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src/controller/DbProcessor/DatabaseOp.py

@@ -26,7 +26,6 @@ def connect_user_db():
             port=3306
         )
         if conn.is_connected():
-            print("Successfully connected to the database")
             return conn
     except Error as e:
         print("Connection error:", e)
@@ -314,6 +313,35 @@ def delete_server_health_log(conn,servername,finaldata):
     @return:
     """
 
+# 通过日期获得当日日志数量
+def getLogNumByDate(conn,date):
+    """
+
+    @param conn:
+    @param date:
+    @return:
+    """
+    cursor = conn.cursor()
+
+    wherequery = f"detect_date = '{date}'"
+
+    query = f"SELECT COUNT(*) FROM serverhealthlog WHERE {wherequery}"
+    # print(query)
+    cursor.execute(query)
+    count = cursor.fetchone()[0]
+
+    # 关闭连接
+    cursor.close()
+    return count
+
+
+# 测试用例
+def test():
+    conn = connect_user_db()
+    date = '2024-6-2'
+    print(getLogNumByDate(conn,date))
+
+# test()
 # 断开数据库连接函数
 def disconnect_db(conn):
     """

src/controller/Interface/KernelToController.py

@@ -0,0 +1,167 @@
+#!/usr/bin/env python
+# encoding: utf-8
+# @author: 原凯峰
+# @contact: 2894340009@qq.com
+# @software: pycharm
+# @file: KernelToController.py
+# @time: 2024/7/2 15:16
+# @desc:
+import requests
+import socket
+import json
+import time
+from datetime import datetime
+
+
+class CustomEncoder(json.JSONEncoder):
+    def __init__(self, *args, **kwargs):
+        super(CustomEncoder, self).__init__(*args, **kwargs)
+        self.prefix = {'number': '1', 'source': 'backend', 'kind': 'server_health_list'}
+
+    def encode(self, obj):
+        if not isinstance(obj, dict):
+            raise TypeError("Object must be a dict.")
+        # 将prefix添加到原始字典的前面
+        encoded_dict = {**self.prefix, **obj}
+        return super(CustomEncoder, self).encode(encoded_dict)
+
+
+def calResponseTime(time_str1, time_str2, time_format):
+    # 将字符串时间转换为datetime对象
+    time1 = datetime.strptime(time_str1, time_format)
+    time2 = datetime.strptime(time_str2, time_format)
+    time_difference = time2 - time1
+    seconds_difference = time_difference.total_seconds()
+    return seconds_difference
+
+
+# 定义时间格式
+time_format = "%a, %d %b %Y %H:%M:%S GMT"
+# 目标URL
+url = 'http://127.0.0.1/status?format=json'
+
+# 发送数据的IP地址和端口
+target_ip = '47.96.136.178'
+target_port = 8080
+# 定义一个数组来存储last_check_time的值
+last_check_times = []
+last_receive_times = []
+last_response_times = []
+tmp = ''
+pos = 0
+while True:
+    # 从URL获取数据
+    response = requests.get(url)
+    data = response.json()
+    tip = 0
+    pos = pos + 1
+    # 检查 'servers' 和 'server' 键是否存在
+    if 'servers' in data and 'server' in data['servers']:
+        # 遍历服务器数据
+        for server in data['servers']['server']:
+            # 将对应的值添加到数组中
+            if (pos == 1):
+                # 数组初添加
+                last_check_times.append(server['last_check_time'])
+            else:
+                # 修正处理json数据
+                server['last_check_time'] = tmp
+                # 存放到数组中
+                last_check_times[tip] = tmp
+
+            # 修正调整
+            if (server['last_receive_time'] == ''):
+                server['last_receive_time'] = 'Null'
+            if (pos == 1):
+                last_receive_times.append(server['last_receive_time'])
+            else:
+                last_receive_times[tip] = server['last_receive_time']
+
+            tip = tip + 1
+    print("last_check_times:", last_check_times)
+    print("last_receive_times:", last_receive_times)
+    ##分析计算出服务器响应时间：
+    for i in range(len(last_check_times)):
+        # 初次添加
+        if (pos == 1):
+            if (last_receive_times[i] == 'Null'):
+                last_response_times.append('Null')
+            if (last_receive_times[i] != 'Null'):
+                responseTime = calResponseTime(last_check_times[i], last_receive_times[i], time_format)
+                last_response_times.append(responseTime)
+        else:
+            if (last_receive_times[i] == 'Null'):
+                last_response_times[i] = 'Null'
+            if (last_receive_times[i] != 'Null'):
+                responseTime = calResponseTime(last_check_times[i], last_receive_times[i], time_format)
+                last_response_times[i] = responseTime
+
+    # 加入到发送数据中：
+    for server, responseTime in zip(data['servers']['server'], last_response_times):
+        server['last_response_time'] = responseTime
+    for i in range(len(last_check_times)):
+        if (last_receive_times[i] != 'Null'):
+            tmp = last_receive_times[i]
+            last_check_times[i] = last_receive_times[i]
+
+    # 打印数组内容，查看存储的值
+
+    print("last_response_times:", last_response_times)
+    # 使用自定义的编码器将数据转换为JSON格式的字符串
+    json_data = json.dumps(data, cls=CustomEncoder)
+
+    print(json_data)
+    # 目标服务器的IP地址和端口
+    target_url = 'http://47.96.136.178:8080'
+
+    # 发送POST请求
+    # 指定发送的内容类型为JSON
+    headers = {'Content-Type': 'application/json'}
+    response = requests.post(target_url, data=json_data, headers=headers)
+
+    # 检查请求是否成功
+    if response.status_code == 200:
+        print("数据成功发送。")
+    else:
+        print("数据发送失败，状态码：", response.status_code)
+
+        print("数据已发送到IP：{}，端口：{}".format(target_ip, target_port))
+    # 等待2.5秒
+    time.sleep(2.5)
+
+
+
+
+# 读取文件
+def read_log(file_path):
+    with open(file_path, 'r') as file:
+        # 读取所有行
+        log_lines = [line.strip() for line in file]
+    return log_lines
+
+# # 指定文件路径
+# file_path = '/usr/local/nginx/logs/access.log'
+#
+# # 指定目标IP和端口
+# targeturl = 'http://47.96.136.178:8080'
+
+# 构造JSON数据并发送
+# def send_json(url, data):
+#     headers = {'Content-Type': 'application/json'}
+#     # 将数据构造为JSON格式
+#     json_data = json.dumps({"logs": data})
+#     response = requests.post(url, data=json_data, headers=headers)
+#     return response
+
+# try:
+#     # 读取日志文件并转换为JSON
+#     log_data = read_log(file_path)
+#
+#     # 发送数据
+#     response = send_json(targeturl, log_data)
+#
+#     # 打印响应状态码和内容
+#     print(f'Status Code: {response.status_code}')
+#     print(f'Response Content: {response.text}')
+# except Exception as e:
+#     print(f'An error occurred: {e}')

src/controller/LogAnalyze/MachineLearningDivider.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python
+# encoding: utf-8
+# @author: 原凯峰
+# @contact: 2894340009@qq.com
+# @software: pycharm
+# @file: MachineLearningDivider.py
+# @time: 2024/6/26 8:21
+# @desc:利用随机森林法进行模型训练，能够通过平均响应时间、故障率等数据计算出服务器的健康状态
+
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report, accuracy_score
+import pickle
+def trainmodel():
+    # 假设我们有以下数据集
+    X = [
+        [0.3, 0.005],  # 服务器特征：平均响应时间和故障率
+        [2.5, 0.03],
+        [0.7, 0.045],
+        [1.2, 0.002],
+        [3.5, 0.1],
+        [1.3, 0.05],
+        [0.01, 0.15],  # 服务器特征：平均响应时间和故障率
+        [5, 0.03],
+        [0.7, 0.015],
+        [1.4, 0.02],
+        [0.15, 0.2],
+        [1.3, 0.005],
+
+    ]
+    y = ['良好', '一般', '一般', '良好', '差', '一般', '一般', '差', '良好', '差', '差', '良好']  # 对应的健康状态标签
+
+    # 将健康状态标签转换为数值
+    label_mapping = {'一般': 0, '良好': 1, '差': 2}
+    y_encoded = [label_mapping[label] for label in y]
+
+    # 划分训练集和测试集
+    X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.4, random_state=42)
+
+    # 选择模型，这里使用随机森林分类器
+    model = RandomForestClassifier(n_estimators=100, random_state=42)
+
+    # 训练模型
+    model.fit(X_train, y_train)
+
+    # 预测测试集
+    y_pred = model.predict(X_test)
+
+    # 评估模型
+    print(classification_report(y_test, y_pred))
+    print("Accuracy:", accuracy_score(y_test, y_pred))
+
+    # 保存模型
+    with open('server_health_model.pkl', 'wb') as file:
+        pickle.dump(model, file)
+
+
+# trainmodel()
+# 定义一个函数来加载模型并进行预测
+def load_model_and_predict(new_data):
+    with open('../LogAnalyze/server_health_model.pkl', 'rb') as file:
+        loaded_model = pickle.load(file)
+    predictions = loaded_model.predict(new_data)
+    return predictions
+
+# 定义一个函数来将预测结果转换为健康等级
+def predict_health_status(new_data):
+    label_mapping = {'一般': 0, '良好': 1, '差': 2}
+    predictions = load_model_and_predict(new_data)
+    # 创建逆向映射字典
+    inverse_label_mapping = {value: key for key, value in label_mapping.items()}
+    # 使用逆向映射字典转换预测结果
+    health_status = [inverse_label_mapping[pred] for pred in predictions]
+    return health_status
+
+# 测试函数
+def testcase():
+    new_data = [[0.4, 0.01]]  # 新的服务器数据
+    health_status = predict_health_status(new_data)
+    print("预测的健康状态：", health_status)
+
+# testcase()

src/controller/MessageHandler/BackendProcessor.py

@@ -45,9 +45,11 @@ class BackendProcessor():
                 # 需要填入数据库的消息
                 server_name =serverInfo["name"]
                 upstream = serverInfo["upstream"]
-                status = serverInfo["status"]
+                status = 0
-                detect_date = serverInfo["detect_time"]#TODO 这个字段和下面这个字段都需要后续修改名字。
+                if serverInfo["status"] == 'up':
-                responseTime = serverInfo["responseTime"]#
+                    status = 1
+                detect_date = serverInfo["last_check_time"]#TODO 这个字段和下面这个字段都需要后续修改名字。
+                responseTime = serverInfo["last_response_time"]#
                 protocol = serverInfo["type"]
 
                 # 先判断是否该服务器已经存在；
@@ -77,8 +79,8 @@ class BackendProcessor():
             return 0,[]
 
 def backendTest():
-    json_str = '{"source": "backend", "kind": "server_health_list", "servers": {"total":2,"generation":1, "server":[{"index":0,"type":"tcp","responseTime":"111","name":"1.195.201.255","status":0,"upstream":"cluster","detect_time":111},{"index":0,"type":"tcp","responseTime":"111","name":"1.205.204.0","status":1,"upstream":"cluster","detect_time":111}]}}'
+    json_str = '{"source": "backend", "kind": "server_health_list", "servers": {"total":2,"generation":1, "server":[{"index":0,"type":"tcp","last_response_time":"30","name":"1.205.204.0","status":"up","upstream":"cluster","last_check_time":"2024-6-5"}]}}'
     data_dict = json.loads(json_str)
     a = BackendProcessor(data_dict)
 
-backendTest()
+# backendTest()

src/controller/MessageHandler/FrontendProcessor.py

@@ -7,6 +7,7 @@
 # @desc:
 from DbProcessor.DatabaseOp import *
 from LogAnalyze import MachineLearningDivider as mldivider
+from datetime import datetime, timedelta
 import json
 
 class FrontendProcessor():
@@ -150,9 +151,13 @@ class FrontendProcessor():
         # 使用json.dumps将字典转换为JSON格式的字符串
         backMessage = json.dumps(stats)
 
-        return backMessage , DisabledRate
+        return backMessage
 
     def getAllServerStatus(self):
+        """
+        给列表页返回信息
+        @return:
+        """
         conn = connect_user_db()
         allServerStatusList = select_server_health_list(conn)
 
@@ -200,13 +205,30 @@ class FrontendProcessor():
         healthDict= {}
         healthCnt = 0 # 统计总服务器列表中的健康状态信息，设计所有的服务器，而不是某个服务器的将抗日志统计
 
+        # 前端总揽界面所需要的前三个服务器的七天内的序列数据，直接通过重用类中本身就有的getServerInfo函数进行获取
+        datasets = []#todo
+        # 以后考虑把这几个字段放在数据库里，入库的时候就算好
+        cnt = 3
+
         for serverInfo in serverList:
             province = self.queryIpLocation(serverInfo['server_name'])
-            disableRate = self.getDisabledRate(serverInfo['server_name'])
+            disableRate = self.getDisabledRate(conn,serverInfo['server_name'])
-            avgReponsetime = self.getAvgResponseTime(serverInfo['server_name'])[0]
+            avgReponsetime = self.getAvgResponseTime(conn,serverInfo['server_name'])[0]
+
+            if cnt:
+                server_dict = json.loads(self.getServerInfo(serverInfo['server_name']))
+                timeArray = server_dict['ResponseTimeArray']
+                length = min(6,len(timeArray))
+                parse_server_dict = {
+                    'label': serverInfo['server_name'],
+                    'data' : timeArray[:length]
+                }
+                datasets.append(parse_server_dict)
+                cnt -= 1
+
 
             # 处理健康情况信息
-            if int(serverInfo['health']):
+            if int(serverInfo['status']):
                 healthCnt += 1
 
             # 处理省份信息
@@ -218,9 +240,8 @@ class FrontendProcessor():
 
             # 存活度信息
             testdata = [[avgReponsetime/1000, disableRate]]
-            health = mldivider.predict_health_status(testdata)
+            health = mldivider.predict_health_status(testdata)[0]
             # 处理存活度信息
-            # @todo  fuck liuzuai
             if health in healthDict:
                 tmp = healthDict[health]
                 healthDict[health] = tmp+1 #计数加一
@@ -228,23 +249,58 @@ class FrontendProcessor():
                 healthDict[health] = 1
 
         provinceDistribution = self.getDictRate(provinceDict,len(serverList))
-        healthDistribution = self.getDictRate(healthdict,len(serverList))
+        healthDistribution = self.getDictRate(healthDict,len(serverList))
         overallHealthRate = float(healthCnt)/len(serverList)
 
+        # 日志统计数据
+        dateArray, logNumArray = self.getLogNumOnDate(conn)
+
+        datadict = {
+            'labels':['周一','周二','周三','周四','周五','周六','周日'],
+            'datasets':datasets
+        }
         overallMessage = {
-            'provinceDistribution': provinceDistribution,
+            'AveSurRate': overallHealthRate,
-            'healthDistribution': healthDistribution,
+            'TimeArray': dateArray,
-            'overallHealthRate': overallHealthRate
+            'DataArray': logNumArray,
+            'data1': provinceDistribution,
+            'data2': healthDistribution,
+            'data': datadict
         }
         return overallMessage
 
-    def getDisabledRate(self,serverName):
+    def getLogNumOnDate(self,conn):
+        """
+        从数据库中获取关于服务器健康状态的日志信息，返回结果为每一天的日志信息数量，这里的时间格式需要进行自己的定义
+        @param conn:
+        @return: 可以用于建立柱形图的信息。
+        """
+        dateArray = []
+        logNumArray = []
+        # 定义时间格式
+        date_format = "%Y-%m-%d"
+
+        # 计算当前日期之前15天的日期
+        for day in range(1,16):
+            delta = timedelta(days=-day)
+            previous_date = (datetime.now() + delta).strftime(date_format)
+            # 去除前导零
+            previous_date = previous_date.replace("-0", "-")
+
+            dateArray.append(previous_date)
+            logNum = getLogNumByDate(conn,previous_date)
+            logNumArray.append(logNum)
+
+        return dateArray, logNumArray
+
+
+    def getDisabledRate(self,conn,serverName):
         """
         @disc 为了更加方便调用，把这个功能单独拿出来
         @param serverName:
         @return: 返回故障率
         """
-        conn = connect_user_db()
+
         serverHealthLog = select_server_health_log(conn, serverName)
         DisabledTime = 0
         AbledTime = 0
@@ -261,13 +317,13 @@ class FrontendProcessor():
 
         return DisabledRate
 
-    def getAvgResponseTime(self,serverName):
+    def getAvgResponseTime(self,conn,serverName):
         """
         获取某个服务器的平均响应时间
         @param serverName:
         @return:
         """
-        conn = connect_user_db()
+
         serverHealthLog = select_server_health_log(conn, serverName)
 
         ResponseTimeList = []
@@ -293,13 +349,17 @@ class FrontendProcessor():
         @param dict:
         @return:
         """
-        rateDict = {}
+        ret = []
-        for key, value in Dict.keys(),Dict.values():
+
-            rateDict[key] = float(value)/num
+        for key in Dict.keys():
+            rateDict = {}
+            rateDict['value'] = Dict[key]/float(num)
+            rateDict['name'] = key
+            ret.append(rateDict)
+        return ret
 
-        return rateDict
 def test_case_serverinfo():
-    di = {"source":"frontend" , "type": "getserverinfo_message","server_name":"abb"}
+    di = {"source":"frontend" , "type": "getserverinfo_message","server_name":"1.205.204.0"}
     a = FrontendProcessor(di)
     print(a.returnMessage)
 

src/controller/MessageHandler/try.py

@@ -0,0 +1,22 @@
+from datetime import datetime, timedelta
+
+# 定义时间格式
+date_format = "%Y-%m-%d"
+
+# 获取当前日期，并按照定义的格式转换
+current_date = datetime.now().strftime(date_format)
+# 去除前导零
+current_date = current_date.replace("-0", "-")
+# 打印当前日期
+print("当前日期:", current_date)
+
+for day in range(1,16):
+
+    # 计算当前日期之前15天的日期
+    delta = timedelta(days=-day)
+    previous_date = (datetime.now() + delta).strftime(date_format)
+    # 去除前导零
+    previous_date = previous_date.replace("-0", "-")
+    print(previous_date)
+# 打印之前15天的日期
+print("之前15天的日期:", previous_date)

src/controller_completed/LogAnalyze/MachineLearningDivider.py

@@ -1,74 +0,0 @@
-#!/usr/bin/env python
-# encoding: utf-8
-# @author: 原凯峰
-# @contact: 2894340009@qq.com
-# @software: pycharm
-# @file: MachineLearningDivider.py
-# @time: 2024/6/26 8:21
-# @desc:利用随机森林法进行模型训练，能够通过平均响应时间、故障率等数据计算出服务器的健康状态
-
-
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.metrics import classification_report, accuracy_score
-import pickle
-# 假设我们有以下数据集
-X = [
-    [0.3, 0.005],  # 服务器特征：平均响应时间和故障率
-    [2.5, 0.03],
-    [0.7, 0.045],
-    [1.2, 0.002]
-]
-y = ['良好', '差', '差', '良好']  # 对应的健康状态标签
-
-# 将健康状态标签转换为数值
-label_mapping = {'一般': 0, '良好': 1, '差': 2, '极差': 3}
-y_encoded = [label_mapping[label] for label in y]
-
-# 划分训练集和测试集
-X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.25, random_state=42)
-
-# 选择模型，这里使用随机森林分类器
-model = RandomForestClassifier(n_estimators=100, random_state=42)
-
-# 训练模型
-model.fit(X_train, y_train)
-
-# 预测测试集
-y_pred = model.predict(X_test)
-
-# 评估模型
-print(classification_report(y_test, y_pred))
-print("Accuracy:", accuracy_score(y_test, y_pred))
-
-with open('server_health_model.pkl', 'wb') as file:
-    pickle.dump(model, file)
-
-
-# 保存模型
-with open('server_health_model.pkl', 'wb') as file:
-    pickle.dump(model, file)
-
-# 定义一个函数来加载模型并进行预测
-def load_model_and_predict(new_data):
-    with open('server_health_model.pkl', 'rb') as file:
-        loaded_model = pickle.load(file)
-    predictions = loaded_model.predict(new_data)
-    return predictions
-
-# 定义一个函数来将预测结果转换为健康等级
-def predict_health_status(new_data):
-    predictions = load_model_and_predict(new_data)
-    # 创建逆向映射字典
-    inverse_label_mapping = {value: key for key, value in label_mapping.items()}
-    # 使用逆向映射字典转换预测结果
-    health_status = [inverse_label_mapping[pred] for pred in predictions]
-    return health_status
-
-# 测试函数
-def testcase():
-    new_data = [[0.4, 0.01]]  # 新的服务器数据
-    health_status = predict_health_status(new_data)
-    print("预测的健康状态：", health_status)
-
-testcase()

src/controller_completed/MessageHandler/try.py

@@ -1,2 +0,0 @@
-dic = {'a':1,'b':2,'c':3}
-for i in dic.keys():