CN114117066A - Recommendation method and device for audit retrieval data folder - Google Patents
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Abstract
The invention discloses a recommendation method and a recommendation device for an audit retrieval data folder, which can be used in the field of finance, wherein the method comprises the following steps: acquiring project data and corresponding folder data of a historical audit project; utilizing a machine learning algorithm to perform entity extraction on project data of the historical audit project to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF; according to project characteristic data of a historical audit project, an entity relation is constructed by utilizing a semantic analysis technology; constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project; and recommending audit retrieval data folders according to the knowledge graph of the historical audit project. The invention can recommend the audit retrieval data folder, and improves the working efficiency and the accuracy.
Description
Technical Field
The invention relates to the field of finance, in particular to a recommendation method and device for an audit retrieval data folder. The method and the device for recommending the audit retrieval data folder can be used in the financial field and can also be used in any field except the financial field, and the application field of the method and the device for recommending the audit retrieval data folder is not limited.
Background
The data retrieval is an important link in the audit inspection, and in the inspection process, the audit project group members can send retrieval data lists to the audited units to require related personnel to upload the data to specified positions within specified dates for the project group to inspect and obtain evidence. The location, i.e., the folder in which project team members are set within the audit system according to project needs (e.g., by audit authorities, business type, description of doubt, review recommendations, audit issues, etc.) is specified. The setting of the folder requires that project group members analyze and sort audited organizations, service types, suspicious point descriptions, inspection suggestions and the like of audit projects, and set different dimensions and different levels. At present, when an audit project group member sets a folder for retrieving data, a manual mode is adopted to analyze and comb a plurality of items of information of an audit project, more time is consumed, and the accuracy of setting the folder is low.
Accordingly, there is a need for a recommendation for audit retrieval of data folders that overcomes the above-mentioned problems.
Disclosure of Invention
The embodiment of the invention provides a recommendation method for auditing and reading data folders, which is used for recommending the auditing and reading data folders and improving the working efficiency and the accuracy, and comprises the following steps:
acquiring project data and corresponding folder data of a historical audit project;
utilizing a machine learning algorithm to perform entity extraction on project data of the historical audit project to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF;
according to project characteristic data of a historical audit project, an entity relation is constructed by utilizing a semantic analysis technology;
constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project;
and recommending audit retrieval data folders according to the knowledge graph of the historical audit project.
The embodiment of the invention provides a recommendation device for auditing and reading data folders, which is used for recommending the auditing and reading data folders and improving the working efficiency and the accuracy, and comprises the following components:
the first data acquisition module is used for acquiring project data of a historical audit project and corresponding folder data;
the entity extraction module is used for performing entity extraction on the project data of the historical audit project by using a machine learning algorithm to obtain project characteristic data of the historical audit project, and the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF;
the entity relationship building module is used for building entity relationships by utilizing a semantic analysis technology according to the project characteristic data of the historical audit projects;
the knowledge map construction module is used for constructing a knowledge map of the historical audit project according to project characteristic data and entity relations of the historical audit project;
and the folder recommendation module is used for recommending the audit retrieval data folder according to the knowledge graph of the historical audit project.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the recommendation method for auditing the retrieval data folder is realized when the processor executes the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the recommendation method for auditing and retrieving the data folder is stored in the computer-readable storage medium.
Compared with the technical scheme of analyzing and carding a plurality of items of information of an audit project in a manual mode in the prior art, the embodiment of the invention obtains the project data of a historical audit project and the corresponding folder data; utilizing a machine learning algorithm to perform entity extraction on project data of the historical audit project to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF; according to project characteristic data of a historical audit project, an entity relation is constructed by utilizing a semantic analysis technology; constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project; and recommending audit retrieval data folders according to the knowledge graph of the historical audit project. According to the embodiment of the invention, the knowledge map of the historical audit project is constructed according to the project characteristic data of the historical audit project and the entity relationship constructed by the semantic analysis technology, so that the audit retrieval data folder is automatically recommended to the members of the audit project group according to the knowledge map of the historical audit project, the time for a user to manually sort and analyze the dimensions and the hierarchy of the folder and set the folder is saved, the accuracy and the reasonability of setting the folder are improved, the times for adjusting the structure of the folder in the process of uploading and looking up the retrieval data are reduced, and the working efficiency of the whole flow of the audit project is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a diagram illustrating a method for recommending audit retrieval data folders in an embodiment of the present invention;
FIG. 2 is a diagram illustrating exemplary folder data corresponding to project data in an embodiment of the present invention;
FIG. 3 is a diagram illustrating another exemplary method for recommending audit data folders according to an embodiment of the present invention;
FIG. 4 is a diagram of a structure of a recommendation device for auditing and reviewing data folders in an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
In order to recommend an audit retrieval document folder and improve work efficiency and accuracy, an embodiment of the present invention provides a recommendation method for an audit retrieval document folder, as shown in fig. 1, the method may include:
102, performing entity extraction on project data of a historical audit project by using a machine learning algorithm to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF;
103, constructing an entity relationship by utilizing a semantic analysis technology according to project characteristic data of a historical audit project;
104, constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project;
and 105, recommending audit retrieval data folders according to the knowledge graph of the historical audit project.
As can be known from the illustration in FIG. 1, the embodiment of the present invention obtains the project data and the corresponding folder data of the historical audit project; utilizing a machine learning algorithm to perform entity extraction on project data of the historical audit project to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF; according to project characteristic data of a historical audit project, an entity relation is constructed by utilizing a semantic analysis technology; constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project; and recommending audit retrieval data folders according to the knowledge graph of the historical audit project. According to the embodiment of the invention, the knowledge map of the historical audit project is constructed according to the project characteristic data of the historical audit project and the entity relationship constructed by the semantic analysis technology, so that the audit retrieval data folder is automatically recommended to the members of the audit project group according to the knowledge map of the historical audit project, the time for a user to manually sort and analyze the dimensions and the hierarchy of the folder and set the folder is saved, the accuracy and the reasonability of setting the folder are improved, the times for adjusting the structure of the folder in the process of uploading and looking up the retrieval data are reduced, and the working efficiency of the whole flow of the audit project is improved.
In the embodiment, project data and corresponding folder data of a historical audit project are obtained;
in this embodiment, the item data includes: audited organization data, service types, suspicious description data, inspection suggestion data and audit question data or any combination thereof.
In specific implementation, project data information which is mainly concerned by people needs to be extracted from multiple data of an audit project, wherein the project data comprises data of an audited organization, business types, suspicious description data, inspection suggestion data, audit problem data and the like, the audited organization comprises a head office, Guangdong province branches, Guangzhou regions and the like, and the business types comprise line-agency business, personal business and the like, category-currency issuing, risk assessment and the like. The corresponding folder data, i.e., the paging material folder that other items have created, is shown in fig. 2, for example.
In the embodiment, entity extraction is carried out on the project data of the historical audit project by using a machine learning algorithm to obtain the project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden markov model algorithm HMM or conditional random factory algorithm CRF. And according to the project characteristic data of the historical audit project, constructing an entity relationship by utilizing a semantic analysis technology. And constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project.
In specific implementation, the extraction of entities is completed by a manual labeling mode and a statistical machine learning algorithm, including a Hidden Markov Model (HMM) algorithm and a conditional random factory algorithm (CRF), the entity relationship is constructed by using a semantic analysis technology, an entity-relationship-entity triple is formed, and a knowledge graph is constructed.
In the embodiment, recommendation of the audit retrieval data folder is carried out according to the knowledge graph of the historical audit project.
In this embodiment, according to the knowledge graph of the historical audit item, recommendation of an audit retrieval data folder is performed, including:
acquiring project data of an audit project to be processed;
utilizing a machine learning algorithm to perform entity extraction on project data of an audit project to be processed to obtain project characteristic data of the audit project to be processed;
matching the project characteristic data of the audit project to be processed with the knowledge map of the historical audit project by using a search model, and determining the matching degree of the folder;
and recommending audit retrieval data folders according to the folder matching degree.
In this embodiment, as shown in fig. 3, the recommendation method for auditing the retrieval folder further includes:
301, obtaining response processing data fed back by a user according to a recommended result;
and 302, optimizing the knowledge graph of the historical audit project according to the response processing data.
In specific implementation, the search model is used for carrying out feature matching on project feature data extracted from the audit project to be processed and the knowledge graph, and the matching degree of the new audit project and the folder is calculated. Wherein the search model comprises an FM model or an LR model. And recommending the folder with the matching degree larger than or equal to the set matching threshold value to the audit item to be processed. The recommended folders are used as references and the operator can make adjustments based on the recommendations. And the operator confirms that the used folder is used as historical folder data in subsequent automatic recommendation and participates in the construction of the knowledge graph. Namely, the response and the processing of the recommendation result by the user are also used as input data for the subsequent optimization of the knowledge graph. The response of the user to the recommendation result comprises: and using the recommended folder, using the recommended folder after adjustment and using the unused recommended folder, and enabling the user to confirm that the used folder is used as historical folder data in subsequent automatic recommendation and participate in the construction of the knowledge graph.
The embodiment of the invention constructs the knowledge map of the audit project and the retrieval data folder, automatically recommends the audit retrieval data folder to the members of the audit project group, and saves the time for manually combing and analyzing the dimensions and the levels of the folder and setting the folder for a user. The accuracy and the rationality of setting up the folder are improved, the number of times of adjusting the folder structure in the process of uploading and looking up the retrieval data is reduced, and the work efficiency of the whole flow of the audit project is improved.
Based on the same inventive concept, the embodiment of the present invention further provides a recommendation device for auditing and retrieving the document folder, as described in the following embodiments. Because the principles for solving the problems are similar to the recommendation method for auditing and retrieving the document folder, the implementation of the recommendation device for auditing and retrieving the document folder can be referred to the implementation of the method, and repeated parts are not repeated.
Fig. 4 is a structural diagram of a recommendation apparatus for auditing and reviewing a document folder in an embodiment of the present invention, and as shown in fig. 4, the recommendation apparatus for auditing and reviewing a document folder includes:
a first data obtaining module 401, configured to obtain project data of a historical audit project and corresponding folder data;
an entity extraction module 402, configured to perform entity extraction on the item data of the historical audit item by using a machine learning algorithm to obtain item feature data of the historical audit item, where the machine learning algorithm includes: hidden Markov model algorithm HMM or conditional random factory algorithm CRF;
an entity relationship construction module 403, configured to construct an entity relationship by using a semantic analysis technique according to the project feature data of the historical audit project;
a knowledge graph construction module 404, configured to construct a knowledge graph of the historical audit project according to project feature data and entity relationships of the historical audit project;
and the folder recommendation module 405 is configured to recommend the audit retrieval data folder according to the knowledge graph of the historical audit project.
In one embodiment, the project data includes: audited organization data, service types, suspicious description data, inspection suggestion data and audit question data or any combination thereof.
In one embodiment, the folder recommendation module 405 is further configured to:
acquiring project data of an audit project to be processed;
utilizing a machine learning algorithm to perform entity extraction on project data of an audit project to be processed to obtain project characteristic data of the audit project to be processed;
matching the project characteristic data of the audit project to be processed with the knowledge map of the historical audit project by using a search model, and determining the matching degree of the folder;
and recommending audit retrieval data folders according to the folder matching degree.
In one embodiment, the recommending apparatus for auditing the retrieved document folder further comprises:
the second data acquisition module is used for acquiring response processing data fed back by the user according to the recommended result;
and the knowledge graph optimizing module is used for optimizing the knowledge graph of the historical audit project according to the response processing data.
In summary, in the embodiments of the present invention, the item data of the historical audit item and the corresponding folder data are obtained; utilizing a machine learning algorithm to perform entity extraction on project data of the historical audit project to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF; according to project characteristic data of a historical audit project, an entity relation is constructed by utilizing a semantic analysis technology; constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project; and recommending audit retrieval data folders according to the knowledge graph of the historical audit project. According to the embodiment of the invention, the knowledge map of the historical audit project is constructed according to the project characteristic data of the historical audit project and the entity relationship constructed by the semantic analysis technology, so that the audit retrieval data folder is automatically recommended to the members of the audit project group according to the knowledge map of the historical audit project, the time for a user to manually sort and analyze the dimensions and the hierarchy of the folder and set the folder is saved, the accuracy and the reasonability of setting the folder are improved, the times for adjusting the structure of the folder in the process of uploading and looking up the retrieval data are reduced, and the working efficiency of the whole flow of the audit project is improved.
Based on the aforementioned inventive concept, as shown in fig. 5, the present invention further provides a computer device 500, which includes a memory 510, a processor 520, and a computer program 530 stored on the memory 510 and executable on the processor 520, wherein the processor 520 executes the computer program 530 to implement the aforementioned method for recommending audit survey data folders.
Based on the foregoing inventive concept, the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the foregoing recommendation method for auditing and reviewing a data folder.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A recommendation method for auditing and retrieving a document folder is characterized by comprising the following steps:
acquiring project data and corresponding folder data of a historical audit project;
utilizing a machine learning algorithm to perform entity extraction on project data of the historical audit project to obtain project characteristic data of the historical audit project, wherein the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF;
according to project characteristic data of a historical audit project, an entity relation is constructed by utilizing a semantic analysis technology;
constructing a knowledge graph of the historical audit project according to project characteristic data and entity relations of the historical audit project;
and recommending audit retrieval data folders according to the knowledge graph of the historical audit project.
2. A recommendation method for audit retrieval of a data folder as claimed in claim 1 wherein said project data includes: audited organization data, service types, suspicious description data, inspection suggestion data and audit question data or any combination thereof.
3. The recommendation method for audit retrieval material folders as claimed in claim 1, wherein the recommendation of audit retrieval material folders is performed according to the knowledge map of the historical audit items, comprising:
acquiring project data of an audit project to be processed;
utilizing a machine learning algorithm to perform entity extraction on project data of an audit project to be processed to obtain project characteristic data of the audit project to be processed;
matching the project characteristic data of the audit project to be processed with the knowledge map of the historical audit project by using a search model, and determining the matching degree of the folder;
and recommending audit retrieval data folders according to the folder matching degree.
4. A recommendation method for auditing a review material folder as recited in claim 1, further comprising:
obtaining response processing data fed back by the user according to the recommended result;
and optimizing the knowledge graph of the historical audit project according to the response processing data.
5. A recommendation device for auditing and retrieving a document folder, comprising:
the first data acquisition module is used for acquiring project data of a historical audit project and corresponding folder data;
the entity extraction module is used for performing entity extraction on the project data of the historical audit project by using a machine learning algorithm to obtain project characteristic data of the historical audit project, and the machine learning algorithm comprises the following steps: hidden Markov model algorithm HMM or conditional random factory algorithm CRF;
the entity relationship building module is used for building entity relationships by utilizing a semantic analysis technology according to the project characteristic data of the historical audit projects;
the knowledge map construction module is used for constructing a knowledge map of the historical audit project according to project characteristic data and entity relations of the historical audit project;
and the folder recommendation module is used for recommending the audit retrieval data folder according to the knowledge graph of the historical audit project.
6. A recommender for audit-retrieval of material folders as in claim 5 wherein said project data includes: audited organization data, service types, suspicious description data, inspection suggestion data and audit question data or any combination thereof.
7. The recommendation device for audit reviewed documentation folders of claim 5, wherein the folder recommendation module is further to:
acquiring project data of an audit project to be processed;
utilizing a machine learning algorithm to perform entity extraction on project data of an audit project to be processed to obtain project characteristic data of the audit project to be processed;
matching the project characteristic data of the audit project to be processed with the knowledge map of the historical audit project by using a search model, and determining the matching degree of the folder;
and recommending audit retrieval data folders according to the folder matching degree.
8. A recommender for audit recall of a data folder as in claim 5 further comprising:
the second data acquisition module is used for acquiring response processing data fed back by the user according to the recommended result;
and the knowledge graph optimizing module is used for optimizing the knowledge graph of the historical audit project according to the response processing data.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116562785A (en) * | 2023-03-17 | 2023-08-08 | 广东铭太信息科技有限公司 | Auditing and welcome system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116562785A (en) * | 2023-03-17 | 2023-08-08 | 广东铭太信息科技有限公司 | Auditing and welcome system |
| CN116562785B (en) * | 2023-03-17 | 2023-12-15 | 广东铭太信息科技有限公司 | Auditing and welcome system |
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