Multi-version space-time consistency verification method and system for test case and medium

Abstract

The invention discloses a multi-version space-time consistency verification method and system for a test case and a medium, mainly relates to the technical field of test cases, and is used for solving the problem of low efficiency of large-scale data verification due to multi-focus time or space single-dimension verification in the prior art. Comprising the following steps: randomly extracting a preset number of analysis nodes from a plurality of deployment nodes, and obtaining formula programs of test cases on the analysis nodes; extracting a preset non-core formula program and all preset core formula programs in a preset proportion, and comparing output data of the preset non-core formula program with output data of all the preset core formula programs to obtain a comparison result; comparing the sampling similarity of the sampling data in the same automatic comparison combination through the deployment nodes in the same automatic comparison combination; sampling similarities corresponding to all the automatic comparison combinations are obtained; and determining that multiple versions of the test cases involved in the automatic comparison combination with the sampling similarity greater than a preset similarity threshold are consistent in time and space, otherwise, inconsistent in time and space.

Description

Technical Field

[0001] The present application relates to the technical field of version spatiotemporal consistency verification, and in particular to a multi-version spatiotemporal consistency verification method, system, and medium for testing a test case. Background Art

[0002] As software systems become more complex and iterations become more frequent, parallel testing of multiple versions (such as A / B testing, phased releases, and transitions between legacy and new systems) has become the norm. However, traditional testing methods have the following limitations in temporal and spatial consistency verification: 1. Single-Dimension Verification Flaws: Existing technologies often focus on single-dimensional verification, either in time or space. For example, time series comparison tools are used to check version iteration results, but they fail to consider spatial differences across different deployment environments (e.g., cloud and edge nodes). 2. Inefficient Large-Scale Data Verification: Traditional full-data comparison methods (e.g., comparing database records row by row) suffer from high computing resource consumption and significant response delays when faced with massive test cases.

[0003] Therefore, there is an urgent need for a multi-version spatiotemporal consistency verification method, system, and medium for test cases to solve the problems that existing technologies focus on single-dimensional verification of time or space and are inefficient in large-scale data verification. Summary of the Invention

[0004] The present application provides a multi-version spatiotemporal consistency verification method, system, and medium for test cases to solve the problems of existing technologies that focus on single-dimensional verification of time or space and have low efficiency in large-scale data verification.

[0005] In a first aspect, the present application provides a method for verifying the spatiotemporal consistency of multiple versions of a test case, the method comprising: Randomly extract a preset number of analysis nodes from several deployment nodes, and obtain the formula program of the test case on the analysis node; wherein, the execution data includes the preset non-core formula program and the preset core formula program, and the test case version on the deployment node is the preset configuration version of the current deployment node server type; extract the preset non-core formula program and all the preset core formula programs in a preset ratio, and obtain the comparison result by comparing the output data of the preset non-core formula program and all the preset core formula programs; when the comparison result is all consistent, obtain the location information of each deployment node, obtain the distance information between the deployment nodes, and establish a Automated comparison combination; obtain the data generation interval of the test case in the deployment node, determine the sampling interval of each deployment node based on the data generation interval, send the same test data to the same automated comparison combination, and obtain the sampling data; compare the sampling similarity of the sampling data between the same automated comparison combination through the deployment nodes in the same automated comparison combination; when the comparison results are not completely consistent, send the inconsistent data to the preset maintenance terminal; obtain the sampling similarity corresponding to all automated comparison combinations; determine that the multiple versions of the test cases involved in the automated comparison combination with a sampling similarity greater than a preset similarity threshold are consistent in time and space, otherwise they are inconsistent in time and space.

[0006] In one implementation of the present application, there is a preset formula comparison table, and the preset formula comparison table stores the evolution relationship between formula programs in different versions of test cases; By comparing the output data of the preset non-core formula program and all the preset core formula programs, the comparison results are obtained, including: Based on the evolution relationship in the preset formula comparison table, the formula program is divided into several groups; Input the same preset input data into each formula program in the same group to obtain several output data; Compare the output data in the same group to see if they are the same data, and obtain the sub-comparison result of the current group; The sub-alignment results of all groups are taken as the alignment results.

[0007] In one implementation of the present application, obtaining the location information of each deployment node, obtaining the distance information between the deployment nodes, and establishing an automated comparison combination based on the distance information specifically includes: Obtain the location information of each deployment node; the location information includes latitude and longitude; According to the location information, the distance between the two deployed nodes is calculated to obtain the distance information between the two deployed nodes; Nodes will be deployed in pairs based on their geographical location, from east to west and from south to north, and based on the principle of closest distance, to establish an automated comparison combination. When only one deployment node remains, the last deployment node is added to the previous automated comparison combination.

[0008] In one implementation of the present application, obtaining the data generation interval of the test case in the deployment node, determining the sampling interval of each deployment node based on the data generation interval, sending the same test data to the same automated comparison combination, and obtaining the sampled data specifically includes: Obtaining a data generation interval for a test case within a deployment node, and determining a sampling interval for each deployment node based on the data generation interval; wherein the ratio between the data generation interval and the sampling interval of all deployment nodes is the same; The same test data is sent to the test cases of the deployment nodes in the same automated comparison combination, and the test cases are run. Each deployment node collects and obtains the sampling data of the current deployment node according to its own sampling interval.

[0009] In one implementation of the present application, comparing the sampling similarity of sampled data between the same automated comparison combinations by deployment nodes within the same automated comparison combination specifically includes: Determine the deployment node with the largest computing power within the same automated comparison combination as the computing node; Configure data comparison algorithms within computing nodes; Obtain the sample data uploaded by other deployment nodes in the same automated comparison combination through the computing node; The sampling similarity between the sampling data involved in the same automated comparison combination is determined by the data comparison algorithm built into the computing node.

[0010] In a second aspect, the present application provides a multi-version spatiotemporal consistency verification system for a test case, the system comprising: The acquisition module is used to randomly extract a preset number of analysis nodes from several deployment nodes and obtain the formula program of the test case on the analysis node; wherein, the execution data includes the preset non-core formula program and the preset core formula program, and the test case version on the deployment node is the preset configuration version of the current deployment node server type; the comparison module is used to extract the preset non-core formula program and all the preset core formula programs of a preset ratio, and obtain the comparison result by comparing the output data of the preset non-core formula program and all the preset core formula programs; the sampling module is used to obtain the location information of each deployment node and the distance information between the deployment nodes when the comparison results are all consistent, and obtain the distance information between the deployment nodes according to the distance. The system establishes an automated comparison combination based on the information; obtains the data generation interval of the test case in the deployment node, determines the sampling interval of each deployment node based on the data generation interval, sends the same test data to the same automated comparison combination, and obtains the sampling data; compares the sampling similarity of the sampling data between the same automated comparison combination through the deployment nodes in the same automated comparison combination; a sending module is used to send the inconsistent data to the preset maintenance terminal when the comparison results are not all consistent; a determination module is used to obtain the sampling similarity corresponding to all automated comparison combinations; determines that the multiple versions of the test cases involved in the automated comparison combination whose sampling similarity is greater than the preset similarity threshold are consistent in time and space, otherwise they are inconsistent in time and space.

[0011] In one implementation of the present application, there is a preset formula comparison table, and the preset formula comparison table stores the evolution relationship between formula programs in different versions of test cases; The comparison module includes a comparison unit, Used to divide formula programs into several groups based on the evolution relationship in the preset formula comparison table; Input the same preset input data into each formula program in the same group to obtain several output data; Compare the output data in the same group to see if they are the same data, and obtain the sub-comparison result of the current group; The sub-alignment results of all groups are taken as the alignment results.

[0012] In one implementation of the present application, the sampling module includes an adding unit, Used to obtain the location information of each deployment node; the location information includes latitude and longitude; According to the location information, the distance between the two deployed nodes is calculated to obtain the distance information between the two deployed nodes; Nodes will be deployed in pairs based on their geographical location, from east to west and from south to north, and based on the principle of closest distance, to establish an automated comparison combination. When only one deployment node remains, the last deployment node is added to the previous automated comparison combination.

[0013] In one implementation of the present application, the sampling module includes an obtaining unit, Used to obtain the data generation interval of the test case in the deployment node, and determine the sampling interval of each deployment node based on the data generation interval; wherein the ratio between the data generation interval and the sampling interval of all deployment nodes is the same; The same test data is sent to the test cases of the deployment nodes in the same automated comparison combination, and the test cases are run. Each deployment node collects and obtains the sampling data of the current deployment node according to its own sampling interval.

[0014] In a third aspect, the present application provides a non-volatile computer storage medium having computer instructions stored thereon, which, when executed, implement a multi-version spatiotemporal consistency verification method for a test case such as any one of the above items.

[0015] It can be seen from the above technical solutions that this application has the following advantages: 1. Multi-dimensional verification integration breaks through the limitations of single verification: An automated comparison combination is established based on the geographical distance of the deployed nodes. The sampling frequency is dynamically adjusted based on the data generation interval of each node. The time series similarity (version iteration consistency) and spatial topology correlation (cross-node data synchronization integrity) are integrated for analysis, overcoming the limitations of traditional methods that only focus on a single dimension of time or space.

[0016] ‌2. Layered verification mechanism improves efficiency of large-scale data:‌ For preset core formula programs, ensure zero omission of key business data; for preset non-core formula programs (such as log cleaning scripts), conduct random sampling verification in proportion (such as 30%) to reduce the amount of non-critical data verification, taking into account both efficiency and coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for the description. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flow chart of a multi-version spatiotemporal consistency verification method for a test case provided in an embodiment of the present application.

[0019] Figure 2 This is a schematic diagram of the internal structure of a multi-version spatiotemporal consistency verification system for a test case provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] It should be understood by those skilled in the art that the embodiments described below are merely preferred embodiments of the present disclosure and do not imply that the present disclosure can only be implemented through these preferred embodiments. These preferred embodiments are merely intended to explain the technical principles of the present disclosure and are not intended to limit the scope of protection of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by those skilled in the art without creative effort should still fall within the scope of protection of the present disclosure.

[0022] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0023] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0024] The embodiment provides a method for checking the spatiotemporal consistency of multiple versions of a test case, such as Figure 1 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 110: randomly select a preset number of analysis nodes from a number of deployment nodes, and obtain formula programs for test cases on the analysis nodes.

[0025] The execution data includes preset non-core formula programs and preset core formula programs, and the test case version on the deployment node is the preset configuration version of the current deployment node server type.

[0026] It should be noted that this step constructs a flexible verification network by randomly selecting a preset number of analysis nodes to achieve intelligent load balancing of computing resources, thereby avoiding resource bottlenecks that may be caused by fixed nodes.

[0027] Application Example: Assume a system has 100 deployed nodes. 20% (i.e., 20 analysis nodes) are randomly selected based on a preset ratio. If the node types include Linux and Windows servers, 10 nodes are randomly selected from each type of server, forming a heterogeneous verification network.

[0028] Core formula program: corresponds to the initialized global variables in the data segment, such as the core interest calculation algorithm of the financial system.

[0029] Non-core formula program: corresponds to the dynamically generated data in the heap or stack, such as the formatting function of user behavior logs.

[0030] Step 120 : extracting a preset proportion of preset non-core formula programs and all preset core formula programs, and obtaining a comparison result by comparing output data of the preset non-core formula programs and all preset core formula programs.

[0031] It should be noted that this application uses a stratified random sampling strategy, sampling non-core formula programs at a preset ratio (e.g., 20%-40%), while fully verifying core formula programs. This design ensures the integrity verification of key business logic.

[0032] Example: Core program: Risk control algorithm for financial trading systems (full verification); Non-core programs: encryption functions for user login logs (sampled at a 30% ratio).

[0033] A preset formula comparison table exists, and it stores the evolutionary relationships between formula programs in different versions of test cases (version evolution relationship mapping: the preset formula comparison table records the version iteration path of the formula program in the form of a directed acyclic graph (DAG). It should be noted that the evolutionary relationship represents program A in version 1 and the corresponding evolved program in version 2.

[0034] By comparing the output data of the preset non-core formula program and all the preset core formula programs, the comparison results are obtained, including: Based on the evolution relationship in the preset formula comparison table, the formula program is divided into several groups; Input the same preset input data into each formula program in the same group to obtain several output data; Compare the output data in the same group to see if they are the same data, and obtain the sub-comparison result of the current group; The sub-alignment results of all groups are taken as the alignment results.

[0035] Step 130: When the comparison results are all consistent, obtain the location information of each deployment node, obtain the distance information between the deployment nodes, and establish an automated comparison combination based on the distance information; obtain the data generation interval of the test case in the deployment node, determine the sampling interval of each deployment node based on the data generation interval, send the same test data to the same automated comparison combination, and obtain sampled data; compare the sampling similarity of the sampled data between the deployment nodes in the same automated comparison combination through the deployment nodes in the same automated comparison combination.

[0036] It should be noted that the physical distance is calculated based on the geographical coordinates (such as longitude and latitude) of the deployment nodes, and a multi-dimensional topology model is constructed in combination with network latency indicators.

[0037] For example, in a cross-regional data center scenario, the physical distance between the Beijing node (116.4°E, 39.9°N) and the Shanghai node (121.5°E, 31.2°N) is approximately 1,068 kilometers.

[0038] The location information of each deployment node is obtained, the distance information between the deployment nodes is obtained, and an automated comparison combination is established based on the distance information. Specifically, the following steps can be performed: Obtain the location information of each deployment node; the location information includes latitude and longitude; According to the location information, the distance between the two deployed nodes is calculated to obtain the distance information between the two deployed nodes; Nodes will be deployed in pairs based on their geographical location, from east to west and from south to north, and based on the principle of closest distance, to establish an automated comparison combination. When only one deployment node remains, the last deployment node is added to the previous automated comparison combination.

[0039] The data generation interval of the test case in the deployment node is obtained, the sampling interval of each deployment node is determined based on the data generation interval, the same test data is sent to the same automated comparison combination, and the sampling data is obtained. Specifically, it can be: Obtaining a data generation interval for a test case within a deployment node, and determining a sampling interval for each deployment node based on the data generation interval; wherein the ratio between the data generation interval and the sampling interval of all deployment nodes is the same; The same test data is sent to the test cases of the deployment nodes in the same automated comparison combination, and the test cases are run. Each deployment node collects and obtains the sampling data of the current deployment node according to its own sampling interval.

[0040] The sampling similarity of the sampled data between the same automated comparison combination is compared by the deployment nodes within the same automated comparison combination, which can be specifically: Determine the deployment node with the largest computing power within the same automated comparison combination as the computing node; Configure data comparison algorithms within computing nodes; Obtain the sample data uploaded by other deployment nodes in the same automated comparison combination through the computing node; The sampling similarity between the sampling data involved in the same automated comparison combination is determined by the data comparison algorithm built into the computing node.

[0041] Step 140: When the comparison results are not completely consistent, the inconsistent data is sent to a preset maintenance terminal.

[0042] Step 150: Obtain sampling similarities corresponding to all automated comparison combinations; determine that the test case versions involved in the automated comparison combinations whose sampling similarities are greater than a preset similarity threshold are consistent in time and space, otherwise they are inconsistent in time and space.

[0043] Based on the description above, it can be seen that this embodiment achieves time-space dual-dimensional cross-validation by establishing an automated comparison combination, overcoming the limitations of single-dimensional verification of traditional methods. The dynamic comparison network established based on the deployment node location information not only takes into account the geographical spatial distribution characteristics, but also achieves accurate matching of time series through dynamic sampling of data generation intervals. Adopting the core / non-core formula program classification processing strategy, under the premise of ensuring the integrity of the verification, the data scale of the traditional full verification is reduced through the intelligent dimensionality reduction method of preset proportion sampling, which is particularly suitable for the massive data verification scenario of large-scale distributed systems. By analyzing the node random extraction mechanism to build a flexible verification network, combined with the topological perception capability of the deployment node spacing, intelligent load balancing of computing resources is achieved. Compared with the fixed node verification mode, resource utilization is improved. The intelligent sampling interval determination method based on the data generation interval can dynamically adjust the sampling frequency according to the actual operating status of the system, establish the optimal balance between data freshness and processing efficiency, and ensure the timeliness of real-time verification. A hierarchical alerting strategy is employed to automatically handle non-core program discrepancies, while a closed-loop "verification-alert-maintenance" process is established for core program discrepancies, shortening the average response time for critical anomalies. A multi-verification mechanism with preset similarity thresholds has been established, encompassing spatial topology verification, time series verification, and core program verification, enhancing the confidence level of verification results. A server-type-based configuration version matching mechanism effectively addresses the challenge of multi-version compatibility verification in heterogeneous environments, supporting full-lifecycle consistency verification across platforms and versions.

[0044] In addition, this application Figure 2 A multi-version spatiotemporal consistency verification system for a test case provided in an embodiment of the present application. Figure 2 As shown, the system provided in the embodiment of the present application mainly includes: An acquisition module 210 is configured to randomly select a preset number of analysis nodes from a plurality of deployment nodes and obtain formula programs for test cases on the analysis nodes; wherein the execution data includes a preset non-core formula program and a preset core formula program, and the test case version on the deployment node is a preset configuration version of the current deployment node server type; A comparison module 220 is configured to extract a preset proportion of preset non-core formula programs and all preset core formula programs, and obtain a comparison result by comparing output data of the preset non-core formula programs with output data of all preset core formula programs; There is a preset formula comparison table, and the preset formula comparison table stores the evolution relationship between formula programs in different versions of test cases; The comparison module 220 includes a comparison unit, Used to divide formula programs into several groups based on the evolution relationship in the preset formula comparison table; Input the same preset input data into each formula program in the same group to obtain several output data; Compare the output data in the same group to see if they are the same data, and obtain the sub-comparison result of the current group; The sub-alignment results of all groups are taken as the alignment results.

[0045] The sampling module 230 is configured to, when the comparison results are all consistent, obtain the location information of each deployment node, obtain the distance information between the deployment nodes, and establish an automated comparison combination based on the distance information; obtain the data generation interval of the test case within the deployment node, determine the sampling interval of each deployment node based on the data generation interval, send the same test data to the same automated comparison combination, and obtain sampled data; and compare the sampling similarity of the sampled data between the deployment nodes within the same automated comparison combination. The sampling module 230 includes an adding unit, Used to obtain the location information of each deployment node; the location information includes latitude and longitude; According to the location information, the distance between the two deployed nodes is calculated to obtain the distance information between the two deployed nodes; Nodes will be deployed in pairs based on their geographical location, from east to west and from south to north, and based on the principle of closest distance, to establish an automated comparison combination. When only one deployment node remains, the last deployment node is added to the previous automated comparison combination.

[0046] The sampling module 230 includes an acquisition unit, Used to obtain the data generation interval of the test case in the deployment node, and determine the sampling interval of each deployment node based on the data generation interval; wherein the ratio between the data generation interval and the sampling interval of all deployment nodes is the same; The same test data is sent to the test cases of the deployment nodes in the same automated comparison combination, and the test cases are run. Each deployment node collects and obtains the sampling data of the current deployment node according to its own sampling interval.

[0047] The sending module 240 is used to send the inconsistent data to a preset maintenance terminal when the comparison results are not completely consistent; The determination module 250 is used to obtain the sampling similarities corresponding to all automated comparison combinations; determine that the test case versions involved in the automated comparison combinations with sampling similarities greater than a preset similarity threshold are consistent in time and space, otherwise they are inconsistent in time and space.

[0048] Based on the description above, it can be seen that this embodiment achieves time-space dual-dimensional cross-validation by establishing an automated comparison combination, overcoming the limitations of single-dimensional verification of traditional methods. The dynamic comparison network established based on the deployment node location information not only takes into account the geographical spatial distribution characteristics, but also achieves accurate matching of time series through dynamic sampling of data generation intervals. Adopting the core / non-core formula program classification processing strategy, under the premise of ensuring the integrity of the verification, the data scale of the traditional full verification is reduced through the intelligent dimensionality reduction method of preset proportion sampling, which is particularly suitable for the massive data verification scenario of large-scale distributed systems. By analyzing the node random extraction mechanism to build a flexible verification network, combined with the topological perception capability of the deployment node spacing, intelligent load balancing of computing resources is achieved. Compared with the fixed node verification mode, resource utilization is improved. The intelligent sampling interval determination method based on the data generation interval can dynamically adjust the sampling frequency according to the actual operating status of the system, establish the optimal balance between data freshness and processing efficiency, and ensure the timeliness of real-time verification. A hierarchical alerting strategy is employed to automatically handle non-core program discrepancies, while a closed-loop "verification-alert-maintenance" process is established for core program discrepancies, shortening the average response time for critical anomalies. A multi-verification mechanism with preset similarity thresholds has been established, encompassing spatial topology verification, time series verification, and core program verification, enhancing the confidence level of verification results. A server-type-based configuration version matching mechanism effectively addresses the challenge of multi-version compatibility verification in heterogeneous environments, supporting full-lifecycle consistency verification across platforms and versions.

[0049] In addition, an embodiment of the present application further provides a non-volatile computer storage medium on which executable instructions are stored. When the executable instructions are executed, a multi-version spatiotemporal consistency verification method of a test case as described above is implemented.

[0050] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for checking the spatiotemporal consistency of multiple versions of a test case, characterized in that: The method comprises: Randomly extracting a preset number of analysis nodes from a number of deployment nodes, and obtaining formula programs for test cases on the analysis nodes; wherein the execution data includes a preset non-core formula program and a preset core formula program, and the test case version on the deployment node is a preset configuration version of the current deployment node server type; Extracting a preset proportion of preset non-core formula programs and all preset core formula programs, and obtaining a comparison result by comparing output data of the preset non-core formula programs and all preset core formula programs; When the comparison results are all consistent, the location information of each deployment node is obtained, the distance information between the deployment nodes is obtained, and an automated comparison combination is established based on the distance information; the data generation interval of the test case within the deployment node is obtained, and the sampling interval of each deployment node is determined based on the data generation interval. The same test data is sent to the same automated comparison combination to obtain sampled data; the sampling similarity of the sampled data between the deployment nodes in the same automated comparison combination is compared; When the comparison results are not completely consistent, the inconsistent data will be sent to the preset maintenance terminal; Obtain the sampling similarities corresponding to all automated comparison combinations; determine that the multiple versions of the test cases involved in the automated comparison combinations whose sampling similarities are greater than a preset similarity threshold are consistent in time and space, otherwise they are inconsistent in time and space.

2. The multi-version spatiotemporal consistency verification method for test cases according to claim 1, characterized in that: There is a preset formula comparison table, and the preset formula comparison table stores the evolution relationship between formula programs in different versions of test cases; By comparing the output data of the preset non-core formula program and all the preset core formula programs, the comparison results are obtained, including: Based on the evolution relationship in the preset formula comparison table, the formula program is divided into several groups; Input the same preset input data into each formula program in the same group to obtain several output data; Compare the output data in the same group to see if they are the same data, and obtain the sub-comparison result of the current group; The sub-alignment results of all groups are taken as the alignment results.

3. The multi-version spatiotemporal consistency verification method for test cases according to claim 1, characterized in that: Obtain the location information of each deployment node, obtain the distance information between deployment nodes, and establish an automated comparison combination based on the distance information, specifically including: Obtain the location information of each deployment node; the location information includes latitude and longitude; According to the location information, the distance between the two deployed nodes is calculated to obtain the distance information between the two deployed nodes; Nodes will be deployed in pairs based on their geographical location, from east to west and from south to north, and based on the principle of closest distance, to establish an automated comparison combination. When only one deployment node remains, the last deployment node is added to the previous automated comparison combination.

4. The multi-version spatiotemporal consistency verification method for test cases according to claim 1, characterized in that: Obtain the data generation interval of the test cases in the deployment nodes, determine the sampling interval of each deployment node based on the data generation interval, send the same test data to the same automated comparison combination, and obtain the sampled data, specifically including: Obtaining a data generation interval for a test case within a deployment node, and determining a sampling interval for each deployment node based on the data generation interval; wherein the ratio between the data generation interval and the sampling interval of all deployment nodes is the same; The same test data is sent to the test cases of the deployment nodes in the same automated comparison combination, and the test cases are run. Each deployment node collects and obtains the sampling data of the current deployment node according to its own sampling interval.

5. The multi-version spatiotemporal consistency verification method for test cases according to claim 1, characterized in that: The sampling similarity of the sampled data between the deployment nodes in the same automated comparison combination is compared, specifically including: Determine the deployment node with the largest computing power within the same automated comparison combination as the computing node; Configure data comparison algorithms within computing nodes; Obtain the sample data uploaded by other deployment nodes in the same automated comparison combination through the computing node; The sampling similarity between the sampling data involved in the same automated comparison combination is determined by the data comparison algorithm built into the computing node.

6. A multi-version spatiotemporal consistency verification system for test cases, characterized in that: The system comprises: An acquisition module is used to randomly extract a preset number of analysis nodes from a number of deployment nodes and obtain formula programs for test cases on the analysis nodes; wherein the execution data includes preset non-core formula programs and preset core formula programs, and the test case version on the deployment node is a preset configuration version of the current deployment node server type; a comparison module for extracting a preset proportion of preset non-core formula programs and all preset core formula programs, and obtaining a comparison result by comparing output data of the preset non-core formula programs and all preset core formula programs; The sampling module is used to obtain the location information of each deployment node and the distance information between the deployment nodes when the comparison results are all consistent, and establish an automated comparison combination based on the distance information; obtain the data generation interval of the test case within the deployment node, determine the sampling interval of each deployment node based on the data generation interval, send the same test data to the same automated comparison combination, and obtain sampling data; compare the sampling similarity of the sampling data between the deployment nodes in the same automated comparison combination; The sending module is used to send the inconsistent data to the preset maintenance terminal when the comparison results are not completely consistent; The determination module is used to obtain the sampling similarity corresponding to all automated comparison combinations; determine that the multiple versions of the test cases involved in the automated comparison combination with a sampling similarity greater than a preset similarity threshold are consistent in time and space, otherwise they are inconsistent in time and space.

7. The multi-version spatiotemporal consistency verification system for test cases according to claim 6, characterized in that: There is a preset formula comparison table, and the preset formula comparison table stores the evolution relationship between formula programs in different versions of test cases; The comparison module includes a comparison unit, Used to divide formula programs into several groups based on the evolution relationship in the preset formula comparison table; Input the same preset input data into each formula program in the same group to obtain several output data; Compare the output data in the same group to see if they are the same data, and obtain the sub-comparison result of the current group; The sub-alignment results of all groups are taken as the alignment results.

8. The multi-version spatiotemporal consistency verification system for test cases according to claim 6, characterized in that: The sampling module includes an adding unit, Used to obtain the location information of each deployment node; the location information includes latitude and longitude; According to the location information, the distance between the two deployed nodes is calculated to obtain the distance information between the two deployed nodes; Nodes will be deployed in pairs based on their geographical location, from east to west and from south to north, and based on the principle of closest distance, to establish an automated comparison combination. When only one deployment node remains, the last deployment node is added to the previous automated comparison combination.

9. The multi-version spatiotemporal consistency verification system for test cases according to claim 6, characterized in that: The sampling module includes an acquisition unit, Used to obtain the data generation interval of the test case in the deployment node, and determine the sampling interval of each deployment node based on the data generation interval; wherein the ratio between the data generation interval and the sampling interval of all deployment nodes is the same; The same test data is sent to the test cases of the deployment nodes in the same automated comparison combination, and the test cases are run. Each deployment node collects and obtains the sampling data of the current deployment node according to its own sampling interval.

10. A non-volatile computer storage medium, characterized in that Computer instructions are stored thereon, and when the computer instructions are executed, the multi-version spatiotemporal consistency verification method of a test case according to any one of claims 1 to 5 is implemented.

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