CN115147081B - Policy matching method, system and storage medium based on artificial intelligence - Google Patents
Policy matching method, system and storage medium based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses an artificial intelligence-based policy matching method, an artificial intelligence-based policy matching system and a storage medium. The policy matching method based on artificial intelligence comprises the steps of extracting basic information corresponding to each enterprise subsidy policy, basic information corresponding to each registered enterprise and associated browsing information corresponding to each registered enterprise in a policy platform from a policy issuing platform background; analyzing attention indexes and adaptation indexes corresponding to the subsidy policies of the enterprises by the registered enterprises; analyzing the reporting value index corresponding to each enterprise subsidy policy; comprehensively matching and analyzing each registered enterprise and each enterprise subsidy policy, and feeding back; the invention effectively solves the problem that the matching dimension of the current matching method is too single, improves the matching efficiency and the matching convenience of enterprises and the policies of all enterprises, realizes the multidimensional matching of the enterprises and the policies of all enterprises, and improves the reliability, the rationality and the accuracy of the matching analysis result of the policies of the enterprises.
Description
Technical Field
The invention belongs to the technical field of policy matching, and relates to an artificial intelligence-based policy matching method, an artificial intelligence-based policy matching system and a storage medium.
Background
In the social development process, enterprises play a role, and for any enterprise, the accuracy of policy matching is self-evident to the enterprise, so that the importance of a policy matching method is highlighted;
the current policy matching method mainly comprises two modes of personnel matching and platform matching, wherein the first mode has larger workload and higher requirements on project personnel, the second mode has higher requirements on policy data in a platform, and the matching is performed only by a keyword matching mode, so that obviously, the two current policy matching modes have the defects that the following aspects are particularly presented:
1. the current policy matching belongs to a general matching mode, the matching dimension is single, matching analysis is not carried out from information such as the release time, the reporting time and the like of the policy, the release time and the enterprise knowledge time of the policy are often asynchronous, an enterprise needs a certain surplus time to prepare materials, the sufficiency of the preparation time of the materials for enterprise reporting cannot be guaranteed only through keyword matching at present, the showing weight of the policies for enterprise reporting cannot be improved, the reporting timeliness of the policies for enterprise reporting cannot be guaranteed, and the matching effect and the reference value of the policies for enterprise cannot be improved;
2. when the enterprise policy is matched currently, the matching is only carried out from the feasibility aspect of the enterprise to the policy declaration, the comprehensive matching of the value degree of the enterprise to the policy and the attention degree of the enterprise to the policy is not considered, the rationality and the accuracy of the enterprise and the policy matching cannot be effectively improved, the individuation, humanization and high-efficiency matching of the enterprise policy cannot be realized, the intelligent level is not high, and the matching requirement of the enterprise to the declaration policy cannot be met to the greatest extent.
Disclosure of Invention
In view of this, in order to solve the problems posed in the background art described above, an artificial intelligence-based policy matching method, system and storage medium are now proposed;
the aim of the invention can be achieved by the following technical scheme:
the first aspect of the present invention provides an artificial intelligence based policy matching method comprising the steps of:
the method comprises the steps of firstly, extracting the number of the released enterprise subsidy policies, basic information corresponding to the enterprise subsidy policies and basic information corresponding to each registered enterprise from a background of a administrative policy release platform, numbering each registered enterprise according to a set sequence, and marking the number as 1,2, j, m, and 1,2, i, n;
extracting relevant browsing information corresponding to each registered enterprise in the policy platform from the background of the policy platform;
third, based on the relevant browse information of each registered enterprise corresponding to the policy platform, analyzing to obtain the attention index of each registered enterprise corresponding to each enterprise subsidy policy, and recording asi representsThe number to which the patch policy corresponds, i=1, 2,.. j represents the number corresponding to each registered business, j=1, 2.
Fourth, based on the basic information corresponding to the auxiliary policies of each enterprise and the basic information corresponding to each registered enterprise, analyzing to obtain the adaptation index corresponding to each registered enterprise and each enterprise auxiliary policy, and recording as
Fifth, extracting the reporting start time, reporting stop time and subsidy amount from the basic information corresponding to each enterprise subsidy policy, analyzing to obtain the reporting value index corresponding to each enterprise subsidy policy, and recording as J i ；
Sixth, based on attention indexes and adaptation indexes of each registered enterprise corresponding to each enterprise subsidy policy and reporting value indexes of each enterprise subsidy policy, comprehensively analyzing to obtain comprehensive matching indexes of each registered enterprise corresponding to each enterprise subsidy policy, sequencing the comprehensive matching indexes of each registered enterprise corresponding to each enterprise subsidy policy according to a sequence from big to small, extracting the enterprise subsidy policies with three top ranks, taking the enterprise subsidy policies as preferred reporting policies corresponding to each registered enterprise, and extracting reporting links corresponding to each preferred reporting policy from a administrative policy issuing platform background;
and a seventh step of sending reporting links corresponding to the preferred reporting policies corresponding to the registered enterprises to the login interfaces corresponding to the registered enterprises.
In a preferred embodiment of the present invention, the basic information corresponding to each corporate subsidy policy is a policy theme, a release time, a subsidy amount, reporting rule information and reporting time information, wherein the reporting time information includes a reporting start time point and a reporting end time point, and the reporting rule information is a reporting corporation type, a scale, a region, a registration duration interval, an annual average income total interval and an annual average income increase rate interval.
In a preferred embodiment of the present invention, the basic information corresponding to each registered enterprise includes enterprise type, registration time, registration area, employee number, total amount of income corresponding to each registration period, and income increase rate corresponding to each registration period.
In a preferred embodiment of the present invention, the associated browsing information corresponding to each registered enterprise in the policy platform specifically includes the number of browsing policies, a theme corresponding to each browsing policy, and browsing times and browsing durations corresponding to each browsing policy.
In a preferred embodiment of the present invention, the specific analysis process includes the following steps:
extracting policy subjects from basic information corresponding to each enterprise subsidy policy, extracting each keyword corresponding to each enterprise subsidy policy through a keyword extraction technology, constructing a keyword set corresponding to each enterprise subsidy policy, and marking the keyword set as A i ；
Extracting topics corresponding to each browsing policy from associated browsing information corresponding to each registered enterprise in a policy platform, extracting keywords corresponding to the browsing policies through a keyword extraction technology, constructing keyword sets corresponding to each browsing policy in each registered enterprise, and marking as B j y Y represents a number corresponding to each browsing policy, y=1, 2.
Analysis by analytical formulasObtaining the matching degree of each enterprise subsidy policy and each browsing policy in each registered user, and taking the browsing policy with the highest matching degree with each enterprise subsidy policy in each registered user as the associated policy of each enterprise subsidy policy;
extracting release time from basic information corresponding to each enterprise patch policy, matching and comparing the release time with attention impact weight corresponding to each set policy release time to obtain attention impact weight corresponding to each enterprise patch policy, and marking the attention impact weight as mu i ；
Extracting browsing times and browsing time length corresponding to each browsing policy from associated browsing information corresponding to each registered enterprise in the policy platform, and respectively marking as c j y And t j y By analysis of the formulaAnalyzing to obtain attention indexes of each registered enterprise to each browsing policy, wherein a1 and a2 are respectively represented as the corresponding duty ratio weights of each browsing time duration of the browsing times, and p represents the number of the browsing policies;
based on the attention index of each registered enterprise to each browsing policy, the attention index of each registered user to the corresponding association policy of each enterprise subsidy policy is extracted from the attention index and marked as epsilon' j i, through analysis formulaAnalyzing and obtaining attention indexes of each registered user to the corresponding related policies of each enterprise subsidy policy, wherein sigma is a set enterprise subsidy policy attention assessment correction factor, and K is a set reference constant.
In a preferred embodiment of the present invention, the specific analysis process includes:
extracting declaration rule information from basic information corresponding to each enterprise subsidy policy, and further obtaining declaration enterprise types, scales, registration duration intervals, annual average income total interval, annual average income increase rate interval and regions corresponding to each enterprise subsidy policy;
extracting employee numbers from the basic information corresponding to each registered enterprise, matching and comparing the employee numbers corresponding to each registered enterprise with the employee number intervals corresponding to the set enterprise scale, and screening to obtain the scale corresponding to each registered enterprise;
extracting the corresponding total income amount and the corresponding income increase rate in each registration period from the basic information corresponding to each registration enterprise, and calculating the corresponding total annual income amount and the corresponding annual income increase rate of each registration enterprise in a mean value calculation mode;
extracting registration time from the basic information corresponding to each registration enterprise, thereby obtaining the current corresponding accumulated registration duration of each registration enterprise;
basic information corresponding to each registered enterpriseExtracting enterprise types and registration areas from the information, correspondingly comparing the enterprise types, registration areas, scales, annual average income total sum and annual average income increase rate and accumulated registration time length corresponding to each registration enterprise with the reporting enterprise types, areas, scales, annual average income total sum interval, annual average income increase rate interval and registration time length interval corresponding to each enterprise subsidy policy, and if comparison failure of certain basic information in a certain registration enterprise and certain reporting rule information in a certain enterprise subsidy policy occurs, recording the matching degree of reporting rules corresponding to the registration enterprise and the enterprise subsidy policy asIf the basic information of a certain registered enterprise is successfully compared with the information of the reporting rule of the enterprise subsidy policy, the reporting rule corresponding to the registered enterprise and the enterprise subsidy policy is adapted to be marked as +.>Thereby obtaining the adaptation degree of the reporting rule corresponding to the subsidy policy of each registered enterprise and marking as +.>The value is +.>Or->
Extracting reporting time information from basic information corresponding to each enterprise subsidy policy, further obtaining reporting deadline corresponding to each enterprise subsidy policy, if the current time exceeds the reporting deadline corresponding to a certain enterprise subsidy policy, marking the reporting time adaptation degree of each registered enterprise corresponding to the enterprise subsidy policy as phi, otherwise marking the reporting time adaptation degree of each registered enterprise corresponding to the enterprise subsidy policy as phi ', thereby obtaining the reporting time adaptation degree of each registered enterprise corresponding to each enterprise subsidy policy, and marking as phi'The value is phi or phi ', phi'>φ；
Based on the reporting rule adaptation degree corresponding to each enterprise subsidy policy and the reporting time adaptation degree corresponding to each enterprise subsidy policy of each registered enterprise, the method comprises the following steps ofAnalyzing to obtain the adaptation index of each registered enterprise corresponding to each enterprise subsidy policy, and performing ++>Respectively a preset reporting rule adaptation degree and a weighting factor corresponding to a reporting time adaptation degree.
In a preferred embodiment of the present invention, the specific analysis formula of the reporting value index corresponding to each enterprise subsidy policy is as followsWherein t is Initiation i 、t Cut-off i 、M i Respectively expressed as the declaration starting time, the declaration ending time and the subsidy amount corresponding to the ith enterprise subsidy policy, t Currently, the method is that For the current time, M Reference to And (5) the amount of subsidy for the set reference.
In a preferred embodiment of the present invention, the specific calculation formula of the comprehensive matching index corresponding to each registered enterprise and each enterprise subsidy policy is as followsThe comprehensive matching indexes corresponding to the j-th registered enterprise and the i enterprise auxiliary policies are represented as the attention indexes of the enterprise auxiliary policies, the adaptation degree corresponding to the enterprise auxiliary policies and the weight factors corresponding to the reporting value indexes corresponding to the enterprise auxiliary policies, wherein tau 1, tau 2 and tau 3 are respectively represented as the attention indexes of the enterprise auxiliary policies.
A second aspect of the present invention provides an artificial intelligence based policy matching system comprising:
the policy and enterprise information acquisition module is used for extracting the number of the released enterprise subsidy policies, the basic information corresponding to each enterprise subsidy policy and the basic information corresponding to each registered enterprise from the background of the policy release platform;
the enterprise browsing information acquisition module is used for extracting corresponding associated browsing information of each registered enterprise in the policy platform from the background of the policy platform;
the policy attention analysis module is used for analyzing and obtaining attention indexes corresponding to the subsidy policies of the registered enterprises based on the associated browsing information corresponding to the registered enterprises in the policy platform;
the policy adaptation analysis module is used for analyzing and obtaining adaptation indexes of each registered enterprise and each enterprise subsidy policy based on the basic information corresponding to each enterprise subsidy policy and the basic information corresponding to each registered enterprise;
the policy value analysis module is used for extracting the reporting starting time, the reporting ending time and the subsidy amount from the basic information corresponding to each enterprise subsidy policy, so as to analyze and obtain the reporting value index corresponding to each enterprise subsidy policy;
the policy comprehensive matching analysis processing module is used for comprehensively analyzing and obtaining comprehensive matching degrees of each registered enterprise corresponding to each enterprise subsidy policy based on attention indexes and adaptation indexes of each registered enterprise corresponding to each enterprise subsidy policy and reporting value indexes of each enterprise subsidy policy, sequencing the comprehensive matching degrees of each registered enterprise corresponding to each enterprise subsidy policy according to the order from large to small, extracting the enterprise subsidy policies with three digits before ranking, taking the enterprise subsidy policies as the preferential reporting policies corresponding to each registered enterprise, and extracting reporting links corresponding to the preferential reporting policies from the backstage of the administrative policy issuing platform;
and the matching feedback terminal is used for sending the reporting links corresponding to the preferred reporting policies corresponding to the registered enterprises to the login interfaces corresponding to the registered enterprises.
The third aspect of the present invention provides an artificial intelligence based policy matching storage medium, where the artificial intelligence based policy matching storage medium is burned with a computer program, and the computer program implements the artificial intelligence based policy matching method of the present invention when running in a memory of a server.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the artificial intelligence-based policy matching method, basic information corresponding to each enterprise subsidy policy, basic information corresponding to each registered enterprise and browsing information corresponding to each registered enterprise are extracted from a government policy issuing platform background, so that the attention degree, the adaptation degree and the value degree corresponding to each enterprise subsidy policy of each registered enterprise are analyzed, the comprehensive matching index of each registered enterprise to each enterprise policy is output, on one hand, the problem that the matching dimension of the two current matching methods is too single is effectively solved, the matching efficiency and the matching convenience of the enterprise and each enterprise policy are improved, the influence of personnel and policy data on the matching result of the enterprise is eliminated, the multidimensional matching of the enterprise and each enterprise policy is realized, sufficient material preparation time is provided for the enterprise, the reporting timeliness of the enterprise reporting policy and the preferred reporting policy is further improved, and the matching effect and the reference value of the enterprise and each enterprise policy are further improved; on the other hand, through carrying out policy matching analysis from three dimensions of the attention degree, the adaptation degree and the corresponding value degree of each enterprise policy, the reliability, the rationality and the accuracy of the enterprise policy matching analysis result are improved, personalized, humanized and efficient matching of the enterprise policy is realized, the intelligent level is high, and the matching requirement of the enterprise to the enterprise policy is met to the greatest extent.
(2) According to the invention, the attention degree of each registered enterprise to each enterprise subsidy policy is analyzed according to the corresponding associated browsing information of each registered enterprise in the policy platform, so that the attention trend and the attention strength of each registered enterprise to the policy are effectively highlighted, the matching basis of each registered enterprise and each enterprise subsidy policy is expanded, meanwhile, the fitting property of a subsequent matching result is improved through carrying out policy matching analysis from the aspect of enterprise requirements, and the fitting degree of the subsequent enterprise preference reporting policy and enterprise reporting intention is ensured.
(3) According to the invention, the policy value analysis is carried out according to the reporting starting time, the reporting ending time and the subsidy amount corresponding to each enterprise subsidy policy, so that the multidirectional analysis of the enterprise subsidy policy value is realized, the referential of the enterprise subsidy policy value analysis result is improved, meanwhile, a reliable reference basis is provided for the follow-up enterprise preferential reporting policy analysis, the rationality of the enterprise preferential reporting policy analysis is ensured, and the development of enterprises is promoted to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the steps of the method of the present invention.
FIG. 2 is a schematic diagram of the connection of the modules of the system of the present invention.
Detailed Description
The foregoing is merely illustrative of the principles of the invention, and various modifications, additions and substitutions for those skilled in the art will be apparent to those having ordinary skill in the art without departing from the principles of the invention or from the scope of the invention as defined in the accompanying claims.
Referring to fig. 1, the present invention provides an artificial intelligence based policy matching method, which includes the steps of:
the method comprises the steps of firstly, extracting the number of the released enterprise subsidy policies, basic information corresponding to the enterprise subsidy policies and basic information corresponding to each registered enterprise from a background of a administrative policy release platform, numbering each registered enterprise according to a set sequence, and marking the number as 1,2, j, m, and 1,2, i, n;
in the above description, the basic information corresponding to each enterprise subsidy policy is policy theme, issue time, subsidy amount, reporting rule information and reporting time information, the reporting time information includes a reporting start time point and a reporting end time point, and the reporting rule information is a reporting enterprise type, scale, region, registration duration interval, annual average income total interval and annual average income increase rate interval.
The basic information corresponding to each registered enterprise specifically includes enterprise type, registration time, registration area, employee number, total income corresponding to each registration period and income increase rate corresponding to each registration period.
And a second step of extracting relevant browsing information corresponding to each registered enterprise in the policy platform from the background of the policy platform, wherein the relevant browsing information specifically comprises the number of browsing policies, topics corresponding to each browsing policy, the browsing times and browsing duration corresponding to each browsing policy.
Third, based on the relevant browse information of each registered enterprise corresponding to the policy platform, analyzing to obtain the attention index of each registered enterprise corresponding to each enterprise subsidy policy, and recording asi represents the number to which the patch policy corresponds, i=1, 2,.. j represents the number corresponding to each registered business, j=1, 2.
Illustratively, the attention index corresponding to each enterprise subsidy policy by each registered enterprise, and the specific analysis process comprises the following steps:
extracting policy subjects from basic information corresponding to each enterprise subsidy policy, extracting each keyword corresponding to each enterprise subsidy policy through a keyword extraction technology, constructing a keyword set corresponding to each enterprise subsidy policy, and marking the keyword set as A i ；
Extracting topics corresponding to each browsing policy from associated browsing information corresponding to each registered enterprise in a policy platform, extracting keywords corresponding to the browsing policies through a keyword extraction technology, constructing keyword sets corresponding to each browsing policy in each registered enterprise, anddenoted as B j y Y represents a number corresponding to each browsing policy, y=1, 2.
It should be noted that the keyword extraction technology is a relatively mature technology, and will not be described herein.
Analysis by analytical formulasObtaining the matching degree of each enterprise subsidy policy and each browsing policy in each registered user, and taking the browsing policy with the highest matching degree with each enterprise subsidy policy in each registered user as the associated policy of each enterprise subsidy policy;
extracting release time from basic information corresponding to each enterprise patch policy, matching and comparing the release time with attention impact weight corresponding to each set policy release time to obtain attention impact weight corresponding to each enterprise patch policy, and marking the attention impact weight as mu i ；
Extracting browsing times and browsing time length corresponding to each browsing policy from associated browsing information corresponding to each registered enterprise in the policy platform, and respectively marking as c j y And t j y By analysis of the formulaAnalyzing to obtain attention indexes of each registered enterprise to each browsing policy, wherein a1 and a2 are respectively represented as the corresponding duty ratio weights of each browsing time duration of the browsing times, and p represents the number of the browsing policies;
based on the attention index of each registered enterprise to each browsing policy, the attention index of each registered user to the corresponding association policy of each enterprise subsidy policy is extracted from the attention index and marked as epsilon' j i, through analysis formulaAnalyzing and obtaining attention indexes of each registered user to the corresponding related policies of each enterprise subsidy policy, wherein sigma is a set enterprise subsidy policy attention assessment correction factor, and K is a set reference constant.
The larger the attention impact weight is, the lower the probability of being noted is, and under the circumstance that the attention impact weight is large, the higher the attention index of the enterprise subsidy policy corresponding to the associated policy is, the stronger the attention of the enterprise to the enterprise subsidy policy is.
According to the embodiment of the invention, the attention degree of each registered enterprise to each enterprise subsidy policy is analyzed according to the corresponding associated browsing information of each registered enterprise in the policy platform, so that the attention trend and the attention strength of each registered enterprise to the policy are effectively highlighted, the matching basis of each registered enterprise and each enterprise subsidy policy is expanded, meanwhile, the fitting property of a subsequent matching result is improved through carrying out policy matching analysis from the aspect of the enterprise, and the fitting degree of the preferred reporting policy of the subsequent enterprise and the enterprise reporting intention is ensured.
Fourth, based on the basic information corresponding to the auxiliary policies of each enterprise and the basic information corresponding to each registered enterprise, analyzing to obtain the adaptation index corresponding to each registered enterprise and each enterprise auxiliary policy, and recording as
In the foregoing, the specific analysis process includes:
extracting declaration rule information from basic information corresponding to each enterprise subsidy policy, and further obtaining declaration enterprise types, scales, registration duration intervals, annual average income total interval, annual average income increase rate interval and regions corresponding to each enterprise subsidy policy;
extracting employee numbers from the basic information corresponding to each registered enterprise, matching and comparing the employee numbers corresponding to each registered enterprise with the employee number intervals corresponding to the set enterprise scale, and screening to obtain the scale corresponding to each registered enterprise;
extracting the corresponding total income amount and the corresponding income increase rate in each registration period from the basic information corresponding to each registration enterprise, and calculating the corresponding total annual income amount and the corresponding annual income increase rate of each registration enterprise in a mean value calculation mode;
extracting registration time from the basic information corresponding to each registration enterprise, thereby obtaining the current corresponding accumulated registration duration of each registration enterprise;
extracting enterprise types and registration areas from basic information corresponding to each registered enterprise, correspondingly comparing the enterprise types, registration areas, scales, annual average income sum, annual average income increase rate and accumulated registration time length corresponding to each registered enterprise subsidy policy with the declared enterprise types, areas, scales, annual average income sum intervals, annual average income increase rate intervals and registration time length intervals corresponding to each enterprise subsidy policy, and if comparison failure of certain basic information in a certain registered enterprise and certain declaration rule information in a certain enterprise subsidy policy occurs, recording the matching degree of the declaration rules corresponding to the registered enterprise and the enterprise subsidy policy asIf the basic information of a certain registered enterprise is successfully compared with the information of the reporting rule of the enterprise subsidy policy, the reporting rule corresponding to the registered enterprise and the enterprise subsidy policy is adapted to be marked as +.>Thereby obtaining the adaptation degree of the reporting rule corresponding to the subsidy policy of each registered enterprise and marking as +.>The value is +.>Or->
Extracting declaration time information from basic information corresponding to each enterprise subsidy policy, further obtaining declaration deadline corresponding to each enterprise subsidy policy, if the current time exceeds the declaration deadline corresponding to a certain enterprise subsidy policy, marking the declaration time adaptation degree of each registered enterprise corresponding to the enterprise subsidy policy as phi, otherwise, marking each registered enterprise and the enterprise subsidy policy as phiThe reporting time adaptation degree corresponding to the paste policy is marked as phi', so that the reporting time adaptation degree corresponding to each registered enterprise and each enterprise paste policy is obtained and marked as phiThe value is phi or phi ', phi'>φ；
Based on the reporting rule adaptation degree corresponding to each enterprise subsidy policy and the reporting time adaptation degree corresponding to each enterprise subsidy policy of each registered enterprise, the method comprises the following steps ofAnalyzing to obtain the adaptation index of each registered enterprise corresponding to each enterprise subsidy policy, and performing ++>Respectively a preset reporting rule adaptation degree and a weighting factor corresponding to a reporting time adaptation degree.
Fifth, extracting the reporting start time, reporting stop time and subsidy amount from the basic information corresponding to each enterprise subsidy policy, analyzing to obtain the reporting value index corresponding to each enterprise subsidy policy, and recording as J i ；
In the above, the specific analysis formula of the declaration value index corresponding to each enterprise subsidy policy is as followsWherein t is Initiation i 、t Cut-off i 、M i Respectively expressed as the declaration starting time, the declaration ending time and the subsidy amount corresponding to the ith enterprise subsidy policy, t Currently, the method is that For the current time, M Reference to And (5) the amount of subsidy for the set reference.
According to the embodiment of the invention, the policy value analysis is carried out according to the reporting starting time, the reporting ending time and the subsidy amount corresponding to each enterprise subsidy policy, so that the multidirectional analysis of the enterprise subsidy policy value is realized, the referential of the enterprise subsidy policy value analysis result is improved, meanwhile, a reliable reference basis is provided for the follow-up enterprise preferential reporting policy analysis, the rationality of the enterprise preferential reporting policy analysis is ensured, and the development of enterprises is promoted to a certain extent.
Sixth, based on attention indexes and adaptation indexes of each registered enterprise corresponding to each enterprise subsidy policy and reporting value indexes of each enterprise subsidy policy, comprehensively analyzing to obtain comprehensive matching indexes of each registered enterprise corresponding to each enterprise subsidy policy, sequencing the comprehensive matching indexes of each registered enterprise corresponding to each enterprise subsidy policy according to a sequence from big to small, extracting the enterprise subsidy policies with three top ranks, taking the enterprise subsidy policies as preferred reporting policies corresponding to each registered enterprise, and extracting reporting links corresponding to each preferred reporting policy from a administrative policy issuing platform background;
in the above, the specific calculation formula of the comprehensive matching index corresponding to each registered enterprise and each enterprise auxiliary policy is as followsThe comprehensive matching indexes corresponding to the j-th registered enterprise and the i enterprise auxiliary policies are represented as the attention indexes of the enterprise auxiliary policies, the adaptation degree corresponding to the enterprise auxiliary policies and the weight factors corresponding to the reporting value indexes corresponding to the enterprise auxiliary policies, wherein tau 1, tau 2 and tau 3 are respectively represented as the attention indexes of the enterprise auxiliary policies.
And a seventh step of sending reporting links corresponding to the preferred reporting policies corresponding to the registered enterprises to the login interfaces corresponding to the registered enterprises.
According to the embodiment of the invention, the basic information corresponding to each enterprise subsidy policy, the basic information corresponding to each registered enterprise and the browsing information corresponding to each registered enterprise are extracted from the background of the administrative policy issuing platform, so that the attention degree, the adaptation degree and the value degree corresponding to each enterprise subsidy policy of each registered enterprise are analyzed, the comprehensive matching index of each registered enterprise to each enterprise policy is output, on one hand, the problem that the matching dimension of the two current matching methods is too single is effectively solved, the matching efficiency and the matching convenience of the enterprise and each enterprise policy are improved, the influence of personnel and policy data on the matching result of the enterprise is eliminated, the multidimensional matching of the enterprise and each enterprise policy is realized, sufficient material preparation time is provided for the enterprise, the reporting weight of the enterprise reporting policy and the preferred reporting policy is further improved, the reporting timeliness of the enterprise to the reporting policy and the preferred reporting policy is further ensured, and the matching effect and the reference value of the enterprise and each enterprise are further improved; on the other hand, through carrying out policy matching analysis from three dimensions of the attention degree, the adaptation degree and the corresponding value degree of each enterprise policy, the reliability, the rationality and the accuracy of the enterprise policy matching analysis result are improved, personalized, humanized and efficient matching of the enterprise policy is realized, the intelligent level is high, and the matching requirement of the enterprise to the enterprise policy is met to the greatest extent.
Referring to fig. 2, the invention provides an artificial intelligence-based policy matching system, which comprises a policy and enterprise information acquisition module, an enterprise browsing information acquisition module, a policy attention analysis module, a policy adaptation analysis module, a policy value analysis module, a policy comprehensive matching analysis processing module and a matching feedback terminal;
the policy and enterprise information acquisition module is respectively connected with the policy attention analysis module, the policy adaptation analysis module and the policy value analysis module, the policy attention analysis module is connected with the enterprise browsing information acquisition module, and the policy comprehensive matching analysis processing module is respectively connected with the policy attention analysis module, the policy adaptation analysis module, the policy value analysis module and the matching feedback terminal;
the policy and enterprise information acquisition module is used for extracting the number of the released enterprise subsidy policies, the basic information corresponding to each enterprise subsidy policy and the basic information corresponding to each registered enterprise from the background of the policy release platform;
the enterprise browsing information acquisition module is used for extracting corresponding associated browsing information of each registered enterprise in the policy platform from the background of the policy platform;
the policy attention analysis module is used for analyzing and obtaining attention indexes corresponding to the enterprise subsidy policies of each registered enterprise based on the corresponding associated browsing information of each registered enterprise in the policy platform;
the policy adaptation analysis module is used for analyzing and obtaining adaptation indexes of each registered enterprise corresponding to each enterprise subsidy policy based on the basic information corresponding to each enterprise subsidy policy and the basic information corresponding to each registered enterprise;
the policy value analysis module is used for extracting the reporting starting time, the reporting ending time and the subsidy amount from the basic information corresponding to each enterprise subsidy policy, so as to analyze and obtain the reporting value index corresponding to each enterprise subsidy policy;
the policy comprehensive matching analysis processing module is used for comprehensively analyzing and obtaining comprehensive matching degrees of each registered enterprise corresponding to each enterprise subsidy policy based on attention indexes, adaptation indexes and reporting value indexes of each registered enterprise corresponding to each enterprise subsidy policy, sequencing the comprehensive matching degrees of each registered enterprise corresponding to each enterprise subsidy policy according to the order from large to small, extracting the enterprise subsidy policies with three top ranks, taking the enterprise subsidy policies as preferred reporting policies corresponding to each registered enterprise, and extracting reporting links corresponding to each preferred reporting policy from a administrative policy issuing platform background;
and the matching feedback terminal is used for sending the reporting links corresponding to the preferred reporting policies corresponding to the registered enterprises to the login interfaces corresponding to the registered enterprises.
The invention also provides an artificial intelligence-based policy matching storage medium, which is burnt with a computer program, and the computer program realizes the artificial intelligence-based policy matching method when running in the memory of the server.
The foregoing is merely illustrative and explanatory of the principles of the invention, as various modifications and additions may be made to the specific embodiments described, or similar thereto, by those skilled in the art, without departing from the principles of the invention or beyond the scope of the appended claims.
Claims (9)
1. The policy matching method based on artificial intelligence is characterized in that: the method comprises the following steps:
the method comprises the steps of firstly, extracting the number of the released enterprise subsidy policies, basic information corresponding to the enterprise subsidy policies and basic information corresponding to each registered enterprise from a background of a administrative policy release platform, numbering each registered enterprise according to a set sequence, and marking the number as 1,2, j, m, and 1,2, i, n;
extracting relevant browsing information corresponding to each registered enterprise in the policy platform from the background of the policy platform;
third, based on the relevant browse information of each registered enterprise corresponding to the policy platform, analyzing to obtain the attention index of each registered enterprise corresponding to each enterprise subsidy policy, and recording asi represents the number to which the patch policy corresponds, i=1, 2,.. j represents the number corresponding to each registered business, j=1, 2.
Fourth, based on the basic information corresponding to the auxiliary policies of each enterprise and the basic information corresponding to each registered enterprise, analyzing to obtain the adaptation index corresponding to each registered enterprise and each enterprise auxiliary policy, and recording as
Fifth, extracting the reporting start time, reporting stop time and subsidy amount from the basic information corresponding to each enterprise subsidy policy, analyzing to obtain the reporting value index corresponding to each enterprise subsidy policy, and recording as J i ；
Sixth, based on attention indexes and adaptation indexes of each registered enterprise corresponding to each enterprise subsidy policy and reporting value indexes of each enterprise subsidy policy, comprehensively analyzing to obtain comprehensive matching indexes of each registered enterprise corresponding to each enterprise subsidy policy, sequencing the comprehensive matching indexes of each registered enterprise corresponding to each enterprise subsidy policy according to a sequence from big to small, extracting the enterprise subsidy policies with three top ranks, taking the enterprise subsidy policies as preferred reporting policies corresponding to each registered enterprise, and extracting reporting links corresponding to each preferred reporting policy from a administrative policy issuing platform background;
a seventh step of sending reporting links corresponding to the preferred reporting policies corresponding to the registered enterprises to login interfaces corresponding to the registered enterprises;
the specific analysis process of the attention index corresponding to the subsidy policy of each registered enterprise comprises the following steps:
extracting policy subjects from basic information corresponding to each enterprise subsidy policy, extracting each keyword corresponding to each enterprise subsidy policy through a keyword extraction technology, constructing a keyword set corresponding to each enterprise subsidy policy, and marking the keyword set as A i ；
Extracting topics corresponding to each browsing policy from associated browsing information corresponding to each registered enterprise in a policy platform, extracting keywords corresponding to the browsing policies through a keyword extraction technology, constructing keyword sets corresponding to each browsing policy in each registered enterprise, and marking as B j y Y represents a number corresponding to each browsing policy, y=1, 2.
Analysis by analytical formulasObtaining the matching degree of each enterprise subsidy policy and each browsing policy in each registered user, and taking the browsing policy with the highest matching degree with each enterprise subsidy policy in each registered user as the associated policy of each enterprise subsidy policy;
extracting release time from basic information corresponding to each enterprise patch policy, matching and comparing the release time with attention impact weight corresponding to each set policy release time to obtain attention impact weight corresponding to each enterprise patch policy, and marking the attention impact weight as mu i ；
Extracting browsing times and browsing time length corresponding to each browsing policy from associated browsing information corresponding to each registered enterprise in the policy platform, and respectively marking as c j y And t j y By analysis of the formulaAnalyzing to obtain attention indexes of each registered enterprise to each browsing policy, wherein a1 and a2 are respectively expressed as the corresponding duty ratio weight of each browsing duration of the browsing times, and p represents the number of the browsing policies;
based on the attention index of each registered enterprise to each browsing policy, the attention index of each registered user to the corresponding association policy of each enterprise subsidy policy is extracted from the attention index and marked as epsilon' j i, through analysis formulaAnalyzing and obtaining attention indexes of each registered user to the corresponding related policies of each enterprise subsidy policy, wherein sigma is a set enterprise subsidy policy attention assessment correction factor, and K is a set reference constant.
2. The artificial intelligence based policy matching method according to claim 1, wherein: the basic information corresponding to the subsidy policies of each enterprise is policy theme, release time, subsidy amount, reporting rule information and reporting time information, wherein the reporting time information comprises reporting starting time points and reporting ending time points, and the reporting rule information is reporting enterprise types, scales, areas, registration duration intervals, annual average income total interval and annual average income increase rate intervals.
3. The artificial intelligence based policy matching method according to claim 2, wherein: the basic information corresponding to each registered enterprise specifically comprises enterprise type, registration time, registration area, employee number, total income corresponding to each registration period and income increase rate corresponding to each registration period.
4. The artificial intelligence based policy matching method according to claim 3, wherein: the associated browsing information corresponding to each registered enterprise in the policy platform specifically comprises the number of browsing policies, topics corresponding to each browsing policy, browsing times corresponding to each browsing policy and browsing duration.
5. The artificial intelligence based policy matching method according to claim 3, wherein: the adaptation index corresponding to each registered enterprise and each enterprise subsidy policy is specifically analyzed, and the specific analysis process comprises the following steps:
extracting declaration rule information from basic information corresponding to each enterprise subsidy policy, and further obtaining declaration enterprise types, scales, registration duration intervals, annual average income total interval, annual average income increase rate interval and regions corresponding to each enterprise subsidy policy;
extracting employee numbers from the basic information corresponding to each registered enterprise, matching and comparing the employee numbers corresponding to each registered enterprise with the employee number intervals corresponding to the set enterprise scale, and screening to obtain the scale corresponding to each registered enterprise;
extracting the corresponding total income amount and the corresponding income increase rate in each registration period from the basic information corresponding to each registration enterprise, and calculating the corresponding total annual income amount and the corresponding annual income increase rate of each registration enterprise in a mean value calculation mode;
extracting registration time from the basic information corresponding to each registration enterprise, thereby obtaining the current corresponding accumulated registration duration of each registration enterprise;
extracting enterprise types and registration areas from basic information corresponding to each registered enterprise, correspondingly comparing the enterprise types, registration areas, scales, annual average income sum, annual average income increase rate and accumulated registration time length corresponding to each registered enterprise subsidy policy with the declared enterprise types, areas, scales, annual average income sum intervals, annual average income increase rate intervals and registration time length intervals corresponding to each enterprise subsidy policy, and if comparison failure of certain basic information in a certain registered enterprise and certain declaration rule information in a certain enterprise subsidy policy occurs, recording the matching degree of the declaration rules corresponding to the registered enterprise and the enterprise subsidy policy asIf the basic information of a certain registered enterprise is successfully compared with the information of the reporting rule of the enterprise subsidy policy, the reporting rule corresponding to the registered enterprise and the enterprise subsidy policy is adapted to be marked as +.>Thereby obtaining the adaptation degree of the reporting rule corresponding to the subsidy policy of each registered enterprise and marking as +.>The value is +.>Or->
Extracting reporting time information from basic information corresponding to each enterprise subsidy policy, further obtaining reporting deadline corresponding to each enterprise subsidy policy, if the current time exceeds the reporting deadline corresponding to a certain enterprise subsidy policy, marking the reporting time adaptation degree of each registered enterprise corresponding to the enterprise subsidy policy as phi, otherwise marking the reporting time adaptation degree of each registered enterprise corresponding to the enterprise subsidy policy as phi ', thereby obtaining the reporting time adaptation degree of each registered enterprise corresponding to each enterprise subsidy policy, and marking as phi'The value is phi or phi ', phi'>φ；
Based on the reporting rule adaptation degree corresponding to each enterprise subsidy policy and the reporting time adaptation degree corresponding to each enterprise subsidy policy of each registered enterprise, the method comprises the following steps ofAnalyzing to obtain the adaptation index of each registered enterprise corresponding to each enterprise subsidy policy, and performing ++>Respectively a preset reporting rule adaptation degree and a weighting factor corresponding to a reporting time adaptation degree.
6. The artificial intelligence based policy matching method according to claim 1, wherein: the specific analysis formula of the declaration value index corresponding to each enterprise subsidy policy is as followsWherein t is Initiation i 、t Cut-off i 、M i Respectively expressed as the declaration starting time, the declaration ending time and the subsidy amount corresponding to the ith enterprise subsidy policy, t Currently, the method is that For the current time, M Reference to And (5) the amount of subsidy for the set reference.
7. The artificial intelligence based policy matching method according to claim 1, wherein: the specific calculation formula of the comprehensive matching index corresponding to each registered enterprise and each enterprise subsidy policy is that The comprehensive matching indexes corresponding to the j-th registered enterprise and the i enterprise auxiliary policies are represented as the attention indexes of the enterprise auxiliary policies, the adaptation degree corresponding to the enterprise auxiliary policies and the weight factors corresponding to the reporting value indexes corresponding to the enterprise auxiliary policies, wherein tau 1, tau 2 and tau 3 are respectively represented as the attention indexes of the enterprise auxiliary policies.
8. An artificial intelligence based policy matching system for performing the method of any of the preceding claims 1-7, characterized by: the system comprises:
the policy and enterprise information acquisition module is used for extracting the number of the released enterprise subsidy policies, the basic information corresponding to each enterprise subsidy policy and the basic information corresponding to each registered enterprise from the background of the policy release platform;
the enterprise browsing information acquisition module is used for extracting corresponding associated browsing information of each registered enterprise in the policy platform from the background of the policy platform;
the policy attention analysis module is used for analyzing and obtaining attention indexes corresponding to the subsidy policies of the registered enterprises based on the associated browsing information corresponding to the registered enterprises in the policy platform;
the policy adaptation analysis module is used for analyzing and obtaining adaptation indexes of each registered enterprise and each enterprise subsidy policy based on the basic information corresponding to each enterprise subsidy policy and the basic information corresponding to each registered enterprise;
the policy value analysis module is used for extracting the reporting starting time, the reporting ending time and the subsidy amount from the basic information corresponding to each enterprise subsidy policy, so as to analyze and obtain the reporting value index corresponding to each enterprise subsidy policy;
the policy comprehensive matching analysis processing module is used for comprehensively analyzing and obtaining comprehensive matching degrees of each registered enterprise corresponding to each enterprise subsidy policy based on attention indexes and adaptation indexes of each registered enterprise corresponding to each enterprise subsidy policy and reporting value indexes of each enterprise subsidy policy, sequencing the comprehensive matching degrees of each registered enterprise corresponding to each enterprise subsidy policy according to the order from large to small, extracting the enterprise subsidy policies with three digits before ranking, taking the enterprise subsidy policies as the preferential reporting policies corresponding to each registered enterprise, and extracting reporting links corresponding to the preferential reporting policies from the backstage of the administrative policy issuing platform;
and the matching feedback terminal is used for sending the reporting links corresponding to the preferred reporting policies corresponding to the registered enterprises to the login interfaces corresponding to the registered enterprises.
9. An artificial intelligence based policy matching storage medium, characterized in that: the artificial intelligence based policy matching storage medium has a computer program burned in it, which when run in the memory of a server implements the method of any of the preceding claims 1-7.
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