A terminal storage function detection method and system, a terminal, and a storage medium

Abstract

The application relates to a terminal storage function detection method and system, a terminal and a storage medium, relates to the technical field of data storage verification, and comprises the following steps: obtaining a to-be-tested module of a preset storage terminal; analyzing the to-be-tested module to determine module requirement data, a data requirement range and a regional association rule; analyzing the module requirement data and the regional association rule to determine module detection data, global detection data and a theoretical return result; obtaining current storage data; and analyzing the to-be-tested module, the module detection data, the global detection data, the theoretical return result, the data requirement range and the current storage data, controlling a preset detection device to detect the storage terminal, and determining a storage detection result. The application has the effect of improving storage function detection efficiency.

Description

Technical Field

[0001] This application relates to the technical field of data storage verification, and in particular to a method, system, terminal, and storage medium for detecting terminal storage function. Background Technology

[0002] Storage function testing refers to the process of testing the data storage, retrieval, and backup functions of a storage terminal to determine its operational status.

[0003] In related technologies, when testing storage functions, a manual black-box testing method is used. Testers manually retrieve historical data and compare it with preset values ​​to verify data consistency. Then, the current storage data in the storage device is mirrored and copied. Based on different simulated scenarios, different test data are input into the mirrored data for testing one by one, and finally the test results of the storage function are determined.

[0004] Regarding the aforementioned technologies, when mirroring stored data, all data in the storage system is first completely copied to generate mirror data. Then, the storage function is tested sequentially based on the complete mirror data according to different testing requirements. This can lead to low efficiency in storage function testing and inaccurate fault location, indicating room for improvement. Summary of the Invention

[0005] To improve the efficiency of storage function testing, this application provides a terminal storage function testing method, system, terminal, and storage medium.

[0006] Firstly, this application provides a method for detecting terminal storage function, employing the following technical solution:

[0007] A method for detecting terminal storage function, comprising:

[0008] Obtain the module to be tested from the preset storage terminal;

[0009] Analyze the modules to be tested to determine the module's required data, the scope of data requirements, and the regional association rules;

[0010] Analyze the module requirement data and regional association rules to determine the module detection data, global detection data, and theoretical return results;

[0011] Get the currently stored data;

[0012] The system analyzes the module under test, module test data, global test data, theoretical return results, data requirement range, and current storage data, and controls the preset test device to test the storage terminal to determine the storage test results.

[0013] Optionally, the steps of analyzing the module to be tested to determine the module's required data, the scope of data requirements, and regional association rules include:

[0014] The module to be tested is searched in a preset historical database to determine the module's required data and the scope of data requirements.

[0015] Extract data from the historical database to identify historical stored logs;

[0016] Input historical storage logs into a preset sliding window model to determine regional time correlations;

[0017] Input historical storage logs into a preset linear regression model to determine the logical relationships between regions;

[0018] Integrate regional temporal association and regional logical association to determine regional association rules.

[0019] Optionally, the steps of analyzing module requirement data and regional association rules to determine module detection data, global detection data, and theoretical return results include:

[0020] Obtain historical input data for the module to be tested;

[0021] Data features are extracted from historical input data to determine quantifiable data characteristics;

[0022] Identify the key test functions of the module to be tested;

[0023] Module detection data and global detection data are generated based on quantitative data characteristics and key testing functions;

[0024] The global detection data and regional association rules are analyzed to determine the theoretical return results.

[0025] Optionally, the steps of analyzing the module to be tested, module test data, global test data, theoretical return results, data requirement range, and current stored data, and controlling the preset test device to test the storage terminal to determine the storage test results include:

[0026] Analyze the scope of data requirements and the currently stored data to determine the cutting mirror data and cutting location information;

[0027] Based on the module detection data and the cut image data, the detection device is controlled to perform parallel detection on the module to be detected in the storage device in order to determine the partial call detection results.

[0028] The mirror-cut data is stitched together based on the cutting position information to determine the complete mirror data;

[0029] The detection device is controlled to perform global storage detection on the storage device based on global detection data and complete image data, and the detection return data is verified based on the theoretical return results to determine the global call detection results;

[0030] The partial call detection results are integrated with the complete call detection results to determine the stored detection results.

[0031] Optionally, the steps of analyzing the data requirement scope and currently stored data to determine the split mirror data and split location information include:

[0032] Extract data from the currently stored data based on the scope of data requirements to identify independent data segments;

[0033] Integrate the scope of data requirements to determine the total independent scope;

[0034] Determine whether the total independent range covers the preset complete data range;

[0035] If so, then generate the split mirror data based on the independent part data, and locate it in the current stored data according to the data requirement range corresponding to the independent part data to determine the split position information;

[0036] If not, analyze the data requirement scope, total independent scope, independent partial data, and currently stored data to determine the split mirror data and split location information.

[0037] Optionally, the steps of analyzing the data requirement scope, total independent scope, independent data portions, and currently stored data to determine the split mirror data and split location information include:

[0038] Compare the independent data portions with the currently stored data to identify unallocated data and adjacent data portions;

[0039] Perform data volume analysis on adjacent data sections to determine the minimum amount of data;

[0040] Unassigned data is concatenated with the minimum amount of data to generate redundant overlay data;

[0041] The redundant overlay data is located to determine its cutting position information in the current stored data;

[0042] Data mirroring is performed on independent portions of data and redundant overlay data to determine the segmentation of mirrored data.

[0043] Optionally, the steps of stitching the mirrored cut data according to the cutting position information to determine the complete mirrored data include:

[0044] Data extraction of cutting location information is used to determine the cutting timestamp and storage module location;

[0045] The image-sharing data is sorted according to the cutting timestamp and storage module location to determine the time-series cutting data;

[0046] Redundancy is removed from the time-series segmented data, and the data is then stitched together to determine the complete mirror data.

[0047] Secondly, this application provides a storage function testing system, which adopts the following technical solution:

[0048] A storage function testing system, comprising:

[0049] The acquisition module is used to acquire the module to be tested and the currently stored data.

[0050] A memory for storing a program for a terminal storage function detection method as described in any of the preceding claims;

[0051] The processor and the program in the memory can be loaded and executed by the processor to implement a terminal storage function detection method as described in any of the above.

[0052] Thirdly, this application provides a smart terminal, which adopts the following technical solution:

[0053] A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any of the preceding claims for a terminal storage function detection method.

[0054] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, which facilitates improved storage function detection efficiency, and adopts the following technical solution:

[0055] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the aforementioned terminal storage function detection methods.

[0056] In summary, this application includes at least one of the following beneficial technical effects:

[0057] 1. By analyzing the modules to be tested, the module requirement data, the corresponding data requirement range, and the regional association rules of the storage data between modules are determined. Based on the module requirement data, module test data for testing each module to be tested, global test data for testing the global storage status, and theoretical return results for verifying the global test data are generated. After testing the detection function with the module test data and the global test data respectively, the data is verified based on the theoretical return results. Finally, the storage function test results are obtained. Thus, parallel testing is performed on the modules to be tested first, and then global storage is tested and verified, thereby improving the accuracy of the storage function test results.

[0058] 2. By analyzing the data requirement range and the current stored data, the system determines the segmented mirror data generated for each module to be tested and the segmentation position information of the segmented mirror data in the current stored data. Then, based on the module detection data and the segmented mirror data, the system controls the detection device to perform parallel detection on the modules to be tested in the storage device, determines the partial call detection results, thereby improving the efficiency of storage function detection. Then, based on the segmentation position information, the mirrored segmented data is spliced ​​to determine the complete mirror data, and the global storage detection of the storage function is completed based on the complete mirror data. Thus, the system first mirrors the storage data module by module, performs parallel detection on the modules to be tested, and then splices the segmented mirror data into complete mirror data, improving the efficiency of mirror data generation and thus improving the efficiency of storage function detection.

[0059] 3. By comparing the independent data segments with the current stored data, the unallocated data not included in the independent data segments and the adjacent data segments adjacent to the unallocated data segments are identified. The smallest data segment among the adjacent data segments is then spliced ​​with the unallocated data to generate the cut mirror data, thereby ensuring the coverage integrity of the cut mirror data and improving the accuracy of the storage function test results. Attached Figure Description

[0060] Figure 1 This is a flowchart of a terminal storage function detection method in an embodiment of this application.

[0061] Figure 2 This is a flowchart illustrating the analysis of the module to be tested in this embodiment of the application to determine the module's required data, the scope of data requirements, and the regional association rules.

[0062] Figure 3 This is a flowchart illustrating the analysis of module requirement data and regional association rules in this embodiment of the application to determine module detection data, global detection data, and theoretical return results.

[0063] Figure 4This is a flowchart in this application embodiment that analyzes the module to be tested, module detection data, global detection data, theoretical return results, data requirement range, and current stored data, and controls a preset detection device to detect the storage terminal in order to determine the storage detection result.

[0064] Figure 5 This is a flowchart in this application embodiment that analyzes the data requirement range and currently stored data to determine the cutting mirror data and cutting position information.

[0065] Figure 6 This is a flowchart in this application embodiment that analyzes the data requirement range, the total independent range, the independent part data, and the currently stored data to determine the cutting mirror data and cutting position information.

[0066] Figure 7 This is a flowchart in this application embodiment of stitching together mirror-cut data based on cutting position information to determine complete mirror data. Detailed Implementation

[0067] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 7 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0068] This application discloses a method, system, terminal, and storage medium for testing terminal storage functions. Specifically, it discloses a processing terminal and a storage terminal, which are connected to each other for information interaction and control. The processing terminal acquires the module to be tested and the current storage data of the storage terminal. It analyzes the module to be tested to determine the module requirement data, the data requirement range corresponding to the module requirement data, and the regional association rules of the storage data between modules. Based on the module requirement data, it generates module test data for testing each module to be tested, global test data for testing the overall storage status, and theoretical return results for verifying the global test data. After testing the detection function using the module test data and the global test data, it verifies the data based on the theoretical return results and finally determines the storage function test result. This method first performs parallel testing on the modules to be tested and then performs global storage testing and verification, thereby improving the accuracy of the storage function test result.

[0069] Reference Figure 1 This application discloses a method for detecting terminal storage function, including the following steps:

[0070] Step S100: Obtain the module to be tested from the preset storage terminal.

[0071] Among them, storage terminal refers to a terminal that has storage-related functions such as data storage, read / write and backup.

[0072] The module to be tested refers to the storage module for different data in the storage terminal, such as embedded storage module, peak storage module, etc. It is determined by the processing terminal through data partitioning and functional partitioning to divide the storage terminal into different modules.

[0073] Step S101: Analyze the module to be tested to determine the module's required data, the scope of data requirements, and the regional association rules.

[0074] The module requirement data refers to all data that each module under test can directly read and write during operation. This data is determined by the processing terminal after identifying the module under test, based on a database lookup. Specific analysis steps are detailed below. Figure 2 The steps in the process.

[0075] The data requirement scope refers to the storage location of the module requirement data within the current storage data. This location is determined by the processing terminal after identifying the module requirement data, and is located within the current storage data. Specific analysis steps are detailed below. Figure 2 The steps in the process.

[0076] Regional association rules refer to the association rules between different modules when storing data globally. These include temporal and logical association rules. For example, if the core area stores 11.2kA of peak current data, it is synchronized to the backup area within 10ms, and the backup data remains unchanged. The processing terminal determines the data association rules between modules by analyzing the modules under test. Specific analysis steps are detailed below. Figure 2 The steps in the process.

[0077] Step S102: Analyze the module requirement data and regional association rules to determine the module detection data, global detection data, and theoretical return results.

[0078] Module detection data refers to the detection data used to detect each module to be tested. Global detection data refers to the detection data used to perform global storage detection on the entire storage function. This detection data is the complete input information during the normal operation of the storage function and includes complete detection parameters for the storage and linkage of each module. Theoretical return results refer to the theoretical storage return results of the coverage area association rules, used to verify the global detection results, thereby improving the accuracy of the storage function detection results. All three are determined by the processing terminal through analysis of module requirement data and area association rules. The specific analysis steps refer to... Figure 3 The steps in the process.

[0079] Step S103: Obtain the currently stored data.

[0080] The currently stored data refers to all the data currently stored in the storage terminal, which is determined by the processing terminal by directly retrieving the database data of the storage terminal.

[0081] Step S104: Analyze the module to be tested, module detection data, global detection data, theoretical return results, data requirement range, and current storage data, and control the preset detection device to detect the storage terminal to determine the storage detection results.

[0082] The testing device refers to an automatic testing equipment used to test the storage function of the storage terminal. It is based on a core architecture of a 485 module, an HPLC module, an FPGA module, and a programming module. The 485 module provides a standard RS-485 channel to complete the rapid issuance and readback of DL / T645 / 698.45 commands. The HPLC module uses high-speed power line carrier to achieve long-distance, interference-resistant communication under complex topologies in the transformer area. The FPGA module has built-in programmable logic and a high-speed DAC, which can generate and inject 2040 channels of simulated data such as power, power, freeze, and events in real time, while capturing the terminal response timing. The programming module is responsible for communicating with the terminal chip and can complete the test process arrangement, coverage statistics, and fault location with one click.

[0083] Storage test results refer to the overall test results obtained after the storage terminal is tested by the testing device through module-by-module testing and global testing. The processing terminal analyzes the module to be tested, module test data, global test data, theoretical return results, data requirement range, and current storage data after determining the module test data, global test data, theoretical return results, and current storage data. This process controls the testing device to first perform module-by-module testing on the storage terminal, then perform global testing and verification, and finally determine the storage test results, thereby improving the accuracy of storage function test results. Specific analysis steps are detailed below. Figure 4 The steps in the process.

[0084] Reference Figure 2 The steps for analyzing the module to be tested to determine the module's required data, data requirement scope, and regional association rules include:

[0085] Step S200: Search the preset historical database according to the module to be tested to determine the module's required data and the scope of data requirements.

[0086] The historical database refers to the operation records of the storage terminal, including data reading and writing, the corresponding data writing module and data reading module, and the data stored in the storage terminal.

[0087] The module requirement data is consistent with the module requirement data in step S102. It is determined by the processing terminal by searching the historical database according to the module to be tested and extracting all read and write data of the module to be tested.

[0088] The data requirement range is consistent with the data requirement range in step S102. After determining the module requirement data, the processing terminal locates the module requirement data in all the data stored in the storage terminal to determine the data range location of the data read and written by the module to be tested.

[0089] Step S201: Extract data from the historical database to identify historical storage logs.

[0090] Among them, historical storage logs refer to the operation logs of storage terminals, including records of reading and writing and corresponding changes in storage data, which are determined by the processing terminal through data extraction from the historical database.

[0091] Step S202: Input the historical storage logs into the preset sliding window model to determine the regional time correlation.

[0092] Among them, the sliding window model refers to an algorithm model that uses a sliding window to analyze the correlation and patterns between continuous time series data and determine the temporal correlation logic of the data. By setting a sliding window of fixed duration, it slides evenly along the time axis to extract local features and finally determine the temporal correlation logic.

[0093] Regional time correlation refers to the data time correlation model between different modules to be detected. For example, after the core records the peak current, the normal region records the power data within 1 second. The processing terminal determines this by inputting the historical stored logs into the sliding window model.

[0094] Step S203: Input the historical storage logs into the preset linear regression model to determine the logical association between regions.

[0095] Among them, the linear regression model refers to the algorithmic model that determines the correlation between variables by linearly fitting a straight line equation.

[0096] Regional logical association refers to the logical association rules between different modules. For example, peak current data stored in the core area will be synchronized to the backup area. This is determined by the processing terminal by inputting historical storage logs into the linear regression model.

[0097] Step S204: Integrate the regional time association and the regional logical association to determine the regional association rules.

[0098] The regional association rule is consistent with the regional association rule in step S102. After determining the regional temporal association and the regional logical association, the processing terminal integrates and determines the regional logical association with its corresponding regional temporal association.

[0099] Reference Figure 3 The steps for analyzing module requirement data and regional association rules to determine module detection data, global detection data, and theoretical return results include:

[0100] Step S300: Obtain historical input data of the module to be tested.

[0101] Historical input data refers to the input sequence corresponding to the module under test, which is determined by the processing terminal by searching for its relevant input data in the historical database.

[0102] Step S301: Extract data features from historical input data to determine quantified data features.

[0103] Among them, quantitative data features refer to the quantitative characteristics of historical input data, such as current data that is stable between 2kA and 6kA and fluctuates within a range of no more than 3kA. These features are determined by the processing terminal through convolutional neural networks to extract features from historical input data.

[0104] Step S302: Obtain the key test functions of the module to be tested.

[0105] Among them, the key testing functions refer to the storage functions in the module to be tested that need to be tested in a key way, such as the fault tolerance function for abnormal data, which are determined by the processing terminal in combination with the storage terminal detection requirements and the reading and writing speed of the storage device.

[0106] Step S303: Generate module detection data and global detection data based on the quantitative data characteristics and key testing functions.

[0107] The module detection data is consistent with the module detection data in step S102. The processing terminal determines the detection data that is consistent with the quantitative data characteristics after determining the quantitative data characteristics and key test functions of the module to be detected. The detection data is generated for the key test functions and is consistent with the quantitative data characteristics.

[0108] The global detection data is consistent with the global detection data in step S102. After determining the quantitative data characteristics of the global input and the key test functions, the processing terminal generates detection data consistent with the quantitative data characteristics for the key test functions.

[0109] Step S304: Analyze the global detection data and regional association rules to determine the theoretical return result.

[0110] The theoretical return result refers to the theoretical return value obtained by inputting global detection data under the derivation of regional association rules. After determining the global detection data, the processing terminal inputs the storage module of the global detection data and the specific values ​​into the regional association rules to obtain the theoretical return result. This provides data support for subsequent verification of the returned data to obtain more accurate returned data, thereby improving the accuracy of storage function detection.

[0111] Reference Figure 4 The steps for analyzing the module to be tested, module test data, global test data, theoretical return results, data requirement range, and current stored data, and controlling the preset test device to test the storage terminal to determine the storage test results include:

[0112] Step S400: Analyze the data requirement range and the currently stored data to determine the cut mirror data and cut location information.

[0113] Specifically, the segmented mirror data refers to the segmented mirror data obtained by dividing the current stored data in the storage function into segments based on the required data of the module to be tested. This segmented mirror data is determined by the processing terminal through analysis of the data requirement range and the current stored data. Specific steps are detailed below. Figure 5 The steps in the process.

[0114] The cutting location information refers to the position and information of the cut mirror data within the current stored data. For example, the complete cut mirror data corresponds to storage blocks S3-S11 in the current stored data, along with the timestamps of data storage within those blocks. This information provides data support for subsequently locating the cut mirror data position and related information, and for completing the stitching of the complete mirror data. For specific analysis steps, please refer to... Figure 5 The steps in the process.

[0115] Step S401: Based on the module detection data and the cut image data, control the detection device to perform parallel detection on the module to be detected in the storage device to determine the partial call detection results.

[0116] Among them, the partial call detection result refers to the detection result of the module to be detected. The processing terminal performs detection-based storage of the cut image data based on the module detection data after determining the module detection data and the cut image data. At the same time, the detection of all modules to be detected is carried out in parallel, thereby improving the detection efficiency of the storage function.

[0117] Step S402: The mirror cut data is spliced ​​according to the cut position information to determine the complete mirror data.

[0118] Among them, complete mirror data refers to a complete mirror image of the currently stored data, which is determined by the processing terminal by splicing the mirrored data according to the segmentation information. Specific analysis steps are detailed below. Figure 7 The steps in the process.

[0119] Step S403: Control the detection device to perform global storage detection on the storage device based on the global detection data and complete image data, and verify the detection return data based on the theoretical return results to determine the global call detection results.

[0120] The global call detection result refers to the detection result obtained after performing a global storage detection on the complete mirror data of the current stored data based on the global detection data. After determining the global detection data and the complete mirror data, the processing terminal controls the detection device to input the global detection data into the complete mirror data for global storage detection, and verifies the returned data obtained after global detection based on the theoretical return result to further determine the storage function detection result, thereby improving the accuracy of storage function detection.

[0121] Step S404: Integrate the partial call detection results with the complete call detection results to determine the stored detection results.

[0122] The storage detection result is consistent with the storage detection result in step S103. After determining the partial call detection result and the complete call detection result, the processing terminal will correlate and integrate the partial call detection result and the complete call detection result to determine the storage terminal through two detection methods: modular detection and global detection. Finally, the detection results will be correlated and integrated to improve the accuracy of fault location of storage function.

[0123] Reference Figure 5 The steps for analyzing the scope of data requirements and currently stored data to determine the cutting mirror data and cutting location information include:

[0124] Step S500: Extract data from the currently stored data according to the data requirement range to determine the independent data segments.

[0125] Independent data refers to data corresponding to the data requirement range, which is extracted and determined by the processing terminal from the currently stored data according to the data requirement range.

[0126] Step S501: Integrate the data requirement scope to determine the total independent scope.

[0127] The total independent range refers to the total coverage of all the data requirements of the modules to be tested in the current stored data, which is determined by the processing terminal by integrating all the data requirement ranges.

[0128] Step S502: Determine whether the total independent range covers the preset complete data range.

[0129] The complete data range refers to the complete data range covered by the current stored data. For example, if the complete data range of the current stored data is S000-S176 storage blocks, the operator will directly input the total range into the system after determining the total data range of the current stored data.

[0130] The processing terminal determines whether the total independent range covers the complete data range, thereby determining whether the data requirement range needs to be expanded to ensure that the data requirement range can completely cover all currently stored data.

[0131] Step S5021: If so, generate cut mirror data based on the independent part data, and locate it in the current stored data according to the data requirement range corresponding to the independent part data to determine the cut position information.

[0132] If the processing terminal determines that the total independent range covers the complete data range, it indicates that there is no need to expand the data requirement range. Therefore, mirrored data is generated based on the independent data, and the data requirement range corresponding to the independent data is located in the current stored data to determine the cutting position information.

[0133] Step S5022: If not, analyze the data requirement range, total independent range, independent part data and current stored data to determine the cut mirror data and cut location information.

[0134] If the processing terminal determines that the total independent range does not cover the complete data range, it indicates that the data requirement range needs to be expanded. Therefore, the data requirement range, the total independent range, the independent data, and the currently stored data are analyzed to determine the cut mirror data and cut location information after expanding the data requirement range. This ensures data integrity when generating complete mirror data from the cut mirror data, improving the accuracy of storage function testing. Specific analysis steps are detailed below. Figure 6 The steps in the process.

[0135] Reference Figure 6 The steps for analyzing the data requirement scope, total independent scope, independent data portions, and currently stored data to determine the split mirror data and split location information include:

[0136] Step S600: Compare the independent data with the currently stored data to determine the unallocated data and adjacent data.

[0137] Unallocated data refers to currently stored data that is not covered by independent data, and is determined by the processing terminal by comparing all independent data with the currently stored data.

[0138] Adjacent data refers to independent data that is adjacent to unallocated data. For example, storage blocks S108-S163 and S165-S170 are independent data, while S164 is unallocated data. These two types of independent data are adjacent data. After determining the unallocated data, the processing terminal compares the data with the independent data to determine the data boundary adjacency. The adjacent data is then determined based on the overlapping data boundaries.

[0139] Step S601: Perform data volume analysis on adjacent data to determine the minimum amount of data.

[0140] Among them, the minimum data refers to the adjacent data with the smallest data volume. The processing terminal determines the data volume of each adjacent data by analyzing the data volume of the adjacent data, and then selects the adjacent data with the smallest data volume as the minimum data.

[0141] Step S602: Concatenate the unallocated data with the minimum amount of data to generate redundant overlay data.

[0142] Among them, redundant overlay data refers to the data after expanding the minimum amount of data based on the unallocated data, which is determined by the processing terminal by splicing the hazardous waste allocation data with the minimum amount of data.

[0143] Step S603: Locate the redundant overlay data to determine its cutting position information in the current stored data.

[0144] Once redundant overlay data is identified, a hash positioning algorithm is used to locate the redundant overlay data in the current stored data, thereby determining the cutting position information of the redundant overlay location in the current stored data.

[0145] Step S604: Perform data mirroring on the independent partial data and redundant overlay data to determine the cut mirror data.

[0146] Once the independent data segment is determined, the independent data segment and the redundant overlay data are mirrored to generate a segmented mirror data that completely covers the currently stored data, providing data support for subsequent determination of partial call detection results and complete mirror data.

[0147] Reference Figure 7 The steps for stitching together the mirrored data based on the cutting position information to determine the complete mirrored data include:

[0148] Step S700: Extract data from the cutting position information to determine the cutting timestamp and the location of the storage module.

[0149] The cutting timestamp refers to the timestamp range of the currently stored data corresponding to the cutting mirror data. The processing terminal extracts data from the cutting position information to determine the timestamp range corresponding to each data in the cutting mirror data, which is the cutting timestamp.

[0150] Storage module location refers to the process of extracting information from the cutting position of the image data to determine the storage module in the current storage data. This process is called storage module location.

[0151] Step S701: Sort the mirror cutting data according to the cutting timestamp and storage module location to determine the time-series cutting data.

[0152] Among them, time-series split data refers to mirrored split data blocks that are consistent with the current stored data order. The processing terminal first splits and sorts the mirrored split data according to each corresponding storage module, and then sorts the data by the split timestamp after the storage modules are sorted.

[0153] Step S702: After removing redundancy from the time-series segmented data, the data is spliced ​​together to determine the complete mirror data.

[0154] The complete mirror data is consistent with the complete mirror data in step S402. After determining the time-series segmented data, the processing terminal removes redundant data through a deduplication algorithm and then splices the deduplicated time-series segmented data through an address mapping splicing algorithm.

[0155] Based on the same inventive concept, embodiments of this application provide a storage function detection system, including:

[0156] The acquisition module is used to acquire the module to be tested, currently stored data, historical input data, and key test functions.

[0157] A memory used to store a program for a terminal storage function detection method;

[0158] The processor and memory can load and execute programs to implement a terminal storage function detection method.

[0159] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0160] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as a terminal storage function detection method.

[0161] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0162] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded and executed by the processor to perform a terminal storage function detection method.

[0163] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0164] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A terminal storage function detection method characterized by comprising: The method comprises the following steps: acquiring a to-be-tested module of a preset storage terminal; analyzing the to-be-tested module to determine module requirement data, data requirement range and regional association rule; analyzing the module requirement data and the regional association rule to determine module detection data, global detection data and theoretical return result; acquiring current storage data; analyzing the to-be-tested module, the module detection data, the global detection data, the theoretical return result, the data requirement range and the current storage data, and controlling a preset detection device to detect the storage terminal to determine storage detection result; the step of analyzing the to-be-tested module, the module detection data, the global detection data, the theoretical return result, the data requirement range and the current storage data, and controlling a preset detection device to detect the storage terminal to determine storage detection result comprises the following steps: analyzing the data requirement range and the current storage data to determine cutting mirror image data and cutting position information; controlling the detection device to detect the to-be-detected module of the storage device in parallel according to the module detection data and the cutting mirror image data to determine partial call detection result; splicing the mirror cutting data according to the cutting position information to determine complete mirror image data; controlling the detection device to detect the storage device globally according to the global detection data and the complete mirror image data, and verifying the detection return data according to the theoretical return result to determine global call detection result; integrating the partial call detection result and the complete call detection result to determine the storage detection result; the step of analyzing the data requirement range and the current storage data to determine cutting mirror image data and cutting position information comprises the following steps: extracting data from the current storage data according to the data requirement range to determine independent partial data; integrating the data requirement range to determine total independent range; determining whether the total independent range covers a preset complete data range; if yes, generating cutting mirror image data according to the independent partial data, and positioning in the current storage data according to the data requirement range corresponding to the independent partial data to determine cutting position information; if no, analyzing the data requirement range, the total independent range, the independent partial data and the current storage data to determine cutting mirror image data and cutting position information; the step of analyzing the data requirement range, the total independent range, the independent partial data and the current storage data to determine cutting mirror image data and cutting position information comprises the following steps: comparing the independent partial data and the current storage data to determine unallocated data and adjacent partial data; analyzing the adjacent partial data to determine minimum amount data; splicing the unallocated data and the minimum amount data to generate redundant coverage data; positioning the redundant coverage data to determine its cutting position information in the current storage data; mirroring the independent partial data and the redundant coverage data to determine cutting mirror image data.

2. The method of claim 1, wherein the terminal storage function detection method is characterized by, the step of analyzing the to-be-tested module to determine module requirement data, data requirement range and regional association rule comprises the following steps: finding the to-be-tested module in a preset historical database to determine module requirement data and data requirement range; extracting data from the historical database to determine historical storage logs; inputting the historical storage logs into a preset sliding window model to determine regional time correlations; inputting the historical storage logs into a preset linear regression model to determine regional logical correlations; integrating the regional time correlations and the regional logical correlations to determine regional correlation rules.

3. The method of claim 1, wherein the terminal storage function detection method is characterized by, The steps of analyzing the module requirement data and the regional correlation rules to determine the module detection data, the global detection data, and the theoretical return result include: obtaining historical input data of the module to be tested; extracting data features from the historical input data to determine quantitative data features; obtaining key test functions of the module to be tested; generating the module detection data and the global detection data according to the quantitative data features and the key test functions; analyzing the global detection data and the regional correlation rules to determine the theoretical return result.

4. The method of claim 1, wherein the terminal storage function detection method is characterized by, The steps of splicing the mirror cutting data according to the cutting position information to determine the complete mirror data include: extracting data from the cutting position information to determine cutting time stamps and storage module positions; sorting the mirror cutting data according to the cutting time stamps and the storage module positions to determine time sequence cutting data; splicing the time sequence cutting data after removing redundancies to determine the complete mirror data.

5. A storage function detection system characterized by comprising: comprise: a module for obtaining a module to be tested and current storage data; a memory for storing a program of a terminal storage function detection method according to any one of claims 1 to 4; a processor, the program in the memory can be loaded and executed by the processor, and the terminal storage function detection method according to any one of claims 1 to 4 is implemented.

6. A smart terminal, characterized by comprise a memory and a processor, the memory has a computer program which can be loaded and executed by the processor to implement the terminal storage function detection method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, a computer program which can be loaded and executed by the processor to implement the terminal storage function detection method according to any one of claims 1 to 4.

Images