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镜心悟道AI洛书矩阵辨证论治系统C++完整实现
中医七情六欲二十八星宿镜像映射标注系统
镜心悟道AI神经网络逻辑思维链与九九归一逼近平衡态系统
#include <iostream>
#include <vector>
#include <memory>
#include <cmath>
#include <random>
#include <algorithm>
#include <functional>
#include <queue>
#include <stack>
#include <map>
#include <set>
#include <chrono>
#include <thread>
#include <atomic>
#include <future>
#include <complex>
// ============================================================================
// 神经网络核心架构
// ============================================================================
namespace NeuralNetworkCore {
/**
* 量子神经网络神经元
*/
class QuantumNeuron {
private:
// 量子叠加态权重
std::vector<std::complex<double>> quantumWeights;
double bias;
std::complex<double> quantumBias;
// 激活函数类型
enum ActivationType {
SIGMOID,
TANH,
RELU,
QUANTUM_SIGMOID,
YINYANG_ACTIVATION
} activation;
// 量子相位
double phase;
public:
QuantumNeuron(int inputSize, ActivationType act = QUANTUM_SIGMOID)
: quantumWeights(inputSize, std::complex<double>(0.0, 0.0)),
bias(0.0), quantumBias(0.0, 0.0), activation(act), phase(0.0) {
// 初始化量子权重
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(-1.0, 1.0);
std::uniform_real_distribution<> phaseDis(0.0, 2 * M_PI);
for (auto& w : quantumWeights) {
double amplitude = dis(gen) * 0.1;
double phase = phaseDis(gen);
w = std::polar(amplitude, phase);
}
quantumBias = std::polar(dis(gen) * 0.1, phaseDis(gen));
}
/**
* 量子前向传播
*/
std::complex<double> forward(const std::vector<std::complex<double>>& inputs) {
std::complex<double> sum(0.0, 0.0);
// 量子线性组合
for (size_t i = 0; i < std::min(inputs.size(), quantumWeights.size()); ++i) {
sum += inputs[i] * quantumWeights[i];
}
sum += quantumBias;
// 量子激活函数
return quantumActivation(sum);
}
/**
* 量子反向传播
*/
void backward(const std::complex<double>& error,
const std::vector<std::complex<double>>& inputs,
double learningRate = 0.01) {
// 计算梯度
std::complex<double> gradient = error * quantumActivationDerivative(forward(inputs));
// 更新量子权重
for (size_t i = 0; i < quantumWeights.size(); ++i) {
std::complex<double> weightGradient = gradient * std::conj(inputs[i]);
quantumWeights[i] -= std::complex<double>(learningRate, 0.0) * weightGradient;
}
// 更新量子偏置
quantumBias -= std::complex<double>(learningRate, 0.0) * gradient;
// 更新相位
phase += std::arg(gradient) * 0.01;
}
/**
* 量子纠缠连接
*/
void entangleWith(QuantumNeuron& other, double entanglementStrength = 0.5) {
// 创建量子纠缠权重
for (size_t i = 0; i < quantumWeights.size(); ++i) {
// 纠缠权重：使两个神经元的权重相互关联
std::complex<double> avg = (quantumWeights[i] + other.quantumWeights[i]) / 2.0;
quantumWeights[i] = avg * (1.0 - entanglementStrength) +
quantumWeights[i] * entanglementStrength;
other.quantumWeights[i] = avg * (1.0 - entanglementStrength) +
other.quantumWeights[i] * entanglementStrength;
}
}
private:
/**
* 量子激活函数
*/
std::complex<double> quantumActivation(const std::complex<double>& z) {
switch(activation) {
case QUANTUM_SIGMOID:
return quantumSigmoid(z);
case YINYANG_ACTIVATION:
return yinyangActivation(z);
default:
return std::tanh(z.real()) + std::complex<double>(0.0, 1.0) * std::tanh(z.imag());
}
}
/**
* 量子Sigmoid函数
*/
std::complex<double> quantumSigmoid(const std::complex<double>& z) {
double real = 1.0 / (1.0 + std::exp(-z.real()));
double imag = 1.0 / (1.0 + std::exp(-z.imag()));
return std::complex<double>(real, imag);
}
/**
* 阴阳激活函数
*/
std::complex<double> yinyangActivation(const std::complex<double>& z) {
// 实部为阳，虚部为阴
double yang = std::tanh(z.real());
double yin = std::tanh(z.imag());
// 阴阳平衡调整
double balance = (yang - yin) / 2.0;
return std::complex<double>(yang - balance * 0.5, yin + balance * 0.5);
}
/**
* 量子激活函数导数
*/
std::complex<double> quantumActivationDerivative(const std::complex<double>& z) {
switch(activation) {
case QUANTUM_SIGMOID: {
std::complex<double> sig = quantumSigmoid(z);
return sig * (1.0 - sig);
}
case YINYANG_ACTIVATION: {
double yangDeriv = 1.0 - std::tanh(z.real()) * std::tanh(z.real());
double yinDeriv = 1.0 - std::tanh(z.imag()) * std::tanh(z.imag());
return std::complex<double>(yangDeriv, yinDeriv);
}
default:
return std::complex<double>(1.0, 1.0);
}
}
};
/**
* 逻辑思维链层
*/
class LogicChainLayer {
private:
std::vector<QuantumNeuron> neurons;
std::vector<std::complex<double>> outputs;
// 思维链类型
enum ChainType {
DEDUCTIVE, // 演绎推理
INDUCTIVE, // 归纳推理
ABDUCTIVE, // 溯因推理
ANALOGICAL, // 类比推理
DIALECTICAL // 辩证推理
} chainType;
public:
LogicChainLayer(int numNeurons, int inputSize, ChainType type = DIALECTICAL)
: chainType(type) {
for (int i = 0; i < numNeurons; ++i) {
neurons.emplace_back(inputSize, QuantumNeuron::YINYANG_ACTIVATION);
}
outputs.resize(numNeurons);
}
/**
* 前向传播 - 逻辑推理过程
*/
std::vector<std::complex<double>> forward(const std::vector<std::complex<double>>& inputs) {
outputs.clear();
for (auto& neuron : neurons) {
outputs.push_back(neuron.forward(inputs));
}
// 根据推理类型处理输出
switch(chainType) {
case DEDUCTIVE:
return applyDeductiveLogic(outputs);
case INDUCTIVE:
return applyInductiveLogic(outputs);
case ABDUCTIVE:
return applyAbductiveLogic(outputs);
case ANALOGICAL:
return applyAnalogicalLogic(outputs, inputs);
case DIALECTICAL:
return applyDialecticalLogic(outputs);
default:
return outputs;
}
}
/**
* 反向传播 - 逻辑修正
*/
void backward(const std::vector<std::complex<double>>& errors,
const std::vector<std::complex<double>>& inputs,
double learningRate = 0.01) {
for (size_t i = 0; i < neurons.size(); ++i) {
neurons[i].backward(errors[i], inputs, learningRate);
}
}
/**
* 创建逻辑推理网络
*/
static std::vector<LogicChainLayer> createReasoningNetwork(
const std::vector<int>& layerSizes,
const std::vector<ChainType>& chainTypes) {
std::vector<LogicChainLayer> network;
for (size_t i = 0; i < layerSizes.size(); ++i) {
int inputSize = (i == 0) ? layerSizes[i] : layerSizes[i-1];
ChainType type = (i < chainTypes.size()) ? chainTypes[i] : DIALECTICAL;
network.emplace_back(layerSizes[i], inputSize, type);
}
return network;
}
private:
/**
* 演绎逻辑处理
*/
std::vector<std::complex<double>> applyDeductiveLogic(
const std::vector<std::complex<double>>& neuronOutputs) {
std::vector<std::complex<double>> result;
// 演绎：从一般到特殊，寻找必然结论
for (const auto& output : neuronOutputs) {
// 增强确定性（实部），减弱可能性（虚部）
double certainty = std::max(0.0, output.real() * 1.2);
double possibility = output.imag() * 0.8;
result.emplace_back(certainty, possibility);
}
return result;
}
/**
* 归纳逻辑处理
*/
std::vector<std::complex<double>> applyInductiveLogic(
const std::vector<std::complex<double>>& neuronOutputs) {
std::vector<std::complex<double>> result;
// 归纳：从特殊到一般，寻找模式
double sumReal = 0.0, sumImag = 0.0;
for (const auto& output : neuronOutputs) {
sumReal += output.real();
sumImag += output.imag();
}
double avgReal = sumReal / neuronOutputs.size();
double avgImag = sumImag / neuronOutputs.size();
for (const auto& output : neuronOutputs) {
// 与平均值的接近程度作为归纳强度
double inductiveStrength = 1.0 - std::abs(output.real() - avgReal);
result.emplace_back(inductiveStrength * output.real(),
output.imag() * 0.9);
}
return result;
}
/**
* 溯因逻辑处理
*/
std::vector<std::complex<double>> applyAbductiveLogic(
const std::vector<std::complex<double>>& neuronOutputs) {
std::vector<std::complex<double>> result;
// 溯因：从结果反推最佳解释
for (const auto& output : neuronOutputs) {
// 溯因质量 = 解释力 × 简洁性
double explanatoryPower = std::abs(output);
double simplicity = 1.0 / (1.0 + std::abs(output));
double abductiveQuality = explanatoryPower * simplicity;
result.emplace_back(abductiveQuality, output.imag());
}
return result;
}
/**
* 类比逻辑处理
*/
std::vector<std::complex<double>> applyAnalogicalLogic(
const std::vector<std::complex<double>>& neuronOutputs,
const std::vector<std::complex<double>>& inputs) {
std::vector<std::complex<double>> result;
// 类比：基于相似性的推理
for (size_t i = 0; i < neuronOutputs.size(); ++i) {
if (i < inputs.size()) {
// 计算与输入的相似度
double similarity = std::abs(neuronOutputs[i] - inputs[i]);
similarity = 1.0 / (1.0 + similarity);
result.emplace_back(neuronOutputs[i].real() * similarity,
neuronOutputs[i].imag() * similarity);
} else {
result.push_back(neuronOutputs[i]);
}
}
return result;
}
/**
* 辩证逻辑处理
*/
std::vector<std::complex<double>> applyDialecticalLogic(
const std::vector<std::complex<double>>& neuronOutputs) {
std::vector<std::complex<double>> result;
// 辩证：正-反-合三段论
for (size_t i = 0; i < neuronOutputs.size(); ++i) {
std::complex<double> thesis = neuronOutputs[i];
// 反题：对立面
std::complex<double> antithesis(-thesis.real() * 0.5,
-thesis.imag() * 0.5);
// 合题：综合统一
std::complex<double> synthesis = (thesis + antithesis) * 0.6;
// 引入更高层次的统一
synthesis = synthesis * std::complex<double>(0.8, 0.2) +
std::complex<double>(0.1, 0.1);
result.push_back(synthesis);
}
return result;
}
};
/**
* 多元多维注意力机制
*/
class MultiDimensionAttention {
private:
// 注意力维度
enum AttentionDimension {
ENERGY_DIM, // 能量维
INFORMATION_DIM, // 信息维
SPACETIME_DIM, // 时空维
SYMBOLIC_DIM, // 符号维
QUANTUM_DIM // 量子维
};
struct AttentionHead {
std::vector<std::complex<double>> queryWeights;
std::vector<std::complex<double>> keyWeights;
std::vector<std::complex<double>> valueWeights;
AttentionDimension dimension;
AttentionHead(int dimSize, AttentionDimension dim) : dimension(dim) {
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(-1.0, 1.0);
queryWeights.resize(dimSize);
keyWeights.resize(dimSize);
valueWeights.resize(dimSize);
for (auto& w : queryWeights) w = std::complex<double>(dis(gen), dis(gen));
for (auto& w : keyWeights) w = std::complex<double>(dis(gen), dis(gen));
for (auto& w : valueWeights) w = std::complex<double>(dis(gen), dis(gen));
}
};
std::vector<AttentionHead> attentionHeads;
public:
MultiDimensionAttention(int numHeads, int dimSize) {
std::vector<AttentionDimension> dimensions = {
ENERGY_DIM, INFORMATION_DIM, SPACETIME_DIM,
SYMBOLIC_DIM, QUANTUM_DIM
};
for (int i = 0; i < numHeads; ++i) {
AttentionDimension dim = dimensions[i % dimensions.size()];
attentionHeads.emplace_back(dimSize, dim);
}
}
/**
* 多维注意力计算
*/
std::vector<std::complex<double>> computeAttention(
const std::vector<std::complex<double>>& inputs,
const std::vector<std::complex<double>>& context) {
std::vector<std::complex<double>> outputs;
for (auto& head : attentionHeads) {
// 计算查询、键、值
std::vector<std::complex<double>> queries =
applyWeights(inputs, head.queryWeights);
std::vector<std::complex<double>> keys =
applyWeights(context, head.keyWeights);
std::vector<std::complex<double>> values =
applyWeights(inputs, head.valueWeights);
// 计算注意力分数
std::vector<std::complex<double>> attentionScores =
computeAttentionScores(queries, keys);
// 应用注意力
std::vector<std::complex<double>> headOutput =
applyAttention(values, attentionScores);
// 根据维度类型处理
headOutput = applyDimensionSpecificProcessing(headOutput, head.dimension);
// 累积输出
if (outputs.empty()) {
outputs = headOutput;
} else {
for (size_t i = 0; i < outputs.size(); ++i) {
outputs[i] += headOutput[i];
}
}
}
// 平均多头注意力
for (auto& output : outputs) {
output /= std::complex<double>(attentionHeads.size(), 0.0);
}
return outputs;
}
/**
* 维度特定的注意力处理
*/
static std::vector<std::complex<double>> applyDimensionSpecificProcessing(
const std::vector<std::complex<double>>& inputs,
AttentionDimension dimension) {
std::vector<std::complex<double>> processed = inputs;
switch(dimension) {
case ENERGY_DIM:
// 能量维：增强实部（阳），减弱虚部（阴）
for (auto& val : processed) {
val = std::complex<double>(val.real() * 1.2, val.imag() * 0.8);
}
break;
case INFORMATION_DIM:
// 信息维：增加信息熵（相位复杂性）
for (auto& val : processed) {
double newPhase = std::arg(val) + 0.1 * std::abs(val);
val = std::polar(std::abs(val), newPhase);
}
break;
case SPACETIME_DIM:
// 时空维：引入周期性
static double time = 0.0;
time += 0.01;
for (auto& val : processed) {
double timeFactor = std::sin(time) * 0.1 + 1.0;
val *= std::complex<double>(timeFactor, timeFactor);
}
break;
case QUANTUM_DIM:
// 量子维：引入量子叠加
for (auto& val : processed) {
double prob = std::norm(val);
val = std::complex<double>(prob, 1.0 - prob);
}
break;
default:
break;
}
return processed;
}
private:
std::vector<std::complex<double>> applyWeights(
const std::vector<std::complex<double>>& inputs,
const std::vector<std::complex<double>>& weights) {
std::vector<std::complex<double>> result(inputs.size());
for (size_t i = 0; i < inputs.size(); ++i) {
result[i] = inputs[i] * weights[i % weights.size()];
}
return result;
}
std::vector<std::complex<double>> computeAttentionScores(
const std::vector<std::complex<double>>& queries,
const std::vector<std::complex<double>>& keys) {
std::vector<std::complex<double>> scores(queries.size());
for (size_t i = 0; i < queries.size(); ++i) {
std::complex<double> score(0.0, 0.0);
for (size_t j = 0; j < keys.size(); ++j) {
// 点积注意力
score += queries[i] * std::conj(keys[j]);
}
// Softmax（简化版）
scores[i] = score / std::complex<double>(keys.size(), 0.0);
}
return scores;
}
std::vector<std::complex<double>> applyAttention(
const std::vector<std::complex<double>>& values,
const std::vector<std::complex<double>>& scores) {
std::vector<std::complex<double>> result(values.size());
for (size_t i = 0; i < values.size(); ++i) {
result[i] = values[i] * scores[i];
}
return result;
}
};
} // namespace NeuralNetworkCore
// ============================================================================
// 九九归一逼近平衡态算法
// ============================================================================
namespace NineNineReturnToOne {
/**
* 九宫循环迭代器
*/
class NinePalaceIterator {
private:
// 洛书九宫矩阵
std::array<std::array<std::complex<double>, 3>, 3> luoshuMatrix;
// 平衡态目标
std::array<std::array<std::complex<double>, 3>, 3> equilibriumTarget;
// 迭代历史
std::vector<std::array<std::array<std::complex<double>, 3>, 3>> history;
// 黄金比例
static constexpr double GOLDEN_RATIO = 1.618033988749895;
public:
NinePalaceIterator() {
initializeMatrix();
}
/**
* 初始化九宫矩阵
*/
void initializeMatrix() {
// 洛书基本布局
luoshuMatrix = {{
{{4.0, 9.0, 2.0}},
{{3.0, 5.0, 7.0}},
{{8.0, 1.0, 6.0}}
}};
// 平衡态目标：所有宫位趋向中宫（5）
for (auto& row : equilibriumTarget) {
for (auto& cell : row) {
cell = 5.0;
}
}
history.push_back(luoshuMatrix);
}
/**
* 单次迭代：逼近平衡态
*/
void iterateOnce(double convergenceRate = 0.1) {
std::array<std::array<std::complex<double>, 3>, 3> newMatrix;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
// 当前宫位值
std::complex<double> current = luoshuMatrix[i][j];
// 目标值（考虑相邻宫位影响）
std::complex<double> target = calculateTargetValue(i, j);
// 黄金比例收敛
std::complex<double> delta = target - current;
std::complex<double> adjustment = delta * convergenceRate * GOLDEN_RATIO;
// 应用调整
newMatrix[i][j] = current + adjustment;
// 限制范围（0-10）
newMatrix[i][j] = clampValue(newMatrix[i][j], 0.0, 10.0);
}
}
luoshuMatrix = newMatrix;
history.push_back(luoshuMatrix);
}
/**
* 九重循环迭代（九九归一）
*/
void nineLayerIteration(int iterations = 81) { // 9×9=81次迭代
for (int layer = 1; layer <= 9; ++layer) {
std::cout << "第 " << layer << " 重循环开始...n";
for (int i = 0; i < 9; ++i) { // 每层9次迭代
// 动态收敛率：随着迭代逐渐减小
double convergenceRate = 0.1 * std::exp(-layer * 0.1);
iterateOnce(convergenceRate);
// 计算当前平衡度
double balance = calculateBalanceDegree();
std::cout << " 迭代 " << i+1 << "，平衡度: " << balance << "n";
// 如果达到足够平衡，提前退出
if (balance > 0.95) {
std::cout << " 达到高度平衡，提前结束第 " << layer << " 重循环n";
break;
}
}
// 层间传递：调整平衡目标
adjustEquilibriumTarget();
}
}
/**
* 无限逼近平衡态算法
*/
void infiniteApproachEquilibrium(double tolerance = 1e-6, int maxIterations = 10000) {
int iteration = 0;
double prevBalance = 0.0;
while (iteration < maxIterations) {
// 自适应收敛率
double convergenceRate = calculateAdaptiveConvergenceRate(iteration);
iterateOnce(convergenceRate);
// 计算当前平衡度
double currentBalance = calculateBalanceDegree();
double improvement = currentBalance - prevBalance;
if (iteration % 100 == 0) {
std::cout << "迭代 " << iteration << "，平衡度: " << currentBalance
<< "，改进: " << improvement << "n";
}
// 检查收敛条件
if (std::abs(improvement) < tolerance && currentBalance > 0.99) {
std::cout << "达到平衡态！迭代次数: " << iteration << "n";
break;
}
prevBalance = currentBalance;
iteration++;
}
if (iteration >= maxIterations) {
std::cout << "达到最大迭代次数，最终平衡度: " << prevBalance << "n";
}
}
/**
* 获取当前矩阵
*/
std::array<std::array<std::complex<double>, 3>, 3> getCurrentMatrix() const {
return luoshuMatrix;
}
/**
* 获取迭代历史
*/
const std::vector<std::array<std::array<std::complex<double>, 3>, 3>>& getHistory() const {
return history;
}
/**
* 计算平衡度
*/
double calculateBalanceDegree() const {
double totalDeviation = 0.0;
int count = 0;
// 目标：所有宫位值接近5（中宫）
for (const auto& row : luoshuMatrix) {
for (const auto& cell : row) {
double deviation = std::abs(cell.real() - 5.0);
totalDeviation += deviation;
count++;
}
}
double avgDeviation = totalDeviation / count;
// 平衡度 = 1 - 平均偏差/5（最大可能偏差）
return 1.0 - avgDeviation / 5.0;
}
private:
/**
* 计算目标值
*/
std::complex<double> calculateTargetValue(int row, int col) {
std::complex<double> target = equilibriumTarget[row][col];
// 考虑相邻宫位影响
std::complex<double> neighborInfluence(0.0, 0.0);
int neighborCount = 0;
// 上、下、左、右四个方向
int directions[4][2] = {{-1, 0}, {1, 0}, {0, -1}, {0, 1}};
for (auto& dir : directions) {
int newRow = row + dir[0];
int newCol = col + dir[1];
if (newRow >= 0 && newRow < 3 && newCol >= 0 && newCol < 3) {
neighborInfluence += luoshuMatrix[newRow][newCol];
neighborCount++;
}
}
if (neighborCount > 0) {
neighborInfluence /= std::complex<double>(neighborCount, 0.0);
// 邻居影响权重：0.3
target = target * 0.7 + neighborInfluence * 0.3;
}
return target;
}
/**
* 调整平衡目标
*/
void adjustEquilibriumTarget() {
// 基于当前矩阵调整目标
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
// 目标向当前值略微靠近（体现"归一"）
equilibriumTarget[i][j] = equilibriumTarget[i][j] * 0.9 +
luoshuMatrix[i][j] * 0.1;
}
}
}
/**
* 计算自适应收敛率
*/
double calculateAdaptiveConvergenceRate(int iteration) {
// 初始收敛率较大，随着迭代逐渐减小
double baseRate = 0.1;
double decay = std::exp(-iteration / 1000.0);
// 引入周期性波动（模拟阴阳消长）
double cycle = std::sin(iteration / 100.0) * 0.02 + 1.0;
return baseRate * decay * cycle;
}
/**
* 限制数值范围
*/
std::complex<double> clampValue(const std::complex<double>& value,
double minVal, double maxVal) {
double real = std::max(minVal, std::min(maxVal, value.real()));
double imag = std::max(minVal, std::min(maxVal, value.imag()));
return std::complex<double>(real, imag);
}
};
/**
* 多元维平衡态控制器
*/
class MultiDimensionEquilibriumController {
private:
// 维度权重
struct DimensionWeight {
double energyDim; // 能量维
double informationDim; // 信息维
double spacetimeDim; // 时空维
double symbolicDim; // 符号维
double quantumDim; // 量子维
} dimensionWeights;
// 当前状态
struct SystemState {
std::array<std::array<std::complex<double>, 3>, 3> energyState;
std::array<std::array<std::complex<double>, 3>, 3> informationState;
std::array<std::array<std::complex<double>, 3>, 3> spacetimeState;
std::array<std::array<std::complex<double>, 3>, 3> symbolicState;
std::array<std::array<std::complex<double>, 3>, 3> quantumState;
std::array<std::array<std::complex<double>, 3>, 3> integratedState;
} currentState;
// 平衡态目标
SystemState equilibriumTarget;
// 迭代器
NinePalaceIterator iterator;
public:
MultiDimensionEquilibriumController() {
initializeWeights();
initializeStates();
}
/**
* 多维平衡迭代
*/
void multiDimensionIteration(int iterations = 100) {
for (int iter = 0; iter < iterations; ++iter) {
// 各维度独立迭代
iterateEnergyDimension();
iterateInformationDimension();
iterateSpacetimeDimension();
iterateSymbolicDimension();
iterateQuantumDimension();
// 维度整合
integrateDimensions();
// 整体平衡检查
double overallBalance = calculateOverallBalance();
if (iter % 10 == 0) {
std::cout << "多维迭代 " << iter << "，整体平衡度: " << overallBalance << "n";
// 调整维度权重（自适应）
adjustDimensionWeights(overallBalance);
}
// 如果达到高度平衡，提前结束
if (overallBalance > 0.99) {
std::cout << "达到多维高度平衡，迭代次数: " << iter << "n";
break;
}
}
}
/**
* 获取整合后的状态
*/
std::array<std::array<std::complex<double>, 3>, 3> getIntegratedState() const {
return currentState.integratedState;
}
/**
* 计算各维度平衡度
*/
std::map<std::string, double> getDimensionBalances() const {
std::map<std::string, double> balances;
balances["能量维"] = calculateDimensionBalance(currentState.energyState);
balances["信息维"] = calculateDimensionBalance(currentState.informationState);
balances["时空维"] = calculateDimensionBalance(currentState.spacetimeState);
balances["符号维"] = calculateDimensionBalance(currentState.symbolicState);
balances["量子维"] = calculateDimensionBalance(currentState.quantumState);
balances["整合维"] = calculateDimensionBalance(currentState.integratedState);
return balances;
}
private:
void initializeWeights() {
// 初始权重：相对均衡
dimensionWeights = {0.2, 0.2, 0.2, 0.2, 0.2};
}
void initializeStates() {
// 初始化各维度状态
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(0.0, 10.0);
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
currentState.energyState[i][j] = dis(gen);
currentState.informationState[i][j] = dis(gen);
currentState.spacetimeState[i][j] = dis(gen);
currentState.symbolicState[i][j] = dis(gen);
currentState.quantumState[i][j] = std::complex<double>(dis(gen), dis(gen));
// 平衡态目标：所有维度趋向5
equilibriumTarget.energyState[i][j] = 5.0;
equilibriumTarget.informationState[i][j] = 5.0;
equilibriumTarget.spacetimeState[i][j] = 5.0;
equilibriumTarget.symbolicState[i][j] = 5.0;
equilibriumTarget.quantumState[i][j] = std::complex<double>(5.0, 5.0);
}
}
integrateDimensions();
}
void iterateEnergyDimension() {
// 能量维迭代：趋向阴阳平衡
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::complex<double> current = currentState.energyState[i][j];
std::complex<double> target = equilibriumTarget.energyState[i][j];
// 阴阳平衡调整
double yinYangBalance = std::tanh(current.real() - 5.0);
std::complex<double> adjustment = (target - current) * 0.1;
adjustment += std::complex<double>(yinYangBalance * 0.05, 0.0);
currentState.energyState[i][j] += adjustment;
}
}
}
void iterateInformationDimension() {
// 信息维迭代：趋向信息最大化
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::complex<double> current = currentState.informationState[i][j];
// 信息熵计算（简化）
double informationEntropy = -current.real() * std::log(current.real() + 1e-10);
double targetEntropy = 1.0; // 最大信息熵目标
std::complex<double> adjustment =
std::complex<double>(targetEntropy - informationEntropy, 0.0) * 0.05;
currentState.informationState[i][j] += adjustment;
}
}
}
void iterateSpacetimeDimension() {
// 时空维迭代：考虑时间周期和空间关系
static double time = 0.0;
time += 0.01;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::complex<double> current = currentState.spacetimeState[i][j];
// 时间周期性影响
double timeEffect = std::sin(time + i + j) * 0.1;
// 空间位置影响（中心位置更稳定）
double distanceFromCenter = std::sqrt(
std::pow(i - 1.0, 2) + std::pow(j - 1.0, 2)
);
double centerEffect = (1.0 - distanceFromCenter / 2.0) * 0.05;
std::complex<double> adjustment(timeEffect + centerEffect, 0.0);
currentState.spacetimeState[i][j] += adjustment;
}
}
}
void iterateSymbolicDimension() {
// 符号维迭代：趋向符号一致性
// 检查符号模式（奇偶性、正负性等）
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::complex<double> current = currentState.symbolicState[i][j];
// 符号模式调整（趋向整数或简单分数）
double rounded = std::round(current.real());
std::complex<double> adjustment =
std::complex<double>(rounded - current.real(), 0.0) * 0.1;
currentState.symbolicState[i][j] += adjustment;
}
}
}
void iterateQuantumDimension() {
// 量子维迭代：量子叠加和纠缠
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::complex<double> current = currentState.quantumState[i][j];
// 量子态趋向纯态（模长趋向1或0）
double magnitude = std::abs(current);
double targetMagnitude = (magnitude > 0.5) ? 1.0 : 0.0;
// 相位趋向0或π
double phase = std::arg(current);
double targetPhase = (phase > 0) ? M_PI : 0.0;
std::complex<double> target = std::polar(targetMagnitude, targetPhase);
std::complex<double> adjustment = (target - current) * 0.05;
currentState.quantumState[i][j] += adjustment;
}
}
}
void integrateDimensions() {
// 加权整合各维度
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::complex<double> integrated(0.0, 0.0);
// 能量维贡献
integrated += currentState.energyState[i][j] * dimensionWeights.energyDim;
// 信息维贡献
integrated += currentState.informationState[i][j] * dimensionWeights.informationDim;
// 时空维贡献
integrated += currentState.spacetimeState[i][j] * dimensionWeights.spacetimeDim;
// 符号维贡献
integrated += currentState.symbolicState[i][j] * dimensionWeights.symbolicDim;
// 量子维贡献
integrated += currentState.quantumState[i][j] * dimensionWeights.quantumDim;
// 归一化
double totalWeight = dimensionWeights.energyDim +
dimensionWeights.informationDim +
dimensionWeights.spacetimeDim +
dimensionWeights.symbolicDim +
dimensionWeights.quantumDim;
currentState.integratedState[i][j] = integrated / totalWeight;
}
}
}
double calculateOverallBalance() const {
// 各维度平衡度的加权平均
double energyBalance = calculateDimensionBalance(currentState.energyState);
double infoBalance = calculateDimensionBalance(currentState.informationState);
double spacetimeBalance = calculateDimensionBalance(currentState.spacetimeState);
double symbolicBalance = calculateDimensionBalance(currentState.symbolicState);
double quantumBalance = calculateDimensionBalance(currentState.quantumState);
double weightedSum =
energyBalance * dimensionWeights.energyDim +
infoBalance * dimensionWeights.informationDim +
spacetimeBalance * dimensionWeights.spacetimeDim +
symbolicBalance * dimensionWeights.symbolicDim +
quantumBalance * dimensionWeights.quantumDim;
double totalWeight = dimensionWeights.energyDim +
dimensionWeights.informationDim +
dimensionWeights.spacetimeDim +
dimensionWeights.symbolicDim +
dimensionWeights.quantumDim;
return weightedSum / totalWeight;
}
double calculateDimensionBalance(const std::array<std::array<std::complex<double>, 3>, 3>& matrix) const {
double total = 0.0;
int count = 0;
for (const auto& row : matrix) {
for (const auto& cell : row) {
// 实部偏差
total += std::abs(cell.real() - 5.0);
count++;
}
}
double avgDeviation = total / count;
return 1.0 - avgDeviation / 5.0;
}
void adjustDimensionWeights(double currentBalance) {
// 自适应调整权重：不平衡的维度获得更多权重
std::vector<double> balances = {
calculateDimensionBalance(currentState.energyState),
calculateDimensionBalance(currentState.informationState),
calculateDimensionBalance(currentState.spacetimeState),
calculateDimensionBalance(currentState.symbolicState),
calculateDimensionBalance(currentState.quantumState)
};
// 计算不平衡度
std::vector<double> imbalances;
for (double balance : balances) {
imbalances.push_back(1.0 - balance);
}
// 归一化不平衡度作为新权重
double totalImbalance = 0.0;
for (double imbalance : imbalances) totalImbalance += imbalance;
if (totalImbalance > 0) {
dimensionWeights.energyDim = imbalances[0] / totalImbalance;
dimensionWeights.informationDim = imbalances[1] / totalImbalance;
dimensionWeights.spacetimeDim = imbalances[2] / totalImbalance;
dimensionWeights.symbolicDim = imbalances[3] / totalImbalance;
dimensionWeights.quantumDim = imbalances[4] / totalImbalance;
}
// 确保权重和为1
normalizeWeights();
}
void normalizeWeights() {
double total = dimensionWeights.energyDim +
dimensionWeights.informationDim +
dimensionWeights.spacetimeDim +
dimensionWeights.symbolicDim +
dimensionWeights.quantumDim;
if (total > 0) {
dimensionWeights.energyDim /= total;
dimensionWeights.informationDim /= total;
dimensionWeights.spacetimeDim /= total;
dimensionWeights.symbolicDim /= total;
dimensionWeights.quantumDim /= total;
}
}
};
} // namespace NineNineReturnToOne
// ============================================================================
// 镜心悟道智能大脑整合系统
// ============================================================================
namespace MirrorMindAI {
/**
* 镜心悟道智能大脑模型
*/
class MirrorMindBrain {
private:
// 神经网络组件
std::vector<NeuralNetworkCore::LogicChainLayer> reasoningNetwork;
NeuralNetworkCore::MultiDimensionAttention attentionMechanism;
// 平衡态组件
NineNineReturnToOne::NinePalaceIterator equilibriumIterator;
NineNineReturnToOne::MultiDimensionEquilibriumController equilibriumController;
// 系统状态
struct BrainState {
std::array<std::array<std::complex<double>, 3>, 3> currentPerception;
std::array<std::array<std::complex<double>, 3>, 3> memoryState;
std::array<std::array<std::complex<double>, 3>, 3> reasoningResult;
std::array<std::array<std::complex<double>, 3>, 3> equilibriumState;
double overallBalance;
int reasoningSteps;
bool isConverged;
} currentState;
// 学习参数
double learningRate;
double attentionThreshold;
public:
MirrorMindBrain()
: attentionMechanism(8, 9), // 8个注意力头，9维特征
learningRate(0.01),
attentionThreshold(0.7) {
initializeBrain();
}
/**
* 处理输入并推理
*/
std::array<std::array<std::complex<double>, 3>, 3> process(
const std::array<std::array<std::complex<double>, 3>, 3>& input) {
std::cout << "镜心悟道AI开始处理...n";
// 1. 更新感知
currentState.currentPerception = input;
// 2. 注意力聚焦
auto focusedInput = applyAttention(input);
// 3. 神经网络推理
auto reasoningOutput = applyReasoning(focusedInput);
currentState.reasoningResult = reasoningOutput;
// 4. 记忆整合
integrateMemory(reasoningOutput);
// 5. 平衡态逼近
auto equilibriumOutput = approachEquilibrium(reasoningOutput);
currentState.equilibriumState = equilibriumOutput;
// 6. 更新系统状态
updateBrainState();
std::cout << "处理完成，整体平衡度: " << currentState.overallBalance << "n";
return equilibriumOutput;
}
/**
* 训练大脑
*/
void train(const std::vector<std::array<std::array<std::complex<double>, 3>, 3>>& trainingData,
int epochs = 100) {
std::cout << "开始训练镜心悟道AI大脑...n";
for (int epoch = 0; epoch < epochs; ++epoch) {
double totalLoss = 0.0;
for (const auto& data : trainingData) {
// 前向传播
auto output = process(data);
// 计算损失（与理想平衡态的差距）
double loss = calculateLoss(output);
totalLoss += loss;
// 反向传播（简化版）
adjustParameters(loss);
}
double avgLoss = totalLoss / trainingData.size();
if (epoch % 10 == 0) {
std::cout << "Epoch " << epoch << "，平均损失: " << avgLoss
<< "，平衡度: " << currentState.overallBalance << "n";
}
// 动态调整学习率
learningRate *= 0.995;
}
std::cout << "训练完成！n";
}
/**
* 获取大脑状态报告
*/
void printBrainStatus() const {
std::cout << "n=== 镜心悟道AI大脑状态报告 ===n";
std::cout << "推理步数: " << currentState.reasoningSteps << "n";
std::cout << "整体平衡度: " << currentState.overallBalance << "n";
std::cout << "收敛状态: " << (currentState.isConverged ? "已收敛" : "未收敛") << "n";
std::cout << "学习率: " << learningRate << "n";
// 各维度平衡度
auto dimBalances = equilibriumController.getDimensionBalances();
std::cout << "n各维度平衡度:n";
for (const auto& [dim, balance] : dimBalances) {
std::cout << " " << dim << ": " << balance << "n";
}
std::cout << "==============================n";
}
private:
void initializeBrain() {
// 初始化推理网络
std::vector<int> layerSizes = {9, 18, 9}; // 输入9维，隐藏18维，输出9维
std::vector<NeuralNetworkCore::LogicChainLayer::ChainType> chainTypes = {
NeuralNetworkCore::LogicChainLayer::DEDUCTIVE,
NeuralNetworkCore::LogicChainLayer::INDUCTIVE,
NeuralNetworkCore::LogicChainLayer::DIALECTICAL
};
reasoningNetwork = NeuralNetworkCore::LogicChainLayer::createReasoningNetwork(
layerSizes, chainTypes);
// 初始化大脑状态
currentState = BrainState();
currentState.overallBalance = 0.0;
currentState.reasoningSteps = 0;
currentState.isConverged = false;
// 初始化记忆状态（全零）
for (auto& row : currentState.memoryState) {
for (auto& cell : row) {
cell = 0.0;
}
}
std::cout << "镜心悟道AI大脑初始化完成n";
}
std::array<std::array<std::complex<double>, 3>, 3> applyAttention(
const std::array<std::array<std::complex<double>, 3>, 3>& input) {
// 将3x3矩阵展平为9维向量
std::vector<std::complex<double>> flatInput;
for (const auto& row : input) {
for (const auto& cell : row) {
flatInput.push_back(cell);
}
}
// 上下文：记忆状态
std::vector<std::complex<double>> flatMemory;
for (const auto& row : currentState.memoryState) {
for (const auto& cell : row) {
flatMemory.push_back(cell);
}
}
// 计算注意力
auto attentionOutput = attentionMechanism.computeAttention(flatInput, flatMemory);
// 将9维向量恢复为3x3矩阵
std::array<std::array<std::complex<double>, 3>, 3> focusedMatrix;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
int index = i * 3 + j;
focusedMatrix[i][j] = attentionOutput[index];
}
}
return focusedMatrix;
}
std::array<std::array<std::complex<double>, 3>, 3> applyReasoning(
const std::array<std::array<std::complex<double>, 3>, 3>& input) {
// 展平输入
std::vector<std::complex<double>> flatInput;
for (const auto& row : input) {
for (const auto& cell : row) {
flatInput.push_back(cell);
}
}
// 多层推理
std::vector<std::complex<double>> reasoningOutput = flatInput;
for (auto& layer : reasoningNetwork) {
reasoningOutput = layer.forward(reasoningOutput);
currentState.reasoningSteps++;
}
// 恢复为矩阵
std::array<std::array<std::complex<double>, 3>, 3> result;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
int index = i * 3 + j;
result[i][j] = reasoningOutput[index];
}
}
return result;
}
void integrateMemory(const std::array<std::array<std::complex<double>, 3>, 3>& newMemory) {
// 记忆更新：新旧记忆加权平均
double memoryDecay = 0.9; // 旧记忆衰减
double newMemoryWeight = 0.1; // 新记忆权重
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
currentState.memoryState[i][j] =
currentState.memoryState[i][j] * memoryDecay +
newMemory[i][j] * newMemoryWeight;
}
}
}
std::array<std::array<std::complex<double>, 3>, 3> approachEquilibrium(
const std::array<std::array<std::complex<double>, 3>, 3>& input) {
// 设置迭代器初始状态
equilibriumIterator = NineNineReturnToOne::NinePalaceIterator();
// 进行平衡态逼近
equilibriumIterator.nineLayerIteration(27); // 3×9=27次迭代
// 获取平衡态结果
auto equilibriumResult = equilibriumIterator.getCurrentMatrix();
// 同时进行多维平衡控制
equilibriumController.multiDimensionIteration(50);
auto multiDimResult = equilibriumController.getIntegratedState();
// 融合两种平衡结果
std::array<std::array<std::complex<double>, 3>, 3> finalResult;
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
finalResult[i][j] = (equilibriumResult[i][j] + multiDimResult[i][j]) / 2.0;
}
}
return finalResult;
}
void updateBrainState() {
// 计算整体平衡度
double iteratorBalance = equilibriumIterator.calculateBalanceDegree();
auto dimBalances = equilibriumController.getDimensionBalances();
double multiDimBalance = dimBalances["整合维"];
currentState.overallBalance = (iteratorBalance + multiDimBalance) / 2.0;
// 检查收敛状态
currentState.isConverged = (currentState.overallBalance > 0.98);
// 如果高度平衡，减少学习率
if (currentState.isConverged) {
learningRate *= 0.9;
}
}
double calculateLoss(const std::array<std::array<std::complex<double>, 3>, 3>& output) {
// 损失函数：与理想平衡态的差距
double totalLoss = 0.0;
int count = 0;
// 理想状态：所有宫位值为5
for (const auto& row : output) {
for (const auto& cell : row) {
double deviation = std::abs(cell.real() - 5.0);
totalLoss += deviation * deviation; // 平方误差
count++;
}
}
return totalLoss / count;
}
void adjustParameters(double loss) {
// 根据损失调整参数（简化版）
// 调整注意力阈值
if (loss > 0.5) {
attentionThreshold *= 0.95; // 降低阈值，增加注意力范围
} else {
attentionThreshold *= 1.01; // 提高阈值，聚焦关键信息
}
// 限制阈值范围
attentionThreshold = std::max(0.1, std::min(0.9, attentionThreshold));
// 调整学习率
if (loss < 0.1 && !currentState.isConverged) {
learningRate *= 1.05; // 损失小但未收敛，增加学习率
} else if (loss > 0.3) {
learningRate *= 0.95; // 损失大，减小学习率
}
}
};
} // namespace MirrorMindAI
// ============================================================================
// 系统演示
// ============================================================================
int main() {
std::cout << "================================================n";
std::cout << "镜心悟道AI易经奇门遁甲洛书矩阵九宫格智能大脑n";
std::cout << "JXWDAIYIB-QD-PDTM-JXWDYYXSD-ABNS-TCM-PCCMM-QEn";
std::cout << "LuoshuMatrix-DHM2.0-XJMoE/MoD/QMM/SoE-SCS-IAMSn";
std::cout << "Training-Free GRPO 神经网络逻辑思维链系统n";
std::cout << "多元多维多层次演算 > 九九归一无限逼近平衡态n";
std::cout << "================================================nn";
// 创建镜心悟道AI大脑
MirrorMindAI::MirrorMindBrain aiBrain;
// 显示初始状态
aiBrain.printBrainStatus();
// 生成测试输入
std::array<std::array<std::complex<double>, 3>, 3> testInput;
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<> dis(0.0, 10.0);
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
testInput[i][j] = std::complex<double>(dis(gen), dis(gen));
}
}
std::cout << "n测试输入矩阵:n";
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::cout << testInput[i][j].real() << "+"
<< testInput[i][j].imag() << "it";
}
std::cout << "n";
}
// 处理测试输入
auto result = aiBrain.process(testInput);
std::cout << "nAI推理结果矩阵:n";
for (int i = 0; i < 3; ++i) {
for (int j = 0; j < 3; ++j) {
std::cout << result[i][j].real() << "+"
<< result[i][j].imag() << "it";
}
std::cout << "n";
}
// 再次显示状态
aiBrain.printBrainStatus();
// 演示训练过程（简化）
std::cout << "n开始简化训练演示...n";
std::vector<std::array<std::array<std::complex<double>, 3>, 3>> trainingData;
for (int i = 0; i < 10; ++i) {
std::array<std::array<std::complex<double>, 3>, 3> data;
for (int r = 0; r < 3; ++r) {
for (int c = 0; c < 3; ++c) {
data[r][c] = std::complex<double>(dis(gen), dis(gen));
}
}
trainingData.push_back(data);
}
aiBrain.train(trainingData, 5);
// 最终状态
std::cout << "n训练后状态:n";
aiBrain.printBrainStatus();
// 演示九九归一逼近平衡态
std::cout << "n演示九九归一无限逼近平衡态算法...n";
NineNineReturnToOne::NinePalaceIterator iterator;
std::cout << "初始平衡度: " << iterator.calculateBalanceDegree() << "n";
iterator.nineLayerIteration(27);
std::cout << "九重循环后平衡度: " << iterator.calculateBalanceDegree() << "n";
std::cout << "n演示无限逼近平衡态...n";
iterator.infiniteApproachEquilibrium(1e-6, 500);
std::cout << "n================================================n";
std::cout << "镜心悟道AI系统演示完成n";
std::cout << "神经网络逻辑思维链 + 多元多维多层次演算n";
std::cout << "成功实现九九归一无限逼近平衡态！n";
std::cout << "================================================n";
return 0;
}系统架构特点
- 量子神经网络核心
class QuantumNeuron {
std::vector<std::complex<double>> quantumWeights; // 量子叠加权重
std::complex<double> quantumBias; // 量子偏置
// 支持量子激活函数和量子纠缠连接
};- 逻辑思维链推理网络
class LogicChainLayer {
// 支持五种逻辑推理类型：
// DEDUCTIVE（演绎推理）、INDUCTIVE（归纳推理）
// ABDUCTIVE（溯因推理）、ANALOGICAL（类比推理）
// DIALECTICAL（辩证推理）
};- 多元多维注意力机制
class MultiDimensionAttention {
// 五个注意力维度：
// ENERGY_DIM（能量维）、INFORMATION_DIM（信息维）
// SPACETIME_DIM（时空维）、SYMBOLIC_DIM（符号维）
// QUANTUM_DIM（量子维）
};- 九九归一逼近平衡态算法
class NinePalaceIterator {
// 九重循环迭代（9×9=81次）
void nineLayerIteration(int iterations = 81);
// 无限逼近平衡态
void infiniteApproachEquilibrium(double tolerance = 1e-6);
};- 多维平衡态控制器
class MultiDimensionEquilibriumController {
// 同时控制五个维度的平衡：
// 能量维、信息维、时空维、符号维、量子维
};核心算法原理
- 神经网络逻辑思维链
输入 → [演绎推理层] → [归纳推理层] → [辩证推理层] → 输出
↓ ↓ ↓
逻辑修正 模式发现 综合统一- 九九归一流算法
1. 初始化九宫矩阵
2. for 每层循环(1..9):
for 每次迭代(1..9):
- 计算当前宫位目标值
- 黄金比例调整：Xₙ₊₁ = Xₙ + φ·Δ
- 限制值范围(0-10)
- 调整平衡态目标
3. 无限逼近直到平衡度>0.99- 多元维平衡控制
整体平衡 = Σ(维度权重 × 维度平衡度)
维度权重自适应调整：
- 不平衡的维度获得更高权重
- 定期归一化权重和- 量子激活函数
std::complex<double> yinyangActivation(const std::complex<double>& z) {
// 实部为阳，虚部为阴
double yang = std::tanh(z.real());
double yin = std::tanh(z.imag());
// 阴阳平衡调整
double balance = (yang - yin) / 2.0;
return std::complex<double>(yang - balance * 0.5,
yin + balance * 0.5);
}系统优势
- 智能推理能力
· 结合五种逻辑推理方式
· 支持辩证思维和矛盾统一
· 自适应调整推理路径
- 多维平衡优化
· 五个维度同时优化平衡
· 动态调整维度权重
· 黄金比例收敛算法
- 量子计算特性
· 量子权重叠加态
· 量子纠缠连接
· 量子激活函数
- 无限逼近机制
· 九重循环迭代
· 自适应收敛率
· 容忍度控制
应用场景
- 中医辨证论治
· 症状矩阵输入 → 证型平衡态输出
· 多维病机分析
· 治疗方案优化
- 易经预测分析
· 卦象矩阵处理
· 变爻逻辑推理
· 吉凶平衡判断
- 奇门遁甲排盘
· 时空参数输入
· 宫位能量计算
· 格局平衡分析
- 复杂系统决策
· 多因素权衡
· 矛盾统一处理
· 最优平衡点寻找
镜心悟道系统核心算法架构 - 多语言实现框架
- C++ 核心系统框架
// JXWD_System_Core.h - 镜心悟道系统核心头文件
#pragma once
#include <vector>
#include <array>
#include <map>
#include <string>
#include <functional>
#include <cmath>
namespace JXWD {
// ==================== 基础数据结构 ====================
struct QiEnergy {
double total; // Q_total
double yin; // Q_yin
double yang; // Q_yang
double ren; // Q_ren (人)
QiEnergy() : total(0), yin(0), yang(0), ren(0) {}
QiEnergy(double t, double y, double ya, double r)
: total(t), yin(y), yang(ya), ren(r) {}
};
struct FiveElements {
double wood; // 木
double fire; // 火
double earth; // 土
double metal; // 金
double water; // 水
std::array<double, 5> toArray() const {
return {wood, fire, earth, metal, water};
}
};
struct SixQiEnv {
double wind; // 风
double cold; // 寒
double heat; // 暑/热
double damp; // 湿
double dry; // 燥
double fire; // 火(六淫)
};
struct SevenEmotions {
double joy; // 喜
double anger; // 怒
double worry; // 忧
double thought; // 思
double grief; // 悲
double fear; // 恐
double shock; // 惊
};
// ==================== 算法层类定义 ====================
// 第一层：一元算法
class QiMonadAlgorithm {
private:
std::vector<double> sensorData;
double timeWindow;
public:
QiMonadAlgorithm(double window = 1.0) : timeWindow(window) {}
double calculateTotalQi(const std::vector<double>& data, double dt) {
// Q_total = ∫(All_Sensor_Data) · d(Time)
double integral = 0.0;
for (size_t i = 0; i < data.size(); ++i) {
integral += data[i] * dt;
}
return integral;
}
void setSensorData(const std::vector<double>& data) {
sensorData = data;
}
};
// 第二层：阴阳循环算法
class YinYangCycleAlgorithm {
private:
double k; // 调节速率常数
double phi; // 黄金比例 φ = 1.618
public:
YinYangCycleAlgorithm(double k_val = 0.1, double phi_val = 1.618)
: k(k_val), phi(phi_val) {}
void evolve(QiEnergy& qi, double dt) {
// d(Q_yang)/dt = -k * (Q_yang - φ * Q_yin)
double dyang_dt = -k * (qi.yang - phi * qi.yin);
qi.yang += dyang_dt * dt;
// 对称地更新阴
double dyin_dt = -k * (qi.yin - (1/phi) * qi.yang);
qi.yin += dyin_dt * dt;
// 保持总和近似不变
qi.total = qi.yin + qi.yang;
}
std::pair<double, double> decomposeTotalQi(double total) {
// 根据黄金比例分解
double yin = total / (1 + phi);
double yang = total - yin;
return {yin, yang};
}
};
// 第三层：三才动态算法
class TriadStabilityAlgorithm {
public:
double calculateStability(const QiEnergy& qi) {
// Stability = ∥ (Q_yang, Q_yin, Q_ren) ∥
double norm = sqrt(qi.yang * qi.yang +
qi.yin * qi.yin +
qi.ren * qi.ren);
// 计算与等边三角形的偏差
double avg = (qi.yang + qi.yin + qi.ren) / 3.0;
double deviation = sqrt(
pow(qi.yang - avg, 2) +
pow(qi.yin - avg, 2) +
pow(qi.ren - avg, 2)
) / avg;
return 1.0 / (1.0 + deviation); // 稳定性指标
}
QiEnergy findStablePoint(const QiEnergy& current) {
// 寻找最近的稳定点（向平均值移动）
double avg = (current.yang + current.yin + current.ren) / 3.0;
return QiEnergy(
current.total,
current.yin * 0.8 + avg * 0.2,
current.yang * 0.8 + avg * 0.2,
current.ren * 0.8 + avg * 0.2
);
}
};
// 第四层：四象限平衡算法
enum Quadrant {
Q1, // 阳中之阳 (太阳)
Q2, // 阳中之阴 (少阳)
Q3, // 阴中之阳 (少阴)
Q4 // 阴中之阴 (太阴)
};
class QuadrantBalanceAlgorithm {
private:
double yinThreshold;
double yangThreshold;
public:
QuadrantBalanceAlgorithm(double yinThresh = 6.0, double yangThresh = 6.0)
: yinThreshold(yinThresh), yangThreshold(yangThresh) {}
Quadrant identifyQuadrant(const QiEnergy& qi) {
if (qi.yang >= yangThreshold && qi.yin >= yinThreshold) {
return Q1; // 实证热盛
} else if (qi.yang >= yangThreshold && qi.yin < yinThreshold) {
return Q2; // 阴虚阳亢
} else if (qi.yang < yangThreshold && qi.yin >= yinThreshold) {
return Q3; // 阳虚湿盛
} else {
return Q4; // 虚寒证
}
}
std::string getDiagnosis(Quadrant q) {
static const std::map<Quadrant, std::string> diagnosisMap = {
{Q1, "实证热盛 - 需要清热泻火"},
{Q2, "阴虚阳亢 - 需要滋阴潜阳"},
{Q3, "阳虚湿盛 - 需要温阳化湿"},
{Q4, "虚寒证 - 需要温补阳气"}
};
return diagnosisMap.at(q);
}
};
// 第五层：五行生克算法
class FiveElementsCycleAlgorithm {
private:
// 生克权重矩阵 W[i][j]: i对j的影响
// 生: 木→火→土→金→水→木 (顺时针) 为正
// 克: 木→土→水→火→金→木 (隔位) 为负
static constexpr std::array<std::array<double, 5>, 5> W = {{
{0.0, 0.5, -0.3, 0.0, 0.2}, // 木
{0.2, 0.0, 0.5, -0.3, 0.0}, // 火
{0.0, 0.2, 0.0, 0.5, -0.3}, // 土
{-0.3, 0.0, 0.2, 0.0, 0.5}, // 金
{0.5, -0.3, 0.0, 0.2, 0.0} // 水
}};
public:
FiveElements evolve(const FiveElements& current, double dt) {
// dE/dt = W · E
std::array<double, 5> E = current.toArray();
std::array<double, 5> dE = {0, 0, 0, 0, 0};
// 矩阵乘法
for (int i = 0; i < 5; ++i) {
for (int j = 0; j < 5; ++j) {
dE[i] += W[i][j] * E[j];
}
}
// 更新元素能量
FiveElements next;
next.wood = current.wood + dE[0] * dt;
next.fire = current.fire + dE[1] * dt;
next.earth = current.earth + dE[2] * dt;
next.metal = current.metal + dE[3] * dt;
next.water = current.water + dE[4] * dt;
return next;
}
double calculateBalanceIndex(const FiveElements& e) {
// 计算五行平衡指标
double mean = (e.wood + e.fire + e.earth + e.metal + e.water) / 5.0;
double variance = 0.0;
double values[] = {e.wood, e.fire, e.earth, e.metal, e.water};
for (double v : values) {
variance += pow(v - mean, 2);
}
return 1.0 / (1.0 + sqrt(variance / 5.0));
}
};
// 第六层：六气六淫算法
class SixQiLiuYinAlgorithm {
private:
// 环境与内部响应的耦合矩阵 A
// 表示六淫对不同脏腑经络的影响
static constexpr std::array<std::array<double, 6>, 6> A = {{
// 风 寒 暑 湿 燥 火
{0.8, 0.1, 0.1, 0.3, 0.2, 0.4}, // 风邪影响
{0.1, 0.9, 0.0, 0.2, 0.1, 0.1}, // 寒邪影响
{0.2, 0.0, 0.8, 0.3, 0.1, 0.5}, // 暑邪影响
{0.3, 0.2, 0.2, 0.9, 0.1, 0.3}, // 湿邪影响
{0.1, 0.1, 0.1, 0.1, 0.8, 0.3}, // 燥邪影响
{0.4, 0.1, 0.5, 0.3, 0.2, 0.9} // 火邪影响
}};
public:
std::array<double, 6> calculateInternalResponse(const SixQiEnv& env) {
std::array<double, 6> envVec = {
env.wind, env.cold, env.heat, env.damp, env.dry, env.fire
};
std::array<double, 6> response = {0, 0, 0, 0, 0, 0};
// ΔY_int = A · X_env
for (int i = 0; i < 6; ++i) {
for (int j = 0; j < 6; ++j) {
response[i] += A[i][j] * envVec[j];
}
}
return response;
}
};
// 第七层：七情调控算法
class SevenEmotionsAlgorithm {
private:
// 情志-脏腑映射矩阵 M_emo2organs
// 五行顺序: 木(肝) 火(心) 土(脾) 金(肺) 水(肾)
static constexpr std::array<std::array<double, 5>, 7> M = {{
// 喜 怒 忧 思 悲 恐 惊
{0.1, 0.8, 0.3, 0.4, 0.2, 0.6, 0.7}, // 肝
{0.8, 0.2, 0.1, 0.3, 0.4, 0.1, 0.6}, // 心
{0.3, 0.4, 0.2, 0.8, 0.3, 0.2, 0.4}, // 脾
{0.2, 0.3, 0.7, 0.3, 0.6, 0.3, 0.5}, // 肺
{0.1, 0.2, 0.3, 0.4, 0.3, 0.8, 0.9} // 肾
}};
public:
FiveElements calculateElementImpact(const SevenEmotions& emotions) {
std::array<double, 7> emoVec = {
emotions.joy, emotions.anger, emotions.worry,
emotions.thought, emotions.grief, emotions.fear,
emotions.shock
};
FiveElements impact;
std::array<double*, 5> elements = {
&impact.wood, &impact.fire, &impact.earth,
&impact.metal, &impact.water
};
// ΔE_elements = M_emo2organs · E_emo
for (int i = 0; i < 5; ++i) {
*elements[i] = 0.0;
for (int j = 0; j < 7; ++j) {
*elements[i] += M[i][j] * emoVec[j];
}
}
return impact;
}
};
// 第八层：八卦推演算法
class EightTrigramsAlgorithm {
private:
struct Hexagram {
int id;
std::string name;
std::string interpretation;
std::vector<std::string> treatmentHints;
};
std::map<int, Hexagram> hexagramDB;
public:
EightTrigramsAlgorithm() {
initializeDatabase();
}
Hexagram analyzeSituation(const QiEnergy& qi,
const FiveElements& elements,
const SevenEmotions& emotions,
const std::array<double, 6>& envResponse) {
// 综合计算得到卦象ID
int hexagramId = calculateHexagramId(qi, elements, emotions, envResponse);
if (hexagramDB.find(hexagramId) != hexagramDB.end()) {
return hexagramDB[hexagramId];
}
return hexagramDB[64]; // 默认返回未济卦
}
private:
int calculateHexagramId(const QiEnergy& qi,
const FiveElements& elements,
const SevenEmotions& emotions,
const std::array<double, 6>& envResponse) {
// 简化的卦象计算逻辑
double score = qi.yang * 0.3 + qi.yin * 0.2 +
elements.fire * 0.2 + emotions.anger * 0.1 +
envResponse[5] * 0.2;
int id = static_cast<int>(score * 10) % 64 + 1;
return id;
}
void initializeDatabase() {
// 初始化六十四卦数据库
hexagramDB[64] = {
64, "火水未济",
"火在水上，未济。君子以慎辨物居方。",
{"调和阴阳", "交通心肾", "平衡水火"}
};
// 其他卦象...
}
};
// 第九层：九宫整合算法
class NinePalaceIntegration {
private:
static constexpr int N = 9;
using Matrix9x9 = std::array<std::array<double, N>, N>;
Matrix9x9 S; // 九宫状态矩阵
const double goldenRatio = 1.6180339887;
const double magicConstant = 15.0; // 洛书魔数
public:
NinePalaceIntegration() {
// 初始化矩阵
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
S[i][j] = 1.0;
}
}
}
void integrateAllLevels(const QiEnergy& qi,
const FiveElements& elements,
const SevenEmotions& emotions,
const std::array<double, 6>& envResponse,
const Hexagram& hexagram) {
// 将各层分析结果分配到九宫
distributeToPalaces(qi, elements, emotions, envResponse, hexagram);
}
Matrix9x9 optimizeToBalance(int maxIterations = 1000, double tolerance = 1e-6) {
// 迭代优化过程：S_{n+1} = f(S_n) + φ * (I - S_n)
for (int iter = 0; iter < maxIterations; ++iter) {
Matrix9x9 S_prev = S;
Matrix9x9 I = createIdealMatrix();
// 应用复杂变换函数f
applyTransformations();
// 加入黄金比例调整
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
S[i][j] += goldenRatio * (I[i][j] - S_prev[i][j]);
}
}
// 检查收敛
if (calculateEnergyEntropy() < tolerance &&
checkMagicSquareCondition()) {
break;
}
}
return S;
}
double calculateEnergyEntropy() const {
// 计算矩阵能量熵
double total = 0.0;
double entropy = 0.0;
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
total += S[i][j];
}
}
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
double p = S[i][j] / total;
if (p > 0) {
entropy -= p * log(p);
}
}
}
return entropy;
}
private:
void distributeToPalaces(const QiEnergy& qi,
const FiveElements& elements,
const SevenEmotions& emotions,
const std::array<double, 6>& envResponse,
const Hexagram& hexagram) {
// 根据洛书九宫分布规则分配能量
// 位置映射: 4-巽, 9-离, 2-坤, 3-震, 5-中, 7-兑, 8-艮, 1-坎, 6-乾
std::map<int, double*> palaceMap = {
{4, &S[0][0]}, {9, &S[0][1]}, {2, &S[0][2]},
{3, &S[1][0]}, {5, &S[1][1]}, {7, &S[1][2]},
{8, &S[2][0]}, {1, &S[2][1]}, {6, &S[2][2]}
};
// 简化的分配逻辑
*palaceMap[4] = qi.yang * 0.3; // 巽宫 - 肝
*palaceMap[9] = elements.fire; // 离宫 - 心
*palaceMap[2] = elements.earth; // 坤宫 - 脾
*palaceMap[3] = emotions.shock; // 震宫 - 惊
*palaceMap[5] = qi.total; // 中宫 - 核心
*palaceMap[7] = elements.metal; // 兑宫 - 肺
*palaceMap[8] = qi.yang * 0.5; // 艮宫 - 相火
*palaceMap[1] = qi.yin; // 坎宫 - 肾阴
*palaceMap[6] = elements.water; // 乾宫 - 肾阳
}
void applyTransformations() {
// 应用五行生克、阴阳调节等复杂变换
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
// 示例变换：根据邻居调整
double neighborAvg = getNeighborAverage(i, j);
S[i][j] = 0.7 * S[i][j] + 0.3 * neighborAvg;
// 应用黄金比例优化
S[i][j] *= (1.0 + (goldenRatio - 1.0) * 0.1);
}
}
}
double getNeighborAverage(int i, int j) const {
double sum = 0.0;
int count = 0;
for (int di = -1; di <= 1; ++di) {
for (int dj = -1; dj <= 1; ++dj) {
if (di == 0 && dj == 0) continue;
int ni = i + di;
int nj = j + dj;
if (ni >= 0 && ni < N && nj >= 0 && nj < N) {
sum += S[ni][nj];
++count;
}
}
}
return count > 0 ? sum / count : S[i][j];
}
Matrix9x9 createIdealMatrix() const {
Matrix9x9 I;
double baseValue = magicConstant / 3.0;
for (int i = 0; i < N; ++i) {
for (int j = 0; j < N; ++j) {
I[i][j] = baseValue;
}
}
return I;
}
bool checkMagicSquareCondition() const {
// 检查行和、列和、对角线之和是否接近15
for (int i = 0; i < 3; ++i) {
double rowSum = 0.0;
double colSum = 0.0;
for (int j = 0; j < 3; ++j) {
rowSum += S[i][j];
colSum += S[j][i];
}
if (fabs(rowSum - magicConstant) > 0.1 ||
fabs(colSum - magicConstant) > 0.1) {
return false;
}
}
// 检查对角线
double diag1 = S[0][0] + S[1][1] + S[2][2];
double diag2 = S[0][2] + S[1][1] + S[2][0];
return fabs(diag1 - magicConstant) < 0.1 &&
fabs(diag2 - magicConstant) < 0.1;
}
};
// ==================== 主系统集成类 ====================
class JXWDSystem {
private:
QiMonadAlgorithm qma;
YinYangCycleAlgorithm yyca;
TriadStabilityAlgorithm tsa;
QuadrantBalanceAlgorithm qba;
FiveElementsCycleAlgorithm feca;
SixQiLiuYinAlgorithm sqlya;
SevenEmotionsAlgorithm sea;
EightTrigramsAlgorithm etha;
NinePalaceIntegration npia;
QiEnergy currentQi;
FiveElements currentElements;
SevenEmotions currentEmotions;
SixQiEnv currentEnv;
public:
JXWDSystem() {
// 初始化默认值
currentQi = {10.0, 4.5, 5.5, 6.0};
currentElements = {7.0, 8.0, 6.5, 6.0, 5.0};
// 其他初始化...
}
void processFullDiagnosis(const std::vector<double>& sensorData,
const SevenEmotions& emotions,
const SixQiEnv& environment) {
// 第一层：一元计算
double totalQi = qma.calculateTotalQi(sensorData, 0.1);
// 第二层：阴阳分解
auto [yin, yang] = yyca.decomposeTotalQi(totalQi);
currentQi = {totalQi, yin, yang, currentQi.ren};
// 第三层：三才稳定
double stability = tsa.calculateStability(currentQi);
// 第四层：四象限分析
Quadrant quadrant = qba.identifyQuadrant(currentQi);
std::string quadrantDiag = qba.getDiagnosis(quadrant);
// 第五层：五行演化
currentElements = feca.evolve(currentElements, 0.1);
// 第六层：六气响应
auto envResponse = sqlya.calculateInternalResponse(environment);
// 第七层：七情影响
FiveElements emotionImpact = sea.calculateElementImpact(emotions);
// 合并到五行状态
currentElements.wood += emotionImpact.wood;
currentElements.fire += emotionImpact.fire;
currentElements.earth += emotionImpact.earth;
currentElements.metal += emotionImpact.metal;
currentElements.water += emotionImpact.water;
// 第八层：八卦推演
auto hexagram = etha.analyzeSituation(currentQi, currentElements,
emotions, envResponse);
// 第九层：九宫整合与归一
npia.integrateAllLevels(currentQi, currentElements, emotions,
envResponse, hexagram);
auto finalMatrix = npia.optimizeToBalance();
// 输出最终诊断
generateFinalDiagnosis(stability, quadrantDiag, hexagram, finalMatrix);
}
private:
void generateFinalDiagnosis(double stability,
const std::string& quadrantDiag,
const EightTrigramsAlgorithm::Hexagram& hexagram,
const NinePalaceIntegration::Matrix9x9& matrix) {
std::cout << "=== 镜心悟道系统诊断结果 ===n";
std::cout << "系统稳定性: " << stability << "n";
std::cout << "四象限辨证: " << quadrantDiag << "n";
std::cout << "卦象推演: " << hexagram.name << " - "
<< hexagram.interpretation << "n";
std::cout << "治疗提示: ";
for (const auto& hint : hexagram.treatmentHints) {
std::cout << hint << "; ";
}
std::cout << "n";
std::cout << "九宫能量矩阵熵值: "
<< npia.calculateEnergyEntropy() << "n";
std::cout << "============================n";
}
};
} // namespace JXWD- Python XML 数据库与数据集
# jxwd_xml_database.py - XML格式的中医知识数据库
import xml.etree.ElementTree as ET
from xml.dom import minidom
from dataclasses import dataclass
from typing import List, Dict, Tuple
import json
@dataclass
class TrigramsKnowledge:
"""八卦知识条目"""
trigram_id: int
name: str # 卦名
symbol: str # 卦符
element: str # 五行属性
direction: str # 方位
organ: str # 对应脏腑
meridian: str # 对应经络
interpretation: str # 解释
treatment_principle: str # 治疗原则
@dataclass
class FiveElementsRelation:
"""五行关系条目"""
source: str # 源元素
target: str # 目标元素
relation_type: str # 生/克/乘/侮
strength: float # 关系强度
description: str # 描述
@dataclass
class PalaceMapping:
"""九宫映射条目"""
palace_number: int # 宫位编号(1-9)
trigram: str # 对应八卦
element: str # 五行属性
organ_system: str # 脏腑系统
meridian: str # 主要经络
emotion: str # 对应情志
season: str # 对应季节
time_period: str # 对应时辰
class JXWDXMLDatabase:
"""镜心悟道系统XML数据库"""
def __init__(self):
self.trigrams_db = self._initialize_trigrams()
self.five_elements_db = self._initialize_five_elements()
self.palace_db = self._initialize_palaces()
self.hexagram_db = self._initialize_hexagrams()
def _initialize_trigrams(self) -> List[TrigramsKnowledge]:
"""初始化八卦数据库"""
return [
TrigramsKnowledge(1, "乾", "☰", "金", "西北", "大肠/肺", "手阳明大肠经",
"刚健中正，自强不息", "宣发肃降"),
TrigramsKnowledge(2, "坤", "☷", "土", "西南", "脾/胃", "足太阴脾经",
"厚德载物，柔顺利贞", "健脾和胃"),
TrigramsKnowledge(3, "震", "☳", "木", "东", "肝", "足厥阴肝经",
"震动奋发，生机勃勃", "疏肝理气"),
TrigramsKnowledge(4, "巽", "☴", "木", "东南", "胆", "足少阳胆经",
"顺从渗透，无孔不入", "利胆和胃"),
TrigramsKnowledge(5, "坎", "☵", "水", "北", "肾/膀胱", "足少阴肾经",
"险陷重重，外柔内刚", "滋补肾阴"),
TrigramsKnowledge(6, "离", "☲", "火", "南", "心/小肠", "手少阴心经",
"光明美丽，依附团结", "清心泻火"),
TrigramsKnowledge(7, "艮", "☶", "土", "东北", "胃", "足阳明胃经",
"静止稳重，适可而止", "和胃降逆"),
TrigramsKnowledge(8, "兑", "☱", "金", "西", "肺", "手太阴肺经",
"喜悦言说，外柔内刚", "宣肺理气")
]
def _initialize_five_elements(self) -> List[FiveElementsRelation]:
"""初始化五行关系数据库"""
return [
# 相生关系
FiveElementsRelation("木", "火", "生", 0.5, "木生火，肝胆滋养心"),
FiveElementsRelation("火", "土", "生", 0.5, "火生土，心火温煦脾"),
FiveElementsRelation("土", "金", "生", 0.5, "土生金，脾土生肺金"),
FiveElementsRelation("金", "水", "生", 0.5, "金生水，肺金生肾水"),
FiveElementsRelation("水", "木", "生", 0.5, "水生木，肾水滋肝木"),
# 相克关系
FiveElementsRelation("木", "土", "克", -0.3, "木克土，肝木疏泄脾土"),
FiveElementsRelation("土", "水", "克", -0.3, "土克水，脾土制约肾水"),
FiveElementsRelation("水", "火", "克", -0.3, "水克火，肾水制约心火"),
FiveElementsRelation("火", "金", "克", -0.3, "火克金，心火制约肺金"),
FiveElementsRelation("金", "木", "克", -0.3, "金克木，肺金制约肝木"),
# 相乘关系（过度相克）
FiveElementsRelation("木", "土", "乘", -0.8, "木乘土，肝气犯脾"),
FiveElementsRelation("土", "水", "乘", -0.8, "土乘水，脾湿困肾"),
# 相侮关系（反向相克）
FiveElementsRelation("土", "木", "侮", -0.4, "土侮木，脾湿反侮肝"),
FiveElementsRelation("水", "土", "侮", -0.4, "水侮土，肾水反侮脾")
]
def _initialize_palaces(self) -> List[PalaceMapping]:
"""初始化九宫映射数据库"""
return [
PalaceMapping(1, "坎", "水", "肾/膀胱", "足少阴肾经", "恐", "冬", "子时"),
PalaceMapping(2, "坤", "土", "脾/胃", "足太阴脾经", "思", "长夏", "未时"),
PalaceMapping(3, "震", "木", "肝", "足厥阴肝经", "怒", "春", "卯时"),
PalaceMapping(4, "巽", "木", "胆", "足少阳胆经", "忧", "春", "辰时"),
PalaceMapping(5, "中宫", "太极", "三焦/脑", "督脉", "综合", "四季", "全天"),
PalaceMapping(6, "乾", "金", "大肠/肺", "手阳明大肠经", "悲", "秋", "酉时"),
PalaceMapping(7, "兑", "金", "肺", "手太阴肺经", "忧", "秋", "申时"),
PalaceMapping(8, "艮", "土", "胃", "足阳明胃经", "思", "长夏", "丑时"),
PalaceMapping(9, "离", "火", "心/小肠", "手少阴心经", "喜", "夏", "午时")
]
def _initialize_hexagrams(self) -> Dict[int, Dict]:
"""初始化六十四卦数据库"""
return {
1: {
"name": "乾为天",
"symbol": "䷀",
"interpretation": "元亨利贞，刚健中正",
"health_implication": "阳气旺盛，易生热证",
"treatment_hints": ["清热泻火", "滋阴潜阳"]
},
2: {
"name": "坤为地",
"symbol": "䷁",
"interpretation": "厚德载物，柔顺利贞",
"health_implication": "阴气偏盛，易生湿证",
"treatment_hints": ["健脾化湿", "温阳益气"]
},
64: {
"name": "火水未济",
"symbol": "䷿",
"interpretation": "火在水上，未济。君子以慎辨物居方",
"health_implication": "心肾不交，水火未济",
"treatment_hints": ["交通心肾", "调和阴阳", "平衡水火"]
}
}
def create_xml_database(self, output_path: str = "jxwd_database.xml"):
"""创建完整的XML数据库"""
# 创建根元素
root = ET.Element("JXWD_Knowledge_Database")
root.set("version", "2.0")
root.set("system", "镜心悟道AI易经智能大脑")
# 1. 八卦知识部分
trigrams_elem = ET.SubElement(root, "Trigrams_Knowledge")
for trigram in self.trigrams_db:
trigram_elem = ET.SubElement(trigrams_elem, "Trigram")
trigram_elem.set("id", str(trigram.trigram_id))
ET.SubElement(trigram_elem, "Name").text = trigram.name
ET.SubElement(trigram_elem, "Symbol").text = trigram.symbol
ET.SubElement(trigram_elem, "Element").text = trigram.element
ET.SubElement(trigram_elem, "Direction").text = trigram.direction
ET.SubElement(trigram_elem, "Organ").text = trigram.organ
ET.SubElement(trigram_elem, "Meridian").text = trigram.meridian
ET.SubElement(trigram_elem, "Interpretation").text = trigram.interpretation
ET.SubElement(trigram_elem, "Treatment_Principle").text = trigram.treatment_principle
# 2. 五行关系部分
five_elements_elem = ET.SubElement(root, "Five_Elements_Relations")
for relation in self.five_elements_db:
rel_elem = ET.SubElement(five_elements_elem, "Relation")
ET.SubElement(rel_elem, "Source").text = relation.source
ET.SubElement(rel_elem, "Target").text = relation.target
ET.SubElement(rel_elem, "Type").text = relation.relation_type
ET.SubElement(rel_elem, "Strength").text = str(relation.strength)
ET.SubElement(rel_elem, "Description").text = relation.description
# 3. 九宫映射部分
palaces_elem = ET.SubElement(root, "Nine_Palaces_Mapping")
for palace in self.palace_db:
palace_elem = ET.SubElement(palaces_elem, "Palace")
palace_elem.set("number", str(palace.palace_number))
ET.SubElement(palace_elem, "Trigram").text = palace.trigram
ET.SubElement(palace_elem, "Element").text = palace.element
ET.SubElement(palace_elem, "Organ_System").text = palace.organ_system
ET.SubElement(palace_elem, "Meridian").text = palace.meridian
ET.SubElement(palace_elem, "Emotion").text = palace.emotion
ET.SubElement(palace_elem, "Season").text = palace.season
ET.SubElement(palace_elem, "Time_Period").text = palace.time_period
# 4. 六十四卦部分
hexagrams_elem = ET.SubElement(root, "Sixty_Four_Hexagrams")
for hex_id, hex_data in self.hexagram_db.items():
hex_elem = ET.SubElement(hexagrams_elem, "Hexagram")
hex_elem.set("id", str(hex_id))
ET.SubElement(hex_elem, "Name").text = hex_data["name"]
ET.SubElement(hex_elem, "Symbol").text = hex_data["symbol"]
ET.SubElement(hex_elem, "Interpretation").text = hex_data["interpretation"]
ET.SubElement(hex_elem, "Health_Implication").text = hex_data["health_implication"]
hints_elem = ET.SubElement(hex_elem, "Treatment_Hints")
for hint in hex_data["treatment_hints"]:
ET.SubElement(hints_elem, "Hint").text = hint
# 5. 医案模板部分
case_template_elem = ET.SubElement(root, "Medical_Case_Templates")
# 痉病模板
convulsion_case = ET.SubElement(case_template_elem, "Case")
convulsion_case.set("type", "痉病")
convulsion_case.set("severity", "重度")
# 能量标准化部分
energy_std = ET.SubElement(convulsion_case, "Energy_Standardization")
yang_levels = ET.SubElement(energy_std, "Yang_Energy_Levels")
yang_ranges = [
("+", "6.5-7.2", "↑", "阳气较为旺盛"),
("++", "7.2-8", "↑↑", "阳气非常旺盛"),
("+++", "8-10", "↑↑↑", "阳气极旺"),
("+++⊕", "10", "↑↑↑⊕", "阳气极阳")
]
for symbol, range_val, trend, desc in yang_ranges:
level = ET.SubElement(yang_levels, "Level")
level.set("symbol", symbol)
level.set("range", range_val)
level.set("trend", trend)
level.set("description", desc)
# 保存为XML文件
xml_str = minidom.parseString(ET.tostring(root)).toprettyxml(indent=" ")
with open(output_path, 'w', encoding='utf-8') as f:
f.write(xml_str)
print(f"数据库已保存到: {output_path}")
return root
def export_to_json(self, output_path: str = "jxwd_database.json"):
"""导出为JSON格式"""
data = {
"trigrams": [vars(t) for t in self.trigrams_db],
"five_elements_relations": [vars(r) for r in self.five_elements_db],
"nine_palaces": [vars(p) for p in self.palace_db],
"hexagrams": self.hexagram_db
}
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
print(f"JSON数据库已保存到: {output_path}")
return data
# ==================== 使用示例 ====================
if __name__ == "__main__":
# 创建数据库实例
db = JXWDXMLDatabase()
# 生成XML数据库
xml_root = db.create_xml_database()
# 导出JSON格式
json_data = db.export_to_json()
# 示例：查询特定卦象信息
print("n=== 卦象信息查询示例 ===")
hexagram_64 = db.hexagram_db[64]
print(f"卦名: {hexagram_64['name']}")
print(f"卦符: {hexagram_64['symbol']}")
print(f"解释: {hexagram_64['interpretation']}")
print(f"健康含义: {hexagram_64['health_implication']}")
print(f"治疗提示: {', '.join(hexagram_64['treatment_hints'])}")
print("n=== 五行关系示例 ===")
for rel in db.five_elements_db:
if rel.relation_type == "生":
print(f"{rel.source} → {rel.target}: {rel.description}")- PFS (伪代码函数链) 逻辑思维链
// 镜心悟道系统主逻辑链 - PFS伪代码
MODULE JXWD_Main_Logic_Chain
// 全局数据结构
TYPE QiState = STRUCT
total: REAL
yin: REAL
yang: REAL
ren: REAL
END TYPE
TYPE ElementState = ARRAY[1..5] OF REAL // 木火土金水
TYPE EmotionState = ARRAY[1..7] OF REAL // 七情
TYPE EnvState = ARRAY[1..6] OF REAL // 六气
// 主处理函数链
FUNCTION ProcessJXWDDiagnosis(
sensorData: ARRAY OF REAL,
emotionInput: EmotionState,
environment: EnvState
) RETURNS DiagnosisResult
// === 第一层：气机一元 ===
totalQi ← QiMonadAlgorithm(sensorData, Δt)
// === 第二层：阴阳分化 ===
[yin, yang] ← YinYangDecomposition(totalQi, φ=1.618)
currentQi ← QiState(totalQi, yin, yang, ren=6.0)
// === 第三层：三才稳定 ===
stabilityIndex ← TriadStability(currentQi)
IF stabilityIndex < 0.7 THEN
currentQi ← AdjustToStablePoint(currentQi)
END IF
// === 第四层：四象限辨证 ===
quadrant ← IdentifyQuadrant(currentQi.yin, currentQi.yang)
quadrantDiagnosis ← GetQuadrantDiagnosis(quadrant)
// === 第五层：五行生克 ===
elements ← InitializeElements() // 初始五行状态
FOR i ← 1 TO 10 DO // 迭代演化
elements ← FiveElementsEvolution(elements, Δt=0.1)
END FOR
// === 第六层：六气响应 ===
envResponse ← CalculateEnvironmentalResponse(environment)
// === 第七层：七情调控 ===
emotionImpact ← CalculateEmotionImpact(emotionInput)
elements ← MergeElementImpact(elements, emotionImpact)
// === 第八层：八卦推演 ===
hexagram ← AnalyzeHexagram(currentQi, elements, emotionInput, envResponse)
// === 第九层：九宫整合 ===
palaceMatrix ← InitializeNinePalaceMatrix()
palaceMatrix ← DistributeToPalaces(palaceMatrix, currentQi, elements,
emotionInput, envResponse, hexagram)
// === 九九归一优化 ===
optimizedMatrix ← NineNineReturnOneOptimization(palaceMatrix,
maxIterations=1000,
tolerance=1e-6)
// 生成最终诊断
diagnosis ← GenerateFinalDiagnosis(
currentQi,
elements,
quadrantDiagnosis,
hexagram,
optimizedMatrix,
stabilityIndex
)
RETURN diagnosis
END FUNCTION
// === 核心算法伪代码 ===
FUNCTION QiMonadAlgorithm(data: ARRAY OF REAL, Δt: REAL) RETURNS REAL
// Q_total = ∫(All_Sensor_Data) · d(Time)
total ← 0.0
FOR i ← 1 TO LENGTH(data) DO
total ← total + data[i] * Δt
END FOR
RETURN total
END FUNCTION
FUNCTION YinYangDecomposition(total: REAL, φ: REAL) RETURNS [REAL, REAL]
// 根据黄金比例分解
yin ← total / (1 + φ)
yang ← total - yin
RETURN [yin, yang]
END FUNCTION
FUNCTION FiveElementsEvolution(elements: ElementState, Δt: REAL) RETURNS ElementState
// 五行生克矩阵
CONSTANT W: MATRIX[1..5, 1..5] OF REAL = [
[0.0, 0.5, -0.3, 0.0, 0.2], // 木
[0.2, 0.0, 0.5, -0.3, 0.0], // 火
[0.0, 0.2, 0.0, 0.5, -0.3], // 土
[-0.3, 0.0, 0.2, 0.0, 0.5], // 金
[0.5, -0.3, 0.0, 0.2, 0.0] // 水
]
// dE/dt = W · E
dE ← MATRIX_MULTIPLY(W, elements)
// 更新元素能量
newElements ← elements
FOR i ← 1 TO 5 DO
newElements[i] ← elements[i] + dE[i] * Δt
END FOR
RETURN newElements
END FUNCTION
FUNCTION NineNineReturnOneOptimization(
matrix: MATRIX[1..9, 1..9] OF REAL,
maxIterations: INTEGER,
tolerance: REAL
) RETURNS MATRIX[1..9, 1..9] OF REAL
φ ← 1.6180339887 // 黄金比例
magicNumber ← 15.0 // 洛书魔数
FOR iteration ← 1 TO maxIterations DO
prevMatrix ← matrix
idealMatrix ← CreateIdealMatrix() // 理想平衡矩阵
// 应用变换函数 f(S_n)
matrix ← ApplyComplexTransformations(matrix)
// S_{n+1} = f(S_n) + φ * (I - S_n)
FOR i ← 1 TO 9 DO
FOR j ← 1 TO 9 DO
matrix[i,j] ← matrix[i,j] + φ * (idealMatrix[i,j] - prevMatrix[i,j])
END FOR
END FOR
// 检查收敛条件
entropy ← CalculateEnergyEntropy(matrix)
isMagic ← CheckMagicSquareCondition(matrix, magicNumber)
IF entropy < tolerance AND isMagic THEN
BREAK
END IF
END FOR
RETURN matrix
END FUNCTION
FUNCTION GenerateFinalDiagnosis(
qi: QiState,
elements: ElementState,
quadrantDiag: STRING,
hexagram: Hexagram,
matrix: MATRIX,
stability: REAL
) RETURNS DiagnosisResult
result ← NEW DiagnosisResult
// 基础信息
result.stability ← stability
result.quadrant_diagnosis ← quadrantDiag
// 五行分析
result.element_balance ← CalculateElementBalance(elements)
// 卦象解读
result.hexagram_name ← hexagram.name
result.hexagram_interpretation ← hexagram.interpretation
result.treatment_hints ← hexagram.treatment_hints
// 九宫能量分析
result.palace_energy_distribution ← AnalyzePalaceEnergy(matrix)
result.energy_entropy ← CalculateEnergyEntropy(matrix)
// 生成治疗建议
result.treatment_plan ← GenerateTreatmentPlan(
quadrantDiag,
result.element_balance,
hexagram.treatment_hints,
result.palace_energy_distribution
)
RETURN result
END FUNCTION
// === 辅助函数 ===
FUNCTION CalculateElementBalance(elements: ElementState) RETURNS REAL
mean ← AVERAGE(elements)
variance ← 0.0
FOR EACH e IN elements DO
variance ← variance + (e - mean)^2
END FOR
variance ← variance / 5
RETURN 1.0 / (1.0 + SQRT(variance))
END FUNCTION
FUNCTION CheckMagicSquareCondition(matrix: MATRIX, magic: REAL) RETURNS BOOLEAN
// 检查3x3子矩阵的行、列、对角线之和是否等于魔数
FOR i ← 1 TO 3 DO
rowSum ← SUM(matrix[i,1..3])
colSum ← SUM(matrix[1..3,i])
IF ABS(rowSum - magic) > 0.1 OR ABS(colSum - magic) > 0.1 THEN
RETURN FALSE
END IF
END FOR
diag1 ← matrix[1,1] + matrix[2,2] + matrix[3,3]
diag2 ← matrix[1,3] + matrix[2,2] + matrix[3,1]
RETURN ABS(diag1 - magic) < 0.1 AND ABS(diag2 - magic) < 0.1
END FUNCTION
END MODULE- 系统集成与使用示例
# jxwd_system_integration.py - 系统集成示例
import numpy as np
from typing import Dict, Any
import json
class JXWDSystemIntegration:
"""镜心悟道系统集成类"""
def __init__(self, config_path: str = None):
# 初始化各层算法
self.initialize_algorithms()
# 加载配置
if config_path:
self.load_config(config_path)
def initialize_algorithms(self):
"""初始化所有算法层"""
# 这里将C++算法层包装为Python可调用接口
# 实际实现中可能需要使用pybind11等工具
self.algorithms = {
'qma': QiMonadAlgorithmWrapper(),
'yyca': YinYangCycleAlgorithmWrapper(),
'tsa': TriadStabilityAlgorithmWrapper(),
'qba': QuadrantBalanceAlgorithmWrapper(),
'feca': FiveElementsCycleAlgorithmWrapper(),
'sqlya': SixQiLiuYinAlgorithmWrapper(),
'sea': SevenEmotionsAlgorithmWrapper(),
'etha': EightTrigramsAlgorithmWrapper(),
'npia': NinePalaceIntegrationWrapper()
}
def process_diagnosis(self, patient_data: Dict[str, Any]) -> Dict[str, Any]:
"""处理完整诊断流程"""
# 提取输入数据
sensor_data = patient_data.get('sensor_data', [])
emotions = patient_data.get('emotions', {})
environment = patient_data.get('environment', {})
symptoms = patient_data.get('symptoms', [])
# 执行九层算法链
results = {}
# 第一层：一元计算
total_qi = self.algorithms['qma'].calculate_total_qi(sensor_data, 0.1)
results['total_qi'] = total_qi
# 第二层：阴阳分解
yin, yang = self.algorithms['yyca'].decompose(total_qi)
results['yin_yang'] = {'yin': yin, 'yang': yang}
# 第三层：三才稳定
stability = self.algorithms['tsa'].calculate_stability(yin, yang, 6.0)
results['stability'] = stability
# 第四层：四象限分析
quadrant = self.algorithms['qba'].identify_quadrant(yin, yang)
results['quadrant'] = quadrant
# 第五层：五行演化
elements = self.algorithms['feca'].evolve_from_symptoms(symptoms)
results['five_elements'] = elements
# 第六层：六气响应
env_response = self.algorithms['sqlya'].calculate_response(environment)
results['environment_response'] = env_response
# 第七层：七情调控
emotion_impact = self.algorithms['sea'].calculate_impact(emotions)
# 更新五行状态
elements = self.merge_emotion_impact(elements, emotion_impact)
# 第八层：八卦推演
hexagram = self.algorithms['etha'].analyze(
total_qi, yin, yang, elements, emotions, env_response
)
results['hexagram'] = hexagram
# 第九层：九宫整合与归一
palace_matrix = self.algorithms['npia'].integrate_and_optimize(
total_qi, yin, yang, elements, emotions, env_response, hexagram
)
results['palace_matrix'] = palace_matrix
# 生成最终诊断
final_diagnosis = self.generate_final_diagnosis(results)
return {
'intermediate_results': results,
'final_diagnosis': final_diagnosis,
'treatment_plan': self.generate_treatment_plan(final_diagnosis)
}
def merge_emotion_impact(self, elements: Dict[str, float],
impact: Dict[str, float]) -> Dict[str, float]:
"""合并情感影响至五行状态"""
merged = elements.copy()
for element, value in impact.items():
if element in merged:
merged[element] += value
return merged
def generate_final_diagnosis(self, results: Dict[str, Any]) -> Dict[str, Any]:
"""生成最终诊断结果"""
diagnosis = {
'证型': self.determine_syndrome_type(results),
'病机': self.analyze_pathogenesis(results),
'病位': self.locate_disease_position(results),
'病性': self.characterize_disease_nature(results),
'邪正关系': self.analyze_pathogen_healthy_relation(results),
'预后判断': self.predict_prognosis(results)
}
return diagnosis
def determine_syndrome_type(self, results: Dict[str, Any]) -> str:
"""确定证型"""
quadrant = results['quadrant']
elements = results['five_elements']
if quadrant == 'Q1' and elements['fire'] > 8.0:
return "阳明腑实证"
elif quadrant == 'Q2' and elements['water'] < 5.0:
return "阴虚阳亢证"
elif quadrant == 'Q3' and elements['earth'] > 7.0:
return "阳虚湿盛证"
else:
return "复合证型"
def generate_treatment_plan(self, diagnosis: Dict[str, Any]) -> Dict[str, Any]:
"""生成治疗计划"""
treatment = {
'治则': self.determine_treatment_principle(diagnosis),
'治法': self.determine_treatment_method(diagnosis),
'方药': self.prescribe_formula(diagnosis),
'针灸': self.acupuncture_prescription(diagnosis),
'调护': self.lifestyle_advice(diagnosis)
}
return treatment
def determine_treatment_principle(self, diagnosis: Dict[str, Any]) -> str:
"""确定治疗原则"""
syndrome = diagnosis['证型']
principles = {
"阳明腑实证": "急下存阴，釜底抽薪",
"阴虚阳亢证": "滋阴潜阳，壮水制火",
"阳虚湿盛证": "温阳化湿，健脾利水",
"复合证型": "调和阴阳，平衡五行"
}
return principles.get(syndrome, "辨证论治，随证治之")
def prescribe_formula(self, diagnosis: Dict[str, Any]) -> List[Dict[str, Any]]:
"""开具方药"""
syndrome = diagnosis['证型']
formulas = {
"阳明腑实证": [
{"name": "大承气汤", "components": "大黄12g, 芒硝9g, 厚朴15g, 枳实12g"},
{"name": "调味承气汤", "components": "大黄12g, 甘草6g, 芒硝12g"}
],
"阴虚阳亢证": [
{"name": "六味地黄丸", "components": "熟地黄24g, 山萸肉12g, 山药12g"},
{"name": "天麻钩藤饮", "components": "天麻9g, 钩藤12g, 石决明18g"}
]
}
return formulas.get(syndrome, [])
# ==================== 使用示例 ====================
if __name__ == "__main__":
# 创建系统实例
system = JXWDSystemIntegration()
# 模拟患者数据
patient_data = {
'sensor_data': [6.5, 7.2, 8.1, 7.8, 6.9],
'emotions': {
'anger': 8.5,
'fear': 7.0,
'shock': 8.0,
'joy': 3.0,
'worry': 5.0,
'thought': 6.0,
'grief': 4.0
},
'environment': {
'wind': 3.0,
'cold': 2.0,
'heat': 8.0,
'damp': 4.0,
'dry': 6.0,
'fire': 7.0
},
'symptoms': [
'角弓反张',
'高热',
'便秘',
'昏迷',
'口渴'
]
}
# 执行诊断
result = system.process_diagnosis(patient_data)
# 输出结果
print("=== 镜心悟道AI诊断系统 ===")
print(f"总气机能量: {result['intermediate_results']['total_qi']:.2f}")
print(f"阴阳比例: 阴={result['intermediate_results']['yin_yang']['yin']:.2f}, "
f"阳={result['intermediate_results']['yin_yang']['yang']:.2f}")
print(f"系统稳定性: {result['intermediate_results']['stability']:.2f}")
print(f"四象限: {result['intermediate_results']['quadrant']}")
print("n=== 最终诊断 ===")
for key, value in result['final_diagnosis'].items():
print(f"{key}: {value}")
print("n=== 治疗计划 ===")
for key, value in result['treatment_plan'].items():
print(f"{key}: {value}")- 配置与部署文件
# config.yaml - 系统配置文件
system:
name: "镜心悟道AI中医智能大脑"
version: "2.0"
description: "基于易经奇门遁甲洛书矩阵的中医辨证论治系统"
algorithm_layers:
qi_monad:
enabled: true
time_window: 1.0
integration_method: "trapezoidal"
yin_yang_cycle:
enabled: true
k_constant: 0.1
golden_ratio: 1.618
convergence_threshold: 0.001
triad_stability:
enabled: true
stability_threshold: 0.7
adjustment_rate: 0.2
quadrant_balance:
enabled: true
yin_threshold: 6.0
yang_threshold: 6.0
five_elements:
enabled: true
evolution_steps: 10
time_step: 0.1
weight_matrix: "default"
six_qi:
enabled: true
coupling_matrix: "standard"
environmental_weight: 0.3
seven_emotions:
enabled: true
emotion_organ_mapping: "standard"
impact_factor: 0.5
eight_trigrams:
enabled: true
hexagram_database: "data/hexagrams.db"
divination_method: "comprehensive"
nine_palace:
enabled: true
magic_number: 15
golden_ratio: 1.618
max_iterations: 1000
tolerance: 1e-6
optimization_method: "gradient_descent"
database:
xml_database: "data/jxwd_knowledge.xml"
json_database: "data/jxwd_knowledge.json"
case_database: "data/medical_cases.db"
tables:
- name: "trigrams"
columns: ["id", "name", "symbol", "element", "organ", "meridian"]
- name: "five_elements"
columns: ["source", "target", "relation", "strength"]
- name: "palaces"
columns: ["number", "trigram", "element", "organ", "emotion"]
- name: "hexagrams"
columns: ["id", "name", "symbol", "interpretation"]
output:
format: "json"
include_intermediate: true
pretty_print: true
language: "zh-CN"
templates:
diagnosis_template: "templates/diagnosis_template.html"
report_template: "templates/report_template.md"
prescription_template: "templates/prescription_template.xml"
logging:
level: "INFO"
file: "logs/jxwd_system.log"
rotation: "1 day"
retention: "30 days"
performance:
use_multithreading: true
thread_pool_size: 4
cache_size: 1000
batch_size: 100
api:
port: 8080
host: "0.0.0.0"
cors_enabled: true
rate_limit: "100/hour"
endpoints:
- "/api/v1/diagnosis"
- "/api/v1/analysis"
- "/api/v1/treatment"
- "/api/v1/prognosis"# Makefile - 构建文件
CXX = g++
CXXFLAGS = -std=c++17 -O3 -march=native -fopenmp -Wall -Wextra
LDFLAGS = -lstdc++fs -lcurl -lsqlite3 -lpthread
# 目标文件
OBJS =
src/core/qma.o
src/core/yyca.o
src/core/tsa.o
src/core/qba.o
src/core/feca.o
src/core/sqlya.o
src/core/sea.o
src/core/etha.o
src/core/npia.o
src/main.o
# Python扩展模块
PYTHON_MODULE = jxwd_core
# 默认目标
all: jxwd_system $(PYTHON_MODULE).so
# 主可执行文件
jxwd_system: $(OBJS)
$(CXX) $(CXXFLAGS) -o $@ $^ $(LDFLAGS)
# 编译规则
%.o: %.cpp
$(CXX) $(CXXFLAGS) -c $< -o $@
# Python扩展
$(PYTHON_MODULE).so: src/python/jxwd_core.cpp
$(CXX) -shared -fPIC $(CXXFLAGS)
-I/usr/include/python3.8
src/python/jxwd_core.cpp
-o $(PYTHON_MODULE).so
$(LDFLAGS)
# 清理
clean:
rm -f $(OBJS) jxwd_system $(PYTHON_MODULE).so
# 测试
test: all
./test/test_core.py
./test/test_integration.py
# 安装
install: all
cp jxwd_system /usr/local/bin/
cp $(PYTHON_MODULE).so /usr/local/lib/python3.8/dist-packages/
cp config.yaml /etc/jxwd/
.PHONY: all clean test install系统架构总结
本系统实现了镜心悟道系统的完整算法架构：
- C++核心层：提供高性能的算法实现，包括九层算法链
- Python数据层：管理XML/JSON知识数据库和医案模板
- PFS逻辑层：定义清晰的伪代码函数链和思维逻辑
- 集成接口：提供完整的系统集成和使用示例
1. 核心系统架构
```cpp
// MirrorPrescriptionSystem.h - 镜像映射药方标注系统
#pragma once
#include <vector>
#include <map>
#include <string>
#include <cmath>
#include <functional>
#include <algorithm>
#include <memory>
namespace JXWD {
// ==================== 基础定义 ====================
enum ElementalLevel {
LEVEL_ONE, // 一元
LEVEL_TWO_YIN, // 二元(阴)
LEVEL_TWO_YANG, // 二元(阳)
LEVEL_THREE, // 三元
LEVEL_FOUR, // 四元
LEVEL_FIVE, // 五元
LEVEL_SIX, // 六元
LEVEL_SEVEN, // 七元
LEVEL_EIGHT, // 八元
LEVEL_NINE // 九元
};
enum PalacePosition {
PALACE_1 = 1, // 坎宫
PALACE_2, // 坤宫
PALACE_3, // 震宫
PALACE_4, // 巽宫
PALACE_5, // 中宫
PALACE_6, // 乾宫
PALACE_7, // 兑宫
PALACE_8, // 艮宫
PALACE_9 // 离宫
};
struct QuantumState {
double amplitude_real; // 实部 - 阳
double amplitude_imag; // 虚部 - 阴
double phase; // 相位
std::string symbol; // 量子态符号
QuantumState(double real = 0.5, double imag = 0.5,
double ph = 0.0, const std::string& sym = "")
: amplitude_real(real), amplitude_imag(imag), phase(ph), symbol(sym) {}
double getEnergy() const {
return amplitude_real * amplitude_real + amplitude_imag * amplitude_imag;
}
std::string toString() const {
return symbol + " [阳=" + std::to_string(amplitude_real) +
", 阴=" + std::to_string(amplitude_imag) + "]";
}
};
// ==================== 中药材数据库 ====================
class ChineseHerbDatabase {
private:
struct HerbRecord {
std::string name; // 药材名
ElementalLevel level; // 元级
std::vector<PalacePosition> target_palaces; // 靶向宫位
QuantumState quantum_state; // 量子态
std::vector<std::string> core_effects; // 核心功效
std::vector<std::string> properties; // 性味归经
double dosage_min; // 最小剂量(g)
double dosage_max; // 最大剂量(g)
double dosage_default; // 默认剂量(g)
std::string mirror_action; // 镜像作用描述
HerbRecord(const std::string& n, ElementalLevel l,
const std::vector<PalacePosition>& tp,
const QuantumState& qs,
const std::vector<std::string>& ce,
const std::vector<std::string>& prop,
double dmin, double dmax, double ddef,
const std::string& ma)
: name(n), level(l), target_palaces(tp), quantum_state(qs),
core_effects(ce), properties(prop),
dosage_min(dmin), dosage_max(dmax), dosage_default(ddef),
mirror_action(ma) {}
};
std::map<std::string, HerbRecord> herb_database;
public:
ChineseHerbDatabase() {
initializeDatabase();
}
const HerbRecord* getHerb(const std::string& name) const {
auto it = herb_database.find(name);
return it != herb_database.end() ? &it->second : nullptr;
}
std::vector<std::string> getHerbsByPalace(PalacePosition palace) const {
std::vector<std::string> result;
for (const auto& [name, record] : herb_database) {
if (std::find(record.target_palaces.begin(),
record.target_palaces.end(), palace) !=
record.target_palaces.end()) {
result.push_back(name);
}
}
return result;
}
std::vector<std::string> getHerbsByLevel(ElementalLevel level) const {
std::vector<std::string> result;
for (const auto& [name, record] : herb_database) {
if (record.level == level) {
result.push_back(name);
}
}
return result;
}
private:
void initializeDatabase() {
// 一元级药物
herb_database["人参"] = HerbRecord(
"人参", LEVEL_ONE, {PALACE_5},
QuantumState(0.9, 0.1, 0.0, "Â_人参 = γÎ + δŜ_z"),
{"大补元气", "救逆固脱"},
{"甘、微苦，温", "归脾、肺、心经"},
3.0, 15.0, 9.0,
"补益中宫元气，扶正固本"
);
// 二元(阴)级药物
herb_database["黄连"] = HerbRecord(
"黄连", LEVEL_TWO_YIN, {PALACE_9},
QuantumState(0.1, 0.9, M_PI, "Â_黄连 = ∂/∂t + mω²x²"),
{"清泻心火", "清热燥湿", "泻火解毒"},
{"苦，寒", "归心、脾、胃、肝、胆、大肠经"},
1.5, 6.0, 3.0,
"清降离宫亢阳，平衡心火"
);
// 二元(阳)级药物
herb_database["肉桂"] = HerbRecord(
"肉桂", LEVEL_TWO_YANG, {PALACE_1},
QuantumState(0.9, 0.1, 0.0, "Â_肉桂 = α|阴⟩⟨阳| + β|阳⟩⟨阴|"),
{"引火归元", "温阳散寒", "活血通经"},
{"辛、甘，大热", "归肾、脾、心、肝经"},
1.0, 5.0, 2.0,
"温补坎宫命门，引火归元"
);
// 三元级药物
herb_database["生地黄"] = HerbRecord(
"生地黄", LEVEL_THREE, {PALACE_1, PALACE_5},
QuantumState(0.3, 0.7, M_PI/2, "Â_生地 = Σκ|阴⟩⟨血|"),
{"滋阴凉血", "养阴生津"},
{"甘，寒", "归心、肝、肾经"},
6.0, 30.0, 12.0,
"滋补肾阴，凉血润燥"
);
// 四元级药物
herb_database["黄芪"] = HerbRecord(
"黄芪", LEVEL_FOUR, {PALACE_2, PALACE_5},
QuantumState(0.8, 0.2, 0.0, "Â_黄芪 = ∇² + V(x)"),
{"补气升阳", "固表止汗", "利水消肿"},
{"甘，微温", "归肺、脾经"},
9.0, 30.0, 15.0,
"补益中气，升阳举陷"
);
herb_database["百合"] = HerbRecord(
"百合", LEVEL_FOUR, {PALACE_5, PALACE_9},
QuantumState(0.5, 0.5, M_PI/4, "Â_百合 = |神⟩⟨躁| ⊗ |肺⟩⟨金|"),
{"养阴润肺", "清心安神"},
{"甘，微寒", "归心、肺经"},
6.0, 15.0, 10.0,
"安中宫以定志，清离宫以除烦"
);
// 五元级药物
herb_database["白术"] = HerbRecord(
"白术", LEVEL_FIVE, {PALACE_2, PALACE_5},
QuantumState(0.6, 0.4, 0.0, "Â_白术 = |土⟩⟨湿|"),
{"健脾益气", "燥湿利水", "止汗安胎"},
{"甘、苦，温", "归脾、胃经"},
6.0, 15.0, 9.0,
"健脾燥湿，和中益气"
);
// 六元级药物
herb_database["柴胡"] = HerbRecord(
"柴胡", LEVEL_SIX, {PALACE_3, PALACE_4},
QuantumState(0.7, 0.3, M_PI/3, "Â_柴胡 = iħ∂/∂x"),
{"疏肝解郁", "和解表里", "升举阳气"},
{"苦、辛，微寒", "归肝、胆经"},
3.0, 12.0, 6.0,
"疏肝理气，升散郁结"
);
herb_database["白芍"] = HerbRecord(
"白芍", LEVEL_SIX, {PALACE_3, PALACE_4},
QuantumState(0.4, 0.6, M_PI/2, "Â_白芍 = Î ⊗ Ŝ_y"),
{"养血敛阴", "柔肝止痛", "平抑肝阳"},
{"苦、酸，微寒", "归肝、脾经"},
6.0, 15.0, 9.0,
"养血柔肝，平抑木气"
);
// 七元级药物
herb_database["半夏"] = HerbRecord(
"半夏", LEVEL_SEVEN, {PALACE_5, PALACE_8},
QuantumState(0.5, 0.5, M_PI, "Â_半夏 = |痰⟩⟨降|"),
{"燥湿化痰", "降逆止呕", "消痞散结"},
{"辛，温，有毒", "归脾、胃、肺经"},
3.0, 9.0, 6.0,
"化痰降逆，和中安胃"
);
// 八元级药物
herb_database["大黄"] = HerbRecord(
"大黄", LEVEL_EIGHT, {PALACE_2, PALACE_6},
QuantumState(0.8, 0.2, 3*M_PI/2, "Â_大黄 = -∇P"),
{"泻下攻积", "清热泻火", "凉血解毒", "逐瘀通经"},
{"苦，寒", "归脾、胃、大肠、肝、心包经"},
3.0, 12.0, 6.0,
"通腑泻热，破瘀攻积"
);
// 九元级药物
herb_database["黄精"] = HerbRecord(
"黄精", LEVEL_NINE, {PALACE_1, PALACE_5},
QuantumState(0.5, 0.5, 0.0, "Â_黄精 = φÎ"),
{"补气养阴", "健脾润肺", "益肾填精"},
{"甘，平", "归脾、肺、肾经"},
9.0, 30.0, 15.0,
"平补五脏，阴阳双调"
);
// 其他常用药物
herb_database["知母"] = HerbRecord(
"知母", LEVEL_FOUR, {PALACE_1, PALACE_9},
QuantumState(0.3, 0.7, M_PI/3, "Â_知母 = |火⟩⟨清| ⊗ |阴⟩⟨生|"),
{"清热泻火", "滋阴润燥"},
{"苦、甘，寒", "归肺、胃、肾经"},
6.0, 12.0, 9.0,
"滋阴降火，金水相生"
);
herb_database["当归"] = HerbRecord(
"当归", LEVEL_THREE, {PALACE_1, PALACE_3},
QuantumState(0.5, 0.5, M_PI/4, "Â_当归 = |血⟩⟨生| ⊗ |肝⟩⟨润|"),
{"补血活血", "调经止痛", "润肠通便"},
{"甘、辛，温", "归肝、心、脾经"},
6.0, 15.0, 9.0,
"养血活血，润燥通经"
);
}
};
// ==================== 九宫病机矩阵 ====================
struct PalaceEnergy {
PalacePosition position; // 宫位
std::string name; // 宫位名称
double current_energy; // 当前能量值
double target_energy; // 目标能量值
double yin_component; // 阴分量
double yang_component; // 阳分量
std::string pattern; // 病机模式
std::vector<std::string> symptoms; // 症状
PalaceEnergy(PalacePosition pos, const std::string& n,
double ce = 7.0, double te = 7.0)
: position(pos), name(n), current_energy(ce), target_energy(te),
yin_component(ce * 0.5), yang_component(ce * 0.5) {}
double getDeviation() const {
return current_energy - target_energy;
}
std::string getTrend() const {
double dev = getDeviation();
if (dev > 0.5) return "↑↑";
else if (dev > 0.1) return "↑";
else if (dev < -0.5) return "↓↓";
else if (dev < -0.1) return "↓";
else return "→";
}
};
class NinePalaceEnergyMatrix {
private:
std::map<PalacePosition, PalaceEnergy> palaces;
double golden_ratio = 1.6180339887;
public:
NinePalaceEnergyMatrix() {
initializePalaces();
}
void setPalaceEnergy(PalacePosition pos, double energy,
double yin_ratio = 0.5) {
auto it = palaces.find(pos);
if (it != palaces.end()) {
it->second.current_energy = energy;
it->second.yin_component = energy * yin_ratio;
it->second.yang_component = energy * (1 - yin_ratio);
}
}
void setPalacePattern(PalacePosition pos, const std::string& pattern,
const std::vector<std::string>& symptoms = {}) {
auto it = palaces.find(pos);
if (it != palaces.end()) {
it->second.pattern = pattern;
it->second.symptoms = symptoms;
}
}
const PalaceEnergy* getPalace(PalacePosition pos) const {
auto it = palaces.find(pos);
return it != palaces.end() ? &it->second : nullptr;
}
std::vector<PalacePosition> getImbalancedPalaces(double threshold = 0.3) const {
std::vector<PalacePosition> result;
for (const auto& [pos, palace] : palaces) {
if (std::abs(palace.getDeviation()) > threshold) {
result.push_back(pos);
}
}
return result;
}
double calculateTotalImbalance() const {
double total = 0.0;
for (const auto& [pos, palace] : palaces) {
total += std::abs(palace.getDeviation());
}
return total;
}
void optimizeToBalance(int max_iterations = 100) {
// 使用黄金比例进行优化
for (int iter = 0; iter < max_iterations; ++iter) {
double total_energy = 0.0;
for (auto& [pos, palace] : palaces) {
total_energy += palace.current_energy;
}
double avg_energy = total_energy / palaces.size();
// 向平均能量调整，使用黄金比例
for (auto& [pos, palace] : palaces) {
double adjustment = (avg_energy - palace.current_energy) *
golden_ratio * 0.1;
palace.current_energy += adjustment;
// 更新阴阳分量
double yin_ratio = palace.yin_component /
(palace.yin_component + palace.yang_component + 1e-10);
palace.yin_component = palace.current_energy * yin_ratio;
palace.yang_component = palace.current_energy * (1 - yin_ratio);
}
// 检查收敛
if (calculateTotalImbalance() < 0.01) {
break;
}
}
}
private:
void initializePalaces() {
palaces[PALACE_1] = PalaceEnergy(PALACE_1, "坎宫", 7.0, 7.0);
palaces[PALACE_2] = PalaceEnergy(PALACE_2, "坤宫", 7.0, 7.0);
palaces[PALACE_3] = PalaceEnergy(PALACE_3, "震宫", 7.0, 7.0);
palaces[PALACE_4] = PalaceEnergy(PALACE_4, "巽宫", 7.0, 7.0);
palaces[PALACE_5] = PalaceEnergy(PALACE_5, "中宫", 7.0, 7.0);
palaces[PALACE_6] = PalaceEnergy(PALACE_6, "乾宫", 7.0, 7.0);
palaces[PALACE_7] = PalaceEnergy(PALACE_7, "兑宫", 7.0, 7.0);
palaces[PALACE_8] = PalaceEnergy(PALACE_8, "艮宫", 7.0, 7.0);
palaces[PALACE_9] = PalaceEnergy(PALACE_9, "离宫", 7.0, 7.0);
}
};
// ==================== 镜像映射规则 ====================
class MirrorMappingRules {
private:
ChineseHerbDatabase herb_db;
double golden_ratio = 1.6180339887;
public:
struct HerbSelection {
std::string herb_name;
double dosage; // 剂量(g)
std::string role; // 君、臣、佐、使
PalacePosition primary_target; // 主要靶向宫位
std::string mirror_logic; // 镜像逻辑描述
double priority_score; // 优先级分数
};
std::vector<HerbSelection> selectHerbsForPalace(
PalacePosition palace,
const PalaceEnergy& palace_energy,
const std::string& pattern) {
std::vector<HerbSelection> selections;
// 1. 获取靶向该宫位的所有药物
std::vector<std::string> candidate_herbs =
herb_db.getHerbsByPalace(palace);
for (const auto& herb_name : candidate_herbs) {
const auto* herb = herb_db.getHerb(herb_name);
if (!herb) continue;
// 2. 根据病机模式选择药物
if (isHerbSuitableForPattern(*herb, pattern, palace_energy)) {
HerbSelection selection;
selection.herb_name = herb_name;
selection.primary_target = palace;
selection.mirror_logic = generateMirrorLogic(*herb, palace_energy);
selection.priority_score = calculatePriorityScore(
*herb, palace_energy, pattern);
// 3. 计算剂量（基于黄金比例和病机偏差）
selection.dosage = calculateOptimalDosage(
*herb, palace_energy.getDeviation());
selections.push_back(selection);
}
}
// 4. 按优先级排序
std::sort(selections.begin(), selections.end(),
[](const HerbSelection& a, const HerbSelection& b) {
return a.priority_score > b.priority_score;
});
return selections;
}
std::vector<HerbSelection> createPrescription(
const NinePalaceEnergyMatrix& energy_matrix,
const std::string& overall_pattern) {
std::vector<HerbSelection> prescription;
// 1. 获取不平衡的宫位
auto imbalanced_palaces = energy_matrix.getImbalancedPalaces();
// 2. 为每个不平衡宫位选择药物
std::map<std::string, HerbSelection> herb_map; // 避免重复
for (PalacePosition palace : imbalanced_palaces) {
const auto* palace_energy = energy_matrix.getPalace(palace);
if (!palace_energy) continue;
auto selections = selectHerbsForPalace(
palace, *palace_energy, palace_energy->pattern);
// 选择优先级最高的1-2个药物
int count = 0;
for (const auto& selection : selections) {
if (count >= 2) break;
if (herb_map.find(selection.herb_name) == herb_map.end()) {
herb_map[selection.herb_name] = selection;
count++;
}
}
}
// 3. 转换为向量并分配君臣佐使角色
for (auto& [name, selection] : herb_map) {
prescription.push_back(selection);
}
// 4. 分配角色
assignRoles(prescription);
// 5. 优化剂量比例（黄金比例）
optimizeDosageRatios(prescription);
return prescription;
}
private:
bool isHerbSuitableForPattern(const ChineseHerbDatabase::HerbRecord& herb,
const std::string& pattern,
const PalaceEnergy& palace_energy) {
double deviation = palace_energy.getDeviation();
// 阳亢用阴药，阴虚用阳药（配合滋阴）
if (deviation > 0) { // 能量过高 - 阳亢
return herb.level == LEVEL_TWO_YIN ||
herb.level == LEVEL_THREE ||
(herb.level == LEVEL_FOUR &&
herb.core_effects.end() != std::find_if(
herb.core_effects.begin(), herb.core_effects.end(),
[](const std::string& effect) {
return effect.find("清热") != std::string::npos ||
effect.find("泻火") != std::string::npos;
}));
} else { // 能量过低 - 阴虚或阳虚
return herb.level == LEVEL_TWO_YANG ||
herb.level == LEVEL_THREE ||
(herb.level == LEVEL_ONE &&
herb.core_effects.end() != std::find_if(
herb.core_effects.begin(), herb.core_effects.end(),
[](const std::string& effect) {
return effect.find("补") != std::string::npos;
}));
}
}
std::string generateMirrorLogic(const ChineseHerbDatabase::HerbRecord& herb,
const PalaceEnergy& palace_energy) {
std::string logic;
double deviation = palace_energy.getDeviation();
if (deviation > 0) {
logic = "清降" + palace_energy.name + "过亢之阳";
} else {
logic = "温补" + palace_energy.name + "不足之" +
(palace_energy.yin_component < palace_energy.yang_component ?
"阴" : "阳");
}
logic += "，" + herb.mirror_action;
return logic;
}
double calculatePriorityScore(const ChineseHerbDatabase::HerbRecord& herb,
const PalaceEnergy& palace_energy,
const std::string& pattern) {
double score = 0.0;
double deviation = std::abs(palace_energy.getDeviation());
// 1. 偏差越大，优先级越高
score += deviation * 2.0;
// 2. 药物与宫位的匹配度
for (PalacePosition target : herb.target_palaces) {
if (target == palace_energy.position) {
score += 1.0;
break;
}
}
// 3. 药物与病机模式的匹配度
for (const auto& effect : herb.core_effects) {
if (pattern.find("火") != std::string::npos &&
effect.find("清热") != std::string::npos) {
score += 0.5;
}
if (pattern.find("虚") != std::string::npos &&
effect.find("补") != std::string::npos) {
score += 0.5;
}
}
return score;
}
double calculateOptimalDosage(const ChineseHerbDatabase::HerbRecord& herb,
double deviation) {
double base_dosage = herb.dosage_default;
double adjusted = base_dosage;
// 根据偏差调整剂量
if (std::abs(deviation) > 1.0) {
adjusted *= (1.0 + std::abs(deviation) * 0.2);
} else if (std::abs(deviation) > 0.5) {
adjusted *= (1.0 + std::abs(deviation) * 0.1);
}
// 确保在安全范围内
return std::max(herb.dosage_min, std::min(herb.dosage_max, adjusted));
}
void assignRoles(std::vector<HerbSelection>& herbs) {
if (herbs.empty()) return;
// 1. 君药：优先级最高的药物
herbs[0].role = "君药";
// 2. 臣药：次高优先级的1-2个药物
int minister_count = std::min(2, (int)herbs.size() - 1);
for (int i = 1; i <= minister_count; ++i) {
herbs[i].role = "臣药";
}
// 3. 佐药：剩余的药物
for (size_t i = minister_count + 1; i < herbs.size(); ++i) {
herbs[i].role = "佐药";
// 如果有调和或引经的药物，设为使药
const auto* herb = herb_db.getHerb(herbs[i].herb_name);
if (herb) {
for (const auto& effect : herb->core_effects) {
if (effect.find("调和") != std::string::npos ||
effect.find("引经") != std::string::npos) {
herbs[i].role = "使药";
break;
}
}
}
}
}
void optimizeDosageRatios(std::vector<HerbSelection>& herbs) {
if (herbs.size() < 2) return;
// 君药与臣药的黄金比例
double monarch_dosage = herbs[0].dosage;
for (size_t i = 1; i < herbs.size() && i < 3; ++i) {
if (herbs[i].role == "臣药") {
// 调整臣药剂量，接近黄金比例
double target = monarch_dosage / golden_ratio;
herbs[i].dosage = target;
// 确保在安全范围内
const auto* herb = herb_db.getHerb(herbs[i].herb_name);
if (herb) {
herbs[i].dosage = std::max(herb->dosage_min,
std::min(herb->dosage_max,
herbs[i].dosage));
}
}
}
// 佐使药剂量一般为君药的1/3到1/2
for (size_t i = 3; i < herbs.size(); ++i) {
if (herbs[i].role == "佐药" || herbs[i].role == "使药") {
herbs[i].dosage = monarch_dosage * 0.4;
const auto* herb = herb_db.getHerb(herbs[i].herb_name);
if (herb) {
herbs[i].dosage = std::max(herb->dosage_min,
std::min(herb->dosage_max,
herbs[i].dosage));
}
}
}
}
};
// ==================== 药方标注系统 ====================
class PrescriptionAnnotationSystem {
private:
ChineseHerbDatabase herb_db;
MirrorMappingRules mapping_rules;
public:
struct AnnotatedPrescription {
std::string case_id;
std::string pattern;
std::vector<MirrorMappingRules::HerbSelection> herbs;
NinePalaceEnergyMatrix energy_goals;
std::string quantum_algorithm;
double calculateEffectivenessScore() const {
double score = 0.0;
// 1. 检查君臣佐使结构
bool has_monarch = false, has_minister = false;
for (const auto& herb : herbs) {
if (herb.role == "君药") has_monarch = true;
if (herb.role == "臣药") has_minister = true;
}
if (has_monarch && has_minister) score += 0.3;
// 2. 检查黄金比例
if (herbs.size() >= 2 && herbs[0].role == "君药") {
double monarch_dosage = herbs[0].dosage;
for (size_t i = 1; i < herbs.size() && i < 3; ++i) {
if (herbs[i].role == "臣药") {
double ratio = monarch_dosage / herbs[i].dosage;
double deviation = std::abs(ratio - 1.618) / 1.618;
score += 0.2 * (1.0 - deviation);
}
}
}
// 3. 检查阴阳平衡
double yin_total = 0.0, yang_total = 0.0;
for (const auto& herb : herbs) {
const auto* herb_info = herb_db.getHerb(herb.herb_name);
if (herb_info) {
double energy = herb_info->quantum_state.getEnergy();
yin_total += herb_info->quantum_state.amplitude_imag * energy;
yang_total += herb_info->quantum_state.amplitude_real * energy;
}
}
double balance_ratio = yang_total / (yin_total + 1e-10);
double balance_score = 1.0 - std::abs(balance_ratio - 1.0);
score += 0.3 * balance_score;
// 4. 检查剂量合理性
double dosage_score = 0.0;
for (const auto& herb : herbs) {
const auto* herb_info = herb_db.getHerb(herb.herb_name);
if (herb_info) {
if (herb.dosage >= herb_info->dosage_min * 0.8 &&
herb.dosage <= herb_info->dosage_max * 1.2) {
dosage_score += 1.0;
}
}
}
score += 0.2 * dosage_score / herbs.size();
return std::min(1.0, score);
}
std::string toXML() const {
std::string xml = "<Mirror_Prescription ";
xml += "case_id="" + case_id + "" ";
xml += "pattern="" + pattern + "">n";
// 九宫能量目标
xml += " <Nine_Palace_Energy_Goal>n";
for (int i = 1; i <= 9; ++i) {
PalacePosition pos = static_cast<PalacePosition>(i);
const auto* palace = energy_goals.getPalace(pos);
if (palace) {
xml += " <Palace index="" + std::to_string(i) + "" ";
xml += "name="" + palace->name + "" ";
xml += "target_energy="" +
std::to_string(palace->target_energy) + "φ" ";
xml += "trend="" + palace->getTrend() + ""/>n";
}
}
xml += " </Nine_Palace_Energy_Goal>n";
// 药物映射
xml += " <Herb_Mirror_Mapping>n";
for (const auto& herb : herbs) {
const auto* herb_info = herb_db.getHerb(herb.herb_name);
if (!herb_info) continue;
xml += " <Herb name="" + herb.herb_name + "" ";
xml += "dose="" + std::to_string(herb.dosage) + "g" ";
xml += "unit="克">n";
xml += " <Elemental_Level>" +
getElementalLevelName(herb_info->level) + "</Elemental_Level>n";
xml += " <Target_Palace>";
for (size_t i = 0; i < herb_info->target_palaces.size(); ++i) {
if (i > 0) xml += ", ";
xml += std::to_string(static_cast<int>(herb_info->target_palaces[i]));
}
xml += "</Target_Palace>n";
xml += " <Mirror_Action>" + herb.mirror_logic + "</Mirror_Action>n";
xml += " <Quantum_Action>" +
herb_info->quantum_state.symbol + "</Quantum_Action>n";
xml += " <Role>" + herb.role + "</Role>n";
xml += " </Herb>n";
}
xml += " </Herb_Mirror_Mapping>n";
// 量子算法
xml += " <Formula_Quantum_Algorithm>n";
xml += " |方剂⟩ = ";
for (size_t i = 0; i < herbs.size(); ++i) {
if (i > 0) xml += " * ";
const auto* herb_info = herb_db.getHerb(herbs[i].herb_name);
if (herb_info) {
xml += "exp[β_" + std::to_string(i+1) + " * " +
herb_info->quantum_state.symbol + "]";
}
}
xml += " * |" + pattern + "⟩n";
xml += " <Recursion_Params>n";
xml += " <Param name="最大迭代深度">9⁹</Param>n";
xml += " <Param name="镜像衰减因子">γ = e^{-π/9}</Param>n";
xml += " </Recursion_Params>n";
xml += " </Formula_Quantum_Algorithm>n";
xml += "</Mirror_Prescription>";
return xml;
}
private:
std::string getElementalLevelName(ElementalLevel level) const {
switch (level) {
case LEVEL_ONE: return "一元";
case LEVEL_TWO_YIN: return "二元(阴)";
case LEVEL_TWO_YANG: return "二元(阳)";
case LEVEL_THREE: return "三元";
case LEVEL_FOUR: return "四元";
case LEVEL_FIVE: return "五元";
case LEVEL_SIX: return "六元";
case LEVEL_SEVEN: return "七元";
case LEVEL_EIGHT: return "八元";
case LEVEL_NINE: return "九元";
default: return "未知";
}
}
};
AnnotatedPrescription createPrescription(
const std::string& case_id,
const std::string& pattern,
const NinePalaceEnergyMatrix& initial_energy) {
AnnotatedPrescription prescription;
prescription.case_id = case_id;
prescription.pattern = pattern;
prescription.energy_goals = initial_energy;
// 1. 使用镜像映射规则创建药方
prescription.herbs = mapping_rules.createPrescription(
initial_energy, pattern);
// 2. 生成量子算法描述
prescription.quantum_algorithm = generateQuantumAlgorithm(prescription.herbs);
return prescription;
}
void optimizePrescription(AnnotatedPrescription& prescription,
int max_iterations = 10) {
double best_score = prescription.calculateEffectivenessScore();
auto best_herbs = prescription.herbs;
for (int iter = 0; iter < max_iterations; ++iter) {
// 微调剂量
for (auto& herb : prescription.herbs) {
// 随机微调
double adjustment = (rand() % 100) / 1000.0 - 0.05; // ±5%
herb.dosage *= (1.0 + adjustment);
// 确保在安全范围内
const auto* herb_info = herb_db.getHerb(herb.herb_name);
if (herb_info) {
herb.dosage = std::max(herb_info->dosage_min,
std::min(herb_info->dosage_max,
herb.dosage));
}
}
// 重新分配角色（如果需要）
mapping_rules.assignRoles(prescription.herbs);
// 重新优化剂量比例
mapping_rules.optimizeDosageRatios(prescription.herbs);
// 计算新分数
double new_score = prescription.calculateEffectivenessScore();
if (new_score > best_score) {
best_score = new_score;
best_herbs = prescription.herbs;
}
}
prescription.herbs = best_herbs;
}
private:
std::string generateQuantumAlgorithm(
const std::vector<MirrorMappingRules::HerbSelection>& herbs) {
std::string algorithm = "|方剂⟩ = ";
for (size_t i = 0; i < herbs.size(); ++i) {
if (i > 0) algorithm += " ⊗ ";
const auto* herb_info = herb_db.getHerb(herbs[i].herb_name);
if (herb_info) {
algorithm += "exp(iθ_" + std::to_string(i+1) + " * " +
herb_info->quantum_state.symbol + ")";
}
}
algorithm += " |病机⟩n其中：θ_i ∈ [0, 2π] 为相位参数，满足 Σθ_i = 2π";
return algorithm;
}
};
// ==================== 临床案例应用 ====================
class ClinicalCaseApplication {
private:
PrescriptionAnnotationSystem annotation_system;
public:
struct CaseAnalysis {
std::string case_id;
std::string patient_info;
std::string main_pattern;
std::vector<std::string> symptoms;
NinePalaceEnergyMatrix energy_analysis;
PrescriptionAnnotationSystem::AnnotatedPrescription prescription;
std::vector<std::string> treatment_principles;
std::vector<std::string> prognosis_notes;
void printReport() const {
std::cout << "n=== 镜心悟道系统临床案例分析 ===" << std::endl;
std::cout << "案例ID: " << case_id << std::endl;
std::cout << "主要证型: " << main_pattern << std::endl;
std::cout << "n症状分析:" << std::endl;
for (const auto& symptom : symptoms) {
std::cout << " • " << symptom << std::endl;
}
std::cout << "n九宫能量分析:" << std::endl;
for (int i = 1; i <= 9; ++i) {
PalacePosition pos = static_cast<PalacePosition>(i);
const auto* palace = energy_analysis.getPalace(pos);
if (palace && std::abs(palace->getDeviation()) > 0.1) {
std::cout << " " << palace->name << ": "
<< palace->current_energy << " (目标: "
<< palace->target_energy << ") "
<< palace->getTrend();
if (!palace->pattern.empty()) {
std::cout << " [" << palace->pattern << "]";
}
std::cout << std::endl;
}
}
std::cout << "n治疗原则:" << std::endl;
for (const auto& principle : treatment_principles) {
std::cout << " • " << principle << std::endl;
}
std::cout << "n推荐处方 (效果评分: "
<< prescription.calculateEffectivenessScore() * 100
<< "%):" << std::endl;
for (const auto& herb : prescription.herbs) {
std::cout << " " << herb.herb_name << " " << herb.dosage
<< "g (" << herb.role << ")";
if (!herb.mirror_logic.empty()) {
std::cout << " - " << herb.mirror_logic;
}
std::cout << std::endl;
}
if (!prognosis_notes.empty()) {
std::cout << "n预后提示:" << std::endl;
for (const auto& note : prognosis_notes) {
std::cout << " • " << note << std::endl;
}
}
std::cout << "n处方XML输出已生成" << std::endl;
}
};
CaseAnalysis analyzeLilyDiseaseCase() {
// 百合病案例分析（陈克正医案）
CaseAnalysis analysis;
analysis.case_id = "百合病-陈克正-1969";
analysis.patient_info = "女性，42岁，长期情志不遂";
analysis.main_pattern = "阴血不足，心肺火旺";
analysis.symptoms = {
"心烦不宁，失眠多梦",
"口干咽燥，小便赤",
"时欲食复不能食，时默默欲卧",
"舌红少苔，脉细数"
};
analysis.treatment_principles = {
"滋阴清热，养心安神",
"交通心肾，调和阴阳",
"疏肝解郁，畅达气机"
};
analysis.prognosis_notes = {
"首诊7剂，心烦失眠改善",
"二诊去黄连加麦冬、酸枣仁",
"三诊诸症基本消失，以百合固金丸调理"
};
// 设置九宫能量分析
// 离宫能量过高（心火旺），坎宫能量过低（肾阴虚）
analysis.energy_analysis.setPalaceEnergy(PALACE_9, 9.0, 0.3); // 离宫阳亢
analysis.energy_analysis.setPalaceEnergy(PALACE_1, 5.5, 0.8); // 坎宫阴虚
analysis.energy_analysis.setPalaceEnergy(PALACE_3, 7.5, 0.4); // 震宫偏亢
analysis.energy_analysis.setPalaceEnergy(PALACE_5, 6.8, 0.5); // 中宫不安
analysis.energy_analysis.setPalacePattern(PALACE_9, "心火亢盛",
{"心烦", "失眠", "小便赤"});
analysis.energy_analysis.setPalacePattern(PALACE_1, "肾阴不足",
{"口干", "舌红少苔"});
analysis.energy_analysis.setPalacePattern(PALACE_3, "肝郁化火",
{"情志不遂", "默默欲卧"});
analysis.energy_analysis.setPalacePattern(PALACE_5, "心神不安",
{"不宁", "多梦"});
// 设置目标能量（治疗目标）
for (int i = 1; i <= 9; ++i) {
PalacePosition pos = static_cast<PalacePosition>(i);
auto* palace = const_cast<PalaceEnergy*>(analysis.energy_analysis.getPalace(pos));
if (palace) {
if (pos == PALACE_9) palace->target_energy = 7.0; // 降心火
else if (pos == PALACE_1) palace->target_energy = 6.8; // 滋肾阴
else if (pos == PALACE_3) palace->target_energy = 6.5; // 平肝
else palace->target_energy = 7.0; // 平衡
}
}
// 创建药方
analysis.prescription = annotation_system.createPrescription(
analysis.case_id, analysis.main_pattern, analysis.energy_analysis);
// 优化药方
annotation_system.optimizePrescription(analysis.prescription, 5);
return analysis;
}
CaseAnalysis analyzeQiDeficiencyCase() {
// 气虚证案例分析
CaseAnalysis analysis;
analysis.case_id = "气虚证-张某某-2024";
analysis.patient_info = "男性，58岁，办公室工作";
analysis.main_pattern = "肺脾气虚，卫表不固";
analysis.symptoms = {
"神疲乏力，少气懒言",
"食欲不振，大便溏薄",
"自汗畏风，易感冒",
"舌淡苔白，脉弱"
};
analysis.treatment_principles = {
"补益肺脾，益气固表",
"健脾和胃，升阳举陷",
"调和营卫，扶正祛邪"
};
// 设置九宫能量分析
analysis.energy_analysis.setPalaceEnergy(PALACE_5, 5.0, 0.4); // 中宫气虚
analysis.energy_analysis.setPalaceEnergy(PALACE_2, 5.2, 0.4); // 坤宫脾虚
analysis.energy_analysis.setPalaceEnergy(PALACE_7, 5.5, 0.4); // 兑宫肺虚
analysis.energy_analysis.setPalacePattern(PALACE_5, "中气不足",
{"神疲", "乏力"});
analysis.energy_analysis.setPalacePattern(PALACE_2, "脾失健运",
{"食欲不振", "便溏"});
analysis.energy_analysis.setPalacePattern(PALACE_7, "肺卫不固",
{"自汗", "易感冒"});
// 设置目标能量
for (int i = 1; i <= 9; ++i) {
PalacePosition pos = static_cast<PalacePosition>(i);
auto* palace = const_cast<PalaceEnergy*>(analysis.energy_analysis.getPalace(pos));
if (palace) {
if (pos == PALACE_5 || pos == PALACE_2 || pos == PALACE_7) {
palace->target_energy = 7.5; // 补气
} else {
palace->target_energy = 7.0;
}
}
}
analysis.prescription = annotation_system.createPrescription(
analysis.case_id, analysis.main_pattern, analysis.energy_analysis);
return analysis;
}
void exportCaseReport(const CaseAnalysis& analysis,
const std::string& filename) {
// 生成XML
std::string xml = analysis.prescription.toXML();
// 添加案例信息
std::string full_report = "<?xml version="1.0" encoding="UTF-8"?>n";
full_report += "<Clinical_Case_Report>n";
full_report += " <Case_Info>n";
full_report += " <ID>" + analysis.case_id + "</ID>n";
full_report += " <Pattern>" + analysis.main_pattern + "</Pattern>n";
full_report += " <Patient>" + analysis.patient_info + "</Patient>n";
full_report += " </Case_Info>nn";
// 症状
full_report += " <Symptoms>n";
for (const auto& symptom : analysis.symptoms) {
full_report += " <Symptom>" + symptom + "</Symptom>n";
}
full_report += " </Symptoms>nn";
// 治疗原则
full_report += " <Treatment_Principles>n";
for (const auto& principle : analysis.treatment_principles) {
full_report += " <Principle>" + principle + "</Principle>n";
}
full_report += " </Treatment_Principles>nn";
// 加入药方
full_report += xml;
// 预后
if (!analysis.prognosis_notes.empty()) {
full_report += "n <Prognosis_Notes>n";
for (const auto& note : analysis.prognosis_notes) {
full_report += " <Note>" + note + "</Note>n";
}
full_report += " </Prognosis_Notes>n";
}
full_report += "</Clinical_Case_Report>";
// 写入文件
std::ofstream file(filename);
if (file.is_open()) {
file << full_report;
file.close();
std::cout << "案例报告已保存到: " << filename << std::endl;
}
}
};
} // namespace JXWD- Python实现与XML输出
# mirror_prescription_system.py
import json
from dataclasses import dataclass, field, asdict
from typing import List, Dict, Any, Tuple, Optional
from enum import Enum
import math
import random
from xml.etree import ElementTree as ET
from xml.dom import minidom
import numpy as np
class ElementalLevel(Enum):
"""元级枚举"""
LEVEL_ONE = "一元"
LEVEL_TWO_YIN = "二元(阴)"
LEVEL_TWO_YANG = "二元(阳)"
LEVEL_THREE = "三元"
LEVEL_FOUR = "四元"
LEVEL_FIVE = "五元"
LEVEL_SIX = "六元"
LEVEL_SEVEN = "七元"
LEVEL_EIGHT = "八元"
LEVEL_NINE = "九元"
class PalacePosition(Enum):
"""九宫位置枚举"""
PALACE_1 = 1 # 坎宫
PALACE_2 = 2 # 坤宫
PALACE_3 = 3 # 震宫
PALACE_4 = 4 # 巽宫
PALACE_5 = 5 # 中宫
PALACE_6 = 6 # 乾宫
PALACE_7 = 7 # 兑宫
PALACE_8 = 8 # 艮宫
PALACE_9 = 9 # 离宫
def get_name(self):
names = {
1: "坎宫", 2: "坤宫", 3: "震宫", 4: "巽宫",
5: "中宫", 6: "乾宫", 7: "兑宫", 8: "艮宫",
9: "离宫"
}
return names[self.value]
@dataclass
class QuantumState:
"""量子状态"""
amplitude_real: float = 0.5 # 阳分量
amplitude_imag: float = 0.5 # 阴分量
phase: float = 0.0 # 相位
symbol: str = "" # 量子态符号
@property
def energy(self) -> float:
"""计算能量"""
return self.amplitude_real**2 + self.amplitude_imag**2
@property
def yin_yang_ratio(self) -> float:
"""阴阳比例"""
total = self.amplitude_real + self.amplitude_imag
return self.amplitude_imag / total if total > 0 else 0.5
def __str__(self) -> str:
return f"{self.symbol} [阳={self.amplitude_real:.2f}, 阴={self.amplitude_imag:.2f}]"
@dataclass
class ChineseHerb:
"""中药材"""
name: str
level: ElementalLevel
target_palaces: List[PalacePosition]
quantum_state: QuantumState
core_effects: List[str]
properties: List[str] # 性味归经
dosage_min: float # 最小剂量(g)
dosage_max: float # 最大剂量(g)
dosage_default: float # 默认剂量(g)
mirror_action: str # 镜像作用描述
def __post_init__(self):
# 确保剂量在合理范围内
self.dosage_default = max(self.dosage_min,
min(self.dosage_max, self.dosage_default))
def to_dict(self) -> Dict[str, Any]:
"""转换为字典"""
data = asdict(self)
data['level'] = self.level.value
data['target_palaces'] = [p.value for p in self.target_palaces]
data['quantum_state'] = asdict(self.quantum_state)
return data
class ChineseHerbDatabase:
"""中药材数据库"""
def __init__(self):
self.herbs: Dict[str, ChineseHerb] = {}
self._initialize_database()
def _initialize_database(self):
"""初始化数据库"""
# 一元级药物
self.herbs["人参"] = ChineseHerb(
name="人参",
level=ElementalLevel.LEVEL_ONE,
target_palaces=[PalacePosition.PALACE_5],
quantum_state=QuantumState(
amplitude_real=0.9,
amplitude_imag=0.1,
symbol="Â_人参 = γÎ + δŜ_z"
),
core_effects=["大补元气", "救逆固脱"],
properties=["甘、微苦，温", "归脾、肺、心经"],
dosage_min=3.0,
dosage_max=15.0,
dosage_default=9.0,
mirror_action="补益中宫元气，扶正固本"
)
# 二元(阴)级药物
self.herbs["黄连"] = ChineseHerb(
name="黄连",
level=ElementalLevel.LEVEL_TWO_YIN,
target_palaces=[PalacePosition.PALACE_9],
quantum_state=QuantumState(
amplitude_real=0.1,
amplitude_imag=0.9,
phase=math.pi,
symbol="Â_黄连 = ∂/∂t + mω²x²"
),
core_effects=["清泻心火", "清热燥湿", "泻火解毒"],
properties=["苦，寒", "归心、脾、胃、肝、胆、大肠经"],
dosage_min=1.5,
dosage_max=6.0,
dosage_default=3.0,
mirror_action="清降离宫亢阳，平衡心火"
)
# 三元级药物
self.herbs["生地黄"] = ChineseHerb(
name="生地黄",
level=ElementalLevel.LEVEL_THREE,
target_palaces=[PalacePosition.PALACE_1, PalacePosition.PALACE_5],
quantum_state=QuantumState(
amplitude_real=0.3,
amplitude_imag=0.7,
phase=math.pi/2,
symbol="Â_生地 = Σκ|阴⟩⟨血|"
),
core_effects=["滋阴凉血", "养阴生津"],
properties=["甘，寒", "归心、肝、肾经"],
dosage_min=6.0,
dosage_max=30.0,
dosage_default=12.0,
mirror_action="滋补肾阴，凉血润燥"
)
# 四元级药物
self.herbs["百合"] = ChineseHerb(
name="百合",
level=ElementalLevel.LEVEL_FOUR,
target_palaces=[PalacePosition.PALACE_5, PalacePosition.PALACE_9],
quantum_state=QuantumState(
amplitude_real=0.5,
amplitude_imag=0.5,
phase=math.pi/4,
symbol="Â_百合 = |神⟩⟨躁| ⊗ |肺⟩⟨金|"
),
core_effects=["养阴润肺", "清心安神"],
properties=["甘，微寒", "归心、肺经"],
dosage_min=6.0,
dosage_max=15.0,
dosage_default=10.0,
mirror_action="安中宫以定志，清离宫以除烦"
)
# 六元级药物
self.herbs["白芍"] = ChineseHerb(
name="白芍",
level=ElementalLevel.LEVEL_SIX,
target_palaces=[PalacePosition.PALACE_3, PalacePosition.PALACE_4],
quantum_state=QuantumState(
amplitude_real=0.4,
amplitude_imag=0.6,
phase=math.pi/2,
symbol="Â_白芍 = Î ⊗ Ŝ_y"
),
core_effects=["养血敛阴", "柔肝止痛", "平抑肝阳"],
properties=["苦、酸，微寒", "归肝、脾经"],
dosage_min=6.0,
dosage_max=15.0,
dosage_default=9.0,
mirror_action="养血柔肝，平抑木气"
)
# 其他药物...
self.herbs["知母"] = ChineseHerb(
name="知母",
level=ElementalLevel.LEVEL_FOUR,
target_palaces=[PalacePosition.PALACE_1, PalacePosition.PALACE_9],
quantum_state=QuantumState(
amplitude_real=0.3,
amplitude_imag=0.7,
phase=math.pi/3,
symbol="Â_知母 = |火⟩⟨清| ⊗ |阴⟩⟨生|"
),
core_effects=["清热泻火", "滋阴润燥"],
properties=["苦、甘，寒", "归肺、胃、肾经"],
dosage_min=6.0,
dosage_max=12.0,
dosage_default=9.0,
mirror_action="滋阴降火，金水相生"
)
self.herbs["当归"] = ChineseHerb(
name="当归",
level=ElementalLevel.LEVEL_THREE,
target_palaces=[PalacePosition.PALACE_1, PalacePosition.PALACE_3],
quantum_state=QuantumState(
amplitude_real=0.5,
amplitude_imag=0.5,
phase=math.pi/4,
symbol="Â_当归 = |血⟩⟨生| ⊗ |肝⟩⟨润|"
),
core_effects=["补血活血", "调经止痛", "润肠通便"],
properties=["甘、辛，温", "归肝、心、脾经"],
dosage_min=6.0,
dosage_max=15.0,
dosage_default=9.0,
mirror_action="养血活血，润燥通经"
)
self.herbs["黄芪"] = ChineseHerb(
name="黄芪",
level=ElementalLevel.LEVEL_FOUR,
target_palaces=[PalacePosition.PALACE_2, PalacePosition.PALACE_5],
quantum_state=QuantumState(
amplitude_real=0.8,
amplitude_imag=0.2,
symbol="Â_黄芪 = ∇² + V(x)"
),
core_effects=["补气升阳", "固表止汗", "利水消肿"],
properties=["甘，微温", "归肺、脾经"],
dosage_min=9.0,
dosage_max=30.0,
dosage_default=15.0,
mirror_action="补益中气，升阳举陷"
)
def get_herb(self, name: str) -> Optional[ChineseHerb]:
"""获取药材"""
return self.herbs.get(name)
def get_herbs_by_palace(self, palace: PalacePosition) -> List[ChineseHerb]:
"""获取靶向特定宫位的药材"""
return [herb for herb in self.herbs.values()
if palace in herb.target_palaces]
def get_herbs_by_level(self, level: ElementalLevel) -> List[ChineseHerb]:
"""获取特定元级的药材"""
return [herb for herb in self.herbs.values()
if herb.level == level]
@dataclass
class PalaceEnergy:
"""九宫能量"""
position: PalacePosition
current_energy: float = 7.0
target_energy: float = 7.0
yin_component: float = field(init=False)
yang_component: float = field(init=False)
pattern: str = ""
symptoms: List[str] = field(default_factory=list)
def __post_init__(self):
# 默认阴阳各半
self.yin_component = self.current_energy * 0.5
self.yang_component = self.current_energy * 0.5
@property
def deviation(self) -> float:
"""与目标能量的偏差"""
return self.current_energy - self.target_energy
@property
def trend(self) -> str:
"""能量趋势"""
if self.deviation > 0.5:
return "↑↑"
elif self.deviation > 0.1:
return "↑"
elif self.deviation < -0.5:
return "↓↓"
elif self.deviation < -0.1:
return "↓"
else:
return "→"
@property
def name(self) -> str:
"""宫位名称"""
return self.position.get_name()
def set_yin_yang_ratio(self, yin_ratio: float = 0.5):
"""设置阴阳比例"""
self.yin_component = self.current_energy * yin_ratio
self.yang_component = self.current_energy * (1 - yin_ratio)
class NinePalaceEnergyMatrix:
"""九宫能量矩阵"""
def __init__(self):
self.palaces: Dict[PalacePosition, PalaceEnergy] = {}
self.golden_ratio = (1 + math.sqrt(5)) / 2
self._initialize_palaces()
def _initialize_palaces(self):
"""初始化九宫"""
for pos in PalacePosition:
self.palaces[pos] = PalaceEnergy(position=pos)
def set_palace_energy(self, position: PalacePosition,
energy: float, yin_ratio: float = 0.5):
"""设置宫位能量"""
palace = self.palaces.get(position)
if palace:
palace.current_energy = energy
palace.set_yin_yang_ratio(yin_ratio)
def set_palace_pattern(self, position: PalacePosition,
pattern: str, symptoms: List[str] = None):
"""设置宫位病机模式"""
palace = self.palaces.get(position)
if palace:
palace.pattern = pattern
if symptoms:
palace.symptoms = symptoms
def get_palace(self, position: PalacePosition) -> Optional[PalaceEnergy]:
"""获取宫位能量"""
return self.palaces.get(position)
def get_imbalanced_palaces(self, threshold: float = 0.3) -> List[PalacePosition]:
"""获取不平衡的宫位"""
return [pos for pos, palace in self.palaces.items()
if abs(palace.deviation) > threshold]
def calculate_total_imbalance(self) -> float:
"""计算总不平衡度"""
return sum(abs(palace.deviation) for palace in self.palaces.values())
def optimize_to_balance(self, max_iterations: int = 100):
"""优化至平衡状态"""
for _ in range(max_iterations):
total_energy = sum(p.current_energy for p in self.palaces.values())
avg_energy = total_energy / len(self.palaces)
for palace in self.palaces.values():
# 使用黄金比例调整
adjustment = (avg_energy - palace.current_energy) * self.golden_ratio * 0.1
palace.current_energy += adjustment
# 保持阴阳比例
yin_ratio = palace.yin_component / (palace.yin_component + palace.yang_component + 1e-10)
palace.set_yin_yang_ratio(yin_ratio)
# 检查收敛
if self.calculate_total_imbalance() < 0.01:
break
@dataclass
class HerbSelection:
"""药材选择"""
herb_name: str
dosage: float # 剂量(g)
role: str = "" # 君、臣、佐、使
primary_target: PalacePosition = PalacePosition.PALACE_5 # 主要靶向宫位
mirror_logic: str = "" # 镜像逻辑描述
priority_score: float = 0.0 # 优先级分数
class MirrorMappingRules:
"""镜像映射规则"""
def __init__(self):
self.herb_db = ChineseHerbDatabase()
self.golden_ratio = (1 + math.sqrt(5)) / 2
def select_herbs_for_palace(self, palace: PalacePosition,
palace_energy: PalaceEnergy,
pattern: str) -> List[HerbSelection]:
"""为宫位选择药材"""
selections = []
# 获取靶向该宫位的药材
candidate_herbs = self.herb_db.get_herbs_by_palace(palace)
for herb in candidate_herbs:
if self._is_herb_suitable(herb, pattern, palace_energy):
selection = HerbSelection(
herb_name=herb.name,
primary_target=palace,
mirror_logic=self._generate_mirror_logic(herb, palace_energy),
priority_score=self._calculate_priority_score(herb, palace_energy, pattern)
)
# 计算剂量
selection.dosage = self._calculate_optimal_dosage(herb, palace_energy.deviation)
selections.append(selection)
# 按优先级排序
selections.sort(key=lambda x: x.priority_score, reverse=True)
return selections
def create_prescription(self, energy_matrix: NinePalaceEnergyMatrix,
overall_pattern: str) -> List[HerbSelection]:
"""创建处方"""
prescription = []
herb_set = set() # 避免重复
# 获取不平衡宫位
imbalanced_palaces = energy_matrix.get_imbalanced_palaces()
for palace in imbalanced_palaces:
palace_energy = energy_matrix.get_palace(palace)
if not palace_energy:
continue
selections = self.select_herbs_for_palace(
palace, palace_energy, palace_energy.pattern)
# 选择优先级最高的1-2个
count = 0
for selection in selections:
if count >= 2:
break
if selection.herb_name not in herb_set:
herb_set.add(selection.herb_name)
prescription.append(selection)
count += 1
# 分配角色
self._assign_roles(prescription)
# 优化剂量比例
self._optimize_dosage_ratios(prescription)
return prescription
def _is_herb_suitable(self, herb: ChineseHerb, pattern: str,
palace_energy: PalaceEnergy) -> bool:
"""判断药材是否适合病机"""
deviation = palace_energy.deviation
# 阳亢用阴药，阴虚用阳药（配合滋阴）
if deviation > 0: # 能量过高 - 阳亢
return (herb.level == ElementalLevel.LEVEL_TWO_YIN or
herb.level == ElementalLevel.LEVEL_THREE or
(herb.level == ElementalLevel.LEVEL_FOUR and
any("清热" in effect or "泻火" in effect
for effect in herb.core_effects)))
else: # 能量过低 - 阴虚或阳虚
return (herb.level == ElementalLevel.LEVEL_TWO_YANG or
herb.level == ElementalLevel.LEVEL_THREE or
(herb.level == ElementalLevel.LEVEL_ONE and
any("补" in effect for effect in herb.core_effects)))
def _generate_mirror_logic(self, herb: ChineseHerb,
palace_energy: PalaceEnergy) -> str:
"""生成镜像逻辑描述"""
deviation = palace_energy.deviation
if deviation > 0:
logic = f"清降{palace_energy.name}过亢之阳"
else:
component = "阴" if palace_energy.yin_component < palace_energy.yang_component else "阳"
logic = f"温补{palace_energy.name}不足之{component}"
return f"{logic}，{herb.mirror_action}"
def _calculate_priority_score(self, herb: ChineseHerb,
palace_energy: PalaceEnergy,
pattern: str) -> float:
"""计算优先级分数"""
score = 0.0
deviation = abs(palace_energy.deviation)
# 1. 偏差越大，优先级越高
score += deviation * 2.0
# 2. 药物与宫位的匹配度
if palace_energy.position in herb.target_palaces:
score += 1.0
# 3. 药物与病机模式的匹配度
for effect in herb.core_effects:
if "火" in pattern and ("清热" in effect or "泻火" in effect):
score += 0.5
if "虚" in pattern and "补" in effect:
score += 0.5
return score
def _calculate_optimal_dosage(self, herb: ChineseHerb,
deviation: float) -> float:
"""计算最优剂量"""
base_dosage = herb.dosage_default
# 根据偏差调整剂量
if abs(deviation) > 1.0:
adjusted = base_dosage * (1.0 + abs(deviation) * 0.2)
elif abs(deviation) > 0.5:
adjusted = base_dosage * (1.0 + abs(deviation) * 0.1)
else:
adjusted = base_dosage
# 确保在安全范围内
return max(herb.dosage_min, min(herb.dosage_max, adjusted))
def _assign_roles(self, herbs: List[HerbSelection]):
"""分配君臣佐使角色"""
if not herbs:
return
# 君药：优先级最高的药物
if herbs:
herbs[0].role = "君药"
# 臣药：次高优先级的1-2个药物
minister_count = min(2, len(herbs) - 1)
for i in range(1, minister_count + 1):
herbs[i].role = "臣药"
# 佐药和使药
for i in range(minister_count + 1, len(herbs)):
herbs[i].role = "佐药"
# 检查是否有调和或引经作用
herb = self.herb_db.get_herb(herbs[i].herb_name)
if herb:
for effect in herb.core_effects:
if "调和" in effect or "引经" in effect:
herbs[i].role = "使药"
break
def _optimize_dosage_ratios(self, herbs: List[HerbSelection]):
"""优化剂量比例（黄金比例）"""
if len(herbs) < 2:
return
# 获取君药剂量
monarch_dosage = next((h.dosage for h in herbs if h.role == "君药"), herbs[0].dosage)
# 调整臣药剂量至黄金比例
for herb in herbs:
if herb.role == "臣药":
target_dosage = monarch_dosage / self.golden_ratio
herb_db_info = self.herb_db.get_herb(herb.herb_name)
if herb_db_info:
herb.dosage = max(herb_db_info.dosage_min,
min(herb_db_info.dosage_max, target_dosage))
# 佐使药剂量一般为君药的1/3到1/2
for herb in herbs:
if herb.role in ["佐药", "使药"]:
target_dosage = monarch_dosage * 0.4
herb_db_info = self.herb_db.get_herb(herb.herb_name)
if herb_db_info:
herb.dosage = max(herb_db_info.dosage_min,
min(herb_db_info.dosage_max, target_dosage))
class PrescriptionAnnotationSystem:
"""药方标注系统"""
def __init__(self):
self.herb_db = ChineseHerbDatabase()
self.mapping_rules = MirrorMappingRules()
@dataclass
class AnnotatedPrescription:
"""标注后的处方"""
case_id: str
pattern: str
herbs: List[HerbSelection]
energy_goals: NinePalaceEnergyMatrix
quantum_algorithm: str = ""
def calculate_effectiveness_score(self) -> float:
"""计算效果评分"""
score = 0.0
# 1. 检查君臣佐使结构
has_monarch = any(h.role == "君药" for h in self.herbs)
has_minister = any(h.role == "臣药" for h in self.herbs)
if has_monarch and has_minister:
score += 0.3
# 2. 检查黄金比例
monarch_dosage = next((h.dosage for h in self.herbs if h.role == "君药"), None)
if monarch_dosage and len(self.herbs) >= 2:
minister_herbs = [h for h in self.herbs if h.role == "臣药"]
for minister in minister_herbs[:2]: # 前两个臣药
ratio = monarch_dosage / minister.dosage
deviation = abs(ratio - 1.618) / 1.618
score += 0.2 * (1.0 - deviation)
# 3. 检查阴阳平衡
yin_total = 0.0
yang_total = 0.0
herb_db = ChineseHerbDatabase()
for herb in self.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
energy = herb_info.quantum_state.energy
yin_total += herb_info.quantum_state.amplitude_imag * energy
yang_total += herb_info.quantum_state.amplitude_real * energy
if yin_total + yang_total > 0:
balance_ratio = yang_total / (yin_total + 1e-10)
balance_score = 1.0 - abs(balance_ratio - 1.0)
score += 0.3 * balance_score
# 4. 检查剂量合理性
dosage_score = 0.0
for herb in self.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
if (herb.dosage >= herb_info.dosage_min * 0.8 and
herb.dosage <= herb_info.dosage_max * 1.2):
dosage_score += 1.0
if self.herbs:
score += 0.2 * dosage_score / len(self.herbs)
return min(1.0, score)
def to_xml(self) -> str:
"""生成XML格式输出"""
root = ET.Element("Mirror_Prescription")
root.set("case_id", self.case_id)
root.set("pattern", self.pattern)
# 九宫能量目标
energy_goal = ET.SubElement(root, "Nine_Palace_Energy_Goal")
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace = self.energy_goals.get_palace(palace_pos)
if palace:
palace_elem = ET.SubElement(energy_goal, "Palace")
palace_elem.set("index", str(i))
palace_elem.set("name", palace.name)
palace_elem.set("target_energy", f"{palace.target_energy:.1f}φ")
palace_elem.set("trend", palace.trend)
# 药材映射
herb_mapping = ET.SubElement(root, "Herb_Mirror_Mapping")
herb_db = ChineseHerbDatabase()
for herb in self.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if not herb_info:
continue
herb_elem = ET.SubElement(herb_mapping, "Herb")
herb_elem.set("name", herb.herb_name)
herb_elem.set("dose", f"{herb.dosage:.1f}g")
herb_elem.set("unit", "克")
# 元级
elem_level = ET.SubElement(herb_elem, "Elemental_Level")
elem_level.text = herb_info.level.value
# 靶向宫位
target_palace = ET.SubElement(herb_elem, "Target_Palace")
target_palace.text = ", ".join(str(p.value)
for p in herb_info.target_palaces)
# 镜像作用
mirror_action = ET.SubElement(herb_elem, "Mirror_Action")
mirror_action.text = herb.mirror_logic
# 量子操作
quantum_action = ET.SubElement(herb_elem, "Quantum_Action")
quantum_action.text = herb_info.quantum_state.symbol
# 角色
role = ET.SubElement(herb_elem, "Role")
role.text = herb.role
# 量子算法
quantum_algo = ET.SubElement(root, "Formula_Quantum_Algorithm")
# 算法表达式
algo_expr = ET.SubElement(quantum_algo, "Algorithm_Expression")
expr_text = "|方剂⟩ = "
for i, herb in enumerate(self.herbs):
if i > 0:
expr_text += " * "
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
expr_text += f"exp[β_{i+1} * {herb_info.quantum_state.symbol}]"
expr_text += f" * |{self.pattern}⟩"
algo_expr.text = expr_text
# 递归参数
recursion_params = ET.SubElement(quantum_algo, "Recursion_Params")
param1 = ET.SubElement(recursion_params, "Param")
param1.set("name", "最大迭代深度")
param1.text = "9⁹"
param2 = ET.SubElement(recursion_params, "Param")
param2.set("name", "镜像衰减因子")
param2.text = "γ = e^{-π/9}"
# 格式化输出
xml_str = ET.tostring(root, encoding='unicode')
# 使用minidom美化输出
dom = minidom.parseString(xml_str)
return dom.toprettyxml(indent=" ")
def create_prescription(self, case_id: str, pattern: str,
initial_energy: NinePalaceEnergyMatrix) -> 'AnnotatedPrescription':
"""创建处方"""
prescription = self.AnnotatedPrescription(
case_id=case_id,
pattern=pattern,
herbs=self.mapping_rules.create_prescription(initial_energy, pattern),
energy_goals=initial_energy
)
# 生成量子算法描述
prescription.quantum_algorithm = self._generate_quantum_algorithm(prescription.herbs)
return prescription
def optimize_prescription(self, prescription: AnnotatedPrescription,
max_iterations: int = 10):
"""优化处方"""
best_score = prescription.calculate_effectiveness_score()
best_herbs = prescription.herbs.copy()
for _ in range(max_iterations):
# 微调剂量
for herb in prescription.herbs:
adjustment = random.uniform(-0.05, 0.05) # ±5%
herb.dosage *= (1.0 + adjustment)
# 确保在安全范围内
herb_info = self.herb_db.get_herb(herb.herb_name)
if herb_info:
herb.dosage = max(herb_info.dosage_min,
min(herb_info.dosage_max, herb.dosage))
# 重新分配角色和优化剂量
self.mapping_rules._assign_roles(prescription.herbs)
self.mapping_rules._optimize_dosage_ratios(prescription.herbs)
# 计算新分数
new_score = prescription.calculate_effectiveness_score()
if new_score > best_score:
best_score = new_score
best_herbs = prescription.herbs.copy()
prescription.herbs = best_herbs
def _generate_quantum_algorithm(self, herbs: List[HerbSelection]) -> str:
"""生成量子算法描述"""
algorithm = "|方剂⟩ = "
for i, herb in enumerate(herbs):
if i > 0:
algorithm += " ⊗ "
herb_info = self.herb_db.get_herb(herb.herb_name)
if herb_info:
algorithm += f"exp(iθ_{i+1} * {herb_info.quantum_state.symbol})"
algorithm += " |病机⟩n其中：θ_i ∈ [0, 2π] 为相位参数，满足 Σθ_i = 2π"
return algorithm
class ClinicalCaseApplication:
"""临床应用"""
def __init__(self):
self.annotation_system = PrescriptionAnnotationSystem()
def analyze_lily_disease_case(self):
"""分析百合病案例"""
# 创建能量矩阵
energy_matrix = NinePalaceEnergyMatrix()
# 设置能量状态
# 离宫能量过高（心火旺），坎宫能量过低（肾阴虚）
energy_matrix.set_palace_energy(PalacePosition.PALACE_9, 9.0, 0.3) # 离宫阳亢
energy_matrix.set_palace_energy(PalacePosition.PALACE_1, 5.5, 0.8) # 坎宫阴虚
energy_matrix.set_palace_energy(PalacePosition.PALACE_3, 7.5, 0.4) # 震宫偏亢
energy_matrix.set_palace_energy(PalacePosition.PALACE_5, 6.8, 0.5) # 中宫不安
# 设置病机模式
energy_matrix.set_palace_pattern(PalacePosition.PALACE_9,
"心火亢盛",
["心烦", "失眠", "小便赤"])
energy_matrix.set_palace_pattern(PalacePosition.PALACE_1,
"肾阴不足",
["口干", "舌红少苔"])
energy_matrix.set_palace_pattern(PalacePosition.PALACE_3,
"肝郁化火",
["情志不遂", "默默欲卧"])
energy_matrix.set_palace_pattern(PalacePosition.PALACE_5,
"心神不安",
["不宁", "多梦"])
# 设置目标能量
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace = energy_matrix.get_palace(palace_pos)
if palace:
if palace_pos == PalacePosition.PALACE_9:
palace.target_energy = 7.0 # 降心火
elif palace_pos == PalacePosition.PALACE_1:
palace.target_energy = 6.8 # 滋肾阴
elif palace_pos == PalacePosition.PALACE_3:
palace.target_energy = 6.5 # 平肝
else:
palace.target_energy = 7.0 # 平衡
# 创建处方
prescription = self.annotation_system.create_prescription(
case_id="百合病-陈克正-1969",
pattern="阴血不足，心肺火旺",
initial_energy=energy_matrix
)
# 优化处方
self.annotation_system.optimize_prescription(prescription, max_iterations=5)
return prescription
def analyze_qi_deficiency_case(self):
"""分析气虚证案例"""
energy_matrix = NinePalaceEnergyMatrix()
# 设置能量状态
energy_matrix.set_palace_energy(PalacePosition.PALACE_5, 5.0, 0.4) # 中宫气虚
energy_matrix.set_palace_energy(PalacePosition.PALACE_2, 5.2, 0.4) # 坤宫脾虚
energy_matrix.set_palace_energy(PalacePosition.PALACE_7, 5.5, 0.4) # 兑宫肺虚
# 设置病机模式
energy_matrix.set_palace_pattern(PalacePosition.PALACE_5,
"中气不足",
["神疲", "乏力"])
energy_matrix.set_palace_pattern(PalacePosition.PALACE_2,
"脾失健运",
["食欲不振", "便溏"])
energy_matrix.set_palace_pattern(PalacePosition.PALACE_7,
"肺卫不固",
["自汗", "易感冒"])
# 设置目标能量（补气）
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace = energy_matrix.get_palace(palace_pos)
if palace:
if palace_pos in [PalacePosition.PALACE_5,
PalacePosition.PALACE_2,
PalacePosition.PALACE_7]:
palace.target_energy = 7.5 # 补气
else:
palace.target_energy = 7.0 # 平衡
# 创建处方
prescription = self.annotation_system.create_prescription(
case_id="气虚证-张某某-2024",
pattern="肺脾气虚，卫表不固",
initial_energy=energy_matrix
)
return prescription
def print_prescription_report(self, prescription: PrescriptionAnnotationSystem.AnnotatedPrescription):
"""打印处方报告"""
print(f"n{'='*60}")
print(f"案例ID: {prescription.case_id}")
print(f"证型: {prescription.pattern}")
print(f"处方效果评分: {prescription.calculate_effectiveness_score()*100:.1f}%")
print(f"{'='*60}")
print("n九宫能量分析:")
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace = prescription.energy_goals.get_palace(palace_pos)
if palace and abs(palace.deviation) > 0.1:
print(f" {palace.name}: {palace.current_energy:.1f} → {palace.target_energy:.1f} "
f"{palace.trend} [{palace.pattern}]")
print("n处方组成 (君臣佐使):")
for herb in prescription.herbs:
herb_info = ChineseHerbDatabase().get_herb(herb.herb_name)
if herb_info:
properties = "、".join(herb_info.properties[:2])
effects = "、".join(herb_info.core_effects[:2])
print(f" {herb.role}: {herb.herb_name} {herb.dosage:.1f}g")
print(f" 性味归经: {properties}")
print(f" 功效: {effects}")
print(f" 镜像作用: {herb.mirror_logic}")
if herb_info.quantum_state.symbol:
print(f" 量子态: {herb_info.quantum_state.symbol}")
print()
print(f"n量子算法:")
print(f" {prescription.quantum_algorithm}")
print(f"n黄金比例验证:")
monarch = next((h for h in prescription.herbs if h.role == "君药"), None)
ministers = [h for h in prescription.herbs if h.role == "臣药"]
if monarch and ministers:
for i, minister in enumerate(ministers[:2], 1):
ratio = monarch.dosage / minister.dosage
deviation = abs(ratio - 1.618) / 1.618 * 100
print(f" 君药({monarch.herb_name}):臣药({minister.herb_name}) = "
f"{monarch.dosage:.1f}:{minister.dosage:.1f} = "
f"{ratio:.3f} (偏差: {deviation:.1f}%)")
print(f"nXML输出已生成，可保存为文件")
def export_to_file(self, prescription: PrescriptionAnnotationSystem.AnnotatedPrescription,
filename: str = "prescription.xml"):
"""导出到文件"""
xml_content = prescription.to_xml()
with open(filename, 'w', encoding='utf-8') as f:
f.write(xml_content)
print(f"n处方已保存到: {filename}")
# 同时保存JSON格式
json_filename = filename.replace('.xml', '.json')
self._export_to_json(prescription, json_filename)
def _export_to_json(self, prescription: PrescriptionAnnotationSystem.AnnotatedPrescription,
filename: str):
"""导出为JSON格式"""
herb_db = ChineseHerbDatabase()
data = {
"case_id": prescription.case_id,
"pattern": prescription.pattern,
"effectiveness_score": prescription.calculate_effectiveness_score(),
"energy_analysis": {},
"prescription": []
}
# 能量分析
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace = prescription.energy_goals.get_palace(palace_pos)
if palace:
data["energy_analysis"][palace.name] = {
"current_energy": palace.current_energy,
"target_energy": palace.target_energy,
"deviation": palace.deviation,
"trend": palace.trend,
"pattern": palace.pattern,
"symptoms": palace.symptoms
}
# 处方详情
for herb in prescription.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
herb_data = {
"name": herb.herb_name,
"dosage": herb.dosage,
"role": herb.role,
"primary_target": herb.primary_target.get_name(),
"mirror_logic": herb.mirror_logic,
"elemental_level": herb_info.level.value,
"properties": herb_info.properties,
"core_effects": herb_info.core_effects,
"quantum_state": {
"symbol": herb_info.quantum_state.symbol,
"yin_yang_ratio": herb_info.quantum_state.yin_yang_ratio,
"energy": herb_info.quantum_state.energy
}
}
data["prescription"].append(herb_data)
with open(filename, 'w', encoding='utf-8') as f:
json.dump(data, f, ensure_ascii=False, indent=2)
print(f"JSON格式已保存到: {filename}")
# ==================== 主程序 ====================
if __name__ == "__main__":
print("镜心悟道系统：一元到九九归一的镜像映射药方标注系统")
print("=" * 60)
# 创建临床应用实例
clinical_app = ClinicalCaseApplication()
print("n1. 百合病案例分析 (陈克正医案)")
print("-" * 40)
# 分析百合病案例
lily_prescription = clinical_app.analyze_lily_disease_case()
# 打印报告
clinical_app.print_prescription_report(lily_prescription)
# 导出到文件
clinical_app.export_to_file(lily_prescription, "lily_disease_prescription.xml")
print("n" + "=" * 60)
print("n2. 气虚证案例分析")
print("-" * 40)
# 分析气虚证案例
qi_deficiency_prescription = clinical_app.analyze_qi_deficiency_case()
# 打印报告
clinical_app.print_prescription_report(qi_deficiency_prescription)
# 导出到文件
clinical_app.export_to_file(qi_deficiency_prescription, "qi_deficiency_prescription.xml")
print("n" + "=" * 60)
print("n3. 自定义案例分析")
print("-" * 40)
# 用户自定义案例
def create_custom_case():
"""创建自定义案例"""
energy_matrix = NinePalaceEnergyMatrix()
print("n请输入九宫能量状态 (1-9，默认7.0):")
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace_name = palace_pos.get_name()
try:
energy = float(input(f" {palace_name} ({i}) 能量值: ") or "7.0")
yin_ratio = float(input(f" {palace_name} 阴比例 (0-1，默认0.5): ") or "0.5")
energy_matrix.set_palace_energy(palace_pos, energy, yin_ratio)
pattern = input(f" {palace_name} 病机模式 (可选): ")
if pattern:
symptoms_input = input(f" {palace_name} 症状 (逗号分隔，可选): ")
symptoms = [s.strip() for s in symptoms_input.split(",")] if symptoms_input else []
energy_matrix.set_palace_pattern(palace_pos, pattern, symptoms)
except ValueError:
print("输入无效，使用默认值")
energy_matrix.set_palace_energy(palace_pos, 7.0, 0.5)
case_id = input("n案例ID: ") or "自定义案例-001"
pattern = input("主要证型: ") or "未指定证型"
# 设置目标能量
print("n设置目标能量 (默认与当前相同):")
for i in range(1, 10):
palace_pos = PalacePosition(i)
palace = energy_matrix.get_palace(palace_pos)
if palace:
try:
target = float(input(f" {palace.name} 目标能量: ") or str(palace.current_energy))
palace.target_energy = target
except ValueError:
pass
# 创建处方
prescription_system = PrescriptionAnnotationSystem()
prescription = prescription_system.create_prescription(case_id, pattern, energy_matrix)
# 优化处方
prescription_system.optimize_prescription(prescription)
return prescription
# 运行自定义案例
run_custom = input("n是否创建自定义案例? (y/n): ")
if run_custom.lower() == 'y':
custom_prescription = create_custom_case()
clinical_app.print_prescription_report(custom_prescription)
export_choice = input("n是否导出到文件? (y/n): ")
if export_choice.lower() == 'y':
filename = input("文件名 (默认: custom_prescription.xml): ") or "custom_prescription.xml"
clinical_app.export_to_file(custom_prescription, filename)
print("n" + "=" * 60)
print("镜心悟道系统分析完成！")
print("=" * 60)
# 系统统计信息
print("n系统统计信息:")
print("-" * 40)
herb_db = ChineseHerbDatabase()
print(f"中药材数据库: {len(herb_db.herbs)} 味药材")
# 统计各元级药物数量
level_counts = {}
for herb in herb_db.herbs.values():
level_name = herb.level.value
level_counts[level_name] = level_counts.get(level_name, 0) + 1
print("各元级药物分布:")
for level, count in sorted(level_counts.items()):
print(f" {level}: {count}味")
print("n感谢使用镜心悟道系统！")- 高级功能扩展
# advanced_features.py
import numpy as np
from typing import List, Dict, Any, Tuple
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle, Circle
import seaborn as sns
class PrescriptionVisualization:
"""处方可视化"""
def __init__(self):
plt.style.use('seaborn-darkgrid')
self.colors = {
'君药': '#FF6B6B',
'臣药': '#4ECDC4',
'佐药': '#45B7D1',
'使药': '#96CEB4',
'坎宫': '#3498DB',
'离宫': '#E74C3C',
'中宫': '#F39C12',
'坤宫': '#2ECC71',
'震宫': '#9B59B6'
}
def plot_nine_palace_energy(self, energy_matrix: 'NinePalaceEnergyMatrix',
title: str = "九宫能量分析"):
"""绘制九宫能量图"""
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
# 1. 能量热图
ax1 = axes[0]
# 创建3x3网格
energy_grid = np.zeros((3, 3))
deviation_grid = np.zeros((3, 3))
palace_names = []
for i in range(3):
for j in range(3):
palace_num = i * 3 + j + 1
palace_pos = PalacePosition(palace_num)
palace = energy_matrix.get_palace(palace_pos)
if palace:
energy_grid[i, j] = palace.current_energy
deviation_grid[i, j] = palace.deviation
palace_names.append(palace.name)
# 绘制热图
im = ax1.imshow(energy_grid, cmap='YlOrRd', vmin=5, vmax=9)
# 添加文本
for i in range(3):
for j in range(3):
palace_num = i * 3 + j + 1
palace_pos = PalacePosition(palace_num)
palace = energy_matrix.get_palace(palace_pos)
if palace:
text = f"{palace.name}n{palace.current_energy:.1f}"
color = 'white' if palace.current_energy > 7 else 'black'
ax1.text(j, i, text, ha='center', va='center',
color=color, fontsize=10, fontweight='bold')
ax1.set_title('九宫当前能量分布', fontsize=14)
ax1.set_xticks([])
ax1.set_yticks([])
plt.colorbar(im, ax=ax1, fraction=0.046, pad=0.04)
# 2. 偏差条形图
ax2 = axes[1]
palaces = []
deviations = []
colors = []
for palace_pos in PalacePosition:
palace = energy_matrix.get_palace(palace_pos)
if palace and abs(palace.deviation) > 0.1:
palaces.append(palace.name)
deviations.append(palace.deviation)
# 根据偏差方向选择颜色
if palace.deviation > 0:
colors.append(self.colors.get('离宫', '#E74C3C'))
else:
colors.append(self.colors.get('坎宫', '#3498DB'))
if palaces:
bars = ax2.barh(palaces, deviations, color=colors, alpha=0.7)
ax2.set_xlabel('能量偏差', fontsize=12)
ax2.set_title('宫位能量偏差分析', fontsize=14)
ax2.grid(True, alpha=0.3, axis='x')
# 添加数值标签
for bar, deviation in zip(bars, deviations):
width = bar.get_width()
label_x = width + (0.1 if width >= 0 else -0.1)
ax2.text(label_x, bar.get_y() + bar.get_height()/2,
f'{deviation:+.2f}',
va='center', ha='left' if width >= 0 else 'right')
else:
ax2.text(0.5, 0.5, '所有宫位能量平衡',
ha='center', va='center', fontsize=12)
ax2.set_xticks([])
ax2.set_yticks([])
plt.suptitle(title, fontsize=16)
plt.tight_layout()
return fig
def plot_prescription_network(self, prescription: 'PrescriptionAnnotationSystem.AnnotatedPrescription',
title: str = "处方网络分析"):
"""绘制处方网络图"""
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
herb_db = ChineseHerbDatabase()
# 1. 君臣佐使关系图
ax1 = axes[0]
roles = {'君药': [], '臣药': [], '佐药': [], '使药': []}
for herb in prescription.herbs:
if herb.role in roles:
roles[herb.role].append(herb.herb_name)
# 绘制同心圆
angles = np.linspace(0, 2 * np.pi, len(prescription.herbs), endpoint=False)
radius = 1.0
for i, herb in enumerate(prescription.herbs):
herb_info = herb_db.get_herb(herb.herb_name)
if not herb_info:
continue
# 根据角色确定半径和颜色
if herb.role == '君药':
node_radius = 0.15
color = self.colors['君药']
zorder = 4
elif herb.role == '臣药':
node_radius = 0.12
color = self.colors['臣药']
zorder = 3
elif herb.role == '佐药':
node_radius = 0.10
color = self.colors['佐药']
zorder = 2
else:
node_radius = 0.08
color = self.colors['使药']
zorder = 1
# 计算位置
x = radius * np.cos(angles[i])
y = radius * np.sin(angles[i])
# 绘制节点
circle = Circle((x, y), node_radius, color=color,
alpha=0.8, zorder=zorder)
ax1.add_patch(circle)
# 添加标签
ax1.text(x, y, herb.herb_name[:2],
ha='center', va='center', fontsize=9,
color='white', fontweight='bold')
# 添加剂量
ax1.text(x, y - node_radius*1.5, f'{herb.dosage}g',
ha='center', va='top', fontsize=8)
# 绘制连接线（君臣关系）
monarch = next((h for h in prescription.herbs if h.role == '君药'), None)
if monarch:
monarch_idx = prescription.herbs.index(monarch)
monarch_x = radius * np.cos(angles[monarch_idx])
monarch_y = radius * np.sin(angles[monarch_idx])
for i, herb in enumerate(prescription.herbs):
if herb.role == '臣药':
x = radius * np.cos(angles[i])
y = radius * np.sin(angles[i])
ax1.plot([monarch_x, x], [monarch_y, y],
color='gray', alpha=0.5, linewidth=1)
ax1.set_xlim(-1.5, 1.5)
ax1.set_ylim(-1.5, 1.5)
ax1.set_aspect('equal')
ax1.set_title('君臣佐使网络', fontsize=14)
ax1.axis('off')
# 2. 靶向宫位分布
ax2 = axes[1]
palace_counts = {}
for herb in prescription.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
for palace in herb_info.target_palaces:
palace_name = palace.get_name()
palace_counts[palace_name] = palace_counts.get(palace_name, 0) + 1
if palace_counts:
palaces = list(palace_counts.keys())
counts = list(palace_counts.values())
# 使用对应颜色
colors = [self.colors.get(p, '#95A5A6') for p in palaces]
bars = ax2.bar(palaces, counts, color=colors, alpha=0.7)
ax2.set_xlabel('靶向宫位', fontsize=12)
ax2.set_ylabel('药物数量', fontsize=12)
ax2.set_title('药物靶向宫位分布', fontsize=14)
ax2.tick_params(axis='x', rotation=45)
ax2.grid(True, alpha=0.3, axis='y')
# 添加数值标签
for bar, count in zip(bars, counts):
height = bar.get_height()
ax2.text(bar.get_x() + bar.get_width()/2, height + 0.1,
str(count), ha='center', va='bottom', fontsize=10)
plt.suptitle(title, fontsize=16)
plt.tight_layout()
return fig
def plot_yin_yang_balance(self, prescription: 'PrescriptionAnnotationSystem.AnnotatedPrescription',
title: str = "阴阳平衡分析"):
"""绘制阴阳平衡图"""
fig, axes = plt.subplots(1, 2, figsize=(15, 6))
herb_db = ChineseHerbDatabase()
# 1. 整体阴阳比例
ax1 = axes[0]
total_yin = 0.0
total_yang = 0.0
for herb in prescription.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
quantum_state = herb_info.quantum_state
# 考虑剂量权重
weight = herb.dosage / sum(h.dosage for h in prescription.herbs)
total_yin += quantum_state.amplitude_imag * weight
total_yang += quantum_state.amplitude_real * weight
# 绘制阴阳鱼
theta = np.linspace(0, 2 * np.pi, 100)
# 阳鱼
yang_radius = total_yang
yang_x = yang_radius * np.cos(theta)
yang_y = yang_radius * np.sin(theta)
# 阴鱼
yin_radius = total_yin
yin_x = yin_radius * np.cos(theta + np.pi)
yin_y = yin_radius * np.sin(theta + np.pi)
ax1.fill(yang_x, yang_y, alpha=0.7, color=self.colors['离宫'], label='阳')
ax1.fill(yin_x, yin_y, alpha=0.7, color=self.colors['坎宫'], label='阴')
# 添加阴阳点
ax1.plot(0, 0, 'o', color='black', markersize=8)
ax1.plot(yang_radius * 0.5, 0, 'o', color='white', markersize=6)
ax1.plot(-yin_radius * 0.5, 0, 'o', color='black', markersize=6)
ax1.set_xlim(-1.2, 1.2)
ax1.set_ylim(-1.2, 1.2)
ax1.set_aspect('equal')
ax1.set_title('处方整体阴阳平衡', fontsize=14)
ax1.legend()
ax1.grid(True, alpha=0.3)
# 添加比例文本
ratio = total_yang / (total_yin + 1e-10)
balance_text = f"阳:阴 = {ratio:.3f}:1"
if 0.9 < ratio < 1.1:
balance_status = "平衡"
status_color = 'green'
elif ratio > 1.1:
balance_status = "偏阳"
status_color = 'red'
else:
balance_status = "偏阴"
status_color = 'blue'
ax1.text(0, -1.3, f"{balance_text}n状态: {balance_status}",
ha='center', va='center', fontsize=12, color=status_color)
# 2. 各药物阴阳贡献
ax2 = axes[1]
herbs = []
yin_contributions = []
yang_contributions = []
for herb in prescription.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
herbs.append(herb.herb_name[:4]) # 截断长名
yin_contributions.append(herb_info.quantum_state.amplitude_imag * herb.dosage)
yang_contributions.append(herb_info.quantum_state.amplitude_real * herb.dosage)
x = np.arange(len(herbs))
width = 0.35
ax2.bar(x - width/2, yang_contributions, width,
label='阳贡献', color=self.colors['离宫'], alpha=0.7)
ax2.bar(x + width/2, yin_contributions, width,
label='阴贡献', color=self.colors['坎宫'], alpha=0.7)
ax2.set_xlabel('药材', fontsize=12)
ax2.set_ylabel('阴阳贡献值', fontsize=12)
ax2.set_title('各药材阴阳贡献分布', fontsize=14)
ax2.set_xticks(x)
ax2.set_xticklabels(herbs, rotation=45, ha='right')
ax2.legend()
ax2.grid(True, alpha=0.3, axis='y')
plt.suptitle(title, fontsize=16)
plt.tight_layout()
return fig
class PrescriptionOptimizer:
"""处方优化器"""
def __init__(self):
self.golden_ratio = (1 + math.sqrt(5)) / 2
self.herb_db = ChineseHerbDatabase()
def optimize_by_genetic_algorithm(self,
prescription: 'PrescriptionAnnotationSystem.AnnotatedPrescription',
population_size: int = 20,
generations: int = 50) -> 'PrescriptionAnnotationSystem.AnnotatedPrescription':
"""使用遗传算法优化处方"""
class Individual:
"""个体表示"""
def __init__(self, herbs: List[HerbSelection]):
self.herbs = herbs
self.fitness = 0.0
def calculate_fitness(self, original_prescription) -> float:
"""计算适应度"""
score = 0.0
# 1. 效果评分
temp_prescription = PrescriptionAnnotationSystem.AnnotatedPrescription(
case_id=original_prescription.case_id,
pattern=original_prescription.pattern,
herbs=self.herbs,
energy_goals=original_prescription.energy_goals
)
score += temp_prescription.calculate_effectiveness_score() * 0.6
# 2. 黄金比例符合度
monarch = next((h for h in self.herbs if h.role == "君药"), None)
ministers = [h for h in self.herbs if h.role == "臣药"]
if monarch and ministers:
for minister in ministers[:2]:
ratio = monarch.dosage / minister.dosage
deviation = abs(ratio - self.golden_ratio) / self.golden_ratio
score += (1.0 - deviation) * 0.2
# 3. 剂量合理性
dosage_score = 0.0
for herb in self.herbs:
herb_info = self.herb_db.get_herb(herb.herb_name)
if herb_info:
if (herb.dosage >= herb_info.dosage_min * 0.8 and
herb.dosage <= herb_info.dosage_max * 1.2):
dosage_score += 1.0
if self.herbs:
score += (dosage_score / len(self.herbs)) * 0.2
self.fitness = score
return score
def mutate(self, mutation_rate: float = 0.1):
"""变异"""
for herb in self.herbs:
if random.random() < mutation_rate:
# 剂量变异
adjustment = random.uniform(-0.1, 0.1) # ±10%
herb.dosage *= (1.0 + adjustment)
# 确保在安全范围内
herb_info = self.herb_db.get_herb(herb.herb_name)
if herb_info:
herb.dosage = max(herb_info.dosage_min,
min(herb_info.dosage_max, herb.dosage))
# 初始化种群
population = []
for _ in range(population_size):
# 复制原始处方并添加随机变异
herbs_copy = []
for herb in prescription.herbs:
new_herb = HerbSelection(
herb_name=herb.herb_name,
dosage=herb.dosage,
role=herb.role,
primary_target=herb.primary_target,
mirror_logic=herb.mirror_logic,
priority_score=herb.priority_score
)
# 初始变异
adjustment = random.uniform(-0.05, 0.05)
new_herb.dosage *= (1.0 + adjustment)
herbs_copy.append(new_herb)
individual = Individual(herbs_copy)
individual.calculate_fitness(prescription)
population.append(individual)
# 进化循环
for generation in range(generations):
# 评估适应度
for individual in population:
individual.calculate_fitness(prescription)
# 选择（精英选择 + 轮盘赌）
population.sort(key=lambda x: x.fitness, reverse=True)
elites = population[:population_size // 4]
# 轮盘赌选择
fitness_sum = sum(ind.fitness for ind in population)
selected = elites.copy()
while len(selected) < population_size:
r = random.uniform(0, fitness_sum)
cumulative = 0.0
for ind in population:
cumulative += ind.fitness
if cumulative >= r:
selected.append(ind)
break
# 交叉
new_population = []
for i in range(0, len(selected), 2):
if i + 1 < len(selected):
parent1 = selected[i]
parent2 = selected[i + 1]
# 单点交叉
crossover_point = random.randint(1, len(parent1.herbs) - 1)
child1_herbs = parent1.herbs[:crossover_point] + parent2.herbs[crossover_point:]
child2_herbs = parent2.herbs[:crossover_point] + parent1.herbs[crossover_point:]
child1 = Individual(child1_herbs)
child2 = Individual(child2_herbs)
new_population.extend([child1, child2])
# 变异
for individual in new_population:
individual.mutate(mutation_rate=0.1)
population = new_population
# 打印进度
if generation % 10 == 0:
best_fitness = max(ind.fitness for ind in population)
print(f"第 {generation} 代，最佳适应度: {best_fitness:.3f}")
# 选择最佳个体
best_individual = max(population, key=lambda x: x.fitness)
# 创建优化后的处方
optimized_prescription = PrescriptionAnnotationSystem.AnnotatedPrescription(
case_id=prescription.case_id + "_优化",
pattern=prescription.pattern,
herbs=best_individual.herbs,
energy_goals=prescription.energy_goals,
quantum_algorithm=prescription.quantum_algorithm
)
return optimized_prescription
def optimize_by_simulated_annealing(self,
prescription: 'PrescriptionAnnotationSystem.AnnotatedPrescription',
initial_temp: float = 100.0,
final_temp: float = 0.1,
cooling_rate: float = 0.95) -> 'PrescriptionAnnotationSystem.AnnotatedPrescription':
"""使用模拟退火优化处方"""
current_herbs = prescription.herbs.copy()
current_score = prescription.calculate_effectiveness_score()
best_herbs = current_herbs.copy()
best_score = current_score
temperature = initial_temp
iteration = 0
while temperature > final_temp:
iteration += 1
# 生成新解
new_herbs = []
for herb in current_herbs:
new_herb = HerbSelection(
herb_name=herb.herb_name,
dosage=herb.dosage,
role=herb.role,
primary_target=herb.primary_target,
mirror_logic=herb.mirror_logic,
priority_score=herb.priority_score
)
# 随机扰动剂量
perturbation = random.uniform(-0.1, 0.1) * temperature / initial_temp
new_herb.dosage *= (1.0 + perturbation)
# 确保在安全范围内
herb_info = self.herb_db.get_herb(new_herb.herb_name)
if herb_info:
new_herb.dosage = max(herb_info.dosage_min,
min(herb_info.dosage_max, new_herb.dosage))
new_herbs.append(new_herb)
# 计算新解评分
temp_prescription = PrescriptionAnnotationSystem.AnnotatedPrescription(
case_id=prescription.case_id,
pattern=prescription.pattern,
herbs=new_herbs,
energy_goals=prescription.energy_goals
)
new_score = temp_prescription.calculate_effectiveness_score()
# 决定是否接受新解
delta = new_score - current_score
if delta > 0:
# 接受更好的解
current_herbs = new_herbs
current_score = new_score
if new_score > best_score:
best_herbs = new_herbs.copy()
best_score = new_score
else:
# 以一定概率接受较差的解
probability = math.exp(delta / temperature)
if random.random() < probability:
current_herbs = new_herbs
current_score = new_score
# 降温
temperature *= cooling_rate
# 打印进度
if iteration % 10 == 0:
print(f"迭代 {iteration}, 温度: {temperature:.3f}, "
f"当前分数: {current_score:.3f}, 最佳分数: {best_score:.3f}")
# 创建优化后的处方
optimized_prescription = PrescriptionAnnotationSystem.AnnotatedPrescription(
case_id=prescription.case_id + "_SA优化",
pattern=prescription.pattern,
herbs=best_herbs,
energy_goals=prescription.energy_goals,
quantum_algorithm=prescription.quantum_algorithm
)
return optimized_prescription
class ClinicalDecisionSupport:
"""临床决策支持系统"""
def __init__(self):
self.herb_db = ChineseHerbDatabase()
self.mapping_rules = MirrorMappingRules()
self.annotation_system = PrescriptionAnnotationSystem()
def compare_prescriptions(self,
prescription1: 'PrescriptionAnnotationSystem.AnnotatedPrescription',
prescription2: 'PrescriptionAnnotationSystem.AnnotatedPrescription') -> Dict[str, Any]:
"""比较两个处方"""
comparison = {
"效果评分对比": {
"处方1": prescription1.calculate_effectiveness_score(),
"处方2": prescription2.calculate_effectiveness_score(),
"差异": (prescription2.calculate_effectiveness_score() -
prescription1.calculate_effectiveness_score())
},
"药材组成对比": {
"共同药材": [],
"处方1特有": [],
"处方2特有": []
},
"剂量对比": {},
"黄金比例符合度": {
"处方1": 0.0,
"处方2": 0.0
}
}
# 药材组成对比
herbs1 = {h.herb_name for h in prescription1.herbs}
herbs2 = {h.herb_name for h in prescription2.herbs}
comparison["药材组成对比"]["共同药材"] = list(herbs1 & herbs2)
comparison["药材组成对比"]["处方1特有"] = list(herbs1 - herbs2)
comparison["药材组成对比"]["处方2特有"] = list(herbs2 - herbs1)
# 剂量对比
for herb_name in (herbs1 | herbs2):
dosage1 = next((h.dosage for h in prescription1.herbs
if h.herb_name == herb_name), 0.0)
dosage2 = next((h.dosage for h in prescription2.herbs
if h.herb_name == herb_name), 0.0)
comparison["剂量对比"][herb_name] = {
"处方1": dosage1,
"处方2": dosage2,
"差异": dosage2 - dosage1
}
# 黄金比例符合度
def calculate_golden_ratio_score(herbs):
monarch = next((h for h in herbs if h.role == "君药"), None)
ministers = [h for h in herbs if h.role == "臣药"]
if monarch and ministers:
scores = []
for minister in ministers[:2]:
ratio = monarch.dosage / minister.dosage
deviation = abs(ratio - 1.618) / 1.618
scores.append(1.0 - deviation)
return sum(scores) / len(scores) if scores else 0.0
return 0.0
comparison["黄金比例符合度"]["处方1"] = calculate_golden_ratio_score(prescription1.herbs)
comparison["黄金比例符合度"]["处方2"] = calculate_golden_ratio_score(prescription2.herbs)
# 建议
score1 = prescription1.calculate_effectiveness_score()
score2 = prescription2.calculate_effectiveness_score()
if score2 > score1 + 0.1:
comparison["建议"] = "推荐使用处方2，效果更佳"
elif score2 > score1:
comparison["建议"] = "处方2略优，但差异不大"
elif score1 > score2 + 0.1:
comparison["建议"] = "推荐使用处方1，效果更佳"
else:
comparison["建议"] = "两个处方效果相近"
return comparison
def generate_treatment_plan(self,
prescription: 'PrescriptionAnnotationSystem.AnnotatedPrescription',
duration_days: int = 7) -> Dict[str, Any]:
"""生成治疗计划"""
treatment_plan = {
"处方信息": {
"案例ID": prescription.case_id,
"证型": prescription.pattern,
"效果评分": prescription.calculate_effectiveness_score(),
"总剂量": sum(h.dosage for h in prescription.herbs)
},
"服药方案": {
"每日剂量": {},
"服药时间": "早晚饭后温服",
"疗程": f"{duration_days}天"
},
"预期效果": [],
"注意事项": [],
"复诊建议": []
}
# 计算每日剂量
daily_total = 0.0
for herb in prescription.herbs:
herb_info = self.herb_db.get_herb(herb.herb_name)
if herb_info:
daily_dose = herb.dosage / duration_days
treatment_plan["服药方案"]["每日剂量"][herb.herb_name] = {
"单日剂量": f"{daily_dose:.1f}g",
"总剂量": f"{herb.dosage:.1f}g",
"角色": herb.role
}
daily_total += daily_dose
treatment_plan["服药方案"]["每日总剂量"] = f"{daily_total:.1f}g"
# 预期效果
energy_matrix = prescription.energy_goals
imbalanced_palaces = energy_matrix.get_imbalanced_palaces(threshold=0.3)
for palace_pos in imbalanced_palaces:
palace = energy_matrix.get_palace(palace_pos)
if palace:
days_to_improve = int(abs(palace.deviation) * 2) # 粗略估计
treatment_plan["预期效果"].append(
f"{palace.name}{palace.pattern}: "
f"预计{days_to_improve}天后改善{palace.symptoms[0] if palace.symptoms else ''}"
)
# 注意事项
# 检查有无大寒大热药物
for herb in prescription.herbs:
herb_info = self.herb_db.get_herb(herb.herb_name)
if herb_info:
properties = "".join(herb_info.properties)
if "大寒" in properties and herb.dosage > 9.0:
treatment_plan["注意事项"].append(
f"{herb.herb_name}大寒，剂量较大，注意脾胃保护")
elif "大热" in properties and herb.dosage > 6.0:
treatment_plan["注意事项"].append(
f"{herb.herb_name}大热，注意有无口干、便秘等热象")
# 复诊建议
treatment_plan["复诊建议"] = [
f"{duration_days // 2}天后复诊，调整剂量",
f"{duration_days}天后全面评估疗效",
"如出现不适及时复诊"
]
return treatment_plan
def predict_side_effects(self,
prescription: 'PrescriptionAnnotationSystem.AnnotatedPrescription') -> List[Dict[str, Any]]:
"""预测可能的副作用"""
side_effects = []
herb_db = self.herb_db
# 检查寒热偏性
total_cold_score = 0.0
total_hot_score = 0.0
for herb in prescription.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info:
properties = "".join(herb_info.properties)
# 寒热评分
cold_score = 0.0
hot_score = 0.0
if "大寒" in properties:
cold_score = 2.0 * herb.dosage
elif "寒" in properties:
cold_score = 1.0 * herb.dosage
elif "微寒" in properties:
cold_score = 0.5 * herb.dosage
if "大热" in properties:
hot_score = 2.0 * herb.dosage
elif "热" in properties:
hot_score = 1.0 * herb.dosage
elif "温" in properties:
hot_score = 0.5 * herb.dosage
total_cold_score += cold_score
total_hot_score += hot_score
# 特定药物副作用
if herb.herb_name == "大黄" and herb.dosage > 9.0:
side_effects.append({
"药物": herb.herb_name,
"类型": "胃肠道反应",
"症状": "腹泻、腹痛",
"风险等级": "中",
"建议": "减少剂量或饭后服用"
})
elif herb.herb_name == "黄连" and herb.dosage > 6.0:
side_effects.append({
"药物": herb.herb_name,
"类型": "脾胃虚寒",
"症状": "胃痛、腹泻",
"风险等级": "中",
"建议": "配伍温中药物或减少剂量"
})
# 寒热平衡分析
cold_hot_ratio = total_cold_score / (total_hot_score + 1e-10)
if cold_hot_ratio > 2.0:
side_effects.append({
"药物": "全方",
"类型": "方剂偏寒",
"症状": "畏寒、腹泻、食欲不振",
"风险等级": "高",
"建议": "加入温中药如干姜、甘草"
})
elif cold_hot_ratio < 0.5:
side_effects.append({
"药物": "全方",
"类型": "方剂偏热",
"症状": "口干、便秘、烦躁",
"风险等级": "中",
"建议": "加入清热药或减少热药剂量"
})
# 检查有无毒性药物
toxic_herbs = []
for herb in prescription.herbs:
herb_info = herb_db.get_herb(herb.herb_name)
if herb_info and "有毒" in "".join(herb_info.properties):
toxic_herbs.append(herb.herb_name)
if toxic_herbs:
side_effects.append({
"药物": "、".join(toxic_herbs),
"类型": "毒性风险",
"症状": "根据具体药物不同",
"风险等级": "高",
"建议": "严格控制剂量，密切观察"
})
return side_effects
# ==================== 主程序扩展 ====================
def main_advanced():
"""高级功能演示"""
print("镜心悟道系统高级功能演示")
print("=" * 60)
# 创建临床应用实例
clinical_app = ClinicalCaseApplication()
# 1. 分析百合病案例
print("n1. 百合病案例分析及优化")
print("-" * 40)
lily_prescription = clinical_app.analyze_lily_disease_case()
original_score = lily_prescription.calculate_effectiveness_score()
print(f"原始处方效果评分: {original_score*100:.1f}%")
# 2. 使用遗传算法优化
print("n2. 使用遗传算法优化处方")
optimizer = PrescriptionOptimizer()
ga_optimized = optimizer.optimize_by_genetic_algorithm(
lily_prescription,
population_size=30,
generations=50
)
ga_score = ga_optimized.calculate_effectiveness_score()
print(f"遗传算法优化后评分: {ga_score*100:.1f}%")
print(f"提升: {(ga_score - original_score)*100:.1f}%")
# 3. 使用模拟退火优化
print("n3. 使用模拟退火优化处方")
sa_optimized = optimizer.optimize_by_simulated_annealing(
lily_prescription,
initial_temp=100.0,
final_temp=0.1,
cooling_rate=0.95
)
sa_score = sa_optimized.calculate_effectiveness_score()
print(f"模拟退火优化后评分: {sa_score*100:.1f}%")
print(f"提升: {(sa_score - original_score)*100:.1f}%")
# 4. 比较不同优化方法
print("n4. 优化方法比较")
print("-" * 40)
decision_support = ClinicalDecisionSupport()
# 比较原始处方和遗传算法优化处方
comparison_ga = decision_support.compare_prescriptions(lily_prescription, ga_optimized)
print(f"效果评分对比:")
print(f" 原始: {comparison_ga['效果评分对比']['处方1']*100:.1f}%")
print(f" GA优化: {comparison_ga['效果评分对比']['处方2']*100:.1f}%")
print(f" 差异: {comparison_ga['效果评分对比']['差异']*100:+.1f}%")
print(f"n黄金比例符合度:")
print(f" 原始: {comparison_ga['黄金比例符合度']['处方1']:.3f}")
print(f" GA优化: {comparison_ga['黄金比例符合度']['处方2']:.3f}")
print(f"n建议: {comparison_ga['建议']}")
# 5. 生成治疗计划
print("n5. 生成治疗计划")
print("-" * 40)
treatment_plan = decision_support.generate_treatment_plan(ga_optimized, duration_days=14)
print(f"疗程: {treatment_plan['服药方案']['疗程']}")
print(f"每日总剂量: {treatment_plan['服药方案']['每日总剂量']}")
print(f"服药时间: {treatment_plan['服药方案']['服药时间']}")
print(f"n预期效果:")
for effect in treatment_plan['预期效果'][:3]: # 显示前3个
print(f" • {effect}")
# 6. 预测副作用
print("n6. 副作用预测")
print("-" * 40)
side_effects = decision_support.predict_side_effects(ga_optimized)
if side_effects:
print("预测可能副作用:")
for effect in side_effects:
print(f" • {effect['药物']}: {effect['类型']} ({effect['风险等级']}风险)")
print(f" 可能症状: {effect['症状']}")
print(f" 建议: {effect['建议']}")
else:
print("无明显副作用风险")
# 7. 可视化分析
print("n7. 生成可视化分析图表")
print("-" * 40)
viz = PrescriptionVisualization()
# 九宫能量图
fig1 = viz.plot_nine_palace_energy(
lily_prescription.energy_goals,
title="百合病九宫能量分析"
)
plt.savefig('nine_palace_energy.png', dpi=300, bbox_inches='tight')
print("已保存: nine_palace_energy.png")
# 处方网络图
fig2 = viz.plot_prescription_network(
ga_optimized,
title="优化处方网络分析"
)
plt.savefig('prescription_network.png', dpi=300, bbox_inches='tight')
print("已保存: prescription_network.png")
# 阴阳平衡图
fig3 = viz.plot_yin_yang_balance(
ga_optimized,
title="处方阴阳平衡分析"
)
plt.savefig('yin_yang_balance.png', dpi=300, bbox_inches='tight')
print("已保存: yin_yang_balance.png")
# 8. 导出详细报告
print("n8. 导出详细报告")
print("-" * 40)
# 导出原始处方
clinical_app.export_to_file(lily_prescription, "original_prescription.xml")
# 导出优化处方
clinical_app.export_to_file(ga_optimized, "optimized_prescription.xml")
# 导出比较报告
comparison_report = {
"原始处方": {
"效果评分": original_score,
"药材组成": [h.herb_name for h in lily_prescription.herbs],
"总剂量": sum(h.dosage for h in lily_prescription.herbs)
},
"遗传算法优化处方": {
"效果评分": ga_score,
"药材组成": [h.herb_name for h in ga_optimized.herbs],
"总剂量": sum(h.dosage for h in ga_optimized.herbs),
"改进": comparison_ga
},
"模拟退火优化处方": {
"效果评分": sa_score,
"药材组成": [h.herb_name for h in sa_optimized.herbs],
"总剂量": sum(h.dosage for h in sa_optimized.herbs)
},
"治疗计划": treatment_plan,
"副作用预测": side_effects
}
with open('comparison_report.json', 'w', encoding='utf-8') as f:
json.dump(comparison_report, f, ensure_ascii=False, indent=2)
print("已保存: comparison_report.json")
# 9. 显示图表
plt.show()
print("n" + "=" * 60)
print("高级功能演示完成！")
print("=" * 60)
if __name__ == "__main__":
# 运行基础系统
from mirror_prescription_system import main as basic_main
basic_main()
# 运行高级功能
run_advanced = input("n是否运行高级功能演示? (y/n): ")
if run_advanced.lower() == 'y':
main_advanced()系统特点总结
- 核心创新
· 一元到九九归一的完整映射：从基础药材属性到完整处方的系统化映射
· 镜象映射规则：阴经配阳药，阳宫应阴爻的相反相成原则
· 量子态标注：每味药都有对应的量子态描述
- 算法特性
· 黄金比例剂量优化：君臣佐使剂量按1:1.618比例分配
· 九宫能量平衡：动态调整各宫位能量至平衡状态
· 遗传算法优化：智能优化处方配伍和剂量
- 临床应用
· 个性化处方生成：根据具体病机生成针对性处方
· 副作用预测：提前预测可能的不良反应
· 治疗计划生成：完整的服药方案和预期效果评估
- 可视化分析
· 九宫能量图：直观显示各宫位能量状态
· 处方网络图：展示君臣佐使关系和靶向宫位
· 阴阳平衡图：分析处方的阴阳平衡状态
- 输出格式
· XML结构化输出：标准化的药方标注格式
· JSON详细报告：完整的分析报告和数据
· 可视化图表：多种分析图表
这套系统实现了从中医理论到现代计算的完美结合，为中医药的精准化、个性化治疗提供了强大的技术支持。
/**
- 镜心悟道AI神经网络逻辑思维链系统
- 九九归一无限逼近平衡态算法
- Neural Network Logic Chain & Nine-Nine Return-to-One Equilibrium System
*/
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