Deployment automation verification method and system

Abstract

The invention provides a deployment automation verification method and system, and relates to the technical field of computers, and the method comprises the steps: obtaining a multi-dimensional feature; the multi-dimensional features are a plurality of execution environment features when the to-be-deployed database is automatically deployed on the target operating system; inputting the multi-dimensional features into a depth decision tree model, and performing multi-dimensional feature conjoint analysis to obtain a test rule list output by the depth decision tree model; and based on the checking rule list, checking the to-be-deployed database to obtain an automatic deployment result of the to-be-deployed database on the target operating system. Through the mode, the problem of high misjudgment rate caused by neglecting the influence of the execution environment variables can be avoided, the verification misjudgment rate is reduced, the problems of poor adaptability, inaccurate verification, high maintenance cost and the like caused by using a static rule base can be effectively avoided, the adaptability and verification accuracy of the verification method are improved, and the manual maintenance cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and in particular, to a deployment automation verification method and system. BACKGROUND

[0002] In the database automation deployment scenario, the result of the database automation deployment needs to be verified to determine whether the database is successfully deployed.

[0003] In the prior art, the database automation deployment verification is mainly realized by the following two ways: one is to use a verification system based on static rule matching for verification, when running the installation program and the deployment script of the database, the installation program and the deployment script will output the execution information in the database automation deployment process (such as the success of a certain step or the failure of a certain step, etc.), the verification system based on static rule matching can match the output result of the script or program by using the pre-defined regular expression or keyword, so as to determine whether the database is successfully deployed; the other is to use the manual inspection method for verification, which is to check the execution log and the expected result in the database deployment process by the operation and maintenance personnel, so as to determine whether the database is successfully deployed. The above method can be realized by the Ansible verification technology, the Shell script return value detection and other schemes.

[0004] However, the existing database automation deployment verification method has the following defects: one is high false positive rate, since the verification system based on static rule matching cannot dynamically identify the influence of the environment variable, it is easy to produce false alarm when non-fatal warning information appears in the database automation deployment process; two is poor adaptability, the verification system based on static rule matching needs to be manually maintained and updated, however, such verification system is difficult to automatically adapt to different versions of the database and deployment architecture; three is inaccurate verification, lacking of multi-dimensional feature collection of the deployment process, which cannot detect hidden configuration conflict problems; four is high maintenance cost, each time the deployment environment is changed, the verification rules need to be re-written, consuming a large amount of human resources.

[0005] In summary, the existing database automation deployment verification method has the problems of high false positive rate, poor adaptability, inaccurate verification and high maintenance cost, resulting in poor verification effect. SUMMARY

[0006] The present application provides a deployment automation verification method and system, which solves the defects of the existing database automation deployment verification method, such as high false positive rate, poor adaptability, inaccurate verification and high maintenance cost, resulting in poor verification effect.

[0007] The application provides a deployment automation verification method, comprising: obtaining multi-dimensional features; the multi-dimensional features are a plurality of execution environment features of a to-be-deployed database during automatic deployment on a target operating system; inputting the multi-dimensional features into a deep decision tree model for joint analysis of the multi-dimensional features to obtain a verification rule list output by the deep decision tree model; and verifying the to-be-deployed database based on the verification rule list to obtain an automatic deployment result of the to-be-deployed database on the target operating system.

[0008] According to the application, a deployment automation verification method is provided, and multi-dimensional features are obtained, comprising: obtaining a running script, system configuration information of a target operating system and operation instruction information of the target operating system required during automatic deployment of a to-be-deployed database on the target operating system; performing script semantic analysis on the running script based on an abstract syntax tree to obtain a script semantic analysis result, and extracting initial multi-dimensional features based on the script semantic analysis result, the system configuration information and the operation instruction information; and performing data cleaning processing, feature fusion processing and space-time alignment processing on the initial multi-dimensional features to generate the multi-dimensional features.

[0009] According to the application, a deployment automation verification method is provided, and a deep decision tree model is used to generate a verification rule list based on a detection dimension weight of each execution environment feature, and the detection dimension weight of each execution environment feature is determined based on an automatic deployment process of a to-be-deployed database.

[0010] According to the application, a deployment automation verification method is provided, and after verifying a to-be-deployed database based on a verification rule list to obtain an automatic deployment result of the to-be-deployed database on a target operating system, the method further comprises: if the automatic deployment result is that the to-be-deployed database fails to pass the deployment verification, optimizing the verification rule list based on a genetic algorithm to generate an optimized rule list; verifying the to-be-deployed database based on the optimized rule list and updating the automatic deployment result of the to-be-deployed database on the target operating system.

[0011] According to the application, a deployment automation verification method is provided, and after verifying a to-be-deployed database based on a verification rule list to obtain an automatic deployment result of the to-be-deployed database on a target operating system, the method further comprises: generating a visual verification report based on the automatic deployment result; generating a three-dimensional topology graph based on the automatic deployment result; the three-dimensional topology graph is a topology graph of a verification process of the to-be-deployed database, and the three-dimensional topology graph comprises a verification path of the to-be-deployed database; and displaying the visual verification report and the three-dimensional topology graph in response to a user operation instruction.

[0012] The application further provides a deployment automation verification system, comprising: a dynamic feature extractor configured to acquire multi-dimensional features; the multi-dimensional features are a plurality of execution environment features of a to-be-deployed database during automatic deployment of the to-be-deployed database on a target operating system; an intelligent verification engine configured to input the multi-dimensional features into a deep decision tree model, perform joint analysis on the multi-dimensional features, and obtain a verification rule list output by the deep decision tree model; and based on the verification rule list, the to-be-deployed database is verified to obtain an automatic deployment result of the to-be-deployed database on the target operating system.

[0013] According to the application, the deployment automation verification system comprises a dynamic feature extractor configured to: during automatic deployment of a to-be-deployed database on a target operating system, acquire a running script required for the automatic deployment, system configuration information of the target operating system, and operation instruction information of the target operating system; perform script semantic analysis on the running script based on an abstract syntax tree to obtain a script semantic analysis result, and extract initial multi-dimensional features based on the script semantic analysis result, the system configuration information, and the operation instruction information; and perform data cleaning processing, feature fusion processing, and space-time alignment processing on the initial multi-dimensional features to generate multi-dimensional features.

[0014] According to the application, the deep decision tree model is configured to generate a verification rule list based on a detection dimension weight of each execution environment feature, and the detection dimension weight of each execution environment feature is determined based on an automatic deployment process of the to-be-deployed database.

[0015] According to the application, the deployment automation verification system further comprises a rule evolution system configured to: if the automatic deployment result is that the to-be-deployed database fails to pass the deployment verification, optimize the verification rule list based on a genetic algorithm to generate an optimized rule list; and based on the optimized rule list, verify the to-be-deployed database to update the automatic deployment result of the to-be-deployed database on the target operating system.

[0016] According to the application, the deployment automation verification system further comprises a visual trace interface configured to: based on the automatic deployment result, generate a visual verification report; based on the automatic deployment result, generate a three-dimensional topology graph; the three-dimensional topology graph is a topology graph of a verification process of the to-be-deployed database, and the three-dimensional topology graph comprises a verification path of the to-be-deployed database; and in response to an operation instruction of a user, display the visual verification report and the three-dimensional topology graph.

[0017] The application provides a deployment automation verification method and system, multi-dimensional features are acquired; the multi-dimensional features are a plurality of execution environment features of a to-be-deployed database when the to-be-deployed database is automatically deployed on a target operating system; the multi-dimensional features are input into a deep decision tree model, multi-dimensional feature joint analysis is performed, and a verification rule list output by the deep decision tree model is acquired; based on the verification rule list, the to-be-deployed database is verified, and an automatic deployment result of the to-be-deployed database on the target operating system is acquired. In the foregoing manner, a dynamic feature analysis mechanism is introduced, a plurality of execution environment features of the target operating system are collected as multi-dimensional features when the to-be-deployed database is automatically deployed on the target operating system, context perception of an execution environment in a deployment process is realized, the multi-dimensional features are analyzed by using the deep decision tree model, a verification rule list is dynamically generated, the influence of an execution environment variable in the deployment process is introduced in the dynamically generated verification rule list, and then the to-be-deployed database is verified based on the verification rule list, so that the automatic deployment result of the to-be-deployed database on the target operating system is acquired, the problem of a high false rejection rate caused by neglecting the influence of the execution environment variable can be avoided, and the verification false rejection rate is reduced. At the same time, since the verification rule list is dynamically generated by the deep decision tree model according to the multi-dimensional features, pre-written static rules are not needed, and the static rules do not need to be manually maintained and updated, the problems of poor adaptability, inaccurate verification, and high maintenance cost caused by using a static rule library can be effectively avoided, the adaptability and verification accuracy of the verification method are improved, and the manual maintenance cost is effectively reduced. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0019] Figure 1 is one of the flowcharts of the deployment automation verification method provided by the application.

[0020] Figure 2 is the second flowchart of the deployment automation verification method provided by the application.

[0021] Figure 3 is the structural schematic diagram of the deployment automation verification system provided by the application. DETAILED DESCRIPTION

[0022] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0023] Please refer to Figure 1 and Figure 2 , Figure 1 is one of flowcharts of the deployment automation verification method provided by the present application, Figure 2 is another flowchart of the deployment automation verification method provided by the present application. As shown in Figure 1 , in the present embodiment, the deployment automation verification method is applied to a deployment automation verification system, and the deployment automation verification method comprises steps S110 to S130, and each step is specifically as follows: S110: acquiring multi-dimensional features.

[0024] The multi-dimensional features are a plurality of execution environment features of the to-be-deployed database when the to-be-deployed database is automatically deployed on a target operating system.

[0025] In the present embodiment, the deployment automation verification system comprises a dynamic feature extractor, an intelligent verification engine, a rule evolution system and a visual trace interface.

[0026] Specifically, as shown in Figure 2 , when the to-be-deployed database is automatically deployed on a target operating system, the dynamic feature extractor of the deployment automation verification system can start an environment probe, collect system configuration information of the target operating system through the environment probe, and extract operation instruction information of the target operating system and a running script required for automatic deployment through a deployment script parser.

[0027] Optionally, the deployment automation verification system further comprises an execution process monitoring module; in the process of data collection by the dynamic feature extractor, the execution process monitoring module can record real-time state data of the to-be-deployed database and the target operating system.

[0028] Further, the dynamic feature extractor can perform script semantic analysis on the running script required for automatic deployment based on an Abstract Syntax Tree (AST), capture execution environment context features in real time, obtain script semantic analysis results, and realize context-aware verification of script execution results.

[0029] Further, based on the script semantic analysis results, the system configuration information and the operation instruction information, a plurality of initial multi-dimensional features are extracted.

[0030] Further, the dynamic feature extractor looks based on multi-source feature fusion technology, and performs data cleaning processing, feature fusion processing and space-time alignment processing on structured log data, unstructured data, system performance index information and the like in multiple initial multi-dimensional features, to generate multi-dimensional features. The multi-dimensional features are multiple execution environment features of the to-be-deployed database when performing automatic deployment on the target operating system.

[0031] It should be noted that the deployment automation verification system of the embodiment constructs a multi-dimensional verification system, that is, multiple execution environment features are extracted at the same time to participate in the verification process, and the number of execution environment features is at least 20, so as to avoid the problem of single verification dimension.

[0032] Optionally, the multi-dimensional features include environment state features, configuration file features, process relationship features and the like.

[0033] S120: inputting the multi-dimensional features into a deep decision tree model, performing multi-dimensional feature joint analysis, and obtaining a verification rule list output by the deep decision tree model.

[0034] Specifically, after obtaining the multi-dimensional features, the intelligent verification engine of the deployment automation verification system can input the multi-dimensional features into a deep decision tree model, perform multi-dimensional feature joint analysis; the deep decision tree model introduces a dynamic weight distribution mechanism, which can dynamically adjust the detection dimension weight of each execution environment feature according to each execution phase of the to-be-deployed database in the automatic deployment process, and generate a verification rule list based on each execution environment feature and the corresponding detection dimension weight.

[0035] The verification rule list includes multiple verification rules suitable for the to-be-deployed database.

[0036] It should be noted that the deployment automation verification system of the embodiment introduces an intelligent rule generation mechanism, dynamically generates a verification rule list suitable for different to-be-deployed database types and versions through a deep decision tree model, without using pre-written static rules or manually maintaining and updating static rules, which can effectively avoid the problems of poor adaptability, inaccurate verification and high maintenance cost caused by using a static rule library, improve the adaptability and verification accuracy of the verification method, and effectively reduce the manual maintenance cost.

[0037] S130: verifying the to-be-deployed database based on the verification rule list, and obtaining an automatic deployment result of the to-be-deployed database on the target operating system.

[0038] Specifically, after obtaining the test rule list, the intelligent verification engine of the deployment automation verification system can further utilize the test rule list to verify the to-be-deployed database by a composite verification strategy combining strong rule verification and weak feature analysis, and obtain an automated deployment result of the to-be-deployed database on the target operating system.

[0039] The automated deployment result includes that the to-be-deployed database fails to pass the deployment verification (i.e., the to-be-deployed database fails to be deployed on the target operating system) and that the to-be-deployed database passes the deployment verification (i.e., the to-be-deployed database is successfully deployed on the target operating system).

[0040] The deployment automation verification method provided in this embodiment acquires multi-dimensional features, the multi-dimensional features being a plurality of execution environment features of the to-be-deployed database when the to-be-deployed database is automatically deployed on the target operating system, inputs the multi-dimensional features into a deep decision tree model, performs joint analysis on the multi-dimensional features, and obtains a test rule list output by the deep decision tree model. Based on the test rule list, the to-be-deployed database is verified, and an automated deployment result of the to-be-deployed database on the target operating system is obtained. In this way, by introducing a dynamic feature analysis mechanism, a plurality of execution environment features of the target operating system are collected as multi-dimensional features when the to-be-deployed database is automatically deployed on the target operating system, context perception of the execution environment during the deployment process is realized, the multi-dimensional features are analyzed by using the deep decision tree model, a test rule list is dynamically generated, the influence of the execution environment variables during the deployment process is introduced in the dynamically generated test rule list, and then the to-be-deployed database is verified based on the test rule list, thereby obtaining the automated deployment result of the to-be-deployed database on the target operating system. This can avoid the problem of high false positive rate caused by neglecting the influence of the execution environment variables, reduce the verification false positive rate, and improve the adaptability and verification accuracy of the verification method, thereby effectively reducing the manual maintenance cost.

[0041] In some embodiments, acquiring the multi-dimensional features includes: when the to-be-deployed database is automatically deployed on the target operating system, acquiring a running script required for the automated deployment, system configuration information of the target operating system, and operation instruction information of the target operating system; performing script semantic analysis on the running script based on an abstract syntax tree to obtain a script semantic analysis result, and extracting initial multi-dimensional features based on the script semantic analysis result, the system configuration information, and the operation instruction information; and performing data cleaning processing, feature fusion processing, and space-time alignment processing on the initial multi-dimensional features to generate the multi-dimensional features.

[0042] In the embodiment, the deployment automation verification system comprises a dynamic feature extractor, an intelligent verification engine, a rule evolution system and a visual trace interface.

[0043] Specifically, as shown in the figure, when the to-be-deployed database is automatically deployed on the target operating system, the dynamic feature extractor of the deployment automation verification system can start an environment probe, collect system configuration information of the target operating system through the environment probe, and extract operation instruction information and running scripts required for automatic deployment of the target operating system through a deployment script parser. Figure 2

[0044] Optionally, the deployment automation verification system further comprises an execution process monitoring module; during the data collection by the dynamic feature extractor, the execution process monitoring module can record real-time state data of the to-be-deployed database and the target operating system.

[0045] Further, the dynamic feature extractor can perform script semantic analysis on the running scripts required for automatic deployment based on an Abstract Syntax Tree (AST), capture execution environment context features in real time, obtain script semantic analysis results, and realize context-aware verification of script execution results.

[0046] Further, based on the script semantic analysis results, the system configuration information and the operation instruction information, a plurality of initial multi-dimensional features are extracted.

[0047] Further, the dynamic feature extractor can perform data cleaning processing, feature fusion processing and space-time alignment processing on structured log data, unstructured data and system performance index information in the plurality of initial multi-dimensional features based on multi-source feature fusion technology, to generate multi-dimensional features, which are a plurality of execution environment features of the to-be-deployed database when it is automatically deployed on the target operating system.

[0048] It should be noted that the deployment automation verification system of the embodiment builds a multi-dimensional verification system, that is, a plurality of execution environment features are extracted to participate in the verification process, and the number of execution environment features is at least 20, so as to avoid the problem of single verification dimension.

[0049] Optionally, the multi-dimensional features comprise environment state features, configuration file features, process relationship features and the like.

[0050] In some embodiments, the deep decision tree model generates a test rule list based on a detection dimension weight of each execution environment feature, and the detection dimension weight of each execution environment feature is determined based on the automatic deployment process of the to-be-deployed database.

[0051] ​Specifically, after obtaining the multi-dimensional features, the intelligent verification engine of the deployment automation verification system can input the multi-dimensional features into a deep decision tree model for joint analysis of the multi-dimensional features. The deep decision tree model introduces a dynamic weight distribution mechanism, which can dynamically adjust the detection dimension weight of each execution environment feature according to the execution stages of the to-be-deployed database in the automation deployment process, and generate a verification rule list based on each execution environment feature and its corresponding detection dimension weight.

[0052] The verification rule list includes a plurality of verification rules applicable to the to-be-deployed database.

[0053] In some embodiments, after verifying the to-be-deployed database based on the verification rule list and obtaining the automation deployment result of the to-be-deployed database on the target operating system, the method further includes: if the automation deployment result is that the to-be-deployed database fails the deployment verification, optimizing the verification rule list based on a genetic algorithm to generate an optimized rule list; verifying the to-be-deployed database based on the optimized rule list and updating the automation deployment result of the to-be-deployed database on the target operating system.

[0054] Specifically, if the automation deployment result is that the to-be-deployed database fails the deployment verification (i.e., the to-be-deployed database fails to deploy on the target operating system), the rule evolution system of the deployment automation verification system can combine and optimize the plurality of verification rules in the verification rule list based on a genetic algorithm to generate an optimized rule list, which includes a plurality of verification rules (i.e., verification strategies) adapted to the current specific deployment scenario of the to-be-deployed database.

[0055] Alternatively, the rule evolution system is a self-learning feedback system or an incremental learning system, which can realize real-time updating of the verification rules based on a machine learning algorithm to continuously optimize the accuracy and coverage of the verification rules.

[0056] Further, verifying the to-be-deployed database based on the optimized rule list and updating the automation deployment result of the to-be-deployed database on the target operating system.

[0057] In some embodiments, after verifying the to-be-deployed database based on the verification rule list and obtaining the automation deployment result of the to-be-deployed database on the target operating system, the method further includes: generating a visual verification report based on the automation deployment result; generating a three-dimensional topology graph based on the automation deployment result, wherein the three-dimensional topology graph is a topology graph of the verification process of the to-be-deployed database, and the three-dimensional topology graph includes a verification path of the to-be-deployed database; and displaying the visual verification report and the three-dimensional topology graph in response to a user operation instruction.

[0058] Specifically, after obtaining the automatic deployment result of the to-be-deployed database on the target operating system, the visualization trace interface of the deployment automation verification system can generate a visualization verification report according to the automatic deployment result by using a result analyzer, and generate a three-dimensional topology graph based on the automatic deployment result, the three-dimensional topology graph being a topology graph of a verification process of the to-be-deployed database.

[0059] Further, a user (for example, an operation and maintenance personnel or a system administrator) can generate an operation instruction by means of mouse clicking, text input, voice input and the like; and the deployment automation verification system can display the visualization verification report and the three-dimensional topology graph on the visualization trace interface in response to the operation instruction of the user, show the verification process to the user through the three-dimensional topology graph, and support the user to click to view a detailed verification path in the three-dimensional topology graph.

[0060] Optionally, the deployment automation verification system further comprises a cross-platform adapter to support automatic adaptation of 26 kinds of mainstream databases such as Oracle, MySQL and MongoDB.

[0061] It is proved by actual tests that the deployment automation verification method provided in the embodiment has achieved significant improvement in the following aspects: (1) The misjudgment rate is reduced by 82%: the context analysis on the execution environment can effectively distinguish the warning information from the real errors.

[0062] (2) The detection coverage is improved by 95%: more than 10 dimensions of feature detection functions are added, which can effectively improve the detection coverage, and especially in the stress test of the cloud environment, 23 kinds of configuration conflict scenarios that are missed by the traditional method can be successfully identified.

[0063] (3) The efficiency of database automation deployment is improved by 60%: the automation rate of the verification process is improved from 43% to 98%.

[0064] (4) The maintenance cost is reduced by 75%: since the rule evolution system can automatically iterate and optimize the verification rules, the manual maintenance cost can be effectively reduced.

[0065] The application further provides a deployment automation verification system. Please refer to Figure 3 , Figure 3 is a structural schematic diagram of the deployment automation verification system provided by the application. In the embodiment, the deployment automation verification system comprises a dynamic feature extractor, an intelligent verification engine, a rule evolution system and a visualization trace interface.

[0066] The dynamic feature extractor is used to acquire multi-dimensional features.

[0067] The multi-dimensional features are a plurality of execution environment features of the to-be-deployed database during the automatic deployment on the target operating system.

[0068] The intelligent verification engine is configured to input multi-dimensional features into a deep decision tree model, perform joint analysis on the multi-dimensional features, and obtain a verification rule list output by the deep decision tree model; and perform verification on the to-be-deployed database based on the verification rule list, and obtain an automatic deployment result of the to-be-deployed database on the target operating system.

[0069] In some embodiments, the dynamic feature extractor is configured to: acquire a running script required for the automatic deployment, system configuration information of the target operating system, and operation instruction information of the target operating system when the to-be-deployed database is automatically deployed on the target operating system; perform script semantic analysis on the running script based on an abstract syntax tree, and obtain a script semantic analysis result; and extract initial multi-dimensional features based on the script semantic analysis result, the system configuration information, and the operation instruction information; and perform data cleaning processing, feature fusion processing, and space-time alignment processing on the initial multi-dimensional features, and generate the multi-dimensional features.

[0070] In some embodiments, the deep decision tree model is configured to generate the verification rule list based on a detection dimension weight of each execution environment feature, and the detection dimension weight of each execution environment feature is determined based on an automatic deployment process of the to-be-deployed database.

[0071] In some embodiments, the deployment automation verification system further includes a rule evolution system, which is configured to: if the automatic deployment result indicates that the to-be-deployed database fails the deployment verification, optimize the verification rule list based on a genetic algorithm, and generate an optimized rule list; and perform verification on the to-be-deployed database based on the optimized rule list, and update the automatic deployment result of the to-be-deployed database on the target operating system.

[0072] In some embodiments, the deployment automation verification system further includes a visual trace interface, which is configured to: generate a visual verification report based on the automatic deployment result; generate a three-dimensional topology graph based on the automatic deployment result, wherein the three-dimensional topology graph is a topology graph of a verification process of the to-be-deployed database, and the three-dimensional topology graph includes a verification path of the to-be-deployed database; and display the visual verification report and the three-dimensional topology graph in response to an operation instruction of a user.

[0073] The apparatus embodiments described above are merely illustrative, and the units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e., they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiments according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0074] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A deployment automation verification method, characterized in that: include: Acquire multi-dimensional features; the multi-dimensional features are multiple execution environment features when the database to be deployed is automatically deployed on the target operating system; Input the multi-dimensional features into a deep decision tree model, perform a multi-dimensional feature joint analysis, and obtain a list of inspection rules output by the deep decision tree model; Based on the verification rule list, the database to be deployed is verified to obtain an automatic deployment result of the database to be deployed on the target operating system.

2. The deployment automation verification method according to claim 1, characterized in that: The obtaining of multi-dimensional features includes: When the database to be deployed is automatically deployed on the target operating system, obtaining a running script required for the automatic deployment, system configuration information of the target operating system, and operation instruction information of the target operating system; Performing script semantic parsing on the running script based on an abstract syntax tree to obtain a script semantic analysis result, and extracting initial multi-dimensional features based on the script semantic analysis result, the system configuration information, and the operation instruction information; The initial multi-dimensional features are subjected to data cleaning processing, feature fusion processing and spatiotemporal alignment processing to generate the multi-dimensional features.

3. The deployment automation verification method according to claim 1, characterized in that: The deep decision tree model generates the inspection rule list based on the detection dimension weight of each execution environment feature, and the detection dimension weight of each execution environment feature is determined based on the automated deployment process of the database to be deployed.

4. The deployment automation verification method according to claim 1, characterized in that: After verifying the database to be deployed based on the verification rule list and obtaining the automatic deployment result of the database to be deployed on the target operating system, the method further includes: If the automated deployment result is that the database to be deployed fails the deployment verification, optimizing the verification rule list based on a genetic algorithm to generate an optimized rule list; Based on the optimization rule list, the database to be deployed is verified, and the automatic deployment result of the database to be deployed on the target operating system is updated.

5. The deployment automation verification method according to claim 1, characterized in that: After verifying the database to be deployed based on the verification rule list and obtaining the automatic deployment result of the database to be deployed on the target operating system, the method further includes: Generate a visual verification report based on the automated deployment result; Based on the automated deployment result, a three-dimensional topology map is generated; the three-dimensional topology map is a topology map of the verification process of the database to be deployed, and the three-dimensional topology map includes a verification path of the database to be deployed; In response to a user's operation instruction, the visual verification report and the three-dimensional topology map are displayed.

6. A deployment automation verification system, characterized in that: include: Dynamic feature extractor, used to obtain multi-dimensional features; The multi-dimensional features are multiple execution environment features when the database to be deployed is automatically deployed on the target operating system; An intelligent verification engine, configured to input the multi-dimensional features into a deep decision tree model, perform a joint analysis of the multi-dimensional features, and obtain a list of verification rules output by the deep decision tree model; Based on the verification rule list, the database to be deployed is verified to obtain an automatic deployment result of the database to be deployed on the target operating system.

7. The deployment automation verification system according to claim 6, characterized in that: The dynamic feature extractor is used to: When the database to be deployed is automatically deployed on the target operating system, obtaining a running script required for the automatic deployment, system configuration information of the target operating system, and operation instruction information of the target operating system; Performing script semantic parsing on the running script based on an abstract syntax tree to obtain a script semantic analysis result, and extracting initial multi-dimensional features based on the script semantic analysis result, the system configuration information, and the operation instruction information; The initial multi-dimensional features are subjected to data cleaning processing, feature fusion processing and spatiotemporal alignment processing to generate the multi-dimensional features.

8. The deployment automation verification system according to claim 6, characterized in that: The deep decision tree model generates the inspection rule list based on the detection dimension weight of each execution environment feature, and the detection dimension weight of each execution environment feature is determined based on the automated deployment process of the database to be deployed.

9. The deployment automation verification system according to claim 6, characterized in that: Also includes a rule evolution system; The rule evolution system is used to: If the automated deployment result is that the database to be deployed fails the deployment verification, optimizing the verification rule list based on a genetic algorithm to generate an optimized rule list; Based on the optimization rule list, the database to be deployed is verified, and the automatic deployment result of the database to be deployed on the target operating system is updated.

10. The deployment automation verification system according to claim 6, characterized in that: It also includes a visual traceability interface; The visual tracing interface is used to: Generate a visual verification report based on the automated deployment result; Based on the automated deployment result, a three-dimensional topology map is generated; The three-dimensional topology diagram is a topology diagram of the verification process of the database to be deployed, and the three-dimensional topology diagram includes a verification path of the database to be deployed; In response to a user's operation instruction, the visual verification report and the three-dimensional topology map are displayed.

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