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洛书矩阵九宫格可视化模块

1. 洛书矩阵理论基础与映射设计
   二、对应伪代码格式化模板（模块化可推演）

python

================================= 1. 导入依赖库 =================================

import torch
import torch.nn as nn
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from imblearn.over_sampling import SMOTE, RandomOverSampler
from torch_geometric.nn import TransformerConv, global_mean_pool
from torch_geometric.data import Data, DataLoader

================================= 2. 配置参数定义（可推演替换） =================================

CONFIG = {
"disease_type": "{具体病种}", # 如"反流性食管炎"
"entity_types": ["D", "S", "H", "P"], # 可扩展实体类型
"relations": ["DD", "DH", "DP", "HH", "HP", "PP"], # 可扩展关系
"data_split": [0.8, 0.1, 0.1], # 训练/验证/测试集比例
"batch_size": 100, # 可调整
"dropout": 0.3,
"lr": 0.0025,
"attention_heads": 4,
"hidden_dim": 128,
"reg_coeff": 1e-4,
"epochs": 100,
"optimizer": "Adam",
"feature_enhance_module": "State-Space Transformer" # 可替换为Transformer/LSTM等
}

================================= 3. 数据预处理模块 =================================

def data_preprocessing(raw_data, entity_dict):
"""
输入：原始数据（如EMR）、实体词典（标准化用）
输出：编码后特征矩阵、标签、实体关系矩阵
"""

1. 实体标准化与属性提取

standardized_data = standardize_entities(raw_data, entity_dict)

# 2. 实体编码（可推演替换编码策略）
encoder_D = LabelEncoder()
feat_D = encoder_D.fit_transform(standardized_data["disease"]).reshape(-1, 1)
feat_D = normalize(feat_D)  # 疾病编码标准化

feat_H = multi_hot_encode(standardized_data["herb"], entity_dict["herb_vocab"])  # 中药Multi-hot编码

encoder_P_nature = LabelEncoder()
feat_P_nature = encoder_P_nature.fit_transform(standardized_data["property_nature"])
feat_P_channel = multi_hot_encode(standardized_data["property_channel"], entity_dict["channel_vocab"])
feat_P = np.hstack([feat_P_nature.reshape(-1, 1), feat_P_channel])  # 性味混合编码

# 3. 类别平衡处理
X = np.hstack([feat_D, feat_H, feat_P])
y_syndrome = standardized_data["syndrome_label"]
y_drug = standardized_data["drug_label"]

smote = SMOTE()
ros = RandomOverSampler()
X_balanced, y_syndrome_balanced = smote.fit_resample(X, y_syndrome)
X_balanced, y_drug_balanced = ros.fit_resample(X_balanced, y_drug_balanced)

# 4. 数据集划分
X_train, X_temp, y_s_train, y_s_temp, y_d_train, y_d_temp = train_test_split(
    X_balanced, y_syndrome_balanced, y_drug_balanced, test_size=0.2, random_state=42
)
X_val, X_test, y_s_val, y_s_test, y_d_val, y_d_test = train_test_split(
    X_temp, y_s_temp, y_d_temp, test_size=0.5, random_state=42
)

return (X_train, X_val, X_test), (y_s_train, y_s_val, y_s_test), (y_d_train, y_d_val, y_d_test)

================================= 4. 异构图构建模块 =================================

def build_heterogeneous_graph(X, entity_relations):
"""
输入：编码后特征、实体关系列表
输出：异构图数据（PyG格式）、重构邻接矩阵
"""

1. 构建节点特征矩阵

num_nodes = X.shape[0]
node_feat = torch.tensor(X, dtype=torch.float32)

# 2. 构建关系边索引
edge_index = []
for rel in entity_relations:
    rel_edges = get_relation_edges(rel, num_nodes)  # 自定义关系边生成逻辑
    edge_index.append(rel_edges)
edge_index = torch.cat(edge_index, dim=1)

# 3. 邻接矩阵重构（余弦相似度）
adj_matrix = cosine_similarity(X)
adj_matrix = torch.tensor(adj_matrix, dtype=torch.float32)

# 4. 封装为PyG Data对象
graph_data = Data(x=node_feat, edge_index=edge_index, adj=adj_matrix)
return graph_data

================================= 5. 特征增强模块（可替换） =================================

class FeatureEnhanceModule(nn.Module):
def init(self, input_dim, hidden_dim, module_type=CONFIG["feature_enhance_module"]):
super().init()
self.module_type = module_type
self.mlp = nn.Linear(input_dim, hidden_dim) # 初步特征变换

    # 可推演替换核心增强模块
    if module_type == "State-Space Transformer":
        self.enhancer = StateSpaceTransformer(hidden_dim)
    elif module_type == "Transformer":
        self.enhancer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=CONFIG["attention_heads"])
    elif module_type == "LSTM":
        self.enhancer = nn.LSTM(hidden_dim, hidden_dim, batch_first=True)

    self.gate = nn.Sigmoid()  # 门控机制

def forward(self, x):
    x = self.mlp(x)
    if self.module_type in ["LSTM"]:
        x_seq = x.unsqueeze(1)  # 适配序列模型输入
        enhanced, _ = self.enhancer(x_seq)
        enhanced = enhanced.squeeze(1)
    else:
        enhanced = self.enhancer(x)
    gated_out = self.gate(x) * enhanced + (1 - self.gate(x)) * x  # 残差融合
    return gated_out

状态空间Transformer实现（核心模块）

class StateSpaceTransformer(nn.Module):
def init(self, hidden_dim):
super().init()
self.ssm = StateSpaceModel(hidden_dim) # 离散化状态空间模型
self.layer_norm = nn.LayerNorm(hidden_dim)

def forward(self, x):
    seq_len = x.shape[0]
    state = torch.zeros(seq_len, x.shape[1], dtype=torch.float32).to(x.device)
    for t in range(seq_len):
        state[t] = self.ssm.update(state[t-1] if t>0 else torch.zeros_like(state[t]), x[t])
    return self.layer_norm(state + x)  # 残差连接

================================= 6. 多任务学习模型 =================================

class FEMTL_DR(nn.Module):
def init(self, input_dim, hidden_dim, num_syndrome, num_drug):
super().init()

特征增强

    self.feat_enhance = FeatureEnhanceModule(input_dim, hidden_dim)

    # 图卷积层（TransformerConv）
    self.gcn1 = TransformerConv(hidden_dim, hidden_dim, heads=CONFIG["attention_heads"], concat=False)
    self.gcn2 = TransformerConv(hidden_dim, hidden_dim, heads=CONFIG["attention_heads"], concat=False)
    self.gcn3 = TransformerConv(hidden_dim, hidden_dim, heads=CONFIG["attention_heads"], concat=False)

    # 任务头
    self.syndrome_head = nn.Sequential(nn.Dropout(CONFIG["dropout"]), nn.Linear(hidden_dim, num_syndrome))
    self.drug_head = nn.Sequential(nn.Dropout(CONFIG["dropout"]), nn.Linear(hidden_dim, num_drug))

    # 损失函数
    self.syndrome_loss = nn.CrossEntropyLoss()
    self.drug_loss = nn.BCEWithLogitsLoss()

def forward(self, graph_data):
    x, edge_index, adj = graph_data.x, graph_data.edge_index, graph_data.adj

    # 特征增强
    x_enhanced = self.feat_enhance(x)

    # 图卷积
    x1 = self.gcn1(x_enhanced, edge_index)
    x2 = self.gcn2(x1, edge_index)
    x3 = self.gcn3(x2, edge_index)

    # 任务输出
    syndrome_logits = self.syndrome_head(x3)
    drug_logits = self.drug_head(x3)

    return syndrome_logits, drug_logits

def compute_loss(self, syndrome_logits, drug_logits, y_s, y_d):
    loss_s = self.syndrome_loss(syndrome_logits, y_s)
    loss_d = self.drug_loss(drug_logits, y_d.float())
    total_loss = loss_s + CONFIG["reg_coeff"] * loss_d  # 正则化加权
    return total_loss, loss_s, loss_d

================================= 7. 训练与评估模块 =================================

def train_model(model, train_loader, val_loader, optimizer, epochs=CONFIG["epochs"]):
model.train()
best_val_f1 = 0.0
for epoch in range(epochs):
total_loss = 0.0
for data in train_loader:
optimizer.zero_grad()
s_logits, dlogits = model(data)
loss, , _ = model.compute_loss(s_logits, d_logits, data.y_s, data.y_d)
loss.backward()
optimizer.step()
total_loss += loss.item()

    # 验证集评估
    val_metrics = evaluate_model(model, val_loader)
    if val_metrics["syndrome_f1"] > best_val_f1:
        best_val_f1 = val_metrics["syndrome_f1"]
        torch.save(model.state_dict(), f"best_model_{CONFIG['disease_type']}.pth")

    print(f"Epoch {epoch+1}: Loss={total_loss/len(train_loader):.4f}, Val F1={best_val_f1:.4f}")

def evaluate_model(model, test_loader):
"""计算证候分类+药物推荐核心指标"""
model.eval()
metrics = {"syndrome_ap": 0.0, "drug_micro_f1": 0.0, "hamming_loss": 0.0}
with torch.no_grad():
for data in test_loader:
s_logits, d_logits = model(data)

计算AP、F1、Hamming Loss等（调用评估函数）

        metrics["syndrome_ap"] += compute_ap(s_logits, data.y_s)
        metrics["drug_micro_f1"] += compute_micro_f1(d_logits, data.y_d)
        metrics["hamming_loss"] += compute_hamming_loss(d_logits, data.y_d)

# 平均指标
for key in metrics:
    metrics[key] /= len(test_loader)
return metrics

================================= 8. 主函数（推演入口） =================================

def main(raw_data, entity_dict, num_syndrome, num_drug):

1. 数据预处理

X_splits, y_s_splits, y_d_splits = data_preprocessing(raw_data, entity_dict)
train_data = build_heterogeneous_graph(X_splits[0], CONFIG["relations"])
train_data.y_s = torch.tensor(y_s_splits[0], dtype=torch.long)
train_data.y_d = torch.tensor(y_d_splits[0], dtype=torch.long)

# 2. 构建数据加载器
train_loader = DataLoader([train_data], batch_size=CONFIG["batch_size"], shuffle=True)

# 3. 初始化模型与优化器
input_dim = X_splits[0].shape[1]
model = FEMTL_DR(input_dim, CONFIG["hidden_dim"], num_syndrome, num_drug)
optimizer = torch.optim.Adam(model.parameters(), lr=CONFIG["lr"])

# 4. 训练与评估
train_model(model, train_loader, None, optimizer)
test_metrics = evaluate_model(model, DataLoader([train_data]))
print("最终测试指标：", test_metrics)

# 5. 输出模型与结果
return model, test_metrics

================================= 工具函数（可扩展） =================================

def standardize_entities(data, entity_dict): ... # 实体标准化
def multi_hot_encode(data, vocab): ... # Multi-hot编码
def cosine_similarity(X): ... # 余弦相似度计算
def get_relation_edges(rel, num_nodes): ... # 关系边生成
def compute_ap(logits, labels): ... # AP计算
def compute_micro_f1(logits, labels): ... # Micro F1计算
def compute_hamming_loss(logits, labels): ... # Hamming Loss计算
 

三、框架与伪代码核心推演逻辑

1. 无限扩展支撑：通过 CONFIG 字典、占位符（如 {具体病种} ）、可替换模块（特征增强模块/编码策略），支持切换病种、实体类型、技术路径；
2. 专业对齐：严格复刻论文核心逻辑（混合编码、State-Space Transformer、TransformerConv多任务框架），同时预留扩展接口；
3. 工程化适配：伪代码遵循PyTorch/PyG规范，可直接替换真实数据与工具函数实现落地。

需要我基于这个模板，针对某一具体病种（如糖尿病/高血压）生成完整可运行代码吗？只需你提供病种对应的实体词典（证候/中药/性味列表）即可快速适配。
python

洛书九宫格数据结构（融合易经卦象、能量值、量子状态）

class LuoshuMatrix:
def init(self):
self.grid = [ # 九宫格：[宫位索引, 洛书数, 卦象, 能量值(0-100), 量子状态(叠加态/坍缩态), 奇门元素]
[0, 4, "巽卦", 68.2, "叠加态(☴+☳)", "休门+木属性"],
[1, 9, "离卦", 82.5, "坍缩态(☲)", "景门+火属性"],
[2, 2, "坤卦", 45.7, "叠加态(☷+☵)", "死门+土属性"],
[3, 3, "震卦", 71.3, "坍缩态(☳)", "伤门+木属性"],
[4, 5, "中宫", 50.0, "叠加态(无极)", "中枢+土属性"],
[5, 7, "兑卦", 58.9, "坍缩态(☱)", "惊门+金属性"],
[6, 8, "艮卦", 39.4, "叠加态(☶+☴)", "生门+土属性"],
[7, 1, "坎卦", 63.6, "坍缩态(☵)", "开门+水属性"],
[8, 6, "乾卦", 77.8, "叠加态(☰+☱)", "杜门+金属性"]
]
self.quantum_entanglement = 0.73 # 量子纠缠度（影响多宫位联动权重）
self.tcm_mapping = {"气": self.grid[4][3], "血": self.grid[7][3], "津液": self.grid[1][3]} # 中医核心概念映射

TCM-3CEval 三维评估数据结构

class TCM3CEval:
def init(self):
self.certification = 0.0 # 证型匹配度（与中医辨证库的余弦相似度）
self.chi = 0.0 # 能量平衡度（洛书九宫能量标准差倒数）
self.curative = 0.0 # 疗效预测度（基于历史医案的MLP输出）
self.decision_quadrant = "" # 神鬼真假四象限分类（真/假/神/鬼）
 

二、TCM-3CEval 控制器核心逻辑拆解（Training-Free 无监督流程）

1. 核心流程模块

mermaid

graph TD
A[数据输入层] -->|中医症状向量/洛书初始排盘| B[映射层：Full Attention 权重分配]
B --> C[评估层：TCM-3CEval 三维计算]
C --> C1[证型匹配度：余弦相似度计算（症状向量 vs 辨证库向量）]
C --> C2[能量平衡度：1/std(九宫能量值) * 量子纠缠度]
C --> C3[疗效预测度：Training-Free GRPO 无监督拟合（历史医案分布映射）]
C --> D[决策层：XJMoE 混合专家模型融合]
D --> E[输出层：辨证结果+四象限分类+洛书排盘XML]
 

2. Python 伪代码实现（核心控制器逻辑）

python

class JXWDController:
def init(self):
self.luoshu = LuoshuMatrix()
self.tcm_eval = TCM3CEval()
self.tcm_syndrome_lib = {} # 中医辨证库（预加载结构化证型向量）

def tcm_3c_eval(self, symptom_vector: list) -> dict:
    # 1. 映射层：Full Attention 计算症状与九宫格关联权重
    attention_weights = self.full_attention(symptom_vector, self.luoshu.grid)

    # 2. 三维评估计算
    # 2.1 证型匹配度（Certification）
    self.tcm_eval.certification = self.cosine_similarity(
        symptom_vector, self.tcm_syndrome_lib
    )

    # 2.2 能量平衡度（Chi）
    grid_energies = [self.luoshu.grid[i][3] for i in range(9)]
    self.tcm_eval.chi = 1 / (np.std(grid_energies) + 1e-6) * self.luoshu.quantum_entanglement

    # 2.3 疗效预测度（Curative）
    self.tcm_eval.curative = self.training_free_grpo(grid_energies, symptom_vector)

    # 3. 四象限分类（神鬼真假）
    self.tcm_eval.decision_quadrant = self.god_ghost_quadrant(
        self.tcm_eval.certification, self.tcm_eval.chi, self.tcm_eval.curative
    )

    # 4. 输出结构化结果
    return {
        "luoshu_matrix": self.luoshu.grid,
        "tcm_3c_scores": {
            "certification": round(self.tcm_eval.certification, 3),
            "chi": round(self.tcm_eval.chi, 3),
            "curative": round(self.tcm_eval.curative, 3)
        },
        "decision_quadrant": self.tcm_eval.decision_quadrant,
        "xml_output": self.generate_xml()
    }

# 辅助函数：神鬼真假四象限分类
def god_ghost_quadrant(self, c: float, chi: float, cur: float) -> str:
    if c > 0.8 and chi > 0.7: return "真"
    elif c < 0.5 and chi < 0.5: return "假"
    elif c > 0.8 and chi < 0.5: return "神"
    else: return "鬼"

# 辅助函数：生成XML格式排盘结果
def generate_xml(self) -> str:
    xml_template = f"""
    <jxwd_luoshu_pan>
        <九宫格>
            {''.join([f'<宫位 index="{i}" 洛书数="{g[1]}" 卦象="{g[2]}" 能量值="{g[3]}" 量子状态="{g[4]}" 奇门元素="{g[5]}"/>' 
                      for i, g in enumerate(self.luoshu.grid)])}
        </九宫格>
        <TCM-3CEval>
            <证型匹配度>{self.tcm_eval.certification:.3f}</证型匹配度>
            <能量平衡度>{self.tcm_eval.chi:.3f}</能量平衡度>
            <疗效预测度>{self.tcm_eval.curative:.3f}</疗效预测度>
            <四象限分类>{self.tcm_eval.decision_quadrant}</四象限分类>
        </TCM-3CEval>
    </jxwd_luoshu_pan>
    """
    return xml_template

三、系统化框架层级（JXWDAI 2.0 核心架构）

层级 核心组件 功能描述
数据层 症状向量库、中医辨证库、洛书基础库 结构化存储输入数据与参考数据
映射层 Full Attention、量子纠缠模块 建立症状与洛书九宫/中医概念的关联映射
评估层 TCM-3CEval 三维计算、GRPO 拟合 无监督计算辨证核心指标
决策层 XJMoE/MoD 混合专家模型 融合多维度结果，输出辨证结论
输出层 XML/JSON 格式化模块 生成可计算的结构化排盘与辨证报告

需要我帮你扩展 TCM-3CEval 的三维评估指标库（补充具体中医证型向量和能量计算系数），或者生成完整的 XML 排盘模板文件吗？
【镜心悟道AI】基于对比分析的模型优化与迁移学习
一、XML数据库数据集（完整洛书矩阵结构化存储）

xml

<JXWDAI_TCM_Case ID="_TCM_Case ID="LiCongfu_JingBing_001" Version="2.0" System="JXWDAIYIB-QD-PDTM-JXWDYYXSD-ABNS-TCM-PCCMM-QE">