Communication network flow management system

Abstract

The invention relates to the technical field of flow management, in particular to a communication network flow management system which comprises a flow monitoring module, an abnormal flow processing module, a flow priority adjusting module and a network load balancing module. According to the method, the abnormal data flow can be identified by analyzing the distribution characteristics of the communication data flow in real time, the abnormal level can be quantitatively evaluated according to the abnormal influence, the flow limiting is more targeted by positioning the source node of the abnormal flow and implementing the bandwidth limitation according to the abnormal level, the flow limiting degree can be adjusted as required, unnecessary broadband waste is avoided, and the flow limiting efficiency is improved. By considering the importance and real-time performance indexes of services, when the priority of communication data is adjusted, the performance of key applications is guaranteed preferentially, and by monitoring the delay and packet loss conditions of nodes in real time and intelligently adjusting node bandwidth allocation according to the flow priority, the overall operation efficiency and stability of a network are improved, and the network performance is improved. And the network performance management is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and in particular to a communication network traffic management system. Background Art

[0002] The field of traffic management technology focuses on effectively controlling and optimizing the transmission of data in communication networks to ensure the reasonable allocation and efficient use of network resources. It includes monitoring, controlling and scheduling the flow of data packets in the network to optimize the performance and response time of the entire network. Traffic management uses various mechanisms such as bandwidth allocation, priority setting, congestion avoidance and traffic shaping to ensure the quality of service and support multiple business types and data flows. In addition, this field also covers cross-network traffic control, handling data transmission and management issues between different networks, and dynamic resource allocation technology to cope with changes in network status and the needs of different applications.

[0003] Among them, the communication network traffic management system is a system used to maintain and optimize the data flow within the data network. The main purpose of the system is to ensure the stable operation of the network, prevent overload, improve data transmission efficiency, and ensure the performance of key applications. By monitoring network traffic in real time and adjusting and controlling it according to preset rules, the traffic management system can dynamically allocate network bandwidth and resources, optimize the overall performance of the network, and ensure that all users and applications have the necessary bandwidth and resources.

[0004] Traditional management systems lack sufficient dynamics and adaptability when processing network traffic. They rely on preset rules and static policies to adjust traffic, which leads to untimely responses or unreasonable resource allocation when the network status changes rapidly. For example, when a large amount of data flow suddenly appears on the network, the traditional system cannot quickly identify and adjust the priority, resulting in the obstruction of key services and affecting user experience. The traffic monitoring and management in the traditional system relies on preset rules, which will lead to excessive or insufficient traffic control, resulting in waste of network resources, affecting the normal operation of the business and the security of data. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a communication network traffic management system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a communication network traffic management system, the system comprising: The traffic monitoring module analyzes the distribution characteristics of communication data traffic based on real-time network communication data, identifies atypical traffic patterns, and evaluates the abnormal level according to the impact of abnormal traffic to obtain abnormal traffic identification results; The abnormal traffic processing module locates the source node of the abnormal traffic based on the abnormal traffic identification result, limits the bandwidth of the source node, and adjusts the flow limitation degree according to the abnormal level to obtain an abnormal processing record; The traffic priority adjustment module adjusts the priority of the communication data based on the exception processing record, reconfigures the priority according to the business importance and real-time performance indicators of each traffic, and records the parameters and execution time of the adjustment operation to obtain a priority adjustment log; The network load balancing module monitors the delay and packet loss data of each network node based on the priority adjustment log, evaluates the performance of the network node, and adjusts the bandwidth allocation of the network communication node in combination with the real-time load situation and traffic priority of the network node to obtain load balancing adjustment information.

[0007] The present invention is improved in that the method for identifying atypical traffic patterns is: Based on the real-time network communication data, extract data flow characteristics, including flow size, duration, frequency, etc., to obtain a data flow characteristic data group; Based on the data flow characteristic data group, through the formula: ; Calculate standard scores for the data; in, Indicates The flow characteristic value of the data point, represents the mean, represents the standard deviation, is the standard score of the data, Indicates the total number of data points; Based on the standard score of the data, it is compared with a preset standard threshold. If the standard score of the data is greater than the preset standard threshold, the data flow is marked as an atypical traffic pattern, and an atypical traffic pattern recognition result is obtained.

[0008] The present invention is improved in that the steps of obtaining the abnormal traffic identification result are: Based on the atypical traffic pattern recognition result, characteristic data of a single abnormal traffic event is extracted, including traffic peak value, duration and frequency, to obtain abnormal traffic correlation data; Based on the abnormal traffic correlation data, the formula is: ; Evaluate the impact of abnormal traffic, calculate the abnormal level, and obtain abnormal traffic identification results; in, is the peak flow rate, is the duration, is the frequency, is the weight coefficient of the traffic peak, is the weight coefficient of duration, is the frequency weight coefficient, It is an abnormal level.

[0009] The present invention is improved in that the method for locating the source node of abnormal traffic is: Based on the abnormal traffic identification result, data packet information associated with the identified atypical traffic is collected, including source IP addresses and destination IP addresses, to obtain address association data; Based on the address association data, frequency analysis is performed on each source IP address, and the number of times each source IP address sends abnormal traffic is counted, using the formula: ; Calculate the traffic contribution rate of each source IP address; in, is the total number of abnormal traffic events, is the traffic contribution rate, Indicates the number of abnormal traffic events sent from the source IP address; Based on the traffic contribution rate of each source IP address, the traffic contribution rate values ​​are sorted, and the source IP address with the highest traffic contribution rate value is selected as the source node to obtain the source node identification result.

[0010] The present invention is improved in that the step of obtaining the exception handling record is: Based on the source node identification results and abnormal traffic identification results, the abnormal level is extracted through the formula: ; Calculate bandwidth limit percentage; in, For abnormal level, is the basic coefficient for bandwidth adjustment, is the logarithmic adjustment coefficient, is the bandwidth limit percentage; Based on the bandwidth limitation percentage and the source node identification result, the bandwidth of the source node is limited to obtain an exception processing record.

[0011] The present invention is improved in that the step of obtaining the priority adjustment log is: Based on the exception handling records, extract the business importance and real-time performance indicators of each type of traffic to obtain priority-related data; Based on the priority association data, by the formula: ; Calculate the priority of each type of traffic; in, Represents business importance, Represents real-time performance, , and is the weight coefficient, and is the stability constant, is the traffic priority; Based on the priority of each type of traffic, the parameters and execution time of the adjustment operation are recorded to obtain a priority adjustment log.

[0012] The present invention is improved in that the method for evaluating the performance of a network node is: Through network monitoring tools, the delay and packet loss data of network nodes are collected at preset fixed intervals to obtain network performance correlation data; Based on the network performance correlation data, through the formula: ; Calculate the performance index of each network node to obtain the network node performance evaluation result; in, is the data packet loss rate of the node, is the node delay, and To adjust the parameters, is the performance index of the network node, is the base of natural logarithms.

[0013] The present invention is improved in that the step of acquiring the load balancing adjustment information is: Based on the priority adjustment log and the network node performance evaluation result, extract the performance index and traffic priority of the network node to obtain load association data; Based on the load association data, by the formula: ; Calculate bandwidth adjustment percentage; in, is the performance index of the network node, is the traffic priority, and To adjust the parameters, is the base of natural logarithms, Adjust the percentage for bandwidth; Based on the bandwidth adjustment percentage, the bandwidth allocation of the network communication node to the data flow is adjusted to obtain load balancing adjustment information.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by real-time analysis of the distribution characteristics of communication data traffic, abnormal data flows can be identified, and the abnormal level can be quantitatively evaluated according to the abnormal impact. By locating the source node of the abnormal traffic and implementing bandwidth limitation according to the abnormal level, the flow limitation can be made more targeted, and the degree of flow limitation can be adjusted as needed to avoid unnecessary bandwidth waste. By considering the importance of the business and real-time performance indicators, the priority of communication data is adjusted to ensure the performance priority of key applications. By real-time monitoring of the node delay and packet loss, and intelligently adjusting the node bandwidth allocation according to the traffic priority, the overall operation efficiency and stability of the network are improved, and the network performance management is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart for identifying atypical traffic patterns for the present invention; Figure 3 A flow chart of obtaining abnormal traffic identification results according to the present invention; Figure 4 A flow chart of locating the source node of abnormal traffic for the present invention; Figure 5 A flowchart for obtaining an exception handling record for the present invention; Figure 6 A flow chart for obtaining a priority adjustment log for the present invention; Figure 7 A flow chart for evaluating network node performance according to the present invention; Figure 8 The present invention is a flow chart of obtaining load balancing adjustment information. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1The present invention provides a technical solution: a communication network traffic management system, the system comprising: The traffic monitoring module analyzes the distribution characteristics of communication data traffic based on real-time network communication data, identifies atypical traffic patterns, and evaluates the abnormal level according to the impact of abnormal traffic to obtain abnormal traffic identification results; The abnormal traffic processing module locates the source node of the abnormal traffic based on the abnormal traffic identification results, limits the bandwidth of the source node, and adjusts the flow restriction degree according to the abnormal level to obtain the abnormal processing record; The traffic priority adjustment module adjusts the priority of communication data based on the exception processing record, reconfigures the priority according to the business importance and real-time performance indicators of each traffic, and records the parameters and execution time of the adjustment operation to obtain the priority adjustment log; The network load balancing module monitors the delay and packet loss data of each network node based on the priority adjustment log, evaluates the performance of the network nodes, and adjusts the bandwidth allocation of the network communication nodes in combination with the real-time load situation and traffic priority of the network nodes to obtain the load balancing adjustment information; The abnormal traffic identification results include the abnormality type, potential risk area and abnormality level. The abnormality handling records include the implemented flow limiting strategy and adjusted bandwidth limit. The priority adjustment log includes the priority information and key application traffic identification before and after the adjustment. The load balancing adjustment information includes the node information after load balancing, the list of nodes involved in the adjustment and the time point when the adjustment was implemented.

[0019] See also Figure 2 , the method to identify atypical traffic patterns is: Based on the real-time network communication data, extract data flow characteristics, including flow size, duration, frequency, etc., to obtain a data flow characteristic data group; Based on the data traffic characteristic data set, through the formula: ; Calculate standard scores for the data; in, Indicates The flow characteristic value of the data point, represents the mean, represents the standard deviation, is the standard score of the data, represents the total number of data points; The standard score based on the data is compared with the preset standard threshold. If the standard score of the data is greater than the preset standard threshold, the data flow is marked as an atypical traffic pattern, and an atypical traffic pattern recognition result is obtained.

[0020] formula: ; Parameter details and how to obtain them: :Indicates the Traffic characteristic values ​​of a data point, such as traffic volume or duration. These data points are obtained through real-time network traffic monitoring systems and are usually exported directly from network management tools or APIs.

[0021] : represents a normal data point The average value is used to determine the center of the data. By summing all data points and dividing by the total number of data points Get, that is .

[0022] : represents all data points The standard deviation is a measure of how far the data points deviate from the mean. is calculated by comparing each data point with The square root of the mean of the sum of squared deviations is .

[0023] Calculation example: Set five data points to be monitored , we need to calculate these data points and , and calculate the standard score .

[0024] Calculate the average : ; Calculate standard deviation : ; Calculating Standard Scores : ; Calculation results The sum of standard scores representing atypical traffic patterns, which is used to measure the degree of abnormality of the data. A value of 0 indicates that the monitored traffic pattern deviates significantly from the normal pattern, indicating unusual activity in the network.

[0025] See also Figure 3 ,The steps to obtain abnormal traffic identification results are: Based on the atypical traffic pattern recognition results, the characteristic data of a single abnormal traffic event is extracted, including traffic peak, duration and frequency, to obtain abnormal traffic correlation data; Based on the abnormal traffic correlation data, the formula is: ; Evaluate the impact of abnormal traffic, calculate the abnormal level, and obtain abnormal traffic identification results; in, is the peak flow rate, is the duration, is the frequency, is the weight coefficient of the traffic peak, is the weight coefficient of duration, is the frequency weight coefficient, It is an abnormal level.

[0026] formula: ; Parameter details and how to obtain them: : Peak traffic rate is the maximum data transmission rate recorded during network monitoring, usually provided directly by network traffic analysis tools.

[0027] Duration: refers to the specific length of time that the abnormal traffic is maintained, which is also directly measured by the network monitoring system.

[0028] : Frequency, which indicates the number of times an abnormal event occurs during the monitoring period, recorded and calculated by the event log.

[0029] , , :The weight coefficient is set according to the importance of each parameter in the historical data to the system. For example, in a traffic-sensitive network environment, May be set higher.

[0030] Calculation example: The set of abnormal traffic data observed is as follows: Mbps (peak traffic), seconds (duration), Times / hour (frequency). The weight coefficient is set as: , , .

[0031] Calculation process: Calculate the weighted value of the traffic peak: ; Calculate the weighted value of duration: ; Compute the weighted values ​​of the frequencies: ; calculate : ; Calculated Value is This value is obtained by comprehensively considering the influence of traffic peak, duration and frequency. Values ​​indicate greater potential risk and require appropriate safety measures.

[0032] See also Figure 4 ,The method to locate the source node of abnormal traffic is: Based on the abnormal traffic identification results, collect the data packet information associated with the identified atypical traffic, including the source IP address and the destination IP address, to obtain address association data; Based on the address association data, frequency analysis is performed on each source IP address, and the number of times each source IP address sends abnormal traffic is counted. The formula is: ; Calculate the traffic contribution rate of each source IP address; in, is the total number of abnormal traffic events, is the traffic contribution rate, Indicates the number of abnormal traffic events sent from the source IP address; Based on the traffic contribution rate of each source IP address, the traffic contribution rate values ​​are sorted, and the source IP address with the highest traffic contribution rate value is selected as the source node to obtain the source node identification result.

[0033] formula: ; Parameter details and how to obtain them: : Indicates the number of abnormal traffic events sent from the source IP address. This is calculated by analyzing the number of packets that match a known abnormal pattern within a specified time window.

[0034] : The total number of abnormal traffic events, which represents the number of all traffic events identified as abnormal during the observation window, and is statistically obtained from the traffic monitoring data of the entire network.

[0035] Calculation example: In a monitoring window, a total of 100 abnormal traffic events are detected ( ), where traffic events from IP address 192.168.1.1 For 20 times.

[0036] Calculation process: Calculate the traffic contribution rate of a single source IP address 192.168.1.1 : ; The calculation results show that IP address 192.168.1.1 contributes 20% of the abnormal traffic, which means that the abnormal traffic sent from IP address 192.168.1.1 accounts for 20% of the total abnormal traffic monitored. If this ratio is the highest among all source IPs, this IP address will be identified as the main source of abnormal traffic. In this way, network administrators can take measures against this source node, such as bandwidth limitation, to reduce its impact on the network.

[0037] See also Figure 5 , the steps to obtain exception handling records are: Based on the source node identification results and abnormal traffic identification results, the abnormal level is extracted through the formula: ; Calculate bandwidth limit percentage; in, For abnormal level, is the basic coefficient for bandwidth adjustment, is the logarithmic adjustment coefficient, is the bandwidth limit percentage; Based on the bandwidth limit percentage and the source node identification result, the bandwidth of the source node is limited to obtain an exception processing record.

[0038] formula: ; Parameter details and how to obtain them: : Abnormality level, calculated by the previous step.

[0039] : Basic bandwidth adjustment coefficient, determined by network security policy, used to balance the basic ratio of bandwidth adjustment. The coefficient is set based on the network's capacity and historical security policy.

[0040] : Logarithmic adjustment coefficient, used to fine-tune the impact of the anomaly level on the bandwidth limit, to ensure that the bandwidth adjustment responds to the anomaly level with appropriate sensitivity. The coefficient is customized based on past anomaly handling experience and network response requirements.

[0041] Calculation example: Set the following setting parameters: Abnormal Level , indicating that the identified traffic is abnormally serious, and the basic bandwidth adjustment coefficient , indicating that the network is very sensitive to abnormal traffic, and the adjustment coefficient , which is used to ensure that the logarithmic adjustment does not result in small Overreacting to change.

[0042] Calculation process: Calculating bandwidth limit percentage : ; Calculated bandwidth limit percentage This means that the bandwidth of the source node will be reduced by about 22%. This adjustment reflects the relatively high level of abnormality, and the network administrator will adjust the egress bandwidth of the source node accordingly to reduce the impact that abnormal traffic may have on the entire network.

[0043] See also Figure 6 , the steps to obtain the priority adjustment log are: Based on the exception processing records, the business importance and real-time performance indicators of each type of traffic are extracted to obtain priority-related data; Based on the priority associated data, through the formula: ; Calculate the priority of each type of traffic; in, Represents business importance, Represents real-time performance, , and is the weight coefficient, and is the stability constant, is the traffic priority; Based on the priority of each type of traffic, the parameters and execution time of the adjustment operation are recorded to obtain a priority adjustment log.

[0044] formula: ; Parameter details and how to obtain them: : Business importance, usually scored by the business department based on the impact of traffic on business operations. This parameter is directly obtained from business analysis data.

[0045] : Real-time performance metrics, including latency and bandwidth utilization, calculated from real-time data provided by network monitoring tools.

[0046] , and : Weight coefficient, set according to the priority of business and technical strategies to ensure a reasonable balance between business importance and the impact of performance indicators.

[0047] and : stability constant, It is used to ensure that the logarithmic function is always positive to avoid calculation errors. Usually, a suitable value is determined based on historical data analysis. It is used to prevent the denominator from being zero and to increase the mathematical stability of the formula. It is usually a very small positive number.

[0048] Calculation example: Set the following parameter values: : Indicates that the traffic is highly important to the business. : Real-time performance indicators, such as low latency. , , : Weight coefficient, set according to business strategy. : Ensure positive value of logarithm. : Small constant to prevent division by zero errors.

[0049] Calculation process: Compute the value inside the logarithm function: ; Apply logarithms and weighting factors: ; Calculated priority , indicating that traffic should be given relatively low network resource priority given the given business importance and real-time performance indicators. The numerical value helps network administrators adjust the configuration of routers and switches, optimize network performance, and ensure that critical business traffic is given priority.

[0050] See also Figure 7 , the method to evaluate the performance of network nodes is: Through network monitoring tools, the delay and packet loss data of network nodes are collected at preset fixed intervals to obtain network performance correlation data; Based on the network performance correlation data, the formula is: ; Calculate the performance index of each network node to obtain the network node performance evaluation result; in, is the data packet loss rate of the node, is the node delay, and To adjust the parameters, is the performance index of the network node, is the base of natural logarithms.

[0051] formula: ; Parameter details and how to obtain them: : The data packet loss rate of the node, which is real-time data obtained directly from the network performance monitoring tool. It reflects the proportion of data packets lost by network nodes when transmitting data.

[0052] : The node latency, in milliseconds, is also monitored in real time through network monitoring tools. Latency refers to the time required for data to travel from the sending point to the receiving point, and is one of the key indicators for evaluating network performance.

[0053] and : Adjustment parameters to balance the impact of packet loss and latency in performance evaluation. The coefficient values ​​are optimized based on historical performance data and specific network requirements to ensure that the evaluation results accurately reflect the performance status of the node.

[0054] Calculation example: Set the following parameter values: (5% packet loss rate), (delay 100ms), , (Adjustment factor).

[0055] Evaluate expressions inside exponential functions: ; calculate : ; Calculated performance index , indicating that the performance evaluation result of the node is close to neutral, reflecting the actual performance of the node under the current network conditions.

[0056] See also Figure 8 , the steps to obtain load balancing adjustment information are: Based on the priority adjustment log and the network node performance evaluation results, the performance index and traffic priority of the network node are extracted to obtain the load correlation data; Based on the load-related data, the formula is: ; Calculate bandwidth adjustment percentage; in, is the performance index of the network node, is the traffic priority, and To adjust the parameters, is the base of natural logarithms, Adjust the percentage for bandwidth; Based on the bandwidth adjustment percentage, the bandwidth allocation of the network communication node to the data flow is adjusted to obtain load balancing adjustment information.

[0057] formula: ; Parameter details and how to obtain them: : The performance index of the network node, calculated by the previous step.

[0058] : Traffic priority, calculated in the previous step.

[0059] and : Adjustment parameters. The parameters are specifically used to adjust the weight coefficient and offset of traffic priority. To enhance the priority The direct impact of Used to adjust the offset in response to bandwidth adjustments, thereby providing sensitivity control in response to bandwidth adjustments.

[0060] Calculation example: Set the following parameter values: : Indicates that the node has higher performance. : Indicates that the traffic has medium service priority. : Strengthened the impact of priority. : Provides a baseline shift for the response.

[0061] Evaluate expressions inside exponential functions: ; calculate : ; Calculated bandwidth adjustment percentage , indicating that under a given performance index and traffic priority, the network node should have close to 50% bandwidth allocation. This result shows that the node can handle close to half of the network bandwidth under its current performance and traffic priority conditions. The calculation helps network administrators dynamically adjust the bandwidth allocation of each node, optimize the resource usage of the entire network, and improve service quality and response speed.

[0062] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A communication network traffic management system, characterized in that: The system comprises: The traffic monitoring module analyzes the distribution characteristics of communication data traffic based on real-time network communication data, identifies atypical traffic patterns, and evaluates the abnormal level according to the impact of abnormal traffic to obtain abnormal traffic identification results; The abnormal traffic processing module locates the source node of the abnormal traffic based on the abnormal traffic identification result, limits the bandwidth of the source node, and adjusts the flow limitation degree according to the abnormal level to obtain an abnormal processing record; The traffic priority adjustment module adjusts the priority of the communication data based on the exception processing record, reconfigures the priority according to the business importance and real-time performance indicators of each traffic, and records the parameters and execution time of the adjustment operation to obtain a priority adjustment log; The network load balancing module monitors the delay and packet loss data of each network node based on the priority adjustment log, evaluates the performance of the network node, and adjusts the bandwidth allocation of the network communication node in combination with the real-time load situation and traffic priority of the network node to obtain load balancing adjustment information.

2. The communication network traffic management system according to claim 1, characterized in that: The method for identifying atypical traffic patterns is: Based on the real-time network communication data, extract data flow characteristics, including flow size, duration, frequency, etc., to obtain a data flow characteristic data group; Based on the data flow characteristic data group, through the formula: ； Calculate standard scores for the data; in, Indicates The flow characteristic value of the data point, represents the mean, represents the standard deviation, is the standard score of the data, Indicates the total number of data points; Based on the standard score of the data, a comparison is made with a preset standard threshold. If the standard score of the data is greater than the preset standard threshold, the data flow is marked as an atypical traffic pattern, and an atypical traffic pattern recognition result is obtained.

3. The communication network traffic management system according to claim 2, characterized in that: The steps for obtaining the abnormal traffic identification result are: Based on the atypical traffic pattern recognition result, characteristic data of a single abnormal traffic event is extracted, including traffic peak value, duration and frequency, to obtain abnormal traffic correlation data; Based on the abnormal traffic correlation data, the formula is: ； Evaluate the impact of abnormal traffic, calculate the abnormal level, and obtain abnormal traffic identification results; in, is the peak flow rate, is the duration, is the frequency, is the weight coefficient of the traffic peak, is the weight coefficient of duration, is the frequency weight coefficient, It is an abnormal level.

4. The communication network traffic management system according to claim 1, characterized in that: The method for locating the source node of abnormal traffic is: Based on the abnormal traffic identification result, data packet information associated with the identified atypical traffic is collected, including source IP addresses and destination IP addresses, to obtain address association data; Based on the address association data, frequency analysis is performed on each source IP address, and the number of times each source IP address sends abnormal traffic is counted, using the formula: ； Calculate the traffic contribution rate of each source IP address; in, is the total number of abnormal traffic events, is the traffic contribution rate, Indicates the number of abnormal traffic events sent from the source IP address; Based on the traffic contribution rate of each source IP address, the traffic contribution rate values ​​are sorted, and the source IP address with the highest traffic contribution rate value is selected as the source node to obtain the source node identification result.

5. The communication network traffic management system according to claim 4, characterized in that: The steps for obtaining the exception handling record are: Based on the source node identification results and abnormal traffic identification results, the abnormal level is extracted through the formula: ； Calculate bandwidth limit percentage; in, For abnormal level, is the basic coefficient for bandwidth adjustment, is the logarithmic adjustment coefficient, is the bandwidth limit percentage; Based on the bandwidth limitation percentage and the source node identification result, the bandwidth of the source node is limited to obtain an exception processing record.

6. The communication network traffic management system according to claim 1, characterized in that: The steps for obtaining the priority adjustment log are: Based on the exception handling records, extract the business importance and real-time performance indicators of each type of traffic to obtain priority-related data; Based on the priority association data, by the formula: ； Calculate the priority of each type of traffic; in, Represents business importance, Represents real-time performance, , and is the weight coefficient, and is the stability constant, is the traffic priority; Based on the priority of each type of traffic, the parameters and execution time of the adjustment operation are recorded to obtain a priority adjustment log.

7. The communication network traffic management system according to claim 1, characterized in that: The method for evaluating network node performance is: Through network monitoring tools, the delay and packet loss data of network nodes are collected at preset fixed intervals to obtain network performance correlation data; Based on the network performance correlation data, through the formula: ； Calculate the performance index of each network node to obtain the network node performance evaluation result; in, is the data packet loss rate of the node, is the node delay, and To adjust the parameters, is the performance index of the network node, is the base of natural logarithms.

8. The communication network traffic management system according to claim 7, characterized in that: The steps for obtaining the load balancing adjustment information are as follows: Based on the priority adjustment log and the network node performance evaluation result, extract the performance index and traffic priority of the network node to obtain load association data; Based on the load association data, by formula: ； Calculate bandwidth adjustment percentage; in, is the performance index of the network node, is the traffic priority, and To adjust the parameters, is the base of natural logarithms, Adjust the percentage for bandwidth; Based on the bandwidth adjustment percentage, the bandwidth allocation of the network communication node to the data flow is adjusted to obtain load balancing adjustment information.

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