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. The method comprises the steps of obtaining a data analysis requirement and a rule file of a to-be-analyzed project; the rule file is recognized, entry data and check point data corresponding to the to-be-analyzed item are obtained, the entry data are used for representing execution rules of the to-be-analyzed item, and the check point data are used for representing analysis rules needing to be used when the to-be-analyzed item is analyzed; based on the data analysis requirement, the item data and the check point data are used for analyzing the to-be-analyzed item, a data analysis result is obtained, and the data analysis result is used for representing whether the to-be-analyzed item is abnormal or not. According to the method and the device, the technical problem of relatively low accuracy of analyzing the data to determine whether the data is abnormal or not in related technologies is solved.

Description

TECHNICAL FIELD

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

[0002] At present, data analysis technology is widely used to evaluate the compliance of enterprise operation data and detect potential abnormal behaviors, but the existing data analysis technology usually relies on a fixed rule set or simple keyword matching, and when dealing with complex and diversified data, especially when it comes to the application and execution of specific rules, there is a lack of accuracy, and it is difficult to accurately identify whether the data is abnormal.

[0003] In view of the above problems, no effective solution has been proposed so far. SUMMARY

[0004] The embodiments of the present application provide a data analysis method, device, electronic device and storage medium to at least solve the technical problem of low accuracy in data analysis of data to determine whether the data is abnormal in the related art.

[0005] According to an aspect of an embodiment of the present application, a data analysis method is provided, comprising: obtaining data analysis requirements and a rule file of a project to be analyzed; identifying the rule file to obtain entry data and checkpoint data corresponding to the project to be analyzed, wherein the entry data is used to represent the execution rules of the project to be analyzed, and the checkpoint data is used to represent the analysis rules to be used when analyzing the project to be analyzed; based on the data analysis requirements, using the entry data and the checkpoint data to analyze the project to be analyzed to obtain a data analysis result, wherein the data analysis result is used to represent whether the project to be analyzed is abnormal.

[0006] Further, based on the data analysis requirements, using the entry data and the checkpoint data to analyze the project to be analyzed to obtain a data analysis result, comprising: analyzing the data analysis requirements to determine a project file and an approval file of the project to be analyzed, wherein the project file is used to record the data corresponding to the project materials consumed in the execution process of the project to be analyzed, and the approval file is used to record the data corresponding to the project materials provided for the project to be analyzed; using the entry data to analyze the project file to obtain a file analysis result, wherein the file analysis result is used to represent whether the execution process of the project to be analyzed conforms to the execution rules; using the checkpoint data to match the project file and the approval file to obtain a file matching result, wherein the file matching result is used to represent whether the project file matches the approval file; in the case that the file analysis result represents that the execution process of the project to be analyzed conforms to the execution rules, and the file matching result represents that the project file matches the approval file, determining that the data analysis result represents that the project to be analyzed is not abnormal.

[0007] Further, the item file is analyzed by using the entry data to obtain a file analysis result, including: performing feature extraction on the entry data to obtain entry data features; performing feature extraction on the item file to obtain item file features; inputting the entry data features and the item file features into a file analysis model, and analyzing the item file by using the file analysis model to obtain the file analysis result.

[0008] Further, the item file and the approval file are matched by using the checkpoint data to obtain a file matching result, including: performing feature extraction on the checkpoint data to obtain checkpoint data features; performing feature extraction on the item file to obtain item file features; performing feature extraction on the approval file to obtain approval file features; inputting the checkpoint data features, the item file features and the approval file features into a file matching model, and matching the item file and the approval file by using the file matching model to obtain the file matching result.

[0009] Further, the rule file is identified to obtain the entry data and the checkpoint data corresponding to the project to be analyzed, including: splitting the rule file to obtain a plurality of data modules; determining a first module from the plurality of data modules based on module features of the plurality of data modules, wherein the first module includes the entry data; determining a second module based on the first module according to a module mapping relationship, wherein the second module includes the checkpoint data, and the module mapping relationship is used to represent an association relationship between the first module and the second module.

[0010] According to another aspect of the embodiment of the present application, a data analysis device is also provided, including: a file acquisition module, configured to acquire a data analysis requirement and a rule file of a project to be analyzed; a file identification module, configured to identify the rule file to obtain entry data and checkpoint data corresponding to the project to be analyzed, wherein the entry data is used to represent an execution rule of the project to be analyzed, and the checkpoint data is used to represent an analysis rule needed to be used when the project to be analyzed is analyzed; and a project analysis module, configured to analyze the project to be analyzed by using the entry data and the checkpoint data based on the data analysis requirement to obtain a data analysis result, wherein the data analysis result is used to represent whether the project to be analyzed is abnormal.

[0011] Furthermore, the project analysis module is also used to: parse data analysis requirements, determine the project documents and approval documents of the project to be analyzed, wherein the project documents record the data corresponding to the project materials consumed during the execution of the project, and the approval documents record the data corresponding to the project materials provided to the project; analyze the project documents using entry data to obtain document analysis results, wherein the document analysis results are used to characterize whether the execution process of the project to be analyzed conforms to the execution rules; match the project documents and approval documents using checkpoint data to obtain document matching results, wherein the document matching results are used to characterize whether the project documents and approval documents match; if the document analysis results indicate that the execution process of the project to be analyzed conforms to the execution rules, and the document matching results indicate that the project documents and approval documents match, then it is determined that the data analysis results indicate that the project to be analyzed has no anomalies.

[0012] Furthermore, the project analysis module is also used to: extract features from entry data to obtain entry data features; extract features from project files to obtain project file features; input the entry data features and project file features into the file analysis model, and use the file analysis model to analyze the project files to obtain file analysis results.

[0013] Furthermore, the project analysis module is also used to: extract features from checkpoint data to obtain checkpoint data features; extract features from project documents to obtain project document features; extract features from approval documents to obtain approval document features; input the checkpoint data features, project document features, and approval document features into the document matching model, and use the document matching model to match the project documents and approval documents to obtain document matching results.

[0014] Furthermore, the file recognition module is also used to: split the rule file to obtain multiple data modules; determine a first module from the multiple data modules based on the module features of the multiple data modules, wherein the first module includes entry data; and determine a second module based on the first module according to the module mapping relationship, wherein the second module includes checkpoint data, and the module mapping relationship is used to characterize the association between the first module and the second module.

[0015] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0016] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0019] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0020] In this embodiment of the invention, the following approach is adopted: acquiring the data analysis requirements and rule files of the project to be analyzed; identifying the rule files to obtain the entry data and checkpoint data corresponding to the project to be analyzed; and analyzing the project to be analyzed based on the data analysis requirements and the entry data and checkpoint data to obtain the data analysis results. By utilizing the execution rules and analysis rules configured in the rule files for the project to be analyzed, and analyzing the file to be analyzed based on the data analysis requirements, the accuracy of the obtained data analysis results can be effectively improved, solving the technical problem of low accuracy in data analysis to determine whether there are anomalies in related technologies. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a flowchart illustrating a data analysis method according to an embodiment of the present invention;

[0023] Figure 2 This is a structural block diagram of a data analysis device according to an embodiment of this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] According to an embodiment of the present invention, a method embodiment for data analysis is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart illustrating a data analysis method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Obtain the data analysis requirements and rule files for the project to be analyzed.

[0029] The aforementioned data analysis requirements can refer to specific metrics that analysts want to evaluate in an analysis project, such as the number of projects, the number of working papers, or the coverage rate. These metrics can help the analysis system understand the application of policy documents in actual analysis scenarios.

[0030] The aforementioned rule documents can refer to specific policy documents, which contain various items and their document attributes (such as document name, policy area, task section, time range, etc.), as well as checkpoint information under each item, which can be used to guide analysts on how to perform analysis tasks.

[0031] In one optional solution of this embodiment, considering that analysts may need to analyze multiple projects regularly, during the analysis process, errors or omissions in analysis items may occur due to frequent updates of policy documents and increased complexity of analysis projects. Furthermore, given that analysts may also make analysis errors due to the large amount of data to be analyzed when manually analyzing the documents to be analyzed.

[0032] Therefore, in order to improve the efficiency and accuracy of analyzing the files to be analyzed, and to determine whether there are any anomalies in the files and whether the data stored in the files meets the specified accuracy, the analysis system can first obtain the data analysis requirements of the data to be analyzed, as well as the corresponding rule files. In this way, the data analysis requirements can guide the determination of the analysis scope and objectives, and the rule files can ensure the standardization of the analysis process and the consistency of analysis quality, thereby improving the accuracy of subsequent analysis of the files to be analyzed.

[0033] Step S104: Identify the rule file to obtain the entry data and checkpoint data corresponding to the project to be analyzed.

[0034] Among them, the entry data is used to characterize the execution rules of the project to be analyzed, and the checkpoint data is used to characterize the analysis rules that need to be used when analyzing the project to be analyzed.

[0035] The aforementioned data items may be the specific requirements and clauses involved in the generation process of the data corresponding to the document to be analyzed, and are the smallest analyzable unit decomposed from the institutional document.

[0036] The aforementioned checkpoint data can refer to the standardized execution steps and analytical basis associated with each item, used to guide analysts on how to verify the implementation of each item in the analysis project.

[0037] In one optional embodiment, considering that rule documents may be frequently updated to reflect the latest legal regulations, industry standards, or internal policy changes, the analysis system, after obtaining the data analysis requirements and the latest rule documents, can identify the obtained rule documents to determine the specific entry data and checkpoint data corresponding to the project to be analyzed. This allows the entry data to clearly define the execution rules that the analysis project should follow, ensuring that analysts can conduct analysis based on the latest requirements. Furthermore, the checkpoint data provides analysts with the latest analysis processes and methods, guiding them on how to effectively verify the implementation of each entry in the project and avoiding analytical biases caused by using outdated rules.

[0038] For example, the analysis system can automatically compare old and new rule files, identify updated entries and checkpoints, and then update its database to ensure that the latest requirements can be applied in subsequent analysis projects. Furthermore, the system can intelligently classify and label new entries and checkpoints, making it easier for analysts to quickly locate and understand the updates, thereby improving the efficiency of analysis preparation.

[0039] Step S106: Based on the data analysis requirements, analyze the item data and checkpoint data to obtain the data analysis results.

[0040] The data analysis results are used to characterize whether there are any anomalies in the project being analyzed.

[0041] In one optional embodiment, in order to improve the accuracy of the analysis of the document to be analyzed, after obtaining the entry data and checkpoint data, the analysis system can combine the above-mentioned data analysis requirements and analyze the item to be analyzed based on the identified entry data and checkpoint data to obtain the above-mentioned data analysis results, so as to determine whether there are any anomalies in the item to be analyzed.

[0042] For example, the analysis system can first verify whether the number of projects and working papers matches the expected values ​​in the item data. Then, the system will review each checkpoint to confirm whether all checkpoints have been correctly implemented in the project analysis. Finally, the system calculates the ratio of the actual number of applied items to the total number of items, i.e., the coverage rate, to determine the implementation status of the policy documents. Correspondingly, if any anomalies are found, such as the coverage rate being lower than the predetermined threshold or the missing checkpoints in the project, the analysis system can immediately identify and report these anomalies, facilitating timely correction by analysts.

[0043] In this embodiment of the invention, the following approach is adopted: acquiring the data analysis requirements and rule files of the project to be analyzed; identifying the rule files to obtain the entry data and checkpoint data corresponding to the project to be analyzed; and analyzing the project to be analyzed based on the data analysis requirements and the entry data and checkpoint data to obtain the data analysis results. By utilizing the execution rules and analysis rules configured in the rule files for the project to be analyzed, and analyzing the file to be analyzed based on the data analysis requirements, the accuracy of the obtained data analysis results can be effectively improved, solving the technical problem of low accuracy in data analysis to determine whether there are anomalies in related technologies.

[0044] Furthermore, based on data analysis requirements, the project under analysis is analyzed using item data and checkpoint data to obtain data analysis results, including: parsing the data analysis requirements to determine the project documents and approval documents of the project under analysis. The project documents record data corresponding to the project materials consumed during the execution of the project, while the approval documents record data corresponding to the project materials provided to the project. The project documents are analyzed using item data to obtain document analysis results, which characterize whether the execution process of the project under analysis conforms to the execution rules. The project documents and approval documents are matched using checkpoint data to obtain document matching results, which characterize whether the project documents and approval documents match. If the document analysis results indicate that the execution process of the project under analysis conforms to the execution rules, and the document matching results indicate that the project documents and approval documents match, then the data analysis results indicate that the project under analysis does not have any anomalies.

[0045] The aforementioned project documents can refer to all documents and records related to the analysis generated during the implementation of the project, including but not limited to analysis working papers, evidence and data collected during the analysis, meeting minutes, communication records, etc. The aforementioned approval documents can refer to institutional documents, policy guidelines, analysis standards, and any other documents guiding analysts on how to conduct their analysis work.

[0046] In one optional embodiment, in order to accurately analyze the project to be analyzed, the analysis system can first parse the data analysis requirements to determine the project documents and approval documents of the project to be analyzed, so as to provide a data and rule basis for subsequent analysis.

[0047] Then, the analysis system can use the entry data to analyze the project documents and obtain the document analysis results to determine whether the execution process of the project under analysis conforms to the execution rules. At the same time, it can use checkpoint data to match the project documents and approval documents and obtain the document matching results to determine whether the project documents and approval documents match.

[0048] Correspondingly, if the document analysis results indicate that the execution process of the analyzed project conforms to the execution rules, and the document matching results indicate that the project documents match the approval documents, the analysis system can determine that the data analysis results indicate that the analyzed project has no anomalies. However, if the document analysis results indicate that the execution process of the analyzed project does not conform to the execution rules, or if the document matching results indicate that the project documents do not match the approval documents, the analysis system can determine that the data analysis results indicate that the analyzed project has anomalies, requiring further investigation and correction.

[0049] Furthermore, the project documents are analyzed using the entry data to obtain document analysis results, including: extracting features from the entry data to obtain entry data features; extracting features from the project documents to obtain project document features; inputting the entry data features and project document features into the document analysis model, and using the document analysis model to analyze the project documents to obtain document analysis results.

[0050] In one optional embodiment, during the analysis of project documents using entry data, in order to improve the accuracy of the obtained document analysis results, the analysis system can first extract features from the entry data to obtain entry data features, thereby determining information such as entry keywords, entry type, task section, and institutional domain, so that the analysis system can understand the specific requirements of the entry.

[0051] Then, the analysis system can extract features from the project files to obtain project file features, thereby determining information such as keyword frequency, use of specific terms, completeness of operation records, and timestamps in the project files, and thus capturing key information in the project files that is relevant to the analysis.

[0052] Finally, the analysis system can input the entry data features and project document features into the document analysis model, and use the document analysis model to analyze the project documents to obtain the document analysis results. The document analysis model can be a pre-trained machine learning model, which may include, but is not limited to, support vector machines, deep neural networks, or natural language processing models.

[0053] Through this feature extraction and model analysis approach, the analysis system can provide more objective and accurate analysis results, avoiding the subjectivity and inconsistency of human interpretation, while improving the efficiency of analyzing the documents being analyzed and ensuring that all analysis activities follow the prescribed items and standards.

[0054] Furthermore, the checkpoint data is used to match project documents and approval documents to obtain document matching results, including: extracting features from the checkpoint data to obtain checkpoint data features; extracting features from the project documents to obtain project document features; extracting features from the approval documents to obtain approval document features; inputting the checkpoint data features, project document features, and approval document features into the document matching model, and using the document matching model to match the project documents and approval documents to obtain document matching results.

[0055] In one optional embodiment, during the process of matching project documents and approval documents using checkpoint data, in order to improve the accuracy of the obtained document matching results, the analysis system can first extract features from the checkpoint data to obtain checkpoint data features, such as key steps of the checkpoint, required evidence types, relevant regulatory references, etc., to ensure that the analysis system can understand the specific requirements and background of each checkpoint.

[0056] Then, the analysis system can extract features from project documents to obtain project document features, capturing all details related to the analysis within the project documents, such as timestamps on operation records, financial data, and communication records, for comparison with checkpoint data features. Simultaneously, the analysis system can extract features from approval documents to obtain approval document features, extracting regulations, policy guidelines, etc., as standards for compliance judgment.

[0057] Finally, the analysis system can input checkpoint data features, project document features, and approval document features into the document matching model. The model then matches the project documents with the approval documents to obtain matching results, thereby verifying whether the operational processes and results of the project documents are consistent with the provisions of the approval documents. The checkpoint data features, acting as an intermediate matching bridge, ensure that all analysis activities are conducted under the guidance of the approval documents and comply with the rules and standards set in the entry data, thus improving the compliance and accuracy of the analysis work.

[0058] Further, the rule file is identified to obtain the entry data and checkpoint data corresponding to the project to be analyzed, including: splitting the rule file to obtain multiple data modules; determining the first module from the multiple data modules based on the module characteristics of the multiple data modules, wherein the first module includes entry data; and determining the second module according to the module mapping relationship based on the first module, wherein the second module includes checkpoint data, and the module mapping relationship is used to characterize the relationship between the first module and the second module.

[0059] In one optional embodiment, to improve the efficiency and accuracy of rule document identification and ensure that entry data and checkpoint data can be effectively extracted and correctly applied in subsequent analysis processes, the analysis system can first split the rule document into multiple data modules during the rule document identification process. This decomposes a large rule document into smaller, more manageable parts, where different parts can correspond to different institutional clauses or analysis areas, facilitating subsequent feature extraction and module identification. Then, the analysis system can determine the first module, i.e., the module containing entry data, from the multiple data modules based on their module characteristics. By analyzing the characteristics of each module (such as module title, keywords, contextual features, etc.), the system can identify which modules contain the entry information of the institutional document. The entry data, as the core component of the institutional document, can directly define the scope and content of the analysis, thereby improving the efficiency of analyzing the document. Finally, the analysis system can determine the second module, i.e., the module containing checkpoint data, based on the first module and according to the module mapping relationship.

[0060] The module mapping relationship refers to the association between the first module and the second module. Specifically, the module mapping relationship can clarify the correspondence between different data items and different checkpoint data, providing analysts with detailed steps and references when performing analysis tasks, ensuring that analysis activities can accurately meet the requirements of the system documents, and improving the standardization and compliance of analysis work.

[0061] Through the identification of rule documents and the decomposition and determination of data modules described above, the analysis system can extract entry data and checkpoint data more systematically and accurately, providing a clear and accurate data foundation for subsequent analysis work, reducing uncertainty in the analysis process, and improving the efficiency and quality of analysis work. At the same time, this modular processing approach also facilitates the updating and maintenance of rule documents, ensuring that the system can quickly adapt to new regulatory requirements and continuously provide efficient support for analysis work.

[0062] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0063] In the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution.

[0064] According to an embodiment of the present invention, an apparatus embodiment for a data analysis method is provided. It should be noted that the apparatus can be used to execute the above-described data analysis method. Figure 2 This is a structural block diagram of a data analysis device according to an embodiment of this application, such as... Figure 2 As shown, the device includes: a file acquisition module 202, a file recognition module 204, and a project analysis module 206.

[0065] The document acquisition module 202 is used to acquire the data analysis requirements and rule files of the project to be analyzed; the document recognition module 204 is used to recognize the rule files to obtain the entry data and checkpoint data corresponding to the project to be analyzed. The entry data is used to represent the execution rules of the project to be analyzed, and the checkpoint data is used to represent the analysis rules that need to be used when analyzing the project to be analyzed; the project analysis module 206 is used to analyze the project to be analyzed based on the data analysis requirements, using the entry data and checkpoint data to obtain the data analysis results. The data analysis results are used to represent whether there are any anomalies in the project to be analyzed.

[0066] Furthermore, the project analysis module is also used to: parse data analysis requirements, determine the project documents and approval documents of the project to be analyzed, wherein the project documents record the data corresponding to the project materials consumed during the execution of the project, and the approval documents record the data corresponding to the project materials provided to the project; analyze the project documents using entry data to obtain document analysis results, wherein the document analysis results are used to characterize whether the execution process of the project to be analyzed conforms to the execution rules; match the project documents and approval documents using checkpoint data to obtain document matching results, wherein the document matching results are used to characterize whether the project documents and approval documents match; if the document analysis results indicate that the execution process of the project to be analyzed conforms to the execution rules, and the document matching results indicate that the project documents and approval documents match, then it is determined that the data analysis results indicate that the project to be analyzed has no anomalies.

[0067] Furthermore, the project analysis module is also used to: extract features from entry data to obtain entry data features; extract features from project files to obtain project file features; input the entry data features and project file features into the file analysis model, and use the file analysis model to analyze the project files to obtain file analysis results.

[0068] Furthermore, the project analysis module is also used to: extract features from checkpoint data to obtain checkpoint data features; extract features from project documents to obtain project document features; extract features from approval documents to obtain approval document features; input the checkpoint data features, project document features, and approval document features into the document matching model, and use the document matching model to match the project documents and approval documents to obtain document matching results.

[0069] Furthermore, the file recognition module is also used to: split the rule file to obtain multiple data modules; determine a first module from the multiple data modules based on the module features of the multiple data modules, wherein the first module includes entry data; and determine a second module based on the first module according to the module mapping relationship, wherein the second module includes checkpoint data, and the module mapping relationship is used to characterize the association between the first module and the second module.

[0070] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0071] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0072] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0073] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0074] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0075] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0076] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between units or modules may be electrical or other forms.

[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0078] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0079] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0080] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data analysis method, characterized in that, include: Obtain the data analysis requirements and rule files for the project to be analyzed; The rule file is identified to obtain the entry data and checkpoint data corresponding to the item to be analyzed. The entry data is used to characterize the execution rules of the item to be analyzed, and the checkpoint data is used to characterize the analysis rules that need to be used when analyzing the item to be analyzed. Based on the data analysis requirements, the item data and the checkpoint data are used to analyze the item to be analyzed to obtain data analysis results, wherein the data analysis results are used to characterize whether there are any anomalies in the item to be analyzed.

2. The method according to claim 1, characterized in that, Based on the aforementioned data analysis requirements, the item data and checkpoint data are used to analyze the project to be analyzed, and the data analysis results are obtained, including: The data analysis requirements are parsed to determine the project files and approval documents of the project to be analyzed. The project files are used to record the data corresponding to the project materials consumed during the execution of the project to be analyzed, and the approval documents are used to record the data corresponding to the project materials provided to the project to be analyzed. The project file is analyzed using the entry data to obtain file analysis results, wherein the file analysis results are used to characterize whether the execution process of the project to be analyzed conforms to the execution rules; The project file and the approval document are matched using the checkpoint data to obtain a file matching result, wherein the file matching result is used to characterize whether the project file and the approval document match; If the document analysis results indicate that the execution process of the project to be analyzed conforms to the execution rules, and the document matching results indicate that the project document matches the approval document, then it is determined that the data analysis results indicate that the project to be analyzed does not have any anomalies.

3. The method according to claim 2, characterized in that, The project file is analyzed using the entry data to obtain file analysis results, including: Feature extraction is performed on the item data to obtain item data features; Feature extraction is performed on the project files to obtain project file features; The entry data features and the project file features are input into the file analysis model, and the project file is analyzed using the file analysis model to obtain the file analysis results.

4. The method according to claim 2, characterized in that, The project documents and approval documents are matched using the checkpoint data to obtain document matching results, including: Feature extraction is performed on the checkpoint data to obtain checkpoint data features; Feature extraction is performed on the project files to obtain project file features; Feature extraction is performed on the approval document to obtain the approval document features; The checkpoint data features, project document features, and approval document features are input into the document matching model. The document matching model is then used to match the project document and the approval document to obtain the document matching result.

5. The method according to any one of claims 1-4, characterized in that, The rule file is identified to obtain the entry data and checkpoint data corresponding to the item to be analyzed, including: The rule file is split into multiple data modules; Based on the module characteristics of the plurality of data modules, a first module is determined from the plurality of data modules, wherein the first module includes the entry data; Based on the first module, a second module is determined according to the module mapping relationship, wherein the second module includes the checkpoint data, and the module mapping relationship is used to characterize the association between the first module and the second module.

6. A data analysis device, characterized in that, include: The file acquisition module is used to acquire the data analysis requirements and rule files of the project to be analyzed. The file recognition module is used to recognize the rule file and obtain the entry data and checkpoint data corresponding to the item to be analyzed. The entry data is used to characterize the execution rules of the item to be analyzed, and the checkpoint data is used to characterize the analysis rules that need to be used when analyzing the item to be analyzed. The project analysis module is used to analyze the project to be analyzed based on the data analysis requirements, using the entry data and the checkpoint data, and obtain data analysis results, wherein the data analysis results are used to characterize whether there are any anomalies in the project to be analyzed.

7. The apparatus according to claim 6, characterized in that, The project analysis module is also used for: The data analysis requirements are parsed to determine the project files and approval documents of the project to be analyzed. The project files are used to record the data corresponding to the project materials consumed during the execution of the project to be analyzed, and the approval documents are used to record the data corresponding to the project materials provided to the project to be analyzed. The project file is analyzed using the entry data to obtain file analysis results, wherein the file analysis results are used to characterize whether the execution process of the project to be analyzed conforms to the execution rules; The project file and the approval document are matched using the checkpoint data to obtain a file matching result, wherein the file matching result is used to characterize whether the project file and the approval document match; If the document analysis results indicate that the execution process of the project to be analyzed conforms to the execution rules, and the document matching results indicate that the project document matches the approval document, then it is determined that the data analysis results indicate that the project to be analyzed does not have any anomalies.

8. The apparatus according to claim 7, characterized in that, The project analysis module is also used for: Feature extraction is performed on the item data to obtain item data features; Feature extraction is performed on the project files to obtain project file features; The entry data features and the project file features are input into the file analysis model, and the project file is analyzed using the file analysis model to obtain the file analysis results.

9. The apparatus according to claim 7, characterized in that, The project analysis module is also used for: Feature extraction is performed on the checkpoint data to obtain checkpoint data features; Feature extraction is performed on the project files to obtain project file features; Feature extraction is performed on the approval document to obtain the approval document features; The checkpoint data features, project document features, and approval document features are input into the document matching model. The document matching model is then used to match the project document and the approval document to obtain the document matching result.

10. The apparatus according to any one of claims 6-9, characterized in that, The file recognition module is also used for: The rule file is split into multiple data modules; Based on the module characteristics of the plurality of data modules, a first module is determined from the plurality of data modules, wherein the first module includes the entry data; Based on the first module, a second module is determined according to the module mapping relationship, wherein the second module includes the checkpoint data, and the module mapping relationship is used to characterize the association between the first module and the second module.

11. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 5.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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