基于人工智能的绿色债券信用评级系统

# 系统核心模块架构
class GreenBondSystem:
    def __init__(self):
        self.data_layer = DataEngine()      # 数据获取与清洗
        self.model_layer = ModelCluster()   # 双模型协同计算
        self.api_layer = FlaskAPI()        # 结果可视化输出
        
    def pipeline(self, bond_id):
        raw_data = self.data_layer.fetch(bond_id)
        processed_data = self.data_layer.clean(raw_data)
        credit_score = self.model_layer.predict_credit(processed_data)
        green_score = self.model_layer.predict_green(processed_data)
        return self.api_layer.render(credit_score, green_score)

# 卫星遥感数据处理（以PM2.5浓度为例）
def calc_environmental_impact(coordinates):
    from sentinelhub import SHConfig, WmsRequest
    config = SHConfig()
    config.instance_id = 'your_instance_id'
    
    # 获取近三年PM2.5遥感数据
    request = WmsRequest(
        layer='PM25',
        bbox=coordinates,
        time=('2020-01-01', '2023-01-01'),
        image_format=MimeType.TIFF,
        config=config
    )
    images = request.get_data()
    
    # 计算环境改善系数
    delta = np.mean(images[-12:]) - np.mean(images[:12]) 
    return 1 / (1 + math.exp(-delta*0.1))  # Sigmoid标准化

# 绿色项目现金流折现率计算
def green_dcf(project_data):
    base_rate = 0.038  # 基准折现率
    carbon_price = get_carbon_price()  # 实时碳价接口
    
    # 环境溢价因子计算
    risk_factor = 0.3*project_data['env_risk'] \
                + 0.7*(1 - project_data['tech_maturity'])
                
    return base_rate * (1 + risk_factor) \
           - 0.001*carbon_price  # 碳价补偿机制

# 双模型联合训练框架
class DualModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.credit_net = xgb.XGBClassifier(n_estimators=200)
        self.green_net = LSTMModel(input_size=17)
        
    def forward(self, X_credit, X_green):
        credit_out = self.credit_net(X_credit)
        green_out = self.green_net(X_green)
        return 0.6*credit_out + 0.4*green_out  # 动态加权融合

# 自定义评价指标（兼顾财务与环保）  
def hybrid_loss(y_true, y_pred):
    financial_loss = F.binary_cross_entropy(y_pred[:,0], y_true[:,0])
    green_loss = F.mse_loss(y_pred[:,1], y_true[:,1])
    return 0.7*financial_loss + 0.3*green_loss

# Flask API接口核心代码
@app.route('/evaluate', methods=['POST'])
def evaluate():
    data = request.json
    bond_id = data['bond_id']
    
    try:
        # 异步任务处理
        result = executor.submit(pipeline, bond_id).result()
        return jsonify({
            "credit_rating": result['credit'],
            "green_level": result['green'],
            "comprehensive_score": result['total']
        })
    except Exception as e:
        return jsonify({"error": str(e)}), 500

# 知识图谱实体对齐示例
def entity_alignment(entity1, entity2):
    # 计算语义相似度
    sem_sim = sentence_bert.similarity(entity1.desc, entity2.desc)
    # 结构相似度
    struct_sim = jaccard_sim(entity1.relations, entity2.relations)
    return 0.6*sem_sim + 0.4*struct_sim > 0.85