Methods and systems for testing the compatibility of laptop software across multiple platforms

Abstract

This invention provides a method and system for testing the compatibility of laptop software across multiple platforms. First, it acquires a set of runtime status data and a set of platform characteristic data for the laptop software under test on the target test platform. These two sets are then correlated and mapped to obtain a correlation mapping result. Based on this correlation mapping result and preset rules, adaptation conflict features are extracted. Adaptation adjustment schemes are then searched using an adaptation strategy library to generate a set of candidate adaptation adjustment schemes. This set of candidate adaptation adjustment schemes is integrated, and duplicate content is removed to generate a software platform adaptation adjustment strategy for the target test platform. This strategy includes adjustment schemes for software functional modules and resource calling methods, improving the multi-platform compatibility and stability of the software.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer technology, in particular to a notebook software adaptation test method and system applied to multi-platform compatibility. BACKGROUND

[0002] In today's digital office and diversified entertainment era, notebook software needs to be widely adapted to a variety of different platforms to meet the user's use requirements in different devices and system environments. However, different platforms have their own unique hardware configurations and system environment characteristics, which brings great challenges to the compatibility and adaptability of notebook software.

[0003] The existing notebook software test method has obvious deficiencies in dealing with multi-platform adaptation problems. On the one hand, some test methods only focus on the basic function running situation of the software on a single platform, and verify whether the software can normally start and perform common operations through simple function test cases, but ignore the resource occupation situation of the software under different hardware configurations and system environments. For example, on a platform with low hardware performance, the software may run slowly or even crash due to high resource occupation, but the traditional test method is difficult to find such potential problems.

[0004] On the other hand, some test methods consider the multi-platform factor, but lack systematic correlation analysis. They only test the software running state and platform characteristics independently, without effectively correlating the two, and cannot accurately identify the adaptation conflicts of the software on different platforms. For example, the functional abnormality of the software on a certain platform may be caused by the mismatch between the specific hardware configuration of the platform and the resource calling mode of the software, but the traditional method is difficult to find the above deep correlation, so it cannot provide effective adaptation adjustment scheme, and the compatibility and stability of the software on different platforms cannot be guaranteed. SUMMARY

[0005] In view of the above-mentioned problems, in combination with the first aspect of the present application, the embodiments of the present application provide a notebook software adaptation test method applied to multi-platform compatibility, which comprises:

[0006] Obtaining a running state data set of the notebook software to be tested on a target test platform, and obtaining a platform characteristic data set of the target test platform, the running state data set containing functional response data and resource occupation data in the software running process, and the platform characteristic data set containing hardware configuration data and system environment data of the platform;

[0007] Correlation mapping is performed on the operation state data set and the platform characteristic data set, a corresponding relationship between each data item in the operation state data set and each data item in the platform characteristic data set is established, and a correlation mapping result of operation state and platform characteristics is obtained;

[0008] Based on the correlation mapping result and a preset multi-platform adaptation rule set, an adaptation conflict feature of the notebook software to be tested on the target test platform is extracted, an associated item in the correlation mapping result that does not conform to the multi-platform adaptation rule set is identified, and an adaptation conflict feature set is obtained;

[0009] Each adaptation conflict feature in the adaptation conflict feature set is matched based on a preset platform adaptation strategy library, an adaptation adjustment scheme corresponding to each adaptation conflict feature is searched, and a candidate adaptation adjustment scheme set is obtained;

[0010] The candidate adaptation adjustment scheme set is integrated, repeated adaptation adjustment contents are removed, a software platform adaptation adjustment strategy for the target test platform is generated, and the software platform adaptation adjustment strategy includes an adjustment scheme for a software function module and an adjustment scheme for a software resource calling mode.

[0011] In still another aspect, the embodiments of the present application also provide a notebook software adaptation test system applied to multi-platform compatibility, which comprises a processor and a machine readable storage medium, the machine readable storage medium is connected with the processor, the machine readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the machine readable storage medium to realize the above method.

[0012] Based on the above aspects, the embodiment of the application obtains a running state data set of the notebook software to be tested on a target test platform and a platform characteristic data set of the target test platform, covers functional response data, resource occupation data in the software running process, and hardware configuration data and system environment data of the platform, performs associated mapping processing on the running state data set and the platform characteristic data set, establishes a corresponding relationship therebetween, can deeply mine the internal relationship between the software running state and the platform characteristic, and thus more accurately identifies the adaptation conflict features of the software on different platforms, avoids the missing of associated information caused by independent analysis in the traditional method. The adaptation conflict features are extracted based on the associated mapping result and a preset multi-platform adaptation rule set, and a corresponding adaptation adjustment scheme is found in combination with a preset platform adaptation strategy library, and finally a software platform adaptation adjustment strategy for the target test platform is generated, a complete process from problem identification to solution generation is realized, and thus personalized adaptation adjustment schemes can be provided for the software according to the characteristics of different platforms, the compatibility and stability of the notebook software on multiple platforms are effectively improved, and the experience of users when using the software on different platforms is improved. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 is an execution flow schematic diagram of the notebook software adaptation test method for multi-platform compatibility provided by the embodiment of the application.

[0014] Figure 2 is a schematic diagram of exemplary hardware and software components of the notebook software adaptation test system for multi-platform compatibility provided by the embodiment of the application. DETAILED DESCRIPTION

[0015] The application will be specifically described below in combination with the drawings of the specification, Figure 1 is a flow schematic diagram of the notebook software adaptation test method for multi-platform compatibility provided by an embodiment of the application, and the notebook software adaptation test method for multi-platform compatibility will be described in detail below.

[0016] Step S110: Obtain a running state data set of the notebook software to be tested on a target test platform, and simultaneously obtain a platform characteristic data set of the target test platform, the running state data set containing functional response data and resource occupation data in the software running process, and the platform characteristic data set containing hardware configuration data and system environment data of the platform.

[0017] The core purpose of this step is to comprehensively collect data related to the notebook software to be tested and the target test platform. The functional response data in the running state data set can reflect the response condition of the software when performing various operations, such as the rendering speed of the software interface, the response time of user interaction operations, and the completion efficiency of data processing tasks. The resource occupation data reflects the use of system resources during software running, such as the processor occupation ratio, memory usage, and storage device read-write frequency. The hardware configuration data in the platform characteristic data set covers the hardware composition and performance indicators of the target test platform, such as processor model, memory capacity, storage device type, etc. The system environment data includes operating system version information, driver specific conditions, and background running process state, etc.

[0018] Step S111: Perform scene-based data collection on the running process of the notebook software to be tested on the target test platform, divide the collection scenes according to the functional modules of the software, and each collection scene corresponds to the collection of functional response related data and resource occupation related data generated when the functional module runs, forming a scene-based initial running data set.

[0019] In order to more accurately know the state of the software when running different functional modules, scene-based data collection is needed. According to the functional modules of the software, the collection scenes are divided, which can ensure that each scene focuses on a specific functional module. For example, for a notebook software with data management, document editing and graphics processing functional modules, the collection scenes can be divided into data management scene, document editing scene and graphics processing scene. In the data management scene, the functional response data of the module when performing data query, storage and update operations, such as query response time, data update completion time, etc., and the resource occupation data, such as processor occupation during data management operation, memory usage, etc. In the document editing scene, the response data of user input text, format setting, etc. operations, and the resource occupation during editing process are collected. In the graphics processing scene, the response data and resource occupation data of graphics rendering, image editing, etc. operations are collected. The data collected by the above method constitutes a scene-based initial running data set.

[0020] Step S112: Obtain the running data benchmark values of multiple standard platforms, compare the scene-based initial running data set with the running data benchmark values of the corresponding standard platforms, correct the deviation data in the scene-based initial running data set caused by platform hardware differences, and obtain the calibrated scene-based running data set.

[0021] Due to the difference in hardware configuration of different platforms, the scenario-based initial running data set may have deviation. In order to eliminate these deviations, it is necessary to obtain the running data benchmark values of multiple standard platforms. The running data benchmark values of the standard platform are the reference values of various data when the software is normally running under a specific hardware configuration and system environment. Comparing the scenario-based initial running data set with the running data benchmark values of the corresponding standard platform can find the deviation data caused by the difference in platform hardware. For example, if the processor performance of the standard platform is strong, the data processing response time of the software on the platform is short, and the processor performance of the target test platform is weak, the data processing response time in the scenario-based initial running data set may be longer. By comparing with the benchmark values of the standard platform, the degree of deviation can be determined, and the data can be corrected, so as to obtain the calibrated scenario-based running data set, and improve the accuracy and comparability of the data.

[0022] Step S113: According to the function module, the function response related data of each scene in the calibrated scenario-based running data set is classified into the function response data of the corresponding module, and the resource occupation related data is classified into the resource occupation data of the corresponding module, to form a running state data set.

[0023] After obtaining the calibrated scenario-based running data set, the data needs to be sorted and classified. According to the function module, the function response related data of each scene is classified into the function response data of the corresponding module. For example, the data query response time and data update completion time of the data management scene are classified into the function response data of the data management module. The resource occupation related data is classified into the resource occupation data of the corresponding module, such as the processor occupation and memory usage in the data management scene are classified into the resource occupation data of the data management module. Through the above classification, the function response data and resource occupation data of each function module are combined to form a complete running state data set.

[0024] Step S114: According to the hardware core degree, the hardware of the target test platform is divided into a core hardware layer and an auxiliary hardware layer, and the parameter information of the core hardware layer and the auxiliary hardware layer is collected respectively to form layered hardware configuration data. The core hardware layer includes processor, memory and storage device, and the auxiliary hardware layer includes interface device and display device.

[0025] In order to more clearly describe the hardware characteristics of the target test platform, it is necessary to divide the hardware according to the hardware core degree. The core hardware layer includes processors, memories and storage devices, which play a key role in the overall performance of the system. The auxiliary hardware layer includes interface devices and display devices, etc., which provide support for the interaction between the user and the system. The parameter information of the core hardware layer and the auxiliary hardware layer is collected respectively. For the core hardware layer, the parameters such as the model, core number and main frequency of the processor, the capacity, type and frequency of the memory, and the type, capacity and read-write speed of the storage device are collected. For the auxiliary hardware layer, the parameters such as the type, number and transmission rate of the interface device, and the resolution, refresh rate and color mode of the display device are collected. The above collected parameter information is combined together to form layered hardware configuration data.

[0026] Step S115: Real-time record the operating system version change, driver update and background process start-stop situation in the system running process of the target test platform, to form dynamic system environment data.

[0027] The system environment of the target test platform is dynamically changing, and the update of the operating system version, the upgrade of the driver and the start-stop of the background process will all affect the running of the software. Therefore, it is necessary to record these changes in real time. When the operating system is updated, the updated version number, update time and update content, etc. are recorded. When the driver is updated, the updated driver name, updated version, update time, etc. are recorded. For the start-stop situation of the background process, the name of the process, the start time, the stop time and the occupied system resources, etc. are recorded. The above real-time recorded information is integrated together to form dynamic system environment data.

[0028] Step S116: Combine the layered hardware configuration data with the dynamic system environment data, mark the collection time and hardware system level corresponding to each data item, to form a platform characteristic data set.

[0029] The layered hardware configuration data and the dynamic system environment data are combined, and the collection time and hardware system level corresponding to each data item are marked. For example, for the parameter information of the processor, the specific time of collecting the parameter and the fact that the processor belongs to the core hardware layer are marked. For the information of the operating system version update, the update time and the fact that the information belongs to the system environment data are marked. In this way, a platform characteristic data set containing hardware configuration and system environment information, and having time and level marking, is formed.

[0030] Step S120: Perform association mapping processing on the running state data set and the platform characteristic data set, to establish the corresponding relationship between each data item in the running state data set and each data item in the platform characteristic data set, to obtain the association mapping result of the running state and the platform characteristics.

[0031] The purpose of this step is to find the internal relationship between the running state data set and the platform characteristic data set, so as to analyze the mutual influence between the software running state and the platform characteristics in the subsequent analysis. Through the association mapping process, the corresponding relationship between the functional response and resource occupation of the software and the hardware configuration and system environment of the platform can be determined.

[0032] Step S121: For the functional response data in the running state data set, mark the response scene, response type, and response time consumption attribute; for the resource occupation data, mark the occupied hardware type, occupation time period, and occupation peak attribute.

[0033] In order to more accurately perform association mapping, attribute marking is required for the data in the running state data set. For functional response data, marking the response scene can determine which functional scenario the response is generated in, such as data management scenario, document editing scenario, or graphics processing scenario. The response type can distinguish different types of responses, such as interface rendering response, interactive operation response, and data processing response. The response time consumption attribute records the time taken from the start to the end of the response. For resource occupation data, marking the occupied hardware type can determine which hardware device is occupied, such as processor, memory, storage device, etc. The occupation time period marks the time period during which resource occupation occurs, and the occupation peak attribute records the maximum value of resource occupation during that time period.

[0034] Step S122: For the hardware configuration data in the platform characteristic data set, mark the hardware model, hardware performance level, and hardware compatibility range attribute; for the system environment data, mark the system version compatibility, driver support type, and background process resource demand attribute.

[0035] Similarly, attribute marking is also required for the data in the platform characteristic data set. For hardware configuration data, marking the hardware model can determine the specific specifications of the hardware, the hardware performance level can evaluate the performance level of the hardware, and the hardware compatibility range attribute can describe the compatibility of the hardware with different software or other hardware. For system environment data, marking the system version compatibility can determine the support degree of the operating system version for the software, the driver support type can indicate the hardware type and function supported by the driver program, and the background process resource demand attribute can record the system resources required by the background process when running.

[0036] Step S123: According to the hardware performance level and system version compatibility of the target test platform, construct a dynamic mapping dimension adjustment mechanism, determine the initial mapping dimension weight, and allocate the resource occupation mapping dimension weight of the core hardware according to the hardware core degree adaptation requirement, which is different from the allocation proportion of the auxiliary hardware. The functional response mapping dimension weight corresponding to different versions of the system is allocated according to the system version compatibility adaptation requirement.

[0037] Since the hardware performance and system version of the target test platform will affect the adaptation relationship between the software and the platform, it is necessary to build a dynamic mapping dimension adjustment mechanism. According to the hardware performance level and system version compatibility, the initial mapping dimension weight is determined. For core hardware such as processor, memory and storage device, the allocation proportion of the resource occupation mapping dimension weight will be set according to the adaptation needs of the hardware core degree. Because the core hardware has a greater impact on the system performance, its weight allocation will be different from that of the auxiliary hardware. For different versions of the system, the function response mapping dimension weight will be allocated according to the adaptation needs of the system version compatibility, so as to ensure more accurate correlation mapping under different system versions.

[0038] Step S124: Based on the initial mapping dimension weight, determine the first mapping dimension of the function response data and the system environment data, which includes the corresponding relationship between the response scene and the system version compatibility, the corresponding relationship between the response type and the driver support type, and the corresponding relationship between the response time consumption and the background process resource demand.

[0039] After determining the initial mapping dimension weight, the first mapping dimension of the function response data and the system environment data is further determined. The corresponding relationship between the response scene and the system version compatibility can determine whether the specific function scene of the software can normally run under different system versions. The corresponding relationship between the response type and the driver support type can determine whether the driver program can support various response types of the software. The corresponding relationship between the response time consumption and the background process resource demand can analyze the influence of the background process on the software response time.

[0040] Step S125: Determine the second mapping dimension of the resource occupation data and the hardware configuration data, which includes the corresponding relationship between the occupied hardware type and the hardware model, the corresponding relationship between the occupation time period and the hardware performance level, and the corresponding relationship between the occupation peak value and the hardware compatibility range.

[0041] At the same time, the second mapping dimension of the resource occupation data and the hardware configuration data is determined. The corresponding relationship between the occupied hardware type and the hardware model can clearly show the resource occupation of the software to different hardware models. The corresponding relationship between the occupation time period and the hardware performance level can analyze the resource occupation of the software in different time periods under different hardware performance levels. The corresponding relationship between the occupation peak value and the hardware compatibility range can determine whether the resource occupation peak value of the software is within the compatible range of the hardware.

[0042] Step S126: Based on the first mapping dimension, combined with the mapping dimension weight, the association strength of each attribute of the function response data and the corresponding attribute of the system environment data is determined by combining the attribute matching degree and the corresponding weight proportion, to obtain the association degree of the interface rendering response data and the operating system version data, the association degree of the interactive operation response data and the driver program version data, and the association degree of the data processing response data and the background process data.

[0043] Step S1261: The response scene, response type, and response time attribute of the interface rendering response data are extracted, and the system version compatibility, driver support type, and background process resource demand attribute of the operating system version data are extracted.

[0044] In order to determine the association degree of the interface rendering response data and the operating system version data, the relevant attributes need to be extracted. The response scene is extracted from the interface rendering response data to know that the rendering response occurs in which specific function scene of the software, such as in the document browsing scene, the graphic display scene, etc. The response type is extracted to determine whether it is ordinary interface rendering, dynamic interface rendering, or complex graphic interface rendering, etc. The response time attribute is extracted to record the time spent from the start to the completion of the interface rendering. The system version compatibility attribute is extracted from the operating system version data to determine whether the operating system version supports the interface rendering function of the software. The driver support type attribute is extracted to determine whether the driver program can provide necessary support for the interface rendering. The background process resource demand attribute is extracted to know the demand of the background process for system resources during the interface rendering.

[0045] Step S1262: According to the weight determined by the dynamic mapping dimension adjustment mechanism, the weight proportion of each of the system version compatibility, the driver support type, and the background process resource demand is allocated.

[0046] According to the weight determined by the previously constructed dynamic mapping dimension adjustment mechanism, the corresponding weight proportion of the system version compatibility, the driver support type, and the background process resource demand is allocated. The above weight proportion will be adjusted according to the hardware performance level and the system version compatibility, etc. to ensure that the influence of each attribute on the association degree can be accurately reflected under different conditions.

[0047] Step S1263: The attribute matching degree of the response scene and the system version compatibility is determined according to the support degree of the system version for the response scene.

[0048] By evaluating the support of the system version for the response scene, the attribute matching degree of the response scene and the system version compatibility is determined. If the system version can well support the interface rendering function of the software in a specific response scene, the attribute matching degree is higher; otherwise, if the system version has compatibility problems, leading to abnormal interface rendering, the attribute matching degree is lower.

[0049] Step S1264: Determine the attribute matching degree of the response type and the drive support type according to the support degree of the response type to the drive type.

[0050] Similarly, according to the support of the response type to the drive type, the attribute matching degree of the response type and the drive support type is determined. If the driver can support the interface rendering of the specific response type of the software, the attribute matching degree is high; if the driver has functional limitations and cannot support the rendering of some response types, the attribute matching degree is low.

[0051] Step S1265: Determine the attribute matching degree of the response time and the background process resource demand according to whether the response time is within the reasonable range corresponding to the background process resource demand.

[0052] Determine whether the response time of the interface rendering is within the reasonable range corresponding to the background process resource demand. If the response time is too long and exceeds the range allowed by the normal operation of the background process, it indicates that the background process may have a greater impact on the interface rendering, and the attribute matching degree is low; if the response time is within the reasonable range, it indicates that the background process has less impact on the interface rendering, and the attribute matching degree is high.

[0053] Step S1266: Determine the association strength by combining each attribute matching degree and the corresponding weight proportion, and the association strength is expressed in percentage, that is, the association degree of the interface rendering response data and the operating system version data.

[0054] The attribute matching degree of the response scene and the system version compatibility, the attribute matching degree of the response type and the drive support type, and the attribute matching degree of the response time and the background process resource demand are combined and calculated with the corresponding weight proportion, and the result expressed in percentage is the association degree of the interface rendering response data and the operating system version data.

[0055] Step S1267: Extract the operation type, operation frequency, and operation response delay attributes of the interactive operation response data, extract the input device support type, operation response optimization level, and delay control parameter attributes of the driver program version data, and determine the weight according to the dynamic mapping dimension adjustment mechanism.

[0056] For interactive operation response data, the operation type extraction can distinguish different types of interactive operations, such as mouse click operation, keyboard input operation, touch screen operation, etc. The operation frequency records the frequency of user interaction. The operation response delay attribute records the time delay from user operation to system response. For driver version data, the input device support type extraction can determine the input device type supported by the driver, such as mouse, keyboard, touch screen, etc. The operation response optimization level describes the optimization degree of the driver to the interactive operation response. The delay control parameter attribute records the control ability of the driver to the operation response delay. According to the dynamic mapping dimension adjustment mechanism, the weights of these attributes are determined.

[0057] Step S1268: According to the support degree of input device support type to operation type, the adaptation degree of operation response optimization level to operation frequency, and the regulation degree of delay control parameter to operation response delay, the attribute matching degrees are determined respectively.

[0058] According to the support of input device support type to operation type, the attribute matching degree of operation type and input device support type is determined. If the input device type supported by the driver can well meet the operation type demand of the software, the attribute matching degree is high. According to the adaptation of operation response optimization level to operation frequency, the attribute matching degree of operation frequency and operation response optimization level is determined. If the operation response optimization level can adapt to higher operation frequency and ensure the timeliness of operation response, the attribute matching degree is high. According to the regulation of delay control parameter to operation response delay, the attribute matching degree of operation response delay and delay control parameter is determined. If the delay control parameter can effectively reduce the operation response delay, the attribute matching degree is high.

[0059] Step S1269: The association strength is determined by the combination of attribute matching degree and corresponding weight proportion, that is, the association degree of interactive operation response data and driver version data.

[0060] The attribute matching degrees of operation type and input device support type, operation frequency and operation response optimization level, and operation response delay and delay control parameter are combined with the corresponding weight proportions respectively. The result is the association degree of interactive operation response data and driver version data.

[0061] Step S12610: The processing data volume, processing logic complexity, and processing time attributes of data processing response data are extracted, and the process resource occupation, process priority, and process concurrency attributes of background process data are extracted, and the weights are determined according to the dynamic mapping dimension adjustment mechanism.

[0062] For data processing response data, the processing data volume can know the size of the data processing task, the processing logic complexity can describe the complexity of the data processing task, and the processing time consumption attribute records the time spent from the beginning to the completion of the data processing task. For background process data, the process resource occupation volume can know the system resource occupation of the background process when running, the process priority can determine the priority of the background process in the system, and the process concurrency number can describe the number of simultaneously running background processes. According to the dynamic mapping dimension adjustment mechanism, the weights corresponding to these attributes are determined.

[0063] Step S12611: According to the support degree of process resource occupation volume to processing data volume, the adaptation degree of process priority to processing logic complexity, and the influence degree of process concurrency number to processing time consumption, the attribute matching degrees of each attribute are determined respectively.

[0064] According to the support degree of process resource occupation volume to processing data volume, the attribute matching degree of processing data volume and process resource occupation volume is determined. If the process resource occupation volume can meet the demand of the data processing task, the attribute matching degree is high. According to the adaptation degree of process priority to processing logic complexity, the attribute matching degree of processing logic complexity and process priority is determined. If the process priority can ensure that the complex data processing task is processed in time, the attribute matching degree is high. According to the influence degree of process concurrency number to processing time consumption, the attribute matching degree of processing time consumption and process concurrency number is determined. If the process concurrency number will not cause the data processing time consumption to be too long, the attribute matching degree is high.

[0065] Step S12612: The association strength, i.e., the association degree of data processing response data and background process data, is determined by combining each attribute matching degree and the corresponding weight proportion.

[0066] The attribute matching degrees of processing data volume and process resource occupation volume, the attribute matching degrees of processing logic complexity and process priority, and the attribute matching degrees of processing time consumption and process concurrency number are combined with the corresponding weight proportions respectively. The result obtained is the association degree of data processing response data and background process data.

[0067] Step S12613: If any attribute data is missing, reasonable estimation is made based on the same type of data of the target test platform to ensure the integrity of the association strength determination.

[0068] When calculating the association strength, if some attribute data is missing, in order to ensure the integrity of the calculation result, reasonable estimation needs to be made based on the same type of data of the target test platform. For example, if the operation response delay attribute data is missing, the response delay data of other similar interactive operations on the test platform can be used for estimation.

[0069] Step S12614: Record the attribute matching degree, weight allocation, and estimated data source in each correlation degree determination process to form a correlation degree determination report as a supplement to the correlation mapping result.

[0070] To facilitate subsequent analysis and verification, detailed information in each correlation degree determination process needs to be recorded, including attribute matching degree, weight allocation, and estimated data source. The above information is sorted into a correlation degree determination report as a supplement to the correlation mapping result.

[0071] Step S127: Based on the second mapping dimension, the same correlation strength determination method is used to obtain the correlation degree of processor occupation data and processor parameter data, the correlation degree of memory occupation data and memory parameter data, and the correlation degree of storage read-write occupation data and storage device parameter data.

[0072] In the same way as determining the correlation degree of functional response data and system environment data, based on the second mapping dimension, the correlation strength of resource occupation data and hardware configuration data is calculated. For processor occupation data and processor parameter data, the attribute matching degrees are determined through the marked occupation hardware type, occupation time period, and occupation peak attribute, and the hardware model, hardware performance level, and hardware compatibility range attribute, and the correlation degree of processor occupation data and processor parameter data is calculated by combining the corresponding weight proportion. For memory occupation data and memory parameter data, and storage read-write occupation data and storage device parameter data, the correlation degree is calculated in the same way.

[0073] Step S128: According to the real-time running state of the target test platform, if any one hardware occupation rate is in the preset attention interval for a preset time, the resource occupation mapping dimension weight corresponding to the related hardware is adjusted, and the correlation degree is determined again.

[0074] During the real-time running of the target test platform, the hardware occupation rate is monitored. If it is found that the occupation rate of a certain hardware is in the preset attention interval for a preset time, it means that the use of this hardware may have an important impact on the adaptation of software and platform. At this time, the resource occupation mapping dimension weight corresponding to the related hardware needs to be adjusted, and the correlation degree is recalculated to more accurately reflect the adaptation relationship between software and platform.

[0075] Step S129: All the final obtained correlation degrees are combined with the corresponding correlation data item identifier, mapping dimension weight, and calculation time to form the correlation mapping result of running state and platform characteristics.

[0076] The association degrees of the interface rendering response data and the operating system version data, the association degrees of the interactive operation response data and the driver program version data, the association degrees of the data processing response data and the background process data, the association degrees of the processor occupation data and the processor parameter data, the association degrees of the memory occupation data and the memory parameter data, and the association degrees of the storage read-write occupation data and the storage device parameter data are combined with the corresponding association data item identifiers, mapping dimension weights, and calculation times respectively to form a complete association mapping result of the running state and the platform characteristics.

[0077] Step S130: Based on the association mapping result and the preset multi-platform adaptation rule set, an adaptation conflict feature of the notebook software to be tested on the target test platform is extracted, and an association item in the association mapping result that does not conform to the multi-platform adaptation rule set is identified to obtain an adaptation conflict feature set.

[0078] The purpose of this step is to find out the adaptation conflict of the software on the target test platform through the association mapping result and the preset multi-platform adaptation rule set. The preset multi-platform adaptation rule set provides a standard for judging the adaptability between the software and the platform.

[0079] Step S131: The preset multi-platform adaptation rule set is obtained, and the multi-platform adaptation rule set includes a basic adaptation rule and a dynamic adaptation rule. The basic adaptation rule is a fixed function response threshold range and a resource occupation threshold range. The dynamic adaptation rule is dynamically generated according to the platform hardware performance level and the system version. The resource occupation threshold range is set according to the adaptation demand of the hardware performance level, and the function response threshold range is set according to the adaptation demand of the system version.

[0080] The preset multi-platform adaptation rule set is composed of a basic adaptation rule and a dynamic adaptation rule. The basic adaptation rule includes a fixed function response threshold range and a resource occupation threshold range. These thresholds are reasonable limits for software function response and resource occupation under normal circumstances. The dynamic adaptation rule is dynamically generated according to the hardware performance level and the system version of the target test platform. For the resource occupation threshold range, the adaptation demand of the hardware performance level is set, and the hardware with higher performance may allow the software to have higher resource occupation. For the function response threshold range, the adaptation demand of the system version is set, and different versions of the system may have different requirements for the function response of the software.

[0081] Step S132: The multi-platform adaptation rule set is divided into a core rule layer and a secondary rule layer according to the degree of affecting the adaptation stability. The core rule layer includes a processor occupation threshold, a memory occupation threshold, and an interface rendering response threshold. The secondary rule layer includes a storage read-write occupation threshold, an interactive operation response threshold, and a data processing response threshold.

[0082] In order to more accurately identify conflicts, the multi-platform adaptation rule set is divided into a core rule layer and a secondary rule layer according to the degree of influence on adaptation stability. The processor occupation threshold, memory occupation threshold and interface rendering response threshold in the core rule layer have a greater influence on the adaptation stability of software and platforms. Because the processor and memory are core hardware resources of the system, and interface rendering response directly affects user experience. The storage read-write occupation threshold, interactive operation response threshold and data processing response threshold in the secondary rule layer also affect adaptability, but the degree of influence is relatively small compared to the core rule layer.

[0083] Step S133: Extract each correlation degree and the corresponding mapping dimension weight from the association mapping result, preferentially compare the threshold range of the core rule layer, identify the correlation items in the correlation degree of processor occupation data and processor parameter data, the correlation degree of memory occupation data and memory parameter data, and the correlation degree of interface rendering response data and operating system version data that exceed the corresponding threshold range of the core rule layer, and mark them as core candidate conflict association items. At the same time, record the amplitude of the correlation degree exceeding the corresponding threshold range, and set the marking priority according to the amplitude value range.

[0084] From the association mapping result, extract each correlation degree and the corresponding mapping dimension weight. First, compare the correlation degree of the core rule layer with the corresponding threshold range. For the correlation degree of processor occupation data and processor parameter data, the correlation degree of memory occupation data and memory parameter data, and the correlation degree of interface rendering response data and operating system version data, if it exceeds the corresponding threshold range of the core rule layer, mark the above-mentioned correlation items as core candidate conflict association items. At the same time, record the amplitude of the correlation degree exceeding the corresponding threshold range, and set the marking priority according to the amplitude value range. The greater the amplitude, the higher the priority.

[0085] Step S134: Compare the threshold range of the secondary rule layer, identify the correlation items in the correlation degree of storage read-write occupation data and storage device parameter data, the correlation degree of interactive operation response data and driver version data, and the correlation degree of data processing response data and background process data that exceed the corresponding threshold range of the secondary rule layer, and mark them as secondary candidate conflict association items. Also record the amplitude of the correlation degree exceeding the corresponding threshold range.

[0086] After completing the comparison of the core rule layer, compare the correlation degree of the secondary rule layer with the corresponding threshold range. For the correlation degree of storage read-write occupation data and storage device parameter data, the correlation degree of interactive operation response data and driver version data, and the correlation degree of data processing response data and background process data, if it exceeds the corresponding threshold range of the secondary rule layer, mark the above-mentioned correlation items as secondary candidate conflict association items, and record the amplitude of the correlation degree exceeding the corresponding threshold range.

[0087] Step S135: Cross-platform conflict association analysis is performed on the core candidate conflict association items and the secondary candidate conflict association items to find common conflict association items between different platforms, and the common conflict association items are marked as cross-platform common conflicts, and the non-common conflict association items are marked as platform-specific conflicts.

[0088] In order to distinguish the conflict situations between different platforms, cross-platform conflict association analysis is performed on the core candidate conflict association items and the secondary candidate conflict association items. By comparing the conflict association items on different platforms, common conflict association items are found, which are marked as cross-platform common conflicts. The above common conflicts may be caused by problems in the software itself or universal system compatibility problems. Non-common conflict association items are marked as platform-specific conflicts. These conflicts may be caused by the hardware configuration or system environment difference of a specific platform.

[0089] Step S136: The running state data features and platform characteristic data features corresponding to the core candidate conflict association items are extracted in priority order, and the running state data features and platform characteristic data features corresponding to the secondary candidate conflict association items are extracted, and the feature extraction of the cross-platform common conflict includes multi-platform feature comparison information.

[0090] Step S1361: The processor occupation data features corresponding to the processor occupation association item in the core candidate conflict association item are extracted, including the time variation curve of the processor occupation rate, the time node of the occurrence of the occupation peak value, the duration of the occupation peak value, and the occupation rate difference when different functional modules run.

[0091] For the processor occupation association item in the core candidate conflict association item, processor occupation data features are extracted. The time variation curve of the processor occupation rate can intuitively show the occupation of the processor at different times. The time node of the occurrence of the occupation peak value can determine the time when the processor is in high load. The duration of the occupation peak value can reflect the duration of the high load of the processor. The occupation rate difference when different functional modules run can analyze which functional modules have a larger demand for processor resources.

[0092] Step S1362: The processor parameter data features corresponding to the processor occupation association item are extracted, including the processor model, the core number, the frequency range, the cache capacity, and the instruction set type supported by the processor, and the differences between the parameters and the industry average level are marked.

[0093] The processor parameter data features corresponding to the processor occupation association item are extracted, including the processor model, the core number, the frequency range, the cache capacity, and the instruction set type supported by the processor. Marking the differences between these parameters and the industry average level can know the performance characteristics of the processor and the difference between the processor and other processors.

[0094] Step S1363: Extract the memory occupation data features corresponding to the memory occupation correlation item in the core candidate conflict correlation item, including the fluctuation amplitude of the memory occupation rate, the change of the memory read-write speed, the memory fragmentation rate, and the memory occupation change under different data processing tasks.

[0095] For the memory occupation correlation item in the core candidate conflict correlation item, the memory occupation data features are extracted. The fluctuation amplitude of the memory occupation rate can reflect the stability of memory usage. The change of the memory read-write speed can know the change of the memory performance. The memory fragmentation rate can evaluate the fragmentation degree of the memory, which affects the use efficiency of the memory. The memory occupation change under different data processing tasks can analyze which data processing tasks have greater demand for memory resources.

[0096] Step S1364: Extract the memory parameter data features corresponding to the memory occupation correlation item, including the memory capacity, the memory type, the memory frequency, the memory channel number, and the memory timing parameter, and analyze the influence of each parameter on the memory occupation characteristics.

[0097] The memory parameter data features corresponding to the memory occupation correlation item are extracted, including the memory capacity, the memory type, the memory frequency, the memory channel number, and the memory timing parameter. The influence of these parameters on the memory occupation characteristics is analyzed, for example, the memory capacity size may affect the upper limit of the memory occupation rate, and the memory frequency may affect the memory read-write speed.

[0098] Step S1365: Extract the interface rendering response data features corresponding to the interface rendering response correlation item in the core candidate conflict correlation item, including the change of the interface rendering frame rate, the distribution of the rendering delay, the rendering effect difference under different resolutions, and the frequency of rendering abnormalities.

[0099] For the interface rendering response correlation item in the core candidate conflict correlation item, the interface rendering response data features are extracted. The change of the interface rendering frame rate can reflect the smoothness of the interface rendering. The distribution of the rendering delay can know the concentrated area of the rendering delay. The rendering effect difference under different resolutions can analyze the interface rendering adaptation of the software under different resolutions. The frequency of rendering abnormalities can evaluate the stability of the interface rendering.

[0100] Step S1366: Extract the operating system version data features corresponding to the interface rendering response correlation item, including the kernel version of the operating system, the graphic interface version, the rendering engine type, the support degree for specific resolution display, and the rendering feature difference under different operating system versions.

[0101] The operation system version data features corresponding to the response correlation item of the extraction interface rendering are extracted, including the kernel version of the operation system, the graphic interface version, the rendering engine type and the support degree for specific resolution display. By comparing the rendering feature differences under different operation system versions, the influence factors of the operation system version on the interface rendering can be found out.

[0102] Step S1367: Extracting storage read-write occupation data features corresponding to the storage read-write occupation correlation item in the secondary candidate conflict correlation item, including the average level of storage read-write speed and the main distribution interval of read-write delay.

[0103] For the storage read-write occupation correlation item in the secondary candidate conflict correlation item, storage read-write occupation data features are extracted. The average level of storage read-write speed can reflect the read-write performance of the storage device. The main distribution interval of read-write delay can know the delay situation of the storage read-write operation.

[0104] Step S1368: Extracting storage device parameter data features corresponding to the storage read-write occupation correlation item, including the type and interface specification of the storage device.

[0105] The storage device parameter data features corresponding to the storage read-write occupation correlation item are extracted, including the type and interface specification of the storage device. The read-write performance and compatibility of storage devices of different types and interface specifications can be different.

[0106] Step S1369: Extracting corresponding data features of the cross-platform common conflict correlation item on the target test platform, and collecting corresponding data features of other platforms that appear corresponding conflicts, including hardware configuration parameters, system version information and conflict scene definition of each platform.

[0107] For the cross-platform common conflict correlation item, not only the corresponding data features on the target test platform are extracted, but also the corresponding data features of other platforms that appear corresponding conflicts are collected. The above data features include hardware configuration parameters, system version information and conflict scene definition of each platform, so as to compare the multi-platform features.

[0108] Step S13610: Comparing and analyzing the multi-platform features of the cross-platform common conflict, finding out the common features and platform-specific features.

[0109] By comparing and analyzing the multi-platform features of the cross-platform common conflict, the common features and platform-specific features between different platforms are found out. The common features can be the root cause of the cross-platform common conflict, and the platform-specific features can help to know the influence degree and manifestation form of different platforms on the conflict.

[0110] Step S13611: classify the extracted running state data features and platform characteristic data features according to the conflict level, mark the core candidate conflict features with core identifiers, mark the cross-platform common conflict features with cross-platform identifiers and the number of comparison platforms, and form a feature extraction list.

[0111] The extracted running state data features and platform characteristic data features are classified and arranged according to the conflict level. For the features of the core candidate conflict, the core identifier is marked to distinguish the core conflict from the secondary conflict. For the features of the cross-platform common conflict, the cross-platform identifier and the number of comparison platforms are marked to form a complete feature extraction list.

[0112] Step S137: build a conflict feature causal chain for each candidate conflict association item and determine the causal relationship between the running state data features and the platform characteristic data features.

[0113] In order to further understand the causes of the adaptation conflict, a conflict feature causal chain is built for each candidate conflict association item. By analyzing the relationship between the running state data features and the platform characteristic data features, the causal relationship between them is determined. For example, if it is found that the high processor occupancy is due to insufficient processor performance, a causal chain between the processor occupancy data feature and the processor parameter data feature can be established.

[0114] Step S138: combine the conflict feature causal chain, conflict level, and cross-platform marker of each candidate conflict association item to form an adaptation conflict feature, integrate all adaptation conflict features, remove duplicate cross-platform common conflict features, and obtain an adaptation conflict feature set.

[0115] The conflict feature causal chain, conflict level, and cross-platform marker of each candidate conflict association item are combined to form an adaptation conflict feature. All adaptation conflict features are integrated, and duplicate cross-platform common conflict features are removed to obtain a final adaptation conflict feature set.

[0116] Step S140: combine the preset platform adaptation strategy library to match each adaptation conflict feature in the adaptation conflict feature set, find the corresponding adaptation adjustment scheme for each adaptation conflict feature, and obtain a candidate adaptation adjustment scheme set.

[0117] After obtaining the adaptation conflict feature set, each adaptation conflict feature needs to be matched with the preset platform adaptation strategy library to find a suitable adaptation adjustment scheme. The preset platform adaptation strategy library stores adjustment schemes for different types of conflicts, and through matching processing, the method to solve the conflict can be quickly found.

[0118] Step S141: Obtain a preset platform adaptation strategy library, which stores adaptation adjustment schemes according to conflict levels and conflict types. The core conflict corresponds to a core adjustment scheme, the secondary conflict corresponds to a secondary adjustment scheme, the cross-platform common conflict corresponds to a general adjustment scheme, and the platform-specific conflict corresponds to a customized adjustment scheme.

[0119] The preset platform adaptation strategy library is obtained, which is classified and stored according to conflict levels and conflict types. The core conflict corresponds to a core adjustment scheme, which is usually formulated for a conflict that has a greater impact on system adaptation stability, has a higher priority and stronger adjustment strength. The secondary conflict corresponds to a secondary adjustment scheme, which has relatively small adjustment strength. The cross-platform common conflict corresponds to a general adjustment scheme, which can be applied to the same conflict that occurs on multiple platforms. The platform-specific conflict corresponds to a customized adjustment scheme, which is formulated according to the hardware configuration and system environment of a specific platform.

[0120] Step S142: Add an effect evaluation tag to each adaptation adjustment scheme in the platform adaptation strategy library, which includes a historical adaptation success rate, an adaptation after stability improvement period, and an adaptation operation complexity. The historical adaptation success rate is based on the statistical analysis of past adaptation cases with the same conflict characteristics. The adaptation after stability improvement period is the time required for the software to reach stable operation after adaptation. The adaptation operation complexity is determined based on the number of software modules and the code level required to be modified.

[0121] In order to better evaluate the effect of each adaptation adjustment scheme, an effect evaluation tag is added to each adaptation adjustment scheme in the platform adaptation strategy library. The historical adaptation success rate is calculated based on the statistical analysis of past adaptation cases with the same conflict characteristics, reflecting the success probability of the adjustment scheme in solving similar conflicts. The adaptation after stability improvement period records the time required for the software to reach stable operation after adaptation, and the shorter the time, the better the effect of the adjustment scheme. The adaptation operation complexity is determined according to the number of software modules and the code level required to be modified, and the lower the complexity, the easier the implementation of the adjustment scheme.

[0122] Step S143: Select an adaptation conflict feature from the adaptation conflict feature set, and filter the corresponding candidate adaptation adjustment scheme in the platform adaptation strategy library according to its conflict level, conflict type, and cross-platform marker. The core conflict only filters the core adjustment scheme, and the cross-platform common conflict preferentially filters the general adjustment scheme.

[0123] From the set of adaptation conflict features, select one adaptation conflict feature in turn, and according to its conflict level, conflict type and cross-platform label, filter the corresponding candidate adaptation adjustment scheme in the platform adaptation strategy library. For core conflicts, only filter core adjustment schemes to ensure effective solution of core conflicts. For cross-platform common conflicts, prefer to filter general adjustment schemes, because general adjustment schemes have more extensive applicability.

[0124] Step S144: Simulate the running state after implementing the filtered candidate adaptation adjustment scheme on the target test platform, predict the changes of functional response data and resource occupation data of the software, and determine the prediction compliance degree of the prediction data with the set of multi-platform adaptation rules.

[0125] Step S1441: Build a platform hardware parameter model based on the hierarchical hardware configuration data, build a system environment model based on the dynamic system environment data, build a software running model based on the functional module structure and running logic of the software to be tested, and combine to form a running simulation model of the target test platform.

[0126] In order to simulate the running state after implementing the candidate adaptation adjustment scheme on the target test platform, it is necessary to build a running simulation model. Based on the hierarchical hardware configuration data, a platform hardware parameter model is built, which can simulate the hardware performance and resource limitations of the target test platform. Based on the dynamic system environment data, a system environment model is built, which can simulate the influence of system environment factors such as operating system, driver and background process on software running. Based on the functional module structure and running logic of the software to be tested, a software running model is built, which can simulate the state of the software when running in different functional modules. Combine the three models together to form a running simulation model of the target test platform.

[0127] Step S1442: Convert the adjustment content of the candidate adaptation adjustment scheme into parameter adjustment instructions of the simulation model.

[0128] Convert the adjustment content of the candidate adaptation adjustment scheme into parameter adjustment instructions that can be recognized by the simulation model. For example, if the adjustment scheme is to optimize the processor resource call, then the specific parameters of the optimization are converted into parameter adjustment instructions in the platform hardware parameter model.

[0129] Step S1443: Load the running scenario data of the software to be tested from the set of scenario-based initial running data in the simulation model, including the running load, running time length and interactive operation sequence of different functional modules.

[0130] Load the software running scene data in the scenarioized initial running data set to be tested in the simulation model, which contains the running load, running time length and interactive operation sequence of different functional modules. By loading these data, the actual running process of the software can be simulated more realistically.

[0131] Step S1444: Run the simulation model and implement the parameter adjustment instruction to record the software function response data and resource occupation data in real time during the simulation running process at the actual data acquisition frequency.

[0132] Run the simulation model and implement the parameter adjustment instruction to simulate the running state after the candidate adaptation adjustment scheme is implemented on the target test platform. According to the actual data acquisition frequency, the software function response data and resource occupation data such as interface rendering response time, processor occupation rate, etc. during the simulation running process are recorded in real time.

[0133] Step S1445: Extract the interface rendering response time, interactive operation response time and data processing response time after the simulation running, and compare them with the function response threshold range in the multi-platform adaptation rule set to determine the data proportion that meets the threshold range.

[0134] Extract the interface rendering response time, interactive operation response time and data processing response time from the simulation running recorded data, compare the above data with the function response threshold range in the multi-platform adaptation rule set, and calculate the data proportion that meets the threshold range. The higher the data proportion, the more the function response after the simulation running meets the adaptation rule.

[0135] Step S1446: Extract the processor occupation rate, memory occupation rate and storage read-write occupation rate after the simulation running, and compare them with the resource occupation threshold range in the multi-platform adaptation rule set to determine the data proportion that meets the resource occupation threshold range.

[0136] Similarly, extract the processor occupation rate, memory occupation rate and storage read-write occupation rate after the simulation running, and compare them with the resource occupation threshold range in the multi-platform adaptation rule set to calculate the data proportion that meets the resource occupation threshold range. The higher the data proportion, the more the resource occupation after the simulation running meets the adaptation rule.

[0137] Step S1447: Calculate the function response compliance proportion and resource occupation compliance proportion respectively, and calculate the weighted compliance degree according to the importance weight of the function response and resource occupation in the multi-platform adaptation rule set, as the compliance degree of the prediction data with the multi-platform adaptation rule set.

[0138] The function response compliance proportion and the resource occupation compliance proportion are calculated respectively, and the two proportions are weighted calculated according to the importance weights of the function response and the resource occupation in the multi-platform adaptation rule set, and the result is the compliance degree of the prediction data and the multi-platform adaptation rule set.

[0139] Step S145: The historical adaptation success rate, the prediction compliance degree and the adaptation operation complexity are normalized into score indexes of the same dimension respectively, and the normalized score indexes are weighted calculated according to preset weights to obtain a comprehensive score of the candidate adaptation adjustment scheme.

[0140] In order to comprehensively evaluate the pros and cons of the candidate adaptation adjustment scheme, the historical adaptation success rate, the prediction compliance degree and the adaptation operation complexity are normalized into score indexes of the same dimension respectively. Then, the normalized score indexes are weighted calculated according to preset weights to obtain a comprehensive score of the candidate adaptation adjustment scheme.

[0141] Step S146: The preset number of adaptation adjustment schemes ranked in the front according to the set standard are selected as the target adaptation adjustment schemes corresponding to the adaptation conflict features.

[0142] According to the comprehensive score, the candidate adaptation adjustment schemes are ranked according to the set standard, and the preset number of adaptation adjustment schemes ranked in the front are selected as the target adaptation adjustment schemes corresponding to the adaptation conflict features.

[0143] Step S147: The target adaptation adjustment scheme is associated with the conflict feature causal chain of the adaptation conflict feature, so that the adjustment scheme can solve the core problem in the causal chain.

[0144] The target adaptation adjustment scheme is associated with the conflict feature causal chain of the adaptation conflict feature, so that the adjustment scheme can solve the core problem in the causal chain. For example, if the conflict feature causal chain indicates that the high processor occupancy rate is caused by insufficient processor performance, then the adjustment scheme should be designed to improve the processor performance or optimize the processor resource usage.

[0145] Step S148: Repeat the above steps until all adaptation conflict features in the adaptation conflict feature set correspond to target adaptation adjustment schemes.

[0146] The steps of selecting the adaptation conflict features, screening the candidate adaptation adjustment schemes, calculating the comprehensive score, selecting the target adaptation adjustment schemes and associating the causal chains are repeated until all adaptation conflict features in the adaptation conflict feature set have corresponding target adaptation adjustment schemes.

[0147] Step S149: integrate all target adaptation adjustment schemes, mark the corresponding adaptation conflict feature identifier, comprehensive score, and pre-evaluation result of each target adaptation adjustment scheme, and form a candidate adaptation adjustment scheme set.

[0148] Integrate all target adaptation adjustment schemes, and mark the corresponding adaptation conflict feature identifier, comprehensive score, and pre-evaluation result of each target adaptation adjustment scheme to form a candidate adaptation adjustment scheme set.

[0149] Step S150: integrate the candidate adaptation adjustment scheme set, remove duplicate adaptation adjustment contents, and generate a software platform adaptation adjustment strategy for the target test platform. The software platform adaptation adjustment strategy includes adjustment schemes for software function modules and adjustment schemes for software resource calling modes.

[0150] After obtaining the candidate adaptation adjustment scheme set, it needs to be integrated and optimized to generate a software platform adaptation adjustment strategy for the target test platform.

[0151] Step S151: decompose each target adaptation adjustment scheme in the candidate adaptation adjustment scheme set into hardware-related adjustment content, system-related adjustment content, and software module adjustment content according to the adjustment object. The hardware-related adjustment content is for processor and memory hardware resource calling, the system-related adjustment content is for operating system and driver parameter configuration, and the software module adjustment content is for software function module logic optimization.

[0152] Decompose each target adaptation adjustment scheme in the candidate adaptation adjustment scheme set according to the adjustment object. Hardware-related adjustment content mainly involves calling optimization of hardware resources such as processors and memories, for example, adjusting the scheduling strategy of the processor and optimizing the allocation method of the memory. System-related adjustment content mainly adjusts the parameter configuration of operating systems and drivers, for example, updating the driver version and adjusting the resource management parameters of the operating system. Software module adjustment content mainly optimizes the logic of software function modules, for example, optimizing the algorithm of the data processing module and improving the code of the interface rendering module.

[0153] Step S152: establish an association matrix of adjustment content to record the association relationship between different target adaptation adjustment schemes. Processor resource calling optimization and data processing module logic optimization have a cooperative relationship, and memory resource calling optimization and background process management adjustment have a dependent relationship.

[0154] To better integrate the adjustment content, an association matrix of the adjustment content is established. The association matrix records the association relationship between the adjustment contents in different target adaptation adjustment schemes. For example, the processor resource call optimization and the data processing module logic optimization may have a synergistic relationship, because optimizing the logic of the data processing module can reduce the burden of the processor, thereby better achieving processor resource call optimization. The memory resource call optimization and the background process management adjustment may have a dependent relationship, because reasonable management of background processes can reduce the occupation of memory and help achieve memory resource call optimization.

[0155] Step S153: based on the association matrix, the adjustment contents of the same adjustment object are merged, and the synergistic relationship adjustment content is integrated, and the memory resource call optimization is merged. The associated background process management adjustment content is integrated.

[0156] For example, step S1531: extract the adjustment content associated with the memory resource call optimization from the adjustment content association matrix, and determine the background process management adjustment content as the associated adjustment content.

[0157] The adjustment content associated with the memory resource call optimization is found from the adjustment content association matrix, and the background process management adjustment content is determined as the associated adjustment content. Because the running of the background process will occupy the memory resource, the management adjustment of the background process can affect the use of the memory resource.

[0158] Step S1532: obtain all target adaptation adjustment schemes containing memory resource call optimization, and extract the adjustment details of the memory resource call optimization in each scheme, including memory allocation strategy, memory release mechanism, and memory cache management method.

[0159] All target adaptation adjustment schemes containing memory resource call optimization are obtained, and the specific adjustment details of the memory resource call optimization in these schemes are extracted, including memory allocation strategy, memory release mechanism, and memory cache management method. The above details can reflect the specific implementation method of different schemes for memory resource call optimization.

[0160] Step S1533: according to the comprehensive score, the memory allocation strategy, the memory release mechanism, and the memory cache management method in the preset number of schemes with the highest ranking are selected as the core adjustment details.

[0161] According to the comprehensive score, the target adaptation adjustment schemes containing memory resource call optimization are sorted according to the set standard, and the memory allocation strategy, the memory release mechanism, and the memory cache management method in the preset number of schemes with the highest ranking are selected as the core adjustment details. The above core adjustment details have a high comprehensive score and may be a more effective memory resource call optimization method.

[0162] Step S1534: Extract the associated background process management adjustment content, including background process priority setting, process resource limit quota, and process start-stop trigger condition.

[0163] Extract the background process management adjustment content associated with memory resource call optimization, including background process priority setting, process resource limit quota, and process start-stop trigger condition. The above adjustment content can effectively manage the running of background processes, thereby affecting the use of memory resources.

[0164] Step S1535: Verify the compatibility of the background process management adjustment content and the core memory resource call optimization details in the simulation environment, and adjust the process priority setting and resource limit quota according to the verification result to ensure that the memory allocation efficiency and cache demand are within the preset performance tolerance range.

[0165] Verify the compatibility of the background process management adjustment content and the core memory resource call optimization details in the simulation environment. Through simulation, observe the changes in memory allocation efficiency and cache demand. According to the verification result, adjust the process priority setting and resource limit quota to ensure that the memory allocation efficiency and cache demand are within the preset performance tolerance range.

[0166] Step S1536: Combine the adapted background process management adjustment content and the core memory resource call optimization details to form the merged memory resource associated adjustment content; use the same method to merge the adjustment content of other adjustment objects, extract the associated adjustment content, select the core details, confirm the adaptability, and combine to form the merged content.

[0167] Combine the adapted background process management adjustment content and the core memory resource call optimization details to form the merged memory resource associated adjustment content. For other adjustment objects, such as processor resource call optimization and software module logic optimization, use the same method to merge, that is, extract the associated adjustment content, select the core details, confirm the adaptability, and combine to form the merged content.

[0168] Step S154: Simulate the system running state after implementing all the merged adjustment content on the target test platform simultaneously, identify whether there is a compatibility conflict, if there is a compatibility conflict, adjust the conflict content according to the association matrix and comprehensive score, preferentially retain the adjustment content corresponding to the core conflict, or modify the parameter configuration of the conflict content to eliminate the conflict.

[0169] In the simulation environment, all the adjusted contents after merging are implemented at the same time to simulate the system running state of the target test platform. Whether there is a compatibility conflict is observed, for example, some adjusted contents may affect each other, resulting in system performance degradation or abnormality. If there is a compatibility conflict, the conflict content is adjusted according to the correlation matrix and the comprehensive score. The adjusted content corresponding to the core conflict is preferentially retained because the core conflict has a greater impact on the system adaptation stability. The parameter configuration of the conflict content can also be modified to eliminate the conflict.

[0170] Step S155: The adjusted contents that pass the compatibility verification are sorted according to the logic of system association adjustment, hardware association adjustment, and software module adjustment. The same type of adjusted contents are sorted and executed according to the comprehensive score and the set standard.

[0171] The adjusted contents that pass the compatibility verification are sorted. The sorting is performed according to the logical order of system association adjustment, hardware association adjustment, and software module adjustment. The system association adjustment usually involves the setting of the basic system environment such as the operating system and the driver program. The hardware association adjustment mainly aims at the optimization of the calling of hardware resources. The software module adjustment is the logical optimization of the software function module. The same type of adjusted contents are sorted and executed according to the comprehensive score and the set standard, and the adjusted content with a higher comprehensive score is preferentially executed.

[0172] Step S156: A structure framework of the software platform adaptation adjustment strategy is constructed according to the sorted adjusted contents. The structure framework includes an adjustment target, an adjusted content classification, an implementation order, and an expected effect. The adjustment target corresponds to the core requirement of the multi-platform adaptation rule set. The expected effect is based on the pre-evaluation result of the candidate adaptation adjustment scheme.

[0173] A structure framework of the software platform adaptation adjustment strategy is constructed according to the sorted adjusted contents. The structure framework includes an adjustment target, an adjusted content classification, an implementation order, and an expected effect. The adjustment target corresponds to the core requirement of the multi-platform adaptation rule set. The expected effect is based on the pre-evaluation result of the candidate adaptation adjustment scheme.

[0174] Step S157: An implementation description is added to each adjusted content in the structure framework to determine an implementation step, a required tool, and a post-implementation verification index. The implementation description of the memory resource calling optimization includes a memory allocation algorithm modification step, a memory monitoring tool name, and an optimized memory occupancy rate verification index, thereby forming a software platform adaptation adjustment strategy for the target test platform.

[0175] An implementation description is added to each adjustment content in the structural framework of the software platform adaptation adjustment strategy. The implementation description includes an implementation step, i.e., a detailed description of how to implement the adjustment content; a required tool, i.e., a tool or software required for implementing the adjustment content; and a post-implementation verification index, i.e., an index for verifying whether the adjustment content achieves the expected effect. For example, for memory resource call optimization, the implementation description contains a memory allocation algorithm modification step, a memory monitoring tool name, and a post-optimization memory occupancy rate verification index. By adding the implementation description, a complete software platform adaptation adjustment strategy for the target test platform is formed.

[0176] Figure 2 An exemplary hardware and software components of the notebook software adaptation test system 100 for multi-platform compatibility provided by some embodiments of the present application are shown in the schematic diagram. For example, the processor 120 can be used in the notebook software adaptation test system 100 for multi-platform compatibility and used to execute the functions in the present application.

[0177] The notebook software adaptation test system 100 for multi-platform compatibility can be a general server or a special-purpose server, both of which can be used to implement the notebook software adaptation test method for multi-platform compatibility of the present application. Although only one server is shown in the present application, for the sake of convenience, the functions described in the present application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0178] For example, the notebook software adaptation test system 100 for multi-platform compatibility can include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and different forms of storage media 140, such as a disk, a ROM, or a RAM, or any combination thereof. Exemplarily, the notebook software adaptation test system 100 for multi-platform compatibility can also include program instructions stored in a ROM, a RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application can be implemented according to these program instructions. The notebook software adaptation test system 100 for multi-platform compatibility also includes an I / O interface 150 between the computer and other input / output devices.

[0179] For the convenience of description, only one processor is described in the application to multi-platform compatible notebook software adaptation test system 100. However, it should be noted that the application to multi-platform compatible notebook software adaptation test system 100 in the present application can also include multiple processors, and therefore the steps performed by one processor described in the present application can also be jointly performed or separately performed by multiple processors. For example, if the processor of the application to multi-platform compatible notebook software adaptation test system 100 performs steps A and B, it should be understood that steps A and B can also be jointly performed by two different processors or separately performed in one processor. For example, a first processor performs step A, a second processor performs step B, or the first processor and the second processor jointly perform steps A and B.

[0180] In addition, the embodiment of the present application also provides a readable storage medium, wherein computer executable instructions are preset, and when a processor executes the computer executable instructions, the application to multi-platform compatible notebook software adaptation test method is realized.

[0181] It should be noted that, in order to simplify the description of the present application and to help the understanding of one or more embodiments of the present application, in the foregoing description of the embodiments of the present application, various features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A method for notebook software adaptation test applied to multi-platform compatibility, characterized in that, The method comprises: acquiring a running state data set of a notebook software to be tested on a target test platform, and simultaneously acquiring a platform characteristic data set of the target test platform, the running state data set containing functional response data and resource occupation data in a software running process, and the platform characteristic data set containing hardware configuration data and system environment data of the platform; performing associated mapping processing on the running state data set and the platform characteristic data set, establishing a corresponding relationship between each data item in the running state data set and each data item in the platform characteristic data set, and obtaining an associated mapping result of the running state and the platform characteristics; based on the associated mapping result and a preset multi-platform adaptation rule set, extracting an adaptation conflict feature of the notebook software to be tested on the target test platform, identifying an associated item in the associated mapping result that does not conform to the multi-platform adaptation rule set, and obtaining an adaptation conflict feature set; combining a preset platform adaptation strategy library, performing matching processing on each adaptation conflict feature in the adaptation conflict feature set, finding an adaptation adjustment scheme corresponding to each adaptation conflict feature, obtaining a candidate adaptation adjustment scheme set, wherein a specific adaptation conflict feature is selected from the adaptation conflict feature set, and a corresponding candidate adaptation adjustment scheme is screened in the platform adaptation strategy library according to the conflict level, the conflict type and the cross-platform marker of the adaptation conflict feature; only a core adjustment scheme is screened for a core conflict, and a general adjustment scheme is preferentially screened for a cross-platform common conflict; a running state after the selected candidate adaptation adjustment scheme is implemented on the target test platform is simulated, changes in functional response data and resource occupation data of the software are predicted, and a prediction compliance degree of the prediction data with the multi-platform adaptation rule set is determined; a historical adaptation success rate, the prediction compliance degree and an adaptation operation complexity are normalized into scoring indexes of the same dimension, the normalized scoring indexes are weighted and calculated according to a preset weight, and a comprehensive score of the candidate adaptation adjustment scheme is obtained; a preset number of adaptation adjustment schemes that are ranked in the front according to the set standard are selected as target adaptation adjustment schemes corresponding to the adaptation conflict feature; the target adaptation adjustment scheme is associated with a conflict feature causal chain of the adaptation conflict feature, so that the adjustment scheme solves the core problem in the causal chain, and until all adaptation conflict features in the adaptation conflict feature set correspond to target adaptation adjustment schemes; integrating the candidate adaptation adjustment scheme set, removing repeated adaptation adjustment contents, and generating a software platform adaptation adjustment strategy for the target test platform, wherein the software platform adaptation adjustment strategy contains an adjustment scheme for a software functional module and an adjustment scheme for a software resource calling mode.

2. The notebook software adaptation test method for multi-platform compatibility according to claim 1, wherein, The acquisition of the running state data set of the notebook software to be tested on the target test platform and the acquisition of the platform characteristic data set of the target test platform comprise: The running process of the notebook software to be tested on the target test platform is divided into scene data collection, and the collection scenes are divided according to the functional modules of the software. Each collection scene corresponds to the functional response related data and the resource occupation related data generated when the functional module runs, forming a scene-based initial running data set; Obtain the running data benchmark values of multiple standard platforms, compare the scene-based initial running data set with the running data benchmark values of the corresponding standard platforms, correct the deviation data in the scene-based initial running data set caused by platform hardware differences, and obtain a calibrated scene-based running data set; According to the functional modules, the functional response related data in each scene in the calibrated scene-based running data set is classified into functional response data of the corresponding module, and the resource occupation related data is classified into resource occupation data of the corresponding module, to form a running state data set; According to the hardware core degree, the hardware of the target test platform is divided into a core hardware layer and an auxiliary hardware layer, and the parameter information of the core hardware layer and the auxiliary hardware layer is collected respectively to form layered hardware configuration data. The core hardware layer includes processors, memories and storage devices, and the auxiliary hardware layer includes interface devices and display devices; Real-time record the operating system version changes, driver updates and background process start-stop conditions in the system running process of the target test platform to form dynamic system environment data; Combine the layered hardware configuration data and the dynamic system environment data to mark the collection time and hardware system level of each data item to form a platform characteristic data set.

3. The method for notebook software adaptation test applied to multi-platform compatibility according to claim 1, characterized in that, The running state data set and the platform characteristic data set are associated and mapped to establish the corresponding relationship between each data item in the running state data set and each data item in the platform characteristic data set, and the associated mapping result of the running state and the platform characteristics is obtained, including: For the functional response data in the running state data set, mark the response scene, response type and response time consumption attribute, and for the resource occupation data, mark the occupation hardware type, occupation period and occupation peak attribute; For the hardware configuration data in the platform characteristic data set, mark the hardware model, hardware performance level and hardware compatibility range attribute, and for the system environment data, mark the system version compatibility, driver support type and background process resource demand attribute; According to the hardware performance level and system version compatibility of the target test platform, a dynamic mapping dimension adjustment mechanism is constructed to determine the initial mapping dimension weight. The resource occupation mapping dimension weight distribution ratio of the core hardware is set according to the hardware core degree adaptation requirement, which is different from that of the auxiliary hardware. The functional response mapping dimension weight of different version systems is distributed according to the system version compatibility adaptation requirement; Based on the initial mapping dimension weight, the first mapping dimension of the functional response data and the system environment data is determined, which includes the corresponding relationship between the response scene and the system version compatibility, the corresponding relationship between the response type and the driver support type, and the corresponding relationship between the response time and the background process resource demand. determine a second mapping dimension of the resource occupation data and the hardware configuration data, the second mapping dimension including a correspondence between an occupation hardware type and a hardware model, a correspondence between an occupation time period and a hardware performance level, and a correspondence between an occupation peak value and a hardware compatibility range; determine, based on the first mapping dimension and in combination with a mapping dimension weight, an association strength of each attribute of the function response data and a corresponding attribute of the system environment data by combination of each attribute matching degree and a corresponding weight proportion, to obtain an association degree of the interface rendering response data and the operating system version data, an association degree of the interactive operation response data and the driver program version data, and an association degree of the data processing response data and the background process data; determine, based on the second mapping dimension and by using the same association strength determination manner, an association degree of the processor occupation data and the processor parameter data, an association degree of the memory occupation data and the memory parameter data, and an association degree of the storage read-write occupation data and the storage device parameter data; adjust, according to a real-time running state of the target test platform, a resource occupation mapping dimension weight of a related hardware if any one hardware occupation rate is in a preset attention interval for a preset time, and redetermine the association degrees; combine all the obtained association degrees with corresponding association data item identifiers, mapping dimension weights, and calculation times to form an association mapping result of the running state and the platform characteristics.

4. The notebook software adaptation test method for multi-platform compatibility according to claim 3, wherein, The determining, based on the first mapping dimension and in combination with a mapping dimension weight, of an association strength of each attribute of the function response data and a corresponding attribute of the system environment data by combination of each attribute matching degree and a corresponding weight proportion to obtain an association degree of the interface rendering response data and the operating system version data, an association degree of the interactive operation response data and the driver program version data, and an association degree of the data processing response data and the background process data includes: extracting response scene, response type, and response time consumption attributes of the interface rendering response data, and extracting system version compatibility, driver support type, and background process resource demand attributes of the operating system version data; allocating respective weight proportions of the system version compatibility, the driver support type, and the background process resource demand according to the weight determined by the dynamic mapping dimension adjustment mechanism; determining an attribute matching degree of the response scene and the system version compatibility according to a support degree of the system version for the response scene; determining an attribute matching degree of the response type and the driver support type according to a support degree of the driver type for the response type; determining an attribute matching degree of the response time consumption and the background process resource demand according to whether the response time consumption is within a reasonable range corresponding to the background process resource demand; determining the association strength by combination of each attribute matching degree and a corresponding weight proportion, the association strength being in the form of a percentage and serving as the association degree of the interface rendering response data and the operating system version data; extracting operation type, operation frequency, and operation response delay attributes of the interactive operation response data, and extracting input device support type, operation response optimization level, and delay control parameter attributes of the driver program version data, and determining a weight according to the dynamic mapping dimension adjustment mechanism; According to the support degree of the input device support type to the operation type, the adaptation degree of the operation response optimization level to the operation frequency, and the regulation degree of the delay control parameter to the operation response delay, the attribute matching degrees are determined respectively; The association strength is determined by combining the attribute matching degrees and the corresponding weight proportions, and the association strength is used as the association degree of the interactive operation response data and the driver version data; The processing data volume, processing logic complexity, and processing time consumption attributes of the data processing response data are extracted, the process resource occupation amount, process priority, and process concurrency attributes of the background process data are extracted, and the weights are determined according to the dynamic mapping dimension adjustment mechanism; According to the support degree of the process resource occupation amount to the processing data volume, the adaptation degree of the process priority to the processing logic complexity, and the influence degree of the process concurrency to the processing time consumption, the attribute matching degrees are determined respectively; The association strength is determined by combining the attribute matching degrees and the corresponding weight proportions, and the association strength is used as the association degree of the data processing response data and the background process data; If any attribute data is missing, reasonable estimation is made based on the same type of data of the target test platform to ensure the integrity of the association strength determination; The attribute matching degrees, weight allocation, and estimated data sources in each association degree determination process are recorded to form an association degree determination report, which is used as supplementary content of the association mapping result.

5. The notebook software adaptation test method for multi-platform compatibility according to claim 1, wherein, According to the association mapping result and a preset multi-platform adaptation rule set, an adaptation conflict feature of the notebook software to be tested on the target test platform is extracted, an association item in the association mapping result that does not conform to the multi-platform adaptation rule set is identified, and an adaptation conflict feature set is obtained, including: A preset multi-platform adaptation rule set is obtained, the multi-platform adaptation rule set includes basic adaptation rules and dynamic adaptation rules, the basic adaptation rules are fixed function response threshold ranges and resource occupation threshold ranges, and the dynamic adaptation rules are dynamically generated according to platform hardware performance levels and system versions, the resource occupation threshold ranges are set according to hardware performance level adaptation requirements, and the function response threshold ranges are set according to system version adaptation requirements; The multi-platform adaptation rule set is divided into a core rule layer and a secondary rule layer according to the degree of influence on adaptation stability, the core rule layer includes processor occupation threshold values, memory occupation threshold values, and interface rendering response threshold values, and the secondary rule layer includes storage read-write occupation threshold values, interactive operation response threshold values, and data processing response threshold values; From the association mapping result, the association degrees and corresponding mapping dimension weights are extracted, the threshold ranges of the core rule layer are preferentially compared, the association degrees of the processor occupation data and the processor parameter data, the association degrees of the memory occupation data and the memory parameter data, and the association degrees of the interface rendering response data and the operating system version data that exceed the corresponding threshold ranges of the core rule layer are identified, and are marked as core candidate conflict association items, and the amplitudes of the association degrees exceeding the corresponding threshold ranges are recorded, and the marking priorities are set according to the amplitude value ranges; The threshold range of the secondary rule layer is compared, and the correlation between the storage read-write occupation data and the storage device parameter data, the correlation between the data processing response data and the background process data, and the correlation between the data processing response data and the background process data are identified. The correlation items that exceed the corresponding threshold range of the secondary rule layer are marked as secondary candidate conflict correlation items, and the amplitude of the correlation exceeding the corresponding threshold range is also recorded. The core candidate conflict correlation items and the secondary candidate conflict correlation items are analyzed for cross-platform conflict correlation, and common conflict correlation items between different platforms are found. The common conflict correlation items are marked as cross-platform common conflicts, and the non-common conflict correlation items are marked as platform-specific conflicts. The running state data features and platform characteristic data features corresponding to the core candidate conflict correlation items are extracted in priority order, and the running state data features and platform characteristic data features corresponding to the secondary candidate conflict correlation items are extracted. The feature extraction of the cross-platform common conflict includes multi-platform feature comparison information. A conflict feature causal chain is constructed for each candidate conflict correlation item to determine the causal relationship between the running state data features and the platform characteristic data features. The conflict feature causal chain, conflict level, and cross-platform label of each candidate conflict correlation item are combined to form an adaptive conflict feature, all adaptive conflict features are integrated, repeated cross-platform common conflict features are removed, and an adaptive conflict feature set is obtained.

6. The notebook software adaptation test method for multi-platform compatibility according to claim 5, wherein, The running state data features and platform characteristic data features corresponding to the core candidate conflict correlation items are extracted in priority order, and the running state data features and platform characteristic data features corresponding to the secondary candidate conflict correlation items are extracted, including: The processor occupation data features corresponding to the processor occupation correlation items in the core candidate conflict correlation items are extracted, including the time variation curve of the processor occupation rate, the time node of the occurrence of the occupation peak value, the duration of the occupation peak value, and the occupation rate difference when different functional modules are running. The processor parameter data features corresponding to the processor occupation correlation items are extracted, including the processor model, core number, main frequency range, cache capacity, and instruction set type supported by the processor, and the differences between each parameter and the industry average level are marked. The memory occupation data features corresponding to the memory occupation correlation items in the core candidate conflict correlation items are extracted, including the fluctuation amplitude of the memory occupation rate, the change of the memory read-write speed, the memory fragmentation rate, and the memory occupation change under different data processing tasks. The memory parameter data features corresponding to the memory occupation correlation items are extracted, including the memory capacity, memory type, memory frequency, memory channel number, and memory timing parameters, and the influence of each parameter on the memory occupation features is analyzed. The interface rendering response data features corresponding to the interface rendering response correlation items in the core candidate conflict correlation items are extracted, including the change of the interface rendering frame rate, the distribution of the rendering delay, the rendering effect difference under different resolutions, and the frequency of rendering abnormalities. The operating system version data features corresponding to the interface rendering response correlation items are extracted, including the kernel version of the operating system, the graphics interface version, the rendering engine type, and the support degree for specific resolution display. The rendering feature differences under different operating system versions are compared. The storage read-write occupation data features corresponding to the storage read-write occupation association item in the secondary candidate conflict association item are extracted, including the average level of storage read-write speed and the main distribution interval of read-write delay; The storage device parameter data features corresponding to the storage read-write occupation association item are extracted, including the type and interface specification of the storage device; The corresponding data features of the cross-platform commonality conflict association item on the target test platform are extracted, and the corresponding data features of other platforms that appear corresponding conflicts are collected, including the hardware configuration parameters, system version information, and conflict scenario definition of each platform; The multi-platform features of the cross-platform commonality conflict are compared and analyzed to find out the common features and platform-specific features; The extracted running state data features and platform characteristic data features are classified and arranged according to the conflict level, the features of the core candidate conflict are marked with a core identifier, the features of the cross-platform commonality conflict are marked with a cross-platform identifier and the number of comparison platforms, and a feature extraction list is formed.

7. The notebook software adaptation test method for multi-platform compatibility according to claim 1, wherein, The platform adaptation strategy library is pre-set, and each adaptation conflict feature in the adaptation conflict feature set is matched and processed to find the corresponding adaptation adjustment scheme of each adaptation conflict feature, and a candidate adaptation adjustment scheme set is obtained, further comprising: A pre-set platform adaptation strategy library is obtained, which stores adaptation adjustment schemes according to conflict level and conflict type, core conflict corresponding to core adjustment scheme, secondary conflict corresponding to secondary adjustment scheme, cross-platform commonality conflict corresponding to general adjustment scheme, and platform-specific conflict corresponding to customized adjustment scheme; An effect evaluation tag is added to each adaptation adjustment scheme in the platform adaptation strategy library, which includes historical adaptation success rate, adaptation stability improvement period after adaptation, and adaptation operation complexity. The historical adaptation success rate is based on the statistical results of past adaptation cases with the same conflict features. The adaptation stability improvement period is the time for the software to reach stable operation after adaptation. The adaptation operation complexity is determined based on the number of software modules to be modified and the code level; All target adaptation adjustment schemes are integrated, and the corresponding adaptation conflict feature identifier, comprehensive score, and pre-evaluation result of each target adaptation adjustment scheme are labeled to form a candidate adaptation adjustment scheme set.

8. The notebook software adaptation test method for multi-platform compatibility according to claim 7, wherein, The running state of the target test platform after the implementation of the selected candidate adaptation adjustment scheme is simulated, the functional response data and resource occupation data changes of the software are predicted, and the compliance degree of the prediction data with the multi-platform adaptation rule set is determined, including: A platform hardware parameter model is constructed based on hierarchical hardware configuration data, a system environment model is constructed based on dynamic system environment data, a software running model is constructed based on the functional module structure and running logic of the software to be tested, and a running simulation model of the target test platform is formed by combination; The adjustment content of the candidate adaptation adjustment scheme is converted into a parameter adjustment instruction of the simulation model; The running scenario data of the software to be tested from the scenario-based initial running data set is loaded into the simulation model, including the running load, running time, and interactive operation sequence of different functional modules; The simulation model is run and the parameter adjustment instruction is implemented, and the software functional response data and resource occupation data in the simulation running process are recorded in real time according to the actual data collection frequency; The interface rendering response time, the interactive operation response time, and the data processing response time after the simulation operation are extracted, and are compared with the functional response threshold range in the multi-platform adaptation rule set to determine the data proportion that meets the threshold range; The processor occupation rate, the memory occupation rate, and the storage read-write occupation rate after the simulation operation are extracted, and are compared with the resource occupation threshold range in the multi-platform adaptation rule set to determine the data proportion that meets the resource occupation threshold range; The functional response meeting proportion and the resource occupation meeting proportion are calculated respectively, and the weighted meeting degree is calculated according to the importance weight of the functional response and the resource occupation in the multi-platform adaptation rule set, as the meeting degree of the prediction data and the multi-platform adaptation rule set.

9. The notebook software adaptation test method for multi-platform compatibility according to claim 1, wherein, The candidate adaptation adjustment scheme set is integrated, and repeated adaptation adjustment contents are removed to generate a software platform adaptation adjustment strategy for the target test platform, including: Each target adaptation adjustment scheme in the candidate adaptation adjustment scheme set is disassembled into hardware-associated adjustment content, system-associated adjustment content, and software module adjustment content according to the adjustment object, the hardware-associated adjustment content is for processor and memory hardware resource calling, the system-associated adjustment content is for operating system and driver parameter configuration, and the software module adjustment content is for software function module logic optimization; An association matrix of adjustment content is established to record the association relationship between each adjustment content in different target adaptation adjustment schemes, and the processor resource calling optimization has a synergistic relationship with the data processing module logic optimization, and the memory resource calling optimization has a dependent relationship with the background process management adjustment; Based on the association matrix, the adjustment contents of the same adjustment object are merged, and the associated background process management adjustment content is integrated at the same time when the memory resource calling optimization is merged; The system running state after simultaneously implementing all the merged adjustment contents on the target test platform is simulated to identify whether there is a compatibility conflict, if there is a compatibility conflict, the adjustment content corresponding to the core conflict is preferentially retained or the parameter configuration of the conflict content is modified to eliminate the conflict according to the association matrix and the comprehensive score adjustment; The adjustment contents that pass the compatibility verification are sorted according to the logic of system-associated adjustment, hardware-associated adjustment, and software module adjustment, and the same type of adjustment content is sorted and executed according to the set standard according to the comprehensive score; A structure framework of the software platform adaptation adjustment strategy is constructed according to the sorted adjustment contents, the structure framework includes adjustment target, adjustment content classification, implementation sequence, and expected effect, the adjustment target corresponds to the core requirement of the multi-platform adaptation rule set, and the expected effect is based on the pre-evaluation result of the candidate adaptation adjustment scheme; Implementation instructions are added to each adjustment content in the structure framework to determine the implementation steps, required tools, and post-implementation verification indicators, the implementation instructions of the memory resource calling optimization include memory allocation algorithm modification steps, memory monitoring tool name, and optimized memory occupation rate verification indicators, thereby forming a software platform adaptation adjustment strategy for the target test platform.

10. A notebook software adaptation test system applied to multi-platform compatibility, characterized in that, The application of the notebook software adaptation test system for multi-platform compatibility comprises a processor and a memory, the memory is connected with the processor, the memory is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the memory to realize the notebook software adaptation test method for multi-platform compatibility in any one of claims 1-9.

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