添加 edge_hypertension_system_beta.py
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edge_hypertension_system_beta.py
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edge_hypertension_system_beta.py
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# 整合MQTT数据接收与高血压风险评估系统
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from kk_tools.kk_mysql_save_api import save_oxygen_mysql_data,save_ceramic_mysql_data,save_radar_mysql_data,save_oxygen_stroke_mysql_data
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from kk_tools.kk_tfcard_save_api import save_data_manager
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from kk_tools.kk_sftp_save_api import sftp_upload_one_file
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import os
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import paho.mqtt.client as mqtt
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import json
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import time
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from datetime import datetime
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import threading
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import numpy as np
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from scipy import signal
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import warnings
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import logging
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import sys
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import traceback
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import requests
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from collections import deque
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# ==================== 基础配置 ====================
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# MQTT配置
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MQTT_BROKER = "60.204.151.68" # MQTT 代理服务器
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MQTT_PORT = 1883
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MQTT_SUBSCRIBE_TOPIC = "/Radar60SP/E43819258520/sys/property/post" # 数据接收主题
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MQTT_PUBLISH_TOPIC = "HypertensionRisk/mm" # 结果发布主题
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# 设备配置
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SENSER_ID = "H11223344S"
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BASE_PATH = "./temp" # TF卡挂载点
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ROBOT_ID = "d100000001"
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# 数据整合配置
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INTEGRATION_INTERVAL = 10 # 10秒整合一次
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# 禁用警告
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warnings.filterwarnings("ignore")
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# ==================== 日志配置（来自第二段） ====================
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# 设置基础路径
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_container_path = "/config/workspace/Hypertension_Risk_Assessment"
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script_dir = os.path.abspath(os.path.dirname(__file__))
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def _find_cloud_dir(start_dir):
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cur = start_dir
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while True:
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candidate = os.path.join(cur, 'cloud_hypertension_system.py')
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if os.path.exists(candidate):
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return cur
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parent = os.path.dirname(cur)
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if parent == cur:
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return None
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cur = parent
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cloud_dir = _find_cloud_dir(script_dir)
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if cloud_dir:
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BASE_PATH_CLOUD = cloud_dir
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elif os.getenv('USE_CONTAINER_PATH', '0') == '1' and os.path.exists(_container_path) and os.name != 'nt':
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BASE_PATH_CLOUD = _container_path
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else:
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BASE_PATH_CLOUD = script_dir
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EDGE_PATH = os.path.join(BASE_PATH_CLOUD, "artifacts", "edge")
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LOG_PATH = os.path.join(BASE_PATH_CLOUD, "logs")
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# 创建目录
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os.makedirs(EDGE_PATH, exist_ok=True)
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os.makedirs(LOG_PATH, exist_ok=True)
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# 日志文件路径
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SYSTEM_STATUS_LOG = os.path.join(EDGE_PATH, "edge_system_status.log")
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# 配置控制台日志
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console_logger = logging.getLogger("HypertensionSystem")
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console_logger.setLevel(logging.INFO)
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console_handler = logging.StreamHandler(sys.stdout)
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console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
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console_handler.setFormatter(console_formatter)
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console_logger.addHandler(console_handler)
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console_logger.propagate = False
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# ==================== 高血压风险评估核心组件（来自第二段） ====================
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class EdgeConfig:
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"""边缘端配置"""
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def __init__(self):
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# 版本标识
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self.version = "v1.0_edge_realtime"
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self.device_id = "edge_rk3588_001"
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# BCG传感器配置
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self.bcg_config = {
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'sampling_rate': 250,
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'window_size': 30,
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'slide_step': 5,
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'hr_range': (40, 120),
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'rr_range': (8, 25),
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'quality_threshold': 0.5,
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'bed_exit_threshold': 0.3
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}
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# 雷达传感器配置
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self.radar_config = {
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'sampling_rate': 20,
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'presence_threshold': 0.6,
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'bathroom_zone': {
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'x_range': (0.5, 2.5),
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'y_range': (0.5, 2.0),
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'z_range': (0.3, 1.8)
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},
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'min_visit_duration': 30,
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'event_merge_gap': 60,
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'motion_threshold': 0.3
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}
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# 风险评估配置
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self.risk_config = {
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'rmssd_high_risk': 20,
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'rmssd_medium_risk': 30,
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'rmssd_low_risk': 40,
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'hr_high_risk': 85,
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'hr_medium_risk': 75,
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'bathroom_high_risk': 3,
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'bathroom_medium_risk': 2,
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'hr_surge_high_risk': 20,
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'hr_surge_medium_risk': 12,
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'weights': {
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'rmssd': 0.35,
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'sdnn': 0.15,
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'hr_level': 0.20,
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'hr_surge': 0.10,
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'bathroom_freq': 0.20
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}
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}
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# 预警配置
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self.alert_config = {
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'high_risk_threshold': 0.65,
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'medium_risk_threshold': 0.40,
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'alert_cooldown': 300
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}
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class RingBuffer:
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"""高效环形缓冲区"""
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def __init__(self, size):
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self.size = size
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self.buffer = np.zeros(size, dtype=np.float32)
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self.index = 0
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self.is_filled = False
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def append(self, value):
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self.buffer[self.index] = value
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self.index = (self.index + 1) % self.size
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if self.index == 0:
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self.is_filled = True
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def extend(self, values):
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for v in values:
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self.append(v)
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def get_data(self):
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if not self.is_filled:
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return self.buffer[:self.index]
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else:
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return np.concatenate([
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self.buffer[self.index:],
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self.buffer[:self.index]
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])
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class BCGProcessor:
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"""BCG信号处理器"""
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def __init__(self, config):
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self.config = config
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self.fs = config.bcg_config['sampling_rate']
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self.hr_range = config.bcg_config['hr_range']
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self.rr_range = config.bcg_config['rr_range']
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self.hr_filter = self._design_hr_filter()
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self.rr_filter = self._design_rr_filter()
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def _design_hr_filter(self):
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nyq = self.fs / 2
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low = 0.8 / nyq
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high = 3.0 / nyq
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b, a = signal.butter(4, [low, high], btype='band')
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return b, a
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def _design_rr_filter(self):
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nyq = self.fs / 2
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low = 0.1 / nyq
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high = 0.6 / nyq
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b, a = signal.butter(4, [low, high], btype='band')
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return b, a
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def assess_signal_quality(self, bcg_signal):
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try:
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signal_power = np.mean(bcg_signal ** 2)
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noise_estimate = np.var(np.diff(bcg_signal))
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snr = signal_power / (noise_estimate + 1e-6)
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snr_score = np.clip(snr / 10.0, 0, 1)
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freqs, psd = signal.welch(bcg_signal, fs=self.fs, nperseg=min(512, len(bcg_signal)))
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hr_band_mask = (freqs >= 0.8) & (freqs <= 3.0)
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hr_band_power = np.sum(psd[hr_band_mask])
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total_power = np.sum(psd)
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spectrum_score = hr_band_power / (total_power + 1e-6)
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autocorr = np.correlate(bcg_signal - np.mean(bcg_signal),
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bcg_signal - np.mean(bcg_signal),
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mode='same')
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autocorr = autocorr[len(autocorr)//2:]
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peak_pos = np.argmax(autocorr[int(0.3*self.fs):int(1.5*self.fs)]) + int(0.3*self.fs)
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periodicity_score = autocorr[peak_pos] / (autocorr[0] + 1e-6)
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periodicity_score = np.clip(periodicity_score, 0, 1)
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quality = (0.40 * snr_score + 0.30 * spectrum_score + 0.30 * periodicity_score)
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return float(np.clip(quality, 0, 1))
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except Exception as e:
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console_logger.error(f"信号质量评估失败: {e}")
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return 0.0
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def extract_heart_rate(self, bcg_signal):
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try:
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filtered = signal.filtfilt(self.hr_filter[0], self.hr_filter[1], bcg_signal)
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peaks, properties = signal.find_peaks(
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filtered,
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distance=int(0.4 * self.fs),
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prominence=0.3 * np.std(filtered)
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)
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if len(peaks) < 5:
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return None, 0.0, None
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rr_intervals = np.diff(peaks) / self.fs * 1000
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median_rr = np.median(rr_intervals)
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mad = np.median(np.abs(rr_intervals - median_rr))
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valid_mask = np.abs(rr_intervals - median_rr) < 3 * mad
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if np.sum(valid_mask) < 5:
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return None, 0.0, None
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clean_rr = rr_intervals[valid_mask]
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mean_rr_ms = np.mean(clean_rr)
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heart_rate = 60000.0 / mean_rr_ms
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if not (self.hr_range[0] <= heart_rate <= self.hr_range[1]):
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return None, 0.0, None
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cv = np.std(clean_rr) / np.mean(clean_rr)
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confidence = np.clip(1.0 - cv, 0, 1)
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return float(heart_rate), float(confidence), clean_rr
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except Exception as e:
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console_logger.error(f"心率提取失败: {e}")
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return None, 0.0, None
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def calculate_hrv_features(self, rr_intervals):
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try:
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if len(rr_intervals) < 10:
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return None
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sdnn = np.std(rr_intervals)
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diff_rr = np.diff(rr_intervals)
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rmssd = np.sqrt(np.mean(diff_rr ** 2))
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nn50 = np.sum(np.abs(diff_rr) > 50)
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pnn50 = nn50 / len(diff_rr) if len(diff_rr) > 0 else 0
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return {
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'sdnn': float(sdnn),
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'rmssd': float(rmssd),
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'pnn50': float(pnn50)
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}
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except Exception as e:
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console_logger.error(f"HRV特征计算失败: {e}")
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return None
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class RadarProcessor:
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"""雷达信号处理器"""
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def __init__(self, config):
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self.config = config
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self.bathroom_zone = config.radar_config['bathroom_zone']
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self.presence_history = deque(maxlen=3)
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self.current_visit_start = None
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self.bathroom_visits = []
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def detect_presence_in_bathroom(self, point_cloud, timestamp):
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try:
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if len(point_cloud) == 0:
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self.presence_history.append(False)
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return False, 0.0, None
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dynamic_points = [p for p in point_cloud if abs(p[3]) > 0.05]
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bathroom_points = []
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for p in dynamic_points:
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x, y, z = p[0], p[1], p[2]
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if (self.bathroom_zone['x_range'][0] <= x <= self.bathroom_zone['x_range'][1] and
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self.bathroom_zone['y_range'][0] <= y <= self.bathroom_zone['y_range'][1] and
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self.bathroom_zone['z_range'][0] <= z <= self.bathroom_zone['z_range'][1]):
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bathroom_points.append(p)
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point_density = len(bathroom_points) / (len(point_cloud) + 1e-6)
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is_present = point_density > self.config.radar_config['presence_threshold']
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self.presence_history.append(is_present)
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stable_presence = sum(self.presence_history) >= 2
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confidence = point_density if stable_presence else 0.0
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# 检测起夜事件
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visit_event = self._detect_bathroom_visit(timestamp, stable_presence)
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return stable_presence, float(confidence), visit_event
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except Exception as e:
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console_logger.error(f"存在检测失败: {e}")
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return False, 0.0, None
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def _detect_bathroom_visit(self, timestamp, is_present):
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min_duration = self.config.radar_config['min_visit_duration']
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if is_present and self.current_visit_start is None:
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self.current_visit_start = timestamp
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return None
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elif not is_present and self.current_visit_start is not None:
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duration = timestamp - self.current_visit_start
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if duration >= min_duration:
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visit_info = {
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'start': self.current_visit_start,
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'end': timestamp,
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'duration': duration,
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'datetime': datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d %H:%M:%S')
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}
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self.bathroom_visits.append(visit_info)
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self.current_visit_start = None
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return visit_info
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else:
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self.current_visit_start = None
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return None
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return None
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class RiskAssessmentEngine:
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"""风险评估引擎"""
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def __init__(self, config):
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self.config = config
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self.risk_cfg = config.risk_config
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def calculate_risk_score(self, hrv_features, hr, bathroom_visits):
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if not hrv_features:
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return 0.0, "UNKNOWN", {}
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weights = self.risk_cfg['weights']
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rmssd = hrv_features.get('rmssd', 0)
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sdnn = hrv_features.get('sdnn', 0)
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bathroom_freq = len(bathroom_visits)
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# RMSSD风险评分
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if rmssd < self.risk_cfg['rmssd_high_risk']:
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rmssd_risk = 1.0
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elif rmssd < self.risk_cfg['rmssd_medium_risk']:
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rmssd_risk = 0.6
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elif rmssd < self.risk_cfg['rmssd_low_risk']:
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rmssd_risk = 0.4
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else:
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rmssd_risk = 0.2
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# SDNN风险评分
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if sdnn < 30:
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sdnn_risk = 1.0
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elif sdnn < 45:
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sdnn_risk = 0.6
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else:
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sdnn_risk = 0.2
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# 心率风险评分
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if hr > self.risk_cfg['hr_high_risk']:
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hr_risk = 1.0
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elif hr > self.risk_cfg['hr_medium_risk']:
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hr_risk = 0.6
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else:
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hr_risk = 0.2
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# 起夜频率风险评分
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if bathroom_freq >= self.risk_cfg['bathroom_high_risk']:
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bathroom_risk = 1.0
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elif bathroom_freq >= self.risk_cfg['bathroom_medium_risk']:
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bathroom_risk = 0.6
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else:
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bathroom_risk = 0.2
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# 总风险评分
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total_risk = (
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weights['rmssd'] * rmssd_risk +
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weights['sdnn'] * sdnn_risk +
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weights['hr_level'] * hr_risk +
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weights['bathroom_freq'] * bathroom_risk
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)
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# 风险等级
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if total_risk >= self.config.alert_config['high_risk_threshold']:
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risk_level = 'HIGH'
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elif total_risk >= self.config.alert_config['medium_risk_threshold']:
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risk_level = 'MEDIUM'
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else:
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risk_level = 'LOW'
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breakdown = {
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'rmssd_risk': rmssd_risk,
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'sdnn_risk': sdnn_risk,
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'hr_risk': hr_risk,
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'bathroom_risk': bathroom_risk
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}
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return float(total_risk), risk_level, breakdown
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# ==================== 整合后的MQTT数据处理器 ====================
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class MQTTHypertensionHandler:
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def __init__(self, base_path, robot_id):
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self.base_path = base_path
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self.robot_id = robot_id
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self.tf_card_manager = save_data_manager(base_path, robot_id)
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# 初始化数据缓冲区
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self.data_buffer = {} # 存储去重后的数据
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self.buffer_lock = threading.Lock() # 线程锁
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self.last_integration_time = time.time() # 上次整合时间
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# 统计信息
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self.total_messages = 0
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self.processed_messages = 0
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self.deduplicated_messages = 0
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self.integration_count = 0
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# 初始化高血压评估组件
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self.edge_config = EdgeConfig()
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self.bcg_processor = BCGProcessor(self.edge_config)
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self.radar_processor = RadarProcessor(self.edge_config)
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self.risk_engine = RiskAssessmentEngine(self.edge_config)
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||||
self.bcg_buffer = RingBuffer(self.edge_config.bcg_config['window_size'] *
|
||||
self.edge_config.bcg_config['sampling_rate'])
|
||||
|
||||
# 设置MQTT客户端
|
||||
self.mqtt_client = mqtt.Client()
|
||||
self.mqtt_client.on_message = self.on_mqtt_message
|
||||
self.mqtt_client.on_connect = self.on_mqtt_connect
|
||||
|
||||
# MQTT发布客户端（用于发送处理结果）
|
||||
self.mqtt_publish_client = mqtt.Client()
|
||||
|
||||
# 连接MQTT服务器
|
||||
self.mqtt_client.connect(MQTT_BROKER, MQTT_PORT, 60)
|
||||
self.mqtt_publish_client.connect(MQTT_BROKER, MQTT_PORT, 60)
|
||||
|
||||
def on_mqtt_connect(self, client, userdata, flags, rc):
|
||||
print(f"Connected to MQTT broker with result code {rc}")
|
||||
client.subscribe(MQTT_SUBSCRIBE_TOPIC)
|
||||
print(f"Subscribed to topics: {MQTT_SUBSCRIBE_TOPIC}")
|
||||
print(f"数据整合模式: 每 {INTEGRATION_INTERVAL} 秒整合输出一次")
|
||||
print(f"结果发布主题: {MQTT_PUBLISH_TOPIC}")
|
||||
|
||||
def extract_data_key(self, data):
|
||||
"""从数据中提取唯一标识"""
|
||||
try:
|
||||
if isinstance(data, dict):
|
||||
if "params" in data and isinstance(data["params"], dict):
|
||||
params = data["params"]
|
||||
param_keys = sorted(list(params.keys()))
|
||||
if param_keys:
|
||||
return tuple(param_keys)
|
||||
|
||||
version = data.get("version", "")
|
||||
method = data.get("method", "")
|
||||
if version and method:
|
||||
return f"{version}|{method}"
|
||||
|
||||
return str(data)
|
||||
except Exception as e:
|
||||
print(f"提取数据key时出错: {e}")
|
||||
return "unknown"
|
||||
|
||||
def process_sensor_data(self, data):
|
||||
"""处理传感器数据并进行风险评估"""
|
||||
# 记录处理开始时间，用于计算处理延迟
|
||||
process_start_time = time.time()
|
||||
|
||||
risk_result = {
|
||||
'type': 'UNKNOWN',
|
||||
'timestamp': datetime.now().isoformat(),
|
||||
# 新增：生理指标独立字段
|
||||
'heart_rate': None,
|
||||
'rmssd': None,
|
||||
'sdnn': None,
|
||||
# 新增：风险评分与等级独立字段
|
||||
'risk_score': 0.0,
|
||||
'risk_level': 'UNKNOWN',
|
||||
# 新增：系统性能字段
|
||||
'processing_delay_ms': 0.0,
|
||||
'features': {},
|
||||
'error': None
|
||||
}
|
||||
|
||||
try:
|
||||
# 识别数据类型（BCG/雷达）
|
||||
params = data.get('params', {})
|
||||
|
||||
# 处理BCG数据
|
||||
if 'bcg_signal' in params:
|
||||
risk_result['type'] = 'BCG'
|
||||
bcg_signal = np.array(params['bcg_signal'], dtype=np.float32)
|
||||
|
||||
# 信号质量评估
|
||||
quality = self.bcg_processor.assess_signal_quality(bcg_signal)
|
||||
if quality < self.edge_config.bcg_config['bed_exit_threshold']:
|
||||
risk_result['error'] = "低质量BCG信号（可能离床）"
|
||||
# 计算处理延迟
|
||||
risk_result['processing_delay_ms'] = round((time.time() - process_start_time) * 1000, 2)
|
||||
return risk_result
|
||||
|
||||
# 提取心率和HRV特征
|
||||
hr, hr_conf, rr_intervals = self.bcg_processor.extract_heart_rate(bcg_signal)
|
||||
if hr is None:
|
||||
risk_result['error'] = "无法提取有效心率"
|
||||
# 计算处理延迟
|
||||
risk_result['processing_delay_ms'] = round((time.time() - process_start_time) * 1000, 2)
|
||||
return risk_result
|
||||
|
||||
hrv_features = self.bcg_processor.calculate_hrv_features(rr_intervals)
|
||||
if not hrv_features:
|
||||
risk_result['error'] = "无法计算HRV特征"
|
||||
# 计算处理延迟
|
||||
risk_result['processing_delay_ms'] = round((time.time() - process_start_time) * 1000, 2)
|
||||
return risk_result
|
||||
|
||||
# 计算风险评分
|
||||
bathroom_visits = self.radar_processor.bathroom_visits
|
||||
risk_score, risk_level, breakdown = self.risk_engine.calculate_risk_score(
|
||||
hrv_features, hr, bathroom_visits
|
||||
)
|
||||
|
||||
# 赋值独立字段：生理指标
|
||||
risk_result['heart_rate'] = round(hr, 1)
|
||||
risk_result['rmssd'] = round(hrv_features['rmssd'], 2)
|
||||
risk_result['sdnn'] = round(hrv_features['sdnn'], 2)
|
||||
# 赋值独立字段：风险评分与等级
|
||||
risk_result['risk_score'] = round(risk_score, 3)
|
||||
risk_result['risk_level'] = risk_level
|
||||
|
||||
risk_result['features'] = {
|
||||
'hr': hr,
|
||||
'hr_confidence': hr_conf,
|
||||
'hrv': hrv_features,
|
||||
'signal_quality': quality,
|
||||
'bathroom_visits': len(bathroom_visits),
|
||||
'risk_breakdown': breakdown
|
||||
}
|
||||
|
||||
# 处理雷达数据
|
||||
elif 'radar_point_cloud' in params:
|
||||
risk_result['type'] = 'RADAR'
|
||||
point_cloud = params['radar_point_cloud']
|
||||
timestamp = params.get('timestamp', time.time())
|
||||
|
||||
# 检测浴室存在和起夜事件
|
||||
is_present, confidence, visit_event = self.radar_processor.detect_presence_in_bathroom(
|
||||
point_cloud, timestamp
|
||||
)
|
||||
|
||||
risk_result['features'] = {
|
||||
'in_bathroom': is_present,
|
||||
'confidence': confidence,
|
||||
'bathroom_visits': len(self.radar_processor.bathroom_visits),
|
||||
'latest_visit': visit_event
|
||||
}
|
||||
|
||||
# 如果有BCG数据，更新风险评分
|
||||
if self.bcg_buffer.is_filled:
|
||||
bcg_signal = self.bcg_buffer.get_data()
|
||||
hr, _, rr_intervals = self.bcg_processor.extract_heart_rate(bcg_signal)
|
||||
if hr and rr_intervals is not None:
|
||||
hrv_features = self.bcg_processor.calculate_hrv_features(rr_intervals)
|
||||
if hrv_features:
|
||||
risk_score, risk_level, _ = self.risk_engine.calculate_risk_score(
|
||||
hrv_features, hr, self.radar_processor.bathroom_visits
|
||||
)
|
||||
# 赋值独立字段
|
||||
risk_result['heart_rate'] = round(hr, 1)
|
||||
risk_result['rmssd'] = round(hrv_features['rmssd'], 2)
|
||||
risk_result['sdnn'] = round(hrv_features['sdnn'], 2)
|
||||
risk_result['risk_score'] = round(risk_score, 3)
|
||||
risk_result['risk_level'] = risk_level
|
||||
|
||||
# 计算处理延迟（毫秒）
|
||||
risk_result['processing_delay_ms'] = round((time.time() - process_start_time) * 1000, 2)
|
||||
|
||||
except Exception as e:
|
||||
risk_result['error'] = f"处理失败: {str(e)}"
|
||||
# 确保处理延迟字段有值
|
||||
risk_result['processing_delay_ms'] = round((time.time() - process_start_time) * 1000, 2)
|
||||
console_logger.error(f"传感器数据处理失败: {e}")
|
||||
|
||||
return risk_result
|
||||
|
||||
def on_mqtt_message(self, client, userdata, msg):
|
||||
try:
|
||||
# 解析MQTT消息
|
||||
data = json.loads(msg.payload.decode())
|
||||
self.total_messages += 1
|
||||
|
||||
# 处理数据并进行风险评估
|
||||
risk_result = self.process_sensor_data(data)
|
||||
|
||||
# 提取数据的唯一标识
|
||||
data_key = self.extract_data_key(data)
|
||||
|
||||
# 将原始数据+风险评估结果存入缓冲区
|
||||
with self.buffer_lock:
|
||||
if data_key in self.data_buffer:
|
||||
self.data_buffer[data_key] = {
|
||||
"data": data,
|
||||
"risk_result": risk_result,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"count": self.data_buffer[data_key].get("count", 1) + 1,
|
||||
"first_seen": self.data_buffer[data_key].get("first_seen", datetime.now().isoformat())
|
||||
}
|
||||
self.deduplicated_messages += 1
|
||||
else:
|
||||
self.data_buffer[data_key] = {
|
||||
"data": data,
|
||||
"risk_result": risk_result,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"count": 1,
|
||||
"first_seen": datetime.now().isoformat()
|
||||
}
|
||||
self.processed_messages += 1
|
||||
|
||||
# 每10条消息打印一次统计
|
||||
if self.total_messages % 10 == 0:
|
||||
print(f"已接收 {self.total_messages} 条消息, 去重后 {len(self.data_buffer)} 种数据")
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error processing MQTT message: {e}")
|
||||
|
||||
def integrate_and_save_data(self):
|
||||
"""整合数据并发布到MQTT"""
|
||||
with self.buffer_lock:
|
||||
if not self.data_buffer:
|
||||
print("缓冲区为空，跳过本次整合")
|
||||
return
|
||||
|
||||
self.integration_count += 1
|
||||
|
||||
# 准备整合后的数据
|
||||
unique_data_items = {}
|
||||
for data_key, buffer_entry in self.data_buffer.items():
|
||||
key_str = "|".join(data_key) if isinstance(data_key, tuple) else str(data_key)
|
||||
unique_data_items[key_str] = {
|
||||
"data": buffer_entry["data"],
|
||||
"risk_result": buffer_entry["risk_result"],
|
||||
"first_seen": buffer_entry["first_seen"],
|
||||
"last_seen": buffer_entry["timestamp"],
|
||||
"message_count": buffer_entry["count"]
|
||||
}
|
||||
|
||||
# 整合数据
|
||||
integration_data = {
|
||||
"integration_id": self.integration_count,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"total_messages": self.total_messages,
|
||||
"unique_data_count": len(self.data_buffer),
|
||||
"sensor_id": SENSER_ID,
|
||||
"integration_interval": INTEGRATION_INTERVAL,
|
||||
"unique_data_items": unique_data_items,
|
||||
"statistics": {
|
||||
"total_messages_received": self.total_messages,
|
||||
"duplicate_messages_removed": self.deduplicated_messages,
|
||||
"unique_messages_kept": self.processed_messages
|
||||
}
|
||||
}
|
||||
|
||||
# 打印整合信息
|
||||
self.print_integration_stats(integration_data)
|
||||
|
||||
# 发布到MQTT服务器
|
||||
self.publish_to_mqtt(integration_data)
|
||||
|
||||
# 保存到本地/数据库
|
||||
self.save_integrated_data(integration_data)
|
||||
|
||||
# 清空缓冲区
|
||||
self.data_buffer.clear()
|
||||
|
||||
def publish_to_mqtt(self, integration_data):
|
||||
"""发布处理结果到MQTT服务器"""
|
||||
try:
|
||||
# 将数据转换为JSON
|
||||
payload = json.dumps(integration_data, ensure_ascii=False, default=str)
|
||||
|
||||
# 发布消息
|
||||
result = self.mqtt_publish_client.publish(
|
||||
MQTT_PUBLISH_TOPIC,
|
||||
payload,
|
||||
qos=1
|
||||
)
|
||||
|
||||
# 等待发布完成
|
||||
result.wait_for_publish()
|
||||
|
||||
if result.is_published():
|
||||
print(f"整合数据 {self.integration_count} 已发布到MQTT主题: {MQTT_PUBLISH_TOPIC}")
|
||||
else:
|
||||
print(f"整合数据 {self.integration_count} MQTT发布失败")
|
||||
|
||||
except Exception as e:
|
||||
print(f"发布MQTT消息时出错: {e}")
|
||||
|
||||
def save_integrated_data(self, integration_data):
|
||||
"""保存整合后的数据"""
|
||||
try:
|
||||
# 生成保存格式
|
||||
save_data = self.tf_card_manager.generate_fake_sensor_002_data(integration_data)
|
||||
|
||||
# 保存到TF卡
|
||||
save_path = self.tf_card_manager.save_data_to_tfcard_json(
|
||||
save_data, dir_name=SENSER_ID, json_type=SENSER_ID)
|
||||
|
||||
# 上传到SFTP
|
||||
directory = os.path.dirname(save_path)
|
||||
file_name = os.path.basename(save_path)
|
||||
cleaned_path = directory[len(self.base_path + "/"):]
|
||||
down_url = sftp_upload_one_file(save_path, cleaned_path, file_name)
|
||||
|
||||
print(f"整合数据 {self.integration_count} 已保存，SFTP路径: {down_url}")
|
||||
|
||||
# 保存到MySQL
|
||||
save_radar_mysql_data(SENSER_ID, save_data, down_url)
|
||||
|
||||
except Exception as e:
|
||||
print(f"保存整合数据时出错: {e}")
|
||||
|
||||
def print_integration_stats(self, integration_data):
|
||||
"""打印整合统计信息"""
|
||||
unique_count = integration_data["unique_data_count"]
|
||||
|
||||
print("=" * 80)
|
||||
print(f"第 {self.integration_count} 次数据整合完成 (10秒周期)")
|
||||
print("=" * 80)
|
||||
print(f"统计信息:")
|
||||
print(f" - 10秒内接收消息总数: {self.total_messages} 条")
|
||||
print(f" - 去重后保留数据: {unique_count} 种不同数据")
|
||||
print(f" - 去重掉的消息: {self.deduplicated_messages} 条")
|
||||
|
||||
# 打印风险评估结果
|
||||
high_risk_count = 0
|
||||
medium_risk_count = 0
|
||||
|
||||
for item in integration_data["unique_data_items"].values():
|
||||
risk_level = item["risk_result"]["risk_level"]
|
||||
if risk_level == "HIGH":
|
||||
high_risk_count += 1
|
||||
elif risk_level == "MEDIUM":
|
||||
medium_risk_count += 1
|
||||
|
||||
print(f" - 高风险数据: {high_risk_count} 条")
|
||||
print(f" - 中风险数据: {medium_risk_count} 条")
|
||||
print(f" - 低风险/未知: {unique_count - high_risk_count - medium_risk_count} 条")
|
||||
|
||||
print(f"\n下次整合时间: {datetime.fromtimestamp(time.time() + INTEGRATION_INTERVAL).strftime('%H:%M:%S')}")
|
||||
print("=" * 80)
|
||||
|
||||
def check_and_process_integration(self):
|
||||
"""检查并执行数据整合"""
|
||||
current_time = time.time()
|
||||
if current_time - self.last_integration_time >= INTEGRATION_INTERVAL:
|
||||
if self.data_buffer:
|
||||
print(f"\n正在进行第 {self.integration_count + 1} 次数据整合...")
|
||||
self.integrate_and_save_data()
|
||||
else:
|
||||
print(f"\n第 {self.integration_count + 1} 次整合周期结束，缓冲区无数据")
|
||||
|
||||
self.last_integration_time = current_time
|
||||
return True
|
||||
return False
|
||||
|
||||
def run(self):
|
||||
"""主循环"""
|
||||
self.mqtt_client.loop_start()
|
||||
self.mqtt_publish_client.loop_start()
|
||||
|
||||
print(f"\nMQTT高血压风险评估系统启动")
|
||||
print(f"数据整合间隔: {INTEGRATION_INTERVAL} 秒")
|
||||
print(f"接收主题: {MQTT_SUBSCRIBE_TOPIC}")
|
||||
print(f"发布主题: {MQTT_PUBLISH_TOPIC}")
|
||||
print("按 Ctrl+C 停止程序")
|
||||
print("-" * 80)
|
||||
|
||||
try:
|
||||
while True:
|
||||
self.check_and_process_integration()
|
||||
time.sleep(0.1)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n收到停止信号，正在进行最后一次数据整合...")
|
||||
|
||||
# 保存剩余数据
|
||||
with self.buffer_lock:
|
||||
if self.data_buffer:
|
||||
print(f"正在保存缓冲区中剩余的 {len(self.data_buffer)} 条数据...")
|
||||
self.integrate_and_save_data()
|
||||
else:
|
||||
print("缓冲区为空，无需保存")
|
||||
|
||||
# 停止MQTT客户端
|
||||
self.mqtt_client.loop_stop()
|
||||
self.mqtt_publish_client.loop_stop()
|
||||
|
||||
print("程序已停止")
|
||||
print(f"\n最终统计:")
|
||||
print(f" - 总共接收消息: {self.total_messages} 条")
|
||||
print(f" - 执行整合次数: {self.integration_count} 次")
|
||||
print("=" * 80)
|
||||
|
||||
# ==================== 主程序 ====================
|
||||
if __name__ == "__main__":
|
||||
# 安装依赖（如果需要）
|
||||
# pip install numpy scipy requests paho-mqtt
|
||||
|
||||
# 创建并启动整合后的系统
|
||||
handler = MQTTHypertensionHandler(BASE_PATH, ROBOT_ID)
|
||||
handler.run()
|
||||
Loading…
x
Reference in New Issue
Block a user