import pandas as pd
from sklearn.preprocessing import StandardScaler

def collect_climate_data(region_id):
    # 调用气象局API获取实时数据
    temp = get_weather_api(region_id, 'temperature')
    pm25 = get_air_quality_sensor(region_id)
    return pd.DataFrame({
        'temperature': temp,
        'PM2.5': pm25['pm25'],
        'humidity': get_humidity_sensor(region_id),
        'wind_speed': get_wind_data(region_id)
    })

def preprocess_genomic_data(patient_profile):
    scaler = StandardScaler()
    return scaler.fit_transform(
        extract_variant_features(patient_profile)
    )

import tensorflow as tf

class ClimateGeneModel(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.clim_encoder = tf.keras.layers.Dense(256, activation='relu')
        self.gene_encoder = tf.keras.layers.Dense(512, activation='relu')
        self.interaction_layer = tf.keras.layers.Attention()

    def call(self, inputs):
        climate_seq, gene_data = inputs
        climate_emb = self.clim_encoder(climate_seq)
        gene_emb = self.gene_encoder(gene_data)
        return self.interaction_layer([climate_emb, gene_emb])

def generate_intervention_plan(risk_score, patient_profile):
    if risk_score > 80:
        return {
            'immediate': ['启动远程会诊', '发放应急药物包'],
            'medium_term': ['基因编辑治疗方案评估', '环境改造建议'],
            'long_term': ['参与临床试验注册', '建立家族遗传监测']
        }
    elif risk_score > 50:
        return {
            'immediate': ['健康宣教', '环境暴露记录'],
            'medium_term': ['定制营养方案', '定期基因检测'],
            'long_term': ['社区互助计划', '保险衔接']
        }
    else:
        return {
            'immediate': ['常规随访'],
            'medium_term': ['预防性疫苗接种'],
            'long_term': ['遗传咨询预约']
        }