From cbc64b599889224ae4a1b80991acd1a10dab658c Mon Sep 17 00:00:00 2001 From: HuangYixin <237842420@qq.com> Date: Fri, 7 Jun 2024 17:33:57 +0800 Subject: [PATCH] qwee --- .idea/.gitignore | 3 + .idea/AirQuility.iml | 15 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/misc.xml | 7 + .idea/modules.xml | 8 + .idea/vcs.xml | 6 + AirQuilitySys/.idea/vcs.xml | 6 + AirQuilitySys/AirQui.py | 120 +++--- AirQuilitySys/DayAir.py | 123 ++++++ AirQuilitySys/DayAirbing.py | 154 +++++++ AirQuilitySys/DayAirzhu.py | 132 ++++++ AirQuilitySys/LSTM_airqui.py | 63 +++ AirQuilitySys/Login.py | 27 +- AirQuilitySys/airquility.db | 0 AirQuilitySys/data.py | 6 +- AirQuilitySys/model.h5 | Bin 0 -> 148848 bytes AirQuilitySys/replace.py | 10 +- AirQuilitySys/scaler.joblib | Bin 0 -> 719 bytes AirQuilitySys/test.py | 1 + AirQuilitySys/time.py | 382 ++++++++++++++++++ AirQuilitySys/trained_model.h5 | Bin 0 -> 148848 bytes AirQuilitySys/user.py | 44 ++ AirQuilitySys/your_database_path.db | 0 ...设计课程设计报告模板 2024年.doc | Bin 0 -> 740791 bytes ...设计课程设计报告模板 2024年.doc | Bin 0 -> 162 bytes 25 files changed, 1028 insertions(+), 85 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 .idea/AirQuility.iml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/misc.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/vcs.xml create mode 100644 AirQuilitySys/.idea/vcs.xml create mode 100644 AirQuilitySys/DayAir.py create mode 100644 AirQuilitySys/DayAirbing.py create mode 100644 AirQuilitySys/DayAirzhu.py create mode 100644 AirQuilitySys/LSTM_airqui.py create mode 100644 AirQuilitySys/airquility.db create mode 100644 AirQuilitySys/model.h5 create mode 100644 AirQuilitySys/scaler.joblib create mode 100644 AirQuilitySys/test.py create mode 100644 AirQuilitySys/trained_model.h5 create mode 100644 AirQuilitySys/user.py create mode 100644 AirQuilitySys/your_database_path.db create mode 100644 Python程序设计课程设计报告模板 2024年.doc create mode 100644 ~$thon程序设计课程设计报告模板 2024年.doc diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..359bb53 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# 默认忽略的文件 +/shelf/ +/workspace.xml diff --git a/.idea/AirQuility.iml b/.idea/AirQuility.iml new file mode 100644 index 0000000..7a85463 --- /dev/null +++ b/.idea/AirQuility.iml @@ -0,0 +1,15 @@ + + + + + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100644 index 0000000..5cc6554 --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,7 @@ + + + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..ed8021f --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..35eb1dd --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/AirQuilitySys/.idea/vcs.xml b/AirQuilitySys/.idea/vcs.xml new file mode 100644 index 0000000..6c0b863 --- /dev/null +++ b/AirQuilitySys/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/AirQuilitySys/AirQui.py b/AirQuilitySys/AirQui.py index 339e28b..96e7552 100644 --- a/AirQuilitySys/AirQui.py +++ b/AirQuilitySys/AirQui.py @@ -1,76 +1,50 @@ -import matplotlib.pyplot as plt -# 创建图形和坐标轴对象,指定尺寸和DPI -fig, ax = plt.subplots(figsize=(13.33,7.5), dpi = 96) -# 绘制折线 -for country in top_20_countries: - data = df[df['Country Name'] == country] - line = ax.plot(data['Year'], data['GDP'], label=country) -# 添加图例 -ax.legend(loc="best", fontsize=8) -# 创建网格 -ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1) -ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1) -# 重新格式化x轴标签和刻度线标签 -ax.set_xlabel('', fontsize=12, labelpad=10) -# 不需要轴标签 -ax.xaxis.set_label_position("bottom") -ax.xaxis.set_major_formatter(lambda s, i : f'{s:,.0f}') -#以防万一我们需要额外的格式设置 -ax.xaxis.set_major_locator(MaxNLocator(integer=True)) -#以防我们需要额外的格式化 -ax.xaxis.set_tick_params(pad=2, labelbottom=True, bottom=True, labelsize=12, labelrotation=0) -# 重新格式化y轴 -ax.set_ylabel('GDP (Billions USD)', fontsize=12, labelpad=10) -ax.yaxis.set_label_position("left") -ax.yaxis.set_major_formatter(lambda s, i : f'{s*10**-9:,.0f}') -ax.yaxis.set_major_locator(MaxNLocator(integer=True)) -#以防我们需要额外的格式化 -ax.yaxis.set_tick_params(pad=2, labeltop=False, labelbottom=True, bottom=False, labelsize=12) +import tkinter as tk +import subprocess +import sys -# 颜色和线条样式 -colors_dict = {'United States': '#014f86', 'China': '#DC0000', 'Japan': '#ff4d6d', 'Germany': '#403d39', 'India': '#6a994e'} +def run_DayAir(): + """函数用于执行另一个Python脚本""" + try: + subprocess.run(["D:\Pycharm\Project\AirQuilitySys\.venv\Scripts\python.exe", "DayAir.py"], check=True) + except subprocess.CalledProcessError as e: + print(f"执行脚本时发生错误: {e}") -line_styles_dict = {'United States': '-', 'China': '-', 'Japan': '-', 'Germany': '-', 'India': '-'} -# 绘制前5条线 -for country in top_20_countries[:5]: - color = colors_dict.get(country, 'grey') - # 从字典中获取颜色,如果找不到,默认为灰色 - line_style = line_styles_dict.get(country, '-') - # 从字典中获取线条样式,如果未找到,默认为实线 - data = df[df['Country Name'] == country] - line = ax.plot(data['Year'], data['GDP'], color=color, linestyle=line_style, zorder=2, label=country) - # 添加图例 - ax.legend(loc="best", fontsize=8) - # 绘制剩余部分 - for country in top_20_countries[5:]: - data = df[df['Country Name'] == country] - line = ax.plot(data['Year'], data['GDP'], color='grey', linestyle=':', linewidth=0.5, zorder=2) -# 移除边框 -ax.spines[['top','right','bottom']].set_visible(False) -# 加粗左侧边框 -ax.spines['left'].set_linewidth(1.1) -# 在顶部添加红线和矩形 -ax.plot([0.05, .9], [.98, .98], transform=fig.transFigure, clip_on=False, color='#E3120B', linewidth=.6) -ax.add_patch(plt.Rectangle((0.05,.98), 0.04, -0.02, facecolor='#E3120B', transform=fig.transFigure, clip_on=False, linewidth = 0)) -# 添加标题和副标题 -ax.text(x=0.05, y=.93, s="Evolution of the 20 Richest Countries GDP over the Past 50 Years", transform=fig.transFigure, ha='left', fontsize=14, weight='bold', alpha=.8) -ax.text(x=0.05, y=.90, s="Focus on the current 5 richest countries from 1973 to 2022", transform=fig.transFigure, ha='left', fontsize=12, alpha=.8) -# 设置来源文本 -ax.text(x=0.05, y=0.12, s="Source: World Bank - https://databank.worldbank.org/", transform=fig.transFigure, ha='left', fontsize=10, alpha=.7) -# 调整绘图区域周围的边距 -plt.subplots_adjust(left=None, bottom=0.2, right=None, top=0.85, wspace=None, hspace=None) -# 设置白色背景 -fig.patch.set_facecolor('white') -# 绘制前5条线 -for country in top_20_countries[:5]: - color = colors_dict.get(country, 'grey') - # 从字典中获取颜色,如果找不到,默认为黑色 - line_style = line_styles_dict.get(country, '-') - # 从字典中获取线条样式,如果找不到,默认为实线 - data = df[df['Country Name'] == country] - line = ax.plot(data['Year'], data['GDP'], color=color, linestyle=line_style, zorder=2, label = country) - ax.plot(data['Year'].iloc[-1], data['GDP'].iloc[-1], 'o', color=color, markersize=10, alpha=0.3) - ax.plot(data['Year'].iloc[-1], data['GDP'].iloc[-1], 'o', color=color, markersize=5) - # 在图表上添加一些文字 - ax.annotate('During the 2000s,\nChina began experiencing rapid economic growth,\noutpacing all other countries.', (data['Year'].iloc[-18], 2000000000000), xytext=(data['Year'].iloc[-28]-timedelta(days=500), 18000000000000), ha='left', fontsize=9, arrowprops=dict(arrowstyle='-|>', facecolor='k', connectionstyle="arc3,rad=-0.15")) +def run_DayAirzhu(): + """函数用于执行另一个Python脚本""" + try: + subprocess.run(["D:\Pycharm\Project\AirQuilitySys\.venv\Scripts\python.exe", "DayAirzhu.py"], check=True) # 尝试运行另一个Python脚本 + except subprocess.CalledProcessError as e: + print(f"执行脚本时发生错误: {e}") +def run_DayAirbing(): + """函数用于执行另一个Python脚本""" + try: + subprocess.run(["D:\Pycharm\Project\AirQuilitySys\.venv\Scripts\python.exe", "DayAirbing.py"], check=True) # 尝试运行另一个Python脚本 + except subprocess.CalledProcessError as e: + print(f"执行脚本时发生错误: {e}") + +def run_time(): + """函数用于执行另一个Python脚本""" + try: + subprocess.run(["D:\Pycharm\Project\AirQuilitySys\.venv\Scripts\python.exe", "time.py"], check=True) # 尝试运行另一个Python脚本 + except subprocess.CalledProcessError as e: + print(f"执行脚本时发生错误: {e}") + +# 创建主窗口 +window = tk.Tk() +window.title("执行外部程序示例") + +# 创建一个按钮,点击时调用run_another_program函数 +but = tk.Button(window, text="查看每日空气折线分布图", command=run_DayAir) +but.pack(padx=20, pady=20) # 设置按钮布局 + +butt = tk.Button(window, text="查看每日空气柱状图", command=run_DayAirzhu) +butt.pack(padx=20, pady=20) # 设置按钮布局 + +butto = tk.Button(window, text="查看每月pm2.5平均指数", command=run_DayAirbing) +butto.pack(padx=20, pady=20) # 设置按钮布局 + +bu = tk.Button(window, text="查看未来预测数据", command=run_time) +bu.pack(padx=20, pady=20) # 设置按钮布局 +# 运行Tkinter事件循环 +window.mainloop() \ No newline at end of file diff --git a/AirQuilitySys/DayAir.py b/AirQuilitySys/DayAir.py new file mode 100644 index 0000000..7150b68 --- /dev/null +++ b/AirQuilitySys/DayAir.py @@ -0,0 +1,123 @@ +import logging +import pymysql +import subprocess +import numpy as np +from matplotlib import pyplot as plt +from tkinter import Tk +from pyecharts import options as _opts +from pyecharts.charts import Bar + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +def get_data(db_config): + """ + 从数据库中获取ptime和ci数据。 + :param db_config: 数据库配置字典 + :return: 包含ptime和ci的元组列表 + """ + ptime_list = [] # 存储ptime数据的列表 + ci_list = [] # 存储ci数据的列表 + so_list = [] + + + try: + with pymysql.connect(**db_config) as conn: + logging.info("数据库连接成功") + with conn.cursor() as cursor: + # 使用单个查询以提高性能 + sql_ptime = "SELECT ptime FROM May" + cursor.execute(sql_ptime) + data_ptime = cursor.fetchall() + #print(data_ptime) + # 处理查询结果 + #for row in data: + #for row in range(len(data)): + #if row == 0: + #continue + # print(row) + + ptime_list = [row['ptime'] for row in data_ptime if 'ptime' in row] + #row_list=[val for sublist in row for val in sublist] + + sql_ci="SELECT ci FROM may" + cursor.execute(sql_ci) + data_ci=cursor.fetchall() + #print(data_ci) + ci_list = [row['ci'] for row in data_ci if 'ci' in row] + + sql_so = "SELECT so FROM may" + cursor.execute(sql_so) + data_so = cursor.fetchall() + so_list = [row['so'] for row in data_so if 'so' in row] + + sql_no = "SELECT no FROM may" + cursor.execute(sql_no) + data_no = cursor.fetchall() + no_list = [row['no'] for row in data_no if 'no' in row] + + sql_pm = "SELECT pm FROM may" + cursor.execute(sql_pm) + data_pm = cursor.fetchall() + pm_list = [row['pm'] for row in data_pm if 'pm' in row] + + + return ptime_list, ci_list, so_list, no_list, pm_list + except pymysql.err.OperationalError as e: + logging.error(f"数据库操作失败: {e}") + # 根据具体场景,可以选择返回空列表、抛出异常或返回特定错误码 + return [], [], [], [], [] + #except Exception as e: + #logging.error(f"未知错误: {e}") + # 同上,根据具体场景进行处理 + #return [], [] +def visualize_data(ptime_list, ci_list, so_list, no_list, pm_list): + """ + 可视化数据。 + :param ptime_list: ptime数据列表 + :param ci_list: ci数据列表 + :param column_to_plot: 需要绘制的列名 + """ + if not ptime_list or not ci_list or not so_list or not no_list or not pm_list: + logging.error("数据为空,无法进行可视化") + return + #y_list=[] + #for i in range(4,8): + # y_list.append(i) + plt.figure(figsize=(15,10),dpi=80) + + plt.xlabel('Ptime') + + plt.title('CI over time') + plt.plot(ptime_list, ci_list,label='CI') + plt.plot(ptime_list, so_list,label='SO2') + plt.plot(ptime_list, no_list, label='NO2') + plt.plot(ptime_list, pm_list, label='PM2.5') + plt.legend() + plt.show() + + + +if __name__ == "__main__": + db_config = { + "host": '127.0.0.1', + "user": "root", + "password": 'mysql>hyx123', + "db": 'airquility', + "charset": 'utf8', + "cursorclass": pymysql.cursors.DictCursor + } + + ptime_list, ci_list,so_list ,no_list, pm_list= get_data(db_config) + visualize_data(ptime_list, ci_list, so_list, no_list, pm_list) + + print(pymysql.__version__) + +''' + window= Tk() + window.title("每日空气质量") + window.geometry("300x200") +#Label(window, text="图表已生成为air_quality_index.html").pack() + + window.mainloop() + ''' + diff --git a/AirQuilitySys/DayAirbing.py b/AirQuilitySys/DayAirbing.py new file mode 100644 index 0000000..4ed7464 --- /dev/null +++ b/AirQuilitySys/DayAirbing.py @@ -0,0 +1,154 @@ +import logging +import pymysql +import subprocess +import numpy as np +from matplotlib import pyplot as plt +#%matplotlib inline +from tkinter import Tk +from pyecharts import options as _opts +from pyecharts.charts import Bar + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +#db_config={'host':'127.0.0.1','user':'root','password':'mysql>hyx123','db':'airquility','charset':'utf8'} +#ptimelist=[] +#cilist=[] +#data_dict=dict(zip(ptimelist,cilist)) + +jan_list = [] +feb_list = [] +mar_list = [] +apr_list = [] +may_list = [] +ptime_list = [] + +def get_data(db_config): + """ + 从数据库中获取ptime和ci数据。 + :param db_config: 数据库配置字典 + :return: 包含ptime和ci的元组列表 + """ + # 存储ptime数据的列表 + + try: + with pymysql.connect(**db_config) as conn: + logging.info("数据库连接成功") + + with conn.cursor() as cursor: + # 使用单个查询以提高性能 + + sql_ptime = "SELECT ptime FROM May" + cursor.execute(sql_ptime) + data_ptime = cursor.fetchall() + ptime_list = [row['ptime'] for row in data_ptime if 'ptime' in row] + + sql_may = "SELECT pm FROM may" + cursor.execute(sql_may) + data_may = cursor.fetchall() + may_list = [row['pm'] for row in data_may if 'pm' in row] + + sql_feb="SELECT pm FROM feb" + cursor.execute(sql_feb) + data_feb=cursor.fetchall() + #print(data_ci) + feb_list = [row['pm'] for row in data_feb if 'pm' in row] + + sql_jan = "SELECT pm FROM jan" + cursor.execute(sql_jan) + data_jan = cursor.fetchall() + # print(data_ci) + jan_list = [row['pm'] for row in data_jan if 'pm' in row] + + sql_mar = "SELECT pm FROM mar" + cursor.execute(sql_mar) + data_mar = cursor.fetchall() + # print(data_ci) + mar_list = [row['pm'] for row in data_mar if 'pm' in row] + + sql_apr = "SELECT pm FROM apr" + cursor.execute(sql_apr) + data_apr = cursor.fetchall() + # print(data_ci) + apr_list = [row['pm'] for row in data_apr if 'pm' in row] + + return ptime_list, may_list, feb_list, jan_list, mar_list, apr_list + except pymysql.err.OperationalError as e: + logging.error(f"数据库操作失败: {e}") + # 根据具体场景,可以选择返回空列表、抛出异常或返回特定错误码 + return [], [], [], [], [], [] + +def average(str_list): + if not str_list: # 检查列表是否为空 + return 0 # 或者根据需要处理,比如返回None或者抛出异常 + num_list = [float(item) for item in str_list] # 假设可能有浮点数 + return sum(num_list) / len(num_list) + + + +def visualize_data(ptime_list, may_list, feb_list, jan_list, mar_list, apr_list): + """ + 可视化数据。 + :param ptime_list: ptime数据列表 + :param ci_list: ci数据列表 + :param column_to_plot: 需要绘制的列名 + """ + if not may_list or not feb_list or not jan_list: + logging.error("数据为空,无法进行可视化") + return + #y_list=[] + #for i in range(4,8): + # y_list.append(i) + x = np.arange(26) + bar_width = 0.2 + # 调用函数并打印结果 + jan_value = average(jan_list) + feb_value = average(feb_list) + may_value = average(may_list) + mar_value = average(mar_list) + apr_value = average(apr_list) + # 创建图形对象 + fig = plt.figure("average_pm2.5") + + #plt.title('每月pm2.5平均指数') + # 创建子图 + ax = fig.add_axes([0, 0, 1, 1]) + # 让饼状图变成圆形 + ax.axis('equal') + # 准备数据 + #labels = ['jan', 'feb', 'may','mar','apr'] + #num_data = [jan_value, feb_value, may_value, mar_value, apr_value] + plt.figure(figsize=(15, 10), dpi=80) + plt.bar(ptime_list, apr_value, tick_label=ptime_list, width=bar_width) + plt.bar(x + bar_width, may_value, width=bar_width) + # 饼状图之间的间隙大小 + #explode = (0.02, 0.03, 0.02, 0.01, 0.01) + # 画饼 + #ax.pie(num_data, labels=labels, autopct='%1.1f%%', explode=explode) + plt.show() + + + +if __name__ == "__main__": + db_config = { + "host": '127.0.0.1', + "user": "root", + "password": 'mysql>hyx123', + "db": 'airquility', + "charset": 'utf8', + "cursorclass": pymysql.cursors.DictCursor + } + + ptime_list, may_list, feb_list, jan_list, mar_list, apr_list= get_data(db_config) + visualize_data(ptime_list, may_list, feb_list, jan_list, mar_list, apr_list) + + #print(pymysql.__version__) + +''' + window= Tk() + window.title("每日空气质量") + window.geometry("300x200") +#Label(window, text="图表已生成为air_quality_index.html").pack() + + window.mainloop() + ''' + diff --git a/AirQuilitySys/DayAirzhu.py b/AirQuilitySys/DayAirzhu.py new file mode 100644 index 0000000..c9f9fc6 --- /dev/null +++ b/AirQuilitySys/DayAirzhu.py @@ -0,0 +1,132 @@ +import logging +import pymysql +import subprocess +import numpy as np +from matplotlib import pyplot as plt +from tkinter import Tk +from pyecharts import options as _opts +from pyecharts.charts import Bar + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + +#db_config={'host':'127.0.0.1','user':'root','password':'mysql>hyx123','db':'airquility','charset':'utf8'} +#ptimelist=[] +#cilist=[] +#data_dict=dict(zip(ptimelist,cilist)) + +def get_data(db_config): + """ + 从数据库中获取ptime和ci数据。 + :param db_config: 数据库配置字典 + :return: 包含ptime和ci的元组列表 + """ + ptime_list = [] # 存储ptime数据的列表 + ci_list = [] # 存储ci数据的列表 + + + try: + with pymysql.connect(**db_config) as conn: + logging.info("数据库连接成功") + with conn.cursor() as cursor: + # 使用单个查询以提高性能 + sql_ptime = "SELECT ptime FROM May" + cursor.execute(sql_ptime) + data_ptime = cursor.fetchall() + ptime_list = [row['ptime'] for row in data_ptime if 'ptime' in row] + #row_list=[val for sublist in row for val in sublist] + + sql_ci="SELECT ci FROM may" + cursor.execute(sql_ci) + data_ci=cursor.fetchall() + #print(data_ci) + ci_list = [row['ci'] for row in data_ci if 'ci' in row] + + sql_so = "SELECT so FROM may" + cursor.execute(sql_so) + data_so = cursor.fetchall() + so_list = [row['so'] for row in data_so if 'so' in row] + + sql_no = "SELECT no FROM may" + cursor.execute(sql_no) + data_no = cursor.fetchall() + no_list = [row['no'] for row in data_no if 'no' in row] + + sql_pm = "SELECT pm FROM may" + cursor.execute(sql_pm) + data_pm = cursor.fetchall() + pm_list = [row['pm'] for row in data_pm if 'pm' in row] + + + return ptime_list, ci_list, so_list, no_list, pm_list + except pymysql.err.OperationalError as e: + logging.error(f"数据库操作失败: {e}") + # 根据具体场景,可以选择返回空列表、抛出异常或返回特定错误码 + return [], [], [], [], [] + #except Exception as e: + #logging.error(f"未知错误: {e}") + # 同上,根据具体场景进行处理 + #return [], [] +def visualize_data(ptime_list, ci_list, so_list, no_list, pm_list): + """ + 可视化数据。 + :param ptime_list: ptime数据列表 + :param ci_list: ci数据列表 + :param column_to_plot: 需要绘制的列名 + """ + if not ptime_list or not ci_list or not so_list or not no_list or not pm_list: + logging.error("数据为空,无法进行可视化") + return + #y_list=[] + #for i in range(4,8): + # y_list.append(i) + plt.figure(figsize=(15,10),dpi=80) + x = np.arange(26) + bar_width = 0.2 + """ + plt.bar(ptime_list, # 柱状图柱体标签值 + ci_list, # 柱体高度 + align='center', # 柱体对齐方式 + color='red', # 柱体颜色 + tick_label=ptime_list, # 刻度标签值 + hatch='/') # 填充样式,越密集填充就越密 + plt.bar(ptime_list, # 柱状图柱体标签值 + so_list, # 柱体高度 + align='center', # 柱体对齐方式 + color='blue', # 柱体颜色 + tick_label=ptime_list, # 刻度标签值 + hatch='/') # 填充样式,越密集填充就越密 + """ + plt.xlabel('Ptime') + plt.bar(ptime_list, ci_list, tick_label=ptime_list, width=bar_width) + plt.bar(x + bar_width, so_list, width=bar_width) + #plt.bar(x + bar_width, no_list, width=bar_width) + #plt.bar(x + bar_width, pm_list, width=bar_width) + plt.title('CI over time') + plt.show() + + + +if __name__ == "__main__": + db_config = { + "host": '127.0.0.1', + "user": "root", + "password": 'mysql>hyx123', + "db": 'airquility', + "charset": 'utf8', + "cursorclass": pymysql.cursors.DictCursor + } + + ptime_list, ci_list, so_list, no_list, pm_list = get_data(db_config) + visualize_data(ptime_list, ci_list, so_list, no_list, pm_list) + + print(pymysql.__version__) + +''' + window= Tk() + window.title("每日空气质量") + window.geometry("300x200") +#Label(window, text="图表已生成为air_quality_index.html").pack() + + window.mainloop() + ''' + diff --git a/AirQuilitySys/LSTM_airqui.py b/AirQuilitySys/LSTM_airqui.py new file mode 100644 index 0000000..be48538 --- /dev/null +++ b/AirQuilitySys/LSTM_airqui.py @@ -0,0 +1,63 @@ +import keras +import matplotlib.pyplot as plt +import pandas as pd +import tensorflow as tf +import numpy as np +from sklearn.preprocessing import MinMaxScaler +from keras.layers import Dense, LSTM, Activation, Dropout +from keras.optimizers import SGD + +colum = 13 +step = 50 +df = pd.read_csv('文件名') +df_for_training=df[:24000] +df_for_testing=df[24000:] +# print(df_for_testing.iloc[:, 2:]) + +scaler = MinMaxScaler(feature_range=(-1, 1)) +df_for_training_scaled = scaler.fit_transform(df_for_training.iloc[:, 2:]) +print("df_for_training_scaled shape:",df_for_training_scaled.shape) +#print(df_for_training_scaled.shape) +#print(df_for_testing_scaled.shape) + +#抽取特征与标签列 +def createXY(dataset, n_past): + dataX = [] + dataY = [] + for i in range(n_past, len(dataset)): + dataX.append(dataset[i - n_past:i, 1:dataset.shape[1]]) + dataY.append(dataset[i, 0]) + return np.array(dataX), np.array(dataY) +#trainX,testX数据的shape为[samples, steps, features] +trainX, trainY = createXY(df_for_training_scaled, step) +testX, testY = createXY(df_for_testing_scaled, step) + +#构建模型 +def build_model(): + model = tf.keras.models.Sequential() + #经试验,如果下一层还是LSTM的话必须将return_sequences设置为True + model.add(LSTM(20, input_shape=(step, column), return_sequences=True)) + model.add(LSTM(20)) + model.add(Activation('relu')) + model.add(Dropout(0.2)) + model.add(Dense(1)) + model.compile(loss='mae', optimizer=SGD(lr=0.1,momentum=0.3)) + return model + +lstm_net = build_model() +lstm_net.fit(trainX, trainY, batch_size=8, epochs=100) +predictions = lstm_net.predict(testX) + +#进行逆变换 +test_Predict_copy = np.repeat(predictions,column+1,axis=-1) +test_Predict_inverse = scaler.inverse_transform(test_Predict_copy)[:,0] +#test_Predict_inverse = scaler.inverse_transform(test_Predict) +test_label = np.array(df_for_testing['omega_vsg_1'])[50:] +testY_copy = np.repeat(np.reshape(testY,(len(testY), 1)),column+1,axis=-1) + +testY_inverse = scaler = scaler.inverse_transform(testY_copy)[:,0] + +plt.plot(test_Predict_inverse,color='red') +plt.plot(test_label,color='green') +plt.plot(testY_inverse, color='blue') +plt.savefig('result_test.png') \ No newline at end of file diff --git a/AirQuilitySys/Login.py b/AirQuilitySys/Login.py index 8aef3dd..e5d16ea 100644 --- a/AirQuilitySys/Login.py +++ b/AirQuilitySys/Login.py @@ -1,6 +1,19 @@ from tkinter import * +import subprocess +import sys +from tkinter import messagebox user_login={'aaa':'123456','bbb':'888888','ccc':'333333'} count=0 + +def run_AirQui(): + """函数用于执行另一个Python脚本""" + try: + #subprocess.run(["python", "AirQui.py"], check=True) # 尝试运行另一个Python脚本 + #sys.exit() + subprocess.run(["D:\Pycharm\Project\AirQuilitySys\.venv\Scripts\python.exe", "AirQui.py"], check=True) + sys.exit(0) + except subprocess.CalledProcessError as e: + print(f"执行脚本时发生错误: {e}") def login(): global count username=entry_username.get() @@ -10,13 +23,15 @@ def login(): password=entry_password.get() if(password==user_login[username]): label_message.config(text='登录成功!') + run_AirQui() + else: label_message.config(text='密码错误!还可以尝试{}次'.format(2-count)) count=count+1 if count == 3: label_message.config(text='登录失败!') - btn_login.config(state="disabled") - + button.config(state="disabled") +# 创建主窗口 window=Tk() window.title("用户登录") window.geometry("300x200") @@ -33,7 +48,13 @@ label_password.pack() entry_password=Entry(window,show='*') entry_password.pack() +# 创建一个按钮,点击时调用run_another_program函数 +button = Button(window, text="登录", command=login) +button.pack() # 设置按钮布局 + label_message=Label(window,text="") label_message.pack() -window.mainloop() \ No newline at end of file +window.mainloop() +# 运行Tkinter事件循环 + diff --git a/AirQuilitySys/airquility.db b/AirQuilitySys/airquility.db new file mode 100644 index 0000000..e69de29 diff --git a/AirQuilitySys/data.py b/AirQuilitySys/data.py index 4624836..12e9783 100644 --- a/AirQuilitySys/data.py +++ b/AirQuilitySys/data.py @@ -2,6 +2,6 @@ import requests res = requests.get('http://www.daqi110.com/content/environment/index.html') print(res) print(type(res)) ->>> - - \ No newline at end of file +#>>> +# +# \ No newline at end of file diff --git a/AirQuilitySys/model.h5 b/AirQuilitySys/model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..21db3c3255a296664ddf59fc1006c5f78bb06982 GIT binary patch literal 148848 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+b=re.sub(pattern,'/',a) +print(b) diff --git a/AirQuilitySys/scaler.joblib b/AirQuilitySys/scaler.joblib new file mode 100644 index 0000000000000000000000000000000000000000..f2b9322ffbd1abb94d30ad3a6b12f9dc86dc3126 GIT binary patch literal 719 zcma))&ubJh6vt&5lBU) z%3`W@BDw+TKn`fGttDa&wqM7;ksnA>&9dNZ4)=X&z(&G7_Y@Vr(l^VZg^G;sceRh46+1d;1 zPjRpJ1z+KEc+vQ)*lxF93~W?G=;auhtH61-MbZ?cz2?%8vpS6cPr4$dA}3LqQ;p$# z5!IdC#fQV6aby2rwf|t)bkEdM*AO;4>?d1b4R+IEvRiDC-DXX8x5w@|kDAl(KmYf> zaojuTKOE_=xaT30n#?(qvG9Ihyx123'), + "db": os.getenv('DB_NAME', 'airquility'), + "charset": 'utf8', + } + + # 检查环境变量是否已设置 + for key, value in config.items(): + if value is None: + raise ValueError(f"缺少环境变量: {key}") + + return config + + +def create_database_engine(config): + """ + 创建数据库引擎。 + """ + db_string = f'mysql+pymysql://{config["user"]}:{config["password"]}@{config["host"]}/{config["db"]}?charset={config["charset"]}' + + try: + engine = create_engine(db_string) + print(type(engine)) + except Exception as e: + print(f"创建数据库引擎失败: {e}") + # 根据需要处理异常,例如记录日志或重试 + raise # 如果需要将异常继续抛出 + return engine + + +def fetch_data(engine, query): + """ + 从数据库中获取数据。 + 参数: + engine: 数据库连接引擎对象。 + query: SQL查询字符串。 + 返回: + 查询结果的数据框(df)。 + """ + # 确保query是字符串类型 + if not isinstance(query, str): + logging.error("查询字符串类型错误,query应为字符串。") + raise ValueError("查询字符串类型错误,query应为字符串。") + + if not query.strip(): + logging.error("查询字符串为空。") + raise ValueError("查询字符串为空。") + + try: + df = pd.read_sql(text(query), engine) + return df + except Exception as e: + logging.error(f"执行SQL查询失败: {e}") + raise # 重新抛出异常以便上层处理 + +def preprocess_data(df, target_col, default_year=2024, features=None): + """ + 对数据进行预处理,包括日期列转换、特征标准化等。 + """ + # 检查df是否为空 + if df.empty: + logging.error("输入的DataFrame为空") + return None, None, None + + # 检查'ptime'列是否存在 + if 'ptime' not in df.columns: + logging.error("DataFrame中不存在'ptime'列") + return None, None, None + default_year = 2024 + df['ptime'] = df['ptime'].apply(lambda x: datetime.strptime(f"{default_year}/{x}", "%Y/%m/%d")) + # 或者,如果使用pd.to_datetime,并且'ptime'格式特殊,需要指定格式 + # df['ptime'] = pd.to_datetime(df['ptime'], errors='coerce', format='%m/%d').dt.strftime('%Y-%m-%d') + + print(df.head) + + # 如果'ptime'已经是datetime类型,则无需转换 + if df['ptime'].dtype == 'datetime64[ns]': + print("ptime列已经是以日期时间格式存储。") + else: + try: + # 尝试将'ptime'列转换为datetime类型 + df['ptime'] = pd.to_datetime(df['ptime'], format='%m/%d/%Y') + except ValueError: + logging.error("ptime列转换为datetime类型失败,可能是因为格式不正确。") + return None, None, None + + # 设置'ptime'为索引 + #df.set_index('ptime', inplace=True) + + # 确定features列表 + if target_col in df.columns: + features = df.columns.drop(target_col) + print("features:", features) + else: + logging.warning(f"目标列 '{target_col}' 在DataFrame中未找到,将不进行列删除操作。") + features = df.columns + + # 检查features是否被正确设置 + if features is None: + logging.error("未找到任何特征列。") + return None, None, None + + print("@@@@") + print(target_col) + print("@@@@") + try: + df.set_index('ptime', inplace=True) + except KeyError: + print("列 'ptime' 不存在,无法设置为索引。") + # 在这里处理缺少'ptime'的情况,比如跳过相关操作或使用其他列 + # 使用MinMaxScaler进行特征缩放 + scaler = MinMaxScaler() + scaled_features = scaler.fit_transform(df[features]) + scaled_target = scaler.fit_transform(df.index.values.reshape(-1, 1)) + print("~~~") + return scaled_features, scaled_target, scaler + + +def split_dataset_into_train_test(features, target, test_size=0.2): + """ + 切分数据集为训练集和测试集。 + """ + # 检查features和target的类型以及长度是否相等 + if not isinstance(features, np.ndarray) or not isinstance(target, np.ndarray): + raise TypeError("features and target must be numpy arrays") + if len(features) != len(target): + raise ValueError("features and target must have the same length") + + # 检查test_size是否在合理的范围内 + if not 0 < test_size < 1: + raise ValueError("test_size must be between 0 and 1") + + # 计算训练集大小 + train_size = int(len(features) * (1 - test_size)) + + # 使用numpy的切片操作,这不会创建新的数据副本,提高性能 + train_features, test_features = features[:train_size], features[train_size:] + train_target, test_target = target[:train_size], target[train_size:] + print("123456") + print(features) + print(target) + print(train_features) + print(train_target) + print(test_features) + print(test_target) + return train_features, test_features, train_target, test_target + + +def validate_data_shapes(train_features, test_features, n_steps): + """ + 验证训练和测试数据形状是否符合预期。 + """ + if train_features.shape[1] != n_steps or test_features.shape[1] != n_steps: + raise ValueError(f"训练和测试特征的第二维度(时间步长)应为{n_steps}") + print("7890") + + +def build_model(n_steps, lstm_units, dense_units, input_shape): + inputs = Input(shape=input_shape) # 添加Input对象 + x = LSTM(lstm_units)(inputs) # 直接将Input对象传递给LSTM层 + outputs = Dense(dense_units)(x) + model = tf.keras.Model(inputs=inputs, outputs=outputs) + model.compile(optimizer='adam', loss='mse') + print("!!!") + return model + + +def validate_params(epochs, batch_size): + """ + 确保 epochs 和 batch_size 是合法的参数。 + """ + if not isinstance(epochs, int) or epochs <= 0: + raise ValueError("epochs 应该是一个正整数") + if not isinstance(batch_size, int) or batch_size <= 0: + raise ValueError("batch_size 应该是一个正整数") + if epochs <= 0 or batch_size <= 0: + raise ValueError("epochs和batch_size必须大于0") + print("%%%") + + +def ensure_directory_safety(path:str): + """ + 确保路径安全且存在。 + """ + if not os.path.isabs(path): + raise ValueError("路径应该是绝对路径") + directory = os.path.dirname(path) + print(directory) + + try: + # 检查目录是否需要创建 + if not os.path.exists(directory): + # 添加日志记录 + logger.info(f"目录 {directory} 不存在,开始创建。") + # 使用 exist_ok=True 避免在目录已存在时抛出异常 + os.makedirs(directory, exist_ok=True) + logger.info(f"目录 {directory} 创建成功。") + except PermissionError: + # 捕获权限异常,给出清晰的错误提示 + logger.error(f"没有权限在 {directory} 创建目录。") + raise + except Exception as e: + # 捕获其他异常,记录并抛出 + logger.error(f"创建目录 {directory} 时发生未知错误:{e}") + raise + print("===") +def train_model(model: Model, train_features, train_target, test_features, test_target, epochs: int, batch_size: int, + patience: int, save_best_only: bool = True, monitor: str = 'val_loss', mode: str = 'min', + model_path: str = "best_model.h5") -> dict: + """ + 训练模型,并根据早停策略和性能指标保存最佳模型。 + + :param model: Keras模型实例 + :param train_features: 训练特征 + :param train_target: 训练目标 + :param test_features: 测试特征 + :param test_target: 测试目标 + :param epochs: 训练轮数 + :param batch_size: 批量大小 + :param patience: 早停策略的耐心值 + :param save_best_only: 是否只保存最佳模型 + :param monitor: 监控的指标 + :param mode: 监控指标的模式(min/max) + :param model_path: 模型保存路径 + :return: 训练历史记录 + """ + logging.basicConfig(level=logging.INFO) + + model_path = "/path/to/your/model.h5" + validate_params(epochs, batch_size) + ensure_directory_safety(model_path) + + # 使用ModelCheckpoint保存最佳模型 + filepath = model_path + checkpoint = ModelCheckpoint(filepath, monitor=monitor, verbose=1, save_best_only=save_best_only, mode=mode) + + # 定义早停策略 + early_stopping = EarlyStopping(monitor=monitor, patience=patience, verbose=1) + + try: + history = model.fit(train_features, train_target, epochs=epochs, batch_size=batch_size, + validation_data=(test_features, test_target), verbose=1, + callbacks=[early_stopping, checkpoint]) + logging.info("###") + return history + except ValueError as ve: + logging.error(f"参数错误: {ve}") + raise + except OSError as oe: + logging.error(f"文件操作错误: {oe}") + raise + except Exception as e: + logging.error(f"模型训练过程中发生异常: {e}") + raise + +def build_and_train_model(n_steps, features, target, train_features, train_target, test_features, test_target,lstm_units=50, dense_units=1, optimizer='adam', loss='mse', epochs=100, batch_size=32,patience=10, model_save_path='model.h5'): + """ + 构建LSTM模型并进行训练,增加了参数可配置性,早停策略和模型保存。 + """ + # 输入数据验证 + if not (isinstance(train_features, np.ndarray) and isinstance(train_target, np.ndarray) and isinstance(test_features, np.ndarray) and isinstance(test_target, np.ndarray)): + raise ValueError("输入数据train_features, train_target, test_features, test_target必须是numpy数组") + + checkpoint = ModelCheckpoint(filepath="/path/to/your/model.keras", # 注意这里的路径保持为.h5 + monitor='val_loss', # 或您希望监控的指标 + verbose=1, + save_best_only=True, + mode='min') + + # 数据形状验证 + validate_data_shapes(train_features, test_features, n_steps) + + model = build_model(n_steps, lstm_units, dense_units, input_shape) + + # 早停策略 + early_stopping = EarlyStopping(monitor='val_loss', patience=patience, verbose=1) + + history = model.fit(train_features, train_target, + validation_data=(test_features, test_target), + epochs=epochs, + batch_size=batch_size, + callbacks=[checkpoint], + # 其他参数... + ) # 模型保存 + # 增加了路径验证来防止潜在的安全问题,这里简化处理,实际应用中可能需要更复杂的逻辑 + if not model_save_path.endswith('.h5'): + model_save_path += '.h5' + model.save(model_save_path) + + return model, history + + +def evaluate_model(model, scaler, test_target, predictions): + """ + 评估模型性能并反向转换预测结果。 + """ + predictions = scaler.inverse_transform(predictions) + test_target_inv = scaler.inverse_transform(test_target.reshape(-1, 1)) + mse = mean_squared_error(test_target_inv, predictions) + print(f'Mean Squared Error: {mse}') + + return mse + + +if __name__ == "__main__": + + engine = create_database_engine(get_db_config()) + query = "SELECT ptime, ci FROM may" + df = fetch_data(engine, query) + target_col = 'ptime' + features, target, scaler = preprocess_data(df, target_col) + + train_features, test_features, train_target, test_target = split_dataset_into_train_test(features, target, test_size=0.2) + + n_steps = 5 + # 假设train_features和test_features是你的数据,且它们是二维数组 + # 首先,你需要获取或设定一个maxlen,这里假设我们已知或计算出它应该是5 + maxlen = 5 + + # 对训练数据进行填充或截断 + train_features_padded = pad_sequences(train_features, maxlen=maxlen, padding='post', truncating='post') + + # 对测试数据进行同样的处理 + test_features_padded = pad_sequences(test_features, maxlen=maxlen, padding='post', truncating='post') + + input_shape = (n_steps, int(train_features.shape[1])) + + + model, history = build_and_train_model(n_steps=n_steps, + features=features, + target=target, + train_target=train_target, + test_target=test_target, + train_features=train_features_padded, + test_features=test_features_padded) + + predictions = model.predict(test_features) + mse = evaluate_model(model, scaler, test_target, predictions) + # 可视化预测结果(可选) + + #plt.plot(test_target, label='Actual') + plt.plot(predictions, label='Predicted') + plt.legend() + plt.xlabel('Ptime') + plt.ylabel('CI') + #plt.plot(ptime, ci) + plt.show() + + # 保存模型 + model.save('trained_model.h5') + joblib.dump(scaler, 'scaler.joblib') diff --git a/AirQuilitySys/trained_model.h5 b/AirQuilitySys/trained_model.h5 new file mode 100644 index 0000000000000000000000000000000000000000..21db3c3255a296664ddf59fc1006c5f78bb06982 GIT binary patch literal 148848 zcmeFZ30RF?*Ers&QIn{Q6{$!nQ966wg(id$G9*oi6b_*co7wpQGznB3tU80q#N(Ah={tNT}gmnQ@pFe zQ83VHnvIPJ4^oInR$O`ZMnB37LpqP#{vK6f{drCz?C ze~YJ6aRqqztoWU-?wxqL_~GH_yU=?Pe*z62F?^c-jIM`#TmpQ275yEbQYXGZe>ZPm 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