Data analysis method and device, electronic equipment and storage medium

Abstract

The invention discloses a data analysis method and device, electronic equipment and a storage medium, and relates to the technical field of data analysis. The data analysis method comprises the steps of obtaining a to-be-accepted project document, and determining a document category of the to-be-accepted project document; obtaining a target model according to the document category, and determining key features of the to-be-accepted project document according to the target model; determining a target test case rule according to the key features, and generating a test case and a test script based on the target test case rule and the key features; and executing the test case and the test script to obtain a test result of the to-be-accepted project document. According to the embodiment of the invention, the automation of the test process is realized, the test efficiency is improved, and the comprehensiveness and accuracy of the test are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and in particular to a data analysis method and device, electronic equipment and storage medium. BACKGROUND

[0002] Intelligent acceptance of informationization project achievements refers to an automatic and intelligent acceptance process of the final achievements of informationization projects using information technology and intelligent means, aiming to improve acceptance efficiency and accuracy, reduce the influence of human factors, and ensure that the project achievements meet the expected goals and quality standards.

[0003] However, in the current informationization project construction acceptance process, due to the different processes, steps, methods and documents of project acceptance, the compliance evaluation of most project achievement related functions and the content of acceptance documents at this stage still mainly rely on manual inspection and writing. Such manual operation not only consumes time and effort, but also has the risk of omission and misjudgment, which brings certain risks to the promotion and audit of subsequent projects. Therefore, there is an urgent need for a method that can flexibly adapt to various acceptance standards and requirements, and realize the standardization and automation of the acceptance process, steps and documents. SUMMARY

[0004] The present application provides a data analysis method, device, electronic equipment and storage medium to solve the problem that the existing technology cannot automatically accept achievements (such as project documents).

[0005] According to an aspect of the present application, a data analysis method is provided, wherein the method comprises:

[0006] Obtaining a project document to be accepted, determining the document category of the project document to be accepted;

[0007] According to the document category, obtaining a target model, determining the key features of the project document to be accepted according to the target model;

[0008] According to the key features, determining the target test case rule, generating the test case and test script based on the target test case rule and key features;

[0009] Executing the test case and the test script to obtain the test result of the project document to be accepted.

[0010] According to another aspect of the present application, a data analysis device is provided, wherein the device comprises:

[0011] The category determination module is configured to obtain a project document to be accepted, and determine the document category of the project document to be accepted;

[0012] The feature recognition module is configured to acquire a target model according to the document category, and determine key features of the project document to be accepted according to the target model.

[0013] The script generation module is configured to determine a target test case rule according to the key features, and generate a test case and a test script based on the target test case rule and the key features.

[0014] The result generation module is configured to execute the test case and the test script to obtain a test result of the project document to be accepted.

[0015] at least one processor; and

[0016] a memory connected with the at least one processor in communication; wherein

[0017] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data analysis method according to any one of the embodiments of the present application.

[0018] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the data analysis method according to any one of the embodiments of the present application when executed.

[0019] The technical solution of the embodiments of the present application can acquire a project document to be accepted, determine a document category of the project document to be accepted, acquire a target model according to the document category, and determine key features of the project document to be accepted according to the target model, thereby automatically extracting key content in the document and performing context analysis, ensuring accurate understanding of the project technical specification, and improving the standardization level of the acceptance process. The target test case rule is determined according to the key features, the test case and the test script are generated based on the target test case rule and the key features, and the test result of the project document to be accepted is obtained by executing the test case and the test script, thereby realizing the automation of the test process, improving the test efficiency, and ensuring the comprehensiveness and accuracy of the test.

[0020] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiments description. Obviously, the drawings in the following description only show some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative effort based on these drawings.

[0022] Figure 1 is a flow chart of a data analysis method according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of a data analysis method according to an embodiment of the present application;

[0024] Figure 3 is a structural schematic diagram of a data analysis device according to an embodiment of the present application;

[0025] Figure 4 is a structural schematic diagram of an electronic device for implementing a data analysis method according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to make the person skilled in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should be within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily mean a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment one

[0029] Figure 1is a flowchart of a data analysis method according to an embodiment of the present application. The embodiment can be applied to the acceptance of project document of multiple systems. The method can be executed by a data analysis device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. As shown in Figure 1 the method comprises the following steps.

[0030] S110, obtaining a project document to be accepted, and determining a document category of the project document to be accepted.

[0031] The project document to be accepted is a structured document submitted to an acceptance party after a project completes development, testing, and other links, for verifying whether the project meets the requirements, specifications, and contract requirements. It can be understood as a project document that needs to be accepted. Generally, the project document to be accepted can come from multiple platforms, such as a supply chain management (SCM) system, a project management system (PMS), and a contract system. In actual operation, the project document to be accepted can include but is not limited to a contract, a technical specification, and a user manual. The document category can be understood as the category of the project document to be accepted. For example, the document category can include but is not limited to a contract, a technical specification, and a user manual.

[0032] In an embodiment, the project documents of each platform can be collected as the project document to be accepted; alternatively, each platform can actively upload the project document to be accepted. Generally, the project document to be accepted can be collected when a project milestone is reached; alternatively, a time period can be set in advance, and the project document to be accepted can be collected according to the time period. In actual application, the Robotic Process Automation (RPA) technology can be used to automatically collect the project document to be accepted of multiple platforms. The RPA technology is a technology that simulates and executes human operations through software robots, and can realize cross-platform and cross-system automation. The RPA technology can be pre-set with collection rules and collection paths, and relevant system platforms can be logged in to extract the project document to be accepted. The project document to be accepted can be subjected to semantic analysis to determine the document category of the project document to be accepted. Generally, the collected project document to be accepted can be subjected to data preprocessing operations such as text cleaning, word segmentation, and part-of-speech tagging, and the core semantic information such as keywords and topics of the project document to be accepted can be extracted through semantic analysis technology. The document category of the project document to be accepted can be determined according to the core semantic information. In an embodiment, the correspondence between the core semantic information and the document category can be pre-set, and the document category can be matched in the correspondence according to the core semantic information as the document category of the project document to be accepted.

[0033] S120, obtaining a target model according to the document category, and determining the key features of the to-be-accepted project document according to the target model.

[0034] The target model can be understood as a model for determining the key features of the to-be-accepted project document. In actual operation, the target model can be a large language model (LLM). Generally, for different document categories, there can be corresponding target models. The key features can be used to indicate the feature information of the core elements of the to-be-accepted project document. For example, the key features can include but are not limited to functional description and index requirement.

[0035] In an embodiment, the corresponding target model can be matched through the document category, the core semantic information of the to-be-accepted project document is determined, the core semantic information of the to-be-accepted project document is input into the target model, and the functional description and index requirement information corresponding to the core semantic information are identified by the target model as the key features of the to-be-accepted project document. In actual operation, the target model can analyze the context of the core semantic information to understand the relationship between the key content and features in the to-be-accepted project document. Based on the context, the differences are compared to ensure the accuracy and reliability of the parsed key features.

[0036] S130, determining a target test case rule according to the key features, and generating a test case and a test script based on the target test case rule and the key features.

[0037] The target test case rule can be understood as a rule for generating a test case. Generally, the target test case rule corresponding to different key feature information can be pre-set, and the key features and the target test case rule can be stored as a preset mapping relationship table. There can be multiple key features for each to-be-accepted project document, and accordingly, multiple corresponding target test case rules can be extracted. In actual application, the target test case rule can include a check item and an expected result. The test case refers to a structured description of the test scenario, steps, and expected results, such as what to test and how to test, mainly for logical testing. The test script is a code or instruction that converts the test case into an executable code, which depends on specific test tools (such as Python, Postman, etc.), and is used to automatically execute test steps. Generally, the test case can be a natural language, a structured table, or a structured document; the test script can be a code file that can be directly run, each test script corresponds to one or more test cases, and is used to automatically obtain the actual result.

[0038] In an embodiment, the test case rule associated with the key feature can be matched as a target test case rule, the to-be-checked item and the expected result corresponding to the target test case rule are determined, and the key feature is generated into a test case according to the target test case rule. Then, the test case template is determined, the test input is input into the test case template, and the test script is generated by filling the corresponding position in the test case template. In actual application, the test case template can be a preset general test case template, or a test case corresponding to the test case.

[0039] S140, executing the test case and the test script to obtain the test result of the to-be-accepted project document.

[0040] In an embodiment, the operation steps in the test case can be executed item by item, the actual result is recorded and compared with the expected result, if the actual result is exactly the same as the expected result, it is marked as passed; if the actual result is not the same as the expected result, it is marked as failed; if it cannot be executed due to environmental problems, the blocking reason can be recorded, and the comparison result and the execution result are taken as the test case result. Then, the test script is tested by the tool scheduling script to obtain the test script result, and the test case result and the test script result are taken as the test result of the to-be-accepted project document. In an embodiment, the test script can be executed by a multi-protocol adaptation framework, and the test case result is output.

[0041] In the embodiment of the application, the to-be-accepted project document is obtained, the document category of the to-be-accepted project document is determined, the target model is obtained according to the document category, and the key feature of the to-be-accepted project document is determined according to the target model. The key content in the document can be automatically extracted and context analysis is performed, ensuring accurate understanding of the project technical specification, thereby improving the standardization level of the acceptance process. The target test case rule is determined according to the key feature, the test case and the test script are generated based on the target test case rule and the key feature, the test case and the test script are executed to obtain the test result of the to-be-accepted project document, and the automation of the test process is realized. Not only the test efficiency is improved, but also the comprehensiveness and accuracy of the test are ensured.

[0042] In an embodiment, after the test result of the to-be-accepted project document is obtained by executing the test case and the test script, the following steps are further included:

[0043] The test result is uploaded to the blockchain for storage.

[0044] In an embodiment, after the test result of the to-be-accepted project document is determined, the test result can be uploaded to the blockchain, and the test result is stored through the blockchain. The test result is uploaded to the blockchain through the smart contract, ensuring the data unalterability and transparency, providing a reliable basis for subsequent audit and traceability, and enhancing the security of project management.

[0045] In an embodiment, after the test case and test script are executed to obtain the test result of the to-be-accepted project document, the method further comprises:

[0046] According to the preset attention index, target data in the test result is extracted, target information is filled into a target test report template to generate a test report;

[0047] According to a preset chart generation rule, a target chart is generated according to the target data for visual display.

[0048] The preset attention index can be understood as a pre-set index that needs to be paid attention to in the test result. In an embodiment, the preset attention index can be a high-frequency attention index, such as the total number of cases, the pass rate, the failed cases, and the performance index compliance rate. The preset chart generation rule can be understood as a rule for generating a chart. Generally, the preset chart generation rule can include the category of the chart.

[0049] In an embodiment, the preset attention index in the test result can be extracted as target data, a target test report template is extracted, target information is filled into the corresponding position of the target test report template to generate a test report. Then, according to the preset chart generation rule, a target chart is generated according to the target data. In actual application process, the test report can be structured data. The test result can be converted into natural language by using a natural language generation (NLG) technology, the target data in the natural language is extracted, and a test report is generated. In an embodiment, the preset chart generation rule can include an enterprise chart (ECHART) technology. The target chart can be generated by using the ECHART technology, the test result can be intuitively displayed, the readability and understandability of the report are improved, and the project related personnel can quickly master the acceptance situation.

[0050] Embodiment two

[0051] Figure 2 is a flowchart of a data analysis method according to the embodiment two of the present application. The present embodiment is further optimized and expanded based on the above-mentioned embodiments, and can be combined with each optional technical solution in the above-mentioned embodiments. As shown in Figure 2 the method comprises:

[0052] S210, based on a preset trigger condition, collecting project documents of at least one platform as to-be-accepted project documents.

[0053] The preset trigger condition can be understood as a pre-set trigger condition for triggering the collection of the project documents of each platform. For example, the preset trigger condition can include reaching a project milestone or collecting according to a pre-set time period.

[0054] In an embodiment, the project documents of the plurality of platforms can be collected according to a trigger condition, and the collected project documents can be taken as the to-be-accepted project documents. In an embodiment, the RPA technology can be used to automatically collect the to-be-accepted project documents of the plurality of platforms.

[0055] S220, performing data preprocessing on the to-be-accepted project documents to obtain target project documents, and performing natural language processing on the target project documents to obtain core semantic information of the target project documents.

[0056] The core semantic information can be understood as characteristic information used to indicate the core idea of the target project documents. For example, the core semantic information can include, but is not limited to, keywords and topics of the target project documents.

[0057] In an embodiment, the to-be-accepted project documents can be subjected to data cleaning to remove special symbols and redundant spaces, and subjected to word segmentation and filtering of stop words (such as “de”, “le”, and “in” in Chinese, and “the” and “and” in English) and other data preprocessing operations. Then, the to-be-accepted project documents subjected to the data preprocessing are subjected to entity recognition, topic analysis, and relationship extraction and other operations through the natural language processing (NLP) technology, to obtain semantic features of the target project documents, and then extract keywords and topic information of the target project documents as the core semantic information of the target project documents.

[0058] S230, matching a document category associated with the core semantic information as the document category of the to-be-accepted project documents.

[0059] In an embodiment, the core semantic information can be matched in the preset document category library, and the matched document category can be taken as the document category of the to-be-accepted project documents. In actual operation, the association between the core semantic information and the document category can be stored as the preset document category library.

[0060] S240, determining a preset model matched with the document category in a preset model library as a target model.

[0061] The preset model library can be used to store preset models set in advance, and a corresponding preset model can be set for each document category.

[0062] In an embodiment, the document category can be matched in the preset model library, and the matched preset model can be taken as the target model.

[0063] S250, input the core semantic information of the target project document into the target model, determine the function description and index requirement of the core semantic information through the target model, and take the function description and index requirement as the key features of the to-be-accepted project document.

[0064] The target model is composed of a large language model. The function description can be understood as a detailed description of the specific functions, business logic and operation processes that the project needs to implement, and the index requirement can be understood as a quantifiable and verifiable standard for the performance, quality, compliance, ease of use and other dimensions of the function.

[0065] In an embodiment, the core semantic information of the target project document can be input into the target model, analyzed by the target model according to the core semantic information, and combined with the context analysis to understand the relationship between the key content and features in the document. The function description and index requirement corresponding to the core semantic information are obtained, and the function description and index requirement are taken as the key features of the to-be-accepted project document.

[0066] S260, traverse the preset mapping table, match the target test case rule associated with the key feature, and generate the test case corresponding to the key feature according to the target test case rule.

[0067] The preset mapping table refers to a table that is pre-set to store the mapping relationship between the key features and the target test case rule. Generally, the mapping relationship between each key feature and the target test case rule can be determined in advance, and the mapping relationship is stored as a preset mapping table to facilitate the determination of the target test case rule.

[0068] In an embodiment, the target test case rule associated with the key feature can be matched in the preset mapping table. When the number of key features is multiple, the target test case rule can be matched for each key feature respectively. Then, the test case corresponding to the key feature is automatically generated according to the target test case rule. In an embodiment, the test case corresponding to the key feature can be generated by a test case generator.

[0069] S270, determine the test case template matched with the target test case rule, input the test case into the test case template, and generate a test script.

[0070] In an embodiment, the test case template associated with the target test case rule can be matched, and the test case is input into the test case template to obtain the test script. Generally, each test script can correspond to one or more test cases.

[0071] S280, execute the test case to obtain the test case result of the to-be-accepted project document, and execute the test script to obtain the test script result of the to-be-accepted project document.

[0072] The test case result refers to a test result obtained by executing the test case. Generally, the test case result can include execution status (pass / fail / block), execution time, difference between actual result and expected result, and the like. The test script result refers to a test result obtained by executing the test script. Generally, the test script result can include execution log, error stack (if failed), and the like.

[0073] In an embodiment, the operation steps in the test case can be executed one by one, the actual result can be recorded and compared with the expected result, the execution status, the execution time, the difference between the actual result and the expected result, and the like can be obtained, and the execution status, the execution time, the difference between the actual result and the expected result, and the like can be taken as the test case result. The test script result can be obtained by executing the test script through the tool scheduling script, and the test case result and the test script result can be taken as the test result of the to-be-accepted project document.

[0074] S290, generating the test result of the to-be-accepted project document according to the test case result and the test script result.

[0075] In an embodiment, the test case result and the test script result can be summarized to obtain the total number of test cases, the number of passes / fails of each module (such as interface / performance), and the performance index compliance rate (such as response time eligibility rate), and the like. The summary result, the test case result, and the test script result can be taken as the test result of the to-be-accepted project document.

[0076] The embodiment of the application realizes automatic collection of multi-platform project documents by collecting at least one platform project document as a to-be-accepted project document based on a preset trigger condition, performing data preprocessing on the to-be-accepted project document to obtain a target project document, performing natural language processing on the target project document to obtain core semantic information of the target project document, matching a document category associated with the core semantic information as a document category of the to-be-accepted project document, automatically collecting multi-platform project documents, and automatically classifying the documents by using a semantic analysis technology, thereby improving the efficiency and accuracy of document management and reducing the burden of manual operation; the preset model matched with the document category in the preset model library is determined as a target model, the core semantic information of the target project document is input into the target model, the function description and the index requirement of the core semantic information are determined by the target model, the function description and the index requirement are taken as key features of the to-be-accepted project document, a preset mapping table is traversed, a target test case rule associated with the key features is matched, a test case corresponding to the key features is generated according to the target test case rule, a test case template matched with the target test case rule is determined, the test case is input into the test case template, a test script is generated, the test case result of the to-be-accepted project document is obtained by executing the test case, the test script result of the to-be-accepted project document is obtained by executing the test script, the test case result and the test script result are taken as the test result of the to-be-accepted project document, the test case and the test script are automatically generated, the automation of the test process is realized, the acceptance efficiency and accuracy are improved, and the risk caused by manual operation is reduced.

[0077] Embodiment three

[0078] In an embodiment, the embodiment is based on the above-mentioned embodiments, taking RPA collection of to-be-accepted project documents as an example, taking project documents as to-be-accepted project documents, taking identification of core semantic information through NLP as an example, taking LLM as a target model, taking conversion and construction of a test case generator through a rule engine, and taking generation of a test case through the test case generator as an example, a further description of a data analysis method. The method comprises:

[0079] Step 1, automatic collection of RPA-based multi-platform to-be-accepted project documents.

[0080] RPA technology is a technology that simulates and performs human operations through software robots, enabling cross-platform and cross-system automation. The present application supports automatic collection of project documents on multiple platforms, including but not limited to supply chain management systems, project management systems, and contract systems. By setting trigger conditions (such as project milestones, time periods, etc.), RPA robots are automatically triggered to collect documents. RPA robots automatically log into related systems according to pre-set rules and paths, extract project documents (such as contracts, technical specifications, user manuals, etc.). The collected documents are automatically stored in designated folders or databases for subsequent processing.

[0081] Step 2, build a specification analysis engine through NLP+LLM (determine the key features of the project document to be accepted).

[0082] NLP technology is a technology that processes and understands natural language through computers, enabling automatic analysis and understanding of text. The collected documents can be pre-processed, including text cleaning, word segmentation, and part-of-speech tagging. Through semantic analysis technology, the core semantic information of the project document to be accepted is extracted, such as keywords, topics, etc. According to the extracted core semantic information, the project document to be accepted is automatically classified into the corresponding document category. In an embodiment, the document category can include contracts, technical specifications, user manuals, etc. The classification results are stored in the database for subsequent processing.

[0083] LLM technology is a technology that realizes natural language processing through large-scale language models, enabling more accurate text understanding and generation. Through LLM technology, the core semantic information is parsed, and key content and features are extracted. Key features such as function description and index requirements are extracted from the document. Through context analysis, the relationship between key content and features in the document is understood. Based on the context, differential comparison is realized to ensure the accuracy and reliability of the analysis results.

[0084] Step 3, build a test case generator through a rule engine converter to generate test cases and test scripts, and obtain test results.

[0085] Rule engine technology is a technology that realizes automatic decision-making through rule definition and execution, enabling automatic generation of test cases. According to the key content of the project technical specification, rules such as function description and index requirements are defined. Through matching of target test case rules, the key content of the project technical specification is converted into test cases. According to the matching results, test cases and test scripts are automatically generated. The blockchain technology application executes the test script through a multi-protocol adaptation framework and outputs the test results.

[0086] In an embodiment, the test case generation process includes defining target test case rules for generating test cases according to key contents of the project technical specification. The key contents of the project technical specification are converted into test cases through rule matching. Test cases and test scripts are automatically generated according to the matching results. The test scripts are executed through a multi-protocol adaptation framework, and test results are output. The test results are stored in a blockchain node for subsequent processing.

[0087] Step 4, test result chaining and intelligent test report generation.

[0088] Blockchain technology is a distributed ledger technology that can achieve data immutability and transparency. Test results, including test case execution results and test script execution results, are collected. Through a blockchain smart contract, test results are chained to ensure data immutability and transparency. The chained results are stored in the blockchain for subsequent queries and audits.

[0089] Intelligent test report generation includes using NLG technology and ECHART technology to generate intelligent test reports. Analyze test results and extract key information such as test case execution results and test script execution results. Through NLG technology, test results are converted into natural language descriptions to generate intelligent test reports. Through ECHART technology, generate charts to visually display test results. The intelligent test report is output in document format for subsequent viewing and auditing. Through the above technical solutions, the present application realizes the automation, standardization and intelligence of informationization project acceptance, improves the efficiency and accuracy of project acceptance, reduces the risk of project management, and promotes the smooth promotion and application of the project.

[0090] The present application realizes the automation, standardization and intelligence of informationization project acceptance through the above technical solutions, improves the efficiency and accuracy of acceptance, and reduces the risk of project management.

[0091] Embodiment Four

[0092] Figure 3 is a structural schematic diagram of a data analysis device according to an embodiment of the present application. As shown in Figure 3 , the device includes a category determination module 31, a feature recognition module 32, a script generation module 33, and a result generation module 34.

[0093] The category determination module 31 is configured to obtain a to-be-inspected project document and determine the document category of the to-be-inspected project document.

[0094] The feature recognition module 32 is configured to acquire a target model according to the document category, and determine the key features of the to-be-inspected project document according to the target model.

[0095] The script generation module 33 is configured to determine a target test case rule according to the key features, and generate a test case and a test script based on the target test case rule and the key features.

[0096] The result generation module 34 is configured to execute the test case and the test script to obtain the test result of the to-be-inspected project document.

[0097] The technical scheme of the embodiment of the application can acquire the to-be-inspected project document through the category determination module, determine the document category of the to-be-inspected project document, acquire the target model according to the document category through the feature recognition module, and determine the key features of the to-be-inspected project document according to the target model, so as to automatically extract the key content in the document and perform context analysis, ensure the accurate understanding of the project technical specification, and improve the standardization level of the inspection process. The script generation module can determine the target test case rule according to the key features, generate the test case and the test script based on the target test case rule and the key features, and the result generation module can execute the test case and the test script to obtain the test result of the to-be-inspected project document, so as to realize the automation of the test process, improve the test efficiency, and ensure the comprehensiveness and accuracy of the test.

[0098] In an embodiment, the data analysis apparatus further comprises:

[0099] The storage module is configured to upload the test result to the blockchain for storage.

[0100] In an embodiment, the category determination module 31 comprises:

[0101] The document collection unit is configured to collect the project document of at least one platform as the to-be-inspected project document based on a preset trigger condition;

[0102] The semantic determination unit is configured to perform data preprocessing on the to-be-inspected project document to obtain a target project document, and perform natural language processing on the target project document to obtain core semantic information of the target project document;

[0103] The category determination unit is configured to match a document category associated with the core semantic information as the document category of the to-be-inspected project document.

[0104] In an embodiment, the feature recognition module 32 comprises:

[0105] The model matching unit is configured to determine a preset model matched with the document category as the target model in a preset model library;

[0106] The feature recognition unit is configured to input core semantic information of a target project document into a target model, determine a function description and an index requirement of the core semantic information through the target model, and take the function description and the index requirement as key features of a to-be-inspected project document.

[0107] In an embodiment, the script generation module 33 comprises:

[0108] The use case generation unit is configured to traverse a preset mapping table, match a target test use case rule associated with the key feature, and generate a test use case corresponding to the key feature according to the target test use case rule.

[0109] The script generation unit is configured to determine a test use case template matched with the target test use case rule, input the test use case into the test use case template, and generate a test script.

[0110] In an embodiment, the result generation module 34 comprises:

[0111] The initial result generation unit is configured to execute the test use case to obtain a test use case result of the to-be-inspected project document, and execute the test script to obtain a test script result of the to-be-inspected project document.

[0112] The target result generation unit is configured to take the test use case result and the test script result as a test result of the to-be-inspected project document.

[0113] In an embodiment, the data analysis device further comprises:

[0114] The report generation module is configured to extract target data in the test result according to a preset attention index, fill the target information into a target test report template to generate a test report.

[0115] The visualization module is configured to generate a target graph according to the target data through a preset graph generation rule, and perform visual display.

[0116] The data analysis device provided in the embodiments of the present application can execute the data analysis method provided in any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0117] Embodiment five

[0118] Figure 4This is a schematic diagram of an electronic device implementing a data analysis method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0119] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data analysis methods.

[0122] In some embodiments, the data analysis method can be implemented as a computer program tangibly embodied in a computer readable storage medium, e.g., storage unit 18. In some embodiments, parts or all of the computer program can be loaded and / or installed onto electronic device 10 via, e.g., ROM 12 and / or communication unit 19. When the computer program is loaded onto RAM 13 and executed by processor 11, one or more steps of the data analysis method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the data analysis method by way of other means, e.g., with the aid of firmware.

[0123] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0124] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as part of a standalone software package, or entirely on a remote machine or server.

[0125] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0126] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0127] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0128] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0129] It should be understood that the various forms of flow shown above can be used to reorder, add or delete steps. For example, each step described in the present application can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which is not limited herein.

[0130] The above detailed description does not constitute a limitation on the scope of protection of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A data analysis method, characterized by, The method comprises the following steps: acquiring a to-be-inspected project document, and determining a document category of the to-be-inspected project document; acquiring a target model according to the document category, and determining a key feature of the to-be-inspected project document according to the target model; determining a target test case rule according to the key feature, and generating a test case and a test script based on the target test case rule and the key feature; executing the test case and the test script to obtain a test result of the to-be-inspected project document.

2. The method of claim 1, wherein, After the test result of the to-be-inspected project document is obtained by executing the test case and the test script, the method further comprises the following steps: uploading the test result to a block chain for storage.

3. The method of claim 1, wherein, The step of acquiring the to-be-inspected project document and determining the document category of the to-be-inspected project document comprises the following steps: collecting project documents of at least one platform as to-be-inspected project documents based on a preset trigger condition; performing data preprocessing on the to-be-inspected project documents to obtain target project documents, and performing natural language processing on the target project documents to obtain core semantic information of the target project documents; matching a document category associated with the core semantic information as the document category of the to-be-inspected project document.

4. The method of claim 1, wherein, The step of acquiring the target model according to the document category and determining the key feature of the to-be-inspected project document according to the target model comprises the following steps: determining a preset model matched with the document category in a preset model library as a target model; inputting the core semantic information of the target project document into the target model, determining a function description and an index requirement of the core semantic information through the target model, and taking the function description and the index requirement as the key feature of the to-be-inspected project document; wherein the target model is composed of a large language model.

5. The method of claim 1, wherein, The step of determining the target test case rule according to the key feature and generating the test case and the test script based on the target test case rule and the key feature comprises the following steps: traversing a preset mapping table, matching a target test case rule associated with the key feature, and generating a test case corresponding to the key feature according to the target test case rule; determining a test case template matched with the target test case rule, inputting the test case into the test case template, and generating a test script.

6. The method of claim 1, wherein, The step of executing the test case and the test script to obtain the test result of the to-be-inspected project document comprises the following steps: executing the test case to obtain a test case result of the to-be-inspected project document, and executing the test script to obtain a test script result of the to-be-inspected project document; generating a test result of the to-be-inspected project document according to the test case result and the test script result.

7. The method of claim 1, wherein, After the test result of the to-be-inspected project document is obtained by executing the test case and the test script, the method further comprises the following steps: extracting target data in the test result according to a preset attention index, filling the target information into the target test report template to generate a test report, and generating a target graph according to the target data through a preset graph generation rule to perform visual display. The method comprises the following steps:

8. A data analysis device, characterized by a category determination module is configured to acquire a to-be-inspected project document, and determine a document category of the to-be-inspected project document; ​ The feature recognition module is configured to acquire a target model according to the document category, and determine key features of the to-be-inspected project document according to the target model; The script generation module is configured to determine a target test case rule according to the key features, and generate a test case and a test script based on the target test case rule and the key features; The result generation module is configured to execute the test case and the test script to obtain a test result of the to-be-inspected project document.

9. An electronic device, comprising: The electronic device comprises: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data analysis method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the data analysis method of any one of claims 1-7 when executed.

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