Performance test method and system for digital platform

Abstract

The invention discloses a performance test method and system for a digital platform. The method comprises the steps that the types of performance tests of the digital platform are determined, and the types of the performance tests comprise a benchmark test, a load test, a pressure test and a stability test; based on the determined performance test type, corresponding performance test indexes are selected, and the performance test indexes comprise throughput, response time, error rate and server resource use conditions; based on the performance test indexes, a performance test strategy of the digital platform is configured, and the performance test strategy comprises a test tool, server resources, network configuration and database setting; and testing the performance of the digital platform by using the performance test strategy. The technical problems that the testing process is not systematic and the result is not reliable are solved.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a performance testing method and system for a digital platform. Background Art

[0002] As the number of students continues to grow, digital education platforms for graphical programming face increasingly severe system performance challenges during operation. Currently, performance testing of digital platforms usually relies on manual configuration and single test type methods. These methods lack systematicity and flexibility, and are difficult to adapt to diverse testing needs in a timely manner, affecting the accuracy and reference value of test results.

[0003] In addition, in traditional performance testing, the test types are not clearly divided, making it difficult to fully evaluate the performance of the platform under different loads and operating environments. At the same time, the test environment configuration process is complex and prone to configuration errors, resulting in reduced reliability of test results. Therefore, in order to ensure the stable operation of the platform under high concurrency and complex business scenarios, a systematic, flexible and efficient performance testing method is urgently needed to solve the above problems and improve the efficiency and accuracy of the platform's performance testing.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a performance testing method and system for a digital platform, so as to at least solve the technical problems of an unsystematic testing process and unreliable results.

[0006] According to one aspect of an embodiment of the present invention, a performance testing method for a digital platform is provided, comprising: determining a type of performance testing of the digital platform, wherein the type of performance testing includes benchmark testing, load testing, stress testing, and stability testing; selecting corresponding performance testing indicators based on the determined performance testing type, wherein the performance testing indicators include throughput, response time, error rate, and server resource usage; configuring a performance testing strategy for the digital platform based on the performance testing indicators, wherein the performance testing strategy includes testing tools, server resources, network configuration, and database settings; and utilizing the performance testing strategy to test the performance of the digital platform.

[0007] According to another aspect of an embodiment of the present invention, a performance testing system for a digital platform is provided, comprising: a determination module configured to determine a type of performance test of a digital platform, wherein the type of performance test comprises a benchmark test, a load test, a stress test, and a stability test; a selection module configured to select corresponding performance test indicators based on the determined performance test type, wherein the performance test indicators comprise throughput, response time, error rate, and server resource usage; a configuration module configured to configure a performance testing strategy of the digital platform based on the performance testing indicators, wherein the performance testing strategy comprises testing tools, server resources, network configuration, and database settings; and a testing module configured to use the performance testing strategy to test the performance of the digital platform.

[0008] In an embodiment of the present invention, the type of performance test of the digital platform is determined, wherein the type of performance test includes benchmark test, load test, stress test, and stability test; based on the determined performance test type, the corresponding performance test indicator is selected, wherein the performance test indicator includes throughput, response time, error rate, and server resource usage; based on the performance test indicator, the performance test strategy of the digital platform is configured, wherein the performance test strategy includes test tools, server resources, network configuration, and database settings; the performance of the digital platform is tested using the performance test strategy. Through the above solution, the technical problems of unsystematic testing process and unreliable results are solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0010] Figure 1 is a flow chart of a performance testing method for a digital platform according to an embodiment of the present invention;

[0011] Figure 2 is a flow chart of another performance testing method for a digital platform according to an embodiment of the present invention;

[0012] Figure 3 is a schematic diagram of dimensions of performance testing dimensions according to an embodiment of the present invention;

[0013] Figure 4 is a structural diagram of a performance testing system for a digital platform according to an embodiment of the present invention;

[0014] Figure 5 A schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0017] According to an embodiment of the present invention, a method embodiment of a performance testing method for a digital platform is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0018] Figure 1 is a performance testing method of a digital platform according to an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0019] Step S102, determining the type of performance test of the digital platform, wherein the type of performance test includes benchmark test, load test, stress test, and stability test.

[0020] Step S104, based on the determined performance test type, select corresponding performance test indicators, wherein the performance test indicators include throughput, response time, error rate and server resource usage.

[0021] Step S106, configuring the performance testing strategy of the digital platform based on the performance testing indicators, wherein the performance testing strategy includes testing tools, server resources, network configuration, and database settings.

[0022] Step S108: using the performance testing strategy to test the performance of the digital platform.

[0023] First, the performance testing strategy is used to monitor and collect test data of the digital platform, wherein the test data includes at least one of the following: system throughput, response time, error rate, CPU usage, memory usage, disk I / O, and database query performance;

[0024] Then, based on the test data, the test results are analyzed to identify the performance bottleneck of the digital platform, wherein the performance bottleneck includes at least one of the following: CPU overload, insufficient memory, slow database query, and network delay. For example, in the benchmark test, the performance test strategy is used to apply a pressure less than a pressure threshold to the digital platform to evaluate the benchmark capability of the digital platform; for example, in the load test, the performance test strategy is used to gradually increase the pressure on the digital platform until the performance index of the digital platform reaches a safety critical value to evaluate the load capacity of the digital platform; in the stress test, a peak pressure or pressure exceeding the maximum load is applied to evaluate the extreme processing capacity of the digital platform; in the stability test, continuous business pressure is applied to evaluate the long-term operational stability of the digital platform.

[0025] In addition, while using the performance test strategy to test the performance of the digital platform, the test environment configuration parameters can also be dynamically adjusted according to the real-time test results of the digital platform to achieve adaptive performance optimization. For example, according to the real-time test results, the key configuration parameters that affect the performance of the digital platform are identified, wherein the key configuration parameters include server resource allocation, database connection pool size, number of threads, and cache strategy; based on the identified key configuration parameters, an optimization plan is automatically generated, wherein the optimization plan includes resource allocation optimization, load balancing strategy adjustment, and network configuration optimization; based on the optimization plan, the configuration parameters of the performance test strategy are adjusted in real time to improve the performance test results of the digital platform.

[0026] The present application embodiment provides another performance testing method for a digital platform, such as Figure 2 As shown, the following steps are included:

[0027] Step S202, determining the performance test type and performance test index.

[0028] The performance testing type of the digital platform determines the core direction of the testing strategy and test environment configuration, including benchmark testing, load testing, stress testing and stability testing.

[0029] Benchmark testing is used to determine the basic performance of the system under normal load. During the test, a load less than the preset stress threshold is applied to simulate a normal operating environment. Load testing is used to test the system's responsiveness when the pressure is gradually increased until the performance index reaches the safety critical value, and evaluate the system's performance under different load levels. Stress testing is used to simulate extreme scenarios, determine the system's maximum processing capacity by exceeding the maximum load pressure, and identify possible performance bottlenecks. Stability testing is used to evaluate the system's ability to operate stably under long-term high-load environments, and test its resource utilization and responsiveness performance trends.

[0030] Performance test indicators can include throughput, response time, server resource usage, error rate, etc. Throughput is the number of requests successfully processed per unit time, and is a key indicator for measuring the system's processing capacity. Response time is the time from when a request is sent to when the server returns a response, reflecting the system's processing speed. The error rate is the proportion of failed requests generated during the test, indicating the stability of the system. Server resource usage includes CPU usage, memory usage, disk I / O, and network bandwidth usage, which are used to determine the allocation and management efficiency of system resources.

[0031] Based on the determined performance test type, select the applicable performance test indicators. For example, for benchmark testing, you can select throughput and response time as core indicators to evaluate the basic performance of the system under normal operating conditions. For load testing, you can select server resource usage and response time to evaluate resource consumption and responsiveness during load changes. For stress testing, you can select maximum throughput and error rate to identify the system's extreme processing capabilities and potential performance bottlenecks. For stability testing, you can select resource usage change trends and error rates to evaluate the system's ability to operate stably over a long period of time.

[0032] All projects on the digital platform are underlying services of graphical programming. The digital platform has fast business processing capabilities, and other projects can call its interfaces quickly. Once there is a problem with the interface of the digital platform, other projects will also have problems, and an avalanche phenomenon is very likely to occur, so the interface is relatively strict.

[0033] The performance test of this application is measured in two dimensions: one is the core business dimension, and the other is the business complexity dimension. The two dimensions measure a performance test result. All standards are executed according to the configuration of the server with 4 cores, 3G memory, such as Figure 3 As shown, the core business dimension is divided into 5 levels, P0, P1, P2, P3, P4, and the business complexity dimension is divided into 5 levels, C0, C1, C2, C3, C4.

[0034] Specifically, the core business levels are as follows: interface call ratio P0>50%, P1>30%, P2>10%, P3>5%, P4<5%. P0 means that the business or interface is a key interface, and the call frequency is very high. Once the interface is abnormal, it will cause a serious accident, such as an authentication interface. P1 means that the business or interface is an important business, and the high call frequency will affect the user experience. P2 means that the business or interface is a relatively important business, with a high call frequency, which does not affect the normal user process. P3 means that the business or interface is a general business, with a low call frequency, which does not affect the normal user process. P4 means that the business or interface is a business that is not called often, with a low call frequency, which does not affect the business process.

[0035] The business complexity levels are as follows: C0 is the most complex business or interface, with multiple judgment branches, many calls between services, and a high number of database queries. C1 is a relatively high business or interface complexity, with fewer judgments, many calls between services, and a high number of database queries. C2 is a moderate business or interface complexity, with moderate calls between services and a moderate number of database queries. C3 is a low business or interface complexity, with fewer calls between services and fewer database queries. C4 is a low business or interface complexity, with no or very few scheduling between services, very few database queries, or direct queries to the cache.

[0036] In some embodiments, a deep learning model can also be used to determine the type of performance test and the performance test index. In order to accurately capture the semantic information in the user demand description, the method first performs natural language processing analysis on the demand description input by the user to extract keywords and deep semantic features. In the early stage of model training, a language model is used to extract primary embedding vector groups from sample data and generate auxiliary embedding vector groups from manually recognized text. According to the preset extended rank criterion, these two embedding vector groups are multi-dimensionally expanded to enrich their feature expressions. The expanded embedding vector group is subjected to feature coupling and fusion processing of a multi-channel convolution kernel to generate a coupled primary embedding group and an auxiliary embedding group. The multi-channel convolution kernel can extract and fuse the semantic information of the text in different feature spaces, thereby effectively combining text features from different sources and improving the performance of the model in deep semantic understanding. After completing the feature coupling, a nonlinear activation function is applied to optimize the expressive power of the embedding vector to obtain the activated primary embedding group and auxiliary embedding group. Subsequently, the scale consistency of the data is ensured by normalization operation to avoid numerical deviations between feature levels. In order to further extract key information, high-order matrix decomposition is performed on the primary feature matrix and the auxiliary feature matrix respectively. First, these matrices are expanded along the expansion dimension to generate multiple sub-matrices. The core subspace representation and factor matrix of each sub-matrix are extracted through singular value decomposition (SVD). The core subspace representation condenses the main information in the matrix, while the factor matrix reveals the local structure of its features and reflects the intrinsic correlation between features at different levels. Next, the similarity between the primary feature matrix and the auxiliary feature matrix is ​​compared through principal component difference analysis. Specifically, the similarity of the core subspace representation is first calculated, and then the difference of the principal components in the factor matrix is ​​measured to capture the subtle changes between the two sets of data. By comprehensively analyzing the similarity and difference, the system can more accurately identify keywords. Finally, by comparing the principal component differences between the sample data and the manually recognized text, the weight of the loss function is dynamically adjusted. The adjusted loss function can more specifically guide the model to focus on the identification of subtle semantic differences in subsequent training, ensuring that the model has a more accurate grasp of semantic details in language understanding tasks. According to the extracted keywords and deep semantic features, the most relevant performance test type and corresponding performance test indicator are determined by matching with the predefined performance test type and indicator database. The matching process takes into account the semantic relevance of keywords, the similarity of feature vectors, and the results of principal component difference analysis to ensure that the selected performance test solution can accurately reflect the user's demand description and improve the pertinence and effectiveness of the test.

[0037] Step S204, determining a performance testing strategy.

[0038] During the configuration of the test environment, it is necessary to determine the performance testing strategy based on different performance testing types and performance testing indicators, for example, selecting appropriate testing tools, server resources, network configuration, and database settings.

[0039] In terms of test tool configuration, select appropriate performance testing tools (such as JMeter, LoadRunner), and configure load generators and monitoring tools according to the test type. In terms of server resource configuration, configure application servers, database servers, and load balancing servers, adjust the number of CPU cores, memory size, and storage capacity to ensure that the test requirements are met. In terms of network configuration, set up the intranet environment to ensure sufficient network bandwidth and reduce interference caused by network fluctuations. In terms of database settings, initialize the database according to the application scenario to ensure data integrity and consistency.

[0040] Specifically, the shortcomings of the performance testing strategy may be as follows:

[0041] First, simulate the real hardware environment of the production line. The performance test strategy is similar to the production environment architecture. Use a server with 4 cores of CPU and 3G memory to simulate the application server and the database server with shared storage. In the cluster test, use load balancing to simulate the application of the shared center.

[0042] Secondly, the servers are in the same intranet to avoid network problems as much as possible. The performance test servers are located in the same area of ​​Alibaba Cloud servers, and the network interaction between servers is carried out through switches to reduce the impact of the external network environment.

[0043] Taking PV as the starting point, we convert it into TPS which can be quantified by performance testing. Using PV as the starting point of performance testing, we convert the number of visits into TPS, simplify the performance testing model, and weaken the impact of the number of online users and concurrent users. We convert the PV value into a TPS indicator that can be measured by the test tool through the PV calculation model.

[0044] The performance test data is divided into two parts: basic data and business data. The test database and the functional test database are independent of each other. The basic data is used to test the database index optimization space and index capacity, while the business data is used to test the SQL statements and code optimization degree in the system operation.

[0045] Java logs are divided into four levels: info, debug, warn, and error. Log printing consumes server IO and storage resources, and frequent printing affects program execution speed. Therefore, set the log level to warn, and only print warn and error logs to reduce the amount of log input and monitor errors in performance testing.

[0046] In addition, single scenarios are tested first, followed by mixed scenarios, to ensure that each performance bottleneck is optimized. Single scenario testing is used to locate and optimize the performance of specific interfaces. After the optimization is completed, mixed scenario testing is performed to combine various scenarios in proportion to test the performance of the entire application system.

[0047] According to the performance test passing criteria, determine whether the performance point has passed. The performance testing team formulates the pass criteria for CodingCat performance testing based on program requirements, historical test results, production line monitoring results and other data, and conducts scientific and guiding evaluations from multiple dimensions such as server performance and user experience.

[0048] Step S206, evaluating and optimizing the performance testing strategy.

[0049] After developing a performance testing strategy, in order to ensure the effectiveness of the testing work, a pre-test evaluation of the project to be tested is required. The main goal of this evaluation is to determine the performance test points and set test expectations to ensure that the system under test can cope with the expected pressure within a specific time period in the future. The pre-test evaluation includes the following four main dimensions:

[0050] First, you need to confirm whether the project under test involves key business and identify its main business logic points. In particular, the performance requirements for functions involving underlying business or C-end student classes must be higher than other interfaces. If the project or function point does not belong to key business, you can move to other dimensions for further evaluation.

[0051] Secondly, evaluate the daily PV volume or request volume of each functional point in the project. For key business points with high PV volume and high system pressure, list them as the focus of performance testing. For example, the authentication interface is a typical representative of high PV volume because it is frequently called. If the PV volume is not high but the function point is a key business, further judgment needs to be made in combination with other dimensions.

[0052] The logic complexity of the function points of the digital platform under test is also an important evaluation criterion. Even if the daily PV volume is low, function points with high logic complexity should be included in the performance test scope. For example, when the interface contains multiple judgment conditions, frequently accesses the database, or calls other services more than five times, the function point may become a system bottleneck and needs to be used as a performance test point.

[0053] In addition, the future system load pressure can be predicted based on the operation and promotion plan. Performance testing must not only meet the current pressure, but also consider the high load situation in a specific time period in the future. Configuring according to the operation and promotion plan helps to reasonably set performance test points.

[0054] The above four dimensions complement each other in actual applications and together constitute a comprehensive dimension set for pre-test evaluation. Function points that do not fully meet the above standards but have performance risks such as high memory or CPU consumption should still be considered as performance test points to ensure that the overall system performance reaches the expected level.

[0055] Step S208, performing performance testing.

[0056] After the performance test strategy configuration is completed, the performance test process is started, and data monitoring and collection, result analysis and performance bottleneck identification are carried out in sequence.

[0057] During the performance test, the system will continuously monitor and collect the following performance data: system throughput (TPS), response time distribution and average, number and ratio of successful and failed requests, resource usage such as CPU, memory and disk I / O, and database query execution time and number of transactions.

[0058] Based on the collected performance data, the following steps are used to analyze system performance. First, data cleaning and statistical analysis are performed to remove abnormal data and calculate the main performance indicators during the test. Then, performance trend analysis is performed, performance indicator curves are drawn, and the changing trends under different pressure conditions are analyzed. Finally, performance bottlenecks such as CPU overload, insufficient memory, and slow database queries are identified through comparison of key indicators and threshold detection.

[0059] In addition, during the testing process, stress management strategies are implemented for different test types, including benchmark testing phase, load testing phase, stress testing phase and stability testing phase.

[0060] In the benchmark test phase, a load less than the stress threshold is applied to observe the system's operating performance under normal load. In the load test phase, the load is gradually increased and changes in system performance indicators are recorded until the load critical point is reached. In the stress test phase, overload stress is applied to test the system's extreme processing capacity and fault tolerance. In the stability test phase, the load is applied for a long time to detect changes in resource utilization and response time during system operation.

[0061] Step S210: dynamic adjustment and performance optimization.

[0062] According to the real-time test results, the test environment configuration parameters are dynamically adjusted to achieve adaptive performance optimization. In terms of key configuration parameter identification, the real-time test results are analyzed to identify key configuration parameters that limit performance, such as server resource allocation, database connection pool size, number of threads, and cache strategy. In terms of automatic optimization solution generation, based on the identification results, optimization solutions are automatically generated, including resource allocation optimization, load balancing strategy adjustment, and network configuration optimization. In terms of real-time adjustment and test result improvement, the performance test strategy configuration parameters are adjusted according to the optimization solution, the performance test is re-run, and the optimization effect is observed to ensure that the system performance is significantly improved within the test indicator range.

[0063] Specifically, first, AI models (such as time series prediction models based on deep learning) are used to analyze the trends of performance data to predict potential performance bottlenecks and system crash risks. At the same time, the performance bottleneck identification model is used to automatically locate high-load areas and resource tension points, such as database query delays, CPU overloads, and memory leaks. The model can dynamically adapt to different application scenarios and improve the accuracy of performance bottleneck identification.

[0064] Next, based on the analysis results of the AI ​​model, a decision engine is built to automatically generate targeted optimization solutions. The optimization solutions include but are not limited to: Server resource adjustment: Dynamically allocate the number of CPU cores, memory size, and storage capacity. Database connection management: Adjust the database connection pool size and cache strategy to ensure database query efficiency. Load balancing strategy: Adjust the load distribution and the number of service nodes to reduce system bottlenecks and service interruption risks. Network configuration optimization: Optimize network bandwidth and data transmission paths to reduce latency and packet loss. Cache strategy adjustment: Dynamically adjust the application cache strategy to speed up access to commonly used data.

[0065] Subsequently, adaptive parameter adjustments and test strategy reconstruction are performed. The performance test environment configuration is automatically adjusted according to the optimization decision, and key parameters are dynamically modified. These adjustments are immediately applied to the test environment, triggering a new round of performance testing to verify the effectiveness of the optimization solution. Using the AI ​​feedback loop mechanism, the optimization strategy is continuously iterated based on the new test results to ensure significant improvement in system performance. By introducing an adaptive test strategy, the test load and pressure distribution are automatically adjusted to improve the test coverage and reliability of the test results.

[0066] Finally, continuous performance improvement and decision model updates are performed. The test data during the optimization process is stored in the performance improvement database for training and updating AI models to improve their prediction and decision-making capabilities. At the same time, the overall performance test and optimization effects are reviewed and evaluated regularly to ensure the stable operation and performance of the system under different load scenarios. In addition, by introducing multi-model integration technology and combining the prediction results of different algorithm models, the accuracy and robustness of the decision engine are enhanced.

[0067] This application introduces the dynamic adjustment and performance optimization mechanism of AI. This method significantly improves the automation and intelligence level of performance testing, ensuring the stability and efficient operation of the digital platform under high-load environments.

[0068] The present application also provides a performance testing system for a digital platform, such as Figure 4 As shown, it includes: a determination module 42, configured to determine the type of performance test of the digital platform, wherein the type of performance test includes benchmark test, load test, stress test, and stability test; a selection module 44, configured to select corresponding performance test indicators based on the determined performance test type, wherein the performance test indicators include throughput, response time, error rate, and server resource usage; a configuration module 46, configured to configure the performance test strategy of the digital platform based on the performance test indicators, wherein the performance test strategy includes test tools, server resources, network configuration, and database settings; a testing module 48, configured to use the performance test strategy to test the performance of the digital platform.

[0069] It should be noted that the performance test system of the digital platform provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the performance test system of the digital platform provided in the above embodiment and the performance test method embodiment of the digital platform belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0070] Figure 5 FIG. 1 shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiment of the present disclosure. It should be noted that: Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0071] like Figure 5 As shown, the electronic device includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for system operation are also stored. The CPU 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0072] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed.

[0073] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A performance testing method for a digital platform, characterized in that: include: Determine the type of performance test of the digital platform, wherein the type of performance test includes benchmark test, load test, stress test, and stability test; Based on the determined performance test type, select corresponding performance test indicators, wherein the performance test indicators include throughput, response time, error rate, and server resource usage; Based on the performance test indicators, configure the performance test strategy of the digital platform, wherein the performance test strategy includes test tools, server resources, network configuration, and database settings; The performance testing strategy is used to test the performance of the digital platform.

2. The method according to claim 1, characterized in that The performance testing strategy is used to test the performance of the digital platform, including: Using the performance testing strategy to monitor and collect test data of the digital platform, wherein the test data includes at least one of the following: system throughput, response time, error rate, CPU usage, memory usage, disk I / O, and database query performance; Analyze the test results based on the test data to identify the performance bottleneck of the digital platform, wherein the performance bottleneck includes at least one of the following: CPU overload, insufficient memory, slow database query, and network delay.

3. The method according to claim 2, characterized in that The performance testing strategy is used to monitor and collect test data of the digital platform, including: Based on the type of the performance test, applying different pressures to the digital platform using the performance test strategy; During the application of different pressures, test data of the digital platform is monitored and collected.

4. The method according to claim 3, characterized in that Based on the type of the performance test, the performance test strategy is used to apply different pressures to the digital platform, including: In the benchmark test, the performance test strategy is used to apply a pressure less than a pressure threshold to the digital platform to evaluate the benchmark capability of the digital platform; In the load test, the performance test strategy is used to gradually increase the pressure on the digital platform until the performance index of the digital platform reaches a safety critical value, so as to evaluate the load capacity of the digital platform; In stress testing, peak pressure or pressure exceeding the maximum load is applied to evaluate the extreme processing capacity of the digital platform; During the stability test, continuous business pressure is applied to evaluate the long-term operational stability of the digital platform.

5. The method according to claim 1, characterized in that: While using the performance testing strategy to test the performance of the digital platform, the method also includes: dynamically adjusting the test environment configuration parameters according to the real-time test results of the digital platform to achieve adaptive performance optimization.

6. The method according to claim 5, characterized in that Dynamically adjusting the test environment configuration parameters includes: According to the real-time test results, identifying key configuration parameters that affect the performance of the digital platform, wherein the key configuration parameters include server resource allocation, database connection pool size, number of threads, and cache strategy; Automatically generate an optimization plan based on the identified key configuration parameters, wherein the optimization plan includes resource allocation optimization, load balancing strategy adjustment, and network configuration optimization; Based on the optimization scheme, the configuration parameters of the performance testing strategy are adjusted in real time to improve the performance testing results of the digital platform.

7. A performance testing system for a digital platform, characterized in that: include: A determination module is configured to determine a type of performance test of the digital platform, wherein the type of performance test includes a benchmark test, a load test, a stress test, and a stability test; A selection module is configured to select corresponding performance test indicators based on the determined performance test type, wherein the performance test indicators include throughput, response time, error rate and server resource usage; A configuration module, configured to configure a performance test strategy of the digital platform based on the performance test indicator, wherein the performance test strategy includes test tools, server resources, network configuration, and database settings; The testing module is configured to test the performance of the digital platform using the performance testing strategy.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

9. A computer device, characterized in that: include: Memory and processor, The memory stores a computer program; The processor is used to execute the computer program stored in the memory, and when the computer program is run, the processor is enabled to execute the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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