1. influxDB连接

首先用InfluxDBStudio软件连接influxDB数据库来查看所有表：

2. 写sql语句来查询数据

然后和平时写sql查询语句一样，先创建连接client，然后调用其query函数来查询获取数据

self.client = influxdb.InfluxDBClient(host=influx_host, port=influx_port, username=influx_username, password=influx_password, database=influx_database, timeout=influx_timeout)

query_sql = f"""select * from {table_name} \
                where point = '{temperature_point_id}'\
                and nowTime >= {begin_time} and nowTime < {end_time}"""
tables = self.client.query(query_sql)

3. 向influxDB数据库插入数据

插入数据时无需提前新建表，只需要指定表名直接插入即可：

self.influx_conn()
if data:
    for i in range(len(data)):
        data[i].update({"measurement": table_name})  # 加上表名
        data[i].update({"time": now_time})  # 加上时间
    self.client.write_points(data)
else:
    print("data is None!")

4. 完整代码（code）

import influxdb
import pandas as pd
import datetime
import time


influx_host = "132.12.29.18"
influx_port = 8086
influx_username = "admin"
influx_password = "abc32100"
influx_database = "dqcloud"
influx_timeout = 10


def get_time_str(time_stamp):  # 时间戳转字符串
    time_array = time.localtime(int(time_stamp) / 1000)
    time_str = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
    return time_str


class InfluxInfo(object):
    def __init__(self):
        pass

    def influx_conn(self):
        self.client = influxdb.InfluxDBClient(host=influx_host, port=influx_port, username=influx_username,
                                         password=influx_password, database=influx_database, timeout=influx_timeout)

    def query_data(self, table_name, temperature_point_id, begin_time, end_time):
        self.influx_conn()
        if type == "query":
            # point:测温点id，num:测温点编号，lastTime:上次测温时间，nowTime:巡检时间，v:温度值，tr:温升值
            query_sql = f"""select * from {table_name} \
                            where point = '{temperature_point_id}'\
                            and nowTime >= {begin_time} and nowTime < {end_time}"""
            tables = self.client.query(query_sql)
            if len(tables) > 0:
                return tables._get_series()
            else:
                return []

    def insert_data(self, table_name, now_time, data=None):
        self.influx_conn()
        if data:
            for i in range(len(data)):
                data[i].update({"measurement": table_name})  # 加上表名
                data[i].update({"time": now_time})  # 加上时间
            self.client.write_points(data)
        else:
            print("data is None!")


def get_data_by_tum(query_rslt):
    """
    "time": 数据插入时间（时序库自带的，不是自己插入的）
    "log": "巡检记录id"
    "point": "测温点id"
    "num": "测温点编号"
    "lastTime": "上次测温时间"
    "nowTime": "巡检时间"
    "v": "温度值"
    "tr": "温升值"
    "overTempAlert": "超温告警：0-否，1-是"
    "overTempWarning": "超温预警：0-否，1-是"
    "tempRiseAlert": "温升告警：0-否，1-是"
    """
    timelist = []  #
    nowTime_list = []
    lastTime_list = []
    tr_list = []
    valuelist = []
    if len(query_rslt) > 0:
        result = query_rslt[0]['values']
        for r in result:
            if len(r) > 4 and r[-1]:
                timestr = r[0].split(".")[0]
                timestr = timestr.replace("T", " ")
                timelist.append(timestr)
                lastTime_list.append(get_time_str(r[1]))
                nowTime_list.append(get_time_str(r[3]))
                tr_list.append(float(r[-2]))
                valuelist.append(float(r[-1]))
    if len(timelist) > 0:
        returndata = pd.DataFrame({'time': timelist, 'lastTime': lastTime_list, 'nowTime': nowTime_list, 'tr': tr_list, 'value': valuelist})
    else:
        returndata = pd.DataFrame()
    return returndata


if __name__ == '__main__':
    info = InfluxInfo()
    # "point": "测温点id"
    #     "nowTime": "预测时间"
    #     "v": "温度值"
    #     "tr": "温升值"
    #     "overTempWarning": "超温预警：0-否，1-是"
    #     "tempRiseWarning": "温升预警：0-否，1-是"
    data = [
        {
            "tags": {  # 标签，元数据信息和标识数据的键值对
                "point": "85231",
            },
            "fields": {  # 字段，实际的数值数据
                "predictTime": 1698289692244,
                "v": 44,
                "tr": 0.64,
                "overTempWarning": 0,
                "tempRiseWarning": 0
            }
        },
        {
            "tags": {
                "point": "85232",
            },
            "fields": {
                "predictTime": 1698289692292,
                "v": 45,
                "tr": 1.69,
                "overTempWarning": 0,
                "tempRiseWarning": 1
            }
        }
    ]
    now_time = datetime.datetime.strftime(datetime.datetime.now(), "%Y-%m-%dT%H:%M:%SZ")
    info.insert_data("point_predict_data", now_time, data)