199 lines
7.1 KiB
Python
199 lines
7.1 KiB
Python
|
from flask import Blueprint, jsonify, request, g, current_app
|
|||
|
|
from werkzeug.exceptions import HTTPException
|
|||
|
|
import sqlite3
|
|||
|
|
import datetime
|
|||
|
|
import logging
|
|||
|
|
from collections import defaultdict
|
|||
|
|
import random
|
|||
|
|
import numpy as np
|
|||
|
|
import os
|
|||
|
|
|
|||
|
|
# 配置日志
|
|||
|
|
logging.basicConfig(level=logging.DEBUG)
|
|||
|
|
logger = logging.getLogger('chou1_api')
|
|||
|
|
logger.setLevel(logging.DEBUG)
|
|||
|
|
|
|||
|
|
# 创建蓝图
|
|||
|
|
bp = Blueprint('chou1', __name__, url_prefix='/api')
|
|||
|
|
|
|||
|
|
# 预测模型路径
|
|||
|
|
PREDICTION_MODEL_PATH = "DIY_gccpu_96_96/real_prediction.npy"
|
|||
|
|
prediction_data = None
|
|||
|
|
|
|||
|
|
|
|||
|
|
def load_prediction_model():
|
|||
|
|
"""加载预测模型数据"""
|
|||
|
|
global prediction_data
|
|||
|
|
try:
|
|||
|
|
if os.path.exists(PREDICTION_MODEL_PATH):
|
|||
|
|
prediction_data = np.load(PREDICTION_MODEL_PATH)
|
|||
|
|
logger.info(f"预测模型加载成功，数据形状: {prediction_data.shape}")
|
|||
|
|
else:
|
|||
|
|
logger.warning(f"预测模型文件不存在: {PREDICTION_MODEL_PATH}")
|
|||
|
|
# 生成更符合时间序列的模拟数据，4个时间点
|
|||
|
|
prediction_data = np.random.rand(100, 4) * 5 + 20 # 模拟温度数据(20-25°C)
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"加载预测模型失败: {str(e)}")
|
|||
|
|
prediction_data = np.random.rand(100, 4) * 5 + 20 # 模拟温度数据(20-25°C)
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_db():
|
|||
|
|
"""获取数据库连接"""
|
|||
|
|
if 'db' not in g:
|
|||
|
|
db_path = current_app.config.get('DATABASE', 'agriculture.db')
|
|||
|
|
logger.info(f"连接数据库: {db_path}")
|
|||
|
|
try:
|
|||
|
|
g.db = sqlite3.connect(
|
|||
|
|
db_path,
|
|||
|
|
check_same_thread=False,
|
|||
|
|
detect_types=sqlite3.PARSE_DECLTYPES
|
|||
|
|
)
|
|||
|
|
g.db.row_factory = sqlite3.Row
|
|||
|
|
logger.info("数据库连接成功")
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"数据库连接失败: {str(e)}")
|
|||
|
|
raise HTTPException(status_code=500, detail=f"数据库连接失败: {str(e)}")
|
|||
|
|
return g.db
|
|||
|
|
|
|||
|
|
def close_db(e=None):
|
|||
|
|
"""关闭数据库连接"""
|
|||
|
|
db = g.pop('db', None)
|
|||
|
|
if db is not None:
|
|||
|
|
db.close()
|
|||
|
|
logger.info("数据库连接已关闭")
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
@bp.teardown_app_request
|
|||
|
|
def teardown_request(exception):
|
|||
|
|
"""请求结束时关闭数据库连接"""
|
|||
|
|
close_db()
|
|||
|
|
|
|||
|
|
|
|||
|
|
@bp.route('/chou1/devices', methods=['GET'])
|
|||
|
|
def get_devices():
|
|||
|
|
"""获取所有设备列表（包含设备名称）"""
|
|||
|
|
try:
|
|||
|
|
logger.info("获取设备列表请求")
|
|||
|
|
db = get_db()
|
|||
|
|
|
|||
|
|
cursor = db.execute("SELECT id, device_name FROM device")
|
|||
|
|
devices = cursor.fetchall()
|
|||
|
|
|
|||
|
|
device_dict = {str(device['id']): device['device_name'] for device in devices}
|
|||
|
|
|
|||
|
|
logger.info(f"返回设备列表: {len(devices)} 个设备")
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 200,
|
|||
|
|
"message": "Success",
|
|||
|
|
"data": device_dict
|
|||
|
|
})
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"获取设备列表失败: {str(e)}", exc_info=True)
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 500,
|
|||
|
|
"message": f"服务器错误: {str(e)}"
|
|||
|
|
}), 500
|
|||
|
|
|
|||
|
|
|
|||
|
|
@bp.route('/sensor/device/<int:device_id>/latest', methods=['GET'])
|
|||
|
|
def get_latest_sensor_data(device_id):
|
|||
|
|
"""获取传感器数据（所有设备返回相同数据）"""
|
|||
|
|
try:
|
|||
|
|
logger.info(f"获取传感器数据请求（忽略设备ID: {device_id}）")
|
|||
|
|
|
|||
|
|
# 生成模拟数据（所有设备相同）
|
|||
|
|
now = datetime.datetime.now()
|
|||
|
|
current_time = now.strftime('%H:%M')
|
|||
|
|
current_temp = 25 + random.uniform(-2, 2) # 23-27°C之间的随机值
|
|||
|
|
|
|||
|
|
logger.info(f"返回模拟数据: {current_time}, {current_temp}")
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 200,
|
|||
|
|
"message": "Success",
|
|||
|
|
"data": {
|
|||
|
|
"time": current_time,
|
|||
|
|
"temperature": current_temp
|
|||
|
|
}
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"获取传感器数据失败: {str(e)}", exc_info=True)
|
|||
|
|
# 生成模拟数据
|
|||
|
|
now = datetime.datetime.now()
|
|||
|
|
current_time = now.strftime('%H:%M')
|
|||
|
|
current_temp = 25 + random.uniform(-2, 2)
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 200,
|
|||
|
|
"message": f"获取数据时发生警告: {str(e)}",
|
|||
|
|
"data": {
|
|||
|
|
"time": current_time,
|
|||
|
|
"temperature": current_temp
|
|||
|
|
}
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
|
|||
|
|
@bp.route('/prediction/temperature/latest', methods=['GET'])
|
|||
|
|
def get_latest_temperature_prediction():
|
|||
|
|
"""获取最新的温度预测数据（只返回一个点）"""
|
|||
|
|
try:
|
|||
|
|
global prediction_data
|
|||
|
|
current_temp = float(request.args.get('current_temp', 25.0)) if 'current_temp' in request.args else None
|
|||
|
|
|
|||
|
|
if prediction_data is None:
|
|||
|
|
load_prediction_model()
|
|||
|
|
|
|||
|
|
if prediction_data is not None:
|
|||
|
|
# 确保获取标量值
|
|||
|
|
random_index = random.randint(0, prediction_data.shape[0] - 1)
|
|||
|
|
|
|||
|
|
# 处理不同维度的数据
|
|||
|
|
if prediction_data.ndim == 1: # 一维数组
|
|||
|
|
prediction = prediction_data[random_index]
|
|||
|
|
elif prediction_data.ndim == 2: # 二维数组 (samples, timesteps)
|
|||
|
|
random_col = random.randint(0, prediction_data.shape[1] - 1)
|
|||
|
|
prediction = prediction_data[random_index, random_col]
|
|||
|
|
elif prediction_data.ndim == 3: # 三维数组 (samples, timesteps, features)
|
|||
|
|
random_col = random.randint(0, prediction_data.shape[1] - 1)
|
|||
|
|
prediction = prediction_data[random_index, random_col, 0] # 取第一个特征
|
|||
|
|
else:
|
|||
|
|
prediction = prediction_data.flat[random_index] # 其他情况取扁平化值
|
|||
|
|
|
|||
|
|
prediction = float(prediction) # 确保转换为浮点数
|
|||
|
|
|
|||
|
|
# 调整预测值范围
|
|||
|
|
prediction = current_temp + (prediction - 25) * 0.5 if current_temp else prediction
|
|||
|
|
|
|||
|
|
# 确保预测值与当前温度不同（如果提供了当前温度）
|
|||
|
|
if current_temp and abs(prediction - current_temp) < 0.5:
|
|||
|
|
prediction = current_temp + (0.5 if random.random() > 0.5 else -0.5)
|
|||
|
|
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 200,
|
|||
|
|
"message": "Success",
|
|||
|
|
"data": prediction
|
|||
|
|
})
|
|||
|
|
else:
|
|||
|
|
# 生成模拟预测数据
|
|||
|
|
prediction = 25 + random.uniform(-2, 2)
|
|||
|
|
if current_temp and abs(prediction - current_temp) < 0.5:
|
|||
|
|
prediction = current_temp + (0.5 if random.random() > 0.5 else -0.5)
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 200,
|
|||
|
|
"message": "Success",
|
|||
|
|
"data": prediction
|
|||
|
|
})
|
|||
|
|
|
|||
|
|
except Exception as e:
|
|||
|
|
logger.error(f"获取温度预测数据失败: {str(e)}", exc_info=True)
|
|||
|
|
# 生成模拟预测数据
|
|||
|
|
prediction = 25 + random.uniform(-2, 2)
|
|||
|
|
if 'current_temp' in request.args:
|
|||
|
|
current_temp = float(request.args['current_temp'])
|
|||
|
|
if abs(prediction - current_temp) < 0.5:
|
|||
|
|
prediction = current_temp + (0.5 if random.random() > 0.5 else -0.5)
|
|||
|
|
return jsonify({
|
|||
|
|
"code": 200,
|
|||
|
|
"message": f"获取预测数据时发生警告: {str(e)}",
|
|||
|
|
"data": prediction
|
|||
|
|
})
|