Enterprise software and hardware compatibility adaptation service evaluation method based on big data

Abstract

The invention relates to the technical field of compatibility adaptation services, and particularly discloses an enterprise software and hardware compatibility adaptation service evaluation method based on big data, and the method comprises the steps: carrying out the compatibility test of software and hardware, and evaluating the optimization effect of the compatibility adaptation service; the cost of the compatibility adaptation service is obtained, the historical cost and the market cost of the compatibility adaptation service in the same period are collected, and the abnormal degree of the cost of the compatibility adaptation service is analyzed; performing stage division on the compatibility adaptation service, and evaluating the progress management condition of the compatibility adaptation service according to the expected completion time and the actual completion time of each stage; and comprehensively evaluating the compatibility adaptation service to obtain an evaluation result and feeding back the evaluation result. According to the method, a user can be helped to find and solve potential safety hazards and compatibility problems, the overall safety and stability of the system are improved, the working efficiency of software and hardware equipment is improved, and the maintenance cost of the equipment is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of compatibility adaptation services, and in particular to a method for evaluating enterprise software and hardware compatibility adaptation services based on big data. Background Art

[0002] At present, with the acceleration of enterprise digital transformation, more and more enterprises have begun to adopt advanced technologies such as cloud computing, big data, and artificial intelligence. The application of these technologies often requires a large number of software and hardware devices to work together. Software and hardware compatibility adaptation services have become an important support for enterprise digital transformation. With the increasing prominence of network security and data security issues, users have higher and higher requirements for the security and stability of software and hardware. Therefore, providing an enterprise software and hardware compatibility adaptation service evaluation method based on big data can help users discover and solve potential security risks and compatibility issues, and improve the overall security and stability of the system.

[0003] For example, the invention patent with announcement number: CN108776631B, one of the inventions announced relates to a server, a method for obtaining compatibility test equipment and a storage medium, the method comprising: obtaining a mobile device as an original adaptation device based on the number of users; matching the original adaptation device with the device parameters of the test device to obtain the first batch of test equipment; obtaining the historical defect problems of the compatibility test of the mobile device, calculating the sum of the defect weights of each test device based on the historical defect problems and a predetermined similarity algorithm, and obtaining the second batch of test equipment according to the sum of the defect weights; obtaining market information, obtaining the predetermined test equipment or similar test equipment of the new model of mobile device as the third batch of test equipment according to the market information; selecting a simplified version of the test equipment from the first batch of test equipment, the second batch of test equipment and the third batch of test equipment. The application can select simplified and representative test equipment to reduce the workload of mobile device compatibility testing.

[0004] For example, the invention patent with announcement number: CN110750396B, an invention announced relates to a server operating system compatibility testing method, device and storage medium, including the following steps: S1: model configuration identification step; S2: model classification step; S3: system installation step; S4: automatic testing step; S5: log collection step; S6: host adaptation step; S7, test progress identification step; S8, monitoring alarm step.

[0005] Based on the above solution, it is found that there are still some deficiencies in the compatibility adaptation of software and hardware, which are specifically reflected in the following aspects: (1) When performing software and hardware compatibility adaptation, the current evaluation method for software performance is not comprehensive enough and cannot provide a basis for software performance optimization, which has a negative impact on equipment work efficiency, reduces the service life of the equipment, and increases the maintenance cost of the enterprise.

[0006] (2) In the current software and hardware compatibility adaptation service, the evaluation method for the abnormal degree of software resource usage is not comprehensive enough, which is not conducive to improving the resource allocation of the software and has a negative impact on the user experience. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an enterprise software and hardware compatibility adaptation service evaluation method based on big data, which can effectively solve the problems involved in the above-mentioned background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an enterprise software and hardware compatibility adaptation service evaluation method based on big data, including: step one, performing compatibility testing on software and hardware, comparing the changes in software and hardware compatibility before and after optimization, and evaluating the optimization effect of the compatibility adaptation service.

[0009] Step 2: Obtain the cost of the compatibility adaptation service, collect the historical cost of the compatibility adaptation service and the market cost of the same period, and analyze the degree of cost abnormality of the compatibility adaptation service.

[0010] Step 3: Obtain the progress management data of the compatibility adaptation service, divide the compatibility adaptation service into stages, and evaluate the progress management of the compatibility adaptation service based on the expected completion time and actual completion time of each stage.

[0011] Step 4: Based on the optimization effect, cost abnormality and progress management of the compatibility adaptation service, conduct a comprehensive evaluation of the compatibility adaptation service, obtain the evaluation results and provide feedback.

[0012] As a further method, the compatibility test of software and hardware is carried out to compare the changes in software and hardware compatibility before and after optimization. The specific process is: the compatibility of the optimized software and hardware is tested, and the software is put into operation on the hardware platform, and the operation data of the target software and target hardware are collected, including software resource occupancy data and software response performance data, and the degree of optimization of software resource occupancy and software response performance is analyzed.

[0013] Based on the software resource usage data, including the CPU usage rate, memory usage volume and number of GPU cores occupied by each software process, and obtaining the reference standard CPU usage rate, reference standard memory usage volume and reference standard number of GPU cores occupied by each process from the adaptation service database, a comprehensive analysis is performed to obtain the software resource usage abnormality index after the compatibility adaptation service is optimized.

[0014] The software resource occupancy abnormality index before the compatibility adaptation service optimization is obtained, and the software resource occupancy optimization degree evaluation value of the compatibility adaptation service is obtained through comprehensive analysis.

[0015] As a further method, the degree of software response performance optimization specifically includes: based on software response performance data, including software failure rate, software startup time and software response time of each compatibility test, and obtaining the critical software failure rate, critical software startup time and critical software response time of the compatibility test from the adaptation service database, a comprehensive analysis is performed to obtain the software response performance evaluation value after the compatibility adaptation service is optimized.

[0016] The software response performance anomaly index before the compatibility adaptation service optimization is obtained, and the software response performance optimization evaluation value of the compatibility adaptation service is obtained through comprehensive analysis.

[0017] As a further method, the optimization effect of the compatibility adaptation service is evaluated. The specific process is: based on the evaluation value of the optimization degree of software resource occupancy of the compatibility adaptation service and the evaluation value of the software response performance after optimization of the compatibility adaptation service, a comprehensive analysis is performed to obtain an optimization effect evaluation index of the compatibility adaptation service.

[0018] As a further method, the cost anomaly degree of the compatibility adaptation service is analyzed, and the specific analysis process is: deploying several time points within a set historical period, obtaining the historical cost of the compatibility adaptation service at each time point, and collecting the cost of the compatibility adaptation service at the current time point and the service market costs of the same type of adaptation services, and comprehensively analyzing to obtain the cost anomaly degree index of the compatibility adaptation service.

[0019] As a further method, the progress management of the compatibility adaptation service is evaluated, and the specific process is: the progress of the compatibility adaptation service is divided into stages, marked as target operation stages, the expected completion time and actual completion time of each target operation stage are obtained, and the progress abnormality index of the compatibility adaptation service is analyzed.

[0020] As a further method, the compatibility adaptation service is comprehensively evaluated, the evaluation results are obtained and feedback is provided. The specific process is: based on the optimization effect degree index, cost abnormality degree index and progress abnormality degree index of the compatibility adaptation service, a comprehensive analysis is performed to obtain a quality reference evaluation value of the compatibility adaptation service.

[0021] The quality reference evaluation value of the compatibility adaptation service is compared with the quality reference evaluation threshold stored in the adaptation service database. If the quality reference evaluation value of the compatibility adaptation service is greater than or equal to the quality reference evaluation threshold, the compatibility adaptation service quality is marked as qualified. If the quality reference evaluation value of the compatibility adaptation service is less than the quality reference evaluation threshold, the compatibility adaptation service quality is marked as unqualified and a feedback warning is issued.

[0022] As a further method, the quality reference evaluation value of the compatibility adaptation service is a quantitative evaluation data obtained by comprehensively analyzing the optimization effect degree index, cost abnormality degree index and progress abnormality degree index of the compatibility adaptation service, which is used to evaluate the quality of the compatibility adaptation service and provide a basis for the optimization of the compatibility adaptation service.

[0023] As a further method, the software response performance evaluation value is specifically a quantitative evaluation data obtained by analyzing the software failure rate, software startup time and software response time of each compatibility test, which is used to quantitatively evaluate the degree of abnormality of software response performance and provide a basis for software performance optimization.

[0024] As a further method, the cost abnormality index of the compatibility adaptation service is specifically a quantitative evaluation data obtained by comprehensively analyzing the historical cost of the compatibility adaptation service at each time point, the cost of the compatibility adaptation service at the current time point, and the service market costs of the same type of adaptation services. It is used to quantitatively evaluate the degree of software cost abnormality and provide a basis for software optimization.

[0025] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention provides an enterprise software and hardware compatibility adaptation service evaluation method based on big data, monitors the progress of the compatibility adaptation service, and can provide real-time feedback on the adaptation progress, helping team members to better allocate work and optimize work processes, thereby improving adaptation efficiency. It helps the team to better allocate resources and optimize resource configuration, thereby improving adaptation efficiency and quality. It can also discover problems and deficiencies in the adaptation process, so as to make timely adjustments and improvements.

[0026] (2) By analyzing the cost of compatibility adaptation services, the present invention enables enterprises to identify high-cost links or problems and make targeted improvements, which helps enterprises improve service quality, reduce costs, and achieve continuous improvement and development.

[0027] (3) The present invention uses a test tool to collect the software failure rate, software startup time, and software response time of the compatibility test, and comprehensively calculates the software response performance evaluation value, thereby providing a basis for software performance optimization, improving equipment work efficiency, extending equipment service life, and reducing the maintenance cost of the enterprise.

[0028] (4) The present invention uses a test tool to collect software resource occupancy data of compatibility testing and calculates the software resource occupancy abnormality index after the compatibility adaptation service is optimized, thereby providing a basis for software performance optimization, which is conducive to improving software resource allocation and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0030] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions 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, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0032] Reference Figure 1 As shown, the present invention provides an enterprise software and hardware compatibility adaptation service evaluation method based on big data, including: step 1, performing compatibility testing on software and hardware, comparing the compatibility changes of software and hardware before and after optimization, and evaluating the optimization effect of the compatibility adaptation service; Specifically, the compatibility of software and hardware is tested, and the changes in software and hardware compatibility before and after optimization are compared. The specific process is: the compatibility of optimized software and hardware is tested, and the software is put into operation on the hardware platform, and the operating data of the target software and target hardware are collected, including software resource usage data and software response performance data, and the degree of optimization of software resource usage and software response performance is analyzed.

[0033] According to the software resource usage data, including the CPU usage rate, memory usage volume and GPU core number of each process of the software, the reference standard CPU usage rate, reference standard memory usage volume and reference standard GPU core number of each process are obtained from the adaptation service database, and the software resource usage abnormality index after the compatibility adaptation service is optimized is comprehensively calculated. In addition to being obtained by analyzing data through system monitoring tools and performance analysis tools, the software resource usage abnormality index can also be obtained through load testing methods and calculated through formulas. The specific calculation expression is: , where Indicates the abnormality index of software resource usage after compatibility adaptation service optimization. Indicates the number of each process. , Indicates the total number of processes. Indicates CPU usage of each process, Indicates The reference standard CPU usage of each process, Indicates the set allowable deviation CPU usage, Indicates The memory usage of a process, Indicates The reference standard memory usage of each process, Indicates the memory usage of the set allowable deviation. Indicates The number of GPU cores occupied by each process, Indicates The reference standard for each process is the number of GPU cores occupied. Indicates the number of GPU cores occupied by the set allowable deviation. Indicates the impact weight factor corresponding to the set CPU usage. Indicates the impact weight factor corresponding to the set memory usage volume, Indicates the impact weight factor corresponding to the set number of occupied GPU cores.

[0034] It should be understood that the software resource usage abnormality index after optimization by the compatibility adaptation service in this instance is specifically a quantitative evaluation data obtained by comprehensively analyzing the CPU usage, memory usage and number of GPU cores occupied by each process of the software, and the reference standard CPU usage, reference standard memory usage and reference standard number of GPU cores occupied by each process. It is used to quantitatively evaluate the abnormality of software resource usage and provide a basis for software performance optimization.

[0035] In a specific embodiment, by using a test tool to collect software resource usage data of a compatibility test and calculating the software resource usage abnormality index after the compatibility adaptation service is optimized, a basis is provided for software performance optimization, which is beneficial to improving software resource allocation and user experience.

[0036] The software resource occupancy abnormality index before the compatibility adaptation service optimization is obtained, and the software resource occupancy optimization evaluation value of the compatibility adaptation service is obtained by comprehensive calculation. The software resource occupancy optimization evaluation value of the compatibility adaptation service can be obtained by using automated tools and scripts to perform software deployment and automatic monitoring of resource usage in operation and maintenance, or by using the CI / CD process to achieve rapid iteration and automated deployment. It can also be obtained by formula calculation. The specific calculation expression is: , where Indicates the evaluation value of the optimization degree of software resource usage of the compatibility adaptation service. Indicates the software resource usage abnormality index before compatibility adaptation service optimization. Indicates the abnormality index of software resource usage after compatibility adaptation service optimization. Indicates the correction factor corresponding to the abnormal degree of software resource usage.

[0037] It should be explained that in data analysis and machine learning, in order to make data of different ranges and magnitudes comparable, data normalization is often required. In this embodiment, taking the square root of the ratio of the software resource occupancy abnormality index before and after the compatibility adaptation service optimization is one of the common normalization methods, which can map the ratio to a specific interval, so that the data can play a better role in the model training and analysis process, and improve the accuracy and stability of the model.

[0038] It should be explained that the software resource occupancy abnormality index in this embodiment is specifically quantitative evaluation data obtained by analyzing the CPU usage rate, memory usage volume and number of GPU cores occupied by each process of the software. It is used to quantitatively evaluate the abnormality of software resource occupancy and provide a basis for software optimization.

[0039] It should be explained that the software resource occupancy optimization degree evaluation value of the compatibility adaptation service in this embodiment is specifically a quantitative evaluation data obtained by comparing and analyzing the software resource occupancy abnormality index before the compatibility adaptation service optimization and the software resource occupancy abnormality index after the compatibility adaptation service optimization, which is used to quantitatively evaluate the degree of software resource occupancy abnormality and provide a basis for software optimization.

[0040] It should be understood that in this embodiment, the CPU usage, including CPU usage and the number of GPU cores occupied, is obtained from the task manager by using the systeminfo command line tool, and the memory usage information is obtained by using the tasklist command line tool. The CPU usage helps identify which processes or functions occupy too much CPU resources. For multi-core processors, you can view which processes use which cores for better resource allocation and optimization, detect memory leaks or whether a process occupies too much memory, which may cause the system to crash or respond slowly, and help determine whether it is necessary to increase memory resources or optimize memory usage. For applications that require GPU acceleration, it is crucial to understand how processes use GPU resources, which helps ensure that GPU resources are used effectively and avoid resource conflicts.

[0041] In a specific embodiment, the compatibility adaptation of the software and hardware of the compatibility adaptation service is a complex process, which specifically includes: 1. Demand analysis: Understand the functional requirements, performance requirements, safety standards, etc. of the software and hardware systems in detail. Analyze the usage habits and environment of the target user group. 2. Environment preparation: Build a test environment, including the required operating system, hardware configuration, network architecture, etc. Prepare necessary test tools and software versions. 3. Compatibility testing: Perform installation testing to ensure that the software can be successfully installed on different hardware and operating systems. Perform functional testing to verify that all functions of the software can work properly in the target environment. Perform performance testing to evaluate the response speed, resource usage, etc. of the software in the target environment. Perform compatibility testing to check whether the software is compatible with other system components (such as databases, middleware, etc.). 4. Problem diagnosis and repair: If problems are found during the test process, use debugging tools to locate the problem. Depending on the type of problem, it may be necessary to modify the software code, update the driver, or adjust the system configuration. After fixing the problem, retest to verify the repair effect. 5. Optimization and adjustment: Optimize the software based on test results and user feedback to improve performance and user experience. Adjust the system configuration to achieve optimal performance and stability. 6. Documentation: Write compatibility adaptation reports, record the test process, problems and their solutions, optimization measures, etc. Provide user manuals or installation guides to help users install and use the software in different environments. 7. Continuous monitoring and maintenance: After the software is released, continuously monitor user feedback and error reports to find and solve problems in a timely manner. Regularly update the software version, fix known problems and add new features.

[0042] Furthermore, the degree of optimization of software response performance specifically includes: based on software response performance data, including software failure rate, software startup time and software response time of each compatibility test, and obtaining the critical software failure rate, critical software startup time and critical software response time of the compatibility test from the adaptation service database, comprehensively calculating the software response performance evaluation value after the compatibility adaptation service is optimized. In addition to being obtained through performance testing, the software response performance evaluation value can also be calculated through a formula. The specific calculation expression is: , where Indicates the software response performance evaluation value after the compatibility adaptation service is optimized. Indicates the compatibility test number. , Indicates the total number of compatibility tests. Indicates Software failure rate of compatibility tests, represents the critical software failure rate, Indicates Software startup time for compatibility test, Indicates the critical software startup time, Indicates Software response time for compatibility tests, represents the critical software response time, Indicates the impact weight factor corresponding to the set failure rate, Indicates the impact weight factor corresponding to the set start time, Indicates the impact weight factor corresponding to the set response time; obtains the software response performance abnormality index before the compatibility adaptation service is optimized, and comprehensively calculates the software response performance optimization evaluation value of the compatibility adaptation service. The specific calculation expression is: , where Indicates the software response performance optimization evaluation value of the compatibility adaptation service. Indicates the software response performance evaluation value after the compatibility adaptation service is optimized. Indicates the software response performance evaluation value before compatibility adaptation service optimization. Indicates the impact weight factor corresponding to the set software response performance abnormality degree.

[0043] In a specific embodiment, by using a test tool to collect the software failure rate, software startup time and software response time of the compatibility test, the software response performance evaluation value is comprehensively calculated to provide a basis for software performance optimization, improve equipment work efficiency, extend equipment service life, and reduce enterprise maintenance costs.

[0044] It should be explained that the software response performance evaluation value in this embodiment is specifically a quantitative evaluation data obtained by analyzing the software failure rate, software startup time and software response time of each compatibility test, which is used to quantitatively evaluate the degree of abnormality of software response performance and provide a basis for software performance optimization.

[0045] It should be understood that in this embodiment, the startup time and response time are recorded when executing the test by using the automated testing tools Selenium, JMeter and RobotFramework, and the software failure rate is monitored and recorded. The failure rate is an important indicator for measuring software stability. It can help identify the reliability and potential problems of the software in actual use. A low failure rate indicates that the software performs well in a wide range of environments, while a high failure rate may require further investigation and optimization. The startup time affects the user's first impression of the software. A long startup time may result in a decline in user experience. Fast startup time is one of the important goals of software performance optimization. The response time directly affects the user experience. A fast response can improve user satisfaction. Analyzing the response time can help identify performance bottlenecks and take measures to optimize them.

[0046] In a specific embodiment, software performance testing is a systematic process for evaluating the performance of software under specific conditions, including: Defining test objectives: Determine the purpose of performance testing, such as verifying whether the system can meet the predetermined performance requirements or identifying performance bottlenecks. Determine performance indicators: Based on the test objectives, define key performance indicators (KPIs), such as response time, throughput, number of concurrent users, resource utilization, etc. Design test scenarios: Create test scenarios that simulate real user operations, including user behavior patterns, transaction frequency, data volume, etc. Prepare the test environment: Build a test environment similar to the production environment to ensure the accuracy of the test results. Configure test tools: Select appropriate performance testing tools, such as JMeter, LoadRunner, etc., and configure the tools to meet the test requirements. Execute performance testing: Run test scripts or test scenarios to collect performance data of the system under various load conditions. Collect and analyze data: Collect performance data such as response time, system resource usage, error rate, etc. Analyze data to identify performance bottlenecks and problems. Report test results: Write a performance test report, including test results, performance indicators, problems found, and recommended improvement measures. Tuning and retesting: Tune the software for performance based on the test results. After tuning, re-execute the performance test to verify the effectiveness of the improvement measures. Performance benchmarking: Establish performance benchmarks to facilitate future performance comparisons and evaluations. Monitor the production environment: Implement performance monitoring in the production environment to ensure the performance of the system in actual operation. Continuous improvement: Continuously optimize and improve the system's performance based on monitoring data and user feedback.

[0047] Furthermore, the optimization effect of the compatibility adaptation service is evaluated. The specific process is: according to the software resource occupancy optimization evaluation value of the compatibility adaptation service and the software response performance evaluation value after the compatibility adaptation service is optimized, a comprehensive analysis is performed to obtain the optimization effect evaluation index of the compatibility adaptation service: .

[0048] In a specific embodiment, software optimization is a process involving multiple levels, which aims to improve the performance, efficiency, stability and user experience of the software, including: Code optimization: refactoring code, eliminating unnecessary duplicate code, and improving the readability and maintainability of the code. Use more efficient algorithms and data structures, reduce memory usage, optimize loops and conditional judgments. Reduce the call stack depth, optimize function calls, and reduce context switching. Resource management: optimize resource usage, such as memory, CPU, and disk I / O. Implement lazy loading and caching mechanisms to reduce resource consumption and improve response speed. Avoid resource leakage and long-term occupation. Performance tuning: perform performance analysis on key business processes and identify bottlenecks. Use performance monitoring tools to track and optimize resource usage and response time. Implement database optimization, such as index optimization, query optimization, and caching strategies. Concurrency and parallel processing: optimize multi-threading and asynchronous processing to fully utilize the advantages of multi-core CPUs. Avoid race conditions and deadlocks to ensure system stability and reliability. Network optimization: optimize network communication protocols and data transmission formats to reduce network latency and packet size. Use compression algorithms and load balancing to improve network efficiency. User experience optimization: improve user interface design to improve ease of use and intuitiveness. Optimize the interaction process, reduce user operation steps, and improve user satisfaction. Security optimization: Strengthen security measures to prevent malicious attacks and data leakage. Conduct security audits and code reviews regularly to fix potential security vulnerabilities. Testing and quality assurance: Implement automated testing, including unit testing, integration testing, and performance testing. Use code coverage tools to ensure the comprehensiveness of testing. Continuous integration and continuous deployment: Adopt CI / CD processes to automate building, testing, and deployment to speed up software iteration. Use containerization and virtualization technologies to improve deployment flexibility and portability. Monitoring and feedback: Implement a real-time monitoring system to track the performance and health of the software. Collect user feedback and optimize based on user needs and pain points.

[0049] Step 2: Obtain the cost of the compatibility adaptation service, collect the historical cost of the compatibility adaptation service and the market cost of the same period, and analyze the degree of cost abnormality of the compatibility adaptation service.

[0050] Specifically, the analysis of the cost abnormality of the compatibility adaptation service is as follows: deploying several time points within a set historical period, obtaining the historical cost of the compatibility adaptation service at each time point, collecting the cost of the compatibility adaptation service at the current time point and the market cost of each service of the same type of adaptation service, and comprehensively calculating the cost abnormality index of the compatibility adaptation service. In addition to being obtained by tracking and analyzing costs using project management software and cost analysis tools, the cost abnormality index of the compatibility adaptation service can also be obtained through internal auditing and change management, and can also be calculated by formula. The specific calculation expression is: , where Indicates the cost anomaly index of the compatibility adaptation service, Indicates The historical cost of compatibility adaptation services at a point in time, Indicates the number of each time point, , represents the total number of each time point, Indicates the cost of the compatibility adaptation service at the current time point, Indicates Cost of serving the market, Indicates the number of each service market, , represents the total number of service markets, Indicates the variance unit value corresponding to the set historical cost affecting the weight factor, Indicates the impact weight factor corresponding to the set service market cost.

[0051] In a specific embodiment, by analyzing the cost of compatibility adaptation services, enterprises can identify high-cost links or problems and make targeted improvements, which helps enterprises improve service quality, reduce costs, and achieve continuous improvement and development.

[0052] It should be explained that the cost abnormality index of the compatibility adaptation service in this embodiment is a quantitative assessment data obtained by comprehensively analyzing the historical costs of the compatibility adaptation service at each time point, the costs of the compatibility adaptation service at the current time point, and the service market costs of the same type of adaptation services. It is used to quantitatively assess the degree of software cost abnormality and provide a basis for software optimization.

[0053] In a specific embodiment, analyzing the cost of compatibility adaptation services generally includes: clarifying the analysis objectives: determining the purpose of cost analysis, such as controlling costs, improving efficiency, or conducting budget planning; collecting relevant information: collecting all cost data related to the adaptability service, including direct costs and indirect costs; defining cost elements: clarifying all cost elements, distinguishing between fixed costs and variable costs, as well as direct costs and indirect costs; establishing a cost model: creating a cost model and incorporating all cost elements into the model to facilitate analysis and calculation of total costs; analyzing cost drivers: identifying key drivers that affect costs, such as scale, complexity, and resource utilization, and conducting baseline cost assessments: Determine the cost baseline of the current adaptive service as a reference point for future improvements; Evaluate the costs of different options: If there are multiple adaptive service options, evaluate the cost of each option and consider its pros and cons; Conduct sensitivity analysis: Analyze the sensitivity of costs to different variables, such as changing workload, resource allocation, etc.; Identify cost saving opportunities: Identify possible cost saving opportunities, such as adopting more efficient tools, improving processes, and optimizing resource allocation; Develop cost control strategies: Based on the analysis results, develop cost control and management strategies, including budget planning, cost monitoring and adjustment mechanisms; Implementation and monitoring: Implement cost control strategies and regularly monitor cost performance to ensure the effectiveness of cost control measures.

[0054] Step 3: Obtain the progress management data of the compatibility adaptation service, divide the compatibility adaptation service into stages, and evaluate the progress management of the compatibility adaptation service based on the expected completion time and actual completion time of each stage.

[0055] Specifically, the progress management of the compatibility adaptation service is evaluated, and the specific process is as follows: The progress of the compatibility adaptation service is divided into stages and marked as target operation stages. The expected completion time and actual completion time of each target operation stage are obtained, and the progress abnormality index of the compatibility adaptation service is calculated. In addition to being obtained through expert review, the progress abnormality index of the compatibility adaptation service can also be obtained through cost simulation and data visualization, and can also be calculated through formulas. The specific calculation expression is: , where Indicates the progress abnormality index of the compatibility adaptation service. Indicates the number of each target operation stage, , Indicates the total number of target run stages, Indicates The actual completion time of each target operation phase, Indicates The expected completion time of each target operation phase, Indicates the set allowable deviation completion time, Indicates the correction factor corresponding to the set completion time.

[0056] It should be explained that the progress abnormality index of the compatibility adaptation service in this embodiment is specifically quantitative evaluation data obtained by comparing and analyzing the expected completion time and the actual completion time of each target operation stage, which is used to quantitatively evaluate the degree of service progress abnormality and provide a basis for software optimization.

[0057] Step 4: Based on the optimization effect, cost abnormality and progress management of the compatibility adaptation service, conduct a comprehensive evaluation of the compatibility adaptation service, obtain the evaluation results and provide feedback.

[0058] Specifically, the compatibility adaptation service is comprehensively evaluated, the evaluation results are obtained and feedback is provided, and the specific process is: based on the optimization effect degree index, cost abnormality degree index and progress abnormality degree index of the compatibility adaptation service, a comprehensive analysis is performed to obtain a quality reference evaluation value of the compatibility adaptation service.

[0059] The quality reference evaluation value of the compatibility adaptation service is compared with the quality reference evaluation threshold stored in the adaptation service database. If the quality reference evaluation value of the compatibility adaptation service is greater than or equal to the quality reference evaluation threshold, the compatibility adaptation service quality is marked as qualified. If the quality reference evaluation value of the compatibility adaptation service is less than the quality reference evaluation threshold, the compatibility adaptation service quality is marked as unqualified and a feedback warning is issued.

[0060] In a specific embodiment, analyzing the quality reference evaluation value of the compatibility adaptation service specifically includes: defining quality standards: determining the quality standards that the compatibility adaptation service needs to meet; formulating an evaluation plan: formulating a detailed evaluation plan, including evaluation methods, tools, resources and schedules; conducting compatibility testing: using appropriate testing tools and methods to test the compatibility of the adaptation service in different environments; the test should cover various usage scenarios and user operations to ensure the performance of the service in actual use; collecting test data: during the test process, collecting data about the service performance, including success rate, response time, and error rate; analyzing test results: analyzing the collected data to identify any potential problems or deficiencies, and evaluate whether the service has met the predetermined quality standards; determine quality indicators: based on the test results, determine quality indicators, such as service uptime, fault recovery time, and user satisfaction; benchmark testing: conduct benchmark testing to compare the performance of the adapted service with industry standards or competitors to determine its position in the market; user feedback: collect user feedback on the adapted service to understand the user's usage experience and satisfaction; continuous improvement: based on test results and user feedback, continuously improve the adapted service to improve its quality and user experience; monitoring and maintenance: after the service is deployed, continuously monitor its performance and quality to ensure that it can maintain high standards in actual operation.

[0061] Furthermore, the quality reference evaluation value of the compatibility adaptation service is a quantitative evaluation data obtained by comprehensively analyzing the optimization effect degree index, cost abnormality degree index and progress abnormality degree index of the compatibility adaptation service, which is used to evaluate the quality of the compatibility adaptation service and provide a basis for the optimization of the compatibility adaptation service.

[0062] Specifically, the optimization effect evaluation index of the compatibility adaptation service can be obtained by analyzing the logs and monitoring data of the operating system, application program, and performance monitoring tool to identify performance problems and potential bottlenecks, comparing the monitoring data before and after optimization, and evaluating the performance improvement. The optimization effect evaluation index of the compatibility adaptation service can also be obtained by formula calculation. The specific calculation expression is: , where Indicates the optimization effect index of the compatibility adaptation service. Indicates the evaluation value of the optimization degree of software resource usage of the compatibility adaptation service. Indicates the software response performance optimization evaluation value of the compatibility adaptation service. Indicates the impact weight factor corresponding to the set software resource usage optimization evaluation value. Indicates the impact weight factor corresponding to the set software response performance optimization evaluation value.

[0063] It should be understood that the optimization effect evaluation index of the compatibility adaptation service provides a basis for the optimization of software and hardware equipment, which is conducive to comprehensively improving the performance, stability and compatibility of the software, thereby enhancing the user experience, reducing maintenance costs, improving market competitiveness, and ensuring that the software can quickly adapt to the ever-changing technology and market environment.

[0064] Specifically, the quality reference evaluation value of the compatibility adaptation service can be obtained by testing the compatibility of the software under different operating systems, browsers, hardware configurations and network environments, as well as through security and compliance checks, and can also be calculated through formulas. The specific analysis process is as follows: , where Indicates the quality reference evaluation value of the compatibility adaptation service. Indicates the optimization effect index of the compatibility adaptation service. Indicates the cost anomaly index of the compatibility adaptation service. Indicates the progress abnormality index of the compatibility adaptation service. Indicates the influence weight factor corresponding to the set optimization effect index. Indicates the impact weight factor corresponding to the set cost abnormality index, Indicates the impact factor corresponding to the set progress abnormality index.

[0065] In a specific embodiment, by providing an enterprise software and hardware compatibility adaptation service evaluation method based on big data, the progress of the compatibility adaptation service is monitored, and real-time feedback on the adaptation progress can be provided, helping team members to better allocate work and optimize workflows, thereby improving adaptation efficiency. It helps the team to better allocate resources and optimize resource configuration, thereby improving adaptation efficiency and quality, and can discover problems and deficiencies in the adaptation process, so that timely adjustments and improvements can be made.

[0066] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for evaluating enterprise software and hardware compatibility adaptation services based on big data, characterized in that: include: Step 1: Perform compatibility tests on software and hardware, compare the changes in software and hardware compatibility before and after optimization, and evaluate the optimization effect of the compatibility adaptation service; Step 2: Obtain the cost of the compatibility adaptation service, collect the historical cost of the compatibility adaptation service and the market cost of the same period, and analyze the cost abnormality of the compatibility adaptation service; Step 3: Obtain progress management data of the compatibility adaptation service, divide the compatibility adaptation service into stages, and evaluate the progress management of the compatibility adaptation service based on the expected completion time and actual completion time of each stage; Step 4: Based on the optimization effect, cost abnormality and progress management of the compatibility adaptation service, conduct a comprehensive evaluation of the compatibility adaptation service, obtain the evaluation results and provide feedback.

2. According to the big data-based enterprise software and hardware compatibility adaptation service evaluation method of claim 1, it is characterized by: The compatibility test of software and hardware is carried out to compare the compatibility changes of software and hardware before and after optimization. The specific process is as follows: Test the compatibility of optimized software and hardware, run the software on the hardware platform, collect the operation data of the target software and target hardware, including software resource usage data and software response performance data, and analyze the degree of optimization of software resource usage and software response performance; Based on the software resource usage data, including the CPU usage rate, memory usage volume and number of GPU cores occupied by each process of the software, and obtaining the reference standard CPU usage rate, reference standard memory usage volume and reference standard number of GPU cores occupied by each process from the adaptation service database, a comprehensive analysis is performed to obtain the software resource usage abnormality index after the compatibility adaptation service is optimized; The software resource occupancy abnormality index before the compatibility adaptation service optimization is obtained, and the software resource occupancy optimization degree evaluation value of the compatibility adaptation service is obtained through comprehensive analysis.

3. According to the big data-based enterprise software and hardware compatibility adaptation service evaluation method of claim 2, it is characterized by: The software response performance optimization degree specifically includes: Based on the software response performance data, including the software failure rate, software startup time and software response time of each compatibility test, and obtaining the critical software failure rate, critical software startup time and critical software response time of the compatibility test from the adaptation service database, a comprehensive analysis is performed to obtain the software response performance evaluation value after the compatibility adaptation service is optimized; The software response performance anomaly index before the compatibility adaptation service optimization is obtained, and the software response performance optimization evaluation value of the compatibility adaptation service is obtained through comprehensive analysis.

4. According to the big data-based enterprise software and hardware compatibility adaptation service evaluation method of claim 3, it is characterized by: The optimization effect of the compatibility adaptation service is evaluated, and the specific process is as follows: Based on the evaluation value of the software resource occupancy optimization degree of the compatibility adaptation service and the evaluation value of the software response performance after the compatibility adaptation service is optimized, a comprehensive analysis is performed to obtain the optimization effect evaluation index of the compatibility adaptation service.

5. According to the big data-based enterprise software and hardware compatibility adaptation service evaluation method of claim 1, it is characterized by: The analysis of the cost abnormality of the compatibility adaptation service is specifically conducted as follows: Deploy several time points within the set historical period, obtain the historical cost of the compatibility adaptation service at each time point, collect the cost of the compatibility adaptation service at the current time point and the market cost of each service of the same type of adaptation service, and conduct a comprehensive analysis to obtain the cost anomaly index of the compatibility adaptation service.

6. According to the big data-based enterprise software and hardware compatibility adaptation service evaluation method of claim 1, it is characterized by: The specific process of evaluating the progress management of the compatibility adaptation service is as follows: The progress of the compatibility adaptation service is divided into stages and marked as target operation stages. The expected completion time and actual completion time of each target operation stage are obtained, and the progress abnormality index of the compatibility adaptation service is analyzed.

7. According to the big data-based enterprise software and hardware compatibility adaptation service evaluation method of claim 1, it is characterized by: The above-mentioned process of comprehensively evaluating the compatibility adaptation service, obtaining the evaluation results and providing feedback is as follows: Based on the optimization effect index, cost abnormality index and progress abnormality index of the compatibility adaptation service, a comprehensive analysis is conducted to obtain a quality reference evaluation value of the compatibility adaptation service; The quality reference evaluation value of the compatibility adaptation service is compared with the quality reference evaluation threshold stored in the adaptation service database. If the quality reference evaluation value of the compatibility adaptation service is greater than or equal to the quality reference evaluation threshold, the compatibility adaptation service quality is marked as qualified. If the quality reference evaluation value of the compatibility adaptation service is less than the quality reference evaluation threshold, the compatibility adaptation service quality is marked as unqualified and a feedback warning is issued.

8. The enterprise software and hardware compatibility adaptation service evaluation method based on big data according to claim 3 is characterized by: The software response performance evaluation value is specifically a quantitative evaluation data obtained by analyzing the software failure rate, software startup time and software response time of each compatibility test, which is used to quantitatively evaluate the abnormal degree of software response performance and provide a basis for software performance optimization.

9. The enterprise software and hardware compatibility adaptation service evaluation method based on big data according to claim 5 is characterized by: The cost anomaly index of the compatibility adaptation service is a quantitative assessment data obtained by comprehensively analyzing the historical costs of the compatibility adaptation service at each time point, the costs of the compatibility adaptation service at the current time point, and the service market costs of the same type of adaptation services. It is used to quantitatively assess the degree of software cost anomaly and provide a basis for software optimization.

10. The enterprise software and hardware compatibility adaptation service evaluation method based on big data according to claim 7, characterized in that: The quality reference evaluation value of the compatibility adaptation service is a quantitative evaluation data obtained by comprehensively analyzing the optimization effect degree index, cost abnormality degree index and progress abnormality degree index of the compatibility adaptation service, which is used to evaluate the quality of the compatibility adaptation service and provide a basis for the optimization of the compatibility adaptation service.

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