import pandas as pd
from sklearn.preprocessing import StandardScaler

# 模拟暴露组数据采集
def collect_exposure_data(patient_id):
    # 从物联网设备获取实时数据
    air_quality = get_air_sensor_data(patient_id)
    activity_data = get_wearable_data(patient_id)
    return pd.DataFrame({
        'PM2.5': air_quality['pm25'],
        'NO2': air_quality['no2'],
        'step_count': activity_data['steps'],
        'heart_rate': activity_data['hr']
    })

# 数据标准化处理
scaler = StandardScaler()
scaled_data = scaler.fit_transform(collect_exposure_data('P001'))

import tensorflow as tf

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

    def call(self, inputs):
        gene_seq, env_data = inputs
        gene_emb = self.gene_encoder(gene_seq)
        env_emb = self.env_encoder(env_data)
        interaction = self.interaction_layer([gene_emb, env_emb])
        return tf.nn.softmax(interaction)

// Solidity智能合约示例
pragma solidity ^0.8.0;

contract PrivacyPreservation {
    struct EncryptedData {
        bytes32 hash;
        uint256 timestamp;
        address owner;
    }

    mapping(bytes32 => EncryptedData) private dataRegistry;

    function registerData(bytes32 _hash, uint256 _timestamp) public {
        require(!_exists(_hash), "Data already exists");
        dataRegistry[_hash] = EncryptedData({
            hash: _hash,
            timestamp: _timestamp,
            owner: msg.sender
        });
    }

    function _exists(bytes32 _hash) private view returns (bool) {
        return dataRegistry[_hash].timestamp != 0;
    }
}