CN115309987A - Enterprise information matching system - Google Patents
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Abstract
The invention relates to the technical field of information processing, in particular to an enterprise information matching system. The method comprises the following steps: the system comprises a knowledge management unit, a knowledge graph model unit and a self-matching unit; the knowledge management unit comprises a policy project library, a law and regulation library, an intellectual property library, a qualification library and an enterprise database; the knowledge map model unit comprises a policy analysis module, a law and regulation analysis module, an intellectual property analysis module, a qualification analysis module and an enterprise data analysis module; the system is used for performing text semantic analysis on policy items, laws and regulations, intellectual property rights, qualifications and enterprise data information to obtain a semantic analysis result; modularly disassembling semantic analysis results according to topic subsections of a plurality of preset modules, and storing the semantic analysis results into a knowledge graph model unit; the self-matching unit calls the enterprise database information in the knowledge management unit to be compared and matched with the knowledge management unit according to the preset matching steps, and a matching result is generated.
Description
Technical Field
The invention relates to the technical field of information processing, in particular to an enterprise information matching system.
Background
In daily management, enterprises inevitably need to be exposed to laws and regulations, policy items, intellectual property rights, qualifications and various business-related policy information. Due to various laws and regulations, policy items, intellectual property rights, qualifications and other information categories, the system has numerous and various forms and structures, and the issuing time, the issuing channel, the effective period and the like are different. In daily processing, most of the policy information acquisition and screening work is manually completed and is influenced by manual quality factors, so that enterprises are difficult to pay attention to, comprehensively understand and specifically grasp in real time in daily operation management, and therefore some information beneficial to the enterprises is often missed, and unnecessary loss can be caused by the fact that some information is not understood.
For example, the policy item refers to a series of preferential policies formulated by government authorities for enterprises, and the preferential policies are automatically declared by the enterprises to obtain related qualifications, so that corresponding preferential benefits are obtained. However, when an enterprise makes a project application, the first failure is to comprehensively understand the real-time project policy; secondly, the enterprise can not better understand and master the project content and reward policy which are in accordance with each condition of the enterprise, so that the enterprise wastes more manpower and material resources to develop projects with low passing rate, but does not develop the enterprise projects with high success rate.
Disclosure of Invention
The invention aims to provide an enterprise information matching system which is suitable for information integration and autonomous matching of enterprises.
In order to achieve the purpose, the invention provides the following technical scheme:
an enterprise information matching system comprising: the system comprises a knowledge management unit, a knowledge graph model unit and a self-matching unit;
the knowledge management unit comprises a policy project library, a law and regulation library, an intellectual property library, a qualification library and an enterprise library; used for storing policy items, laws and regulations, intellectual property rights, qualification and enterprise data analysis label data;
the knowledge map model unit comprises a policy analysis module, a law and regulation analysis module, an intellectual property analysis module, a qualification analysis module and an enterprise data analysis module; the system is used for performing text semantic analysis on policy items, laws and regulations, intellectual property rights, qualifications and enterprise data information to obtain a semantic analysis result; modularly disassembling semantic analysis results according to topic subsections of a plurality of preset modules; setting corresponding module subject labels for the disassembled modular character paragraphs respectively as policy items, laws and regulations, intellectual property rights, qualifications and enterprise label data; each group of label data is stored into a knowledge graph model unit;
the self-matching unit calls enterprise database information in the knowledge management unit according to preset matching steps for the input enterprise information, and the enterprise database information is matched with a policy item library, a legal regulation library, an intellectual property library and a qualification library in the knowledge management unit to generate a matching result.
Optionally, the policy analysis module disassembles and reconstructs the policy document according to a issuing institution, a issuing date, an effective date, an applicable object, a policy category, basic requirements, variable conditions, rejection conditions, evaluation indexes, evaluation rules, reward and punishment modes, performance requirements, an application period and the like to form a policy knowledge graph model;
the law and regulation analysis module is used for disassembling and reconstructing the law and regulation files according to issuing organs, issuing dates, effective dates, applicable objects, law and regulation types, basic requirements, variable requirements, rejection requirements, evaluation rules, reward and punishment modes, application periods and the like to form a law and regulation knowledge graph model;
the intellectual property analysis module is used for disassembling and reconstructing the intellectual property file according to a publishing organ, a publishing date, an effective date, an applicable object, a category of intellectual property, a basic requirement, a declaration requirement, an authorization requirement, a refute requirement, a current state, a reward and punishment mode, an effective period and the like to form an intellectual property knowledge graph model;
the qualification analysis module is used for disassembling and reconstructing a qualification certification file according to issuing organs, issuing dates, effective dates, applicable objects, qualification types, basic requirements, certification conditions, current states, reward and punishment modes, effective periods and the like to form a qualification knowledge map model;
the enterprise data analysis module disassembles and reconstructs enterprise files according to the established date, the registered fund, the registered address, the operation range, legal representatives, assets, income, profits, personnel, social security, intellectual property ownership type, intellectual property quantity, qualification ownership type, affiliated industry, qualification ownership quantity, operation risk, tax payment state and the like to form an enterprise data knowledge graph model.
Optionally, the self-matching unit includes a knowledge sharing module, a dynamic diverse browsing component, and a knowledge comparison management module;
the knowledge sharing module can be communicated with a knowledge map model unit, the knowledge management unit, the dynamic diverse browsing component and a knowledge comparison management module;
the dynamic multi-browsing component can dynamically crawl data from a webpage and transmit the data to the knowledge graph model unit for analysis; the retrieval and browsing of the information in the knowledge map model unit and the knowledge management unit are dynamically executed;
the knowledge comparison management module performs comparison on control instruction information and information obtained by retrieving and browsing the dynamic multi-browsing component from the knowledge map model unit and the knowledge management unit to form a comparison matching result; and analyzed to form a matching report.
Optionally, the dynamic diverse browsing component crawls policy items, laws and regulations, intellectual property rights, qualifications and enterprise data from a webpage at regular time, transmits the crawled policy items, laws and regulations, intellectual property rights, qualifications and enterprise data to the policy analysis module, the laws and regulations analysis module, the intellectual property rights analysis module, the qualification analysis module and the enterprise data analysis module of the knowledge map model unit, and each analysis module performs text semantic analysis on the crawled data, performs modular disassembly and label setting on each group of data according to semantic analysis results, and stores the data to the knowledge management unit.
Optionally, the matching step of the self-matching unit includes:
s1, receiving enterprise data input by a user, transmitting the enterprise data to an enterprise data analysis module of a knowledge map model unit, performing text semantic analysis on the enterprise data, performing modular disassembly and label setting on each group of data according to a semantic analysis result, and updating an enterprise database stored in a knowledge management unit;
step S2, according to enterprise data input by a receiving user, calling enterprise database information updated in the knowledge management unit, and performing comparison with a policy project library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit;
s3, intelligently matching enterprise data of the updated enterprise database with a plurality of groups of data of the knowledge management unit according to the tags finished by the knowledge map model unit, the enterprise tags and the data types, and the tag systems of a policy item library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit and the tag systems of the enterprise database; then, the user scores the weight of each round of matching results, and a matching report is formed according to the final weight scoring result;
and S4, obtaining a final weight scoring result of the enterprise matching result, screening out the matching result name with the final weight scoring result higher than a threshold value, sequencing the matching results according to the final weight scoring result from high to low, and generating a matching report to be pushed to a user.
Optionally, in step S2, when the enterprise information entered by the user is only an enterprise name, crawling the currently published basic data of the enterprise from the web page by using the enterprise name as a keyword, setting an enterprise tag through a knowledge map model unit, storing the enterprise tag into a knowledge management unit, calling enterprise data of an enterprise database updated by the enterprise of the knowledge management unit, and comparing information of a policy project library, a legal and legal rule library, an intellectual property library and a qualification library of the enterprise according to the currently published basic data;
and meanwhile, sending a non-public basic data acquisition request to the user to acquire non-public basic data related to policy declaration of the enterprise, and combining the non-public basic data with the public basic data to perform secondary comparison.
Optionally, in step S3, the intelligent matching sequentially adopts a forward matching mode and a reverse condition mode:
the forward matching comprises the steps of respectively comparing the enterprise data of the updated enterprise database with label systems of a policy item library, a legal regulation library, an intellectual property library and a qualification library in the knowledge management unit, and when a plurality of items meet the conditions, weighting and scoring the policies meeting the conditions as reportable policies, and bringing the matching results into a matching report;
the reverse condition comprises that the updated enterprise data of the enterprise database is respectively compared with label systems of a policy project library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit, and when the enterprise data is judged not to accord with at least one basic requirement of the policy project library, the law and regulation library, the intellectual property library and the qualification library, the matching result is not brought into the matching report; one or more basic requirements for reporting the policy are preset in each item of policy data, and as long as one condition does not meet the basic requirements, the matching result is not included in the matching report.
Optionally, in step S3, the intelligent matching sequentially adopts a forward matching mode, a reverse condition mode, and a reverse diagnosis mode, where the reverse diagnosis mode includes:
in step S3, the intelligent matching sequentially adopts a forward matching, a reverse condition, and a reverse diagnosis manner, wherein the reverse diagnosis includes:
1) Carrying out reverse diagnosis on matching results of a policy item library, a law and regulation library, an intellectual property library and a qualification library which are marked with labels in enterprise data of an updated enterprise database, deleting the matching results from the matching results, and not bringing the matching results into a matching report;
2) Performing reverse diagnosis on matching results of the policy item library, the legal rule library, the intellectual property library and the qualification library which are not marked with labels in the enterprise data of the updated enterprise database, wherein the matching results are not obtained but have conditions, are brought into a matching report, and can be declared;
3) And carrying out reverse diagnosis on the matching results of the policy item library, the law and regulation library, the intellectual property library and the qualification library which are not marked with labels in the enterprise data of the updated enterprise database, wherein the matching results are brought into the matching report within a preset interval of the difference between the matching results and the declared conditions and are not obtained and have no conditions, and the breeding prospect is prompted.
The beneficial effect of this application:
the method analyzes and arranges policy items, laws and regulations, intellectual property rights, qualifications and enterprise data to form an intellectual map, and performs automatic matching according to customer information to form a matching report. Therefore, the information can be conveniently known and matched by enterprises, and the enterprises can efficiently control the information.
Drawings
Fig. 1 is a schematic flow chart of automatic matching in the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With reference to the accompanying drawings, the application discloses an enterprise information matching system, comprising: the system comprises a knowledge management unit, a knowledge graph model unit and a self-matching unit. The intellectual maps are formed by analyzing and sorting policy items, laws and regulations, intellectual property rights, qualifications and enterprise data, and the matching reports are formed by automatically matching according to the client information. Therefore, the enterprise can conveniently know and match information, and can efficiently handle control information.
The knowledge management unit comprises a policy project library, a law and regulation library, an intellectual property library, a qualification library and an enterprise library; used to store the policy item, law and regulation, intellectual property, qualification, and enterprise data analysis label data.
The knowledge map model unit comprises a policy analysis module, a law and regulation analysis module, an intellectual property analysis module, a qualification analysis module and an enterprise data analysis module; the system is used for performing text semantic analysis on policy items, laws and regulations, intellectual property rights, qualifications and enterprise data information to obtain a semantic analysis result; modularly disassembling semantic analysis results according to topic subsections of a plurality of preset modules; setting corresponding module subject labels for the disassembled modular character paragraphs respectively as policy items, laws and regulations, intellectual property rights, qualifications and enterprise label data; and storing the label data of each group into a knowledge graph model unit by taking the label data of each group as a unit.
The policy analysis module is used for disassembling and reconstructing policy files according to issuing organs, issuing dates, effective dates, applicable objects, policy types, basic requirements, variable conditions, rejection conditions, evaluation indexes, evaluation rules, reward and punishment modes, performance requirements, application periods and the like to form a policy knowledge graph model;
the law and regulation analysis module is used for disassembling and reconstructing the law and regulation files according to issuing organs, issuing dates, effective dates, applicable objects, law and regulation types, basic requirements, variable requirements, rejection requirements, evaluation rules, reward and punishment modes, application periods and the like to form a law and regulation knowledge graph model;
the intellectual property analysis module is used for disassembling and reconstructing the intellectual property file according to a publishing organ, a publishing date, an effective date, an applicable object, a category of intellectual property, a basic requirement, a declaration requirement, an authorization requirement, a refute requirement, a current state, a reward and punishment mode, an effective period and the like to form an intellectual property knowledge graph model;
the qualification analysis module is used for disassembling and reconstructing a qualification certification file according to issuing organs, issuing dates, effective dates, applicable objects, qualification types, basic requirements, certification conditions, current states, reward and punishment modes, effective periods and the like to form a qualification knowledge map model;
the enterprise data analysis module disassembles and reconstructs enterprise files according to the established date, the registered fund, the registered address, the operation range, legal representatives, assets, income, profits, personnel, social security, intellectual property ownership type, intellectual property quantity, qualification ownership type, affiliated industry, qualification ownership quantity, operation risk, tax payment state and the like to form an enterprise data knowledge graph model.
The self-matching unit calls enterprise database information in the knowledge management unit according to preset matching steps for the input enterprise information, and the enterprise database information is matched with a policy project library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit to generate a matching result.
The self-matching unit comprises a knowledge sharing module, a dynamic diversified browsing component and a knowledge comparison management module; the knowledge sharing module can be communicated with a knowledge map model unit, the knowledge management unit, the dynamic diverse browsing component and a knowledge comparison management module; the dynamic multi-browsing component can dynamically crawl data from a webpage and transmit the data to the knowledge graph model unit for analysis; the retrieval and browsing of the information in the knowledge map model unit and the knowledge management unit are dynamically executed; the knowledge comparison management module performs comparison on control instruction information and information obtained by retrieval and browsing of the dynamic multi-browsing component from the knowledge graph model unit and the knowledge management unit to form a comparison matching result; and analyzing to form a matching report.
The dynamic diversified browsing component regularly crawls policy items, laws and regulations, intellectual property rights, qualifications and enterprise data from a webpage, transmits the crawled policy items, laws and regulations, intellectual property rights, qualifications and enterprise data to a policy analysis module, a laws and regulations analysis module, an intellectual property rights analysis module, a qualification analysis module and an enterprise data analysis module of the knowledge graph model unit, and each analysis module performs text semantic analysis on the crawled data, performs modular disassembly and label setting on each group of data according to a semantic analysis result, and stores the data to the knowledge management unit.
The matching step of the self-matching unit comprises the following steps:
step S1, enterprise data input by a user is received, the enterprise data are transmitted to an enterprise data analysis module of the knowledge map model unit, text semantic analysis is carried out on the enterprise data, each group of data are subjected to modular disassembly and label setting according to semantic analysis results, and an enterprise database stored in the knowledge management unit is updated.
And S2, calling the enterprise database information updated in the knowledge management unit according to the enterprise data input by the receiving user, and performing comparison with a policy item library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit.
When the enterprise information input by the user is only an enterprise name, the enterprise name is taken as a keyword to crawl the current published basic data of the enterprise from a webpage, the published basic data is analyzed through a knowledge graph model unit and is stored in an enterprise database, the updated enterprise data is called, and the updated enterprise data is compared with information of a policy project library, a legal and legal regulation library, an intellectual property library and a qualification library according to the updated enterprise data.
And simultaneously, sending a non-public basic data acquisition request to the user to acquire non-public basic data related to the policy declaration of the enterprise, and combining the non-public basic data and the public basic data for secondary comparison.
S3, intelligently matching enterprise data of the updated enterprise database with a plurality of groups of data of the knowledge management unit according to the tags finished by the knowledge map model unit, the enterprise tags and the data types, and the tag systems of a policy item library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit and the tag systems of the enterprise database; then, the user scores the weight of each round of matching results, and a matching report is formed according to the final weight scoring result;
the intelligent matching sequentially adopts a forward matching mode, a reverse condition mode and a reverse diagnosis mode.
The forward matching comprises the steps of respectively comparing the enterprise data of the updated enterprise database with label systems of a policy item library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit, and when a plurality of items meet the conditions, weighting and scoring the policies meeting the conditions as reportable policies, and bringing the matching results into a matching report.
The reverse condition comprises that the updated enterprise data of the enterprise database is respectively compared with label systems of a policy project library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit, and when the enterprise data is judged not to accord with at least one basic requirement of the policy project library, the law and regulation library, the intellectual property library and the qualification library, the matching result is not brought into the matching report; and if one condition does not meet the basic requirement, the matching result is not included in the matching report.
The reverse diagnosis includes:
1) Carrying out reverse diagnosis on matching results of the policy item library, the legal rule library, the intellectual property library and the qualification library which are marked with labels in the enterprise data of the updated enterprise database, deleting the matching results from the matching results, and not bringing the matching results into a matching report;
2) Performing reverse diagnosis on matching results of the policy item library, the law and regulation library, the intellectual property library and the qualification library which are not marked with labels in enterprise data of the updated enterprise database, wherein the matching results are not obtained but have conditions, are brought into a matching report, and can be declared;
3) And carrying out reverse diagnosis on the matching results of the policy item library, the law and regulation library, the intellectual property library and the qualification library which are not marked with labels in the enterprise data of the updated enterprise database, wherein the matching results are brought into the matching report within a preset interval of the difference between the matching results and the declared conditions and are not obtained and have no conditions, and the breeding prospect is prompted.
And S4, obtaining a final weight scoring result of the enterprise matching result, screening out the matching result name with the final weight scoring result higher than a threshold value, sequencing the matching results according to the final weight scoring result from high to low, and generating a matching report to be pushed to a user.
While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An enterprise information matching system, comprising: the system comprises a knowledge management unit, a knowledge graph model unit and a self-matching unit;
the knowledge management unit comprises a policy project library, a law and regulation library, an intellectual property library, a qualification library and an enterprise library; used for storing policy items, laws and regulations, intellectual property rights, qualification and enterprise data analysis label data;
the knowledge graph model unit comprises a policy analysis module, a law and regulation analysis module, an intellectual property analysis module, a qualification analysis module and an enterprise data analysis module; the system is used for performing text semantic analysis on policy items, laws and regulations, intellectual property rights, qualifications and enterprise data information to obtain a semantic analysis result; modularly disassembling semantic parsing results according to topic subsections of a plurality of preset modules; setting corresponding module subject labels for the disassembled modular character paragraphs respectively as policy items, laws and regulations, intellectual property rights, qualifications and enterprise label data; storing the label data of each group into a knowledge graph model unit by taking the label data of each group as a unit;
the self-matching unit calls enterprise database information in the knowledge management unit according to preset matching steps for the input enterprise information, and the enterprise database information is matched with a policy project library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit to generate a matching result.
2. The system of claim 1,
the policy analysis module is used for disassembling and reconstructing policy files according to issuing organs, issuing dates, effective dates, applicable objects, policy types, basic requirements, variable conditions, rejection conditions, evaluation indexes, evaluation rules, reward and punishment modes, performance requirements, application periods and the like to form a policy knowledge graph model;
the law and regulation analysis module is used for disassembling and reconstructing the law and regulation file according to issuing organs, issuing dates, effective dates, applicable objects, law and regulation types, basic requirements, variable requirements, rejection requirements, evaluation rules, reward and punishment modes, applicable periods and the like to form a law and regulation knowledge map model;
the intellectual property analysis module is used for disassembling and reconstructing the intellectual property file according to a publishing organ, a publishing date, an effective date, an applicable object, a category of intellectual property, a basic requirement, a declaration requirement, an authorization requirement, a refute requirement, a current state, a reward and punishment mode, an effective period and the like to form an intellectual property knowledge graph model;
the qualification analysis module is used for disassembling and reconstructing a qualification certification file according to issuing organs, issuing dates, effective dates, applicable objects, qualification types, basic requirements, certification conditions, current states, reward and punishment modes, effective periods and the like to form a qualification knowledge map model;
the enterprise data analysis module disassembles and reconstructs enterprise files according to the established date, the registered fund, the registered address, the operation range, legal representatives, assets, income, profits, personnel, social security, intellectual property ownership type, intellectual property quantity, qualification ownership type, affiliated industry, qualification ownership quantity, operation risk, tax payment state and the like to form an enterprise data knowledge graph model.
3. The system of claim 1,
the self-matching unit comprises a knowledge sharing module, a dynamic diverse browsing component and a knowledge comparison management module;
the knowledge sharing module can be communicated with a knowledge graph model unit, the knowledge management unit, the dynamic diverse browsing component and a knowledge comparison management module;
the dynamic multi-browsing component can dynamically crawl data from a webpage and transmit the data to the knowledge graph model unit for analysis; the retrieval and browsing of the information in the knowledge map model unit and the knowledge management unit are dynamically executed;
the knowledge comparison management module performs comparison on control instruction information and information obtained by retrieving and browsing the dynamic multi-browsing component from the knowledge map model unit and the knowledge management unit to form a comparison matching result; and analyzed to form a matching report.
4. The system of claim 3,
the dynamic diversified browsing component regularly crawls policy items, laws and regulations, intellectual property rights, qualifications and enterprise data from a webpage, transmits the crawled policy items, laws and regulations, intellectual property rights, qualifications and enterprise data to a policy analysis module, a laws and regulations analysis module, an intellectual property rights analysis module, an qualification analysis module and an enterprise data analysis module of the knowledge map model unit, and each analysis module carries out text semantic analysis on the crawled data, carries out modular disassembly and label setting on each group of data according to semantic analysis results, and stores the data to the knowledge management unit.
5. The system of claim 4, wherein,
the matching step of the self-matching unit includes:
step S1, enterprise data input by a user is received, the enterprise data are transmitted to an enterprise data analysis module of a knowledge graph model unit, text semantic analysis is carried out on the enterprise data, each group of data are subjected to modular disassembly and label setting according to semantic analysis results, and an enterprise database stored in a knowledge management unit is updated;
step S2, according to enterprise data input by a receiving user, calling enterprise database information updated in the knowledge management unit, and performing comparison with a policy project library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit;
s3, intelligently matching enterprise data of an updated enterprise database with a plurality of groups of data of the knowledge management unit according to the tags and enterprise tags completed by the knowledge map model unit, the data categories and tag systems of a policy item library, a legal and legal regulation library, an intellectual property library and a qualification library in the knowledge management unit; then, the user scores the weight of each round of matching results, and a matching report is formed according to the final weight scoring result;
and S4, obtaining a final weight scoring result of the enterprise matching result, screening out the matching result name with the final weight scoring result higher than a threshold value, sequencing the matching results according to the final weight scoring result from high to low, and generating a matching report to be pushed to a user.
6. The system of claim 5,
in step S2, when the enterprise information input by the user is only an enterprise name, crawling the currently published basic data of the enterprise from a webpage by taking the enterprise name as a keyword, setting an enterprise label through a knowledge map model unit, storing the enterprise label into a knowledge management unit, calling enterprise data of an enterprise database updated by the enterprise of the knowledge management unit, and comparing information of a policy project library, a legal and legal rule library, an intellectual property library and a qualification library of the enterprise according to the currently published basic data;
and simultaneously, sending a non-public basic data acquisition request to the user to acquire non-public basic data related to the policy declaration of the enterprise, and combining the non-public basic data and the public basic data for secondary comparison.
7. The system of claim 6,
in step S3, the intelligent matching sequentially adopts a forward matching and a reverse condition:
the forward matching comprises the steps of respectively comparing the enterprise data of the updated enterprise database with label systems of a policy item library, a law and regulation library, an intellectual property library and a qualification library in the knowledge management unit, and when a plurality of items meet the conditions, weighting and scoring the policies meeting the conditions as reportable policies, and bringing the matching results into a matching report;
the reverse condition comprises that the updated enterprise data of the enterprise database is respectively compared with label systems of a policy item library, a legal regulation library, an intellectual property library and a qualification library in the knowledge management unit, and when the enterprise data is judged not to accord with at least one basic requirement of the policy item library, the legal regulation library, the intellectual property library and the qualification library, the matching result is not brought into a matching report; one or more basic requirements for reporting the policy are preset in each item of policy data, and as long as one condition does not meet the basic requirements, the matching result is not included in the matching report.
8. The system of claim 7,
in step S3, the intelligent matching sequentially adopts a forward matching, a reverse condition, and a reverse diagnosis manner, wherein the reverse diagnosis includes:
1) Carrying out reverse diagnosis on matching results of the policy item library, the legal rule library, the intellectual property library and the qualification library which are marked with labels in the enterprise data of the updated enterprise database, deleting the matching results from the matching results, and not bringing the matching results into a matching report;
2) Performing reverse diagnosis on matching results of the policy item library, the legal rule library, the intellectual property library and the qualification library which are not marked with labels in the enterprise data of the updated enterprise database, wherein the matching results are not obtained but have conditions, are brought into a matching report, and can be declared;
3) And carrying out reverse diagnosis on the matching results of the policy item library, the law and regulation library, the intellectual property library and the qualification library which are not marked with labels in the enterprise data of the updated enterprise database, wherein the matching results are brought into the matching report within a preset interval of the difference between the matching results and the declared conditions and are not obtained and have no conditions, and the breeding prospect is prompted.
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| CN116701639A (en) * | 2023-07-26 | 2023-09-05 | 广东师大维智信息科技有限公司 | Text analysis-based double-carbon knowledge graph data analysis method and system |
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| CN116701639B (en) * | 2023-07-26 | 2024-03-12 | 广东师大维智信息科技有限公司 | Text analysis-based double-carbon knowledge graph data analysis method and system |
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