class ClimateHealthMonitor:
    def __init__(self):
        self.edge_devices = [EdgeDevice(location) for location in ['Pacific Islands', 'Bangladesh', 'Maldives']]
        self.llm_model = LLMHealthAnalyzer()
        self.data_pipeline = MultiModalPipeline()

    def collect_data(self):
        # 多源数据采集
        health_data = self.data_pipeline.process([
            self.edge_devices[0].get_biosensors(), 
            self.edge_devices[1].get_satellite_imagery(),
            self.edge_devices[2].get_social_media()
        ])
        return self.llm_model.analyze(health_data)

# 边缘计算设备实时处理模块
def edge_processing(data_stream):
    if detect_anomaly(data_stream['temperature']):
        trigger_alert("Heatwave Alert", data_stream['location'])
    return compress_data(data_stream)

% 基于LLM的资源预测模型
function allocate_resources = optimize_distribution(population_data, health_risk_matrix)
    % 计算各区域脆弱指数
    vulnerability_scores = calculate_vulnerability(population_data);

    % 生成资源分配方案
    allocate_resources = LLM.optimize(
        input = [vulnerability_scores, health_risk_matrix], 
        constraints = resource_limits
    );
end

{
  "telemedicine": {
    "language_support": ["82 languages"],
    "diagnosis_accuracy": "89.7%",
    "response_time": "<500ms",
    "cultural_adaptation": {
      "symbol_set": "Unicode 15.0",
      "emoji_usage": "Context-aware"
    }
  }
}