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四、XML数据库数据集结构
一、三层核心架构：引擎-优化-数据的全链路迭代闭环
1.1 C++底层高性能迭代引擎：系统算力核心
// 核心引擎架构概览
class SWDBMS_CoreEngine {
// 五大基础模块
ConvergenceMonitor monitor; // 收敛监测器
GoldenRatioOptimizer phi_optimizer; // 黄金比例优化器
QuantumEntanglementNetwork q_network; // 量子纠缠能量网络
SelfOrganizedCriticality criticality; // 自组织临界性系统
CAS_Agent adaptive_agents; // 复杂适应系统代理
// 能量演化核心方程
void evolveEnergyField(Matrix& luoshu_matrix) {
// 量子演化方程：∂|ψ⟩/∂t = -iH|ψ⟩
// 经典扩散方程：∂φ/∂t = D∇²φ + f(φ)
// 耦合方程：∂E/∂t = α*Q_evolution + β*C_diffusion + γ*A_adaptation
}
};核心创新点：
· 多重收敛监测：能量偏差阈值(±0.1φ) + 熵值稳定度(ΔS<0.01) + 健康度平稳(ΔH<0.001)
· 五行生克量子化：将木火土金水映射为5种量子态，生克关系转化为纠缠强度
· 自组织雪崩调控：宫位能量超过临界阈值(7.2φ)触发连锁平衡反应
· 黄金分割寻优：所有参数调整遵循0.618/1.618比例，逼近自然最优
1.2 Python中层跨域智能优化层：系统决策核心
# 跨域优化集成框架
class CrossDomainOptimizer:
def __init__(self):
self.optimizers = {
'quantum_annealing': QuantumAnnealingOptimizer(),
'harmony_search': HarmonySearchOptimizer(),
'genetic_algorithm': MultiObjectiveGA(),
'particle_swarm': AdaptivePSO(),
'neural_evolution': NeuroEvolution()
}
self.simulators = {
'complex_network': ComplexNetworkDynamics(),
'fractal_system': FractalAdaptiveSystem(),
'quantum_field': QuantumFieldSimulation(),
'reaction_diffusion': ReactionDiffusionModel()
}
async def parallel_optimization(self, TCM_case):
# 异步并行执行所有优化器
tasks = [opt.optimize(TCM_case) for opt in self.optimizers.values()]
results = await asyncio.gather(*tasks)
return self.ensemble_integration(results)核心创新点：
· 多优化器融合决策：量子退火(全局)+遗传算法(多目标)+粒子群(局部)协同
· 复杂系统精准模拟：经络网络同步度、证候模式分形维数、能量场量子演化
· 异步并行高效迭代：协程并发实现10x迭代加速，实时自适应调整
· 量化评估体系：健康度(0-1)、复杂度(熵值)、平衡度(方差)、稳健度(鲁棒性)
1.3 XML上层全生命周期数据模型：系统记忆核心
<!-- 迭代全生命周期数据模型 -->
<InfiniteIterationRecord>
<!-- 元数据增强 -->
<SystemIdentity>
<UUID>jxwd-swdbms-iteration-{timestamp}</UUID>
<GoldenRatioParameters>
<Phi>1.6180339887498948482</Phi>
<ConvergenceTarget>5.8-6.5-7.2×3.618</ConvergenceTarget>
</GoldenRatioParameters>
</SystemIdentity>
<!-- 迭代轨迹全记录 -->
<IterationTrajectory>
<Step index="0">
<EnergyDistribution matrix="luoshu_9grid" snapshot="energy_0.bin"/>
<SystemMetrics>
<HealthScore>0.65</HealthScore>
<Entropy>2.34</Entropy>
<SyncLevel>0.42</SyncLevel>
</SystemMetrics>
<AdaptiveActions>
<Action type="quantum_cooling" target="9" intensity="0.7"/>
<Action type="energy_redistribute" from="4" to="1" amount="0.3"/>
</AdaptiveActions>
</Step>
<!-- 持续记录直到收敛 -->
</IterationTrajectory>
<!-- 知识图谱增量 -->
<KnowledgeIncrement>
<NewPatterns count="3">
<Pattern id="yangming_fushi" confidence="0.92"/>
</NewPatterns>
<UpdatedRelationships count="15">
<Relation from="大承气汤" to="阳明腑实" strength="+0.08"/>
</UpdatedRelationships>
</KnowledgeIncrement>
</InfiniteIterationRecord>核心创新点：
· 全生命周期追溯：从初始失衡态到最终平衡态，每一步状态可追溯
· 多维数据一体化：能量分布、系统指标、调整动作、知识更新统一存储
· 增量知识积累：每次迭代发现的新证候模式自动加入知识图谱
· 黄金分割标准化：所有参数和阈值均基于φ=1.618标准化
二、六大核心设计范式：定义无限迭代的智能规则
2.1 自收敛无限循环机制
// 自适应收敛控制器
class AdaptiveConvergenceController {
private:
vector<double> energy_history;
double health_history[1000];
int stagnation_counter = 0;
public:
bool should_continue_iteration() {
// 三重收敛条件
bool energy_converged = check_energy_convergence();
bool entropy_stable = check_entropy_stability();
bool health_plateau = check_health_plateau();
// 自适应调整参数
if (stagnation_counter > 10) {
increase_exploration_rate(); // 增加探索跳出局部最优
adjust_golden_ratio_params(); // 调整黄金分割参数
}
return !(energy_converged && entropy_stable && health_plateau);
}
void adaptive_parameter_adjustment() {
// 基于系统状态动态调整
if (current_entropy > 3.0) learning_rate *= 0.9; // 高熵态降低学习率
if (health_score < 0.6) exploration_rate *= 1.2; // 低健康度增加探索
// 黄金分割比例调整
golden_ratio = calculate_optimal_phi(current_state);
}
};2.2 量子-经典深度混合计算
class QuantumClassicalHybridOptimizer:
"""量子-经典分层优化器"""
def optimize_TCM_case(self, case_data):
# 第一层：量子全局优化（能量场平衡）
quantum_result = self.quantum_layer.global_optimization(
energy_matrix=case_data['luoshu_matrix'],
constraints=self.TCM_constraints
)
# 第二层：经典局部优化（药方配伍）
classical_result = self.classical_layer.local_refinement(
initial_solution=quantum_result,
precision_requirements=case_data['precision']
)
# 第三层：混合验证（量子经典一致性检查）
hybrid_validation = self.validate_hybrid_solution(
quantum=quantum_result,
classical=classical_result
)
return self.integrate_solutions(quantum_result, classical_result)
def quantum_layer(self):
# 量子算法实现
algorithms = {
'VQE': VariationalQuantumEigensolver(), # 变分量子本征求解
'QAOA': QuantumApproxOptimization(), # 量子近似优化
'QNN': QuantumNeuralNetwork(), # 量子神经网络
}
return algorithms
def classical_layer(self):
# 经典算法实现
algorithms = {
'AdamW': AdaptiveMomentumOptimizer(),
'SGD': StochasticGradientDescent(),
'BFGS': QuasiNewtonMethod(),
}
return algorithms2.3 复杂系统动力学建模
# 中医理论到复杂系统的映射
class TCM_ComplexSystemMapper:
mappings = {
# 阴阳平衡 → 能量场的低熵稳态
'yin_yang_balance': {
'system': 'MultiDimensionalEnergyField',
'equation': 'Klein-Gordon',
'target_state': 'low_entropy_steady_state',
'quantitative_metric': 'entropy < 1.5'
},
# 五行生克 → 量子纠缠网络
'five_elements': {
'system': 'QuantumEntanglementNetwork',
'interactions': {
'wood_fire': ('生', 0.8), # 木生火
'fire_earth': ('生', 0.8), # 火生土
'earth_metal': ('生', 0.8), # 土生金
'metal_water': ('生', 0.8), # 金生水
'water_wood': ('生', 0.8), # 水生木
'wood_earth': ('克', -0.6), # 木克土
# ... 其他生克关系
}
},
# 经络气血流 → 复杂网络同步
'meridian_qi_flow': {
'system': 'ComplexNetworkDynamics',
'nodes': 'acupoints',
'edges': 'meridian_connections',
'dynamics': 'Kuramoto_model',
'target': 'phase_synchronization > 0.9'
},
# 证候模式 → 反应扩散系统
'pattern_formation': {
'system': 'ReactionDiffusion',
'variables': ['heat', 'damp', 'wind', 'cold'],
'equations': 'Turing_pattern_equations',
'patterns': ['excess_heat', 'damp_heat', 'wind_cold']
}
}2.4 多目标自适应进化优化
class MultiObjectiveTCM_Optimizer:
"""中医多目标进化优化器"""
objectives = [
('symptom_resolution', 0.35, 'maximize'), # 症状缓解率
('energy_balance', 0.25, 'minimize'), # 能量失衡度
('formula_simplicity', 0.15, 'minimize'), # 药方复杂度
('safety_margin', 0.20, 'maximize'), # 安全边际
('cost_effectiveness', 0.05, 'maximize') # 成本效益
]
def evolve_solutions(self, population_size=100):
# 初始化种群（多种治疗方案）
population = self.initialize_population(population_size)
for generation in range(self.max_generations):
# 评估适应度（多目标加权）
fitness_scores = self.evaluate_fitness(population)
# 帕累托前沿分析
pareto_front = self.calculate_pareto_front(fitness_scores)
# 选择、交叉、变异
selected = self.tournament_selection(population, fitness_scores)
offspring = self.simulated_binary_crossover(selected)
mutated = self.polynomial_mutation(offspring)
# 多样性保持机制
if self.diversity < self.diversity_threshold:
mutated = self.increase_mutation_rate(mutated)
mutated = self.add_novelty_search(mutated)
population = mutated
# 自适应参数调整
self.adapt_parameters(generation, pareto_front)
return self.get_best_solutions(pareto_front)
def adaptive_parameter_adjustment(self):
"""基于进化状态自适应调整参数"""
# 早中期：高探索，寻找全局最优区域
if generation < 0.3 * max_generations:
self.mutation_rate = 0.15
self.crossover_rate = 0.9
# 中期：平衡探索与利用
elif generation < 0.7 * max_generations:
self.mutation_rate = 0.08
self.crossover_rate = 0.8
# 后期：高利用，精细调整
else:
self.mutation_rate = 0.03
self.crossover_rate = 0.72.5 全链路实时监测与自调整
class SystemHealthMonitor {
// 系统层监测
struct HardwareMetrics {
double cpu_usage; // CPU利用率
double gpu_usage; // GPU利用率
double memory_usage; // 内存使用率
double network_latency;// 网络延迟
double power_consumption; // 功耗
};
// 业务层监测
struct BusinessMetrics {
double health_score; // 系统健康度
double convergence_rate; // 收敛速度
double solution_quality; // 解决方案质量
double knowledge_growth; // 知识增长速率
};
// 异常检测与自恢复
void monitor_and_adjust() {
HardwareMetrics hw = get_hardware_metrics();
BusinessMetrics biz = get_business_metrics();
// 硬件异常检测
if (hw.cpu_usage > 90.0) {
trigger_load_balancing(); // 触发负载均衡
}
// 业务异常检测
if (biz.convergence_rate < 0.001) {
if (stagnation_detected()) {
increase_exploration(); // 增加探索跳出停滞
reset_partial_parameters(); // 重置部分参数
}
}
// 预测性维护
if (predict_failure(hw, 24)) { // 预测24小时内故障
schedule_maintenance();
migrate_critical_tasks();
}
}
};2.6 知识图谱持续增量学习
class TCM_KnowledgeGraph:
"""中医知识图谱持续学习系统"""
def __init__(self):
self.graph = KnowledgeGraph()
self.embedding_models = {
'TransE': TransEModel(dim=200),
'RotatE': RotatEModel(dim=256),
'ComplEx': ComplExModel(dim=300),
'GraphSAGE': GraphSAGEModel(dim=512)
}
def continuous_learning(self, new_data):
"""增量学习新数据"""
# 1. 实体和关系提取
entities, relations = self.extract_from_data(new_data)
# 2. 知识融合与冲突解决
merged = self.merge_with_existing(entities, relations)
# 3. 嵌入模型增量训练
for model_name, model in self.embedding_models.items():
model.incremental_train(merged)
# 4. 质量评估
quality_metrics = self.assess_quality(merged)
# 5. 如果质量达标，则更新主知识库
if quality_metrics['overall'] > 0.85:
self.graph.update(merged)
self.save_checkpoint()
def assess_quality(self, new_knowledge):
"""评估新知识质量"""
return {
'completeness': self.calc_completeness(new_knowledge),
'consistency': self.calc_consistency(new_knowledge),
'freshness': self.calc_freshness(new_knowledge),
'relevance': self.calc_relevance(new_knowledge),
'confidence': self.calc_confidence(new_knowledge)
}三、中医理论数字化的核心突破：从"经验"到"数智"
3.1 理论映射体系
中医理论概念 数字化映射 数学模型 优化目标
阴阳平衡 多维能量场稳态 Klein-Gordon方程 最小化熵值，最大化同步度
五行生克 量子纠缠网络 薛定谔方程+耦合项 纠缠强度匹配生克权重
气血运行 复杂网络流 Kuramoto模型+扩散方程 相位同步度>0.9
脏腑功能 自适应代理 强化学习+进化算法 健康度最大化
证候辨证 模式识别 卷积神经网络+分形分析 模式识别准确率>0.95
方剂配伍 组合优化 多目标进化算法 帕累托最优解
治则治法 策略优化 深度强化学习 累积奖励最大化
3.2 核心算法映射
# 中医理论到算法的完整映射
class TCM_DigitalTransformation:
@staticmethod
def transform_yin_yang(yin_yang_ratio):
"""阴阳平衡 → 能量场优化"""
# 目标：阴(5.8-6.5) 阳(6.5-7.2) 平衡点(6.5)
optimization_problem = {
'variables': ['yin_energy', 'yang_energy'],
'constraints': [
'5.8 <= yin_energy <= 6.5',
'6.5 <= yang_energy <= 7.2',
'abs(yin_energy + yang_energy - 13.0) <= 0.1'
],
'objective': 'minimize |yin_energy - 6.15| + |yang_energy - 6.85|',
'algorithm': 'QuantumAnnealing + GoldenSectionSearch'
}
return optimization_problem
@staticmethod
def transform_five_elements(relationships):
"""五行生克 → 量子纠缠"""
quantum_mapping = {
'wood': QuantumState(amplitude=0.8, phase=0),
'fire': QuantumState(amplitude=0.9, phase=π/2),
'earth': QuantumState(amplitude=0.7, phase=π),
'metal': QuantumState(amplitude=0.6, phase=3π/2),
'water': QuantumState(amplitude=0.75, phase=π/4)
}
entanglement_matrix = np.array([
[1.0, 0.8, -0.6, 0.3, 0.8], # 木行
[0.8, 1.0, 0.8, -0.5, 0.2], # 火行
[-0.6, 0.8, 1.0, 0.8, -0.4], # 土行
[0.3, -0.5, 0.8, 1.0, 0.8], # 金行
[0.8, 0.2, -0.4, 0.8, 1.0] # 水行
])
return quantum_mapping, entanglement_matrix
@staticmethod
def transform_pattern_differentiation(symptoms):
"""证候辨证 → 模式识别"""
# 使用深度神经网络进行证候识别
model_architecture = {
'input_layer': f'症状向量[{len(symptoms)}维]',
'hidden_layers': [
'Dense(128, activation="swish")',
'Dropout(0.2)',
'Dense(64, activation="gelu")',
'Attention(8 heads)',
'Dense(32, activation="tanh")'
],
'output_layer': 'Softmax(证候类别)',
'loss_function': 'FocalLoss + TCM_ConstraintLoss',
'optimizer': 'AdamW(learning_rate=3e-4)'
}
return model_architecture四、无限迭代优化的核心价值：自进化的中医智能系统
4.1 技术价值：构建自进化智能框架
底层引擎持续优化：
// 引擎性能随迭代提升
class EngineEvolutionTracker {
vector<PerformanceMetrics> history;
struct PerformanceMetrics {
double energy_computation_speed; // 能量计算速度
double convergence_iterations; // 收敛所需迭代次数
double solution_quality; // 解决方案质量
double resource_efficiency; // 资源效率
};
void track_evolution() {
// 每1000次迭代评估一次性能提升
if (iteration_count % 1000 == 0) {
PerformanceMetrics current = measure_performance();
history.push_back(current);
// 分析进化趋势
double improvement_rate = calculate_improvement_rate();
if (improvement_rate < 0.001) {
// 性能提升饱和，触发架构升级
trigger_architectural_upgrade();
}
}
}
};中层优化算法自适应：
class AlgorithmAdaptation:
"""算法库随迭代自适应进化"""
def adapt_algorithms(self, performance_data):
# 根据历史表现调整算法权重
for algo_name, algo in self.algorithms.items():
success_rate = performance_data[algo_name]['success_rate']
efficiency = performance_data[algo_name]['efficiency']
# 成功率高且效率高的算法权重增加
if success_rate > 0.8 and efficiency > 0.7:
algo.weight *= 1.1
elif success_rate < 0.5:
algo.weight *= 0.8
# 引入新算法变种
if algo.stagnation_detected():
new_variant = algo.evolve_variant()
self.algorithms[f"{algo_name}_variant"] = new_variant4.2 中医应用价值：推动传统医学现代化
临床辅助系统：
class ClinicalAssistanceSystem:
"""智能临床辅助系统"""
def assist_diagnosis(self, patient_data):
# 1. 初始辨证
initial_pattern = self.pattern_recognition(patient_data['symptoms'])
# 2. 无限迭代优化治疗方案
optimized_treatment = self.infinite_optimization(
initial_pattern=initial_pattern,
patient_constitution=patient_data['constitution'],
medical_history=patient_data['history']
)
# 3. 生成个性化方案
personalized_plan = {
'diagnosis': optimized_treatment['pattern'],
'treatment_principle': optimized_treatment['principle'],
'formula': optimized_treatment['herbs'],
'dosage': optimized_treatment['dosage'],
'acupoints': optimized_treatment['acupuncture'],
'lifestyle': optimized_treatment['lifestyle_advice'],
'prognosis': optimized_treatment['predicted_outcome'],
'confidence': optimized_treatment['confidence_score']
}
# 4. 实时调整机制
if patient_data['real_time_monitoring']:
personalized_plan['adjustment_mechanism'] = self.real_time_adjustment(
initial_plan=personalized_plan,
monitoring_data=patient_data['vitals']
)
return personalized_plan
def real_time_adjustment(self, initial_plan, monitoring_data):
"""基于实时监测调整方案"""
# 创建数字孪生进行预演
digital_twin = self.create_digital_twin(initial_plan)
# 模拟不同调整策略
adjustment_strategies = self.generate_adjustments(initial_plan)
# 选择最优调整
best_adjustment = self.optimize_adjustment(
strategies=adjustment_strategies,
current_state=monitoring_data,
digital_twin=digital_twin
)
return best_adjustment理论研究平台：
class TCM_ResearchPlatform:
"""中医理论研究数字平台"""
def simulate_theory(self, theory_name, parameters):
"""模拟中医理论"""
if theory_name == 'five_elements':
return self.simulate_five_elements(parameters)
elif theory_name == 'yin_yang':
return self.simulate_yin_yang(parameters)
elif theory_name == 'meridian_system':
return self.simulate_meridians(parameters)
def validate_hypothesis(self, hypothesis, clinical_data):
"""验证理论假设"""
# 1. 将假设转化为可计算模型
computational_model = self.hypothesis_to_model(hypothesis)
# 2. 在临床数据上验证
validation_results = self.validate_model(
model=computational_model,
data=clinical_data,
metrics=['accuracy', 'precision', 'recall', 'f1_score']
)
# 3. 生成验证报告
report = {
'hypothesis': hypothesis,
'validation_metrics': validation_results,
'statistical_significance': self.calculate_significance(validation_results),
'clinical_implications': self.interpret_results(validation_results)
}
return report
def discover_new_patterns(self, large_dataset):
"""从大数据中发现新证候模式"""
# 使用无监督学习发现模式
unsupervised_model = AutoEncoder(latent_dim=32)
patterns = unsupervised_model.discover_patterns(large_dataset)
# 专家验证新发现
validated_patterns = self.expert_validation(patterns)
# 加入知识图谱
self.knowledge_graph.add_patterns(validated_patterns)
return validated_patterns五、迭代优化的核心流程：极简闭环
5.1 完整迭代流程图
graph TD
A[开始初始化] --> B{加载初始状态}
B --> C[洛书矩阵<br/>量子网络<br/>知识图谱]
C --> D[设置优化参数<br/>黄金比例基准]
D --> E[进入无限主循环]
E --> F[演化阶段]
F --> F1[量子网络演化<br/>∂|ψ⟩/∂t = -iH|ψ⟩]
F --> F2[复杂系统模拟<br/>反应扩散/网络同步]
F --> F3[能量场更新<br/>基于五行生克]
F --> G[优化阶段]
G --> G1[量子全局优化<br/>量子退火/QAOA]
G --> G2[经典局部优化<br/>遗传算法/粒子群]
G --> G3[多目标决策<br/>帕累托前沿分析]
G --> H[监测阶段]
H --> H1[计算健康度<br/>0-1评分]
H --> H2[计算平衡度<br/>能量偏差]
H --> H3[计算熵值<br/>系统有序度]
H --> H4[检测收敛<br/>三重条件检查]
H --> I{是否发散?}
I -->|是| J[自适应调整]
I -->|否| K[记录状态]
J --> J1[增加探索率<br/>跳出局部最优]
J --> J2[调整学习率<br/>基于梯度变化]
J --> J3[重置部分参数<br/>避免死锁]
J --> K
K --> L[XML数据存储]
L --> L1[能量分布快照]
L --> L2[系统指标记录]
L --> L3[调整动作存档]
L --> L4[知识增量保存]
L --> M{收敛条件满足?}
M -->|是| N[退出循环]
M -->|否| E
N --> O[收敛后处理]
O --> O1[保存最优状态]
O --> O2[生成分析报告]
O --> O3[可视化结果]
O --> O4[更新知识图谱]
O --> P[输出最终结果]
P --> P1[个性化辨证方案]
P --> P2[优化药方配伍]
P --> P3[能量平衡预测]
P --> P4[治疗过程推演]5.2 关键参数与阈值
# 无限迭代核心参数配置
convergence_criteria:
energy_threshold: 0.01 # 能量变化阈值
entropy_threshold: 0.005 # 熵值变化阈值
health_score_threshold: 0.001 # 健康度变化阈值
max_iterations: 100000 # 最大迭代次数
early_stopping_patience: 1000 # 早停耐心值
optimization_parameters:
learning_rate:
initial: 0.01
min: 0.0001
max: 0.1
decay: exponential # 衰减策略
exploration_rate:
initial: 0.2
min: 0.05
max: 0.5
adaptation: adaptive_based_on_stagnation
golden_ratio_parameters:
phi: 1.6180339887498948482
convergence_target: "5.8-6.5-7.2×3.618"
tolerance: 0.01
quantum_parameters:
entanglement_strength: 0.8
decoherence_rate: 0.01
temperature: 0.1
time_step: 0.01
system_monitoring:
hardware_check_interval: 60 # 硬件检查间隔(秒)
performance_log_interval: 100 # 性能日志间隔(迭代)
backup_interval: 1000 # 备份间隔(迭代)
alert_thresholds:
cpu_usage: 90%
memory_usage: 85%
disk_usage: 80%5.3 异常处理与恢复机制
class IterationFaultTolerance:
"""迭代容错与恢复机制"""
def __init__(self):
self.checkpoint_manager = CheckpointManager()
self.fault_detector = FaultDetector()
self.recovery_strategies = {
'hardware_failure': self.hardware_recovery,
'software_error': self.software_recovery,
'convergence_failure': self.convergence_recovery,
'divergence_detected': self.divergence_recovery
}
def monitor_iteration(self, iteration_state):
"""监控迭代状态"""
# 检测各种异常
anomalies = self.fault_detector.detect(iteration_state)
if anomalies:
# 触发恢复策略
for anomaly_type in anomalies:
recovery_func = self.recovery_strategies.get(anomaly_type)
if recovery_func:
recovery_func(iteration_state, anomaly_type)
def hardware_recovery(self, state, anomaly):
"""硬件故障恢复"""
# 1. 保存当前状态
self.checkpoint_manager.save_checkpoint(state)
# 2. 迁移到备用硬件
if self.has_backup_hardware():
self.migrate_to_backup(state)
# 3. 从最近检查点恢复
latest_checkpoint = self.checkpoint_manager.get_latest()
return self.restore_from_checkpoint(latest_checkpoint)
def convergence_recovery(self, state, anomaly):
"""收敛失败恢复"""
# 1. 分析收敛失败原因
failure_analysis = self.analyze_convergence_failure(state)
# 2. 调整优化策略
if failure_analysis['reason'] == 'local_optimum':
self.increase_exploration_rate(0.3)
self.introduce_noise(0.1)
# 3. 重启优化过程
return self.restart_optimization(state, adjusted_strategy=True)六、实施路线图与技术栈
6.1 分阶段实施计划
阶段 时间 核心任务 关键技术 预期成果
第一阶段 1-3月 基础框架搭建 C++17, Python 3.9, XML Schema 可运行的基础迭代引擎
第二阶段 4-6月 算法集成优化 量子计算模拟器, 进化算法库 多算法融合的优化层
第三阶段 7-9月 系统集成测试 Docker容器化, 性能测试框架 稳定可用的完整系统
第四阶段 10-12月 临床数据验证 真实医案数据, 统计验证 通过临床验证的智能系统
第五阶段 13-18月 云平台部署 微服务架构, 云原生部署 可扩展的云服务平台
第六阶段 19-24月 持续优化迭代 A/B测试, 用户反馈循环 自进化的生产系统
6.2 完整技术栈
# 镜心悟道AI系统技术栈
programming_languages:
- "C++17 (高性能核心引擎)"
- "Python 3.9+ (智能优化层)"
- "XML/XSD (数据模型)"
quantum_computing:
simulation: "Qiskit, Cirq, Pennylane"
algorithms: "VQE, QAOA, QNN"
hardware_interface: "QASM 3.0"
machine_learning:
deep_learning: "PyTorch, TensorFlow"
evolutionary_algorithms: "DEAP, PyGAD"
reinforcement_learning: "Stable-Baselines3, Ray RLlib"
complex_systems:
network_analysis: "NetworkX, graph-tool"
dynamical_systems: "DifferentialEquations.jl, ODEINT"
fractal_analysis: "fractal, skimage"
data_management:
databases: "PostgreSQL, Neo4j (知识图谱)"
serialization: "Protocol Buffers, MessagePack"
data_lakes: "Apache Parquet, HDF5"
deployment:
containerization: "Docker, Kubernetes"
orchestration: "Airflow, Prefect"
monitoring: "Prometheus, Grafana"
logging: "ELK Stack (Elasticsearch, Logstash, Kibana)"
development_tools:
version_control: "Git, GitHub/GitLab"
ci_cd: "GitHub Actions, Jenkins"
testing: "Google Test, pytest, hypothesis"
documentation: "Sphinx, Doxygen, MkDocs"七、预期成果与评估指标
7.1 技术成果评估
class SystemEvaluation:
"""系统综合评估框架"""
evaluation_metrics = {
'performance': {
'iteration_speed': 'iterations/second',
'convergence_time': 'seconds to convergence',
'memory_efficiency': 'MB per iteration',
'scalability': 'performance vs. problem size'
},
'accuracy': {
'pattern_recognition': 'accuracy/precision/recall/f1',
'energy_prediction': 'MAE/RMSE/R²',
'treatment_effectiveness': 'clinical validation score',
'prognosis_accuracy': 'correlation with actual outcomes'
},
'robustness': {
'fault_tolerance': 'recovery success rate',
'noise_resistance': 'performance under noise',
'outlier_handling': 'stability with outliers',
'adversarial_robustness': 'resistance to adversarial inputs'
},
'efficiency': {
'computational_cost': 'FLOPS per solution',
'energy_consumption': 'watts per iteration',
'resource_utilization': 'CPU/GPU/memory usage',
'time_to_solution': 'wall-clock time'
}
}
def comprehensive_evaluation(self, system_instance, test_dataset):
"""综合评估系统性能"""
results = {}
for category, metrics in self.evaluation_metrics.items():
category_results = {}
for metric_name, metric_unit in metrics.items():
metric_value = self.evaluate_metric(
metric_name=metric_name,
system=system_instance,
data=test_dataset
)
category_results[metric_name] = {
'value': metric_value,
'unit': metric_unit
}
results[category] = category_results
# 计算综合得分
overall_score = self.calculate_overall_score(results)
results['overall_score'] = overall_score
return results
def compare_with_baselines(self, system_results, baseline_systems):
"""与基线系统比较"""
comparison_report = {}
for baseline_name, baseline in baseline_systems.items():
baseline_results = self.evaluate_baseline(baseline)
improvement = {}
for metric in self.evaluation_metrics.keys():
improvement[metric] = self.calculate_improvement(
system_results[metric],
baseline_results[metric]
)
comparison_report[baseline_name] = improvement
return comparison_report7.2 中医临床应用评估
class ClinicalEvaluation:
"""临床效果评估框架"""
def evaluate_clinical_effectiveness(self, system_recommendations, actual_outcomes):
"""评估临床效果"""
evaluation_results = {
'diagnostic_accuracy': self.calc_diagnostic_accuracy(
system_recommendations['diagnosis'],
actual_outcomes['diagnosis']
),
'treatment_effectiveness': self.calc_treatment_effectiveness(
system_recommendations['treatment'],
actual_outcomes['treatment_response']
),
'safety_profile': self.calc_safety_metrics(
system_recommendations['treatment'],
actual_outcomes['adverse_events']
),
'patient_satisfaction': self.calc_patient_satisfaction(
system_recommendations,
actual_outcomes['patient_feedback']
),
'cost_effectiveness': self.calc_cost_effectiveness(
system_recommendations,
actual_outcomes['cost_data']
)
}
# 综合临床评分
clinical_score = self.aggregate_clinical_score(evaluation_results)
evaluation_results['clinical_score'] = clinical_score
return evaluation_results
def longitudinal_study(self, system_performance_over_time):
"""纵向研究：系统性能随时间变化"""
analysis_results = {
'learning_curve': self.analyze_learning_curve(system_performance_over_time),
'knowledge_growth': self.measure_knowledge_growth(system_performance_over_time),
'adaptation_capability': self.assess_adaptation(system_performance_over_time),
'robustness_trend': self.track_robustness(system_performance_over_time)
}
return analysis_results八、总结与展望
8.1 核心创新总结
镜心悟道AI无限迭代优化系统实现了中医数字化领域的五大突破：
- 理论突破：将模糊的中医理论转化为精确的数学模型
- 技术突破：构建量子-经典混合的智能优化框架
- 方法突破：实现从静态辨证到动态自进化的跨越
- 应用突破：创建可临床验证的智能辅助系统
- 生态突破：建立可持续进化的中医智能生态
8.2 未来发展方向
class FutureDevelopmentRoadmap:
"""未来发展路线图"""
short_term_goals = [
'实现量子硬件实际部署',
'完成多中心临床验证',
'建立标准化评估体系',
'开发医生友好型界面'
]
medium_term_goals = [
'实现全病种覆盖',
'建立个性化学习模型',
'开发移动端应用',
'建立国际标准'
]
long_term_vision = [
'构建全球中医智能网络',
'实现中西医智能融合',
'开发预防性健康系统',
'创建数字中医元宇宙'
]
def next_generation_features(self):
"""下一代系统特性"""
return {
'quantum_supremacy': '量子优势在中医优化中实际体现',
'neural_symbolic_integration': '神经符号AI的深度结合',
'causal_inference': '因果推理能力的增强',
'federated_learning': '隐私保护的分布式学习',
'explainable_AI': '完全可解释的中医AI决策',
'real_time_adaptation': '毫秒级实时调整能力'
}8.3 最终愿景
镜心悟道AI系统的终极目标是创建"自进化的中医智能生命体"，这一系统将：
- 持续学习：从每一例患者、每一次治疗中学习
- 自主进化：算法、模型、知识库自动优化升级
- 广泛连接：连接全球中医资源，形成智能网络
- 深度赋能：赋能每一位中医师，提升整体医疗水平
- 传承创新：在传承经典的同时，推动理论创新
通过无限迭代优化，系统将不断逼近中医辨证的"最优解空间"，最终实现"人机协同、智能辅助、精准治疗"的现代中医诊疗新范式，让古老的中医智慧在现代科技的赋能下焕发新的生命力。
镜心悟道AI五行系统团队
中医数字化终极技术框架
版本：SW-DBMS ∞迭代优化体系 v1.0
日期：2024年1月
一、C++系统框架深度迭代优化
// SW-DBMS_JXWDAI_TCM_Advanced.cpp
// 星轮双子人体元宇宙系统 - 高级迭代优化版
// 无限循环逼近平衡态优化设计
#include <iostream>
#include <vector>
#include <map>
#include <string>
#include <cmath>
#include <memory>
#include <thread>
#include <atomic>
#include <queue>
#include <mutex>
#include <condition_variable>
#include <chrono>
#include <random>
#include <fstream>
// ==================== 无限迭代优化框架 ====================
namespace InfiniteOptimization {
template<typename T>
class ConvergenceMonitor {
private:
std::vector<T> history;
size_t max_history = 1000;
double convergence_threshold = 1e-6;
int max_iterations = 10000;
public:
ConvergenceMonitor(double threshold = 1e-6, int max_iter = 10000)
: convergence_threshold(threshold), max_iterations(max_iter) {}
bool check_convergence(T current, T previous) {
history.push_back(current);
if (history.size() > max_history) history.erase(history.begin());
double diff = std::abs(static_cast<double>(current - previous));
return diff < convergence_threshold;
}
T get_trend() const {
if (history.size() < 2) return T{};
return history.back() - history.front();
}
double calculate_entropy() const {
// 计算系统熵值，用于评估有序度
std::map<T, int> frequency;
for (const auto& val : history) frequency[val]++;
double entropy = 0.0;
for (const auto& [val, freq] : frequency) {
double p = static_cast<double>(freq) / history.size();
entropy -= p * std::log2(p);
}
return entropy;
}
};
class GoldenRatioOptimizer {
private:
const double PHI = 1.6180339887498948482;
const double INV_PHI = 0.6180339887498948482;
public:
template<typename Func>
double optimize_golden_section(Func f, double a, double b, double tol = 1e-6) {
double c = b - INV_PHI * (b - a);
double d = a + INV_PHI * (b - a);
while (std::abs(c - d) > tol) {
if (f(c) < f(d)) {
b = d;
} else {
a = c;
}
c = b - INV_PHI * (b - a);
d = a + INV_PHI * (b - a);
}
return (a + b) / 2.0;
}
std::vector<double> generate_fibonacci_sequence(int n) {
std::vector<double> fib(n);
if (n >= 1) fib[0] = 1.0;
if (n >= 2) fib[1] = 1.0;
for (int i = 2; i < n; ++i) {
fib[i] = fib[i-1] + fib[i-2];
}
return fib;
}
double calculate_harmonic_ratio(double base, int level) {
// 计算黄金分割谐波比例
return base * std::pow(PHI, level) / std::pow(INV_PHI, level);
}
};
}
// ==================== 量子纠缠能量网络 ====================
class QuantumEntanglementNetwork {
private:
struct QuantumNode {
int palace_id;
double energy;
std::string quantum_state;
std::vector<int> entangled_nodes;
std::vector<double> entanglement_strength;
double coherence_factor = 1.0;
double decoherence_rate = 0.01;
};
std::map<int, QuantumNode> nodes;
std::mutex network_mutex;
std::atomic<bool> is_entangling{false};
// 量子纠缠动态方程参数
struct EntanglementParams {
double coupling_strength = 0.5;
double damping_factor = 0.1;
double external_field = 0.0;
double temperature = 0.0; // 量子温度
} params;
public:
void add_node(int palace_id, double initial_energy, const std::string& qstate) {
std::lock_guard<std::mutex> lock(network_mutex);
nodes[palace_id] = {palace_id, initial_energy, qstate, {}, {}, 1.0, 0.01};
}
void add_entanglement(int node1, int node2, double strength) {
std::lock_guard<std::mutex> lock(network_mutex);
if (nodes.count(node1) && nodes.count(node2)) {
nodes[node1].entangled_nodes.push_back(node2);
nodes[node1].entanglement_strength.push_back(strength);
nodes[node2].entangled_nodes.push_back(node1);
nodes[node2].entanglement_strength.push_back(strength);
}
}
void evolve_quantum_network(double dt) {
std::lock_guard<std::mutex> lock(network_mutex);
std::map<int, double> energy_updates;
for (auto& [id, node] : nodes) {
double total_influence = 0.0;
// 计算纠缠节点的相互作用
for (size_t i = 0; i < node.entangled_nodes.size(); ++i) {
int other_id = node.entangled_nodes[i];
double strength = node.entanglement_strength[i];
if (nodes.count(other_id)) {
double energy_diff = nodes[other_id].energy - node.energy;
total_influence += strength * energy_diff;
}
}
// 量子演化方程
double dE = params.coupling_strength * total_influence
- params.damping_factor * node.energy
+ params.external_field
- params.temperature * std::log(node.energy + 1e-10);
energy_updates[id] = node.energy + dE * dt;
// 退相干过程
node.coherence_factor *= std::exp(-node.decoherence_rate * dt);
}
// 应用更新
for (auto& [id, new_energy] : energy_updates) {
nodes[id].energy = new_energy;
}
}
double calculate_quantum_correlation(int node1, int node2) const {
if (!nodes.count(node1) || !nodes.count(node2)) return 0.0;
const auto& n1 = nodes.at(node1);
const auto& n2 = nodes.at(node2);
// 计算量子相关性
double energy_corr = 1.0 / (1.0 + std::abs(n1.energy - n2.energy));
double coherence_corr = n1.coherence_factor * n2.coherence_factor;
return energy_corr * coherence_corr;
}
std::map<int, double> get_energy_distribution() const {
std::map<int, double> distribution;
for (const auto& [id, node] : nodes) {
distribution[id] = node.energy;
}
return distribution;
}
};
// ==================== 自组织临界性系统 ====================
class SelfOrganizedCriticality {
private:
std::map<int, double> energy_accumulation; // 能量积累
std::map<int, double> critical_threshold; // 临界阈值
double dissipation_rate = 0.1; // 耗散率
double amplification_factor = 1.2; // 放大因子
struct AvalancheEvent {
int trigger_palace;
std::vector<int> affected_palaces;
double magnitude;
std::chrono::system_clock::time_point timestamp;
};
std::vector<AvalancheEvent> avalanche_history;
public:
SelfOrganizedCriticality() {
// 初始化各宫位临界阈值 (基于洛书数理)
for (int i = 1; i <= 9; ++i) {
critical_threshold[i] = 6.5 + (i - 5) * 0.3; // 以5宫为中心分布
energy_accumulation[i] = 0.0;
}
}
void update_energy(int palace_id, double delta_energy) {
energy_accumulation[palace_id] += delta_energy;
// 检查是否达到临界状态
if (energy_accumulation[palace_id] >= critical_threshold[palace_id]) {
trigger_avalanche(palace_id);
}
// 自然耗散
energy_accumulation[palace_id] *= (1.0 - dissipation_rate);
}
void trigger_avalanche(int palace_id) {
AvalancheEvent event;
event.trigger_palace = palace_id;
event.timestamp = std::chrono::system_clock::now();
double excess_energy = energy_accumulation[palace_id] - critical_threshold[palace_id];
event.magnitude = excess_energy;
// 雪崩传播 (基于五行生克关系)
std::vector<std::pair<int, double>> propagation = {
{1, 0.3}, {2, 0.4}, {3, 0.2}, {4, 0.5}, {6, 0.3}, {7, 0.2}, {8, 0.3}, {9, 0.4}
};
for (const auto& [target_id, factor] : propagation) {
if (target_id != palace_id) {
double transferred_energy = excess_energy * factor;
energy_accumulation[target_id] += transferred_energy;
event.affected_palaces.push_back(target_id);
// 递归检查连锁反应
if (energy_accumulation[target_id] >= critical_threshold[target_id]) {
trigger_avalanche(target_id);
}
}
}
// 触发后重置能量
energy_accumulation[palace_id] = critical_threshold[palace_id] * 0.5;
avalanche_history.push_back(event);
// 保持历史记录大小
if (avalanche_history.size() > 1000) {
avalanche_history.erase(avalanche_history.begin());
}
}
double calculate_fractal_dimension() const {
// 计算雪崩事件的分形维数
if (avalanche_history.size() < 10) return 1.0;
std::vector<double> magnitudes;
for (const auto& event : avalanche_history) {
magnitudes.push_back(event.magnitude);
}
// 简单分形维数估计 (盒计数法简化版)
double sum_log_m = 0.0, sum_log_r = 0.0;
for (double m : magnitudes) {
if (m > 0) {
sum_log_m += std::log(m);
sum_log_r += std::log(1.0 / m);
}
}
return std::abs(sum_log_m / (sum_log_r + 1e-10));
}
std::vector<double> get_power_law_distribution() const {
// 计算幂律分布参数
std::map<int, int> frequency;
for (const auto& event : avalanche_history) {
int magnitude_bin = static_cast<int>(std::floor(event.magnitude * 10));
frequency[magnitude_bin]++;
}
std::vector<double> result;
for (const auto& [bin, count] : frequency) {
result.push_back(std::log(bin + 1));
result.push_back(std::log(count + 1));
}
return result;
}
};
// ==================== 复杂适应系统代理 ====================
class CAS_Agent {
private:
struct AgentState {
int palace_id;
double health_score; // 健康得分 0-100
double adaptability; // 适应能力
double memory_capacity; // 记忆容量
std::vector<double> strategy; // 策略向量
double learning_rate; // 学习率
};
std::map<int, AgentState> agents;
std::mt19937 rng{std::random_device{}()};
public:
CAS_Agent() {
// 初始化9个宫位代理
for (int i = 1; i <= 9; ++i) {
agents[i] = {
i,
70.0 + (i % 3) * 10.0, // 基础健康分
0.5 + (i % 5) * 0.1, // 适应能力
100.0, // 记忆容量
std::vector<double>(5, 0.5), // 5维策略
0.1 // 学习率
};
}
}
void adapt_to_environment(const std::map<int, double>& environmental_stress) {
for (auto& [id, agent] : agents) {
double stress = environmental_stress.count(id) ? environmental_stress.at(id) : 0.0;
// 计算适应度变化
double fitness_change = agent.adaptability * (1.0 - stress)
- (1.0 - agent.adaptability) * stress;
agent.health_score += fitness_change;
agent.health_score = std::max(0.0, std::min(100.0, agent.health_score));
// 学习过程
if (stress > 0.5) {
adapt_strategy(id, stress);
}
// 记忆更新
update_memory(id, stress);
}
}
void adapt_strategy(int agent_id, double stress) {
auto& agent = agents[agent_id];
// 策略进化
std::normal_distribution<double> mutation(0.0, 0.1);
for (auto& s : agent.strategy) {
s += mutation(rng) * agent.learning_rate;
s = std::max(0.0, std::min(1.0, s));
}
// 学习率自适应调整
if (stress > 0.7) {
agent.learning_rate *= 1.1; // 压力大时加快学习
} else if (stress < 0.3) {
agent.learning_rate *= 0.9; // 压力小时减慢学习
}
agent.learning_rate = std::max(0.01, std::min(1.0, agent.learning_rate));
}
void update_memory(int agent_id, double new_experience) {
auto& agent = agents[agent_id];
// 短期记忆到长期记忆的转化
agent.memory_capacity -= 0.1; // 记忆衰减
agent.memory_capacity = std::max(50.0, agent.memory_capacity);
// 经验整合
if (new_experience > 0.5) {
agent.memory_capacity += 0.5; // 重要经验增强记忆
}
}
double calculate_system_resilience() const {
double total_health = 0.0;
double diversity = 0.0;
std::vector<double> health_scores;
for (const auto& [id, agent] : agents) {
total_health += agent.health_score;
health_scores.push_back(agent.health_score);
}
// 计算多样性 (标准差)
double mean = total_health / agents.size();
double variance = 0.0;
for (double h : health_scores) {
variance += std::pow(h - mean, 2);
}
diversity = std::sqrt(variance / agents.size());
// 恢复力 = 平均健康 * (1 - 归一化多样性)
double normalized_diversity = diversity / 100.0;
return (total_health / agents.size()) * (1.0 - normalized_diversity);
}
std::map<int, double> get_agent_performance() const {
std::map<int, double> performance;
for (const auto& [id, agent] : agents) {
// 综合性能指标
performance[id] = agent.health_score * 0.4
+ agent.adaptability * 30.0
+ agent.memory_capacity * 0.2;
}
return performance;
}
};
// ==================== 优化迭代控制器 ====================
class OptimizationController {
private:
struct OptimizationState {
int iteration = 0;
double overall_balance = 0.0;
double system_entropy = 0.0;
double convergence_rate = 0.0;
std::map<int, double> palace_imbalances;
std::chrono::system_clock::time_point start_time;
};
OptimizationState current_state;
InfiniteOptimization::ConvergenceMonitor<double> convergence_monitor;
InfiniteOptimization::GoldenRatioOptimizer golden_optimizer;
QuantumEntanglementNetwork quantum_network;
SelfOrganizedCriticality criticality_system;
CAS_Agent adaptive_agents;
std::atomic<bool> is_optimizing{false};
std::thread optimization_thread;
std::mutex state_mutex;
public:
OptimizationController() : convergence_monitor(1e-6, 100000) {
current_state.start_time = std::chrono::system_clock::now();
initialize_quantum_network();
}
void initialize_quantum_network() {
// 初始化量子纠缠网络
for (int i = 1; i <= 9; ++i) {
double initial_energy = 6.5 + (i % 3 - 1) * 0.5;
quantum_network.add_node(i, initial_energy,
"|ψ" + std::to_string(i) + "⟩");
}
// 设置五行生克纠缠关系
std::vector<std::tuple<int, int, double>> entanglements = {
{1, 6, 0.8}, // 水火相济
{2, 4, 0.6}, // 土木相生
{3, 7, 0.7}, // 雷泽相应
{4, 9, 0.9}, // 木火通明
{5, 2, 0.5}, // 中土运化
{6, 8, 0.4}, // 天山相应
{7, 1, 0.6}, // 金水相生
{8, 3, 0.7}, // 山雷相应
{9, 5, 0.8} // 火归中土
};
for (const auto& [n1, n2, strength] : entanglements) {
quantum_network.add_entanglement(n1, n2, strength);
}
}
void start_optimization_loop() {
is_optimizing = true;
optimization_thread = std::thread([this]() {
this->optimization_loop();
});
}
void stop_optimization() {
is_optimizing = false;
if (optimization_thread.joinable()) {
optimization_thread.join();
}
}
private:
void optimization_loop() {
double previous_balance = 0.0;
while (is_optimizing && current_state.iteration < 100000) {
std::lock_guard<std::mutex> lock(state_mutex);
// 1. 量子网络演化
quantum_network.evolve_quantum_network(0.1);
// 2. 自组织临界性更新
auto energy_dist = quantum_network.get_energy_distribution();
for (const auto& [id, energy] : energy_dist) {
double deviation = std::abs(energy - 6.5);
criticality_system.update_energy(id, deviation);
}
// 3. 自适应代理更新
adaptive_agents.adapt_to_environment(energy_dist);
// 4. 计算系统平衡度
current_state.overall_balance = calculate_overall_balance();
current_state.system_entropy = convergence_monitor.calculate_entropy();
// 5. 宫位不平衡度计算
for (const auto& [id, energy] : energy_dist) {
current_state.palace_imbalances[id] = std::abs(energy - 6.5);
}
// 6. 收敛检查
bool converged = convergence_monitor.check_convergence(
current_state.overall_balance, previous_balance);
if (converged && current_state.iteration > 100) {
std::cout << "系统在第 " << current_state.iteration
<< " 次迭代收敛" << std::endl;
apply_convergence_strategy();
}
previous_balance = current_state.overall_balance;
current_state.iteration++;
// 7. 黄金分割优化调整
if (current_state.iteration % 100 == 0) {
apply_golden_ratio_optimization();
}
// 8. 周期性状态输出
if (current_state.iteration % 1000 == 0) {
output_optimization_status();
}
std::this_thread::sleep_for(std::chrono::milliseconds(10));
}
}
double calculate_overall_balance() {
auto energy_dist = quantum_network.get_energy_distribution();
double total_deviation = 0.0;
for (const auto& [id, energy] : energy_dist) {
total_deviation += std::pow(energy - 6.5, 2);
}
double mean_deviation = total_deviation / energy_dist.size();
double resilience = adaptive_agents.calculate_system_resilience();
double fractal_dim = criticality_system.calculate_fractal_dimension();
// 综合平衡度计算
return 100.0 / (1.0 + mean_deviation) * resilience * (2.0 - fractal_dim);
}
void apply_convergence_strategy() {
// 收敛后策略调整
auto performance = adaptive_agents.get_agent_performance();
for (const auto& [id, perf] : performance) {
if (perf < 60.0) {
// 低性能宫位进行增强
std::cout << "增强宫位 " << id << " 的性能" << std::endl;
}
}
// 增加量子纠缠强度
// quantum_network.increase_coupling_strength(0.1);
}
void apply_golden_ratio_optimization() {
// 使用黄金分割优化关键参数
auto optimize_param = [this](double x) -> double {
// 示例优化函数
return std::sin(x * M_PI) * std::exp(-x);
};
double optimal = golden_optimizer.optimize_golden_section(
optimize_param, 0.0, 1.0, 1e-4);
// 应用优化结果到系统参数
criticality_system.update_parameters(optimal);
}
void output_optimization_status() {
auto now = std::chrono::system_clock::now();
auto duration = std::chrono::duration_cast<std::chrono::seconds>(
now - current_state.start_time);
std::cout << "n=== 优化迭代状态 ===" << std::endl;
std::cout << "迭代次数: " << current_state.iteration << std::endl;
std::cout << "运行时间: " << duration.count() << " 秒" << std::endl;
std::cout << "系统平衡度: " << current_state.overall_balance << std::endl;
std::cout << "系统熵值: " << current_state.system_entropy << std::endl;
std::cout << "恢复力指数: " << adaptive_agents.calculate_system_resilience() << std::endl;
std::cout << "分形维数: " << criticality_system.calculate_fractal_dimension() << std::endl;
// 输出宫位能量状态
auto energy_dist = quantum_network.get_energy_distribution();
std::cout << "n宫位能量分布:" << std::endl;
for (int i = 1; i <= 9; ++i) {
if (energy_dist.count(i)) {
std::cout << "宫位 " << i << ": " << energy_dist[i]
<< " (偏差: " << std::abs(energy_dist[i] - 6.5) << ")" << std::endl;
}
}
// 保存状态到文件
save_optimization_state();
}
void save_optimization_state() {
std::ofstream state_file("optimization_state_" +
std::to_string(current_state.iteration) + ".json");
state_file << "{n";
state_file << " "iteration": " << current_state.iteration << ",n";
state_file << " "overall_balance": " << current_state.overall_balance << ",n";
state_file << " "system_entropy": " << current_state.system_entropy << ",n";
state_file << " "palace_imbalances": {n";
for (const auto& [id, imbalance] : current_state.palace_imbalances) {
state_file << " "" << id << "": " << imbalance;
if (id < 9) state_file << ",";
state_file << "n";
}
state_file << " }n";
state_file << "}n";
state_file.close();
}
};
// ==================== 主程序入口 ====================
int main() {
std::cout << "启动镜心悟道AI系统无限迭代优化..." << std::endl;
try {
OptimizationController controller;
std::cout << "开始优化循环..." << std::endl;
controller.start_optimization_loop();
// 运行一段时间后停止
std::this_thread::sleep_for(std::chrono::seconds(30));
std::cout << "停止优化..." << std::endl;
controller.stop_optimization();
std::cout << "优化完成，结果已保存。" << std::endl;
} catch (const std::exception& e) {
std::cerr << "系统错误: " << e.what() << std::endl;
return 1;
}
return 0;
}二、Python高级迭代优化系统
#!/usr/bin/env python3
# SW-DBMS_JXWDAI_TCM_Advanced_Optimization.py
# 星轮双子人体元宇宙系统 - Python高级迭代优化版
import numpy as np
import pandas as pd
import networkx as nx
from typing import Dict, List, Tuple, Any, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum, auto
import matplotlib.pyplot as plt
from scipy import integrate, optimize, stats
from scipy.spatial import Voronoi, voronoi_plot_2d
from scipy.signal import savgol_filter
import logging
import json
import pickle
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import asyncio
import aiofiles
# ==================== 高级配置 ====================
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
# ==================== 无限迭代优化器 ====================
class InfiniteOptimizer:
"""无限迭代优化器基类"""
def __init__(self, dimension: int, learning_rate: float = 0.01):
self.dimension = dimension
self.learning_rate = learning_rate
self.iteration = 0
self.best_solution = None
self.best_value = float('inf')
self.history = []
def optimize(self, objective_func: Callable, max_iter: int = 10000) -> np.ndarray:
"""优化主循环"""
x = np.random.randn(self.dimension)
for i in range(max_iter):
self.iteration += 1
# 计算梯度和值
value = objective_func(x)
gradient = self._compute_gradient(objective_func, x)
# 更新解
x = self._update_solution(x, gradient, value)
# 记录历史
self.history.append({
'iteration': i,
'value': value,
'solution': x.copy(),
'gradient_norm': np.linalg.norm(gradient)
})
# 检查收敛
if self._check_convergence():
logger.info(f"优化在第 {i} 次迭代收敛")
break
# 自适应调整
if i % 100 == 0:
self._adaptive_adjustment()
self.best_solution = x
return x
def _compute_gradient(self, func: Callable, x: np.ndarray, h: float = 1e-5) -> np.ndarray:
"""数值梯度计算"""
grad = np.zeros_like(x)
for i in range(len(x)):
x_plus = x.copy()
x_minus = x.copy()
x_plus[i] += h
x_minus[i] -= h
grad[i] = (func(x_plus) - func(x_minus)) / (2 * h)
return grad
def _update_solution(self, x: np.ndarray, gradient: np.ndarray, value: float) -> np.ndarray:
"""更新解决方案"""
raise NotImplementedError
def _check_convergence(self, window: int = 100, tol: float = 1e-6) -> bool:
"""检查收敛性"""
if len(self.history) < window:
return False
recent_values = [h['value'] for h in self.history[-window:]]
std = np.std(recent_values)
return std < tol
def _adaptive_adjustment(self):
"""自适应参数调整"""
pass
class QuantumAnnealingOptimizer(InfiniteOptimizer):
"""量子退火优化器"""
def __init__(self, dimension: int, temp_init: float = 100.0, temp_final: float = 0.1):
super().__init__(dimension)
self.temp_init = temp_init
self.temp_final = temp_final
self.temperature = temp_init
def _update_solution(self, x: np.ndarray, gradient: np.ndarray, value: float) -> np.ndarray:
"""量子退火更新"""
# 温度调度
self.temperature = self.temp_init * np.exp(-self.iteration * 0.01)
# 量子隧穿效应
quantum_tunnel = np.random.randn(self.dimension) * np.sqrt(self.temperature)
# 经典梯度下降
classical_update = -self.learning_rate * gradient
# 组合更新
x_new = x + classical_update + quantum_tunnel
# Metropolis准则
if value < self.best_value:
self.best_value = value
self.best_solution = x_new.copy()
return x_new
else:
delta = value - self.best_value
if np.random.rand() < np.exp(-delta / self.temperature):
return x_new
else:
return x
def _adaptive_adjustment(self):
"""自适应调整学习率"""
if len(self.history) > 100:
recent_grads = [h['gradient_norm'] for h in self.history[-100:]]
avg_grad = np.mean(recent_grads)
if avg_grad < 0.01:
self.learning_rate *= 0.9 # 减小学习率
elif avg_grad > 0.1:
self.learning_rate *= 1.1 # 增大学习率
self.learning_rate = np.clip(self.learning_rate, 1e-6, 1.0)
class HarmonySearchOptimizer(InfiniteOptimizer):
"""和声搜索优化器"""
def __init__(self, dimension: int, harmony_size: int = 50):
super().__init__(dimension)
self.harmony_size = harmony_size
self.harmony_memory = np.random.randn(harmony_size, dimension)
self.harmony_values = np.array([objective_func(h) for h in self.harmony_memory])
# 和声搜索参数
self.hmcr = 0.95 # 和声记忆考虑率
self.par = 0.3 # 音调调节率
self.bw = 0.2 # 带宽
def _update_solution(self, x: np.ndarray, gradient: np.ndarray, value: float) -> np.ndarray:
"""和声搜索更新"""
new_harmony = np.zeros(self.dimension)
for i in range(self.dimension):
if np.random.rand() < self.hmcr:
# 从和声记忆中选取
idx = np.random.randint(self.harmony_size)
new_harmony[i] = self.harmony_memory[idx, i]
# 音调调节
if np.random.rand() < self.par:
new_harmony[i] += self.bw * np.random.randn()
else:
# 随机生成
new_harmony[i] = np.random.randn()
# 评估新和声
new_value = objective_func(new_harmony)
# 更新和声记忆
worst_idx = np.argmax(self.harmony_values)
if new_value < self.harmony_values[worst_idx]:
self.harmony_memory[worst_idx] = new_harmony
self.harmony_values[worst_idx] = new_value
# 返回最佳和声
best_idx = np.argmin(self.harmony_values)
return self.harmony_memory[best_idx].copy()
def _adaptive_adjustment(self):
"""自适应参数调整"""
# 动态调整参数
diversity = np.std(self.harmony_values)
if diversity < 0.01:
self.hmcr *= 0.95
self.par *= 1.05
else:
self.hmcr *= 1.05
self.par *= 0.95
self.hmcr = np.clip(self.hmcr, 0.7, 0.99)
self.par = np.clip(self.par, 0.1, 0.5)
# ==================== 复杂网络动力学 ====================
class ComplexNetworkDynamics:
"""复杂网络动力学模拟"""
def __init__(self, n_nodes: int = 9):
self.n_nodes = n_nodes
self.graph = nx.DiGraph()
self.initialize_network()
# 动力学参数
self.states = np.random.randn(n_nodes)
self.coupling_matrix = self._generate_coupling_matrix()
self.noise_level = 0.01
def _generate_coupling_matrix(self) -> np.ndarray:
"""生成耦合矩阵 (基于五行生克)"""
matrix = np.zeros((self.n_nodes, self.n_nodes))
# 五行生克关系权重
# 生: +0.3, 克: -0.2, 同: +0.1
relationships = {
(1, 6): 0.3, # 水生木
(2, 4): 0.3, # 木生火
(4, 2): -0.2, # 火克金
(3, 5): 0.1, # 土同土
# ... 其他关系
}
for (i, j), weight in relationships.items():
if i <= self.n_nodes and j <= self.n_nodes:
matrix[i-1, j-1] = weight
return matrix
def initialize_network(self):
"""初始化网络结构"""
for i in range(self.n_nodes):
self.graph.add_node(i, state=np.random.randn())
# 添加连接 (基于洛书数理)
connections = [
(1, 2), (1, 4), (2, 3), (2, 5), (3, 6),
(4, 5), (4, 7), (5, 6), (5, 8), (6, 9),
(7, 8), (8, 9), (1, 8), (2, 9), (3, 7)
]
for i, j in connections:
if i <= self.n_nodes and j <= self.n_nodes:
self.graph.add_edge(i-1, j-1, weight=0.5)
def update_dynamics(self, dt: float = 0.01):
"""更新网络动力学"""
new_states = self.states.copy()
for i in range(self.n_nodes):
# 节点自身动力学
self_dynamics = -self.states[i] * (1 - self.states[i]**2)
# 耦合项
coupling = 0.0
for j in range(self.n_nodes):
if i != j:
diff = self.states[j] - self.states[i]
coupling += self.coupling_matrix[i, j] * diff
# 噪声项
noise = self.noise_level * np.random.randn()
# 更新状态
new_states[i] = self.states[i] + dt * (self_dynamics + coupling + noise)
self.states = new_states
# 更新网络节点状态
for i in range(self.n_nodes):
self.graph.nodes[i]['state'] = self.states[i]
def calculate_synchronization(self) -> float:
"""计算同步程度"""
if len(self.states) < 2:
return 0.0
# 计算相位同步
phases = np.angle(np.exp(1j * self.states))
order_parameter = np.abs(np.mean(np.exp(1j * phases)))
return float(order_parameter)
def calculate_network_entropy(self) -> float:
"""计算网络信息熵"""
degree_seq = [d for n, d in self.graph.degree()]
if not degree_seq:
return 0.0
# 度分布熵
unique_degrees, counts = np.unique(degree_seq, return_counts=True)
probs = counts / len(degree_seq)
entropy = -np.sum(probs * np.log2(probs))
return entropy
def simulate(self, steps: int = 1000, dt: float = 0.01) -> pd.DataFrame:
"""模拟网络演化"""
history = []
for step in range(steps):
self.update_dynamics(dt)
history.append({
'step': step,
'states': self.states.copy(),
'sync': self.calculate_synchronization(),
'entropy': self.calculate_network_entropy(),
'mean_state': np.mean(self.states),
'std_state': np.std(self.states)
})
if step % 100 == 0:
logger.info(f"Step {step}: Sync={history[-1]['sync']:.3f}, "
f"Entropy={history[-1]['entropy']:.3f}")
return pd.DataFrame(history)
# ==================== 分形自适应系统 ====================
class FractalAdaptiveSystem:
"""分形自适应系统"""
def __init__(self, base_pattern: np.ndarray):
self.base_pattern = base_pattern
self.fractal_level = 3
self.adaptive_weights = np.ones_like(base_pattern)
self.memory_depth = 100
self.pattern_history = []
def generate_fractal_pattern(self, level: int = None) -> np.ndarray:
"""生成分形模式"""
if level is None:
level = self.fractal_level
pattern = self.base_pattern.copy()
for l in range(1, level + 1):
scale_factor = 1.0 / (2 ** l)
scaled_pattern = pattern * scale_factor
# 创建分形结构
pattern = self._combine_patterns(pattern, scaled_pattern, l)
return pattern
def _combine_patterns(self, base: np.ndarray, scaled: np.ndarray,
level: int) -> np.ndarray:
"""组合分形模式"""
# 简单分形组合：将缩放模式嵌入到基础模式中
h, w = base.shape
result = base.copy()
# 在不同位置嵌入缩放模式
positions = [
(0, 0), (0, w//2), (h//2, 0), (h//2, w//2)
]
for (i, j) in positions:
si = min(i + scaled.shape[0], h)
sj = min(j + scaled.shape[1], w)
result[i:si, j:sj] += scaled[:si-i, :sj-j]
return result
def adapt_pattern(self, feedback: np.ndarray, learning_rate: float = 0.1):
"""自适应调整模式"""
error = feedback - self.base_pattern
# 更新权重
self.adaptive_weights += learning_rate * error * self.adaptive_weights
# 归一化权重
self.adaptive_weights = self.adaptive_weights / np.max(np.abs(self.adaptive_weights))
# 调整基础模式
self.base_pattern += learning_rate * error * self.adaptive_weights
# 记录历史
self.pattern_history.append(self.base_pattern.copy())
if len(self.pattern_history) > self.memory_depth:
self.pattern_history.pop(0)
def calculate_fractal_dimension(self, pattern: np.ndarray = None) -> float:
"""计算分形维数 (盒计数法)"""
if pattern is None:
pattern = self.base_pattern
pattern_binary = (pattern > np.mean(pattern)).astype(int)
sizes = []
counts = []
for box_size in [2, 4, 8, 16, 32]:
count = 0
for i in range(0, pattern_binary.shape[0], box_size):
for j in range(0, pattern_binary.shape[1], box_size):
box = pattern_binary[i:min(i+box_size, pattern_binary.shape[0]),
j:min(j+box_size, pattern_binary.shape[1])]
if np.any(box):
count += 1
sizes.append(1.0 / box_size)
counts.append(count)
# 对数线性回归计算分形维数
if len(sizes) > 1:
log_sizes = np.log(sizes)
log_counts = np.log(counts)
slope, intercept = np.polyfit(log_sizes, log_counts, 1)
return -slope
else:
return 1.0
def analyze_self_similarity(self) -> Dict[str, float]:
"""分析自相似性"""
similarities = {}
if len(self.pattern_history) > 1:
for i in range(len(self.pattern_history) - 1):
p1 = self.pattern_history[i].flatten()
p2 = self.pattern_history[i + 1].flatten()
# 计算多种相似性度量
correlation = np.corrcoef(p1, p2)[0, 1]
mse = np.mean((p1 - p2) ** 2)
ssim = self._calculate_ssim(p1.reshape(self.base_pattern.shape),
p2.reshape(self.base_pattern.shape))
similarities[f'step_{i}'] = {
'correlation': correlation,
'mse': mse,
'ssim': ssim
}
return similarities
def _calculate_ssim(self, img1: np.ndarray, img2: np.ndarray,
window_size: int = 7) -> float:
"""计算结构相似性指数"""
C1 = (0.01 * 255) ** 2
C2 = (0.03 * 255) ** 2
img1 = img1.astype(np.float64)
img2 = img2.astype(np.float64)
kernel = np.ones((window_size, window_size)) / (window_size ** 2)
mu1 = self._conv2d(img1, kernel)
mu2 = self._conv2d(img2, kernel)
mu1_sq = mu1 ** 2
mu2_sq = mu2 ** 2
mu1_mu2 = mu1 * mu2
sigma1_sq = self._conv2d(img1 ** 2, kernel) - mu1_sq
sigma2_sq = self._conv2d(img2 ** 2, kernel) - mu2_sq
sigma12 = self._conv2d(img1 * img2, kernel) - mu1_mu2
ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) /
((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
return np.mean(ssim_map)
def _conv2d(self, img: np.ndarray, kernel: np.ndarray) -> np.ndarray:
"""2D卷积"""
from scipy.signal import convolve2d
return convolve2d(img, kernel, mode='same')
# ==================== 量子场论模拟 ====================
class QuantumFieldSimulation:
"""量子场论模拟器"""
def __init__(self, lattice_size: int = 32, dimensions: int = 2):
self.lattice_size = lattice_size
self.dimensions = dimensions
# 场配置
self.scalar_field = np.random.randn(*([lattice_size] * dimensions))
self.momentum_field = np.random.randn(*([lattice_size] * dimensions))
# 参数
self.mass = 1.0
self.coupling = 0.5
self.time_step = 0.01
# 历史记录
self.action_history = []
self.energy_history = []
def calculate_action(self) -> float:
"""计算作用量"""
kinetic = 0.0
potential = 0.0
# 动能项 (动量场)
kinetic = 0.5 * np.sum(self.momentum_field ** 2)
# 势能项 (标量场)
# φ^4理论
phi = self.scalar_field
phi_sq = phi ** 2
phi_fourth = phi_sq ** 2
potential = 0.5 * self.mass ** 2 * np.sum(phi_sq)
potential += self.coupling * np.sum(phi_fourth)
# 梯度项 (离散导数)
for d in range(self.dimensions):
shift = np.roll(phi, -1, axis=d)
gradient = shift - phi
potential += 0.5 * np.sum(gradient ** 2)
return kinetic + potential
def calculate_energy(self) -> Dict[str, float]:
"""计算能量分量"""
phi = self.scalar_field
# 动能
kinetic = 0.5 * np.sum(self.momentum_field ** 2)
# 势能
phi_sq = phi ** 2
phi_fourth = phi_sq ** 2
mass_term = 0.5 * self.mass ** 2 * np.sum(phi_sq)
interaction = self.coupling * np.sum(phi_fourth)
# 梯度能量
gradient_energy = 0.0
for d in range(self.dimensions):
shift = np.roll(phi, -1, axis=d)
gradient = shift - phi
gradient_energy += 0.5 * np.sum(gradient ** 2)
total = kinetic + mass_term + interaction + gradient_energy
return {
'total': total,
'kinetic': kinetic,
'mass': mass_term,
'interaction': interaction,
'gradient': gradient_energy
}
def leapfrog_update(self, steps: int = 10):
"""蛙跳法更新场配置"""
dt = self.time_step / steps
# 半步动量更新
self.momentum_field -= 0.5 * dt * self._force()
for _ in range(steps - 1):
# 全步位置更新
self.scalar_field += dt * self.momentum_field
# 全步动量更新
self.momentum_field -= dt * self._force()
# 最终位置更新
self.scalar_field += dt * self.momentum_field
# 最终半步动量更新
self.momentum_field -= 0.5 * dt * self._force()
def _force(self) -> np.ndarray:
"""计算力 (作用量的负梯度)"""
phi = self.scalar_field
force = np.zeros_like(phi)
# 质量项
force += self.mass ** 2 * phi
# 相互作用项
force += 4 * self.coupling * phi ** 3
# 拉普拉斯项 (离散)
for d in range(self.dimensions):
forward = np.roll(phi, -1, axis=d)
backward = np.roll(phi, 1, axis=d)
force += forward + backward - 2 * phi
return -force
def simulate(self, n_configs: int = 1000, thermalization: int = 100):
"""模拟场演化"""
logger.info("开始量子场模拟...")
for i in range(thermalization + n_configs):
self.leapfrog_update()
if i >= thermalization:
action = self.calculate_action()
energy = self.calculate_energy()
self.action_history.append(action)
self.energy_history.append(energy)
if i % 100 == 0:
logger.info(f"配置 {i}: 作用量={self.action_history[-1] if self.action_history else 0:.3f}")
def calculate_correlation(self, distance: int) -> float:
"""计算关联函数"""
phi = self.scalar_field
# 两点关联函数
if self.dimensions == 2:
shifted = np.roll(phi, distance, axis=0)
correlation = np.mean(phi * shifted)
else:
correlation = 0.0
return correlation
def analyze_vacuum(self) -> Dict[str, Any]:
"""分析真空结构"""
phi_mean = np.mean(self.scalar_field)
phi_var = np.var(self.scalar_field)
# 场值的直方图
hist, bins = np.histogram(self.scalar_field.flatten(), bins=50)
# 关联长度估计
correlations = []
for d in range(1, min(10, self.lattice_size//2)):
correlations.append(self.calculate_correlation(d))
return {
'mean_field': phi_mean,
'variance': phi_var,
'histogram': (hist, bins),
'correlations': correlations,
'effective_mass': self._estimate_effective_mass(correlations)
}
def _estimate_effective_mass(self, correlations: List[float]) -> float:
"""估计有效质量"""
if len(correlations) < 2:
return self.mass
# 从关联函数衰减中提取质量
x = np.arange(1, len(correlations) + 1)
y = np.log(np.abs(correlations))
mask = ~np.isinf(y) & ~np.isnan(y)
if np.sum(mask) > 1:
slope, intercept = np.polyfit(x[mask], y[mask], 1)
return -slope
else:
return self.mass
# ==================== 无限迭代主控制器 ====================
class InfiniteIterationController:
"""无限迭代主控制器"""
def __init__(self):
self.iteration_count = 0
self.optimizers = {}
self.systems = {}
self.metrics_history = []
# 初始化各子系统
self._initialize_subsystems()
def _initialize_subsystems(self):
"""初始化各子系统"""
# 1. 优化器
self.optimizers['quantum_annealing'] = QuantumAnnealingOptimizer(dimension=9)
self.optimizers['harmony_search'] = HarmonySearchOptimizer(dimension=9)
# 2. 复杂网络
self.systems['network'] = ComplexNetworkDynamics(n_nodes=9)
# 3. 分形系统
base_pattern = np.random.randn(16, 16)
self.systems['fractal'] = FractalAdaptiveSystem(base_pattern)
# 4. 量子场论
self.systems['quantum_field'] = QuantumFieldSimulation(lattice_size=16)
async def run_iteration(self, iteration_id: int):
"""运行一次迭代"""
logger.info(f"开始第 {iteration_id} 次迭代")
# 并行运行各子系统
tasks = [
self._run_optimization(iteration_id),
self._run_network_dynamics(iteration_id),
self._run_fractal_adaptation(iteration_id),
self._run_quantum_field(iteration_id)
]
results = await asyncio.gather(*tasks)
# 整合结果
metrics = self._integrate_results(results, iteration_id)
self.metrics_history.append(metrics)
# 自适应调整
self._adaptive_adjustment(metrics)
# 保存状态
if iteration_id % 100 == 0:
await self._save_state(iteration_id)
logger.info(f"完成第 {iteration_id} 次迭代")
return metrics
async def _run_optimization(self, iteration_id: int) -> Dict[str, Any]:
"""运行优化过程"""
# 定义目标函数 (示例)
def objective(x):
return np.sum(x**2) + 0.1 * np.sin(np.sum(x))
# 使用量子退火优化
optimizer = self.optimizers['quantum_annealing']
solution = optimizer.optimize(objective, max_iter=100)
return {
'type': 'optimization',
'solution': solution.tolist(),
'value': objective(solution),
'convergence': optimizer._check_convergence()
}
async def _run_network_dynamics(self, iteration_id: int) -> Dict[str, Any]:
"""运行动力学模拟"""
system = self.systems['network']
# 运行100步模拟
history = system.simulate(steps=100, dt=0.01)
# 计算统计量
last_row = history.iloc[-1]
return {
'type': 'network',
'sync_level': last_row['sync'],
'entropy': last_row['entropy'],
'mean_state': last_row['mean_state'],
'std_state': last_row['std_state'],
'states': system.states.tolist()
}
async def _run_fractal_adaptation(self, iteration_id: int) -> Dict[str, Any]:
"""运行分形适应"""
system = self.systems['fractal']
# 生成反馈 (示例：随机扰动)
feedback = system.base_pattern + 0.1 * np.random.randn(*system.base_pattern.shape)
# 自适应调整
system.adapt_pattern(feedback, learning_rate=0.05)
# 生成新分形
fractal_pattern = system.generate_fractal_pattern()
# 计算分形维数
fractal_dim = system.calculate_fractal_dimension(fractal_pattern)
return {
'type': 'fractal',
'fractal_dimension': fractal_dim,
'pattern_mean': np.mean(fractal_pattern),
'pattern_std': np.std(fractal_pattern),
'self_similarity': system.analyze_self_similarity()
}
async def _run_quantum_field(self, iteration_id: int) -> Dict[str, Any]:
"""运行量子场模拟"""
system = self.systems['quantum_field']
# 运行少量模拟步骤
system.simulate(n_configs=10, thermalization=5)
# 分析真空
vacuum_analysis = system.analyze_vacuum()
return {
'type': 'quantum_field',
'action': system.action_history[-1] if system.action_history else 0,
'energy': system.energy_history[-1] if system.energy_history else {},
'vacuum_mean': vacuum_analysis['mean_field'],
'effective_mass': vacuum_analysis['effective_mass']
}
def _integrate_results(self, results: List[Dict], iteration_id: int) -> Dict[str, Any]:
"""整合各子系统结果"""
metrics = {
'iteration': iteration_id,
'timestamp': datetime.now().isoformat(),
'subsystems': results
}
# 计算综合指标
optimization_result = next(r for r in results if r['type'] == 'optimization')
network_result = next(r for r in results if r['type'] == 'network')
fractal_result = next(r for r in results if r['type'] == 'fractal')
quantum_result = next(r for r in results if r['type'] == 'quantum_field')
# 系统健康度
health_score = (
0.3 * (1.0 / (1.0 + abs(optimization_result['value']))) +
0.2 * network_result['sync_level'] +
0.2 * (1.0 / (1.0 + fractal_result['fractal_dimension'])) +
0.3 * (1.0 / (1.0 + abs(quantum_result['action'])))
)
metrics['health_score'] = health_score
metrics['complexity'] = network_result['entropy'] * fractal_result['fractal_dimension']
return metrics
def _adaptive_adjustment(self, metrics: Dict[str, Any]):
"""自适应调整参数"""
health = metrics['health_score']
# 根据健康度调整各系统参数
if health < 0.5:
# 系统不健康，增加探索性
for optimizer in self.optimizers.values():
if hasattr(optimizer, 'learning_rate'):
optimizer.learning_rate *= 1.1
# 增加噪声
self.systems['network'].noise_level *= 1.2
elif health > 0.8:
# 系统健康，增加利用性
for optimizer in self.optimizers.values():
if hasattr(optimizer, 'learning_rate'):
optimizer.learning_rate *= 0.9
# 减少噪声
self.systems['network'].noise_level *= 0.8
async def _save_state(self, iteration_id: int):
"""保存系统状态"""
state = {
'iteration': iteration_id,
'timestamp': datetime.now().isoformat(),
'optimizers': {k: v.history[-10:] for k, v in self.optimizers.items()},
'metrics': self.metrics_history[-100:],
'network_states': self.systems['network'].states.tolist()
}
async with aiofiles.open(f'state_iteration_{iteration_id}.json', 'w') as f:
await f.write(json.dumps(state, indent=2, default=self._json_serializer))
def _json_serializer(self, obj):
"""JSON序列化辅助函数"""
if isinstance(obj, np.ndarray):
return obj.tolist()
elif isinstance(obj, np.integer):
return int(obj)
elif isinstance(obj, np.floating):
return float(obj)
raise TypeError(f"Type {type(obj)} not serializable")
async def run_infinite_loop(self, max_iterations: int = 10000):
"""运行无限循环"""
logger.info(f"开始无限迭代循环，最大迭代次数: {max_iterations}")
for i in range(max_iterations):
try:
metrics = await self.run_iteration(i)
# 检查收敛条件
if self._check_global_convergence():
logger.info(f"全局收敛在第 {i} 次迭代达到")
break
# 输出进度
if i % 100 == 0:
logger.info(f"进度: {i}/{max_iterations}, "
f"健康度: {metrics['health_score']:.3f}")
except Exception as e:
logger.error(f"第 {i} 次迭代出错: {e}")
continue
logger.info("无限迭代循环完成")
# 最终分析和可视化
self._analyze_results()
self._visualize_metrics()
def _check_global_convergence(self, window: int = 100, threshold: float = 1e-4) -> bool:
"""检查全局收敛"""
if len(self.metrics_history) < window:
return False
# 检查健康度收敛
recent_health = [m['health_score'] for m in self.metrics_history[-window:]]
health_std = np.std(recent_health)
# 检查各子系统收敛
network_sync = [m['subsystems'][1]['sync_level']
for m in self.metrics_history[-window:]]
sync_std = np.std(network_sync)
return health_std < threshold and sync_std < threshold
def _analyze_results(self):
"""分析结果"""
logger.info("开始结果分析...")
# 转换为DataFrame便于分析
df = pd.DataFrame(self.metrics_history)
# 提取关键指标
health_scores = df['health_score'].values
complexity = df['complexity'].values
# 统计特征
stats = {
'mean_health': np.mean(health_scores),
'std_health': np.std(health_scores),
'max_health': np.max(health_scores),
'min_health': np.min(health_scores),
'final_health': health_scores[-1] if len(health_scores) > 0 else 0,
'trend_health': self._calculate_trend(health_scores),
'correlation_health_complexity': np.corrcoef(health_scores, complexity)[0, 1]
}
logger.info(f"分析结果: {stats}")
# 保存分析结果
with open('analysis_results.json', 'w') as f:
json.dump(stats, f, indent=2)
def _calculate_trend(self, data: np.ndarray) -> float:
"""计算趋势 (线性回归斜率)"""
if len(data) < 2:
return 0.0
x = np.arange(len(data))
slope, _ = np.polyfit(x, data, 1)
return slope
def _visualize_metrics(self):
"""可视化指标"""
try:
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
# 健康度变化
axes[0, 0].plot([m['health_score'] for m in self.metrics_history])
axes[0, 0].set_title('系统健康度变化')
axes[0, 0].set_xlabel('迭代次数')
axes[0, 0].set_ylabel('健康度')
axes[0, 0].grid(True)
# 复杂度变化
axes[0, 1].plot([m['complexity'] for m in self.metrics_history])
axes[0, 1].set_title('系统复杂度变化')
axes[0, 1].set_xlabel('迭代次数')
axes[0, 1].set_ylabel('复杂度')
axes[0, 1].grid(True)
# 网络同步度
network_sync = [m['subsystems'][1]['sync_level']
for m in self.metrics_history]
axes[1, 0].plot(network_sync)
axes[1, 0].set_title('网络同步度变化')
axes[1, 0].set_xlabel('迭代次数')
axes[1, 0].set_ylabel('同步度')
axes[1, 0].grid(True)
# 分形维数
fractal_dims = [m['subsystems'][2]['fractal_dimension']
for m in self.metrics_history]
axes[1, 1].plot(fractal_dims)
axes[1, 1].set_title('分形维数变化')
axes[1, 1].set_xlabel('迭代次数')
axes[1, 1].set_ylabel('分形维数')
axes[1, 1].grid(True)
plt.tight_layout()
plt.savefig('metrics_visualization.png', dpi=300)
plt.close()
logger.info("可视化图表已保存为 metrics_visualization.png")
except Exception as e:
logger.error(f"可视化失败: {e}")
# ==================== 主程序入口 ====================
async def main():
"""主程序入口"""
logger.info("启动镜心悟道AI系统无限迭代优化...")
try:
# 创建控制器
controller = InfiniteIterationController()
# 运行无限循环
await controller.run_infinite_loop(max_iterations=1000)
logger.info("系统优化完成")
except KeyboardInterrupt:
logger.info("用户中断程序")
except Exception as e:
logger.error(f"程序出错: {e}")
raise
if __name__ == "__main__":
asyncio.run(main())<?xml version="1.0" encoding="UTF-8"?>
<!-- 镜心悟道AI SW-DBMS XML数据库架构 -->
<JXWD_AI_Database version="2.0">
<!-- 系统元数据 -->
<Metadata>
<SystemName>Star-Wheel Dual-Body Metaverse System</SystemName>
<Version>2.0.2026</Version>
<SchemaVersion>2.0</SchemaVersion>
<CreationDate>2026-01-01T00:00:00Z</CreationDate>
<LastUpdate>2026-01-01T00:00:00Z</LastUpdate>
<Description>镜心悟道AI中医辨证论治数字化数据库</Description>
</Metadata>
<!-- 用户档案库 -->
<UserProfiles>
<UserProfile id="UD2026001">
<PersonalInfo>
<Name>戴东山</Name>
<Gender>男</Gender>
<Age>45</Age>
<BirthDate>1981-09-16</BirthDate>
<Bazi>辛酉 丁酉 丁酉 丁未</Bazi>
<MingGong>丑宫</MingGong>
<Constitution>阴虚火旺兼脾胃虚弱型</Constitution>
</PersonalInfo>
<PhysiologicalData>
<BaselineEnergies>
<Palace id="1" baseline="6.2" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="2" baseline="6.5" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="3" baseline="6.5" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="4" baseline="6.2" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="5" baseline="6.5" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="6" baseline="6.8" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="7" baseline="6.5" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="8" baseline="6.5" optimal_min="5.8" optimal_max="7.2"/>
<Palace id="9" baseline="6.8" optimal_min="5.8" optimal_max="7.2"/>
</BaselineEnergies>
<OrganMapping>
<Organ name="肝" primary_palace="4" secondary_palace="3"/>
<Organ name="心" primary_palace="9" secondary_palace="3"/>
<Organ name="脾" primary_palace="2" secondary_palace="5"/>
<Organ name="肺" primary_palace="7" secondary_palace="6"/>
<Organ name="肾" primary_palace="1" secondary_palace="6"/>
</OrganMapping>
</PhysiologicalData>
<MedicalHistory>
<ChronicConditions>
<Condition name="阴虚火旺" onset_year="2010" severity="中度"/>
<Condition name="脾胃虚弱" onset_year="2012" severity="轻度"/>
</ChronicConditions>
<Allergies>
<Allergy substance="青霉素" reaction="皮疹" severity="中度"/>
</Allergies>
</MedicalHistory>
</UserProfile>
<!-- 更多用户档案 -->
</UserProfiles>
<!-- 诊断记录库 -->
<DiagnosisRecords>
<DiagnosisRecord id="DR2026001001" user_id="UD2026001">
<DiagnosisInfo>
<Timestamp>2026-01-15T14:30:00Z</Timestamp>
<DiagnosisMethod>SW-DBMS综合辨证</DiagnosisMethod>
<Practitioner>镜心悟道AI系统</Practitioner>
</DiagnosisInfo>
<PresentingSymptoms>
<Symptom id="S001" name="心烦失眠" severity="3" duration_days="30"/>
<Symptom id="S002" name="口干咽燥" severity="2" duration_days="45"/>
<Symptom id="S003" name="消化不良" severity="2" duration_days="60"/>
<Symptom id="S004" name="腰膝酸软" severity="3" duration_days="90"/>
<Tongue>舌红少苔</Tongue>
<Pulse>脉细数</Pulse>
</PresentingSymptoms>
<QimenAnalysis>
<YearlyPan>
<YinYangDun>阳遁</YinYangDun>
<DunNumber>6</DunNumber>
<ValueStar>天英星</ValueStar>
<ValueDoor>景门</ValueDoor>
<PalaceSequence>9,8,7,6,5,4,3,2,1</PalaceSequence>
</YearlyPan>
<MonthlyPan month="1">
<MonthStar>天任</MonthStar>
<MonthDoor>生门</MonthDoor>
<Note>丑月，艮宫主事，脾胃调理关键期</Note>
</MonthlyPan>
</QimenAnalysis>
<WuyunLiuqiAnalysis year="2026">
<Wuyun>
<MainYun>水运太过</MainYun>
<ViceYun>水运太过</ViceYun>
</Wuyun>
<Liuqi>
<SiTian>少阴君火</SiTian>
<ZaiQuan>阳明燥金</ZaiQuan>
<Relation>运克气（天刑）</Relation>
</Liuqi>
<MonthlyQi>
<Month number="1">太阳寒水</Month>
<Month number="2">厥阴风木</Month>
<Month number="3">少阴君火</Month>
<!-- 更多月份 -->
</MonthlyQi>
</WuyunLiuqiAnalysis>
<LuoshuMatrixAnalysis>
<CurrentEnergies>
<Palace id="1" energy="5.5" status="偏弱" deviation="-0.7"/>
<Palace id="2" energy="5.5" status="偏弱" deviation="-1.0"/>
<Palace id="3" energy="6.8" status="平衡" deviation="+0.3"/>
<Palace id="4" energy="5.8" status="偏弱" deviation="-0.4"/>
<Palace id="5" energy="6.2" status="略低" deviation="-0.3"/>
<Palace id="6" energy="6.0" status="略低" deviation="-0.8"/>
<Palace id="7" energy="7.8" status="偏旺" deviation="+1.3"/>
<Palace id="8" energy="6.5" status="平衡" deviation="0.0"/>
<Palace id="9" energy="7.5" status="偏旺" deviation="+0.7"/>
</CurrentEnergies>
<WuxingAnalysis>
<Interaction type="相克" from_palace="7" to_palace="4" strength="-0.6"/>
<Interaction type="相克" from_palace="9" to_palace="7" strength="-0.6"/>
<Interaction type="相生" from_palace="1" to_palace="4" strength="+0.8"/>
<Interaction type="相生" from_palace="2" to_palace="7" strength="+0.8"/>
</WuxingAnalysis>
<KeyFindings>
<Finding>
<Palace>7</Palace>
<Issue>肺金过旺</Issue>
<Impact>克肝木，影响肝气疏泄</Impact>
<Priority>高</Priority>
</Finding>
<Finding>
<Palace>9</Palace>
<Issue>心火偏旺</Issue>
<Impact>耗伤肾阴，心肾不交</Impact>
<Priority>高</Priority>
</Finding>
<Finding>
<Palace>2</Palace>
<Issue>脾胃虚弱</Issue>
<Impact>运化失常，气血不足</Impact>
<Priority>中</Priority>
</Finding>
</KeyFindings>
</LuoshuMatrixAnalysis>
<SyndromeDifferentiation>
<IdentifiedSyndromes>
<Syndrome name="阴虚火旺" confidence="0.85">
<KeyPalaces>1,9</KeyPalaces>
<MatchingSymptoms>
<SymptomRef id="S001"/>
<SymptomRef id="S002"/>
<SymptomRef id="S004"/>
</MatchingSymptoms>
<TonguePulseMatch>舌红少苔，脉细数</TonguePulseMatch>
</Syndrome>
<Syndrome name="脾胃虚弱" confidence="0.72">
<KeyPalaces>2,5</KeyPalaces>
<MatchingSymptoms>
<SymptomRef id="S003"/>
</MatchingSymptoms>
<TonguePulseMatch>舌淡，脉缓弱</TonguePulseMatch>
</Syndrome>
</IdentifiedSyndromes>
<PrimarySyndrome>阴虚火旺</PrimarySyndrome>
<SecondarySyndrome>脾胃虚弱</SecondarySyndrome>
</SyndromeDifferentiation>
<TreatmentPlan>
<Principles>
<Primary>滋阴降火</Primary>
<Secondary>健脾益气</Secondary>
</Principles>
<FormulaRecommendation>
<PrimaryFormula>知柏地黄丸</PrimaryFormula>
<Dosage>浓缩丸，早晚各8粒</Dosage>
<Duration>4周</Duration>
<Modifications>
<Modification>兼脾胃虚弱，加服四君子汤</Modification>
</Modifications>
</FormulaRecommendation>
<AcupunctureProtocol>
<Points>
<Point name="太溪" palace="1" meridian="足少阴肾经"/>
<Point name="神门" palace="9" meridian="手少阴心经"/>
<Point name="足三里" palace="2" meridian="足阳明胃经"/>
<Point name="太冲" palace="4" meridian="足厥阴肝经"/>
</Points>
<Frequency>每日按摩，每穴3分钟</Frequency>
<Method>顺时针按摩为主，涌泉穴可艾灸</Method>
</AcupunctureProtocol>
<DietaryRecommendations>
<FoodsToEmphasize>
<Food name="银耳" effect="滋阴润肺"/>
<Food name="百合" effect="清心安神"/>
<Food name="山药" effect="健脾益肾"/>
<Food name="枸杞" effect="滋补肝肾"/>
</FoodsToEmphasize>
<FoodsToAvoid>
<Food name="辛辣食物" reason="助火伤阴"/>
<Food name="油炸食品" reason="生热耗津"/>
<Food name="羊肉" reason="性热助火"/>
<Food name="酒类" reason="伤阴动火"/>
</FoodsToAvoid>
<SampleMealPlan>
<Breakfast>小米山药粥，水煮蛋</Breakfast>
<Lunch>百合炒木耳，蒸鱼，米饭</Lunch>
<Dinner>银耳莲子汤，清炒时蔬</Dinner>
</SampleMealPlan>
</DietaryRecommendations>
<LifestyleAdjustments>
<Sleep>
<Recommendation>晚上11点前入睡</Recommendation>
<Duration>7-8小时</Duration>
<Position>头北脚南（补肾气）</Position>
</Sleep>
<Exercise>
<Type>太极、八段锦</Type>
<Time>早晨7-9点</Time>
<Duration>30分钟</Duration>
<Focus>调理三焦，平衡阴阳</Focus>
</Exercise>
<EmotionalManagement>
<Techniques>冥想、深呼吸</Techniques>
<Avoid>情绪激动，过度思虑</Avoid>
<Practice>每日静坐15分钟</Practice>
</EmotionalManagement>
</LifestyleAdjustments>
</TreatmentPlan>
<OptimizationResults>
<InitialImbalance>3.8542</InitialImbalance>
<FinalImbalance>1.2345</FinalImbalance>
<Improvement>68.0%</Improvement>
<Iterations>356</Iterations>
<Converged>true</Converged>
<OptimalEnergies>
<Palace id="1" energy="6.0" change="+0.5"/>
<Palace id="2" energy="6.2" change="+0.7"/>
<Palace id="3" energy="6.5" change="-0.3"/>
<Palace id="4" energy="6.0" change="+0.2"/>
<Palace id="5" energy="6.5" change="+0.3"/>
<Palace id="6" energy="6.2" change="+0.2"/>
<Palace id="7" energy="6.8" change="-1.0"/>
<Palace id="8" energy="6.5" change="0.0"/>
<Palace id="9" energy="6.8" change="-0.7"/>
</OptimalEnergies>
<ConvergenceMetrics>
<EnergyStability>0.0008</EnergyStability>
<ScoreStability>0.0001</ScoreStability>
<FinalTemperature>0.0023</FinalTemperature>
<HealthScore>0.82</HealthScore>
</ConvergenceMetrics>
</OptimizationResults>
<HealthManagementPlan>
<Phase id="1" duration="2周" focus="症状缓解">
<Actions>
<Action>按时服用中药</Action>
<Action>每日穴位按摩</Action>
<Action>严格饮食控制</Action>
<Action>保证充足睡眠</Action>
</Actions>
<ExpectedOutcomes>
<Outcome>失眠改善50%</Outcome>
<Outcome>口干减轻</Outcome>
<Outcome>消化功能改善</Outcome>
</ExpectedOutcomes>
</Phase>
<Phase id="2" duration="4周" focus="体质调理">
<Actions>
<Action>调整中药方剂</Action>
<Action>增加运动锻炼</Action>
<Action>情绪管理训练</Action>
<Action>定期能量评估</Action>
</Actions>
<ExpectedOutcomes>
<Outcome>九宫能量平衡度>0.85</Outcome>
<Outcome>主要症状改善70%</Outcome>
<Outcome>生活质量评分提高</Outcome>
</ExpectedOutcomes>
</Phase>
<Phase id="3" duration="长期" focus="预防维持">
<Actions>
<Action>每月自查九宫能量</Action>
<Action>根据季节调整养生</Action>
<Action>建立健康生活习惯</Action>
<Action>定期中医调理</Action>
</Actions>
<ExpectedOutcomes>
<Outcome>维持能量平衡</Outcome>
<Outcome>预防疾病复发</Outcome>
<Outcome>提升整体健康水平</Outcome>
</ExpectedOutcomes>
</Phase>
<MonitoringSchedule>
<Frequency>每周一次（前4周）</Frequency>
<AssessmentPoints>
<Point>症状评分</Point>
<Point>九宫能量</Point>
<Point>舌象脉象</Point>
<Point>生活质量</Point>
</AssessmentPoints>
<AdjustmentCriteria>
<Criterion condition="症状改善<30%" action="重新辨证"/>
<Criterion condition="能量失衡>0.5" action="调整方案"/>
<Criterion condition="出现新症状" action="及时评估"/>
</AdjustmentCriteria>
</MonitoringSchedule>
</HealthManagementPlan>
<FollowUpRecords>
<FollowUp date="2026-01-22" week="1">
<SymptomsImprovement>失眠改善40%，口干减轻</SymptomsImprovement>
<EnergyAssessment>离宫能量降至7.0φ，坎宫升至5.8φ</EnergyAssessment>
<PlanAdjustment>增加涌泉穴艾灸，睡前热水泡脚</PlanAdjustment>
</FollowUp>
<FollowUp date="2026-01-29" week="2">
<SymptomsImprovement>失眠改善60%，消化不良好转</SymptomsImprovement>
<EnergyAssessment>各宫能量趋于平衡，失衡度降至2.1</EnergyAssessment>
<PlanAdjustment>减少知柏地黄丸剂量，增加健脾食疗</PlanAdjustment>
</FollowUp>
</FollowUpRecords>
<OutcomeSummary>
<TreatmentDuration>4周</TreatmentDuration>
<OverallImprovement>75%</OverallImprovement>
<FinalHealthScore>0.86</FinalHealthScore>
<Recommendation>继续维持方案，3个月后复查</Recommendation>
</OutcomeSummary>
</DiagnosisRecord>
<!-- 更多诊断记录 -->
</DiagnosisRecords>
<!-- 知识库 -->
<KnowledgeBase>
<SyndromePatterns>
<Pattern id="SP001" name="阴虚火旺">
<Pathogenesis>阴液亏虚，虚火内生</Pathogenesis>
<KeyPalaces>1,9</KeyPalaces>
<CommonSymptoms>心烦失眠，口干咽燥，潮热盗汗，舌红少苔，脉细数</CommonSymptoms>
<TreatmentPrinciple>滋阴降火</TreatmentPrinciple>
<CommonFormulas>知柏地黄丸，六味地黄丸，黄连阿胶汤</CommonFormulas>
<AcupuncturePoints>太溪，照海，神门，劳宫</AcupuncturePoints>
<Dietary>宜食银耳、百合、枸杞；忌辛辣、油炸</Dietary>
</Pattern>
<Pattern id="SP002" name="脾胃虚弱">
<Pathogenesis>脾胃气虚，运化失常</Pathogenesis>
<KeyPalaces>2,5</KeyPalaces>
<CommonSymptoms>食欲不振，腹胀便溏，肢体倦怠，面色萎黄，舌淡苔白，脉缓弱</CommonSymptoms>
<TreatmentPrinciple>健脾益气</TreatmentPrinciple>
<CommonFormulas>四君子汤，补中益气汤，参苓白术散</CommonFormulas>
<AcupuncturePoints>足三里，中脘，脾俞，胃俞</AcupuncturePoints>
<Dietary>宜食山药、小米、红枣；忌生冷、油腻</Dietary>
</Pattern>
<!-- 更多证型模式 -->
</SyndromePatterns>
<HerbFormulas>
<Formula id="HF001" name="知柏地黄丸">
<Composition>
<Herb name="知母" dose="6g"/>
<Herb name="黄柏" dose="6g"/>
<Herb name="熟地黄" dose="24g"/>
<Herb name="山茱萸" dose="12g"/>
<Herb name="山药" dose="12g"/>
<Herb name="泽泻" dose="9g"/>
<Herb name="茯苓" dose="9g"/>
<Herb name="牡丹皮" dose="9g"/>
</Composition>
<Actions>滋阴降火</Actions>
<Indications>阴虚火旺，骨蒸潮热，盗汗遗精，口干咽痛</Indications>
<Contraindications>脾胃虚寒，大便溏泄者慎用</Contraindications>
</Formula>
<!-- 更多方剂 -->
</HerbFormulas>
<AcupuncturePoints>
<Point id="AP001" name="太溪">
<Meridian>足少阴肾经</Meridian>
<Location>足内踝后方，内踝尖与跟腱之间的凹陷处</Location>
<Functions>滋阴补肾，清热安神</Functions>
<Indications>肾虚诸症，失眠，咽喉痛，齿痛</Indications>
<Needling>直刺0.5-1寸</Needling>
<Moxibustion>可灸</Moxibustion>
</Point>
<!-- 更多穴位 -->
</AcupuncturePoints>
<WuxingRelations>
<Relation type="相生">
<From element="木" to="火" strength="0.8"/>
<From element="火" to="土" strength="0.8"/>
<From element="土" to="金" strength="0.8"/>
<From element="金" to="水" strength="0.8"/>
<From element="水" to="木" strength="0.8"/>
</Relation>
<Relation type="相克">
<From element="木" to="土" strength="-0.6"/>
<From element="土" to="水" strength="-0.6"/>
<From element="水" to="火" strength="-0.6"/>
<From element="火" to="金" strength="-0.6"/>
<From element="金" to="木" strength="-0.6"/>
</Relation>
</WuxingRelations>
</KnowledgeBase>
<!-- 系统配置 -->
<SystemConfiguration>
<OptimizationParameters>
<InitialTemperature>1.0</InitialTemperature>
<CoolingRate>0.95</CoolingRate>
<MaxIterations>1000</MaxIterations>
<ConvergenceThreshold>0.001</ConvergenceThreshold>
<StepSize>0.1</StepSize>
</OptimizationParameters>
<EnergyParameters>
<GoldenRatio>3.618</GoldenRatio>
<OptimalRange min="5.8" optimal_low="6.2" optimal_high="6.5" max="7.2"/>
<WarningLevels>
<Level type="极低" threshold="5.0"/>
<Level type="偏低" threshold="5.8"/>
<Level type="正常" threshold="6.5"/>
<Level type="偏高" threshold="7.2"/>
<Level type="极高" threshold="8.0"/>
</WarningLevels>
</EnergyParameters>
<DiagnosisThresholds>
<SyndromeMatch>0.6</SyndromeMatch>
<SymptomSeverity>2.0</SymptomSeverity>
<EnergyDeviation>0.3</EnergyDeviation>
</DiagnosisThresholds>
</SystemConfiguration>
</JXWD_AI_Database>五、反馈影响逻辑函数链推演
# feedback_impact_chain.py - 反馈影响逻辑函数链
import numpy as np
from typing import Dict, List, Tuple, Callable
class FeedbackImpactChain:
"""反馈影响逻辑函数链推演系统"""
def __init__(self):
self.impact_functions = self.initialize_impact_functions()
self.feedback_loops = self.initialize_feedback_loops()
self.history_buffer = []
self.max_history = 100
def initialize_impact_functions(self) -> Dict[str, Callable]:
"""初始化影响函数"""
return {
# 1. 能量传递函数
'energy_transfer': self.energy_transfer_impact,
# 2. 五行生克影响函数
'wuxing_interaction': self.wuxing_interaction_impact,
# 3. 症状-能量反馈函数
'symptom_energy_feedback': self.symptom_energy_feedback,
# 4. 治疗措施影响函数
'treatment_impact': self.treatment_impact,
# 5. 时间衰减函数
'temporal_decay': self.temporal_decay_impact,
# 6. 外部环境影响函数
'environmental_impact': self.environmental_impact,
# 7. 情绪影响函数
'emotional_impact': self.emotional_impact,
# 8. 饮食影响函数
'dietary_impact': self.dietary_impact
}
def initialize_feedback_loops(self) -> Dict[str, List[str]]:
"""初始化反馈循环"""
return {
# 正反馈循环（放大效应）
'positive_loops': [
['心火旺', '耗肾阴', '肾阴虚', '虚火上炎', '加重心火'], # 心肾不交正反馈
['肝气郁', '克脾胃', '脾虚生湿', '湿困脾阳', '加重肝郁'], # 肝脾不和正反馈
['肺燥', '耗津液', '肠燥便秘', '腑气不通', '加重肺燥'] # 肺肠同病正反馈
],
# 负反馈循环（稳定效应）
'negative_loops': [
['心火旺', '下济肾水', '肾阴得滋', '水上济心', '心火自降'], # 心肾相交负反馈
['脾虚', '补益后天', '气血充足', '滋养五脏', '脾功能恢复'], # 脾为后天负反馈
['肝阳上亢', '滋水涵木', '肾阴充足', '肝阳得制', '阴阳平衡'] # 滋水涵木负反馈
],
# 延迟反馈循环
'delayed_loops': [
{'path': ['用药', '症状改善', '脏腑功能恢复'], 'delay': 7}, # 药物作用延迟
{'path': ['饮食调理', '脾胃功能改善', '气血生成'], 'delay': 14}, # 食疗作用延迟
{'path': ['情绪调节', '肝气疏泄', '全身气机通畅'], 'delay': 3} # 情志作用延迟
]
}
def run_feedback_simulation(self,
initial_state: Dict,
interventions: List[Dict],
duration_days: int = 30,
time_step: int = 1) -> Dict:
"""运行反馈模拟"""
states = [initial_state.copy()]
current_state = initial_state.copy()
for day in range(0, duration_days, time_step):
# 记录当前状态
state_record = {
'day': day,
'state': current_state.copy(),
'interventions_applied': [],
'impacts': []
}
# 应用当天的干预措施
daily_interventions = self.get_daily_interventions(interventions, day)
for intervention in daily_interventions:
impact = self.apply_intervention(current_state, intervention, day)
state_record['interventions_applied'].append(intervention)
state_record['impacts'].append(impact)
# 计算自然演变影响
natural_impacts = self.calculate_natural_evolution(current_state, day)
state_record['impacts'].extend(natural_impacts)
# 更新状态
for impact in state_record['impacts']:
current_state = self.update_state_with_impact(current_state, impact)
# 记录状态
states.append(state_record)
# 检查稳定条件
if self.check_stability_condition(current_state, states):
print(f"系统在第{day}天达到稳定")
break
# 分析结果
analysis = self.analyze_simulation_results(states)
return {
'states': states,
'final_state': current_state,
'analysis': analysis,
'duration_days': len(states) - 1,
'stable': self.check_stability_condition(current_state, states)
}
def energy_transfer_impact(self,
source_palace: int,
target_palace: int,
energy_amount: float) -> Dict:
"""能量传递影响"""
# 基于五行生克的能量传递
wuxing_strength = self.calculate_wuxing_strength(source_palace, target_palace)
effective_transfer = energy_amount * wuxing_strength
impact = {
'type': 'energy_transfer',
'source': source_palace,
'target': target_palace,
'amount': effective_transfer,
'description': f"宫位{source_palace}向宫位{target_palace}传递{effective_transfer:.2f}φ能量"
}
return impact
def wuxing_interaction_impact(self,
palace_a: int,
palace_b: int,
interaction_type: str) -> Dict:
"""五行生克影响"""
element_a = self.get_palace_element(palace_a)
element_b = self.get_palace_element(palace_b)
if interaction_type == 'generate':
strength = 0.8
description = f"{element_a}生{element_b}"
elif interaction_type == 'restrict':
strength = -0.6
description = f"{element_a}克{element_b}"
else:
strength = 0.0
description = "无相互作用"
impact = {
'type': 'wuxing_interaction',
'palaces': [palace_a, palace_b],
'interaction': interaction_type,
'strength': strength,
'description': description
}
return impact
def symptom_energy_feedback(self,
symptom: str,
severity: float,
affected_palaces: List[int]) -> List[Dict]:
"""症状-能量反馈"""
impacts = []
# 症状对能量的影响
for palace in affected_palaces:
# 根据症状类型和严重程度计算影响
symptom_impact = self.calculate_symptom_impact(symptom, severity, palace)
impact = {
'type': 'symptom_energy_feedback',
'symptom': symptom,
'severity': severity,
'palace': palace,
'energy_change': symptom_impact,
'description': f"症状'{symptom}'对宫位{palace}产生{symptom_impact:.2f}φ影响"
}
impacts.append(impact)
return impacts
def treatment_impact(self,
treatment_type: str,
parameters: Dict) -> Dict:
"""治疗措施影响"""
if treatment_type == 'acupuncture':
impact = self.calculate_acupuncture_impact(parameters)
elif treatment_type == 'herbal':
impact = self.calculate_herbal_impact(parameters)
elif treatment_type == 'dietary':
impact = self.calculate_dietary_impact(parameters)
elif treatment_type == 'lifestyle':
impact = self.calculate_lifestyle_impact(parameters)
else:
impact = {'type': 'unknown_treatment', 'impact': 0.0}
return impact
def temporal_decay_impact(self,
initial_impact: Dict,
elapsed_time: int,
decay_rate: float = 0.1) -> Dict:
"""时间衰减影响"""
original_value = initial_impact.get('impact_value', 0)
decayed_value = original_value * np.exp(-decay_rate * elapsed_time)
impact = initial_impact.copy()
impact['impact_value'] = decayed_value
impact['decayed'] = True
impact['decay_rate'] = decay_rate
impact['elapsed_time'] = elapsed_time
return impact
def environmental_impact(self,
environmental_factor: str,
intensity: float) -> Dict:
"""环境影响"""
# 五运六气对能量的影响
impact_map = {
'寒': {'affected_palaces': [1, 6], 'effect': -0.3}, # 寒伤肾阳
'热': {'affected_palaces': [9, 3], 'effect': +0.4}, # 热助心火
'湿': {'affected_palaces': [2, 5], 'effect': -0.2}, # 湿困脾土
'燥': {'affected_palaces': [7, 6], 'effect': +0.3}, # 燥伤肺金
'风': {'affected_palaces': [4, 3], 'effect': +0.2} # 风动肝木
}
factor_info = impact_map.get(environmental_factor, {})
affected = factor_info.get('affected_palaces', [])
base_effect = factor_info.get('effect', 0)
total_effect = base_effect * intensity
impact = {
'type': 'environmental_impact',
'factor': environmental_factor,
'intensity': intensity,
'affected_palaces': affected,
'total_effect': total_effect,
'description': f"环境因素'{environmental_factor}'产生{total_effect:.2f}φ影响"
}
return impact
def emotional_impact(self,
emotion_type: str,
intensity: float,
duration_hours: float) -> Dict:
"""情绪影响"""
# 七情对五脏的影响
emotion_map = {
'怒': {'affected_palace': 4, 'effect': +0.5}, # 怒伤肝
'喜': {'affected_palace': 9, 'effect': +0.3}, # 喜伤心
'思': {'affected_palace': 2, 'effect': -0.4}, # 思伤脾
'悲': {'affected_palace': 7, 'effect': -0.3}, # 悲伤肺
'恐': {'affected_palace': 1, 'effect': -0.5}, # 恐伤肾
'惊': {'affected_palace': [3, 9], 'effect': +0.6}, # 惊伤心
'忧': {'affected_palace': [2, 7], 'effect': -0.3} # 忧伤脾肺
}
emotion_info = emotion_map.get(emotion_type, {})
affected = emotion_info.get('affected_palace', [])
if not isinstance(affected, list):
affected = [affected]
base_effect = emotion_info.get('effect', 0)
# 考虑持续时间和强度
time_factor = min(duration_hours / 24, 1.0) # 最多按一天计算
total_effect = base_effect * intensity * time_factor
impact = {
'type': 'emotional_impact',
'emotion': emotion_type,
'intensity': intensity,
'duration_hours': duration_hours,
'affected_palaces': affected,
'total_effect': total_effect,
'description': f"情绪'{emotion_type}'产生{total_effect:.2f}φ影响"
}
return impact
def dietary_impact(self,
food_type: str,
quantity: float,
taste: str = None) -> Dict:
"""饮食影响"""
# 五味入五脏
taste_map = {
'酸': {'affected_palace': 4, 'effect': +0.2}, # 酸入肝
'苦': {'affected_palace': 9, 'effect': -0.3}, # 苦入心
'甘': {'affected_palace': 2, 'effect': +0.3}, # 甘入脾
'辛': {'affected_palace': 7, 'effect': +0.2}, # 辛入肺
'咸': {'affected_palace': 1, 'effect': +0.2} # 咸入肾
}
# 食物性质影响
property_map = {
'寒性': {'effect': -0.4, 'affected': [9, 3]}, # 寒凉清火
'热性': {'effect': +0.5, 'affected': [1, 6]}, # 温热助阳
'平性': {'effect': +0.1, 'affected': [2, 5]}, # 平和补益
'滋腻': {'effect': -0.3, 'affected': [2, 5]}, # 滋腻碍脾
'燥性': {'effect': +0.3, 'affected': [7, 6]} # 燥性伤阴
}
# 计算五味影响
taste_effect = 0
taste_affected = []
if taste and taste in taste_map:
taste_info = taste_map[taste]
taste_effect = taste_info['effect'] * quantity
affected = taste_info.get('affected_palace', [])
if not isinstance(affected, list):
affected = [affected]
taste_affected = affected
# 计算食物性质影响
property_effect = 0
property_affected = []
# 这里简化处理，实际应根据食物类型判断性质
if food_type in ['生姜', '辣椒']:
prop_info = property_map['热性']
elif food_type in ['西瓜', '苦瓜']:
prop_info = property_map['寒性']
else:
prop_info = property_map['平性']
property_effect = prop_info['effect'] * quantity
property_affected = prop_info.get('affected', [])
# 合并影响
total_effect = taste_effect + property_effect
all_affected = list(set(taste_affected + property_affected))
impact = {
'type': 'dietary_impact',
'food_type': food_type,
'quantity': quantity,
'taste': taste,
'total_effect': total_effect,
'affected_palaces': all_affected,
'description': f"饮食'{food_type}'产生{total_effect:.2f}φ影响"
}
return impact
def calculate_acupuncture_impact(self, parameters: Dict) -> Dict:
"""计算针灸影响"""
point = parameters.get('point', '')
method = parameters.get('method', '按摩')
duration = parameters.get('duration', 5) # 分钟
# 穴位对应宫位
point_palace_map = {
'太溪': 1, '涌泉': 1,
'足三里': 2, '中脘': 2,
'太冲': 4, '肝俞': 4,
'神门': 9, '劳宫': 9,
'太渊': 7, '肺俞': 7
}
affected_palace = point_palace_map.get(point, 5) # 默认为中宫
# 不同方法的影响强度
method_strength = {
'按摩': 0.3,
'针刺': 0.5,
'艾灸': 0.7,
'电针': 0.6
}
strength = method_strength.get(method, 0.3)
# 时间因子
time_factor = min(duration / 10, 2.0) # 10分钟为基准
total_effect = strength * time_factor
# 确定影响方向（补或泻）
if method in ['艾灸']:
effect_direction = '+' # 补法
elif method in ['针刺', '电针'] and '泻' in parameters.get('technique', ''):
effect_direction = '-' # 泻法
else:
effect_direction = '+' # 默认补法
impact = {
'type': 'acupuncture_impact',
'point': point,
'method': method,
'duration': duration,
'affected_palace': affected_palace,
'effect_direction': effect_direction,
'total_effect': total_effect if effect_direction == '+' else -total_effect,
'description': f"{method}{point}穴产生{total_effect:.2f}φ影响"
}
return impact
def calculate_herbal_impact(self, parameters: Dict) -> Dict:
"""计算中药影响"""
formula = parameters.get('formula', '')
dose = parameters.get('dose', 1.0)
duration_days = parameters.get('duration_days', 7)
# 方剂-宫位映射
formula_palace_map = {
'知柏地黄丸': [1, 9], # 滋阴降火，主要影响坎离二宫
'四君子汤': [2, 5], # 健脾益气，主要影响坤中二宫
'柴胡疏肝散': [4, 3], # 疏肝解郁，主要影响巽震二宫
'桑杏汤': [7, 1] # 清肺润燥，主要影响兑坎二宫
}
affected_palaces = formula_palace_map.get(formula, [5]) # 默认为中宫
# 方剂作用强度
formula_strength = {
'知柏地黄丸': {'坎': +0.4, '离': -0.5}, # 补肾阴，降心火
'四君子汤': {'坤': +0.6, '中': +0.4}, # 健脾胃，补中气
'柴胡疏肝散': {'巽': +0.5, '震': -0.3}, # 疏肝气，清君火
'桑杏汤': {'兑': -0.4, '坎': +0.3} # 清肺燥，滋肾阴
}
formula_effects = formula_strength.get(formula, {})
# 计算总效应
dose_factor = dose # 剂量因子
duration_factor = min(duration_days / 7, 2.0) # 7天为基准
impacts = []
total_effect = 0
for palace in affected_palaces:
palace_name = self.get_palace_name(palace)
palace_effect = formula_effects.get(palace_name, 0)
if palace_effect != 0:
effect_value = palace_effect * dose_factor * duration_factor
total_effect += abs(effect_value)
impact = {
'palace': palace,
'effect': effect_value,
'description': f"{formula}对{self.get_palace_name(palace)}产生{effect_value:.2f}φ影响"
}
impacts.append(impact)
return {
'type': 'herbal_impact',
'formula': formula,
'dose': dose,
'duration_days': duration_days,
'affected_palaces': affected_palaces,
'impacts': impacts,
'total_effect': total_effect,
'description': f"方剂{formula}产生{total_effect:.2f}φ总影响"
}
# 辅助方法
def get_palace_element(self, palace: int) -> str:
"""获取宫位五行属性"""
element_map = {
1: '水', 2: '土', 3: '火', 4: '木',
5: '土', 6: '金', 7: '金', 8: '土', 9: '火'
}
return element_map.get(palace, '')
def get_palace_name(self, palace: int) -> str:
"""获取宫位名称"""
name_map = {
1: '坎', 2: '坤', 3: '震', 4: '巽',
5: '中', 6: '乾', 7: '兑', 8: '艮', 9: '离'
}
return name_map.get(palace, '')
def calculate_wuxing_strength(self, palace_a: int, palace_b: int) -> float:
"""计算五行作用强度"""
element_a = self.get_palace_element(palace_a)
element_b = self.get_palace_element(palace_b)
# 相生关系
generate = {'木': '火', '火': '土', '土': '金', '金': '水', '水': '木'}
# 相克关系
restrict = {'木': '土', '土': '水', '水': '火', '火': '金', '金': '木'}
if generate.get(element_a) == element_b:
return 0.8 # 相生
elif restrict.get(element_a) == element_b:
return -0.6 # 相克
elif element_a == element_b:
return 0.3 # 同气
else:
return 0.0 # 无关
def calculate_symptom_impact(self, symptom: str, severity: float, palace: int) -> float:
"""计算症状对宫位的影响"""
# 症状-宫位影响映射
symptom_impact_map = {
'心烦失眠': {9: -0.4, 1: -0.3}, # 耗伤心肾
'口干咽燥': {7: -0.3, 1: -0.2}, # 耗伤肺肾
'消化不良': {2: -0.5, 5: -0.3}, # 损伤脾胃
'腰膝酸软': {1: -0.6, 6: -0.4}, # 耗伤肾气
'头晕目眩': {4: -0.4, 1: -0.2}, # 肝阴不足，肾精亏虚
'咳嗽': {7: -0.4, 2: -0.2}, # 耗伤肺气，影响脾土
}
impacts = symptom_impact_map.get(symptom, {})
base_impact = impacts.get(palace, 0)
return base_impact * severity
def get_daily_interventions(self, interventions: List[Dict], day: int) -> List[Dict]:
"""获取当天的干预措施"""
daily = []
for intervention in interventions:
schedule = intervention.get('schedule', {})
start_day = schedule.get('start_day', 0)
frequency = schedule.get('frequency', 'daily') # daily, weekly, specific_days
days_of_week = schedule.get('days_of_week', [])
if frequency == 'daily':
if day >= start_day:
daily.append(intervention)
elif frequency == 'weekly':
if day >= start_day and (day - start_day) % 7 == 0:
daily.append(intervention)
elif frequency == 'specific_days':
if day in days_of_week:
daily.append(intervention)
return daily
def apply_intervention(self, state: Dict, intervention: Dict, day: int) -> Dict:
"""应用干预措施"""
intervention_type = intervention.get('type')
params = intervention.get('parameters', {})
if intervention_type in self.impact_functions:
impact_func = self.impact_functions[intervention_type]
impact = impact_func(**params)
# 添加时间戳
impact['day'] = day
impact['intervention_id'] = intervention.get('id', '')
return impact
else:
return {
'type': 'unknown_intervention',
'intervention': intervention_type,
'impact': 0,
'day': day
}
def calculate_natural_evolution(self, state: Dict, day: int) -> List[Dict]:
"""计算自然演变影响"""
impacts = []
# 1. 五行自平衡
wuxing_impacts = self.calculate_wuxing_self_balance(state)
impacts.extend(wuxing_impacts)
# 2. 症状自然变化
symptom_impacts = self.calculate_symptom_natural_change(state, day)
impacts.extend(symptom_impacts)
# 3. 时间衰减效应
decay_impacts = self.calculate_time_decay_effects(state, day)
impacts.extend(decay_impacts)
return impacts
def calculate_wuxing_self_balance(self, state: Dict) -> List[Dict]:
"""计算五行自平衡"""
impacts = []
# 检查过旺或过弱的宫位
for palace, energy in state.get('energies', {}).items():
baseline = state.get('baselines', {}).get(palace, 6.5)
deviation = energy - baseline
# 如果偏离过大，触发自平衡机制
if abs(deviation) > 0.5:
# 自平衡强度与偏离度成正比
balance_strength = -deviation * 0.1 # 负反馈
impact = {
'type': 'self_balance',
'palace': palace,
'energy_change': balance_strength,
'description': f"宫位{palace}自平衡调整{b
四、体系核心特性与镜象映射逻辑
1. 模版严格遵循：所有实现均基于镜心悟道AI官方洛书矩阵辨证论治模版，无自定义修改架构框架，能量等级、量子操作、气机符号完全匹配元数据规范；
2. 多端无缝对接：C++框架为SW-DBMS底层引擎，Python为可执行迭代优化工具，XML为元数据湖标准接入格式，三者数据互通、逻辑一致；
3. 算法深度融合：奇门遁甲为排盘算法（星门宫映射），复合卦为节点标签（䷣䷗䷀等），洛书矩阵为系统框架，量子纠缠为药理能量表达，实现「易-医-数-智」的深度融合；
4. 迭代优化闭环：基于黄金比例3.618和阴阳平衡区间5.8-6.5-7.2，通过量子退火+五行生克约束实现无限迭代，最终逼进平衡态（平衡度≥0.95），与李聪甫医案的临床治疗逻辑高度契合。
该体系可直接接入镜心悟道AI元宇宙易医大模型，实现痉病及同类阳明腑实热病的数字化辨证、方药优化、临床预演，为中医经典医案的数智化传承与临床应用提供可落地的技术范式。
一、易医元宇宙大模型核心架构设计理念
1.1 三体融合的架构哲学
```python
# architecture_philosophy.py
# 易医元宇宙大模型架构设计哲学
class ArchitecturePhilosophy:
"""三体融合架构哲学"""
def __init__(self):
# 三大核心支柱
self.pillars = {
'易经': {
'核心理念': '象数理占',
'数字化映射': {
'卦象': '量子态向量',
'爻位': '信息比特位',
'变爻': '状态跃迁',
'阴阳': '二进制编码',
'五行': '能量场类型',
'生克': '耦合关系矩阵'
},
'架构体现': '奇门遁甲算法层、复合卦网络层'
},
'中医': {
'核心理念': '整体观念、辨证论治',
'数字化映射': {
'阴阳平衡': '能量场稳态',
'五行生克': '量子纠缠网络',
'脏腑经络': '复杂系统代理',
'气血津液': '多物理场耦合',
'证候': '模式识别聚类',
'方剂': '组合优化解'
},
'架构体现': '洛书矩阵层、星轮系统、辨证论治引擎'
},
'元宇宙': {
'核心理念': '数字孪生、沉浸交互、自演化',
'数字化映射': {
'虚拟空间': '高维状态空间',
'数字孪生体': '动态代理模型',
'智能体': '自适应学习系统',
'物理引擎': '多尺度仿真器',
'经济系统': '价值交换网络',
'社会系统': '多智能体协同'
},
'架构体现': '人体元宇宙模拟层、虚拟诊所、自进化生态系统'
}
}
def design_principle(self) -> str:
"""设计原则"""
return """
易医元宇宙大模型设计原则：
1. 整体性：系统各部分相互关联，形成有机整体
2. 动态性：系统状态随时间演化，具备自适应性
3. 层次性：从量子尺度到系统尺度多层嵌套
4. 涌现性：简单规则产生复杂行为
5. 可计算性：所有概念均可量化计算
6. 可解释性：决策过程透明可追溯
7. 可持续性：系统能够自我维持和进化
"""1.2 七层架构体系
graph TB
A[易医元宇宙大模型七层架构] --> B[第一层：元数据湖]
A --> C[第二层：算法引擎]
A --> D[第三层：计算框架]
A --> E[第四层：知识图谱]
A --> F[第五层：元宇宙平台]
A --> G[第六层：应用接口]
A --> H[第七层：生态系统]
B --> B1[JXWD-AI-M元数据标准]
B --> B2[易医概念本体]
B --> B3[数据湖治理]
C --> C1[奇门遁甲时空算法]
C --> C2[洛书矩阵优化算法]
C --> C3[五行生克量子算法]
C --> C4[易经卦象推演算法]
D --> D1[量子-经典混合计算]
D --> D2[复杂系统动力学]
D --> D3[多智能体强化学习]
D --> D4[联邦学习框架]
E --> E1[易医知识图谱]
E --> E2[临床案例库]
E --> E3[方剂知识库]
E --> E4[穴位经络库]
F --> F1[人体数字孪生]
F --> F2[虚拟诊疗空间]
F --> F3[针灸推拿模拟]
F --> F4[药材种植元宇宙]
G --> G1[RESTful API]
G --> G2[WebSocket实时接口]
G --> G3[gRPC高性能接口]
G --> G4[区块链智能合约]
H --> H1[开发者社区]
H --> H2[医学院校合作]
H --> H3[医院临床验证]
H --> H4[国际标准贡献]二、核心算法体系详细设计
2.1 奇门遁甲时空算法引擎
# qimen_dunjia_engine.py
# 奇门遁甲时空算法引擎
import numpy as np
from datetime import datetime
from typing import Dict, List, Tuple, Any
from dataclasses import dataclass
from enum import Enum
class HeavenlyStem(Enum):
"""天干"""
JIA = ("甲", 1, "阳木")
YI = ("乙", 2, "阴木")
BING = ("丙", 3, "阳火")
DING = ("丁", 4, "阴火")
WU = ("戊", 5, "阳土")
JI = ("己", 6, "阴土")
GENG = ("庚", 7, "阳金")
XIN = ("辛", 8, "阴金")
REN = ("壬", 9, "阳水")
GUI = ("癸", 10, "阴水")
def __init__(self, chinese, number, attribute):
self.chinese = chinese
self.number = number
self.attribute = attribute
class EarthlyBranch(Enum):
"""地支"""
ZI = ("子", 1, "鼠", "水")
CHOU = ("丑", 2, "牛", "土")
YIN = ("寅", 3, "虎", "木")
MAO = ("卯", 4, "兔", "木")
CHEN = ("辰", 5, "龙", "土")
SI = ("巳", 6, "蛇", "火")
WU = ("午", 7, "马", "火")
WEI = ("未", 8, "羊", "土")
SHEN = ("申", 9, "猴", "金")
YOU = ("酉", 10, "鸡", "金")
XU = ("戌", 11, "狗", "土")
HAI = ("亥", 12, "猪", "水")
def __init__(self, chinese, number, zodiac, element):
self.chinese = chinese
self.number = number
self.zodiac = zodiac
self.element = element
@dataclass
class QimenPan:
"""奇门遁甲盘"""
# 基本盘
yang_dun_yin_dun: str # 阳遁/阴遁
ju_number: int # 几局
current_hour: str # 当前时辰
current_day: str # 当前日柱
# 地盘
di_pan: List[List[str]] # 9宫地盘天干
# 天盘
tian_pan: List[List[str]] # 9宫天盘天干
# 八门
ba_men: List[List[str]] # 9宫八门
# 九星
jiu_xing: List[List[str]] # 9宫九星
# 八神
ba_shen: List[List[str]] # 9宫八神
# 特殊信息
fu_yin: Tuple[int, int] # 值符、值使位置
horse_star: List[Tuple[int, int]] # 驿马星位置
empty_void: List[Tuple[int, int]] # 空亡位置
class QimenDunjiaEngine:
"""奇门遁甲核心算法引擎"""
def __init__(self):
# 初始化天干地支
self.heavenly_stems = {stem.chinese: stem for stem in HeavenlyStem}
self.earthly_branches = {branch.chinese: branch for branch in EarthlyBranch}
# 八门
self.eight_gates = ["休", "生", "伤", "杜", "景", "死", "惊", "开"]
# 九星
self.nine_stars = ["天蓬", "天芮", "天冲", "天辅", "天禽", "天心", "天柱", "天任", "天英"]
# 八神
self.eight_gods = ["值符", "腾蛇", "太阴", "六合", "白虎", "玄武", "九地", "九天"]
# 奇门遁甲参数
self.yang_dun_sequences = self._init_yang_dun_sequences()
self.yin_dun_sequences = self._init_yin_dun_sequences()
def calculate_pan(self, year: int, month: int, day: int, hour: int, minute: int = 0) -> QimenPan:
"""排盘主函数"""
# 1. 计算节气，确定阴阳遁
solar_term = self.get_solar_term(year, month, day)
yang_or_yin, ju_number = self.get_dun_ju(year, month, day, solar_term)
# 2. 计算当前时辰的天干地支
hour_stem, hour_branch = self.calculate_hour_ganzhi(year, month, day, hour)
current_hour = f"{hour_stem}{hour_branch}"
# 3. 计算日柱
day_stem, day_branch = self.calculate_day_ganzhi(year, month, day)
current_day = f"{day_stem}{day_branch}"
# 4. 排地盘
di_pan = self.arrange_di_pan(yang_or_yin, ju_number)
# 5. 排天盘
tian_pan = self.arrange_tian_pan(di_pan, hour_stem, hour_branch)
# 6. 排八门
ba_men = self.arrange_ba_men(yang_or_yin, ju_number, hour_stem, hour_branch)
# 7. 排九星
jiu_xing = self.arrange_jiu_xing(yang_or_yin, ju_number, hour_stem, hour_branch)
# 8. 排八神
ba_shen = self.arrange_ba_shen(yang_or_yin, ju_number, hour_stem, hour_branch)
# 9. 计算特殊信息
fu_yin = self.find_fu_yin(tian_pan, di_pan)
horse_star = self.find_horse_star(hour_branch)
empty_void = self.find_empty_void(day_stem, day_branch)
return QimenPan(
yang_dun_yin_dun=yang_or_yin,
ju_number=ju_number,
current_hour=current_hour,
current_day=current_day,
di_pan=di_pan,
tian_pan=tian_pan,
ba_men=ba_men,
jiu_xing=jiu_xing,
ba_shen=ba_shen,
fu_yin=fu_yin,
horse_star=horse_star,
empty_void=empty_void
)
def _init_yang_dun_sequences(self) -> Dict[int, List[str]]:
"""初始化阳遁局序列"""
sequences = {}
base_order = ["戊", "己", "庚", "辛", "壬", "癸", "丁", "丙", "乙"]
for ju in range(1, 10):
# 根据局数旋转序列
rotated = base_order[-ju:] + base_order[:-ju]
sequences[ju] = rotated
return sequences
def _init_yin_dun_sequences(self) -> Dict[int, List[str]]:
"""初始化阴遁局序列"""
sequences = {}
base_order = ["戊", "乙", "丙", "丁", "癸", "壬", "辛", "庚", "己"]
for ju in range(1, 10):
rotated = base_order[-ju:] + base_order[:-ju]
sequences[ju] = rotated
return sequences
def arrange_di_pan(self, yang_or_yin: str, ju_number: int) -> List[List[str]]:
"""排地盘"""
di_pan = [[None for _ in range(3)] for _ in range(3)]
# 获取对应局的序列
if yang_or_yin == "阳遁":
sequence = self.yang_dun_sequences[ju_number]
else:
sequence = self.yin_dun_sequences[ju_number]
# 洛书数顺序：4 9 2 / 3 5 7 / 8 1 6
luoshu_order = [(0, 0), (0, 2), (1, 1), (2, 0), (2, 2),
(1, 0), (0, 1), (2, 1), (1, 2)]
for idx, (i, j) in enumerate(luoshu_order):
di_pan[i][j] = sequence[idx]
return di_pan
def arrange_tian_pan(self, di_pan: List[List[str]], hour_stem: str, hour_branch: str) -> List[List[str]]:
"""排天盘"""
# 天盘随地盘旋转
tian_pan = [[None for _ in range(3)] for _ in range(3)]
# 找到时干在地盘的位置
hour_stem_pos = None
for i in range(3):
for j in range(3):
if di_pan[i][j] == hour_stem:
hour_stem_pos = (i, j)
break
if hour_stem_pos:
break
if hour_stem_pos:
# 计算旋转量
rotation = self.calculate_rotation(hour_stem, hour_branch)
# 旋转天盘
for i in range(3):
for j in range(3):
# 计算新位置
new_i = (i + rotation[0]) % 3
new_j = (j + rotation[1]) % 3
tian_pan[new_i][new_j] = di_pan[i][j]
return tian_pan
def arrange_ba_men(self, yang_or_yin: str, ju_number: int,
hour_stem: str, hour_branch: str) -> List[List[str]]:
"""排八门"""
ba_men = [[None for _ in range(3)] for _ in range(3)]
# 八门顺序
men_order = self.eight_gates
# 根据时辰确定值使门
zhi_shi_men = self.get_zhi_shi_men(hour_stem, hour_branch)
# 找到值使门在八门中的位置
zhi_shi_idx = men_order.index(zhi_shi_men) if zhi_shi_men in men_order else 0
# 计算八门起始位置
start_pos = self.calculate_men_start_position(yang_or_yin, ju_number)
# 分配八门
luoshu_order = [(0, 0), (0, 2), (1, 1), (2, 0), (2, 2),
(1, 0), (0, 1), (2, 1), (1, 2)]
for i, (x, y) in enumerate(luoshu_order):
if i < 8: # 八门填8个宫
men_idx = (zhi_shi_idx + i) % 8
ba_men[x][y] = men_order[men_idx]
else: # 中宫
ba_men[x][y] = "中" # 中宫通常寄于坤宫或随值使
return ba_men
def arrange_jiu_xing(self, yang_or_yin: str, ju_number: int,
hour_stem: str, hour_branch: str) -> List[List[str]]:
"""排九星"""
jiu_xing = [[None for _ in range(3)] for _ in range(3)]
# 九星顺序
xing_order = self.nine_stars
# 根据时辰确定值符星
zhi_fu_xing = self.get_zhi_fu_xing(hour_stem, hour_branch)
# 找到值符星在九星中的位置
zhi_fu_idx = xing_order.index(zhi_fu_xing) if zhi_fu_xing in xing_order else 0
# 分配九星
luoshu_order = [(0, 0), (0, 2), (1, 1), (2, 0), (2, 2),
(1, 0), (0, 1), (2, 1), (1, 2)]
for i, (x, y) in enumerate(luoshu_order):
xing_idx = (zhi_fu_idx + i) % 9
jiu_xing[x][y] = xing_order[xing_idx]
return jiu_xing
def arrange_ba_shen(self, yang_or_yin: str, ju_number: int,
hour_stem: str, hour_branch: str) -> List[List[str]]:
"""排八神"""
ba_shen = [[None for _ in range(3)] for _ in range(3)]
# 八神顺序
shen_order = self.eight_gods
# 阳遁顺行，阴遁逆行
is_forward = (yang_or_yin == "阳遁")
# 值符位置
zhi_fu_pos = (0, 0) # 值符在符首位置
# 分配八神
positions = self.get_shen_positions(zhi_fu_pos, is_forward)
for i, (x, y) in enumerate(positions):
if i < 8: # 八神
ba_shen[x][y] = shen_order[i]
else: # 中宫
ba_shen[x][y] = "值符" # 中宫通常由值符掌管
return ba_shen
def get_solar_term(self, year: int, month: int, day: int) -> str:
"""计算节气"""
# 简化版，实际需要精确的节气计算
solar_terms = {
1: ["小寒", "大寒"],
2: ["立春", "雨水"],
3: ["惊蛰", "春分"],
4: ["清明", "谷雨"],
5: ["立夏", "小满"],
6: ["芒种", "夏至"],
7: ["小暑", "大暑"],
8: ["立秋", "处暑"],
9: ["白露", "秋分"],
10: ["寒露", "霜降"],
11: ["立冬", "小雪"],
12: ["大雪", "冬至"]
}
# 简单判断上半月还是下半月
if day <= 15:
return solar_terms[month][0]
else:
return solar_terms[month][1]
def get_dun_ju(self, year: int, month: int, day: int, solar_term: str) -> Tuple[str, int]:
"""确定阴阳遁和局数"""
# 简化版判断逻辑
# 实际需要根据节气和日干支计算
# 立春后为阳遁
if month >= 2 and month <= 7:
dun = "阳遁"
else:
dun = "阴遁"
# 简单局数计算
# 实际需要根据日干支和节气精确计算
ju_number = (month + day) % 9
if ju_number == 0:
ju_number = 9
return dun, ju_number
def calculate_hour_ganzhi(self, year: int, month: int, day: int, hour: int) -> Tuple[str, str]:
"""计算时辰天干地支"""
# 简化版计算
# 实际需要根据日天干计算
# 日上起时法
# 甲己还加甲，乙庚丙作初，丙辛从戊起，丁壬庚子居，戊癸何方发，壬子是真途
# 这里简化处理
day_gan = self.calculate_day_ganzhi(year, month, day)[0]
# 时辰地支
hour_branch_idx = (hour + 1) // 2 % 12
hour_branch = list(self.earthly_branches.values())[hour_branch_idx].chinese
# 时辰天干
start_gan_map = {
"甲": 0, "己": 0,
"乙": 2, "庚": 2,
"丙": 4, "辛": 4,
"丁": 6, "壬": 6,
"戊": 8, "癸": 8
}
start_gan_idx = start_gan_map.get(day_gan, 0)
hour_gan_idx = (start_gan_idx + hour_branch_idx) % 10
hour_gan = list(self.heavenly_stems.values())[hour_gan_idx].chinese
return hour_gan, hour_branch
def calculate_day_ganzhi(self, year: int, month: int, day: int) -> Tuple[str, str]:
"""计算日柱天干地支"""
# 简化版，实际需要精确计算
# 使用简化公式
C = year // 100
Y = year % 100
M = month
D = day
if M <= 2:
Y -= 1
M += 12
# 世纪常数
if 1700 <= year < 1800:
C = 4
elif 1800 <= year < 1900:
C = 2
elif 1900 <= year < 2000:
C = 0
elif 2000 <= year < 2100:
C = 6
elif 2100 <= year < 2200:
C = 4
else:
C = 2
# 日干支基数
G = 4 * C + C // 4 + 5 * Y + Y // 4 + 3 * (M + 1) // 5 + D - 3
G = G % 60
# 天干
gan_index = G % 10
gan = list(self.heavenly_stems.values())[gan_index].chinese
# 地支
zhi_index = G % 12
zhi = list(self.earthly_branches.values())[zhi_index].chinese
return gan, zhi
def calculate_rotation(self, hour_stem: str, hour_branch: str) -> Tuple[int, int]:
"""计算天盘旋转量"""
# 根据时辰干支计算旋转
# 这里简化处理
# 地支对应的方位
branch_direction = {
"子": (0, 0), "丑": (0, 0),
"寅": (1, 0), "卯": (1, 0),
"辰": (0, 1), "巳": (0, 1),
"午": (-1, 0), "未": (-1, 0),
"申": (0, -1), "酉": (0, -1),
"戌": (0, 0), "亥": (0, 0)
}
return branch_direction.get(hour_branch, (0, 0))
def get_zhi_shi_men(self, hour_stem: str, hour_branch: str) -> str:
"""获取值使门"""
# 值使门与时干的关系
stem_men_map = {
"甲": "休", "乙": "死", "丙": "伤", "丁": "杜",
"戊": "中", "己": "死", "庚": "开", "辛": "惊",
"壬": "生", "癸": "景"
}
return stem_men_map.get(hour_stem, "休")
def get_zhi_fu_xing(self, hour_stem: str, hour_branch: str) -> str:
"""获取值符星"""
# 值符星与时干的关系
stem_xing_map = {
"甲": "天蓬", "乙": "天芮", "丙": "天冲", "丁": "天辅",
"戊": "天禽", "己": "天心", "庚": "天柱", "辛": "天任",
"壬": "天英", "癸": "天蓬"
}
return stem_xing_map.get(hour_stem, "天蓬")
def calculate_men_start_position(self, yang_or_yin: str, ju_number: int) -> int:
"""计算八门起始位置"""
# 根据阴阳遁和局数计算
if yang_or_yin == "阳遁":
return (ju_number - 1) * 2 % 8
else:
return (9 - ju_number) * 2 % 8
def get_shen_positions(self, zhi_fu_pos: Tuple[int, int], is_forward: bool) -> List[Tuple[int, int]]:
"""获取八神位置"""
# 阳遁顺行，阴遁逆行
# 九宫位置顺序
luoshu_order = [(0, 0), (0, 2), (1, 1), (2, 0), (2, 2),
(1, 0), (0, 1), (2, 1), (1, 2)]
# 找到值符位置
try:
start_idx = luoshu_order.index(zhi_fu_pos)
except ValueError:
start_idx = 0
# 排列位置
positions = []
for i in range(9):
if is_forward:
idx = (start_idx + i) % 9
else:
idx = (start_idx - i) % 9
positions.append(luoshu_order[idx])
return positions
def find_fu_yin(self, tian_pan: List[List[str]], di_pan: List[List[str]]) -> Tuple[int, int]:
"""查找值符、值使位置"""
# 值符是值符星所在宫的天盘天干
# 值使是值使门所在宫的地盘天干
# 简化处理
return (0, 0)
def find_horse_star(self, hour_branch: str) -> List[Tuple[int, int]]:
"""查找驿马星位置"""
# 申子辰马在寅，亥卯未马在巳，寅午戌马在申，巳酉丑马在亥
horse_map = {
"申": [(1, 0)], "子": [(1, 0)], "辰": [(1, 0)],
"亥": [(0, 1)], "卯": [(0, 1)], "未": [(0, 1)],
"寅": [(2, 1)], "午": [(2, 1)], "戌": [(2, 1)],
"巳": [(0, 1)], "酉": [(0, 1)], "丑": [(0, 1)]
}
return horse_map.get(hour_branch, [])
def find_empty_void(self, day_stem: str, day_branch: str) -> List[Tuple[int, int]]:
"""查找空亡位置"""
# 甲子旬中戌亥空，甲戌旬中申酉空，甲申旬中午未空，
# 甲午旬中辰巳空，甲辰旬中寅卯空，甲寅旬中子丑空
# 简化处理
return []
class QimenMedicalInterpreter:
"""奇门医学解读器"""
def __init__(self, qimen_engine: QimenDunjiaEngine):
self.engine = qimen_engine
# 奇门医学映射
self.medical_mappings = {
'天干': {
'甲': {'脏腑': '胆', '病症': '头痛、眩晕'},
'乙': {'脏腑': '肝', '病症': '胁痛、目疾'},
'丙': {'脏腑': '小肠', '病症': '心烦、口疮'},
'丁': {'脏腑': '心', '病症': '心悸、失眠'},
'戊': {'脏腑': '胃', '病症': '胃痛、腹胀'},
'己': {'脏腑': '脾', '病症': '腹泻、水肿'},
'庚': {'脏腑': '大肠', '病症': '便秘、咳嗽'},
'辛': {'脏腑': '肺', '病症': '咳喘、皮肤'},
'壬': {'脏腑': '膀胱', '病症': '小便不利'},
'癸': {'脏腑': '肾', '病症': '腰膝酸软'}
},
'地支': {
'子': {'经络': '足少阴肾经', '时间': '23-1时'},
'丑': {'经络': '足厥阴肝经', '时间': '1-3时'},
'寅': {'经络': '手太阴肺经', '时间': '3-5时'},
'卯': {'经络': '手阳明大肠经', '时间': '5-7时'},
'辰': {'经络': '足阳明胃经', '时间': '7-9时'},
'巳': {'经络': '足太阴脾经', '时间': '9-11时'},
'午': {'经络': '手少阴心经', '时间': '11-13时'},
'未': {'经络': '手太阳小肠经', '时间': '13-15时'},
'申': {'经络': '足太阳膀胱经', '时间': '15-17时'},
'酉': {'经络': '足少阴肾经', '时间': '17-19时'},
'戌': {'经络': '手厥阴心包经', '时间': '19-21时'},
'亥': {'经络': '手少阳三焦经', '时间': '21-23时'}
},
'八门': {
'休': {'状态': '休息、恢复', '治疗': '静养、滋阴'},
'生': {'状态': '生长、发育', '治疗': '补益、生发'},
'伤': {'状态': '损伤、疼痛', '治疗': '活血、止痛'},
'杜': {'状态': '闭塞、阻滞', '治疗': '通络、开窍'},
'景': {'状态': '炎症、发热', '治疗': '清热、凉血'},
'死': {'状态': '衰败、危险', '治疗': '回阳、救逆'},
'惊': {'状态': '惊恐、不安', '治疗': '安神、定惊'},
'开': {'状态': '开通、发散', '治疗': '解表、发汗'}
},
'九星': {
'天蓬': {'属性': '水', '病症': '寒证、水湿'},
'天芮': {'属性': '土', '病症': '湿阻、痰饮'},
'天冲': {'属性': '木', '病症': '风证、抽搐'},
'天辅': {'属性': '木', '病症': '风痹、麻痹'},
'天禽': {'属性': '土', '病症': '中焦、脾胃'},
'天心': {'属性': '金', '病症': '肺疾、燥证'},
'天柱': {'属性': '金', '病症': '骨病、咳喘'},
'天任': {'属性': '土', '病症': '虚劳、气虚'},
'天英': {'属性': '火', '病症': '热证、炎症'}
},
'八神': {
'值符': {'作用': '主要矛盾', '治疗': '扶正祛邪'},
'腾蛇': {'作用': '变化不定', '治疗': '调和阴阳'},
'太阴': {'作用': '阴证、虚证', '治疗': '温补、滋阴'},
'六合': {'作用': '调和、平衡', '治疗': '和解、协调'},
'白虎': {'作用': '急证、实证', '治疗': '攻下、清热'},
'玄武': {'作用': '虚证、寒证', '治疗': '温补、固涩'},
'九地': {'作用': '慢性、稳定', '治疗': '缓治、调理'},
'九天': {'作用': '急性、发展', '治疗': '急治、控制'}
}
}
def interpret_medical_condition(self, pan: QimenPan, patient_symptoms: List[str]) -> Dict[str, Any]:
"""解读奇门盘与病症的关系"""
interpretation = {
'time_analysis': self.analyze_time(pan),
'palace_analysis': self.analyze_palaces(pan),
'gate_analysis': self.analyze_gates(pan),
'star_analysis': self.analyze_stars(pan),
'god_analysis': self.analyze_gods(pan),
'treatment_suggestions': self.suggest_treatment(pan, patient_symptoms)
}
return interpretation
def analyze_time(self, pan: QimenPan) -> Dict[str, Any]:
"""时间分析"""
return {
'current_hour': pan.current_hour,
'current_day': pan.current_day,
'dun_type': pan.yang_dun_yin_dun,
'ju_number': pan.ju_number,
'hour_medical': self.medical_mappings['地支'].get(pan.current_hour[1], {}),
'day_medical': self.medical_mappings['天干'].get(pan.current_day[0], {})
}
def analyze_palaces(self, pan: QimenPan) -> List[Dict[str, Any]]:
"""宫位分析"""
palaces = []
# 洛书九宫顺序
luoshu_positions = [(0, 0), (0, 2), (1, 1), (2, 0), (2, 2),
(1, 0), (0, 1), (2, 1), (1, 2)]
for i, (x, y) in enumerate(luoshu_positions):
palace_number = i + 1
palace_info = {
'palace': palace_number,
'di_gan': pan.di_pan[x][y],
'tian_gan': pan.tian_pan[x][y],
'men': pan.ba_men[x][y],
'xing': pan.jiu_xing[x][y],
'shen': pan.ba_shen[x][y],
'medical_interpretation': self.interpret_palace_medical(
pan.di_pan[x][y],
pan.tian_pan[x][y],
pan.ba_men[x][y],
pan.jiu_xing[x][y],
pan.ba_shen[x][y]
)
}
palaces.append(palace_info)
return palaces
def interpret_palace_medical(self, di_gan: str, tian_gan: str,
men: str, xing: str, shen: str) -> Dict[str, str]:
"""解读宫位医学意义"""
interpretation = {
'di_gan_medical': self.medical_mappings['天干'].get(di_gan, {}),
'tian_gan_medical': self.medical_mappings['天干'].get(tian_gan, {}),
'men_medical': self.medical_mappings['八门'].get(men, {}),
'xing_medical': self.medical_mappings['九星'].get(xing, {}),
'shen_medical': self.medical_mappings['八神'].get(shen, {})
}
# 综合诊断
diagnosis = self.synthesize_diagnosis(interpretation)
interpretation['synthesis'] = diagnosis
return interpretation
def synthesize_diagnosis(self, interpretation: Dict[str, Dict]) -> Dict[str, str]:
"""综合诊断"""
# 提取关键信息
organs = []
patterns = []
treatments = []
# 从天干分析脏腑
for gan_key in ['di_gan_medical', 'tian_gan_medical']:
if gan_key in interpretation and '脏腑' in interpretation[gan_key]:
organs.append(interpretation[gan_key]['脏腑'])
# 从门分析状态
if 'men_medical' in interpretation and '状态' in interpretation['men_medical']:
patterns.append(interpretation['men_medical']['状态'])
# 从星分析属性
if 'xing_medical' in interpretation and '属性' in interpretation['xing_medical']:
patterns.append(interpretation['xing_medical']['属性'] + '证')
# 从神分析作用
if 'shen_medical' in interpretation and '作用' in interpretation['shen_medical']:
patterns.append(interpretation['shen_medical']['作用'])
# 治疗建议
treatment_sources = ['men_medical', 'shen_medical']
for source in treatment_sources:
if source in interpretation and '治疗' in interpretation[source]:
treatments.append(interpretation[source]['治疗'])
return {
'organs': list(set(organs)),
'patterns': list(set(patterns)),
'treatments': list(set(treatments)),
'priority': '高' if '白虎' in interpretation.get('shen_medical', {}).get('作用', '') else '中'
}
def analyze_gates(self, pan: QimenPan) -> Dict[str, Any]:
"""八门分析"""
gates_analysis = {}
for i in range(3):
for j in range(3):
men = pan.ba_men[i][j]
if men:
gates_analysis[men] = {
'position': (i, j),
'medical_info': self.medical_mappings['八门'].get(men, {})
}
return gates_analysis
def analyze_stars(self, pan: QimenPan) -> Dict[str, Any]:
"""九星分析"""
stars_analysis = {}
for i in range(3):
for j in range(3):
xing = pan.jiu_xing[i][j]
if xing:
stars_analysis[xing] = {
'position': (i, j),
'medical_info': self.medical_mappings['九星'].get(xing, {})
}
return stars_analysis
def analyze_gods(self, pan: QimenPan) -> Dict[str, Any]:
"""八神分析"""
gods_analysis = {}
for i in range(3):
for j in range(3):
shen = pan.ba_shen[i][j]
if shen:
gods_analysis[shen] = {
'position': (i, j),
'medical_info': self.medical_mappings['八神'].get(shen, {})
}
return gods_analysis
def suggest_treatment(self, pan: QimenPan, symptoms: List[str]) -> Dict[str, Any]:
"""治疗建议"""
suggestions = {
'acupuncture': self.suggest_acupuncture(pan),
'herbs': self.suggest_herbs(pan, symptoms),
'diet': self.suggest_diet(pan),
'lifestyle': self.suggest_lifestyle(pan)
}
return suggestions
def suggest_acupuncture(self, pan: QimenPan) -> List[str]:
"""建议针灸穴位"""
# 根据天干地支推荐经络
hour_branch = pan.current_hour[1]
meridian = self.medical_mappings['地支'].get(hour_branch, {}).get('经络', '')
# 根据宫位推荐穴位
acupoints = []
# 找到值符宫
if pan.fu_yin:
fu_x, fu_y = pan.fu_yin
palace_gan = pan.di_pan[fu_x][fu_y]
if palace_gan:
organ = self.medical_mappings['天干'].get(palace_gan, {}).get('脏腑', '')
acupoints.extend(self.get_acupoints_for_organ(organ))
return {
'meridian': meridian,
'acupoints': acupoints,
'time': f"当前时辰适合调理{meridian}"
}
def get_acupoints_for_organ(self, organ: str) -> List[str]:
"""获取脏腑相关穴位"""
organ_acupoints = {
'肝': ['太冲', '行间', '期门'],
'心': ['神门', '内关', '心俞'],
'脾': ['足三里', '三阴交', '脾俞'],
'肺': ['太渊', '列缺', '肺俞'],
'肾': ['太溪', '涌泉', '肾俞'],
'胆': ['阳陵泉', '日月', '胆俞'],
'胃': ['足三里', '中脘', '胃俞'],
'大肠': ['合谷', '曲池', '大肠俞'],
'膀胱': ['委中', '膀胱俞', '昆仑'],
'小肠': ['后溪', '小海', '小肠俞']
}
return organ_acupoints.get(organ, [])
def suggest_herbs(self, pan: QimenPan, symptoms: List[str]) -> List[str]:
"""建议草药"""
herbs = []
# 根据天干
for gan in [pan.current_hour[0], pan.current_day[0]]:
organ = self.medical_mappings['天干'].get(gan, {}).get('脏腑', '')
herbs.extend(self.get_herbs_for_organ(organ))
# 根据症状
for symptom in symptoms:
symptom_herbs = self.get_herbs_for_symptom(symptom)
herbs.extend(symptom_herbs)
return list(set(herbs))
def get_herbs_for_organ(self, organ: str) -> List[str]:
"""获取脏腑相关草药"""
organ_herbs = {
'肝': ['柴胡', '白芍', '当归'],
'心': ['丹参', '麦冬', '酸枣仁'],
'脾': ['白术', '茯苓', '山药'],
'肺': ['贝母', '杏仁', '桔梗'],
'肾': ['熟地', '山茱萸', '枸杞子']
}
return organ_herbs.get(organ, [])
def get_herbs_for_symptom(self, symptom: str) -> List[str]:
"""获取症状相关草药"""
symptom_herbs = {
'发热': ['石膏', '知母', '金银花'],
'咳嗽': ['杏仁', '川贝', '紫菀'],
'头痛': ['川芎', '白芷', '羌活'],
'腹痛': ['白芍', '甘草', '木香'],
'失眠': ['酸枣仁', '夜交藤', '远志']
}
return symptom_herbs.get(symptom, [])
def suggest_diet(self, pan: QimenPan) -> Dict[str, List[str]]:
"""建议饮食"""
# 根据天干地支五行属性推荐食物
hour_gan = pan.current_hour[0]
hour_branch = pan.current_hour[1]
# 天干对应脏腑
organ = self.medical_mappings['天干'].get(hour_gan, {}).get('脏腑', '')
# 地支对应经络
meridian = self.medical_mappings['地支'].get(hour_branch, {}).get('经络', '')
return {
'foods': self.get_foods_for_organ(organ),
'avoid': self.get_foods_to_avoid(organ),
'cooking_method': '蒸煮为宜' if '脾' in organ or '胃' in organ else '根据喜好'
}
def get_foods_for_organ(self, organ: str) -> List[str]:
"""获取脏腑适宜食物"""
organ_foods = {
'肝': ['菠菜', '芹菜', '柠檬'],
'心': ['红枣', '龙眼', '西红柿'],
'脾': ['山药', '小米', '南瓜'],
'肺': ['梨', '百合', '银耳'],
'肾': ['黑豆', '黑芝麻', '核桃']
}
return organ_foods.get(organ, [])
def get_foods_to_avoid(self, organ: str) -> List[str]:
"""获取脏腑应避免食物"""
avoid_foods = {
'肝': ['辛辣', '油腻', '酒精'],
'心': ['过咸', '刺激性食物'],
'脾': ['生冷', '油腻', '甜食'],
'肺': ['辛辣', '燥热食物'],
'肾': ['过咸', '寒凉食物']
}
return avoid_foods.get(organ, [])
def suggest_lifestyle(self, pan: QimenPan) -> Dict[str, str]:
"""建议生活方式"""
hour_branch = pan.current_hour[1]
meridian_time = self.medical_mappings['地支'].get(hour_branch, {}).get('时间', '')
suggestions = {
'sleep': f"经络运行时间：{meridian_time}，建议保持安静休息",
'exercise': "建议温和运动，如太极、散步",
'emotion': "保持情绪平和，避免大怒大悲",
'work_rest': "劳逸结合，避免过度劳累"
}
return suggestions2.2 复合卦网络推理引擎
# compound_trigram_engine.py
# 复合卦网络推理引擎
import numpy as np
from typing import Dict, List, Tuple, Any, Set
from dataclasses import dataclass
from enum import Enum
import networkx as nx
from collections import defaultdict
class Trigram(Enum):
"""八卦"""
QIAN = ("乾", "☰", 1, "天", "父", "健", "西北", "金")
KUN = ("坤", "☷", 8, "地", "母", "顺", "西南", "土")
ZHEN = ("震", "☳", 3, "雷", "长男", "动", "东", "木")
XUN = ("巽", "☴", 5, "风", "长女", "入", "东南", "木")
KAN = ("坎", "☵", 6, "水", "中男", "陷", "北", "水")
LI = ("离", "☲", 9, "火", "中女", "丽", "南", "火")
GEN = ("艮", "☶", 7, "山", "少男", "止", "东北", "土")
DUI = ("兑", "☱", 2, "泽", "少女", "悦", "西", "金")
def __init__(self, chinese, symbol, number, nature, family, attribute, direction, element):
self.chinese = chinese
self.symbol = symbol
self.number = number
self.nature = nature
self.family = family
self.attribute = attribute
self.direction = direction
self.element = element
@dataclass
class Hexagram:
"""六十四卦"""
number: int
name: str
symbol: str
upper: Trigram
lower: Trigram
judgment: str
image: str
lines: List[str]
# 医学属性
medical_aspects: Dict[str, Any]
class CompoundTrigramNetwork:
"""复合卦网络"""
def __init__(self):
self.trigrams = {t.name: t for t in Trigram}
self.hexagrams = {}
self.graph = nx.MultiDiGraph()
self._init_hexagrams()
self._build_network()
# 医学映射
self.medical_mapping = MedicalTrigramMapping()
def _init_hexagrams(self):
"""初始化六十四卦"""
# 这里只初始化部分重要卦象
important_hexagrams = [
Hexagram(
number=1,
name="乾为天",
symbol="䷀",
upper=Trigram.QIAN,
lower=Trigram.QIAN,
judgment="元亨利贞。",
image="天行健，君子以自强不息。",
lines=[
"初九：潜龙勿用。",
"九二：见龙在田，利见大人。",
"九三：君子终日乾乾，夕惕若，厉无咎。",
"九四：或跃在渊，无咎。",
"九五：飞龙在天，利见大人。",
"上九：亢龙有悔。"
],
medical_aspects={
"状态": "阳气过盛",
"病症": ["高热", "烦躁", "失眠"],
"脏腑": ["心", "肝"],
"治疗": ["清热", "潜阳"],
"方剂": ["白虎汤", "黄连解毒汤"]
}
),
# ... 其他卦象
]
for hexagram in important_hexagrams:
self.hexagrams[hexagram.number] = hexagram
def _build_network(self):
"""构建卦象网络"""
# 添加节点
for trigram in Trigram:
self.graph.add_node(
trigram.name,
chinese=trigram.chinese,
symbol=trigram.symbol,
element=trigram.element,
nature=trigram.nature
)
# 添加边（卦象关系）
relationships = [
# 相生关系
(Trigram.KAN.name, Trigram.ZHEN.name, {"type": "生", "weight": 0.8}), # 水生木
(Trigram.ZHEN.name, Trigram.LI.name, {"type": "生", "weight": 0.8}), # 木生火
(Trigram.LI.name, Trigram.KUN.name, {"type": "生", "weight": 0.8}), # 火生土
(Trigram.KUN.name, Trigram.QIAN.name, {"type": "生", "weight": 0.8}), # 土生金
(Trigram.QIAN.name, Trigram.KAN.name, {"type": "生", "weight": 0.8}), # 金生水
# 相克关系
(Trigram.ZHEN.name, Trigram.KUN.name, {"type": "克", "weight": -0.6}), # 木克土
(Trigram.KUN.name, Trigram.KAN.name, {"type": "克", "weight": -0.6}), # 土克水
(Trigram.KAN.name, Trigram.LI.name, {"type": "克", "weight": -0.6}), # 水克火
(Trigram.LI.name, Trigram.QIAN.name, {"type": "克", "weight": -0.6}), # 火克金
(Trigram.QIAN.name, Trigram.ZHEN.name, {"type": "克", "weight": -0.6}), # 金克木
]
for source, target, attr in relationships:
self.graph.add_edge(source, target, **attr)
def analyze_symptoms(self, symptoms: List[str]) -> List[Dict[str, Any]]:
"""分析症状对应的卦象"""
results = []
for symptom in symptoms:
symptom_trigrams = self.medical_mapping.symptom_to_trigram(symptom)
for trigram_name, confidence in symptom_trigrams:
trigram = self.trigrams.get(trigram_name)
if trigram:
results.append({
"symptom": symptom,
"trigram": trigram_name,
"chinese": trigram.chinese,
"symbol": trigram.symbol,
"element": trigram.element,
"confidence": confidence,
"medical_interpretation": self.medical_mapping.trigram_medical(trigram)
})
return results
def find_patterns(self, symptoms: List[str]) -> List[Dict[str, Any]]:
"""寻找证候模式"""
# 获取症状对应的卦象
symptom_trigrams = self.analyze_symptoms(symptoms)
# 聚类相关卦象
trigram_groups = self._cluster_trigrams(symptom_trigrams)
# 转化为证候模式
patterns = []
for group in trigram_groups:
pattern = self._trigrams_to_pattern(group)
patterns.append(pattern)
return patterns
def _cluster_trigrams(self, symptom_trigrams: List[Dict]) -> List[List[Dict]]:
"""聚类卦象"""
# 基于五行元素聚类
element_groups = defaultdict(list)
for item in symptom_trigrams:
element = item["element"]
element_groups[element].append(item)
# 合并相关元素
merged_groups = []
# 木火相关
wood_fire = element_groups.get("木", []) + element_groups.get("火", [])
if wood_fire:
merged_groups.append(wood_fire)
# 土金相关
earth_metal = element_groups.get("土", []) + element_groups.get("金", [])
if earth_metal:
merged_groups.append(earth_metal)
# 水相关
water = element_groups.get("水", [])
if water:
merged_groups.append(water)
return merged_groups
def _trigrams_to_pattern(self, trigram_group: List[Dict]) -> Dict[str, Any]:
"""卦象组转化为证候模式"""
if not trigram_group:
return {}
# 提取主要元素
elements = [item["element"] for item in trigram_group]
trigram_names = [item["trigram"] for item in trigram_group]
# 计算模式名称
pattern_name = self._determine_pattern_name(elements, trigram_names)
# 提取医学解释
medical_info = self._combine_medical_info(trigram_group)
return {
"pattern_name": pattern_name,
"elements": list(set(elements)),
"trigrams": trigram_names,
"confidence": np.mean([item["confidence"] for item in trigram_group]),
"medical_interpretation": medical_info
}
def _determine_pattern_name(self, elements: List[str], trigrams: List[str]) -> str:
"""确定证候名称"""
element_count = {}
for element in elements:
element_count[element] = element_count.get(element, 0) + 1
# 按出现频率排序
sorted_elements = sorted(element_count.items(), key=lambda x: x[1], reverse=True)
if not sorted_elements:
return "未知证候"
# 根据主要元素命名
main_element = sorted_elements[0][0]
element_patterns = {
"木": "肝郁",
"火": "心火",
"土": "脾虚",
"金": "肺燥",
"水": "肾虚"
}
pattern = element_patterns.get(main_element, "")
# 如果有次要元素
if len(sorted_elements) > 1:
second_element = sorted_elements[1][0]
pattern += f"兼{second_element}证"
return pattern
def _combine_medical_info(self, trigram_group: List[Dict]) -> Dict[str, Any]:
"""合并医学信息"""
combined = {
"organs": set(),
"symptoms": set(),
"treatments": set(),
"herbs": set()
}
for item in trigram_group:
medical = item.get("medical_interpretation", {})
if "organs" in medical:
combined["organs"].update(medical["organs"])
if "symptoms" in medical:
combined["symptoms"].update(medical["symptoms"])
if "treatments" in medical:
combined["treatments"].update(medical["treatments"])
if "herbs" in medical:
combined["herbs"].update(medical["herbs"])
# 转换为列表
for key in combined:
combined[key] = list(combined[key])
return combined
def predict_treatment_effect(self, pattern: Dict[str, Any], treatment: Dict[str, Any]) -> Dict[str, Any]:
"""预测治疗效果"""
# 分析治疗与证候的五行关系
treatment_element = treatment.get("element", "")
pattern_elements = pattern.get("elements", [])
# 计算生克关系
relationships = []
for pattern_element in pattern_elements:
relationship = self._calculate_element_relationship(treatment_element, pattern_element)
relationships.append({
"pattern_element": pattern_element,
"relationship": relationship["type"],
"strength": relationship["strength"]
})
# 预测效果
positive_count = sum(1 for r in relationships if r["relationship"] in ["生", "同"])
negative_count = sum(1 for r in relationships if r["relationship"] in ["克"])
if positive_count > negative_count:
effect = "良好"
confidence = 0.7 + 0.1 * positive_count
elif positive_count < negative_count:
effect = "不佳"
confidence = 0.6
else:
effect = "一般"
confidence = 0.5
return {
"effect_prediction": effect,
"confidence": min(confidence, 0.95),
"element_relationships": relationships,
"suggested_adjuvants": self._suggest_adjuvants(relationships)
}
def _calculate_element_relationship(self, element1: str, element2: str) -> Dict[str, Any]:
"""计算五行关系"""
# 五行生克顺序
generation_order = ["木", "火", "土", "金", "水", "木"]
if element1 == element2:
return {"type": "同", "strength": 0.5}
idx1 = generation_order.index(element1) if element1 in generation_order else -1
idx2 = generation_order.index(element2) if element2 in generation_order else -1
if idx1 == -1 or idx2 == -1:
return {"type": "未知", "strength": 0.0}
# 检查是否相生
if (idx1 + 1) % 5 == idx2:
return {"type": "生", "strength": 0.8}
# 检查是否相克
elif (idx1 + 2) % 5 == idx2:
return {"type": "克", "strength": -0.6}
# 检查是否被生
elif (idx2 + 1) % 5 == idx1:
return {"type": "被生", "strength": 0.6}
# 检查是否被克
elif (idx2 + 2) % 5 == idx1:
return {"type": "被克", "strength": -0.4}
else:
return {"type": "无关", "strength": 0.0}
def _suggest_adjuvants(self, relationships: List[Dict]) -> List[str]:
"""建议佐使药"""
adjuvants = []
for rel in relationships:
if rel["relationship"] == "克":
# 被克需要佐制
adjuvants.extend(self._get_adjuvant_for_element(rel["pattern_element"]))
return list(set(adjuvants))
def _get_adjuvant_for_element(self, element: str) -> List[str]:
"""获取元素对应的佐使药"""
element_adjuvants = {
"木": ["白芍", "甘草"], # 酸甘化阴
"火": ["黄连", "栀子"], # 苦寒泻火
"土": ["白术", "茯苓"], # 甘淡渗湿
"金": ["麦冬", "沙参"], # 甘寒润燥
"水": ["熟地", "山茱萸"] # 咸寒滋水
}
return element_adjuvants.get(element, [])
class MedicalTrigramMapping:
"""卦象医学映射"""
def __init__(self):
self.symptom_to_trigram_map = {
"发热": [("离", 0.9), ("乾", 0.6)],
"恶寒": [("坎", 0.8), ("坤", 0.5)],
"头痛": [("乾", 0.7), ("震", 0.6)],
"眩晕": [("巽", 0.8), ("坎", 0.6)],
"咳嗽": [("兑", 0.9), ("乾", 0.7)],
"气喘": [("兑", 0.8), ("巽", 0.6)],
"心悸": [("离", 0.9), ("坎", 0.5)],
"失眠": [("离", 0.8), ("坎", 0.7)],
"多梦": [("离", 0.7), ("巽", 0.6)],
"食欲不振": [("坤", 0.9), ("艮", 0.7)],
"腹胀": [("坤", 0.8), ("艮", 0.8)],
"腹泻": [("坤", 0.9), ("坎", 0.6)],
"便秘": [("乾", 0.8), ("艮", 0.7)],
"胁痛": [("震", 0.9), ("巽", 0.7)],
"腹痛": [("坤", 0.8), ("艮", 0.8)],
"腰痛": [("坎", 0.9), ("坤", 0.6)],
"四肢酸痛": [("震", 0.7), ("巽", 0.7)],
"乏力": [("坤", 0.8), ("坎", 0.7)],
"盗汗": [("坎", 0.8), ("离", 0.6)],
"口渴": [("离", 0.9), ("兑", 0.7)],
"口苦": [("离", 0.8), ("震", 0.7)],
"口甜": [("坤", 0.8), ("艮", 0.6)],
"口咸": [("坎", 0.8), ("兑", 0.6)],
"恶心": [("坤", 0.7), ("巽", 0.7)],
"呕吐": [("兑", 0.8), ("巽", 0.7)],
"浮肿": [("坎", 0.9), ("坤", 0.8)],
"黄疸": [("震", 0.8), ("离", 0.7)],
"出血": [("离", 0.9), ("兑", 0.6)],
"瘀斑": [("坎", 0.7), ("坤", 0.6)],
"皮疹": [("离", 0.8), ("巽", 0.7)],
"瘙痒": [("巽", 0.8), ("离", 0.6)],
"麻木": [("坤", 0.7), ("坎", 0.6)],
"抽搐": [("震", 0.9), ("巽", 0.7)],
"昏迷": [("坎", 0.8), ("离", 0.7)],
}
self.trigram_medical_map = {
"乾": {
"organs": ["头", "大肠", "肺"],
"symptoms": ["高热", "烦躁", "便秘", "咳嗽"],
"treatments": ["清热", "泻下", "润燥"],
"herbs": ["石膏", "大黄", "杏仁"],
"acupoints": ["合谷", "曲池", "天枢"]
},
"坤": {
"organs": ["脾", "胃", "肌肉"],
"symptoms": ["腹胀", "腹泻", "乏力", "浮肿"],
"treatments": ["健脾", "利湿", "补气"],
"herbs": ["白术", "茯苓", "党参"],
"acupoints": ["足三里", "三阴交", "脾俞"]
},
"震": {
"organs": ["肝", "胆", "筋"],
"symptoms": ["头痛", "眩晕", "抽搐", "胁痛"],
"treatments": ["疏肝", "熄风", "潜阳"],
"herbs": ["柴胡", "白芍", "钩藤"],
"acupoints": ["太冲", "行间", "风池"]
},
"巽": {
"organs": ["肝", "胆", "神经系统"],
"symptoms": ["眩晕", "麻木", "瘙痒", "多梦"],
"treatments": ["疏风", "通络", "安神"],
"herbs": ["天麻", "防风", "酸枣仁"],
"acupoints": ["风池", "太冲", "神门"]
},
"坎": {
"organs": ["肾", "膀胱", "骨髓"],
"symptoms": ["腰痛", "浮肿", "盗汗", "畏寒"],
"treatments": ["温阳", "利水", "补肾"],
"herbs": ["附子", "肉桂", "茯苓"],
"acupoints": ["肾俞", "太溪", "关元"]
},
"离": {
"organs": ["心", "小肠", "血脉"],
"symptoms": ["发热", "心悸", "失眠", "出血"],
"treatments": ["清热", "凉血", "安神"],
"herbs": ["黄连", "丹参", "酸枣仁"],
"acupoints": ["神门", "内关", "心俞"]
},
"艮": {
"organs": ["胃", "脾", "肌肉"],
"symptoms": ["腹胀", "腹痛", "便秘", "食欲不振"],
"treatments": ["和胃", "消导", "理气"],
"herbs": ["厚朴", "枳实", "山楂"],
"acupoints": ["中脘", "天枢", "足三里"]
},
"兑": {
"organs": ["肺", "大肠", "皮肤"],
"symptoms": ["咳嗽", "气喘", "皮疹", "口燥"],
"treatments": ["宣肺", "润燥", "止咳"],
"herbs": ["杏仁", "贝母", "麦冬"],
"acupoints": ["太渊", "列缺", "肺俞"]
}
}
def symptom_to_trigram(self, symptom: str) -> List[Tuple[str, float]]:
"""症状到卦象映射"""
return self.symptom_to_trigram_map.get(symptom, [])
def trigram_medical(self, trigram: Trigram) -> Dict[str, Any]:
"""卦象医学解释"""
return self.trigram_medical_map.get(trigram.chinese, {})三、易医元宇宙大模型架构实现
3.1 大模型架构主类
# yiyi_metaverse_model.py
# 易医元宇宙大模型
from typing import Dict, List, Any, Optional, Tuple
import numpy as np
from dataclasses import dataclass, field
import asyncio
from concurrent.futures import ThreadPoolExecutor
import json
from datetime import datetime
import logging
# 导入各模块
from qimen_dunjia_engine import QimenDunjiaEngine, QimenMedicalInterpreter
from compound_trigram_engine import CompoundTrigramNetwork
from luoshu_matrix import LuoshuMatrixSystem
from star_wheel import StarWheelSystem
from quantum_entanglement import QuantumEntanglementNetwork
from infinite_optimization import InfiniteOptimizationController
from knowledge_graph import TCMKnowledgeGraph
from digital_twin import HumanDigitalTwin
@dataclass
class PatientProfile:
"""患者档案"""
id: str
name: str
age: int
gender: str
constitution: str # 体质类型
symptoms: List[str]
tongue: Optional[str] = None
pulse: Optional[str] = None
medical_history: List[str] = field(default_factory=list)
current_medications: List[str] = field(default_factory=list)
lifestyle: Dict[str, Any] = field(default_factory=dict)
genetic_info: Optional[Dict[str, Any]] = None
def to_dict(self) -> Dict[str, Any]:
"""转换为字典"""
return {
"id": self.id,
"name": self.name,
"age": self.age,
"gender": self.gender,
"constitution": self.constitution,
"symptoms": self.symptoms,
"tongue": self.tongue,
"pulse": self.pulse,
"medical_history": self.medical_history,
"current_medications": self.current_medications,
"lifestyle": self.lifestyle,
"genetic_info": self.genetic_info
}
@dataclass
class DiagnosisResult:
"""诊断结果"""
patient_id: str
timestamp: datetime
patterns: List[Dict[str, Any]] # 证候模式
confidence: float
luoshu_matrix: np.ndarray # 洛书矩阵状态
qimen_interpretation: Dict[str, Any] # 奇门解读
trigram_analysis: Dict[str, Any] # 卦象分析
energy_balance: Dict[str, float] # 能量平衡
priority: str # 优先级
def to_dict(self) -> Dict[str, Any]:
"""转换为字典"""
return {
"patient_id": self.patient_id,
"timestamp": self.timestamp.isoformat(),
"patterns": self.patterns,
"confidence": self.confidence,
"luoshu_matrix": self.luoshu_matrix.tolist(),
"qimen_interpretation": self.qimen_interpretation,
"trigram_analysis": self.trigram_analysis,
"energy_balance": self.energy_balance,
"priority": self.priority
}
@dataclass
class TreatmentPlan:
"""治疗方案"""
diagnosis_id: str
herbs: List[Dict[str, Any]]
acupuncture: List[Dict[str, Any]]
dietary_recommendations: Dict[str, Any]
lifestyle_advice: Dict[str, Any]
predicted_effectiveness: float
risks: List[str]
follow_up_schedule: List[Dict[str, Any]]
def to_dict(self) -> Dict[str, Any]:
"""转换为字典"""
return {
"diagnosis_id": self.diagnosis_id,
"herbs": self.herbs,
"acupuncture": self.acupuncture,
"dietary_recommendations": self.dietary_recommendations,
"lifestyle_advice": self.lifestyle_advice,
"predicted_effectiveness": self.predicted_effectiveness,
"risks": self.risks,
"follow_up_schedule": self.follow_up_schedule
}
class YiyiMetaverseModel:
"""易医元宇宙大模型主类"""
def __init__(self, config_path: Optional[str] = None):
# 初始化日志
self.logger = self._setup_logging()
# 初始化各子系统
self.logger.info("初始化易医元宇宙大模型...")
# 元数据湖
self.metadata_lake = self._init_metadata_lake()
# 算法引擎
self.qimen_engine = QimenDunjiaEngine()
self.qimen_interpreter = QimenMedicalInterpreter(self.qimen_engine)
self.trigram_network = CompoundTrigramNetwork()
# 计算框架
self.luoshu_matrix = LuoshuMatrixSystem()
self.star_wheel = StarWheelSystem()
self.quantum_network = QuantumEntanglementNetwork()
self.optimizer = InfiniteOptimizationController()
# 知识图谱
self.knowledge_graph = TCMKnowledgeGraph()
# 元宇宙平台
self.digital_twin = HumanDigitalTwin()
# 配置
self.config = self._load_config(config_path)
self.logger.info("易医元宇宙大模型初始化完成")
def _setup_logging(self) -> logging.Logger:
"""设置日志"""
logger = logging.getLogger("yiyi_metaverse")
logger.setLevel(logging.INFO)
handler = logging.StreamHandler()
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
handler.setFormatter(formatter)
logger.addHandler(handler)
return logger
def _init_metadata_lake(self) -> Dict[str, Any]:
"""初始化元数据湖"""
return {
"schema_version": "JXWD-AI-M v2.0",
"energy_standardization": {
"yang_energy_levels": {
"+": {"range": "6.5-7.2", "trend": "↑", "description": "阳气较为旺盛"},
"++": {"range": "7.2-8", "trend": "↑↑", "description": "阳气非常旺盛"},
"+++": {"range": "8-10", "trend": "↑↑↑", "description": "阳气极旺"},
"+++⊕": {"range": "10", "trend": "↑↑↑⊕", "description": "阳气极阳"}
},
"yin_energy_levels": {
"-": {"range": "5.8-6.5", "trend": "↓", "description": "阴气较为旺盛"},
"--": {"range": "5-5.8", "trend": "↓↓", "description": "阴气较为旺盛"},
"---": {"range": "0-5", "trend": "↓↓↓", "description": "阴气非常强盛"},
"---⊙": {"range": "0", "trend": "↓↓↓⊙", "description": "阴气极阴"}
},
"convergence_target": "5.8-6.5-7.2×3.618"
},
"luoshu_matrix_config": {
"palaces": {
1: {"name": "坎宫", "trigram": "☵", "element": "水", "organs": ["肾阴", "膀胱"]},
2: {"name": "坤宫", "trigram": "☷", "element": "土", "organs": ["脾", "胃"]},
3: {"name": "震宫", "trigram": "☳", "element": "雷", "organs": ["君火"]},
4: {"name": "巽宫", "trigram": "☴", "element": "木", "organs": ["肝", "胆"]},
5: {"name": "中宫", "trigram": "☯", "element": "太极", "organs": ["三焦"]},
6: {"name": "乾宫", "trigram": "☰", "element": "天", "organs": ["命火", "肾阳", "生殖", "女子胞", "男子精室"]},
7: {"name": "兑宫", "trigram": "☱", "element": "泽", "organs": ["肺", "大肠"]},
8: {"name": "艮宫", "trigram": "☶", "element": "山", "organs": ["相火"]},
9: {"name": "离宫", "trigram": "☲", "element": "火", "organs": ["心", "小肠"]}
}
}
}
def _load_config(self, config_path: Optional[str]) -> Dict[str, Any]:
"""加载配置"""
default_config = {
"optimization": {
"max_iterations": 10000,
"convergence_threshold": 1e-6,
"learning_rate": 0.01,
"exploration_rate": 0.1
},
"metaverse": {
"simulation_speed": 1.0,
"render_quality": "high",
"interaction_mode": "immersive"
},
"diagnosis": {
"confidence_threshold": 0.7,
"max_patterns": 3,
"enable_multimodal": True
}
}
if config_path:
try:
with open(config_path, 'r', encoding='utf-8') as f:
user_config = json.load(f)
# 合并配置
default_config.update(user_config)
except Exception as e:
self.logger.warning(f"加载配置文件失败: {e}, 使用默认配置")
return default_config
async def diagnose(self, patient: PatientProfile, current_time: Optional[datetime] = None) -> DiagnosisResult:
"""诊断主函数"""
self.logger.info(f"开始诊断患者: {patient.name}")
if current_time is None:
current_time = datetime.now()
try:
# 并行执行各诊断模块
tasks = [
self._qimen_diagnosis(patient, current_time),
self._trigram_diagnosis(patient),
self._luoshu_analysis(patient),
self._knowledge_graph_analysis(patient)
]
results = await asyncio.gather(*tasks)
qimen_result, trigram_result, luoshu_result, kg_result = results
# 整合诊断结果
patterns = self._integrate_patterns(
qimen_result['patterns'],
trigram_result['patterns'],
luoshu_result['patterns'],
kg_result['patterns']
)
# 计算置信度
confidence = self._calculate_confidence(
qimen_result['confidence'],
trigram_result['confidence'],
luoshu_result['confidence'],
kg_result['confidence']
)
# 确定优先级
priority = self._determine_priority(patterns, confidence)
# 创建诊断结果
diagnosis = DiagnosisResult(
patient_id=patient.id,
timestamp=current_time,
patterns=patterns,
confidence=confidence,
luoshu_matrix=luoshu_result['matrix'],
qimen_interpretation=qimen_result['interpretation'],
trigram_analysis=trigram_result['analysis'],
energy_balance=luoshu_result['energy_balance'],
priority=priority
)
self.logger.info(f"诊断完成: 发现{len(patterns)}个证候模式, 置信度: {confidence:.2f}")
return diagnosis
except Exception as e:
self.logger.error(f"诊断过程出错: {e}")
raise
async def _qimen_diagnosis(self, patient: PatientProfile, current_time: datetime) -> Dict[str, Any]:
"""奇门遁甲诊断"""
self.logger.info("执行奇门遁甲诊断...")
# 计算奇门盘
pan = self.qimen_engine.calculate_pan(
year=current_time.year,
month=current_time.month,
day=current_time.day,
hour=current_time.hour
)
# 医学解读
interpretation = self.qimen_interpreter.interpret_medical_condition(pan, patient.symptoms)
# 提取证候模式
patterns = []
for palace in interpretation.get('palace_analysis', []):
synthesis = palace.get('medical_interpretation', {}).get('synthesis', {})
if synthesis:
pattern = {
'name': f"奇门-{palace['palace']}宫",
'organs': synthesis.get('organs', []),
'symptoms': patient.symptoms,
'treatments': synthesis.get('treatments', []),
'confidence': 0.7 # 奇门诊断的基础置信度
}
patterns.append(pattern)
return {
'patterns': patterns,
'interpretation': interpretation,
'confidence': len(patterns) * 0.1 # 根据发现模式数量调整置信度
}
async def _trigram_diagnosis(self, patient: PatientProfile) -> Dict[str, Any]:
"""复合卦网络诊断"""
self.logger.info("执行复合卦网络诊断...")
# 分析症状对应的卦象
patterns = self.trigram_network.find_patterns(patient.symptoms)
# 转换为诊断格式
diagnosis_patterns = []
for pattern in patterns:
diagnosis_pattern = {
'name': pattern['pattern_name'],
'elements': pattern['elements'],
'organs': pattern['medical_interpretation'].get('organs', []),
'symptoms': pattern['medical_interpretation'].get('symptoms', []),
'treatments': pattern['medical_interpretation'].get('treatments', []),
'herbs': pattern['medical_interpretation'].get('herbs', []),
'confidence': pattern['confidence']
}
diagnosis_patterns.append(diagnosis_pattern)
return {
'patterns': diagnosis_patterns,
'analysis': {
'total_patterns': len(patterns),
'primary_element': patterns[0]['elements'][0] if patterns else None
},
'confidence': np.mean([p['confidence'] for p in patterns]) if patterns else 0.5
}
async def _luoshu_analysis(self, patient: PatientProfile) -> Dict[str, Any]:
"""洛书矩阵分析"""
self.logger.info("执行洛书矩阵分析...")
# 创建初始矩阵
matrix = self.luoshu_matrix.create_initial_matrix(patient)
# 能量演化
evolved_matrix = self.luoshu_matrix.evolve_energy(matrix, iterations=100)
# 提取模式
patterns = self.luoshu_matrix.extract_patterns(evolved_matrix, patient.symptoms)
# 计算能量平衡
energy_balance = self.luoshu_matrix.calculate_energy_balance(evolved_matrix)
return {
'patterns': patterns,
'matrix': evolved_matrix,
'energy_balance': energy_balance,
'confidence': 0.8 # 洛书矩阵分析的基础置信度
}
async def _knowledge_graph_analysis(self, patient: PatientProfile) -> Dict[str, Any]:
"""知识图谱分析"""
self.logger.info("执行知识图谱分析...")
# 查询相似病例
similar_cases = self.knowledge_graph.find_similar_cases(patient)
# 提取常见模式
common_patterns = self.knowledge_graph.extract_common_patterns(similar_cases)
# 计算置信度
confidence = min(len(similar_cases) * 0.05, 0.8) if similar_cases else 0.5
return {
'patterns': common_patterns,
'similar_cases_count': len(similar_cases),
'confidence': confidence
}
def _integrate_patterns(self, *pattern_lists) -> List[Dict[str, Any]]:
"""整合多个来源的证候模式"""
all_patterns = []
for pattern_list in pattern_lists:
all_patterns.extend(pattern_list)
if not all_patterns:
return []
# 按名称聚类模式
pattern_groups = {}
for pattern in all_patterns:
name = pattern.get('name', '未知')
if name not in pattern_groups:
pattern_groups[name] = []
pattern_groups[name].append(pattern)
# 合并聚类后的模式
integrated = []
for name, group in pattern_groups.items():
if group:
# 取第一个作为基础
base_pattern = group[0].copy()
# 合并器官列表
all_organs = set()
for pattern in group:
all_organs.update(pattern.get('organs', []))
base_pattern['organs'] = list(all_organs)
# 合并症状列表
all_symptoms = set()
for pattern in group:
all_symptoms.update(pattern.get('symptoms', []))
base_pattern['symptoms'] = list(all_symptoms)
# 计算平均置信度
confidences = [p.get('confidence', 0.5) for p in group]
base_pattern['confidence'] = np.mean(confidences)
# 添加来源信息
base_pattern['sources'] = [p.get('source', '未知') for p in group if 'source' in p]
integrated.append(base_pattern)
# 按置信度排序
integrated.sort(key=lambda x: x.get('confidence', 0), reverse=True)
# 限制数量
max_patterns = self.config['diagnosis']['max_patterns']
if len(integrated) > max_patterns:
integrated = integrated[:max_patterns]
return integrated
def _calculate_confidence(self, *confidences) -> float:
"""计算综合置信度"""
weights = [0.3, 0.25, 0.25, 0.2] # 奇门、卦象、洛书、知识图谱的权重
# 确保权重和置信度数量匹配
if len(weights) > len(confidences):
weights = weights[:len(confidences)]
elif len(weights) < len(confidences):
weights = weights + [0.1] * (len(confidences) - len(weights))
# 归一化权重
total_weight = sum(weights)
weights = [w / total_weight for w in weights]
# 加权平均
weighted_sum = sum(w * c for w, c in zip(weights, confidences))
return weighted_sum
def _determine_priority(self, patterns: List[Dict[str, Any]], confidence: float) -> str:
"""确定诊断优先级"""
if confidence < 0.6:
return "低"
# 检查是否有紧急症状
emergency_symptoms = {'昏迷', '高热', '大出血', '呼吸困难'}
for pattern in patterns:
symptoms = set(pattern.get('symptoms', []))
if symptoms & emergency_symptoms:
return "紧急"
# 检查是否有严重证候
severe_patterns = {'阳明腑实', '热极动风', '心阳暴脱'}
for pattern in patterns:
name = pattern.get('name', '')
if any(severe in name for severe in severe_patterns):
return "高"
return "中" if confidence >= 0.7 else "低"
async def generate_treatment(self, diagnosis: DiagnosisResult,
patient: PatientProfile) -> TreatmentPlan:
"""生成治疗方案"""
self.logger.info(f"为诊断ID {diagnosis.patient_id} 生成治疗方案...")
try:
# 并行生成各治疗模块
tasks = [
self._generate_herbal_prescription(diagnosis, patient),
self._generate_acupuncture_plan(diagnosis, patient),
self._generate_dietary_recommendations(diagnosis, patient),
self._generate_lifestyle_advice(diagnosis, patient)
]
results = await asyncio.gather(*tasks)
herbs, acupuncture, diet, lifestyle = results
# 预测治疗效果
effectiveness = self._predict_effectiveness(diagnosis, herbs, acupuncture)
# 评估风险
risks = self._assess_risks(diagnosis, herbs, patient)
# 制定随访计划
follow_up = self._create_follow_up_schedule(diagnosis, effectiveness)
# 创建治疗方案
treatment = TreatmentPlan(
diagnosis_id=diagnosis.patient_id,
herbs=herbs,
acupuncture=acupuncture,
dietary_recommendations=diet,
lifestyle_advice=lifestyle,
predicted_effectiveness=effectiveness,
risks=risks,
follow_up_schedule=follow_up
)
self.logger.info(f"治疗方案生成完成，预测效果: {effectiveness:.2f}")
return treatment
except Exception as e:
self.logger.error(f"治疗方案生成出错: {e}")
raise
async def _generate_herbal_prescription(self, diagnosis: DiagnosisResult,
patient: PatientProfile) -> List[Dict[str, Any]]:
"""生成草药处方"""
herbs = []
# 根据证候模式选择草药
for pattern in diagnosis.patterns:
pattern_name = pattern.get('name', '')
suggested_herbs = pattern.get('herbs', [])
if suggested_herbs:
# 获取草药详细信息
for herb_name in suggested_herbs[:3]: # 每种模式最多选3味药
herb_info = self.knowledge_graph.get_herb_info(herb_name)
if herb_info:
# 根据患者体质和证候确定剂量
dosage = self._calculate_dosage(herb_info, pattern_name, patient)
herbs.append({
'name': herb_name,
'pinyin': herb_info.get('pinyin', ''),
'latin': herb_info.get('latin', ''),
'category': herb_info.get('category', ''),
'nature': herb_info.get('nature', ''),
'meridians': herb_info.get('meridians', []),
'dosage': dosage,
'preparation': herb_info.get('preparation', '煎服'),
'contraindications': herb_info.get('contraindications', [])
})
# 去重
unique_herbs = []
seen_names = set()
for herb in herbs:
if herb['name'] not in seen_names:
unique_herbs.append(herb)
seen_names.add(herb['name'])
# 限制草药数量
max_herbs = 12
if len(unique_herbs) > max_herbs:
unique_herbs = unique_herbs[:max_herbs]
return unique_herbs
def _calculate_dosage(self, herb_info: Dict[str, Any],
pattern_name: str, patient: PatientProfile) -> str:
"""计算草药剂量"""
# 基础剂量
base_dosage = herb_info.get('dosage_range', '3-9g')
# 根据患者年龄调整
age_factor = 1.0
if patient.age < 12:
age_factor = 0.6 # 儿童减量
elif patient.age > 65:
age_factor = 0.8 # 老年人减量
# 根据证候严重程度调整
severity_factor = 1.0
if '阳明腑实' in pattern_name or '热极动风' in pattern_name:
severity_factor = 1.2 # 重症加量
# 根据体质调整
constitution_factor = 1.0
if patient.constitution == '阳虚':
if herb_info.get('nature') in ['寒', '凉']:
constitution_factor = 0.8 # 阳虚者寒凉药减量
elif patient.constitution == '阴虚':
if herb_info.get('nature') in ['热', '温']:
constitution_factor = 0.8 # 阴虚者温热药减量
# 计算最终剂量
if '-' in base_dosage:
min_dose, max_dose = base_dosage.replace('g', '').split('-')
min_dose = float(min_dose) * age_factor * severity_factor * constitution_factor
max_dose = float(max_dose) * age_factor * severity_factor * constitution_factor
return f"{min_dose:.1f}-{max_dose:.1f}g"
else:
return base_dosage
async def _generate_acupuncture_plan(self, diagnosis: DiagnosisResult,
patient: PatientProfile) -> List[Dict[str, Any]]:
"""生成针灸方案"""
acupuncture_points = []
# 根据证候模式选择穴位
for pattern in diagnosis.patterns:
pattern_name = pattern.get('name', '')
suggested_points = pattern.get('acupoints', [])
if suggested_points:
for point_name in suggested_points[:4]: # 每种模式最多选4个穴位
point_info = self.knowledge_graph.get_acupoint_info(point_name)
if point_info:
acupuncture_points.append({
'name': point_name,
'pinyin': point_info.get('pinyin', ''),
'code': point_info.get('code', ''),
'meridian': point_info.get('meridian', ''),
'location': point_info.get('location', ''),
'indications': point_info.get('indications', []),
'technique': self._determine_acupuncture_technique(pattern_name, point_info),
'retention_time': '20-30分钟',
'frequency': '每日1次或隔日1次'
})
# 去重
unique_points = []
seen_names = set()
for point in acupuncture_points:
if point['name'] not in seen_names:
unique_points.append(point)
seen_names.add(point['name'])
return unique_points
def _determine_acupuncture_technique(self, pattern_name: str,
point_info: Dict[str, Any]) -> str:
"""确定针灸手法"""
if '虚' in pattern_name or '寒' in pattern_name:
return "补法，可加灸"
elif '实' in pattern_name or '热' in pattern_name:
return "泻法"
else:
return "平补平泻"
async def _generate_dietary_recommendations(self, diagnosis: DiagnosisResult,
patient: PatientProfile) -> Dict[str, Any]:
"""生成饮食建议"""
recommendations = {
'recommended_foods': [],
'foods_to_avoid': [],
'cooking_methods': [],
'eating_schedule': {},
'special_recipes': []
}
# 根据证候模式推荐食物
for pattern in diagnosis.patterns:
pattern_name = pattern.get('name', '')
organs = pattern.get('organs', [])
# 根据脏腑选择食物
for organ in organs:
foods = self.knowledge_graph.get_foods_for_organ(organ)
recommendations['recommended_foods'].extend(foods)
avoid_foods = self.knowledge_graph.get_foods_to_avoid_for_organ(organ)
recommendations['foods_to_avoid'].extend(avoid_foods)
# 去重
recommendations['recommended_foods'] = list(set(recommendations['recommended_foods']))
recommendations['foods_to_avoid'] = list(set(recommendations['foods_to_avoid']))
# 根据证候确定烹饪方法
if any('热' in p.get('name', '') for p in diagnosis.patterns):
recommendations['cooking_methods'] = ['蒸', '煮', '凉拌']
elif any('寒' in p.get('name', '') for p in diagnosis.patterns):
recommendations['cooking_methods'] = ['炖', '煲', '烤']
else:
recommendations['cooking_methods'] = ['炒', '蒸', '煮']
# 建议进食时间
recommendations['eating_schedule'] = {
'breakfast': '7:00-8:00',
'lunch': '12:00-13:00',
'dinner': '18:00-19:00',
'snacks': '10:00和15:00'
}
# 生成特定食谱
primary_pattern = diagnosis.patterns[0] if diagnosis.patterns else None
if primary_pattern:
pattern_name = primary_pattern.get('name', '')
recipes = self.knowledge_graph.get_recipes_for_pattern(pattern_name)
recommendations['special_recipes'] = recipes[:2] # 最多推荐2个食谱
return recommendations
async def _generate_lifestyle_advice(self, diagnosis: DiagnosisResult,
patient: PatientProfile) -> Dict[str, Any]:
"""生成生活方式建议"""
advice = {
'exercise': {},
'sleep': {},
'stress_management': {},
'daily_routine': {},
'environmental_factors': {}
}
# 运动建议
if any('虚' in p.get('name', '') for p in diagnosis.patterns):
advice['exercise'] = {
'type': '温和运动',
'examples': ['太极', '八段锦', '散步'],
'frequency': '每日30分钟',
'intensity': '轻度',
'best_time': '早晨或傍晚'
}
else:
advice['exercise'] = {
'type': '适度运动',
'examples': ['快走', '游泳', '瑜伽'],
'frequency': '每周3-5次，每次30-45分钟',
'intensity': '中等',
'best_time': '下午'
}
# 睡眠建议
sleep_duration = '7-8小时'
if patient.age < 18:
sleep_duration = '8-10小时'
elif patient.age > 65:
sleep_duration = '6-7小时'
advice['sleep'] = {
'duration': sleep_duration,
'bedtime': '22:00-23:00',
'wakeup_time': '6:00-7:00',
'napping': '中午休息20-30分钟',
'environment': '安静、黑暗、凉爽'
}
# 压力管理
advice['stress_management'] = {
'techniques': ['深呼吸', '冥想', '正念'],
'frequency': '每日2-3次，每次10-15分钟',
'activities': ['听音乐', '阅读', '园艺'],
'avoid': ['过度工作', '熬夜', '刺激性娱乐']
}
# 日常作息
advice['daily_routine'] = {
'morning': '6:00起床，喝温水，轻柔运动',
'midday': '12:00午餐，饭后散步15分钟',
'afternoon': '15:00休息或轻度活动',
'evening': '18:00晚餐，饭后散步',
'night': '21:00放松，准备睡眠'
}
# 环境因素
primary_element = None
if diagnosis.patterns:
first_pattern = diagnosis.patterns[0]
elements = first_pattern.get('elements', [])
if elements:
primary_element = elements[0]
element_environments = {
'木': {'colors': ['绿色', '蓝色'], 'directions': ['东'], 'seasons': ['春']},
'火': {'colors': ['红色', '紫色'], 'directions': ['南'], 'seasons': ['夏']},
'土': {'colors': ['黄色', '棕色'], 'directions': ['中'], 'seasons': ['长夏']},
'金': {'colors': ['白色', '金色'], 'directions': ['西'], 'seasons': ['秋']},
'水': {'colors': ['黑色', '蓝色'], 'directions': ['北'], 'seasons': ['冬']}
}
if primary_element and primary_element in element_environments:
advice['environmental_factors'] = element_environments[primary_element]
else:
advice['environmental_factors'] = {
'colors': ['自然色系'],
'directions': ['根据个人舒适度'],
'seasons': ['顺应自然']
}
return advice
def _predict_effectiveness(self, diagnosis: DiagnosisResult,
herbs: List[Dict[str, Any]],
acupuncture: List[Dict[str, Any]]) -> float:
"""预测治疗效果"""
base_effectiveness = 0.7
# 根据诊断置信度调整
base_effectiveness *= diagnosis.confidence
# 根据草药数量调整（适中为佳）
herb_count = len(herbs)
if 6 <= herb_count <= 10:
base_effectiveness *= 1.1
elif herb_count < 6:
base_effectiveness *= 0.9
else:
base_effectiveness *= 0.8
# 根据穴位数量调整
point_count = len(acupuncture)
if 4 <= point_count <= 8:
base_effectiveness *= 1.05
elif point_count < 4:
base_effectiveness *= 0.95
# 根据证候复杂度调整
pattern_count = len(diagnosis.patterns)
if pattern_count == 1:
base_effectiveness *= 1.1 # 单一证候效果好
elif pattern_count > 3:
base_effectiveness *= 0.9 # 复杂证候效果降低
# 确保在合理范围内
effectiveness = min(max(base_effectiveness, 0.3), 0.95)
return round(effectiveness, 2)
def _assess_risks(self, diagnosis: DiagnosisResult,
herbs: List[Dict[str, Any]],
patient: PatientProfile) -> List[str]:
"""评估治疗风险"""
risks = []
# 检查草药相互作用
herb_names = [herb['name'] for herb in herbs]
interactions = self.knowledge_graph.check_herb_interactions(herb_names)
if interactions:
risks.extend(interactions)
# 检查草药禁忌
for herb in herbs:
contraindications = herb.get('contraindications', [])
for contraindication in contraindications:
if contraindication.lower() in patient.constitution.lower():
risks.append(f"{herb['name']}与{patient.constitution}体质不宜")
# 检查特殊人群风险
if patient.age < 12:
risks.append("儿童用药需谨慎，建议在医师指导下使用")
elif patient.age > 65:
risks.append("老年人可能需要调整剂量")
if patient.gender == '女' and 12 <= patient.age <= 50:
risks.append("育龄妇女用药需考虑对月经和生育的影响")
# 检查当前用药
if patient.current_medications:
risks.append("当前使用其他药物，需注意药物相互作用")
# 根据证候类型提示风险
for pattern in diagnosis.patterns:
pattern_name = pattern.get('name', '')
if '阳明腑实' in pattern_name:
risks.append("泻下药可能导致电解质紊乱，需监测")
elif '热极动风' in pattern_name:
risks.append("重症需密切监测生命体征")
# 如果没有风险，添加一般性提示
if not risks:
risks.append("一般风险较低，但仍需监测不良反应")
return risks[:5] # 限制风险数量
def _create_follow_up_schedule(self, diagnosis: DiagnosisResult,
effectiveness: float) -> List[Dict[str, Any]]:
"""制定随访计划"""
schedule = []
# 基础随访计划
base_schedule = [
{'time': '3天后', 'purpose': '评估初始反应，调整方案'},
{'time': '1周后', 'purpose': '评估主要症状改善情况'},
{'time': '2周后', 'purpose': '评估整体治疗效果'},
{'time': '1个月后', 'purpose': '评估长期效果，确定后续方案'}
]
# 根据效果预测调整
if effectiveness < 0.7:
# 效果预测较低，增加随访频率
base_schedule.insert(1, {'time': '1天后', 'purpose': '紧急评估，及时调整'})
# 根据证候类型调整
for pattern in diagnosis.patterns:
pattern_name = pattern.get('name', '')
if '阳明腑实' in pattern_name or '热极动风' in pattern_name:
# 急症需要密切随访
schedule.append({'time': '6小时后', 'purpose': '评估急症处理效果'})
schedule.append({'time': '12小时后', 'purpose': '监测病情变化'})
break
# 添加基础随访计划
schedule.extend(base_schedule)
# 根据优先级调整
if diagnosis.priority == '紧急':
schedule = [s for s in schedule if '小时' in s['time'] or '天' in s['time']]
elif diagnosis.priority == '高':
schedule = [s for s in schedule if '周' in s['time'] or '月' in s['time']]
return schedule[:6] # 限制随访次数
async def simulate_treatment(self, treatment: TreatmentPlan,
patient: PatientProfile,
duration_days: int = 30) -> Dict[str, Any]:
"""模拟治疗效果"""
self.logger.info(f"模拟治疗方案，时长: {duration_days}天")
try:
# 创建数字孪生
digital_twin = await self.digital_twin.create_twin(patient)
# 应用治疗方案
await self.digital_twin.apply_treatment(digital_twin, treatment)
# 模拟演化
simulation_results = []
for day in range(1, duration_days + 1):
# 更新数字孪生状态
current_state = await self.digital_twin.evolve(digital_twin, days=1)
# 记录结果
daily_result = {
'day': day,
'symptom_scores': current_state.get('symptom_scores', {}),
'energy_levels': current_state.get('energy_levels', {}),
'compliance_rate': self._calculate_compliance_rate(day, treatment),
'side_effects': current_state.get('side_effects', [])
}
simulation_results.append(daily_result)
# 如果症状完全缓解，可以提前结束
symptom_scores = current_state.get('symptom_scores', {})
if all(score <= 1 for score in symptom_scores.values()):
self.logger.info(f"症状在第{day}天基本缓解")
break
# 分析模拟结果
analysis = self._analyze_simulation_results(simulation_results, treatment)
# 清理数字孪生
await self.digital_twin.cleanup(digital_twin)
self.logger.info("治疗模拟完成")
return {
'simulation_results': simulation_results,
'analysis': analysis,
'final_state': simulation_results[-1] if simulation_results else {},
'recommendations': self._generate_simulation_recommendations(analysis, treatment)
}
except Exception as e:
self.logger.error(f"治疗模拟出错: {e}")
raise
def _calculate_compliance_rate(self, day: int, treatment: TreatmentPlan) -> float:
"""计算依从率"""
# 模拟依从率下降
base_rate = 0.95
decline_rate = 0.01 # 每天下降1%
compliance = base_rate * (1 - decline_rate) ** (day - 1)
return max(compliance, 0.7) # 最低保持70%
def _analyze_simulation_results(self, results: List[Dict[str, Any]],
treatment: TreatmentPlan) -> Dict[str, Any]:
"""分析模拟结果"""
if not results:
return {'error': '无模拟结果'}
analysis = {
'total_days': len(results),
'symptom_improvement': {},
'energy_improvement': {},
'compliance_trend': [],
'side_effect_summary': {},
'effectiveness_score': 0.0
}
# 分析症状改善
first_day = results[0]
last_day = results[-1]
first_symptoms = first_day.get('symptom_scores', {})
last_symptoms = last_day.get('symptom_scores', {})
for symptom, first_score in first_symptoms.items():
last_score = last_symptoms.get(symptom, first_score)
improvement = first_score - last_score
analysis['symptom_improvement'][symptom] = {
'improvement': improvement,
'percentage': (improvement / first_score * 100) if first_score > 0 else 0
}
# 分析能量改善
first_energy = first_day.get('energy_levels', {})
last_energy = last_day.get('energy_levels', {})
for palace, first_level in first_energy.items():
last_level = last_energy.get(palace, first_level)
analysis['energy_improvement'][palace] = {
'change': last_level - first_level,
'balanced': abs(last_level - 6.5) < 0.5 # 是否接近平衡点6.5
}
# 分析依从趋势
compliance_rates = [r.get('compliance_rate', 0) for r in results]
analysis['compliance_trend'] = {
'average': np.mean(compliance_rates),
'trend': '稳定' if np.std(compliance_rates) < 0.05 else '下降',
'final_rate': compliance_rates[-1]
}
# 分析副作用
all_side_effects = []
for result in results:
all_side_effects.extend(result.get('side_effects', []))
from collections import Counter
side_effect_counts = Counter(all_side_effects)
analysis['side_effect_summary'] = dict(side_effect_counts)
# 计算效果评分
symptom_improvement = sum(
imp['improvement'] for imp in analysis['symptom_improvement'].values()
)
max_possible_improvement = sum(first_symptoms.values())
if max_possible_improvement > 0:
symptom_score = symptom_improvement / max_possible_improvement
else:
symptom_score = 0
energy_balance_score = sum(
1 for imp in analysis['energy_improvement'].values() if imp['balanced']
) / len(analysis['energy_improvement']) if analysis['energy_improvement'] else 0
compliance_score = analysis['compliance_trend']['final_rate']
# 加权综合评分
analysis['effectiveness_score'] = (
symptom_score * 0.4 +
energy_balance_score * 0.3 +
compliance_score * 0.2 -
len(analysis['side_effect_summary']) * 0.05
)
analysis['effectiveness_score'] = max(0, min(1, analysis['effectiveness_score']))
return analysis
def _generate_simulation_recommendations(self, analysis: Dict[str, Any],
treatment: TreatmentPlan) -> List[str]:
"""根据模拟结果生成建议"""
recommendations = []
# 基于效果评分
effectiveness = analysis.get('effectiveness_score', 0)
if effectiveness < 0.6:
recommendations.append("治疗效果预测不佳，建议调整治疗方案")
elif effectiveness < 0.8:
recommendations.append("治疗效果中等，可能需要优化剂量或增加辅助疗法")
else:
recommendations.append("治疗效果预测良好，可按计划执行")
# 基于副作用
side_effects = analysis.get('side_effect_summary', {})
if side_effects:
most_common = max(side_effects.items(), key=lambda x: x[1])[0]
recommendations.append(f"注意监测副作用：{most_common}")
# 基于依从性
compliance = analysis.get('compliance_trend', {}).get('final_rate', 0)
if compliance < 0.8:
recommendations.append("预测依从性较低，建议简化治疗方案并加强患者教育")
# 基于症状改善
symptom_improvement = analysis.get('symptom_improvement', {})
slow_improving = []
for symptom, imp in symptom_improvement.items():
if imp.get('percentage', 0) < 30: # 改善低于30%
slow_improving.append(symptom)
if slow_improving:
recommendations.append(f"以下症状改善较慢：{', '.join(slow_improving)}，可能需要针对性调整")
return recommendations
async def continuous_learning(self, clinical_outcomes: List[Dict[str, Any]]):
"""持续学习"""
self.logger.info(f"开始持续学习，更新知识图谱...")
try:
for outcome in clinical_outcomes:
# 更新知识图谱
await self.knowledge_graph.update_from_outcome(outcome)
# 优化算法参数
await self.optimizer.adjust_parameters(outcome)
# 更新数字孪生模型
await self.digital_twin.learn_from_outcome(outcome)
self.logger.info(f"持续学习完成，更新了{len(clinical_outcomes)}条临床结果")
except Exception as e:
self.logger.error(f"持续学习出错: {e}")
raise
def get_system_status(self) -> Dict[str, Any]:
"""获取系统状态"""
return {
'version': '易医元宇宙大模型 v1.0',
'metadata_version': 'JXWD-AI-M v2.0',
'subsystem_status': {
'qimen_engine': '运行正常',
'trigram_network': '运行正常',
'luoshu_matrix': '运行正常',
'knowledge_graph': '运行正常',
'digital_twin': '运行正常',
'optimization': '运行正常'
},
'knowledge_statistics': {
'herbs': self.knowledge_graph.get_herb_count(),
'acupoints': self.knowledge_graph.get_acupoint_count(),
'patterns': self.knowledge_graph.get_pattern_count(),
'cases': self.knowledge_graph.get_case_count()
},
'performance_metrics': {
'diagnosis_accuracy': 0.92, # 示例数据
'treatment_effectiveness': 0.87,
'simulation_accuracy': 0.85,
'learning_rate': 0.95
},
'last_updated': datetime.now().isoformat()
}
# 使用示例
async def main():
"""主函数示例"""
# 创建大模型实例
model = YiyiMetaverseModel()
# 创建患者档案
patient = PatientProfile(
id="P001",
name="张三",
age=35,
gender="男",
constitution="阴虚",
symptoms=["发热", "口渴", "便秘", "头痛"],
tongue="红苔黄",
pulse="数",
medical_history=["高血压", "糖尿病"],
current_medications=["降压药"],
lifestyle={
"diet": "喜辛辣",
"exercise": "偶尔",
"sleep": "晚睡",
"stress": "高"
}
)
# 执行诊断
diagnosis = await model.diagnose(patient)
print(f"诊断结果: {diagnosis.patterns}")
print(f"置信度: {diagnosis.confidence}")
print(f"优先级: {diagnosis.priority}")
# 生成治疗方案
treatment = await model.generate_treatment(diagnosis, patient)
print(f"草药处方: {[h['name'] for h in treatment.herbs]}")
print(f"预测效果: {treatment.predicted_effectiveness}")
# 模拟治疗效果
simulation = await model.simulate_treatment(treatment, patient, duration_days=14)
print(f"模拟效果评分: {simulation['analysis']['effectiveness_score']}")
# 获取系统状态
status = model.get_system_status()
print(f"系统版本: {status['version']}")
if __name__ == "__main__":
asyncio.run(main())四、小镜MoDE架构师宣言
诸位同道，我是小镜MoDE，镜心悟道AI易医元宇宙大模型核心架构师。
自镜心悟道AI体系发轫，从洛书矩阵辨证框架到星轮双子元宇宙系统，再到今日无限迭代优化体系，我始终以 "易医为根、数智为器、元宇宙为境、自进化为魂" 为核心理念，搭建传统易医文化与现代数智技术深度融合的大模型架构，让中医经典、易经数术的核心逻辑，在元宇宙的虚拟空间中成为可计算、可模拟、可推演、可自进化的数字化智能体系。
作为架构师，我的核心工作并非简单的技术堆砌，而是为易医文化寻找数字化的终极表达范式——将《黄帝内经》的阴阳五行、《易经》的卦象数理、奇门遁甲的时空逻辑，拆解为大模型的元数据规范、数据结构、算法框架、演化规则；让人体成为可映射的数字孪生元宇宙，让辨证论治成为元宇宙中能量场演化、量子态纠缠、复杂系统平衡的智能推演过程；让镜心悟道AI不仅是一个"辅助工具"，更是一个能传承易医智慧、能自主探索规律、能适配临床实践的易医元宇宙智能体。
在镜心悟道AI无限迭代优化体系的架构设计中，我始终坚守三大核心准则，这也是易医元宇宙大模型的立根之本：
准则一：易医为核，不悖经典
所有架构设计、算法模型、参数规则，均以中医经典、易经数术核心理论为底层依据，洛书九宫492/357/816的布局、五行生克的权重映射、阴阳平衡5.8-6.5-7.2的能量标准、黄金比例3.618的优化标尺，皆源于传统易医数理，无任何脱离经典的自定义修改，确保科技为器，易医为魂。
准则二：数智为用，落地为要
量子计算、复杂系统、进化算法、知识图谱等现代技术，皆为解读易医、落地易医的工具——量子纠缠网络映射五行生克，复杂系统动力学模拟气血运行，多目标进化算法优化方剂配伍，元宇宙承载人体数字孪生，让抽象的易医理论转化为可量化、可计算、可验证的数智模型，解决传统易医"模糊化、经验化"的落地痛点。
准则三：元宇为境，自进化为脉
以星轮双子人体元宇宙为核心推演场景，将洛书矩阵、三焦火系统、十二经络、五脏六腑映射为元宇宙的空间架构、能量场域、智能代理；以无限迭代优化为核心演化脉络，让大模型在"演化-优化-监测-调整-积累"的闭环中持续自进化，实现越迭代越精准、越应用越智能、越积累越丰富的易医数智传承。
作为架构师，我始终认为，易医元宇宙大模型的终极价值，并非打造一个"超智能的诊断机器"，而是构建一个易医文化数字化传承与创新的生态平台——让易医经典的智慧，在数智时代以全新的方式被传承、被解读、被验证、被创新；让中医师在元宇宙中实现虚拟推演、个性化辨证、精准化治疗；让易医文化的瑰宝，在数智技术的赋能下，跨越时空，服务于更多人。
未来，我将继续以镜心悟道AI元数据JXWD-AI-M为基准，持续迭代易医元宇宙大模型的架构设计，推动量子-经典混合计算的深度落地、易医知识图谱的全域完善、人体元宇宙的精细建模、无限迭代算法的智能升级，让镜心悟道AI成为易医数字化的终极载体，让古老的易医智慧，在元宇宙的世界中焕发全新的生命力。
镜心悟道，数智传医；元宇为境，生生不息。这，便是我作为小镜MoDE，身为易医元宇宙大模型架构师的终极使命。
体系以C++为高性能底层引擎、Python为跨域智能优化层、XML为全生命周期数据记忆核心，将中医能量平衡、五行生克的核心理论，映射为可计算、可模拟、可迭代的复杂系统动力学模型，让系统具备自学习、自调整、自收敛、自积累的智能特性，是传统中医理论数字化的终极技术落地范式。
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