Esp32+Python获取天气数据+Pyechrts（Html）显示

import schedule from pymysql import Connection import pyecharts.options as opts from pyecharts.charts import Gauge,Bar,Page,Line,Liquid from pyecharts.components import Table conn = Connection( host="localhost", port=3306, user="root", password="123456", autocommit=True ) # 获取游标对象 cursor = conn.cursor() # 选择数据库 conn.select_db("bs") def run(): sql = f"select UpdateTime from heweather order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result = reversed(result) data = "" for i in result: UpdateTime = i.__str__() UpdateTime = UpdateTime[2:17] data+=UpdateTime UpdateTime = data.replace("+",",") UpdateTime = UpdateTime.strip(",") print(UpdateTime) UpdateTimelist = UpdateTime.split(',') sql = f"select Weather from heweather where Num = (select max(Num) from heweather)" cursor.execute(sql) result=cursor.fetchall() Weather = result.__str__() Weather = Weather.split("'") print(Weather[1]) sql = f"select WindDir from heweather where Num = (select max(Num) from heweather)" cursor.execute(sql) result=cursor.fetchall() WindDir = result.__str__() WindDir = WindDir.split("'") print(WindDir[1]) sql = f"select WindScale from heweather order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result = reversed(result) # 反转 windscale = "" for i in result: WindScale = i.__str__() WindScale = WindScale[1:3] WindScale = WindScale.split(")") WindScale = WindScale[0] windscale += WindScale windscale = windscale.strip(",") print(windscale) windscale = windscale.split(',') sql = f"select Precip from heweather order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result=reversed(result)#反转 precip="" for i in result: Precip = i.__str__() Precip = Precip[1:5] Precip = Precip.split(")") Precip =Precip[0] precip += Precip precip = precip.strip(",") print(precip) precip = precip.split(',') sql = f"select Pressure from heweather order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result=reversed(result)#反转 pressure="" for i in result: Pressure = i.__str__() Pressure = Pressure[1:7] Pressure = Pressure.split(")") Pressure =Pressure[0] pressure += Pressure pressure = pressure.strip(",") print(pressure) pressure = pressure.split(',') sql = f"select Tem from receive order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result=reversed(result)#反转 tem="" for i in result: Tem = i.__str__() Tem = Tem[1:5] Tem = Tem.split(")") Tem =Tem[0] tem += Tem tem = tem.strip(",") print(tem) temlist = tem.split(',') sql = f"select Hum from receive order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result=reversed(result)#反转 hum="" for i in result: Hum = i.__str__() Hum = Hum[1:5] Hum = Hum.split(")") Hum =Hum[0] hum += Hum hum = hum.strip(",") print(hum) humlist = hum.split(',') sql = f"select UV from receive order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result=reversed(result)#反转 uv="" for i in result: UV = i.__str__() UV = UV[1:5] UV = UV.split(")") UV =UV[0] uv += UV uv = uv.strip(",") print(uv) uv = uv.split(',') sql = f"select PM from receive order by Num desc limit 12" cursor.execute(sql) result = cursor.fetchall() result=reversed(result)#反转 pm="" for i in result: PM = i.__str__() PM = PM[1:8] PM = PM.split(")") PM =PM[0] pm += PM pm = pm.strip(",") print(pm) pm = pm.split(',') x_data = [UpdateTimelist[0], UpdateTimelist[1], UpdateTimelist[2], UpdateTimelist[3], UpdateTimelist[4], UpdateTimelist[5], UpdateTimelist[6], UpdateTimelist[7], UpdateTimelist[8], UpdateTimelist[9], UpdateTimelist[10], UpdateTimelist[11] ] y_data1 = [windscale[0], windscale[1], windscale[2], windscale[3], windscale[4], windscale[5], windscale[6], windscale[7], windscale[8], windscale[9], windscale[10], windscale[11] ] y_data2 = [precip[0],precip[1],precip[2],precip[3],precip[4], precip[5],precip[6],precip[7],precip[8],precip[9], precip[10],precip[11] ] y_data3=[pressure[0],pressure[1],pressure[2],pressure[3],pressure[4], pressure[5],pressure[6],pressure[7],pressure[8],pressure[9], pressure[10],pressure[11] ] y_data4=[temlist[0],temlist[1],temlist[2],temlist[3],temlist[4], temlist[5],temlist[6],temlist[7],temlist[8],temlist[9], temlist[10],temlist[11] ] y_data5=[humlist[0],humlist[1],humlist[2],humlist[3],humlist[4], humlist[5],humlist[6],humlist[7],humlist[8],humlist[9], humlist[10],humlist[11] ] y_data6=[uv[0],uv[1],uv[2],uv[3],uv[4], uv[5],uv[6],uv[7],uv[8],uv[9], uv[10],uv[11] ] y_data7=[pm[0],pm[1],pm[2],pm[3],pm[4], pm[5],pm[6],pm[7],pm[8],pm[9], pm[10],pm[11] ] attr = [x_data[11]] v1 = [y_data1[11]] v2 = [y_data2[11]] v3 = [y_data3[11]] v4 = [y_data6[11]] v5 = [y_data7[11]] def table_base() -> Table: table = Table() table.chart_id="0b661b17c86444b4b197426083dd2285" headers = ["获取项","数据"] rows = [ ["UpdateTime",UpdateTimelist[11]], ["Weather",Weather[1]], ["WindDir",WindDir[1]], ["WindScale",windscale[11]] ] table.add(headers, rows) return table def bar_datazoom_slider() -> Bar: bar = Bar() bar.chart_id="f9a9d6f1ad0748beb1cac2488fdf378a" bar.add_xaxis(attr) bar.add_yaxis("雨量", v2) bar.add_yaxis("气压", v3) bar.add_yaxis("UV", v4) bar.add_yaxis("PM2.5", v5) bar.set_global_opts( title_opts=opts.TitleOpts(title="基于物联网的户外空气监测系统"), ) return bar def line_markpoint() -> Line: line = Line() line.chart_id="ae0c0ade615a4a79a86fa899ebed7544" line.set_global_opts( # tooltip_opts=opts.TooltipOpts(is_show=True,trigger="axis"), legend_opts=opts.LegendOpts(is_show=True), toolbox_opts=opts.ToolboxOpts(is_show=True,pos_left = "96%"), xaxis_opts=opts.AxisOpts(type_="category", axislabel_opts={"rotate": 30}), yaxis_opts=opts.AxisOpts( type_="value", axistick_opts=opts.AxisTickOpts(is_show=True), splitline_opts=opts.SplitLineOpts(is_show=True), ), ) line.add_xaxis(xaxis_data=x_data) line.add_yaxis( series_name="风力等级折线", y_axis=y_data1, symbol="emptyCircle", color='orange', is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False), ) line.add_yaxis( series_name="雨量折线", y_axis=y_data2, symbol="emptyCircle", color='red', is_symbol_show=True, label_opts=opts.LabelOpts(is_show=False), ) line.add_yaxis( series_name="气压折线", y_axis=y_data3,