336 lines
12 KiB
Python
336 lines
12 KiB
Python
from datetime import datetime, timezone, timedelta, date
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import pandas as pd
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import requests
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import json
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from matplotlib import pyplot as plt
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#登录接口认证
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def loginAuth():
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# 接口地址
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global auth_token
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url = "https://xj-tb.maimaiag.com/api/auth/login" # 请替换为实际地址
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# 请求头
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headers = {
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"Content-Type": "application/json"
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}
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# 请求体数据
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payload = {
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"username": "read@xj.com",
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"password": "maimai"
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}
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try:
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# 发送POST请求
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response = requests.post(
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url,
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headers=headers,
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data=json.dumps(payload) # 或者直接使用 json=payload
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)
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# 检查响应状态码
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if response.status_code == 200:
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print("登录成功！")
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print("响应数据:", response.json())
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# 通常登录接口会返回token，可以这样获取
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response_data = response.json()
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auth_token = response_data.get("token") # 根据实际返回字段调整
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if auth_token:
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print("获取到的认证Token:", auth_token)
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else:
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print(f"登录失败，状态码: {response.status_code}")
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print("错误信息:", response.text)
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except requests.exceptions.RequestException as e:
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print(f"请求发生异常: {str(e)}")
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {str(e)}")
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return auth_token
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# 调用数据接口--历史数据
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def getWeatherAndSoilData(auth_token,device_id,startTs,endTs,agg,interval,limit,orderBy,keys):
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# 配置信息
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BASE_URL = "https://xj-tb.maimaiag.com/" # 请替换为实际API地址
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TOKEN = auth_token # 替换为实际的Bearer token
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DEVICE_ID = device_id # 替换为实际的设备ID
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# 构造请求URL
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endpoint = f"/api/plugins/telemetry/DEVICE/{DEVICE_ID}/values/timeseries"
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url = BASE_URL + endpoint
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# 请求头
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headers = {
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"Content-Type": "application/json",
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"X-Authorization": f"Bearer+{TOKEN}"
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}
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# 查询参数
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params = {
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"keys": keys, # 要查询的参数，多个用逗号分隔
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"startTs": startTs, # 开始时间戳（毫秒）- 可选
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"endTs": endTs, # 结束时间戳（毫秒）- 可选
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"agg": agg, # 聚合函数 - 可选
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"interval": interval, # 聚合间隔（毫秒）- 可选
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"limit": limit, # 返回条数 - 可选
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"orderBy": orderBy # 排序方式 - 可选
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}
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try:
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# 发送GET请求
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response = requests.get(
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url,
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headers=headers,
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params=params
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)
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# 检查响应状态码
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if response.status_code == 200:
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# print("请求成功！")
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data = response.json()
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# print("响应数据:", json.dumps(data, indent=2))
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converted_data = {k: convert_timestamp_to_datetime(v) for k, v in data.items()}
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return converted_data
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elif response.status_code == 401:
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print("认证失败，请检查Token是否正确")
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elif response.status_code == 404:
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print("设备不存在或路径错误")
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else:
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print(f"请求失败，状态码: {response.status_code}")
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print("错误信息:", response.text)
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except requests.exceptions.RequestException as e:
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print(f"请求发生异常: {str(e)}")
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {str(e)}")
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print("原始响应:", response.text)
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def convert_timestamp_to_datetime(data_dict):
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"""
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递归地将字典中的所有'timestamp'字段转换为可读的日期时间格式
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保留原始时间戳的同时添加新的'datetime'字段
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"""
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if isinstance(data_dict, dict):
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if "ts" in data_dict:
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# 转换毫秒时间戳为datetime对象
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timestamp_sec = data_dict["ts"] / 1000
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dt = datetime.fromtimestamp(timestamp_sec)
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# 添加新字段，保留原始时间戳
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data_dict["datetime"] = dt.strftime("%Y-%m-%d %H:%M:%S")
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return data_dict
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elif isinstance(data_dict, list):
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return [convert_timestamp_to_datetime(item) for item in data_dict]
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else:
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return data_dict
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def dateToTimestamp(date):
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# dt = datetime.strptime(date, "%Y-%m-%d %H:%M:%S")
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if isinstance(date, datetime):
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# 如果已经是datetime对象，直接使用
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dt = date
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elif isinstance(date, str):
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# 如果是字符串，解析为datetime对象
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dt = datetime.strptime(date, "%Y-%m-%d %H:%M:%S.%f")
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else:
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raise TypeError("date参数必须是字符串或datetime对象")
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timestamp_ms = int(dt.timestamp() * 1000)
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return timestamp_ms
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#调用接口获取未来气象数据
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def getFutureWeather(monitor_date):
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base_url_future="https://api.open-meteo.com/"
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end_point="v1/forecast"
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current_date = datetime.now().date()
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params = {
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"latitude": "41.34252110189814", # 纬度
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"longitude": "86.28810755462379", # 经度
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"hourly": "temperature_2m,precipitation,precipitation_probability"
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}
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# if monitor_date.date()<current_date:
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# # url = base_url_history + end_point
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# params["start_date"] = (monitor_date + timedelta(days=1)).strftime("%Y-%m-%d")
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# params["end_date"] = (monitor_date + timedelta(days=3)).strftime("%Y-%m-%d")
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#
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# else:
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url = base_url_future + end_point
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params["forecast_days"]= 4
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try:
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# 发送GET请求
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response = requests.get(
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url,
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params=params
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)
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# 检查响应状态码
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if response.status_code == 200:
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data = response.json()
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result={}
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if monitor_date.date()<current_date:
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result["qw_day1"] = get_tjvalue(data['hourly']['temperature_2m'][:24])
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result["qw_day2"] = get_tjvalue(data['hourly']['temperature_2m'][24:48])
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result["qw_day3"] = get_tjvalue(data['hourly']['temperature_2m'][48:72])
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result["js_day1"] = get_tjvalue(data['hourly']['precipitation'][:24])
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result["js_day2"] = get_tjvalue(data['hourly']['precipitation'][24:48])
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result["js_day3"] = get_tjvalue(data['hourly']['precipitation'][48:72])
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else:
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result["qw_day1"] = get_tjvalue(data['hourly']['temperature_2m'][24:48])
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result["qw_day2"] = get_tjvalue(data['hourly']['temperature_2m'][48:72])
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result["qw_day3"] = get_tjvalue(data['hourly']['temperature_2m'][72:96])
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result["js_day1"] = get_tjvalue(data['hourly']['precipitation'][24:48])
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result["js_day2"] = get_tjvalue(data['hourly']['precipitation'][48:72])
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result["js_day3"] = get_tjvalue(data['hourly']['precipitation'][72:96])
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return result
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elif response.status_code == 401:
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print("认证失败，请检查Token是否正确")
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elif response.status_code == 404:
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print("设备不存在或路径错误")
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else:
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print(f"请求失败，状态码: {response.status_code}")
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print("错误信息:", response.text)
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except requests.exceptions.RequestException as e:
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print(f"请求发生异常: {str(e)}")
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except json.JSONDecodeError as e:
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print(f"JSON解析错误: {str(e)}")
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print("原始响应:", response.text)
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def get_tjvalue(data):
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result={}
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max_val = max(data)
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min_val = min(data)
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mean_val = sum(data) / len(data)
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sum_val = sum(data)
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result['max']=max_val
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result['min']=min_val
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result['mean']=mean_val
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result['sum'] = sum_val
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return result
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soil_params={
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"1号传感器":{
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"device_id":"31fbb2d0-561b-11f0-a556-4f10f26fc07f"
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},
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"2号传感器": {
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"device_id": "3bf4cc90-561b-11f0-a556-4f10f26fc07f"
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},
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"3号传感器": {
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"device_id": "43a57cf0-561b-11f0-a556-4f10f26fc07f"
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},
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"4号传感器": {
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"device_id": "4ff6b780-561b-11f0-a556-4f10f26fc07f"
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},
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"5号传感器": {
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"device_id": "598c5480-561b-11f0-a556-4f10f26fc07f"
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},
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"6号传感器": {
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"device_id": "6ff07260-561b-11f0-a556-4f10f26fc07f"
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},
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"7号传感器": {
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"device_id": "8637b970-561b-11f0-a556-4f10f26fc07f"
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},
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"8号传感器": {
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"device_id": "955de5f0-561b-11f0-a556-4f10f26fc07f"
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},
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"9号传感器": {
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"device_id": "a24e94d0-561b-11f0-a556-4f10f26fc07f"
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},
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"10号传感器": {
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"device_id": "adb7e060-561b-11f0-a556-4f10f26fc07f"
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},
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"11号传感器": {
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"device_id": "b74c6bf0-561b-11f0-a556-4f10f26fc07f"
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},
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"12号传感器": {
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"device_id": "c0f36e10-561b-11f0-a556-4f10f26fc07f"
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},
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}
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def get_soil_data_dk1(auth_token,startTs, endTs):
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keys_soil = "SOIL_MOISTURE_SURFACE,SOIL_MOISTURE_MIDDLE,SOIL_MOISTURE_BOTTOM"
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limit = ""
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orderBy = ""
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result={}
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for key in soil_params:
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id=soil_params[key]['device_id']
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soil_data = getWeatherAndSoilData(auth_token,id, startTs, endTs, "AVG", 3600000, limit, orderBy,
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keys_soil)
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result[key]=soil_data
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return result
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def get_soil_data_dk2(auth_token,device_id,startTs, endTs):
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keys_soil = "SOIL_MOISTURE_SURFACE,SOIL_MOISTURE_MIDDLE,SOIL_MOISTURE_BOTTOM"
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limit = ""
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orderBy = ""
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result={}
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soil_data = getWeatherAndSoilData(auth_token,device_id, startTs, endTs, "AVG", 3600000, limit, orderBy,
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keys_soil)
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result[device_id]=soil_data
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return result
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weather_params={
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"device_id" : "18d121f0-561b-11f0-a556-4f10f26fc07f",
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"agg" :"AVG",
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"interval" : 3600000, #1小时
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"limit" :"",
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"orderBy" : "",
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"keys":"AIR_TEMPERATURE,WIND_SPEED,AIR_LUX,AIR_HUMIDITY,RAIN_FALL_REALTIME"
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}
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def get_plot(data_list):
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df = pd.DataFrame(data_list)
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df['value'] = pd.to_numeric(df['value'], errors='coerce').round(3)# 转换湿度值为浮点数
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df['datetime'] = pd.to_datetime(df['datetime']) # 转换为datetime类型
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# 设置画布大小
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plt.figure(figsize=(12, 6))
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# 绘制折线图
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plt.plot(df['datetime'], df['value'],
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color='teal',
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linestyle='-',
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linewidth=2,
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marker='o',
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markersize=4,
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markerfacecolor='orange',
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markeredgecolor='orange',
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label='Soil Moisture (%)')
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# 添加22点后的数据高亮（符合你之前获取夜间数据的需求）
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night_data = df[df['datetime'].dt.hour >= 22]
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plt.scatter(night_data['datetime'], night_data['value'],
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color='red',
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s=30,
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label='22:00-06:00 Data')
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# 设置标题和坐标轴标签
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plt.title('Soil Moisture Time Series (2025-06-22 to 2025-06-30)',
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fontsize=14,
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pad=15)
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plt.xlabel('Datetime', fontsize=12, labelpad=10)
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plt.ylabel('Soil Moisture (%)', fontsize=12, labelpad=10)
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# 设置x轴刻度旋转，避免重叠
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plt.xticks(rotation=45, ha='right', fontsize=10)
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# 添加网格线
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plt.grid(axis='y', linestyle='--', alpha=0.7)
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# 添加图例
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plt.legend(loc='upper right', fontsize=10)
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# 调整布局
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plt.tight_layout()
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# 显示图表
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plt.show()
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if __name__ == "__main__":
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auth_token = loginAuth()
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device_id="18d121f0-561b-11f0-a556-4f10f26fc07f"
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agg="AVG"
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interval=21600000
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limit=""
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orderBy=""
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keys="RAIN_FALL_REALTIME"
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date_start = "2025-06-25 00:00:00.123"
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date_end = "2025-07-01 23:59:59.123"
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startTs=dateToTimestamp(date_start)
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endTs=dateToTimestamp(date_end)
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# data=get_soil_data_dk1(auth_token,startTs,endTs)
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# print(data)
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data=getWeatherAndSoilData(auth_token,device_id,startTs,endTs,"AVG",3600000,limit,orderBy,keys)
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print(data['RAIN_FALL'])
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# get_plot(data['1号传感器']['SOIL_MOISTURE_SURFACE'])
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get_plot(data['RAIN_FALL_REALTIME']) |