A data processing method

Abstract

The present application relates to the field of data processing technology, and in particular to a data processing method, device and electronic device, the method comprising: obtaining an unassociated data table set, detecting the data in each unassociated data table in the unassociated data table set based on a preset rule set, obtaining the detection result corresponding to the unassociated data table set, and adjusting at least one unassociated data table in the unassociated data table set based on the detection result, until each unassociated data table in the unassociated data table set is completely associated with the at least one preset data table corresponding to each. Through the method described above, the data of each unassociated data table in the unassociated data table set is associated with at least one data table corresponding to each, and through the method of patrol detection, the detection result of each unassociated data table set reaches the preset threshold, ensuring the integrity of the unassociated data table set for compliance process inspection, and improving the efficiency of compliance process inspection.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method, device and electronic device. Background Art

[0002] Before application development, an application has multiple data tables. Compliance process checks need to be performed for each data table. Compliance process checks involve multiple data check nodes. Each data check node needs to check different data. The compliance process check will end only when it determines that all data tables are 100% complete. The compliance process check process is as follows:

[0003] If the data to be checked is the data in data table A, data table B, data table C, and data table D, the data check nodes are data check node 1, data check node 2, and data check node 3. The compliance process of the compliance process check of each data check node is Q, W, and E. The set association relationship of the data tables is: data table A is associated with data table B and data table C, data table B is associated with data table A, data table C is associated with data table B and data table D, and data table D is associated with data table A, data table B, and data table C respectively. The set time for the compliance process check is T.

[0004] The above four data tables need to be checked together on data check node 1, data check node 2, and data check node 3 respectively. As long as the association relationship of the data tables in the above four data tables is different from the set association relationship of the data table and / or the actual inspection time is greater than T, it means that the completion degree of the current data table A, data table B, data table C, and data table D is not 100%. At this time, it is necessary to modify the data in at least one of the above data tables. If data table A is only associated with data table B, it means that the number of data tables associated with data table A is 1, and it is necessary to modify the data in data table A and the data in data table B and data table C associated with data table A. After the data modification is completed, repeat the above compliance inspection process until the actual inspection time of the compliance process inspection is less than T, and each data table is associated with a pre-set data table, then it can be determined that the completion degree of the compliance process inspection of the above four data tables is 100%.

[0005] At present, compliance process inspections are conducted by multiple staff members. During the compliance process inspection, staff members will inevitably make judgment errors due to the influence of human subjective factors. When obtaining the test results of the compliance process inspection, it will not only consume a lot of time, but also cannot ensure that the integrity of the data corresponding to the compliance process inspection is 100%. Summary of the invention

[0006] The embodiments of the present application provide a data processing method, device and electronic device for improving the efficiency of compliance process inspection and ensuring that the integrity of data in each unrelated data table in a set of unrelated data tables undergoing compliance process inspection is 100%.

[0007] In a first aspect, the present application provides a data processing method, the method comprising:

[0008] Obtaining an unassociated data table set, wherein each unassociated data table in the unassociated data table set is not completely associated with at least one corresponding preset data table;

[0009] Detecting data in each unassociated data table in the unassociated data table set based on a preset rule set, and obtaining a detection result corresponding to the unassociated data table set;

[0010] At least one unassociated data table in the unassociated data table set is adjusted based on the detection result until each unassociated data table in the unassociated data table set is completely associated with the corresponding at least one preset data table.

[0011] Through the method described above, the data in the unassociated data table set is detected based on the preset rules in the preset rule set. When it is detected that the integrity of the data in the unassociated data table set does not reach the preset threshold, the data in the unassociated data table set is adjusted, and the preset rule set is used again to detect the modified unassociated data table set until each unassociated data table in the unassociated data table set is completely associated with at least one corresponding preset data table, thereby ensuring the integrity of each unassociated data table in the unassociated data table set, avoiding the problem of low efficiency of manual compliance process checks on unassociated data table sets, and improving the efficiency of compliance process checks on unassociated data table sets.

[0012] In a possible design, before obtaining the set of unrelated data tables, it includes:

[0013] Receiving an identification number set, extracting data tables respectively associated with each identification number in the identification number set, and taking all data tables corresponding to the identification number set as an initial data table set;

[0014] The unassociated data in each initial data table are screened out based on the initial preset rule set, and unassociated data tables corresponding to each initial data table are generated based on the unassociated data.

[0015] In one possible design, before receiving the identification number set, it includes:

[0016] Obtain a data table stored in the system and read out the data table name of the data table;

[0017] Combine the data table name with a preset storage name to obtain a data table combination name;

[0018] An identification number of the data table is obtained based on the data table combination name, and the identification number is associated with the data table.

[0019] In a possible design, unrelated data in each initial data table is filtered out based on an initial preset rule set, including:

[0020] Determine row data in the initial data table whose column data is empty, and treat the row data as unassociated data; or

[0021] First data including at least one preset keyword in a preset keyword set is screened out from the initial data table, and the first data is used as unassociated data.

[0022] In a possible design, unrelated data in each initial data table is filtered out based on an initial preset rule set, including:

[0023] Filtering out first data from the initial data table based on a preset keyword set, wherein the first data includes at least one keyword in the preset keyword set;

[0024] Acquire second data not associated with a test scope from the first data, and use the second data as the unassociated data, wherein the test scope records the name of each function in the test case; and

[0025] Acquire third data not associated with a test case from the first data, and use the third data as unassociated data, wherein the test case records a test result of each function.

[0026] In a possible design, detecting data in each unrelated data table in the unrelated data table set based on a preset rule set includes:

[0027] Obtain each preset rule in the preset rule set;

[0028] The data in each unrelated data table is detected based on each preset rule.

[0029] In a possible design, the preset rule set is:

[0030] Determining whether the data in the unassociated data table is associated with a test case; and

[0031] Determining whether data in unassociated data tables associated with test cases is associated with test coverage; and

[0032] Determining whether the data in the unassociated data table is associated with a data pass detail table, wherein the data pass detail table records the execution result of each function; and

[0033] Determining whether the data in the unassociated data table is associated with erroneous data; and

[0034] Determine whether the erroneous data corresponding to the data in the unassociated data table having the erroneous data has been repaired; and

[0035] Determine whether the test status corresponding to the data in the unassociated data table is a test success; and

[0036] Determine whether the data in the unassociated data table is associated with a change case, wherein the change case includes a preset process of historical modification.

[0037] In a possible design, after obtaining the detection result corresponding to the unassociated data table set, the following steps are included:

[0038] Obtaining an actual detection time of an unassociated data table set, and detecting whether the actual detection time exceeds a preset detection time;

[0039] When it is determined that the actual detection time exceeds the preset detection time, the preset detection time is increased to the target detection time, and the unassociated data table set is re-detected.

[0040] In a possible design, adjusting at least one unassociated data table in the unassociated data table set based on the detection result includes:

[0041] When the detection result shows a first preset mark, obtaining first data to be modified with the first preset mark in the initial data table set and second data to be modified associated with the first data to be modified, wherein the first preset mark represents that the integrity of the unassociated data table set does not reach a preset threshold;

[0042] The first data to be modified and the second data to be modified are modified.

[0043] In a second aspect, the present application provides a data processing device, the device comprising:

[0044] An acquisition module is used to obtain a set of unassociated data tables;

[0045] A detection module, configured to detect data in each unassociated data table in the unassociated data table set based on a preset rule set, and obtain a detection result corresponding to the unassociated data table set;

[0046] The determination module is used to adjust at least one unassociated data table in the unassociated data table set based on the detection result until each unassociated data table in the unassociated data table set is completely associated with the at least one preset data table corresponding to each other.

[0047] In one possible design, the acquisition module is specifically used to receive an identification number set, and extract the data tables respectively associated with each identification number in the identification number set, and use all the data tables corresponding to the identification number set as an initial data table set, and filter out the unassociated data in each initial data table based on an initial preset rule set, and generate unassociated data tables corresponding to each initial data table based on the unassociated data.

[0048] In a possible design, the acquisition module is also used to obtain a data table saved in the system, and read out the data table name of the data table, combine the data table name with a preset storage name to obtain a data table combination name, obtain an identification number of the data table based on the data table combination name, and associate the identification number with the data table.

[0049] In a possible design, the acquisition module is also used to determine the row data in the initial data table whose column data is empty, and treat the row data as unassociated data, or to filter out the first data containing at least one preset keyword in the preset keyword set from the initial data table, and treat the first data as unassociated data.

[0050] In one possible design, the acquisition module is also used to filter out first data from the initial data table based on a preset keyword set, obtain second data of an unassociated test range from the first data, and use the second data as unassociated data, and obtain third data of an unassociated test case from the first data, and use the third data as unassociated data.

[0051] In a possible design, the detection module is specifically used to obtain each preset rule in the preset rule set, and detect the data in each unassociated data table based on each preset rule.

[0052] In one possible design, the detection module is also used to determine whether the data in the unassociated data table is associated with a test case, and whether the data in the unassociated data table that is associated with a test case is associated with a test scope, and whether the data in the unassociated data table is associated with a data pass detail table, and whether the data in the unassociated data table is associated with error data, and whether the error data corresponding to the data in the unassociated data table with error data has been repaired, and whether the test status corresponding to the data in the unassociated data table is a successful test, and whether the data in the unassociated data table is associated with a change case.

[0053] In one possible design, the detection module is also used to obtain the actual detection time of the unassociated data table set, detect whether the actual detection time exceeds the preset detection time, and when it is determined that the actual detection time exceeds the preset detection time, increase the preset detection time to the target detection time, and re-detect the unassociated data table set.

[0054] In one possible design, the determination module is specifically used to obtain the first data to be modified with the first preset identifier in the initial data table set and the second data to be modified associated with the first data to be modified, and modify the first data to be modified and the second data to be modified when the detection result displays a first preset identifier.

[0055] In a third aspect, the present application provides an electronic device, including:

[0056] Memory, used to store computer programs;

[0057] The processor is used to implement the above-mentioned data processing method steps when executing the computer program stored in the memory.

[0058] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned data processing method are implemented.

[0059] In a fifth aspect, the present application provides a computer program product, which, when executed on a computer, enables the computer to execute the above-mentioned data processing method steps.

[0060] For each of the above-mentioned first to fifth aspects and the technical effects that may be achieved by each of the above-mentioned aspects, please refer to the above-mentioned description of the technical effects that can be achieved by the first aspect or various possible schemes in the first aspect, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1A flowchart of the steps of a data processing method provided in this application;

[0062] Figure 2 A schematic diagram of the structure of a data processing device provided in this application;

[0063] Figure 3 A schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The specific operating methods in the method embodiments can also be applied to device embodiments or system embodiments. It should be noted that in the description of the present application, "multiple" is understood as "at least two". "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A is connected to B, which can represent: A is directly connected to B and A is connected to B through C. In addition, in the description of the present application, words such as "first" and "second" are only used to distinguish the purpose of description, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order.

[0065] In previous technologies, unassociated data table sets are subject to compliance process checks by multiple staff members. When the staff members conduct compliance process checks on the unassociated data table sets, the inspection results of the compliance process checks on the unassociated data table sets will inevitably be affected by human factors. Since the compliance process check needs to ensure the integrity of the unassociated data table set, when the staff member determines that the integrity of the compliance process check on the unassociated data table set is 100%, it cannot be ensured that the preset data tables corresponding to each unassociated data table in the unassociated data table set have been correctly associated, and it will take a lot of time for the staff member to conduct compliance process checks on the unassociated data table set.

[0066] In order to solve the above-described problems, the embodiments of the present application provide a data processing method to improve the efficiency of compliance process inspection of unrelated data table sets, and to ensure the integrity of each unrelated data table in the unrelated data table set that is subject to compliance process inspection. Among them, the method and device described in the embodiments of the present application are based on the same technical concept. Since the principles of the problems solved by the method and the device are similar, the embodiments of the device and the method can refer to each other, and the repeated parts will not be repeated. The acquisition, storage, use, and processing of data in the technical solution of the present application are in compliance with the relevant provisions of national laws and regulations.

[0067] The embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0068] Reference Figure 1 The present application provides a data processing method, which can improve the efficiency of compliance process checking of unrelated data table sets and ensure the integrity of each unrelated data table in the unrelated data table set for compliance process checking. The implementation process of the method is as follows:

[0069] Step S1: Obtain a set of unrelated data tables.

[0070] Since the embodiment of the present application is to improve the efficiency of the compliance process check of the unrelated data table set and at the end of the compliance process check, the integrity of each unrelated data table in the unrelated data table set is 100%, in order to reduce the time of the compliance process check as much as possible, the system's saved data will be stored in a preset manner. The specific process of storing the system's saved data table in a preset manner is as follows:

[0071] First, obtain the data tables saved in the system. The system can be the system where the compliance process check is performed, or it can be other systems. The saved data tables each use a data table name. Since the data table names of the data tables in the system do not have a unified format, when the data tables need to be obtained for the compliance process check process, it will take a long time to obtain the data tables, which is not conducive to reducing the time for the compliance process check. Therefore, the data table names of the saved data tables and the preset storage names will be extracted, and the above data table names will be combined with the preset storage names to obtain the data combination name.

[0072] In order to obtain various types of data tables more quickly when conducting compliance process checks, the identification number of the data table is obtained based on the data combination name of the data table. The identification number is unique. By associating the identification number with the data table, the purpose of finding the data table corresponding to the identification number can be achieved based on the identification number, thereby improving the efficiency of obtaining data tables.

[0073] For example: the data table name is: data function details table; the preset storage name is: asdXsdq.name.source.file, the data table name is entered into name, that is, the data combination name is asd1sdq.data function details table.source.file, get the string prefix asdXsdq in the data combination name, calculate the identification number in the string prefix, the identification number is X, and X is a natural number.

[0074] After the data table is marked with an identification number, when the inspection device performing compliance process inspection receives the identification number set, the inspection device will extract the data table associated with each identification number from the system based on each identification number in the obtained identification number set, and when all the data tables corresponding to the identification number set are obtained, all the data tables will be used as the initial data table set.

[0075] Since the compliance process check will detect whether each data table is completely associated with its corresponding preset data table, the initial data table set obtained above will be screened through the initial preset rule set to screen out the unassociated data in each initial data table in the initial data table set, and obtain the unassociated data table corresponding to the unassociated data, and then obtain the unassociated data table set corresponding to the initial data table set based on the unassociated data table. The specific process of screening out the unassociated data in each initial data table through the initial preset rule set is as follows:

[0076] Since the compliance process check needs to ensure the integrity of the data table and the relationship between the data table and other data tables, the screening of unrelated data in the initial data table will correspond to multiple initial preset rule sets, which include the following initial preset rules:

[0077] Initial preset rule one: When the column data in the initial data table is empty, it can be determined that the integrity of the data table is not compliant. Therefore, it is necessary to extract the row data with empty column data in the initial data table and treat the row data as unassociated data.

[0078] Initial preset rule 2: based on the preset keyword set, first data including at least one preset keyword in the preset keyword set is screened out from the initial data table, and the first data is used as unassociated data.

[0079] Initial preset rule three: obtain the first data, and use the second data in the first data that is not associated with the test scope as unassociated data, where the test scope records the name of each function in the test case.

[0080] For example: the test scope is: login, registration, and retrieval.

[0081] Initial preset rule four: obtain the first data, and use the third data in the first data that is not associated with the test case as unassociated data, where the test case records the test results of each function.

[0082] For example: the test cases are successful login, failed login, successful registration, failed registration, successful retrieval, and failed retrieval.

[0083] Initial preset rule five: obtain the first data and the data corresponding to the first data table through the details table, and display the fourth data that failed in the details table as unassociated data. The details table records the result of the data execution function.

[0084] For example: The data function details table is shown in Table 1:

[0085] The name of the function performed by the data The result of executing the function on the data 1 Log in pass 2 register pass ... ... ...

[0086] Table 1

[0087] According to the login function example in Table 1 above, when the login function is executed, if the login is successful, the function execution result is recorded as passed; when an incorrect account or password is entered and the login function is executed, it is displayed as successful login, or when the correct account and password are entered and the login function is executed, it is displayed as failed login, then the function execution result is recorded as failed.

[0088] The names of other data execution functions and the results of the data execution functions corresponding to the names of the data execution functions can be referred to any one of the examples in Table 1 above, and no further explanation is given here.

[0089] Initial preset rule six: obtain the first data, and treat the fifth data in the first data that is not associated with the erroneous data as the unassociated data, the erroneous data including the data content not being consistent with the row and column where the data is located, and / or an indicator of data error is displayed when the data is executed.

[0090] for example:

[0091] A-type data Type B data C type data 1 a1 b1 b3 2 a2 b2 c2 ... ... ... ...

[0092] Table 2

[0093] Three types of data are recorded in the above Table 2, a1 and a2 are type A data, b1 and b2 are type B data, and in type C data, b3 in the first row obviously does not belong to type C data, b3 belongs to type B data, therefore, b3 is wrong data, b3 can be a document, or a number, or a program, which is not specifically limited here.

[0094] If table 2 is not associated with the error data b3, table 2 is regarded as unassociated data.

[0095] Initial preset rule seven: obtain the first data and the sixth data whose erroneous data has not been repaired, and use the sixth data as unassociated data.

[0096] For example, taking the above Table 2 as an example, the known erroneous data is b3. After b3 is repaired, if b3 is not repaired to c1, Table 2 is regarded as unrelated data.

[0097] Initial preset rule eight: obtain the seventh data whose test status is test failure, and treat the seventh data of test failure as unassociated data.

[0098] For example, when the data in the first data needs to undergo an integration test or an installation test, and the test status column corresponding to the integration test or the installation test displays test failure, the seventh data corresponding to the test failure column is treated as unassociated data.

[0099] Initial preset rule nine: obtain the first data, and take the eighth data of the unassociated change case as the unassociated data. The change case is a preset process containing historical modifications. The preset process can be a purchase process or a test process, which is not specifically limited here.

[0100] Or, the first quantity value of the changed cases in the first data and the second quantity value of the unchanged cases in the first data are counted. When the first quantity value of the changed cases is not equal to the second quantity value of the unchanged cases, it means that not all data in the first data are associated. Therefore, the eighth data in which the first quantity value of the changed cases is not equal to the second quantity value of the unchanged cases is regarded as unassociated data.

[0101] Initial preset rule ten: obtain the first data and a test summary table of the first data, wherein the test summary table includes all test results of the first data, including functional test results, case test results, results of whether the results are associated with erroneous data, and results of whether the results are associated with the laboratory.

[0102] When the total quantity value of the functional test results is 0, or the total data value of the successful functional test results is not equal to the total quantity value of all functional test results, or the case test result is not equal to 0, when the first data meets any of the conditions described above, the ninth data that meets the above conditions will be treated as unassociated data.

[0103] Ten initial preset rules are recorded above. By adopting the above initial preset rules, ten unrelated data can be obtained. Unrelated data tables corresponding to each unrelated data are generated based on the ten unrelated data. Then, an unrelated data table set corresponding to the initial data table set can be generated based on the unrelated data tables.

[0104] Through the method described above, the unassociated data in each initial data table in the initial data table set is filtered out, and an unassociated data table set is generated based on each unassociated data. The unassociated data in the initial data set is detected through the initial preset rule set, ensuring that the unassociated data can be filtered out from the initial data set.

[0105] Step S2: Detecting data in each unassociated data table in the unassociated data table set based on a preset rule set to obtain a detection result corresponding to the unassociated data table set.

[0106] After obtaining the unassociated data table set through the steps described above, in order to detect whether each unassociated data table in the unassociated data table set is completely associated with at least one corresponding preset data table, a preset rule set is required to detect the data in the unassociated data table set. When performing a compliance process check, each preset rule in the preset rule set needs to detect all the data in the unassociated data set. The preset rule set includes the following preset rules:

[0107] Preset rule 1: Determine whether the data in the unassociated data table is associated with the test case.

[0108] Preset rule two: Determine whether the data in the unassociated data table of the associated test case is associated with the test scope.

[0109] Preset rule three: Determine whether the data in the unassociated data table is associated with the data through the details table.

[0110] Preset rule four: Determine whether the data in the unassociated data table is associated with incorrect data.

[0111] Preset rule five: Determine whether the erroneous data corresponding to the data in the unassociated data table with erroneous data has been repaired.

[0112] Preset rule six: Determine whether the test status corresponding to the data in the unassociated data table is a successful test.

[0113] Preset rule seven: Determine whether the data in the unassociated data table is associated with the change case.

[0114] The above change cases include preset processes for historical modifications, and the preset processes may include procurement processes, testing processes, etc.

[0115] The detection results corresponding to the above preset rule sets are:

[0116] The detection result of the preset rule 1: when the data in the unassociated data table is not associated with the test case, the first preset identifier is output; when the data in the unassociated data table is associated with the test case, the second preset identifier is output.

[0117] In the embodiment of the present application, the first preset identifier may be 0, the second preset identifier may be 1, the first preset identifier is different from the second preset identifier, the first preset identifier and the second preset identifier may be other identifiers, and since the first preset identifier and the second preset identifier are only used to mark different detection results, the embodiment of the present application does not make specific limitations.

[0118] The detection result of preset rule 2: when the data in the unassociated data table of the associated test case is not associated with the test scope, the first preset identifier is output; when the data in the unassociated data table of the associated test case is associated with the test scope, the second preset identifier is output.

[0119] The detection result of preset rule three: when the data in the unassociated data table is not associated with the data through the details table, the first preset identifier is output; when the data in the unassociated data table is associated with the data through the details table, the second preset identifier is output.

[0120] The detection result of the preset rule four: when the data in the unassociated data table is not associated with erroneous data, the first preset identifier is output; when the data in the unassociated data table is associated with erroneous data, the second preset identifier is output.

[0121] The detection result of preset rule five: when the erroneous data corresponding to the data in the unassociated data table with erroneous data has not been repaired, the first preset identifier is output; when the erroneous data corresponding to the data in the unassociated data table with erroneous data has been repaired, the second preset identifier is output.

[0122] The detection result of preset rule six: when the test status corresponding to the data in the unassociated data table is test failure, the first preset identifier is output; when the test status corresponding to the data in the unassociated data table is test success, the second preset identifier is output.

[0123] The detection result of preset rule seven: when the data in the unassociated data table is not associated with a change case, the first preset identifier is output; when the data in the unassociated data table is associated with a change case, the second preset identifier is output.

[0124] After the unassociated data table set traverses all detection rules, each unassociated data table will obtain the same detection result as the preset rule quantity value. Based on the detection result, it can be ensured whether the integrity of the unassociated data table set is within the preset threshold.

[0125] In addition, after the unassociated data table set has traversed all the detection rules once, in order to ensure that the detection of the unassociated data table set is completed within the specified time, it is also necessary to obtain the actual detection time of the unassociated data table set, and detect whether the actual detection time exceeds the preset detection time. When the actual detection time exceeds the preset detection time, the preset detection time will be increased to the target detection time.

[0126] Through the method described above, the unassociated data table set is detected through a preset rule set, which avoids the influence of human factors, resulting in inaccurate detection results of the unassociated data table set, improves the detection efficiency of the unassociated data table set, and ensures the accuracy of the data in the unassociated data table set.

[0127] Step S3: adjusting at least one unassociated data table in the unassociated data table set based on the detection result, until each unassociated data table in the unassociated data table set is completely associated with the corresponding at least one preset data table.

[0128] After the unassociated data table set traverses the preset rule set once, when there is at least one unassociated data table in the unassociated data table set whose corresponding detection result is not the second preset identifier, it is necessary to obtain the unassociated data table corresponding to the first preset identifier and the first data to be modified in the unassociated data table, and it is also necessary to obtain the second data to be modified associated with the first data to be modified, and finally adjust the first data to be modified and the second data to be modified, and update the adjusted data to the unassociated data table set.

[0129] Since the completeness of the unmodified unassociated data table set does not reach the preset threshold, the preset threshold in the embodiment of the present application is 100%. Therefore, it is necessary to re-check the data in the unassociated data table set based on the preset rule set. Since the process of checking the data in the unassociated data table set based on the preset rule set has been described above, it will not be elaborated here.

[0130] When the detection results of all the unassociated data tables in the unassociated data table set are the second preset identification, it is proved that the completeness of the current unassociated data table set is 100%. At this time, no more cyclic detection is performed and the compliance process check ends.

[0131] Through the method described above, the data in the unassociated data table set is detected through the preset rule set. When the detection result indicates that the completeness of the current unassociated data table set is not 100%, the unassociated data table set is adjusted based on the detection result, and then the modified unassociated data table set is detected through the preset rule set, thereby avoiding judgment errors due to the influence of human subjective factors, improving the efficiency of compliance process inspection, and at the end of the compliance process inspection, it can ensure that each unassociated data table in the unassociated data table set is completely associated with at least one corresponding preset data table, thereby ensuring that the completeness of the unassociated data table set is 100%.

[0132] Based on the same inventive concept, a data processing device is also provided in the embodiment of the present application. The data processing device is used to implement the function of a data processing method. Figure 2 , the device comprises:

[0133] An acquisition module 201 is used to obtain a set of unassociated data tables;

[0134] A detection module 202, configured to detect data in each unrelated data table in the unrelated data table set based on a preset rule set, and obtain a detection result corresponding to the unrelated data table set;

[0135] The determination module 203 is configured to adjust at least one unassociated data table in the unassociated data table set based on the detection result, until each unassociated data table in the unassociated data table set is completely associated with the corresponding at least one preset data table.

[0136] In one possible design, the acquisition module 201 is specifically used to receive an identification number set, and extract data tables respectively associated with each identification number in the identification number set, and use all data tables corresponding to the identification number set as an initial data table set, and filter out unassociated data in each initial data table based on an initial preset rule set, and generate unassociated data tables corresponding to each initial data table based on the unassociated data.

[0137] In a possible design, the acquisition module 201 is also used to obtain a data table saved in the system, and read out the data table name of the data table, combine the data table name with a preset storage name to obtain a data table combination name, obtain an identification number of the data table based on the data table combination name, and associate the identification number with the data table.

[0138] In a possible design, the acquisition module 201 is also used to determine the row data in the initial data table whose column data is empty, and treat the row data as unassociated data, or to filter out the first data containing at least one preset keyword in the preset keyword set from the initial data table, and treat the first data as unassociated data.

[0139] In one possible design, the acquisition module 201 is also used to filter out first data from the initial data table based on a preset keyword set, obtain second data of an unassociated test range from the first data, and use the second data as unassociated data, and obtain third data of an unassociated test case from the first data, and use the third data as unassociated data.

[0140] In a possible design, the detection module 202 is specifically used to obtain each preset rule in each preset rule set, and detect the data in the unassociated data table based on each preset rule.

[0141] In one possible design, the detection module 202 is also used to determine whether the data in the unassociated data table is associated with a test case, and whether the data in the unassociated data table that is associated with a test case is associated with a test scope, and whether the data in the unassociated data table is associated with a data pass detail table, and whether the data in the unassociated data table is associated with error data, and whether the error data corresponding to the data in the unassociated data table with error data has been repaired, and whether the test status corresponding to the data in the unassociated data table is a successful test, and whether the data in the unassociated data table is associated with a change case.

[0142] In one possible design, the detection module 202 is also used to obtain the actual detection time of the unassociated data table set, detect whether the actual detection time exceeds the preset detection time, and when it is determined that the actual detection time exceeds the preset detection time, increase the preset detection time to the target detection time, and re-detect the unassociated data table set.

[0143] In one possible design, the determination module 203 is specifically used to obtain the first data to be modified with the first preset identifier in the initial data table set and the second data to be modified associated with the first data to be modified, and modify the first data to be modified and the second data to be modified when the detection result displays a first preset identifier.

[0144] Based on the same inventive concept, an electronic device is also provided in an embodiment of the present application, and the electronic device can realize the functions of the aforementioned data processing device, referring to Figure 3 , the electronic device comprises:

[0145] At least one processor 301, and a memory 302 connected to the at least one processor 301. The specific connection medium between the processor 301 and the memory 302 is not limited in the embodiment of the present application. Figure 3 In the example, the processor 301 and the memory 302 are connected via the bus 300. The bus 300 is connected to the memory 302 via the bus 300. Figure 3 The bus 300 is represented by a bold line, and the connection between other components is only for schematic illustration and is not intended to be limiting. The bus 300 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. Alternatively, the processor 301 can also be called a controller, and there is no limitation on the name.

[0146] In the embodiment of the present application, the memory 302 stores instructions that can be executed by at least one processor 301. The at least one processor 301 can execute a data processing method discussed above by executing the instructions stored in the memory 302. The processor 301 can implement Figure 2 The functions of each module in the device shown.

[0147] Among them, the processor 301 is the control center of the device, and can use various interfaces and lines to connect the various parts of the entire control device. By running or executing instructions stored in the memory 302 and calling data stored in the memory 302, the various functions of the device and process data, the device can be monitored as a whole.

[0148] In one possible design, the processor 301 may include one or more processing units, and the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the modem processor may not be integrated into the processor 301. In some embodiments, the processor 301 and the memory 302 may be implemented on the same chip, and in some embodiments, they may also be implemented separately on separate chips.

[0149] The processor 301 may be a general-purpose processor, such as a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of a data processing method disclosed in the embodiments of the present application may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0150] The memory 302 is a non-volatile computer-readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs and modules. The memory 302 may include at least one type of storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory, a random access memory (Random Access Memory, RAM), a static random access memory (Static Random Access Memory, SRAM), a programmable read-only memory (Programmable Read Only Memory, PROM), a read-only memory (Read Only Memory, ROM), an electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), a magnetic memory, a disk, an optical disk, etc. The memory 302 is any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer, but is not limited thereto. The memory 302 in the embodiment of the present application can also be a circuit or any other device that can realize a storage function, for storing program instructions and / or data.

[0151] By designing and programming the processor 301, the code corresponding to a data processing method introduced in the above embodiment can be fixed into the chip, so that the chip can execute the code when running. Figure 1The steps of a data processing method in the embodiment shown are as follows: How to design and program the processor 301 is a technique known to those skilled in the art and will not be described in detail here.

[0152] Based on the same inventive concept, an embodiment of the present application further provides a storage medium, which stores computer instructions. When the computer instructions are executed on a computer, the computer executes a data processing method discussed above.

[0153] In some possible implementations, various aspects of a data processing method provided by the present application may also be implemented in the form of a program product, which includes program code. When the program product is run on an apparatus, the program code is used to enable the control device to execute the steps of a data processing method according to various exemplary implementations of the present application described above in this specification.

[0154] The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0155] The program product of the data processing method provided in the embodiment of the present invention can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a computing device. However, the program product of the present invention is not limited to this. In this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0156] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein readable program code is carried. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium other than a readable storage medium, which may transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0157] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0158] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0159] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to an embodiment of the present invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0160] In addition, although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0161] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0162] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0163] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0165] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A data processing method, It is characterized in that include: Obtain a data table stored in the system and read out the data table name of the data table; Combine the data table name with a preset storage name to obtain a data table combination name; Obtaining an identification number of the data table based on the data table combination name, and associating the identification number with the data table; Receiving an identification number set, extracting data tables respectively associated with each identification number in the identification number set, and taking all data tables corresponding to the identification number set as an initial data table set; Based on the initial preset rule set, unassociated data in each initial data table is respectively screened out, and based on the unassociated data, unassociated data tables corresponding to each initial data table are generated respectively; Obtaining an unassociated data table set, wherein each unassociated data table in the unassociated data table set is not completely associated with at least one corresponding preset data table; Detecting data in each unassociated data table in the unassociated data table set based on a preset rule set, and obtaining a detection result corresponding to the unassociated data table set; When the detection result shows a first preset mark, obtaining first data to be modified with the first preset mark in the initial data table set and second data to be modified associated with the first data to be modified, wherein the first preset mark represents that the integrity of the unassociated data table set does not reach a preset threshold; The first to-be-modified data and the second to-be-modified data are modified until each unassociated data table in the unassociated data table set is completely associated with the at least one preset data table corresponding to each of them.

2. The method as claimed in claim 1, It is characterized in that Based on the initial preset rule set, the unrelated data in each initial data table is filtered out respectively, including: Determine row data in the initial data table whose column data is empty, and treat the row data as unassociated data; or First data including at least one preset keyword in a preset keyword set is screened out from the initial data table, and the first data is used as unassociated data.

3. The method according to claim 1, It is characterized in that Based on the initial preset rule set, the unrelated data in each initial data table is filtered out respectively, including: Filtering out first data from the initial data table based on a preset keyword set, wherein the first data includes at least one keyword in the preset keyword set; Acquire second data not associated with a test scope from the first data, and use the second data as the unassociated data, wherein the test scope records the name of each function in the test case; and Acquire third data not associated with a test case from the first data, and use the third data as unassociated data, wherein the test case records a test result of each function.

4. The method according to claim 1, It is characterized in that Detecting data in each unrelated data table in the unrelated data table set based on a preset rule set includes: Obtain each preset rule in the preset rule set; The data in each unrelated data table is detected based on each preset rule.

5. The method according to claim 1, It is characterized in that The preset rule set is: Determine whether the data in the unassociated data table is associated with a test case; and Determine whether the data in the unassociated data table that has been associated with the test case is associated with the test scope; and Determining whether the data in the unassociated data table is associated with a data pass detail table, wherein the data pass detail table records the execution result of each function; and Determining whether the data in the unassociated data table is associated with erroneous data; and Determine whether the erroneous data corresponding to the data in the unassociated data table having the erroneous data has been repaired; and Determine whether the test status corresponding to the data in the unassociated data table is a test success; and Determine whether the data in the unassociated data table is associated with a change case, wherein the change case includes a preset process of historical modification.

6. The method according to claim 1, It is characterized in that After obtaining the detection result corresponding to the unassociated data table set, the method includes: Obtaining an actual detection time of an unassociated data table set, and detecting whether the actual detection time exceeds a preset detection time; When it is determined that the actual detection time exceeds the preset detection time, the preset detection time is increased to the target detection time, and the unassociated data table set is re-detected.

7. A data processing device, It is characterized in that include: The acquisition module is used to obtain the data table stored in the system and read out the data table name of the data table; Combine the data table name with a preset storage name to obtain a data table combination name; Obtaining an identification number of the data table based on the data table combination name, and associating the identification number with the data table; Receiving an identification number set, extracting data tables respectively associated with each identification number in the identification number set, and taking all data tables corresponding to the identification number set as an initial data table set; Based on the initial preset rule set, unassociated data in each initial data table is respectively screened out, and based on the unassociated data, unassociated data tables corresponding to each initial data table are generated respectively; Get the set of unrelated data tables; A detection module, configured to detect data in each unassociated data table in the unassociated data table set based on a preset rule set, and obtain a detection result corresponding to the unassociated data table set; a determination module, configured to, when the detection result shows a first preset mark, obtain first data to be modified with the first preset mark in the initial data table set and second data to be modified associated with the first data to be modified, wherein the first preset mark represents that the integrity of the unassociated data table set does not reach a preset threshold; The first to-be-modified data and the second to-be-modified data are modified until each unassociated data table in the unassociated data table set is completely associated with the at least one preset data table corresponding to each of them.

8. An electronic device, It is characterized in that include: Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 6 when executing the computer program stored in the memory.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 6 are implemented.

10. A computer program product, It is characterized in that When the computer program product is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 6.

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