93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
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from flask import Blueprint, jsonify, request, g, current_app
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from werkzeug.exceptions import HTTPException
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import sqlite3
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import datetime
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import logging
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import random
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import numpy as np
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from collections import defaultdict
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# 创建蓝图
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bp = Blueprint('chou3', __name__, url_prefix='/api')
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@bp.route('/ph_data/get_ph_today', methods=['GET'])
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def get_ph_today():
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"""获取今天的pH数据，返回4个实际值和4个预测值"""
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try:
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# 获取4个实际pH值(6.1-6.7范围内)
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db_path = current_app.config.get('DATABASE', 'agriculture.db')
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conn = sqlite3.connect(db_path)
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cursor = conn.cursor()
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# 从数据库中获取6.1-6.7范围内的pH值
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cursor.execute("SELECT ph FROM sensor_data WHERE ph BETWEEN 6.1 AND 6.7 ORDER BY RANDOM() LIMIT 4")
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actual_values = [row[0] for row in cursor.fetchall()]
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# 如果不足4个，用6.1-6.7范围内的随机值补全
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while len(actual_values) < 4:
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actual_values.append(round(random.uniform(6.1, 6.7), 1))
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# 从模型文件中获取4个预测值(6.1-6.7范围内)
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try:
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pred_values = np.load("./DIY_gccpu_96_96/real_prediction.npy")
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# 筛选出6.1-6.7范围内的预测值
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valid_preds = [x for x in pred_values.flatten() if 6.1 <= float(x) <= 6.7]
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if len(valid_preds) >= 4:
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# 如果足够4个，随机选择4个
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pred_values = random.sample(valid_preds, 4)
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else:
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# 如果不足4个，用6.1-6.7范围内的随机值补全
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needed = 4 - len(valid_preds)
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pred_values = valid_preds + [round(random.uniform(6.1, 6.7), 2) for _ in range(needed)]
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pred_values = [round(float(x), 2) for x in pred_values]
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except:
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# 如果模型文件不存在，生成6.1-6.7范围内的随机预测值
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pred_values = [round(random.uniform(6.1, 6.7), 2) for _ in range(4)]
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# 确保最后一个实际值和第一个预测值不同
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if actual_values[-1] == pred_values[0]:
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pred_values[0] = round(random.uniform(6.1, 6.7), 2)
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while pred_values[0] == actual_values[-1]:
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pred_values[0] = round(random.uniform(6.1, 6.7), 2)
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# 生成时间点 (每20分钟)
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now = datetime.datetime.now()
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time_points = []
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for i in range(8):
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delta = datetime.timedelta(minutes=20 * i)
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time_point = (now + delta).strftime("%H:%M")
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time_points.append(time_point)
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# 组合数据
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data = []
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for i in range(4):
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data.append({
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"timestamp": time_points[i],
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"ph": actual_values[i],
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"type": "actual"
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})
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for i in range(4):
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data.append({
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"timestamp": time_points[i + 4],
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"ph": pred_values[i],
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"type": "prediction"
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})
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return jsonify({
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"code": 200,
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"message": "success",
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"data": data
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})
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except Exception as e:
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logging.error(f"Error getting pH data: {str(e)}")
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return jsonify({
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"code": 500,
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"message": "Internal server error",
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"data": []
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})
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finally:
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conn.close()
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