# 数据预处理代码示例
import pandas as pd
from transformers import BertTokenizer

def load_real_time_data():
    # 从API获取实时医疗数据
    hospital_data = pd.read_json("https://api.healthcare.gov/hospitals")
    # 文本特征提取
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    def extract_text_features(text):
        return tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    return hospital_data, extract_text_features

class CausalReasoner:
    def __init__(self):
        self.graph_builder = CausalGraphBuilder()
        self.reasoning_engine = ProbabilisticReasoner()

    def build_causal_graph(self, patient_profile):
        # 构建包含年龄、合并症、环境因素的动态因果图
        graph = self.graph_builder.construct(
            age=patient_profile['age'], 
            comorbidities=patient_profile['diseases'],
            environment=patient_profile['location']
        )
        return self.reasoning_engine.infer(graph)

def multi_modal_optimization(resources, constraints):
    # 初始化STGNN模型
    graph_model = STGNN(input_dim=128, hidden_dim=256)
    # DQN强化学习智能体
    agent = DQN(state_size=200, action_size=10)

    while not convergence:
        candidates = graph_model.predict(resources)
        rewards = agent.evaluate(candidates)
        valid_solutions = filter_constraints(candidates, constraints)
        return argmax(valid_solutions, key=rewards)