Network data test algorithm and system for network communication

Abstract

The invention relates to the technical field of network testing, and particularly discloses a network data testing algorithm and system for network communication, and the algorithm comprises the following steps: building a plurality of communication load testing models with different virtual user numbers through a model building module, a plurality of communication load test models with different numbers of virtual users are established to simulate scenes accessed by different numbers of users, and then network transmission data of each communication load test model is combined. Whether network environments of different communication load test models are different or not during network data test operation can be judged, so that subsequent load test data can be acquired when the network environments of the communication load test models are highly similar; and the condition that the reliability of a network data performance evaluation result is influenced by an error in an acquisition result of the key performance index due to different network environments of different communication load test models during testing is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of network testing, and in particular to a network data testing algorithm and system for network communication. Background Art

[0002] Network communication refers to a communication system between terminal devices through a computer network. It has a wide range of application scenarios. With the increasing popularity of online education, the network communication system enables teachers and students to conduct real-time interactive teaching through the Internet, thereby improving education efficiency.

[0003] In the network communication system for online education, since online education is based on the scenario of a large number of users accessing the platform at the same time, in order to ensure the stability of network communication, the network data performance of the network communication is generally load tested, mainly to evaluate the stability of the system under high concurrency. The traditional network data performance testing method generally simulates the scenario of a large number of users accessing the online education platform at the same time, and then evaluates the stability of the system under high concurrency based on key performance indicators such as the response time and throughput of the network communication system to avoid students encountering problems such as video freezes and slow page loading, which affects the learning effect.

[0004] The traditional method for testing the network data performance of network communications is generally to simulate a scenario in which a large number of users simultaneously access an online education platform, and to evaluate the stability of the system under high concurrency based on key performance indicators. However, since the performance indicators of network data are affected by the network speed, if the network environment cannot be measured during the test, the collection results of key performance indicators will be affected by the network environment and errors will occur, which will in turn reduce the reliability of the network data performance evaluation results. Summary of the invention

[0005] The purpose of the present invention is to provide a network data testing algorithm and system for network communication to solve the following technical problems: How to improve the reliability of network data performance evaluation results.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A network data testing algorithm and system for network communication, the algorithm comprising the following steps: S1: Establish multiple communication load test models with different numbers of virtual users through the model establishment module, and perform network data test operations based on different communication load test models; S2: collecting network transmission data and load test data of each communication load test model in a test operation through a data collection module; S3: The data evaluation module determines whether there is a difference in the network environment when different communication load test models perform network data test operations based on the data collected by the data collection module. If yes, step S1 is repeated; otherwise, step S4 is performed. S4: When it is determined that there is no difference in the test environment of all communication load test models when performing network data testing operations, the network performance of network communications in different communication load test models is analyzed by combining the load test data corresponding to each communication load test model through the data analysis module.

[0007] Furthermore, the analysis process of the data analysis module in S3 includes: S31: Calculating the network influence coefficients of all communication load test models in one test operation by combining the network transmission data of different communication load test models in one test operation through the data calculation unit in the data evaluation module; S32: Calculating the network impact coefficient fluctuation values ​​of all communication load test models in one test operation by combining the network impact coefficients of all communication load test models in one test operation through a data calculation unit; S33: The network impact coefficient fluctuation values ​​of all communication load test models in a test operation are compared with a preset fluctuation value threshold through the data comparison unit in the data evaluation module, and whether there are differences in the test environments of different communication load test models is determined based on the comparison results.

[0008] Furthermore, the calculation process in S31 includes: By formula Calculate the network impact coefficient of the ath communication load test model in a test operation ; Where a is any communication load test model, and each communication load test model only has a difference in the number of virtual users, i is a data collection time point at a fixed time interval in a test job, n is the total number of data collection times in a test job, is the network data transmission speed of the ath communication load test model at the i-th time point in the test, For all The average value of The network delay of the ath communication load test model at the i-th time point in the test, is the preset network delay, The bandwidth utilization of the ath communication load test model in the test, is the preset bandwidth utilization, for The standard value of To define a function, if ,make , otherwise, let .

[0009] Furthermore, the calculation process in S32 includes: By formula Calculate the network impact coefficient fluctuation value of all communication load test models in a test job ; Where w is the total number of communication load test models in the test job on one side, The average value of For all The maximum value in For all The minimum value in for The standard value of is the proportionality coefficient, which is set based on empirical fitting.

[0010] Furthermore, the comparison process in S33 includes: By calculating the network impact coefficient fluctuation value of all communication load test models in a test job The preset fluctuation value threshold Make a comparison; like ,It is judged that in a test operation, different communication load test models have different network conditions when testing, which means that the basic test environments of different communication load test models are different, which will interfere with the test results and cause low test data quality; like , it is judged that in a test operation, there is no difference in the network conditions when different communication load test models are tested, which means that there is no difference in the basic test environment of different communication load test models, and there will be no interference with the test results.

[0011] Furthermore, the analysis process in S4 includes: The real-time load test data collected by the data acquisition module is used to establish the CPU usage change curve of the a-th communication load test model in a test operation. Memory usage change curve ; And through the formula Calculate the load risk coefficient of the ath communication load test model in a test operation ; in, is the average response time of virtual user requests in the communication system of the ath communication load test model in a test operation, is the preset response time, is the throughput of the communication system of the ath communication load test model in a test operation, For the preset throughput, The error rate of the communication system in a test operation of the a-th communication load test model, is the preset error rate, for The standard value of is the starting time point of a test operation. is the end time point of a test operation. is an adjustment coefficient comparison table function, wherein the adjustment coefficient comparison table function The value of The values ​​of are in one-to-one correspondence.

[0012] Furthermore, the analysis process in S4 also includes: By combining all communication load test models in one test job with the load risk factor Respectively with the preset load risk factor threshold Make a comparison; If any Greater than or equal to , it is judged that the load test of the communication network fails, which means that the performance of the network communication system is poor, and its stability and reliability are unqualified; If all All less than , judging that the load test of the communication network is qualified, it means that the performance of the network communication system is good, and its stability and reliability are qualified.

[0013] Further, a network data testing system for network communication, the system comprising: A model building module, used to build multiple communication load test models with different numbers of virtual users; A data collection module is used to collect network transmission data and load test data of each communication load test model in a test operation; The data evaluation module includes a data calculation unit and a data comparison unit, which is used to judge whether there are differences in the network environment when different communication load test models perform network data test operations based on the data collected by the data collection module; The data calculation unit is used to calculate the network impact coefficient of all communication load test models in one test operation in combination with the data collected by the data collection module, and calculate the network impact coefficient fluctuation value of all communication load test models in one test operation based on the data; The data comparison unit is used to compare the network impact coefficient fluctuation values ​​of all communication load test models in a test operation with a preset fluctuation value threshold, and determine whether there is a difference in the test environment of different communication load test models according to the comparison result; The data analysis module is used to analyze the network performance of network communications in different communication load test models in combination with the load test data corresponding to each communication load test model.

[0014] Beneficial effects of the present invention: (1) The present invention establishes multiple communication load test models with different numbers of virtual users to simulate scenarios with different numbers of users accessing. Then, by combining the network transmission data of each communication load test model, it can be judged whether there are differences in the network environments of different communication load test models when performing network data testing operations, thereby ensuring that subsequent load test data is collected when the network environments of each communication load test model are highly similar, thereby avoiding the situation where errors in the collection results of key performance indicators due to different network environments of different communication load test models during testing affect the reliability of network data performance evaluation results.

[0015] (2) The present invention calculates the network impact coefficient fluctuation value of all communication load test models in a test operation. The preset fluctuation value threshold By comparing, through this comparison method, it is possible to make an accurate judgment on whether there are differences in the network conditions when testing different communication load test models in a test operation. The judgment result can indicate whether there are differences in the basic test environments of different communication load test models, thereby ensuring that subsequent data collection is based on a highly similar network environment for each communication load test model to ensure the accuracy of data collection.

[0016] (3) The present invention calculates the load risk coefficient of all communication load test models in one test operation. Respectively with the preset load risk factor threshold For comparison, since the data is collected and calculated based on the highly similar network environment of each communication load test model, the reliability of the data is high. Through this comparison method, an accurate judgment can be made on whether the load test of the communication network is qualified, and the performance of the network communication system can be further analyzed, thereby realizing data testing of the communication network data, and data with higher accuracy can further improve the reliability of the network data performance evaluation results.

[0017] (4) The present invention analyzes the network performance of network communications in different communication load test models by combining the load test data corresponding to each communication load test model through a data analysis module. Since the load test data corresponding to each communication load test model is collected based on the same network environment, the accuracy of the data is relatively high. Analyzing the network performance of network communications in different communication load test models based on the data can improve the accuracy of the analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings.

[0019] Figure 1 It is a flow chart of the steps of a network data testing algorithm for network communication in the present invention; Figure 2 is a flow chart of the analysis process of the data analysis module in the present invention; Figure 3 It is a schematic block diagram of a network data testing system for network communication in the present invention. DETAILED DESCRIPTION

[0020] 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 only 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.

[0021] See also Figure 1 As shown, in one embodiment, the present application provides a network data testing algorithm and system for network communication, the algorithm comprising the following steps: S1: Establish multiple communication load test models with different numbers of virtual users through the model establishment module, and perform network data test operations based on different communication load test models; S2: collecting network transmission data and load test data of each communication load test model in a test operation through a data collection module; S3: The data evaluation module determines whether there is a difference in the network environment when different communication load test models perform network data test operations based on the data collected by the data collection module. If yes, step S1 is repeated; otherwise, step S4 is performed. S4: when it is determined that there is no difference in the test environment of all communication load test models when performing the network data test operation, the network performance of network communication in different communication load test models is analyzed by combining the load test data corresponding to each communication load test model through the data analysis module; Through the above technical scheme, this example provides a network data testing algorithm for network communication. First, a plurality of communication load testing models with different numbers of virtual users are established through a model building module, and network data testing operations are performed based on different communication load testing models. Then, the network transmission data and load test data of each communication load testing model in a test operation are collected through a data acquisition module, and a judgment is made through a data evaluation module based on the data collected by the data acquisition module whether there is a difference in the network environment when different communication load testing models are performing network data testing operations. Finally, when it is judged that there is no difference in the test environment of all communication load testing models when performing network data testing operations, the network performance of network communication in different communication load testing models is analyzed through a data analysis module in combination with the load test data corresponding to each communication load testing model. Through such settings, this example simulates scenarios of different numbers of users accessing by establishing multiple communication load test models with different numbers of virtual users. Then, by combining the network transmission data of each communication load test model, it is possible to judge whether there are differences in the network environments of different communication load test models when performing network data testing operations, thereby ensuring that subsequent load test data is collected when the network environments of each communication load test model are highly similar, avoiding the situation where errors in the collection results of key performance indicators due to different network environments of different communication load test models during testing affect the reliability of network data performance evaluation results. Finally, when it is determined that there is no difference in the test environment of all communication load test models when performing network data testing operations, it means that the network environment of each communication load test model during the test is highly similar. Based on this situation, the load test data of the communication load test model in the same network environment can be combined to analyze the network performance of network communications in different communication load test models, thereby evaluating the stability of the system under high concurrency, avoiding students from encountering problems such as video freezes and slow page loading, and thus improving students' learning effects.

[0022] See also Figure 2 As shown, the analysis process of the data analysis module in S3 includes: S31: Calculating the network influence coefficients of all communication load test models in one test operation by combining the network transmission data of different communication load test models in one test operation through the data calculation unit in the data evaluation module; S32: Calculating the network impact coefficient fluctuation values ​​of all communication load test models in one test operation by combining the network impact coefficients of all communication load test models in one test operation through a data calculation unit; S33: comparing the network impact coefficient fluctuation values ​​of all communication load test models in a test operation with a preset fluctuation value threshold through a data comparison unit in a data evaluation module, and judging whether there are differences in the test environments of different communication load test models according to the comparison results; Through the above technical scheme, this example provides an analysis process of the data analysis module. First, the data calculation unit in the data evaluation module combines the network transmission data of different communication load test models in a test operation to calculate the network impact coefficient of all communication load test models in a test operation. Then, the network impact coefficient fluctuation value of all communication load test models in a test operation is calculated by combining the network impact coefficient of all communication load test models in a test operation. Finally, the data comparison unit in the data evaluation module can compare the network impact coefficient fluctuation value of all communication load test models in a test operation with the preset fluctuation value threshold, and judge whether there is a difference in the test environment of different communication load test models according to the comparison result; Through such a setting, this example can first calculate the network impact coefficients of all communication load test models in a test operation by combining the network transmission data of different communication load test models in a test operation. The data reflects the degree to which different communication load test models are affected by the network environment in a test operation. Then, the network impact coefficients of all communication load test models in a test operation are combined to calculate the fluctuation values ​​of the network impact coefficients of all communication load test models in a test operation. The data can reflect the degree of dispersion of the network impact coefficients of all communication load test models in a test operation relative to the average value. The smaller the degree of dispersion, the more similar the sizes of the network impact coefficients of all communication load test models in a test operation are, which means that the different communication load test models are The degree to which the load test models are affected by the network environment in the test operation is similar. Conversely, the greater the degree of discreteness, the more dissimilar the network impact coefficients of all communication load test models in a test operation, which means that the degree to which different communication load test models are affected by the network environment in the test operation varies greatly. Then, by comparing the data with the preset fluctuation value threshold, it can be determined based on the comparison results whether there are differences in the test environments of different communication load test models, thereby ensuring that subsequent load test data is collected when the network environment of each communication load test model is highly similar, avoiding the situation where errors in the collection results of key performance indicators due to different network environments during testing of different communication load test models affect the reliability of network data performance evaluation results.

[0023] The calculation process in S31 includes: By formula Calculate the network impact coefficient of the ath communication load test model in a test operation ; Where a is any communication load test model, and each communication load test model only has a difference in the number of virtual users, i is a data collection time point at a fixed time interval in a test job, n is the total number of data collection times in a test job, is the network data transmission speed of the ath communication load test model at the i-th time point in the test, and is the network data transmission speed of all The average value of The network delay of the ath communication load test model at the i-th time point in the test, is the preset network delay, The bandwidth utilization of the ath communication load test model in the test, is the preset bandwidth utilization, for The standard value can be selected and set according to the allowable error in the empirical data. To define a function, if ,make , otherwise, let ; Through the above technical solution, this example provides the network impact coefficient of the ath communication load test model in a test operation , can be obtained by formula Calculated, where the formula The network data transmission speed fluctuation value of the a-th communication load test model in a test operation can be calculated. Obviously, when the network data transmission speed fluctuation value and bandwidth utilization of the a-th communication load test model in a test operation are larger, and the network delay of the a-th communication load test model at the i-th time point in the test is higher, then the network impact coefficient of the a-th communication load test model in a test operation is The larger it is, the greater the network impact coefficient of the a-th communication load test model is in a test operation. On the contrary, when the network data transmission speed fluctuation value and bandwidth utilization of the a-th communication load test model in a test operation are smaller, and the network delay of the a-th communication load test model at the i-th time point in the test is lower, then the network impact coefficient of the a-th communication load test model in a test operation is The smaller it is, the smaller the a-th communication load test model is affected by the network environment in a test operation; Through this calculation method, the degree of network environment that different communication load test models are subject to in a test operation can be analyzed based on the calculation results, thereby providing accurate data for subsequent judgment whether there are differences in the basic test environments of different communication load test models to ensure the accuracy of the judgment results.

[0024] The calculation process in S32 includes: By formula Calculate the network impact coefficient fluctuation value of all communication load test models in a test job ; Where w is the total number of communication load test models in the test job on one side, For all The average value of For all The maximum value in For all The minimum value in for The standard value can be selected and set according to the allowable error in the empirical data. is the proportionality coefficient, which is set according to empirical fitting; Through the above technical solution, this example provides the network impact coefficient fluctuation value of all communication load test models in a test operation , can be obtained by formula Calculated, through this calculation method, the data can reflect the degree of dispersion of the network impact coefficients of all communication load test models in a test job relative to the average value. When the degree of dispersion is smaller, it means that the sizes of the network impact coefficients of all communication load test models in a test job are more similar, which means that the degree of influence of the network environment on different communication load test models in the test job is similar. On the contrary, when the degree of dispersion is larger, it means that the sizes of the network impact coefficients of all communication load test models in a test job are more dissimilar, which means that the degree of influence of the network environment on different communication load test models in the test job is quite different. Through such a setting, accurate data can be provided for the subsequent judgment whether there are differences in the basic test environments of different communication load test models, so as to ensure the accuracy of the judgment results.

[0025] The comparison process in S33 includes: By calculating the network impact coefficient fluctuation value of all communication load test models in a test job The preset fluctuation value threshold Make a comparison; like ,It is judged that in a test operation, different communication load test models have different network conditions when testing, which means that the basic test environments of different communication load test models are different, which will interfere with the test results and cause low test data quality; like , it is determined that in a test operation, there is no difference in the network conditions of different communication load test models when testing, which means that there is no difference in the basic test environment of different communication load test models, and there will be no interference with the test results; Through the above technical solution, this example calculates the network impact coefficient fluctuation value of all communication load test models in a test operation The preset fluctuation value threshold By comparing, through this comparison method, it is possible to make an accurate judgment on whether there are differences in the network conditions when testing different communication load test models in a test operation. The judgment result can indicate whether there are differences in the basic test environments of different communication load test models, thereby ensuring that subsequent data collection is based on a highly similar network environment for each communication load test model, so as to ensure the accuracy of data collection and thereby improve the reliability of network data performance test results.

[0026] The analysis process in S4 includes: The real-time load test data collected by the data acquisition module is used to establish the CPU usage change curve of the a-th communication load test model in a test operation. Memory usage change curve ; And through the formula Calculate the load risk coefficient of the ath communication load test model in a test operation ; in, is the average response time of virtual user requests in the communication system of the ath communication load test model in a test operation, is the preset response time, is the throughput of the communication system of the ath communication load test model in a test operation, For the preset throughput, The error rate of the communication system in a test operation of the a-th communication load test model, is the preset error rate, for The standard value can be selected and set according to the allowable error in the empirical data. is the starting time point of a test operation. is the end time point of a test operation. is an adjustment coefficient comparison table function, wherein the adjustment coefficient comparison table function The value of The values ​​of are in one-to-one correspondence. Specifically, The value of can be determined based on empirical data. The influence degree of the value range on the load risk coefficient of the ath communication load test model in a test operation is obtained based on the test data; Through the above technical solution, this example provides the load risk coefficient of the ath communication load test model in a test operation , can be obtained by formula Calculated, where the formula With formula The CPU usage change and memory usage change of the a-th communication load test model in a test job can be calculated respectively. Therefore, it is obvious that when the average response time of virtual user requests in the communication system and the communication system error rate of the a-th communication load test model in a test job are higher, the CPU usage change and memory usage change of the a-th communication load test model in a test job are higher and the throughput of the communication system is lower, then the load risk coefficient of the a-th communication load test model in a test job is The higher the value, the worse the stability of the communication system is when the number of users in the communication load test model accesses concurrently. On the contrary, when the average response time of virtual user requests in the communication system and the communication system error rate of the a-th communication load test model in a test operation are lower, the CPU usage change and memory usage change of the a-th communication load test model in a test operation are lower, and the throughput of the communication system is higher, then the load risk coefficient of the a-th communication load test model in a test operation is The lower it is, the higher the stability of the communication system is when the number of users in the communication load test model accesses concurrently; Through this calculation method, the load risk coefficient of the ath communication load test model in a test operation can be The size of the communication load test model is analyzed for the stability of the communication system under concurrent access, and the data is combined with the network impact coefficient of the a-th communication load test model in a test operation The correction can ensure the accuracy and reliability of the data, thereby providing accurate data for subsequent judgment on whether the load test of the communication network is qualified, so as to improve the accuracy of the judgment result.

[0027] The analysis process in S4 further includes: By combining all communication load test models in one test job with the load risk factor Respectively with the preset load risk factor threshold Make a comparison; If any Greater than or equal to , it is judged that the load test of the communication network fails, which means that the performance of the network communication system is poor, and its stability and reliability are unqualified; If all All less than , judging that the load test of the communication network is qualified, it means that the performance of the network communication system is good, and its stability and reliability are qualified; Through the above technical solution, this example calculates the load risk coefficient of all communication load test models in one test operation. Respectively with the preset load risk factor threshold For comparison, since the data is collected and calculated based on the highly similar network environment of each communication load test model, the reliability of the data is high. Through this comparison method, an accurate judgment can be made on whether the load test of the communication network is qualified, and the performance of the network communication system can be further analyzed, thereby realizing data testing of the communication network data, and data with higher accuracy can further improve the reliability of the network data performance evaluation results.

[0028] See also Figure 3 As shown, a network data testing system for network communication, the system comprises: A model building module, used to build multiple communication load test models with different numbers of virtual users; A data collection module is used to collect network transmission data and load test data of each communication load test model in a test operation; The data evaluation module includes a data calculation unit and a data comparison unit, which is used to judge whether there are differences in the network environment when different communication load test models perform network data test operations based on the data collected by the data collection module; The data calculation unit is used to calculate the network impact coefficient of all communication load test models in one test operation in combination with the data collected by the data collection module, and calculate the network impact coefficient fluctuation value of all communication load test models in one test operation based on the data; The data comparison unit is used to compare the network impact coefficient fluctuation values ​​of all communication load test models in a test operation with a preset fluctuation value threshold, and determine whether there is a difference in the test environment of different communication load test models according to the comparison result; A data analysis module, for analyzing the network performance of network communications in different communication load test models in combination with the load test data corresponding to each communication load test model; Through the above technical scheme, this example provides a model building module for establishing multiple communication load test models with different numbers of virtual users. After the communication load test model is established, a network data test operation can be performed based on different communication load test models. Then, the network transmission data and load test data of each communication load test model in a test operation are collected through the data acquisition module, and the data evaluation module is combined with the data collected by the data acquisition module to determine whether there are differences in the network environment when different communication load test models perform network data test operations. Finally, the data analysis module can be combined with the load test data corresponding to each communication load test model to analyze the network performance of network communications in different communication load test models. Through such a setting, by combining the data evaluation module with the data collected by the data acquisition module to judge whether there are differences in the network environment when different communication load test models perform network data testing operations, it can be ensured that the subsequent load test data is collected when the network environment of each communication load test model is highly similar, so as to avoid the situation where errors in the collection results of key performance indicators occur due to the different network environments of different communication load test models during testing, thereby affecting the reliability of the network data performance evaluation results. Finally, the data analysis module is combined with the load test data corresponding to each communication load test model to analyze the network performance of network communications in different communication load test models. Since the load test data corresponding to each communication load test model is obtained based on the same network environment, the accuracy of the data is relatively high. The network performance of network communications in different communication load test models is analyzed based on the data, which can improve the accuracy of the analysis results.

[0029] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A network data testing algorithm for network communication, characterized in that: The algorithm comprises the following steps: S1: Establish multiple communication load test models with different numbers of virtual users through the model establishment module, and perform network data test operations based on different communication load test models; S2: collecting network transmission data and load test data of each communication load test model in a test operation through a data collection module; S3: The data evaluation module determines whether there is a difference in the network environment when different communication load test models perform network data test operations based on the data collected by the data collection module. If yes, step S1 is repeated; otherwise, step S4 is performed. S4: When it is determined that there is no difference in the test environment of all communication load test models when performing network data testing operations, the network performance of network communications in different communication load test models is analyzed by combining the load test data corresponding to each communication load test model through the data analysis module.

2. A network data testing algorithm for network communication according to claim 1, characterized in that: The analysis process of the data analysis module in S3 includes: S31: Calculating the network influence coefficients of all communication load test models in one test operation by combining the network transmission data of different communication load test models in one test operation through the data calculation unit in the data evaluation module; S32: Calculating the network impact coefficient fluctuation values ​​of all communication load test models in one test operation by combining the network impact coefficients of all communication load test models in one test operation through a data calculation unit; S33: The network impact coefficient fluctuation values ​​of all communication load test models in a test operation are compared with a preset fluctuation value threshold through the data comparison unit in the data evaluation module, and whether there are differences in the test environments of different communication load test models is determined based on the comparison results.

3. A network data testing algorithm for network communication according to claim 2, characterized in that: The calculation process in S31 includes: By formula Calculate the network impact coefficient of the ath communication load test model in a test operation ; Where a is any communication load test model, and each communication load test model only has a difference in the number of virtual users, i is a data collection time point at a fixed time interval in a test job, n is the total number of data collection times in a test job, is the network data transmission speed of the ath communication load test model at the i-th time point in the test, For all The average value of The network delay of the ath communication load test model at the i-th time point in the test, is the preset network delay, The bandwidth utilization of the ath communication load test model in the test, is the preset bandwidth utilization, for The standard value of To define a function, if ,make , otherwise, let .

4. A network data testing algorithm for network communication according to claim 2, characterized in that: The calculation process in S32 includes: By formula Calculate the network impact coefficient fluctuation value of all communication load test models in a test job ; Where w is the total number of communication load test models in the test job on one side, For all The average value of For all The maximum value in For all The minimum value in for The standard value of is the proportionality coefficient, which is set based on empirical fitting.

5. A network data testing algorithm for network communication according to claim 2, characterized in that: The comparison process in S33 includes: By calculating the network impact coefficient fluctuation value of all communication load test models in a test job The preset fluctuation value threshold Make a comparison; like ,It is judged that in a test operation, different communication load test models have different network conditions when testing, which means that the basic test environments of different communication load test models are different, which will interfere with the test results and cause low test data quality; like , it is judged that in a test operation, there is no difference in the network conditions when different communication load test models are tested, which means that there is no difference in the basic test environment of different communication load test models, and there will be no interference with the test results.

6. A network data testing algorithm for network communication according to claim 3, characterized in that: The analysis process in S4 includes: The real-time load test data collected by the data acquisition module is used to establish the CPU usage change curve of the a-th communication load test model in a test operation. Memory usage change curve ; And through the formula Calculate the load risk coefficient of the ath communication load test model in a test operation ; in, is the average response time of virtual user requests in the communication system of the ath communication load test model in a test operation, is the preset response time, is the throughput of the communication system of the ath communication load test model in a test operation, is the preset throughput, The error rate of the communication system in a test operation of the a-th communication load test model, is the preset error rate, for The standard value of is the starting time point of a test operation. is the end time point of a test operation. is an adjustment coefficient comparison table function, wherein the adjustment coefficient comparison table function The value of The values ​​of are in one-to-one correspondence.

7. A network data testing algorithm for network communication according to claim 1, characterized in that: The analysis process in S4 further includes: By combining all communication load test models in one test job with the load risk factor Respectively with the preset load risk factor threshold Make a comparison; If any Greater than or equal to , it is judged that the load test of the communication network fails, which means that the performance of the network communication system is poor, and its stability and reliability are unqualified; If all All less than , judging that the load test of the communication network is qualified, it means that the performance of the network communication system is good, and its stability and reliability are qualified.

8. A network data testing system for network communication, suitable for a system of a network data testing algorithm for network communication according to claims 1-7, characterized in that: The system comprises: A model building module, used to build multiple communication load test models with different numbers of virtual users; A data collection module is used to collect network transmission data and load test data of each communication load test model in a test operation; The data evaluation module includes a data calculation unit and a data comparison unit, which is used to judge whether there are differences in the network environment when different communication load test models perform network data test operations based on the data collected by the data collection module; The data calculation unit is used to calculate the network impact coefficient of all communication load test models in one test operation in combination with the data collected by the data collection module, and calculate the network impact coefficient fluctuation value of all communication load test models in one test operation based on the data; The data comparison unit is used to compare the network impact coefficient fluctuation values ​​of all communication load test models in a test operation with a preset fluctuation value threshold, and determine whether there is a difference in the test environment of different communication load test models according to the comparison result; The data analysis module is used to analyze the network performance of network communications in different communication load test models in combination with the load test data corresponding to each communication load test model.

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