Software testing data management system and method based on big data and artificial intelligence

Abstract

The present invention discloses a software test data management system and method based on big data and artificial intelligence, and relates to the technical field of data management. The system discloses a data generation module, an initial storage module, and a complex data module. The node records obtained by the data generation module are analyzed accordingly by the initial storage module, and the software test data are reasonably encrypted according to the analysis results, and the software test data is transmitted to the corresponding storage space for storage. The complex data module is set up, and the re-examined and revised software test data can be re-encrypted and stored. Based on the modification records of the software test data feature data and the storage data in the storage space, the software test data and the storage data in the storage space are intelligently analyzed, and the storage efficiency and storage rationality of each storage space are gradually optimized. On the basis of ensuring the security of the software test data, it is helpful to narrow the review scope of the software test data in each storage space.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and more specifically, to a software testing data management system and method based on big data and artificial intelligence. Background Art

[0002] With the rapid development of computer technology, various mobile phone, computer and television software have gradually entered people's lives, bringing great convenience to people, especially smart interconnection software, which realizes the exchange of information through the interconnection of two individuals.

[0003] However, in order to ensure the normal use of the majority of users, these software need to be tested multiple times before updating in order to find problems in the software. Since a large amount of test data is generated during software testing, the test data needs to be encrypted and stored. The encrypted and stored test data also needs to be reviewed again, and the corresponding feature data in the test data needs to be corrected. The current test data encryption storage rules are chaotic, resulting in the need to rescan the test data in the storage space every time the test data in the storage space needs to be reviewed, resulting in a large scope of review each time, which is inconvenient for the management of software test data. Summary of the invention

[0004] In view of the deficiencies in the prior art, the object of the present invention is to provide a software testing data management system and method based on big data and artificial intelligence.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] Software testing data management system based on big data and artificial intelligence, including data generation module, initial storage module, and complex data module;

[0007] The data generation module is used to generate a node record during software data testing, and obtain software test data after the software test is completed. Specifically, when the software is being tested, a node record is generated every t time, and the software test data is obtained after the software test is completed;

[0008] Obtain software testing data based on big data;

[0009] The initial storage module is used to obtain the historical encryption times of the software test data, and when the historical encryption times of the software test data is 0 or 1, obtain the encryption vector value of the initial storage data, set the encryption key strength of the initial storage data according to the value of the encryption vector value of the initial storage data, encrypt the initial storage data according to the key strength, generate encrypted data, and transfer the encrypted data to the corresponding storage space for storage;

[0010] The complex data module is used to mark the software test data as complex storage data, adjust the key strength of the encrypted storage according to the vector modification value of the complex storage data, obtain the changed storage value of the software test data when the number of historical encryption times of the software test data is greater than 1, and divide the target space of the complex storage data according to the changed storage value, encrypt the complex storage data, generate encrypted data, and transmit the encrypted data to the corresponding target space for storage.

[0011] Furthermore, when it is necessary to modify the encrypted data in the storage space, the encrypted data is taken out from the storage space and decrypted. After the decryption, the software test data is obtained. After each modification, the byte modification amount, byte modification frequency, etc. of each feature data in the software test data are obtained. When the software test data is modified, the historical number of encryptions of the software test data is obtained. When the historical number of encryptions of the software test data is greater than 1, the change storage value of the software test data is obtained, and a change storage threshold is set. When the change storage value is less than the change storage threshold, the storage space last stored is marked as the target space.

[0012] Furthermore, when the change storage value is greater than or equal to the change storage threshold, the similarity calculation method is used to compare the feature matrix of the software test data with the feature matrix of the data stored in the storage space, obtain the similarity between the software test data and the data stored in the storage space, and mark it as q, obtain the throughput of the storage space in T time before the current time of the system, and mark it as dT, obtain the total storage space margin in the storage space, and mark it as Gs, and use the formula Obtain the optimized storage value Z of the storage space, where c1 is the throughput coefficient and c2 is the total margin coefficient of the storage space, and mark the storage space with the largest optimized storage value Z in the storage space as the target space.

[0013] Furthermore, the spatial recommendation value of the software test data is obtained by the following steps: obtain the total number of nodes in the software test and mark it as n, mark the data complexity corresponding to the time length t in each node record as K, mark the data byte corresponding to the time length t in each node record as i, set the weight vector corresponding to the time length t in each node record as ωt, and use the formula Get the recommended spatial value Pj of the current software test data.

[0014] Further, the encrypted vector value Zs of the initially stored data is obtained by the following steps: obtaining the spatial recommendation value Pj of the initially stored data, using the formula The encrypted vector value Zs of the initially stored data is obtained, where a1 is the spatial recommendation coefficient.

[0015] Furthermore, the change storage value of the software test data is obtained by the following steps: obtaining the decryption frequency of the software test data and marking it as e, obtaining the decryption time of each decryption of the software test data and marking it as S, obtaining the number of storage changes of the software test data before the current time of the system and marking it as Kd, and using the formula The changed storage value Fs of the software test data is obtained, wherein b1 is the storage change times coefficient. When the storage space of the software test data stored this time is different from the storage space of the last storage, the storage change times is set to increase by one.

[0016] Further, the vector modification value Ym of the complex storage data is obtained by the following steps: each time the feature data in the complex storage data is modified, a feature modification record is generated, all feature modification records of the complex storage data within R time before the current system time are obtained, the feature data type in the feature modification record is obtained, the byte modification amounts corresponding to all feature modification records with the same feature data type are sorted in the chronological order of the modification times, the difference between the two adjacent byte modification amounts after sorting is calculated and the absolute value is taken to obtain a byte modification variable, all byte modification variables are summed and averaged to obtain an average modification variable, which is marked as Ew, the modification times of all feature modification records with the same feature data type are sorted in chronological order, the time difference between the two adjacent modification times after sorting is calculated to obtain a modification interval, all modification intervals are summed and averaged to obtain an average modification interval, which is marked as Sg, and the formula is used. Get the feature modification value B of the corresponding feature data, where d1 is the average modification variable coefficient, d2 is the average modification interval coefficient, mark the feature data type as s, and use the formula Obtain the vector modification value Ym of the complex stored data, where e1 is the characteristic data modification coefficient.

[0017] Furthermore, the node record includes the data complexity corresponding to the time length t, the data bytes corresponding to the time length t, and the weight vector corresponding to the time length t.

[0018] Furthermore, the software test data management method based on big data and artificial intelligence includes the following steps:

[0019] Step 1: Generate node records during software data testing, and obtain software test data after software testing is completed;

[0020] Step 2: Obtain the historical encryption times of the software test data. When the historical encryption times of the software test data is 0 or 1, obtain the encryption vector value of the initial storage data, set the encryption key strength of the initial storage data according to the value of the encryption vector value of the initial storage data, encrypt the initial storage data according to the key strength, generate encrypted data, and transfer the encrypted data to the corresponding storage space for storage;

[0021] Step 3: When the encrypted data in the storage space needs to be modified, the encrypted data is taken out from the storage space and decrypted, and the software test data is obtained after the decryption process;

[0022] Step 4: After the software test data is modified, the number of historical encryptions of the software test data is obtained. When the number of historical encryptions of the software test data is greater than 1, the target space of the software test data is divided according to the changed storage value, and it is determined whether the key strength of the encrypted storage of the software test data needs to be adjusted.

[0023] Step 5: Encrypt the duplicate storage data to generate encrypted data, and transfer the encrypted data to the corresponding target space for storage.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. The method of the present invention reasonably encrypts and stores software test data. Based on the modification records of the feature data of the software test data and the storage data in the storage space, the software test data is stored in a more reasonable storage space after each modification, thereby improving the overall management efficiency of the software test data, and comprehensively analyzing and adjusting the key strength of the software test data in encrypted storage based on the modification records of the feature data;

[0026] 2. The initial storage module of the present invention performs corresponding analysis on the node records obtained by the data generation module, reasonably encrypts the software test data according to the analysis results, and transmits the software test data to the corresponding storage space for storage. A complex data module is set up to re-encrypt and store the reviewed and revised software test data, and based on the modification records of the software test data feature data and the storage data in the storage space, the software test data and the storage data in the storage space are intelligently analyzed, and the storage efficiency and storage rationality of each storage space are gradually optimized, which helps to narrow the review scope of the software test data in each storage space on the basis of ensuring the security of the software test data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flowchart of the software test data management method based on big data and artificial intelligence;

[0028] Figure 2It is a principle block diagram of the data duplication module of the present invention. DETAILED DESCRIPTION Example 1

[0029] Reference Figure 2 , a software testing data management system based on big data and artificial intelligence, including data generation module, initial storage module and complex data module.

[0030] The data generation module is used to generate node records during software data testing, and obtain software test data after the software test is completed. Specifically, when the software is tested, a node record is generated every t time length, and the software test data is obtained after the software test is completed. The node record includes the data complexity corresponding to the t time length, the data bytes corresponding to the t time length, and the weight vector corresponding to the t time length.

[0031] Obtain software testing data based on big data.

[0032] The initial storage module is used to obtain the historical encryption times of the software test data. When the historical encryption times of the software test data is 0 or 1, the spatial recommendation value Pj of the software test data of that time is obtained. The spatial recommendation value of the software test data of that time is obtained by the following steps: obtain the total number of nodes of the software test of that time and mark it as n, mark the data complexity corresponding to the duration t in each node record as K, mark the data byte corresponding to the duration t in each node record as i, set the weight vector corresponding to the duration t in each node record as ωt, and use the formula Get the recommended spatial value Pj of the current software test data. Set the range of each recommended spatial value Pj to correspond to a storage space, where the range of the recommended spatial value Pj includes (0, P1], (P1, P2], ..., (Pj-1, Pj], and the storage space includes storage space 1, storage space 2, ..., storage space j-1, storage space j. When Pj∈(0, P1], mark storage space 1 as the pre-storage space of the current software test data, mark the data of the current software test as the initial storage data, and obtain the encrypted vector value of the initial storage data. The encrypted vector value Zs of the initial storage data is obtained by the following steps: Get the recommended spatial value Pj of the initial storage data, and use the formula The encryption vector value Zs of the initial storage data is obtained, where a1 is the space recommendation coefficient, and the value of a1 is 0.35. The larger the space recommendation coefficient of the initial storage data, the larger the encryption vector value of the initial storage data. The key strength of the encryption of the initial storage data is set according to the value of the encryption vector value of the initial storage data, and the initial storage data is encrypted according to the key strength to generate encrypted data, and the encrypted data is transferred to the corresponding storage space for storage. The larger the encryption vector value, the higher the key strength, and the higher the key strength corresponds to the higher the data security. For example, the key length is divided into 128, 196, 256, 1024, etc. The data security of the key length of 1024 is higher than that of the key length of 256.

[0033] The data generation module collects node records of software test data, the initial storage module performs corresponding analysis on the node records obtained by the data generation module, reasonably encrypts the software test data according to the analysis results, and transmits the software test data to the corresponding storage space for storage.

[0034] The data generated after software testing needs to be reviewed again after being encrypted and saved. Therefore, the software testing data after the initial encryption needs to be decrypted and analyzed later. During the analysis, the error data in the software testing data needs to be corrected to facilitate the subsequent reasonable analysis of the software testing.

[0035] The data re-data module is used to re-encrypt the modified encrypted data and reselect the storage space for processing, specifically:

[0036] When the encrypted data in the storage space needs to be modified, the encrypted data is taken out of the storage space and decrypted. After the decryption, the software test data is obtained. After each modification, the byte modification amount and byte modification frequency of each characteristic data in the software test data are obtained. The software test data contains multiple characteristic data. For example, when the tested software is a chat software, the characteristic data of the chat software test data includes but is not limited to security test data, compatibility test data, and functional test data. The byte modification amount and byte modification frequency generated by each modification of the security test data, compatibility test data, and functional test data need to be obtained.

[0037] When the software test data is modified, the historical encryption times of the software test data are obtained. When the historical encryption times of the software test data are greater than 1, the change storage value Fs of the software test data is obtained. The change storage value Fs of the software test data is obtained by the following steps: obtain the decryption frequency of the software test data and mark it as e, obtain the decryption time of each decryption of the software test data and mark it as S, obtain the storage change times of the software test data before the current system time and mark it as Kd, and use the formula The change storage value Fs of the software test data is obtained, where b1 is the storage change times coefficient, and the value of b1 is 0.43. When the storage space of the software test data stored this time is different from the storage space of the last storage, the storage change times are set to increase by one. A change storage threshold is set. When the change storage value is less than the change storage threshold, the storage space of the last storage is marked as the target space.

[0038] When the change storage value is greater than or equal to the change storage threshold, the feature matrix of the software test data is generated according to similar information such as the byte modification amount and byte modification frequency of each feature data of the software test data before the current system time, specifically: , based on similar information such as the byte modification amount and byte modification frequency of the characteristic data of the data stored in the storage space before the current time of the system, a characteristic matrix of the data stored in the storage space is generated, specifically: , where i is the characteristic data type and j is similar information. The characteristic data types include but are not limited to security test data, compatibility test data, functional test data, usability test data, stress test data, and regression test data. Similar information includes but is not limited to byte modification amount and byte modification frequency. This embodiment analyzes byte modification amount and byte modification frequency.

[0039] For example, similar information about each modification of software test data is obtained, including the byte modification amount and byte modification frequency, marked as , get similar information of all stored data in the storage space marked as: , using the cosine similarity calculation method, the similarity is marked as: q;

[0040] like , , the similarity between the software test data and the similar information of the data stored in the storage space is: .

[0041] Use the similarity calculation method to compare the feature matrix of the software test data with the feature matrix of the data stored in the storage space, obtain the similarity between the software test data and the data stored in the storage space, and mark it as q, obtain the throughput of the storage space in T time before the current time of the system, and mark it as dT, obtain the total storage space margin in the storage space, and mark it as Gs, and use the formula Get the optimized storage value Z of the storage space, where c1 is the throughput coefficient, c2 is the total margin coefficient of the storage space, the value of c1 is 0.48, the value of c2 is 0.53, and the storage space with the largest optimized storage value Z in the storage space is marked as the target space. For example, there are 8 storage spaces in total, namely storage space 1, storage space 2, ..., storage space 7, storage space 8, and when the storage space 7 has the largest optimized storage value Z among the above 8 storage spaces, the software test data is stored in storage space 7.

[0042] The software test data is marked as re-stored data, and the vector modification value of the re-stored data is obtained. The vector modification value Ym of the re-stored data is obtained by the following steps: each time the feature data in the re-stored data is modified, a feature modification record is generated, and the feature modification record includes the feature data type, the byte modification amount, and the modification time. For example, the feature data type of the feature modification record is functional test data, and the byte modification amount is 20 bytes. The modification time is 15:20:55 on April 18, 2022.

[0043] Obtain all feature modification records of the stored data within R time before the current system time, obtain the feature data type in the feature modification record, sort the byte modification amounts corresponding to all feature modification records with the same feature data type according to the time sequence of the modification time, calculate the difference between the two adjacent byte modification amounts after sorting and take the absolute value to obtain the byte modification variable, sum up all byte modification variables and take the average to obtain the average modification variable, and mark it as Ew, sort the modification times of all feature modification records with the same feature data type according to the time sequence, calculate the time difference between the two adjacent modification times after sorting to obtain the modification interval, sum up all modification intervals and take the average to obtain the average modification interval, and mark it as Sg, using the formula Get the feature modification value B of the corresponding feature data, where d1 is the average modification variable coefficient, d2 is the average modification interval coefficient, d1 is 0.85, and d2 is 0.73. Mark the feature data type as s, and use the formula The vector modification value Ym of the complex stored data is obtained, wherein e1 is the characteristic data modification coefficient, and the value of e1 is 0.98.

[0044] Set the vector enhancement value and the vector weakening value, wherein the vector enhancement value is greater than the vector weakening value. When the vector modification value of the re-stored data is greater than or equal to the vector enhancement value, obtain the key strength of the re-stored data in the last encrypted storage, and enhance the key strength of the re-stored data for this encryption storage on the basis of the key strength of the last encrypted storage. The enhanced key strength may be to modify the key length of 128 to a key length of 196, or to modify the key length of 128 to a key length of 256. The specific modification standard is set by the system default. When the vector modification value of the re-stored data is less than or equal to the vector weakening value, obtain the key strength of the re-stored data in the last encrypted storage, and weaken the key strength of the re-stored data for this encryption storage on the basis of the key strength of the last encrypted storage. Otherwise, keep the key strength of this encryption storage the same as the key strength of the last encryption storage.

[0045] The duplicate storage data is encrypted to generate encrypted data, and the encrypted data is transmitted to the corresponding target space for storage.

[0046] By setting up a complex data module, the reviewed and revised software test data can be re-encrypted and stored. Based on the modification records of the software test data feature data and the storage data in the storage space, the software test data and the storage data in the storage space can be intelligently analyzed. After each revision of the software test data, it will be stored in a more reasonable storage space to improve the overall management efficiency of the software test data. Based on the comprehensive analysis of the modification records of the feature data and the adjustment of the key strength of the software test data in encrypted storage, the storage efficiency and storage rationality of each storage space can be gradually optimized. On the basis of ensuring the security of the software test data, it helps to narrow the review scope of the software test data in each storage space. Example 2

[0047] Reference Figure 1 , a software test data management method based on big data and artificial intelligence includes the following steps:

[0048] Step 1: Generate node records during software data testing, and obtain software test data after software testing is completed.

[0049] Step 2: Obtain the historical number of encryption times of the software test data. When the historical number of encryption times of the software test data is 0 or 1, obtain the encryption vector value of the initially stored data, set the encryption key strength of the initially stored data according to the numerical value of the encryption vector value of the initially stored data, encrypt the initially stored data according to the key strength, generate encrypted data, and transfer the encrypted data to the corresponding storage space for storage.

[0050] In steps one and two, the node records of the software test data are analyzed, and the node records are analyzed accordingly, and the software test data is reasonably encrypted and stored based on the basic data of each node of the software test data.

[0051] Step 3: When the encrypted data in the storage space needs to be modified, the encrypted data is taken out from the storage space and decrypted, and the software test data is obtained after the decryption.

[0052] Step 4: After the software test data is modified, obtain the historical encryption times of the software test data. When the historical encryption times of the software test data is greater than 1, divide the target space of the software test data according to the changed storage value, and determine whether the key strength of the encrypted storage of the software test data needs to be adjusted.

[0053] Step 5: Encrypt the duplicate storage data to generate encrypted data, and transfer the encrypted data to the corresponding target space for storage.

[0054] In steps three, four, and five, based on the modification records of the characteristic data of the software test data and the storage data in the storage space, intelligent analysis is performed on the software test data and the storage data in the storage space. After each modification of the software test data, it is stored in a more reasonable storage space to improve the overall management efficiency of the software test data. Based on the modification records of the characteristic data, a comprehensive analysis is performed and the key strength of the software test data in encrypted storage is adjusted.

[0055] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0056] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Software testing data management system based on big data and artificial intelligence, characterized by: It includes a data generation module, an initial storage module, and a data recovery module; The data generation module is used to generate node records during software data testing, and obtain software test data after the software test is completed. Specifically, when the software is being tested, a node record is generated every t time, and the software test data is obtained after the software test is completed; The initial storage module is used to obtain the historical encryption times of the software test data, and when the historical encryption times of the software test data is 0 or 1, obtain the encryption vector value of the initial storage data, set the encryption key strength of the initial storage data according to the value of the encryption vector value of the initial storage data, encrypt the initial storage data according to the key strength, generate encrypted data, and transfer the encrypted data to the corresponding storage space for storage; The complex data module is used to mark the software test data as complex storage data, adjust the encryption storage key strength according to the vector modification value of the complex storage data, obtain the changed storage value of the software test data when the number of historical encryption times of the software test data is greater than 1, and divide the target space of the complex storage data according to the changed storage value, encrypt the complex storage data, generate encrypted data, and transfer the encrypted data to the corresponding target space for storage; When it is necessary to modify the encrypted data in the storage space, the encrypted data is taken out from the storage space and decrypted. After the decryption, the software test data is obtained. After each modification, the byte modification amount and byte modification frequency of each feature data in the software test data are obtained. When the software test data is modified, the historical encryption times of the software test data are obtained. When the historical encryption times of the software test data are greater than 1, the change storage value of the software test data is obtained, and the change storage threshold is set. When the change storage value is less than the change storage threshold, the storage space last stored is marked as the target space. When the change storage value is greater than or equal to the change storage threshold, the similarity calculation method is used to compare the feature matrix of the software test data with the feature matrix of the data stored in the storage space, obtain the similarity between the software test data and the data stored in the storage space, and mark it as q, obtain the throughput of the storage space in T time before the current system time, and mark it as dT, obtain the total storage space margin in the storage space, and mark it as Gs, and use the formula Obtaining the optimized storage value Z of the storage space, where c1 is the throughput coefficient and c2 is the total margin coefficient of the storage space, and marking the storage space with the largest optimized storage value Z in the storage space as the target space; The encrypted vector value Zs of the initial stored data is obtained by the following steps: Get the spatial recommendation value Pj of the initial stored data, and use the formula Get the encrypted vector value Zs of the initial stored data, where a1 is the spatial recommendation coefficient; The spatial recommendation value is obtained by the following steps: obtain the total number of nodes in the software test and mark it as n, mark the data complexity corresponding to the time length t in each node record as K, mark the data byte corresponding to the time length t in each node record as i, and set the weight vector corresponding to the time length t in each node record as , using the formula Obtain the recommended spatial value Pj of the current software test data, and set the range of each recommended spatial value Pj to correspond to a storage space, wherein the range of the recommended spatial value Pj includes (0, P1], (P1, P2], ..., (Pj-1, Pj]; The change storage value of the software test data is obtained by the following steps: obtain the decryption frequency of the software test data and mark it as e, obtain the decryption time of each decryption of the software test data and mark it as S, obtain the number of storage changes of the software test data before the current time of the system and mark it as Kd, and use the formula Obtaining a change storage value Fs of the software test data, wherein b1 is a storage change times coefficient, and when the storage space of the software test data stored this time is different from the storage space of the last storage, the storage change times is set to increase by one; The vector modification value Ym of the complex storage data is obtained by the following steps: each time the feature data in the complex storage data is modified, a feature modification record is generated, all feature modification records of the complex storage data within R time before the current system time are obtained, the feature data type in the feature modification record is obtained, the byte modification amounts corresponding to all feature modification records with the same feature data type are sorted in the chronological order of the modification time, the difference between the two adjacent byte modification amounts after sorting is calculated and the absolute value is taken to obtain the byte modification variable, all the byte modification variables are summed up and averaged to obtain the average modification variable, which is marked as Ew, the modification times of all feature modification records with the same feature data type are sorted in chronological order, the time difference between the two adjacent modification times after sorting is calculated to obtain the modification interval, all the modification intervals are summed up and averaged to obtain the average modification interval, which is marked as Sg, and the formula is used. Get the feature modification value B of the corresponding feature data, where d1 is the average modification variable coefficient, d2 is the average modification interval coefficient, mark the feature data type as s, and use the formula Obtain the vector modification value Ym of the complex stored data, where e1 is the characteristic data modification coefficient; The node record includes the data complexity corresponding to the duration t, the data bytes corresponding to the duration t, and the weight vector corresponding to the duration t; The encrypted vector value of the initially stored data is obtained by the following steps: obtaining the spatial recommendation value of the software test data, obtaining the encrypted vector value of the initially stored data according to the spatial recommendation value of the software test data, and the feature modification record includes the feature data type, byte modification amount, and modification time.

2. A software test data management method based on big data and artificial intelligence, applied to the software test data management system based on big data and artificial intelligence as claimed in claim 1, characterized in that: The steps include: Step 1: Generate node records during software data testing, and obtain software test data after software testing is completed; Step 2: Obtain the historical encryption times of the software test data. When the historical encryption times of the software test data is 0 or 1, obtain the encryption vector value of the initial storage data, set the encryption key strength of the initial storage data according to the value of the encryption vector value of the initial storage data, encrypt the initial storage data according to the key strength, generate encrypted data, and transfer the encrypted data to the corresponding storage space for storage; Step 3: When the encrypted data in the storage space needs to be modified, the encrypted data is taken out from the storage space and decrypted, and the software test data is obtained after the decryption process; Step 4: After the software test data is modified, the number of historical encryptions of the software test data is obtained. When the number of historical encryptions of the software test data is greater than 1, the target space of the software test data is divided according to the changed storage value, and it is determined whether the key strength of the encrypted storage of the software test data needs to be adjusted. Step 5: Encrypt the duplicate storage data to generate encrypted data, and transfer the encrypted data to the corresponding target space for storage.

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