Data interaction method and device for switch test system

Abstract

The invention relates to the technical field of switch testing, and provides a switch testing system data interaction method and device, and the method comprises the steps: obtaining the operation state information and data packet flow information of each port, carrying out the extraction of the data packet flow information, obtaining a data packet feature parameter matrix, and carrying out the dynamic flow scheduling of a switch testing system, obtaining a port flow regulation scheme, and generating test result data based on the port flow regulation scheme and the operation state information; and performing comprehensive evaluation on the performance indexes of the switch based on the test result data, generating corresponding optimal configuration parameters based on an evaluation result, and applying the optimal configuration parameters to the switch test system. By acquiring the running state information and the data packet flow information of each port, the optimal configuration parameters are generated, efficient optimization and performance improvement of the switch test system are realized, and the problems of low response speed and insufficient flexibility in facing a dynamically changing network environment are improved.

Description

Technical Field

[0001] The present application relates to the technical field of switch testing, and in particular to a data interaction method and device for a switch testing system. Background Art

[0002] With the rapid development of information technology, the performance and stability of switches, as one of the core devices in network communications, directly affect the operation quality and efficiency of the entire network. Therefore, the switch test system is particularly important in ensuring network performance and improving user experience. With the continuous expansion of network scale and the increasing complexity of application requirements, switch test systems are facing higher and higher requirements, and need to be continuously innovated and optimized to meet new challenges.

[0003] In related technical means, switch data interaction is mainly achieved through traditional static traffic scheduling strategies. These strategies are usually based on fixed traffic models, pre-set scheduling and allocation of data packets for each port, and regularly detect the operating status and data packet traffic of each port to generate corresponding test data. This method can reflect the performance indicators and load status of the switch to a certain extent, which helps to discover potential problems and optimize network configuration.

[0004] Regarding the above technical solutions, although the traditional static traffic scheduling strategy can realize the data interaction of the switch test system, it has the problems of slow response speed and insufficient flexibility when facing the dynamically changing network environment. For example, when the network traffic changes suddenly or an abnormal situation occurs, the traditional test system cannot adjust the traffic distribution strategy in time, resulting in inaccurate test results and affecting the reliability of switch performance evaluation. Summary of the invention

[0005] In order to improve the problems of slow response speed and insufficient flexibility when facing a dynamically changing network environment, the present application provides a switch test system data interaction method and device.

[0006] The present application provides a switch test system data interaction method, comprising: obtaining operation status information and data packet flow information of each port in the switch test system, extracting the data packet flow information, and obtaining a data packet characteristic parameter matrix; dynamically scheduling the switch test system based on the data packet characteristic parameter matrix to obtain a port flow adjustment scheme, generating a flow distribution strategy based on the port flow adjustment scheme and the operation status information, matching the flow distribution strategy with the current load state of the switch to obtain a flow distribution result; analyzing the flow distribution result by using a preset data processing model to obtain flow anomaly determination information, adjusting the data transmission path of the switch test system based on the flow anomaly determination information to obtain an optimized data transmission path; rescheduling the test task of the switch test system according to the optimized data transmission path to obtain an execution plan, inputting the execution plan into a switch test control module, and dynamically adjusting the test process of the switch by using the switch test control module to obtain test result data; comprehensively evaluating the performance indicators of the switch based on the test result data, generating corresponding optimization configuration parameters based on the evaluation results, and applying the optimization configuration parameters to the switch test system.

[0007] As a preferred solution, the steps of obtaining the data packet flow information and operation status information of each port in the switch test system, extracting the data packet flow information, and obtaining the data packet characteristic parameter matrix include: obtaining the operation status information and data packet flow information of each port from the switch test system in real time, performing protocol parsing on the data packet flow information through deep data packet detection, extracting data packet header information, and obtaining the source IP address, the destination IP address, and the data packet size; calculating the inflow timestamp and the outflow timestamp of each data packet according to the source IP address and the destination IP address, and deducing the flow duration; and calculating the flow duration per second by using the relationship between the data packet size and the inflow timestamp and the outflow timestamp. The amount of data transmitted; based on the traffic duration and the amount of data transmitted per second, a basic feature set of data packets is generated, and the basic feature set of data packets is clustered using a K-means clustering algorithm to obtain a data stream classification result; based on an entropy analysis method, an entropy analysis is performed on the data stream classification result, and the distribution balance of each type of traffic is calculated to obtain a traffic balance degree and a traffic volatility, an outlier detection is performed on the traffic balance degree to obtain an abnormal traffic interval, a correlation analysis is performed on the traffic volatility to obtain a traffic periodicity, and the abnormal traffic interval is combined with the traffic periodicity to obtain a traffic concentration index; according to the traffic concentration index and the data stream classification result, a data packet feature parameter matrix is ​​constructed.

[0008] As a preferred solution, the steps of dynamically scheduling the switch test system based on the data packet characteristic parameter matrix to obtain a port traffic regulation scheme, generating a traffic distribution strategy based on the port traffic regulation scheme and the operating status information, matching the traffic distribution strategy with the current load status of the switch to obtain a traffic distribution result include: inputting the data packet characteristic parameter matrix into a preset Markov decision process model, predicting the business traffic of the switch test system port, and obtaining a prediction result of the traffic occupancy trend of each port at a future moment; optimizing the business flow of the high-load port based on the prediction result to obtain a port traffic regulation scheme, applying a traffic balancing algorithm to calculate the optimal traffic distribution scheme based on the port traffic regulation scheme and the operating status information to obtain a traffic distribution strategy; matching the traffic distribution strategy with the current load status of the switch to obtain a traffic distribution result.

[0009] As a preferred solution, the step of analyzing the traffic distribution result using a preset data processing model to obtain traffic anomaly determination information, adjusting the data transmission path of the switch test system based on the traffic anomaly determination information, and obtaining an optimized data transmission path includes: analyzing the traffic distribution result using a preset data processing model, in the data processing model, performing time-frequency domain analysis on the port traffic trend in the traffic distribution result based on discrete wavelet transform, detecting abnormal traffic surges or decreases, and obtaining abnormal traffic detection results; applying Kalman filtering to fit the future trend of abnormal traffic points based on the abnormal traffic detection results to obtain traffic anomaly determination information; adjusting the data transmission path of the switch test system through a link load balancing strategy in combination with the traffic anomaly determination information to obtain an optimized data transmission path.

[0010] As a preferred solution, the test tasks of the switch test system are rescheduled according to the optimized data transmission path to obtain an execution plan, the execution plan is input into the switch test control module, and the switch test control module is used to dynamically adjust the test process of the switch to obtain the test result data, including: reallocating the test tasks of the switch test system based on the optimized data transmission path, calculating the optimal execution order of the test tasks, and obtaining a task scheduling plan; applying an ant colony optimization algorithm to optimally map the test tasks of different ports according to the task scheduling plan, and assigning them to different test threads to form a specific execution plan; inputting the execution plan into the switch test control module, and collecting key performance indicators of port throughput, packet loss rate, and delay jitter in real time during the test execution process to generate test result data.

[0011] As a preferred solution, the application of the ant colony optimization algorithm optimally maps the test tasks of different ports according to the task scheduling scheme, and assigns them to different test threads to form a specific execution scheme, including: based on the test task requirements of each port of the switch, using the ant colony optimization algorithm to preliminarily solve the test task requirements to obtain the throughput requirements, delay requirements and packet loss rate tolerance of the port tasks; using the task scheduling scheme to optimize the throughput requirements, delay requirements and packet loss rate tolerance to obtain an execution scheme.

[0012] As a preferred solution, the steps of comprehensively evaluating the performance indicators of the switch based on the test result data, generating corresponding optimized configuration parameters based on the evaluation results, and applying the optimized configuration parameters to the switch test system include: inputting the test result data into a preset performance evaluation model for multi-dimensional analysis, in the performance evaluation model, calculating the weights and scores of various performance indicators of the switch based on the test result data by a weighted average method to obtain an evaluation result of the switch performance; optimizing the evaluation result by using a particle swarm optimization algorithm to generate optimized configuration parameters, and inputting the optimized configuration parameters into the switch test system.

[0013] The present application also provides a switch test system data interaction device, including: an acquisition module, used to acquire the operation status information and data packet flow information of each port in the switch test system, extract the data packet flow information, and obtain a data packet characteristic parameter matrix; a matching module, used to dynamically schedule the switch test system based on the data packet characteristic parameter matrix, obtain a port flow adjustment scheme, generate a flow distribution strategy based on the port flow adjustment scheme and the operation status information, match the flow distribution strategy with the current load state of the switch, and obtain a flow distribution result; an analysis module, used to analyze the flow distribution result using a preset data processing model, obtain flow anomaly determination information, adjust the data transmission path of the switch test system based on the flow anomaly determination information, and obtain an optimized data transmission path; a scheduling module, used to reschedule the test task of the switch test system according to the optimized data transmission path, obtain an execution plan, input the execution plan into the switch test control module, and use the switch test control module to dynamically adjust the test process of the switch to obtain test result data; an interaction module, used to comprehensively evaluate the performance indicators of the switch based on the test result data, generate corresponding optimization configuration parameters based on the evaluation results, and apply the optimization configuration parameters to the switch test system.

[0014] Compared with the prior art, the present application has the following beneficial effects: fast response speed and high flexibility. By acquiring the operation status information and data packet flow information of each port, constructing a data packet characteristic parameter matrix, performing dynamic flow scheduling, generating a flow distribution strategy, and matching the current load state of the switch, the flow distribution result is obtained; the flow distribution result is analyzed by a preset data processing model, flow anomaly determination information is obtained, and the data transmission path is optimized and adjusted; the test task is rescheduled according to the optimized data transmission path, an execution plan is generated, and dynamic adjustments are made during the test process to obtain test result data, and based on the test result data, the switch performance indicators are comprehensively evaluated, and optimized configuration parameters are generated and applied to the switch test system, realizing efficient optimization and performance improvement of the switch test system, and improving the problems of slow response speed and insufficient flexibility when facing a dynamically changing network environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0016] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0017] Figure 1 It is a flow chart of a data interaction method of a switch test system provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of a data interaction device for a switch test system provided by an embodiment of the present invention.

[0018] Description of reference numerals: 10. Switch test system data interaction device; 11. Acquisition module; 12. Matching module; 13. Analysis module; 14. Scheduling module; 15. Interaction module. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0021] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0022] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0024] Embodiment 1: like Figure 1 As shown, the present application provides a switch test system data interaction method, including steps S100 to S500.

[0025] Step S100: Obtain the operation status information and data packet flow information of each port in the switch test system, extract the data packet flow information, and obtain a data packet characteristic parameter matrix.

[0026] In this step, the monitoring module of the switch monitors the operation status information of each port in real time, including the connection status, traffic statistics, error counts, etc. of the port. At the same time, the data packet flow information of each port is collected, and the collected data packet flow information is analyzed and extracted in detail to obtain a data packet feature parameter matrix including packet length, source address, destination address, protocol type, etc. Specifically, the extraction of data packet flow information adopts deep packet inspection technology, and by analyzing the various characteristic parameters of the data packet, a multi-dimensional data packet feature parameter matrix is ​​formed for subsequent dynamic traffic scheduling.

[0027] For example, by deploying a deep packet inspection module in a switch test system, the transmitted data packets can be collected and analyzed in real time to obtain detailed traffic information and a matrix of data packet characteristic parameters.

[0028] Step S200, dynamically schedule the switch test system based on the data packet characteristic parameter matrix to obtain a port traffic regulation scheme, generate a traffic distribution strategy based on the port traffic regulation scheme and the operating status information, match the traffic distribution strategy with the current load status of the switch, and obtain a traffic distribution result.

[0029] In this step, the traffic distribution of each port is dynamically adjusted by analyzing the data packet characteristic parameter matrix. Specifically, according to the current operation status information of each port and the data packet characteristic parameters, a port traffic adjustment scheme is generated to ensure that the traffic of each port fluctuates within the normal range. Based on the port traffic adjustment scheme, a detailed traffic distribution strategy is further formulated, and the strategy is matched with the current load state of the switch to obtain a specific traffic distribution result, ensuring that the traffic distribution of the switch is balanced under different load states.

[0030] For example, by introducing machine learning algorithms, port traffic regulation schemes and traffic allocation strategies can be automatically generated based on historical traffic data and current traffic characteristics, achieving more accurate dynamic traffic scheduling.

[0031] Step S300: Analyze the flow distribution result by using a preset data processing model to obtain flow anomaly determination information, and adjust the data transmission path of the switch test system based on the flow anomaly determination information to obtain an optimized data transmission path.

[0032] In this step, the generated traffic distribution results are analyzed using a preset data processing model. Specifically, the data processing model is used to detect abnormal traffic information in the traffic distribution results and generate traffic anomaly determination information. Based on the traffic anomaly determination information, the data transmission path in the switch test system is optimized and adjusted, and the data flow is replanned and allocated to obtain a more reasonable optimized data transmission path.

[0033] For example, by introducing big data analysis technology, we can conduct in-depth analysis of a large number of traffic distribution results, timely discover and handle abnormal traffic, thereby optimizing data transmission paths and improving transmission efficiency and reliability.

[0034] Step S400: reschedule the test tasks of the switch test system according to the optimized data transmission path to obtain an execution plan, input the execution plan into the switch test control module, and use the switch test control module to dynamically adjust the switch test process to obtain test result data.

[0035] In this step, the test tasks of the switch test system are newly scheduled according to the optimized data transmission path. Specifically, a test task execution plan containing the optimized path is generated and input into the switch test control module. The test control module dynamically adjusts the switch test process in real time according to the execution plan to ensure the smooth progress of the test task and finally obtain detailed test result data.

[0036] For example, by introducing a dynamic scheduling algorithm in the switch test system, the execution order and method of the test tasks can be flexibly adjusted according to the optimized data transmission path, thereby improving the test efficiency and accuracy.

[0037] Step S500: Comprehensively evaluate the performance indicators of the switch based on the test result data, generate corresponding optimized configuration parameters based on the evaluation results, and apply the optimized configuration parameters to the switch test system.

[0038] In this step, the acquired test result data is comprehensively analyzed and evaluated. Specifically, a comprehensive evaluation is performed on each performance indicator in the test data to generate a detailed evaluation report. Based on the results of the evaluation report, optimized configuration parameters are proposed and applied to the switch test system to improve the overall performance and stability of the system.

[0039] For example, by using intelligent analysis tools, each indicator in the test result data can be automatically evaluated and optimized, thereby achieving self-optimization of the switch test system.

[0040] In this embodiment, by acquiring the operation status information and data packet flow information of each port in the switch test system and extracting the data packet flow information, a data packet characteristic parameter matrix is ​​obtained. Then, the switch test system is dynamically scheduled for flow based on the data packet characteristic parameter matrix to obtain a port flow adjustment scheme. Based on the port flow adjustment scheme and the operation status information, a flow distribution strategy is generated, and the flow distribution strategy is matched with the current load state of the switch to obtain a flow distribution result. Then, the flow distribution result is analyzed using a preset data processing model to obtain flow anomaly determination information, and the data transmission path of the switch test system is adjusted based on the flow anomaly determination information to obtain an optimized data transmission path. Finally, the test task of the switch test system is rescheduled according to the optimized data transmission path to obtain an execution plan, and the execution plan is input into the switch test control module, and the switch test control module is used to dynamically adjust the test process of the switch to obtain test result data, and the performance indicators of the switch are comprehensively evaluated based on the test result data, and corresponding optimization configuration parameters are generated based on the evaluation results, and the optimization configuration parameters are applied to the switch test system.

[0041] By using the packet characteristic parameter matrix to dynamically schedule the switch test system, the traffic of each port of the switch can be accurately adjusted, thereby improving the response speed and flexibility of the switch test system. In addition, by analyzing the traffic distribution results and traffic anomaly determination information in real time, the abnormal situation of the data transmission path can be discovered and adjusted in time, further optimizing the data transmission path and improving the reliability of data transmission. Finally, by comprehensively evaluating the test result data and generating optimized configuration parameters, the accuracy of the switch performance evaluation and optimized configuration is effectively improved, ensuring the stable operation of the switch in various network environments, and improving the problems of slow response speed and insufficient flexibility when facing a dynamically changing network environment.

[0042] Embodiment 2: In step S100, the operation status information and data packet flow information of each port are obtained in real time from the switch test system, the data packet flow information is parsed through deep data packet inspection, the data packet header information is extracted, and the source IP address, target IP address and data packet size are obtained.

[0043] The packet flow information is parsed through deep packet inspection, and the packet header information is extracted to obtain the specific source IP address, destination IP address and packet size. Specifically, the detection module monitors the transmission process of the packet in real time, parses the protocol type of the packet, and extracts the packet header containing the source IP address, destination IP address, packet size and other information to form a detailed packet information table.

[0044] For example, using deep packet inspection technology, every data packet passing through the switch can be monitored and analyzed in real time, and its header information can be extracted to provide accurate traffic data.

[0045] According to the source IP address and the destination IP address, the inflow timestamp and outflow timestamp of each data packet are calculated to deduce the traffic duration. The amount of data transmitted per second is calculated by using the relationship between the data packet size and the inflow timestamp and outflow timestamp.

[0046] By analyzing the source IP address and destination IP address of each data packet, the inflow and outflow timestamps of the data packet in the network are calculated, and the flow duration of the data packet is inferred. Specifically, by comparing the inflow and outflow timestamps of the data packet, the residence time of the data packet is calculated, and combined with the data packet size, the amount of data transmitted per second is calculated.

[0047] For example, the timestamp of each data packet entering and leaving the switch can be recorded and combined with its size to calculate the data transmission rate in the network.

[0048] The basic feature set of data packets is generated based on the traffic duration and the amount of data transmitted per second. The basic feature set of data packets is clustered using the K-means clustering algorithm to obtain the data flow classification result.

[0049] By collecting the traffic duration and data volume per second of all data packets, a detailed basic feature set of data packets is generated. Specifically, the K-means clustering algorithm is used to analyze and classify the basic feature set of data packets, clustering data packets with similar features together to form multiple data flow classification results.

[0050] For example, through the K-means clustering algorithm, data packets with similar transmission characteristics can be classified into the same category, helping to identify and manage different data flows.

[0051] Based on the entropy analysis method, entropy analysis is performed on the data flow classification results, and the distribution balance of each type of traffic is calculated to obtain the traffic balance and traffic volatility. Outlier detection is performed on the traffic balance to obtain the abnormal traffic interval. Correlation analysis is performed on the traffic volatility to obtain the traffic periodicity. The abnormal traffic interval and traffic periodicity are combined to obtain the traffic concentration index.

[0052] By performing entropy analysis on the data flow classification results, the distribution balance and volatility of each type of traffic are calculated. Specifically, the entropy analysis method is applied to calculate the balance and volatility of each type of traffic, and outlier detection is used to identify abnormal traffic intervals. Combined with the correlation analysis of traffic periodicity, a comprehensive traffic concentration index is finally obtained.

[0053] For example, through entropy analysis methods, the distribution of various types of traffic in the network can be evaluated, and areas with abnormally high or low traffic can be identified to help optimize traffic management strategies.

[0054] According to the traffic concentration index and data flow classification results, a data packet characteristic parameter matrix is ​​constructed.

[0055] By combining the traffic concentration index with the previously obtained data flow classification results, a detailed and multi-dimensional data packet feature parameter matrix is ​​constructed. Specifically, all collected features and indicators are used to form a matrix to provide comprehensive feature parameters of data packets for subsequent traffic scheduling and optimization.

[0056] For example, by combining the traffic concentration index and the data flow classification results, a complete packet characteristic parameter matrix can be constructed, which is convenient for detailed analysis and optimization of switch traffic.

[0057] In step S200, the data packet characteristic parameter matrix is ​​input into a preset Markov decision process model to predict the service flow of the switch test system port, and obtain the prediction result of the flow occupancy trend of each port at a future moment.

[0058] By inputting the data packet characteristic parameter matrix into the Markov decision process model, the traffic trend of each port at the future moment is predicted. Specifically, the Markov model is used to predict the traffic occupancy of each port in the future period of time based on the current traffic characteristic parameters, and the specific traffic trend prediction results are obtained.

[0059] For example, by using the Markov decision process model, the future traffic change trend of each port can be accurately predicted based on the current data packet characteristic parameter matrix, facilitating early adjustment and optimization.

[0060] Based on the prediction results, the business flow of the high-load port is optimized and diverted to obtain the port traffic regulation plan. The traffic balancing algorithm is applied to calculate the optimal traffic distribution plan based on the port traffic regulation plan and operation status information to obtain the traffic distribution strategy.

[0061] By optimizing the business flow of the high-load ports in the prediction results, a port traffic adjustment plan is formulated. Specifically, according to the high-load ports in the prediction results, the traffic balancing algorithm is applied to optimize the diversion, generate the optimal traffic distribution plan, and form a traffic distribution strategy.

[0062] For example, using a traffic balancing algorithm, traffic from a high-load port can be effectively diverted to other ports with lower loads, thereby achieving balanced traffic distribution.

[0063] The traffic distribution strategy is matched with the current load status of the switch to obtain the traffic distribution result.

[0064] By matching the generated traffic distribution strategy with the current load status of the switch, the specific traffic distribution result is determined. Specifically, the traffic distribution strategy is adjusted in combination with the real-time load information of the switch to ensure load balancing of each port and obtain the final traffic distribution result.

[0065] For example, by combining the load status of the switch, the traffic distribution strategy can be dynamically adjusted to ensure that the traffic on each port is balanced under different load conditions.

[0066] In step S300, the traffic distribution result is analyzed using a preset data processing model. In the data processing model, the port traffic trend in the traffic distribution result is analyzed in the time and frequency domain based on discrete wavelet transform to detect abnormal traffic surges or drops and obtain abnormal traffic detection results.

[0067] By using the preset data processing model to analyze the traffic distribution results in detail, especially based on the discrete wavelet transform technology, the port traffic trend in the traffic distribution results is analyzed in the time and frequency domain. Specifically, the abnormal increase or decrease in the port traffic is detected, and the abnormal traffic detection result is generated.

[0068] For example, through discrete wavelet transform technology, abnormal fluctuations in traffic distribution results can be accurately detected and traffic anomalies can be discovered in a timely manner.

[0069] Based on the abnormal flow detection results, Kalman filtering is applied to fit the future trend of abnormal flow points and obtain the flow anomaly judgment information.

[0070] By applying Kalman filtering to analyze and fit the abnormal flow detection results, the future trend of abnormal flow points can be predicted. Specifically, the Kalman filter algorithm is used to fit the future change trend of the current abnormal flow point data to obtain detailed flow anomaly determination information.

[0071] For example, using the Kalman filter algorithm, trend prediction can be performed on detected abnormal traffic points to help prevent and handle potential traffic problems in advance.

[0072] The data transmission path of the switch test system is adjusted through the link load balancing strategy combined with the traffic anomaly judgment information to obtain the optimized data transmission path.

[0073] By combining the traffic anomaly determination information and adopting the link load balancing strategy, the data transmission path in the switch test system is optimized and adjusted. Specifically, according to the traffic anomaly determination information, the data transmission path is replanned and allocated to ensure the stability and efficiency of the transmission, and the optimized data transmission path is obtained.

[0074] For example, through the link load balancing strategy, the data transmission path can be effectively adjusted and optimized, the transmission bottleneck caused by abnormal traffic can be avoided, and the overall transmission performance of the system can be improved.

[0075] In step S400, the test tasks of the switch test system are reallocated based on the optimized data transmission path, the optimal execution order of the test tasks is calculated, and a task scheduling solution is obtained.

[0076] By reallocating the test tasks of the switch test system based on the optimized data transmission path, the optimal execution order of the test tasks is formulated. Specifically, according to the optimized data transmission path, the optimal task execution order is calculated and a detailed task scheduling plan is generated.

[0077] For example, through the optimized data transmission path, the execution order of test tasks can be reasonably arranged to improve test efficiency and accuracy.

[0078] The ant colony optimization algorithm is applied to optimally map the test tasks of different ports according to the task scheduling plan and assign them to different test threads to form a specific execution plan.

[0079] By applying the ant colony optimization algorithm, the test tasks in the task scheduling scheme are optimally mapped and allocated. Specifically, the ant colony optimization algorithm is used to allocate different test tasks to suitable test threads to form a specific execution plan to ensure the efficient execution of the test tasks.

[0080] For example, the ant colony optimization algorithm can effectively allocate test tasks to appropriate threads, improving the efficiency and accuracy of task scheduling.

[0081] The execution plan is input into the switch test control module, and key performance indicators such as port throughput, packet loss rate, and delay jitter are collected in real time during the test execution to generate test result data.

[0082] By inputting the execution plan into the switch test control module, the key performance indicators of each port during the test process, including throughput, packet loss rate and delay jitter, are collected in real time. Specifically, when executing the test task, the test control module monitors and records various performance indicators and generates detailed test result data.

[0083] For example, the test control module can record the throughput, packet loss rate and delay jitter of each port in real time to form a detailed test report.

[0084] Among them, the ant colony optimization algorithm is used to optimally map the test tasks of different ports according to the task scheduling plan, and assign them to different test threads to form a specific execution plan, including: based on the test task requirements of each port of the switch, the ant colony optimization algorithm is used to preliminarily solve the test task requirements to obtain the throughput requirements, delay requirements and packet loss rate tolerance of the port tasks.

[0085] By using the ant colony optimization algorithm to preliminarily solve the test task requirements of each port of the switch, the throughput requirements, delay requirements and packet loss rate tolerance of each port task are obtained. Specifically, the ant colony optimization algorithm is used to calculate and optimize each demand parameter to generate a preliminary task demand allocation result.

[0086] For example, the ant colony optimization algorithm can preliminarily determine the required throughput, latency, and packet loss rate parameters based on the specific needs of each port.

[0087] The task scheduling scheme is used to optimize the throughput requirements, latency requirements, and packet loss rate tolerance to obtain an execution plan.

[0088] By combining the task scheduling plan, the throughput requirements, latency requirements and packet loss rate tolerance of each port are further optimized to form the final execution plan. Specifically, the optimization algorithm is used to comprehensively consider various requirements, adjust the execution order and method of the test tasks, and obtain the optimal execution plan.

[0089] For example, combined with the task scheduling solution, various test requirements can be optimized to ensure the best execution effect of the test tasks.

[0090] In step S500, the test result data is input into a preset performance evaluation model for multi-dimensional analysis. In the performance evaluation model, the weights and scores of various performance indicators of the switch are calculated based on the test result data by a weighted average method to obtain an evaluation result of the switch performance.

[0091] By inputting the test result data into the preset performance evaluation model, a comprehensive multi-dimensional analysis of the switch performance is performed. Specifically, the weighted average method is applied to calculate the weight and score of each performance indicator based on the test result data, forming a detailed evaluation report and obtaining the performance evaluation results of the switch.

[0092] For example, through the performance evaluation model, indicators such as throughput, packet loss rate and latency can be comprehensively analyzed to accurately evaluate the overall performance of the switch.

[0093] The particle swarm optimization algorithm is used to optimize the evaluation results, generate optimized configuration parameters, and input the optimized configuration parameters into the switch test system.

[0094] By applying the particle swarm optimization algorithm, the performance evaluation results are further optimized to generate optimized configuration parameters. Specifically, the particle swarm optimization algorithm is used to adjust and optimize the switch configuration parameters according to the evaluation results to ensure that its performance reaches the best state. The optimized configuration parameters are input into the switch test system to make corresponding configuration adjustments.

[0095] For example, through the particle swarm optimization algorithm, the configuration parameters of the switch can be accurately adjusted to improve its performance and stability.

[0096] In this embodiment, the operating status information and data packet flow information of each port are obtained in real time from the switch test system, and the data packet flow information is subjected to deep data packet detection and protocol analysis, the source IP address, the target IP address and the data packet size are extracted, the inflow timestamp and outflow timestamp of the data packet are calculated, the flow duration and the amount of data transmitted per second are estimated, and the basic feature set of the data packet is generated. The basic feature set of the data packet is clustered by the K-means clustering algorithm to obtain the data flow classification result, and the flow balance and flow volatility are calculated based on the entropy analysis method to obtain the flow concentration index, and finally the data packet feature parameter matrix is ​​constructed. The data packet feature parameter matrix is ​​input into the Markov decision process model to predict the future flow occupancy trend of each port, and the business flow of the high-load port is optimized based on the prediction results to generate the port flow adjustment scheme and flow distribution strategy. The flow distribution results are analyzed by the preset data processing model, and the abnormal flow detection and prediction are performed based on the discrete wavelet transform and Kalman filtering technology, the data transmission path is adjusted in combination with the link load balancing strategy, the execution order and mapping of the test task are optimized, the execution scheme is generated by the ant colony optimization algorithm, and the key performance indicators are collected in real time during the test. Finally, the test result data is analyzed and optimized in multiple dimensions through the performance evaluation model and particle swarm optimization algorithm, and the optimized configuration parameters are generated and applied to the switch test system. This embodiment effectively improves the data interaction efficiency and accuracy of the switch test system, realizes the comprehensive evaluation and optimized configuration of the switch performance, and thus ensures the stable operation of the switch in different network environments.

[0097] Embodiment 3: like Figure 2 As shown, the present application also provides a switch test system data interaction device 10, including an acquisition module 11, a matching module 12, an analysis module 13, a scheduling module 14 and an interaction module 15.

[0098] The acquisition module 11 is mainly used to acquire the operation status information and data packet flow information of each port in the switch test system, extract the data packet flow information, and obtain a data packet characteristic parameter matrix.

[0099] The matching module 12 is mainly used to dynamically schedule the switch test system based on the data packet characteristic parameter matrix, obtain the port traffic regulation plan, generate the traffic distribution strategy based on the port traffic regulation plan and the operation status information, match the traffic distribution strategy with the current load status of the switch, and obtain the traffic distribution result.

[0100] The analysis module 13 is mainly used to analyze the flow distribution result by using a preset data processing model to obtain flow anomaly determination information, and adjust the data transmission path of the switch test system based on the flow anomaly determination information to obtain an optimized data transmission path.

[0101] The scheduling module 14 is mainly used to reschedule the test tasks of the switch test system according to the optimized data transmission path, obtain an execution plan, input the execution plan into the switch test control module, and use the switch test control module to dynamically adjust the test process of the switch to obtain test result data.

[0102] The interaction module 15 is mainly used to comprehensively evaluate the performance indicators of the switch based on the test result data, generate corresponding optimization configuration parameters based on the evaluation results, and apply the optimization configuration parameters to the switch test system.

[0103] In this embodiment, the acquisition module 11 collects the operating status information and data packet flow information of each port in the switch test system in real time, extracts the source IP address, target IP address, data packet size and other features of the data packet through deep data packet detection and protocol analysis, forms a data packet feature parameter matrix, and provides a detailed data basis for subsequent traffic scheduling and optimization. The matching module 12 performs dynamic traffic scheduling based on the data packet feature parameter matrix, generates a port traffic adjustment plan, and formulates a traffic allocation strategy in combination with the operating status information of the port, matches the strategy with the current load state of the switch, and ensures the balance and efficiency of traffic allocation. The analysis module 13 uses a preset data processing model to comprehensively analyze the traffic allocation results, detects and predicts abnormal traffic points through time-frequency domain analysis and Kalman filtering technology, generates traffic anomaly judgment information, and optimizes and adjusts the data transmission path. The scheduling module 14 reschedules the test task according to the optimized data transmission path, formulates an execution plan, and dynamically adjusts the test process through the switch test control module, collects key performance indicators in real time, and ensures the accurate execution of the test task and the effective acquisition of the results. The interaction module 15 uses the performance evaluation model to conduct a comprehensive evaluation based on the test result data, generates optimized configuration parameters, and further optimizes the switch configuration through the particle swarm optimization algorithm to improve its performance and stability. The device as a whole realizes efficient data interaction, traffic scheduling, path optimization, task scheduling and performance evaluation of the switch test system, effectively improving the test efficiency and operation stability of the switch, and ensuring its optimal performance in various network environments.

[0104] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned switch test system data interaction method embodiment, and will not be repeated here.

[0105] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0106] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A switch test system data interaction method, characterized in that: include: Obtaining the operation status information and data packet flow information of each port in the switch test system, extracting the data packet flow information, and obtaining a data packet characteristic parameter matrix; Dynamically scheduling the switch test system based on the data packet characteristic parameter matrix to obtain a port traffic regulation scheme, generating a traffic distribution strategy based on the port traffic regulation scheme and the operation status information, matching the traffic distribution strategy with the current load status of the switch to obtain a traffic distribution result; Analyze the flow distribution result by using a preset data processing model to obtain flow anomaly determination information, and adjust the data transmission path of the switch test system based on the flow anomaly determination information to obtain an optimized data transmission path; Rescheduling the test tasks of the switch test system according to the optimized data transmission path to obtain an execution plan, inputting the execution plan into the switch test control module, and dynamically adjusting the switch test process by using the switch test control module to obtain test result data; Based on the test result data, a comprehensive evaluation is performed on the performance indicators of the switch, corresponding optimization configuration parameters are generated based on the evaluation results, and the optimization configuration parameters are applied to the switch test system.

2. The switch test system data interaction method according to claim 1, characterized in that: The step of obtaining data packet flow information and operation status information of each port in the switch test system, extracting the data packet flow information, and obtaining a data packet characteristic parameter matrix includes: Acquire the operation status information and data packet flow information of each port in real time from the switch test system, perform protocol parsing on the data packet flow information through deep data packet inspection, extract data packet header information, and obtain the source IP address, destination IP address, and data packet size; According to the source IP address and the destination IP address, calculate the inflow timestamp and outflow timestamp of each data packet, deduce the flow duration, and calculate the amount of data transmitted per second by using the relationship between the data packet size and the inflow timestamp and the outflow timestamp; Generate a basic feature set of data packets based on the flow duration and the amount of data transmitted per second, cluster the basic feature set of data packets using a K-means clustering algorithm to obtain a data flow classification result; Performing entropy analysis on the data flow classification results based on the entropy analysis method, calculating the distribution balance of each type of traffic, obtaining traffic balance and traffic volatility, performing outlier detection on the traffic balance, obtaining abnormal traffic intervals, performing correlation analysis on the traffic volatility, obtaining traffic periodicity, combining the abnormal traffic intervals with the traffic periodicity, and obtaining a traffic concentration index; A data packet characteristic parameter matrix is ​​constructed according to the traffic concentration index and the data flow classification result.

3. The switch test system data interaction method according to claim 1, characterized in that: The step of dynamically scheduling the switch test system based on the data packet characteristic parameter matrix to obtain a port traffic regulation scheme, generating a traffic distribution strategy based on the port traffic regulation scheme and the operation status information, matching the traffic distribution strategy with the current load status of the switch, and obtaining a traffic distribution result includes: Input the data packet characteristic parameter matrix into a preset Markov decision process model, predict the service flow of the switch test system port, and obtain the prediction result of the flow occupancy trend of each port at a future moment; Based on the prediction result, the business flow of the high-load port is optimized for diversion to obtain a port traffic regulation scheme, and a traffic balancing algorithm is applied to calculate an optimal traffic distribution scheme based on the port traffic regulation scheme and the operation status information to obtain a traffic distribution strategy; The flow distribution strategy is matched with the current load state of the switch to obtain a flow distribution result.

4. The switch test system data interaction method according to claim 1, characterized in that: The step of analyzing the flow distribution result by using a preset data processing model to obtain flow anomaly determination information, and adjusting the data transmission path of the switch test system based on the flow anomaly determination information to obtain an optimized data transmission path includes: The flow distribution result is analyzed by using a preset data processing model. In the data processing model, a time-frequency domain analysis is performed on the port flow trend in the flow distribution result based on discrete wavelet transform to detect abnormal flow increase or decrease, thereby obtaining an abnormal flow detection result; Applying Kalman filtering to fit the future trend of the abnormal flow point based on the abnormal flow detection result to obtain flow abnormality determination information; The data transmission path of the switch test system is adjusted through the link load balancing strategy in combination with the traffic anomaly determination information to obtain an optimized data transmission path.

5. The switch test system data interaction method according to claim 1, characterized in that: The step of rescheduling the test task of the switch test system according to the optimized data transmission path to obtain an execution plan, inputting the execution plan into the switch test control module, and dynamically adjusting the test process of the switch by using the switch test control module to obtain the test result data includes: Based on the optimized data transmission path, the test tasks of the switch test system are reallocated, the optimal execution order of the test tasks is calculated, and the task scheduling scheme is obtained; Applying the ant colony optimization algorithm to optimally map the test tasks of different ports according to the task scheduling scheme, and assigning them to different test threads to form a specific execution scheme; The execution scheme is input into the switch test control module, and key performance indicators such as port throughput, packet loss rate, and delay jitter are collected in real time during the test execution process to generate test result data.

6. The switch test system data interaction method according to claim 5, characterized in that: The step of applying the ant colony optimization algorithm to optimally map the test tasks of different ports according to the task scheduling scheme and assigning them to different test threads to form a specific execution scheme includes: Based on the test task requirements of each port of the switch, the ant colony optimization algorithm is used to preliminarily solve the test task requirements to obtain the throughput requirements, delay requirements and packet loss rate tolerance of the port tasks; The task scheduling scheme is used to optimize the throughput requirement, the delay requirement, and the packet loss rate tolerance to obtain an execution scheme.

7. The switch test system data interaction method according to claim 1, characterized in that: The step of comprehensively evaluating the performance indicators of the switch based on the test result data, generating corresponding optimization configuration parameters based on the evaluation results, and applying the optimization configuration parameters to the switch test system includes: Input the test result data into a preset performance evaluation model for multi-dimensional analysis, in which the weights and scores of various performance indicators of the switch are calculated based on the test result data by a weighted average method to obtain an evaluation result of the switch performance; The evaluation result is optimized by using a particle swarm optimization algorithm to generate optimized configuration parameters, and the optimized configuration parameters are input into a switch test system.

8. A data interaction device for a switch test system, characterized in that: include: An acquisition module is used to acquire the operation status information and data packet flow information of each port in the switch test system, extract the data packet flow information, and obtain a data packet characteristic parameter matrix; A matching module, configured to perform dynamic traffic scheduling on the switch test system based on the data packet characteristic parameter matrix, obtain a port traffic regulation scheme, generate a traffic distribution strategy based on the port traffic regulation scheme and the operation status information, match the traffic distribution strategy with the current load status of the switch, and obtain a traffic distribution result; An analysis module is used to analyze the flow distribution result by using a preset data processing model to obtain flow anomaly determination information, and adjust the data transmission path of the switch test system based on the flow anomaly determination information to obtain an optimized data transmission path; A scheduling module, used to reschedule the test tasks of the switch test system according to the optimized data transmission path to obtain an execution plan, input the execution plan into the switch test control module, and use the switch test control module to dynamically adjust the test process of the switch to obtain test result data; The interactive module is used to comprehensively evaluate the performance indicators of the switch based on the test result data, generate corresponding optimization configuration parameters based on the evaluation results, and apply the optimization configuration parameters to the switch test system.

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