CN115204995B - Tax data acquisition and analysis method, system and computer storage medium - Google Patents
Tax data acquisition and analysis method, system and computer storage medium Download PDFInfo
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Abstract
The invention discloses a tax data acquisition and analysis method, a tax data acquisition and analysis system and a computer storage medium. The tax data acquisition and analysis method comprises the steps of extracting basic information corresponding to a specified manufacturing industry in a target area from an enterprise tax declaration platform; positioning tax declaration data corresponding to each registered enterprise in the appointed manufacturing industry from the background of the enterprise tax declaration platform based on basic information corresponding to each registered enterprise in the appointed manufacturing industry; preliminary analysis and deep analysis are carried out on tax declaration data corresponding to each registered enterprise at present; feeding back the analysis result to enterprise tax manager in the target area; the method effectively solves the problem that the tax declaration data of the enterprise is too single at present, realizes multiple analysis of the tax declaration data of the registered enterprise, ensures objectivity and authenticity of the tax declaration data of the enterprise to the greatest extent, and simultaneously provides an accurate management direction for subsequent tax management of each registered enterprise.
Description
Technical Field
The invention belongs to the technical field of tax data analysis, and relates to a tax data acquisition and analysis method, a tax data acquisition and analysis system and a computer storage medium.
Background
Along with the development of social economy, the number of tax payers is continuously increased, and under the trend, the difficulty coefficient of enterprise tax risk management is also increased, and especially, manufacturing enterprises, because the tax elements are more complex, the tax data are more required to be collected and analyzed;
the processing and analysis of tax data of manufacturing enterprises by the current tax related departments are mainly focused on collecting economic data corresponding to single enterprises, and then tax risk analysis is carried out on the enterprises based on the collected economic data corresponding to the single enterprises, and obviously, the current mode of collecting and analyzing the enterprise data has the following problems:
1. the current data acquisition and analysis mode of manufacturing enterprises belongs to a single mode, and the method only analyzes the economic data of the enterprises, so that the objectivity and the authenticity of the enterprise data cannot be effectively ensured, and further the rationality and the accuracy of tax data analysis of the manufacturing enterprises cannot be effectively improved;
2. because materials and equipment used by manufacturing enterprises in the production process are complex, market price fluctuation of the materials is large, when tax data of the manufacturing enterprises are analyzed currently, reference analysis is not performed based on tax conditions of similar enterprises, and therefore the reference of tax analysis results of the manufacturing enterprises cannot be improved, and meanwhile, the subsequent pertinence to enterprise tax management cannot be improved;
3. At present, when tax analysis is performed on manufacturing enterprises, the tax situation of the enterprises is mainly analyzed, the manufacturing enterprises are not classified and analyzed, and further the management efficiency of tax of the manufacturing industries cannot be improved, and precise management direction of tax management of the manufacturing industries cannot be provided.
Disclosure of Invention
In view of this, in order to solve the problems set forth in the above background art, a tax data collection and analysis method, system and computer storage medium for each registered enterprise in the manufacturing industry are now proposed, so as to implement accurate analysis of tax data of the registered enterprise;
the aim of the invention can be achieved by the following technical scheme:
the first aspect of the invention provides a tax data acquisition and analysis method, which comprises the following steps:
step 1, acquiring industry basic information: extracting basic information corresponding to a specified manufacturing industry in a target area from an enterprise tax declaration platform, wherein the basic information corresponding to the specified manufacturing industry comprises the number of registered enterprises corresponding to the specified manufacturing industry currently and basic information corresponding to each registered enterprise of the specified manufacturing industry currently, and numbering each registered enterprise corresponding to the specified manufacturing industry currently according to a preset sequence, wherein the number is marked as 1, 2.
Step 2, collecting tax declaration data of a registered enterprise: positioning tax declaration data corresponding to each registered enterprise in the appointed manufacturing industry from an enterprise tax declaration platform background based on basic information corresponding to each registered enterprise in the appointed manufacturing industry, wherein the tax declaration data comprises enterprise basic declaration information, enterprise quarter operation income data and enterprise quarter operation expenditure data;
step 3, preliminary analysis of tax declaration data of registered enterprises: processing tax declaration data corresponding to each current registered enterprise based on tax declaration data corresponding to each current registered enterprise in the appointed manufacturing industry, further dividing operation categories of each current registered enterprise, carrying out preliminary analysis on each current registered enterprise in each operation category, and counting tax anomaly coefficients corresponding to each current registered enterprise in each operation category;
and 4, deeply analyzing tax declaration data of the registered enterprises: performing deep analysis on each registered enterprise in each operation category based on tax anomaly coefficients corresponding to each registered enterprise in each operation category, counting comprehensive tax anomaly coefficients corresponding to each registered enterprise in each operation category, matching and comparing the comprehensive tax anomaly coefficients corresponding to each registered enterprise in each operation category with pre-set pre-alarm tax anomaly coefficients corresponding to enterprises, if the tax anomaly coefficient corresponding to a certain registered enterprise in a certain operation category is greater than the pre-set pre-alarm tax anomaly coefficients corresponding to enterprises, recording the registered enterprise in the operation category as tax anomaly enterprises, counting the number of tax anomaly enterprises corresponding to each operation category, and analyzing key pre-alarm categories based on the number of tax anomaly enterprises corresponding to each operation category;
Step 5, tax declaration data analysis result feedback: and the system is used for feeding back the tax abnormal enterprises and the key early warning categories corresponding to the operation categories to the enterprise tax manager in the target area.
Preferably, the basic information corresponding to each current registered enterprise specifically includes the number of employees corresponding to each current registered enterprise and the organization code corresponding to each current registered enterprise.
Preferably, the enterprise basic declaration information is enterprise current quarter tax payment amount, the enterprise quarter expense data comprises enterprise quarter basic operation cost data and enterprise quarter employee expense data, the enterprise quarter income data is enterprise quarter comprehensive sales product amount and enterprise quarter sales product amount, the enterprise basic operation cost data specifically comprises enterprise quarter equipment cost amount, enterprise quarter material cost amount and enterprise quarter site cost amount, and the enterprise quarter employee expense data is expense wage amount and expense welfare amount corresponding to each employee in the current quarter of the enterprise.
Preferably, the processing is performed on tax declaration data corresponding to each current registered enterprise, so that each current registered enterprise is classified into categories for extracting quaternary sales product amount corresponding to each current registered enterprise from the tax declaration data corresponding to each current registered enterprise, the quaternary sales product amount corresponding to each current registered enterprise is matched and compared with sales unit intervals corresponding to preset operation categories, the operation categories corresponding to each current registered enterprise are obtained through screening, each current registered enterprise is classified into the operation categories to which each current registered enterprise belongs, and each operation category is numbered according to a preset sequence and is marked as 1, 2.
Preferably, the preliminary analysis is performed on each registered enterprise currently in each operation category, and the specific statistical process of the tax anomaly coefficient corresponding to each registered enterprise currently in each operation category is as follows:
the method comprises the steps of firstly, counting the number of current registered enterprises corresponding to each operation category based on the operation category to which each registered enterprise corresponds, and extracting the number corresponding to each current registered enterprise in each operation category;
step two, positioning tax declaration data corresponding to historical declaration quarters corresponding to each registered enterprise in each operation category from an enterprise tax declaration platform based on numbers corresponding to each registered enterprise in each operation category;
thirdly, acquiring tax declaration data corresponding to each current registered enterprise in each operation category, matching and comparing the tax declaration data corresponding to each current registered enterprise in each operation category with tax declaration data corresponding to each historic declaration quarter, and counting the matching degree of the tax declaration data corresponding to each current registered enterprise in each operation category and each historic declaration quarter;
step four, sorting the matching degree of tax declaration data corresponding to the current registered enterprises in each operation category and the historical declaration quarters of the current registered enterprises according to the sequence from big to small, taking the historical declaration quarter with the first sequence as the reference quarter of the current registered enterprises in each operation category, and extracting the tax payment amount corresponding to the current registered enterprises in each operation category in the reference quarter;
Fifthly, substituting the tax payment amount corresponding to each registered enterprise in each operation category in the reference quarter into a calculation formula
Obtaining tax anomaly coefficients corresponding to each registered enterprise currently in each operation category, NS j r Indicating the tax amount paid in the current quarter of the current r registered enterprise in the j-th operation category,/for>
The method comprises the steps that (1) the current r registered enterprises in the j operation categories pay tax amount corresponding to the current r registered enterprises in the reference quarter, deltaS is expressed as a preset license paying tax amount difference value, sigma is expressed as a compensation coefficient, r is expressed as a number corresponding to each registered enterprise in each operation category, r is expressed as an operation category number, j is expressed as an operation category number, j=1, 2.
Preferably, the specific statistical process of the comprehensive tax abnormal coefficient corresponding to each registered enterprise in each operation category is as follows:
1) Based on the enterprise quarter expense data corresponding to each registered enterprise in each operation category, calculating to obtain the quarter comprehensive expense amount corresponding to each registered enterprise in each operation category, calculating to obtain the quarter average comprehensive expense amount corresponding to each registered enterprise in each operation category by using an average calculation mode, setting expense amount reference data corresponding to each registered enterprise in each operation category based on the average comprehensive expense amount corresponding to each registered enterprise in each operation category, and recording as T j ，
τ is a set constant, ">
Represents the jthThe average comprehensive expenditure amount of the quarters corresponding to the current registered enterprise in the operation enterprise class;
2) Based on the quarterly integrated sales product quantity corresponding to each registered enterprise in each operation category, calculating to obtain the quarterly average integrated sales product quantity corresponding to each registered enterprise in each operation category by means of average calculation, setting the reference product sales quantity corresponding to each registered enterprise in each operation category, and marking as R j ，
Representing the average integrated sales product quantity in the quarter corresponding to the current registered enterprise in the j-th operation enterprise class, wherein kappa is a set constant;
3) Based on the tax payment amount of each registered enterprise in each operation category in the current quarter, calculating the average tax payment amount corresponding to the current registered enterprise in each operation category by using an average calculation method, setting the reference tax payment amount of the corresponding enterprise in each operation category, and recording as Q j ，
Representing the average tax payment amount corresponding to the current registered enterprise in the j-th operation category, wherein omega is a set constant;
4) The abnormal information coefficient corresponding to each registered enterprise in each operation category is obtained through recognition of the abnormal information recognition algorithm of the declaration data and is recorded as YC j r ，YC j r Representing an abnormal coefficient corresponding to the current r registered enterprise declaration information in the j-th operation enterprise class;
5) Substituting the reporting information abnormal coefficient corresponding to each registered enterprise in each operation category and the tax abnormal coefficient corresponding to each registered enterprise in each operation category into a calculation formula
Obtaining comprehensive tax anomaly coefficients corresponding to each registered enterprise currently in each operation category, and adding ∈>
Representing the comprehensive tax anomaly coefficient corresponding to the current r registered enterprise in the j-th operation class,/->
For the preset coefficient, ++>
Preferably, the specific recognition process of the declared data anomaly recognition algorithm is as follows: importing and reporting data anomaly identification algorithm for quaternary comprehensive expenditure amount, comprehensive sales product amount and current tax payment amount corresponding to current registered enterprises in each operation category
In the method, the reporting information anomaly coefficient corresponding to each registered enterprise in each operation category is identified and obtained, and the ZC j r Representing the quaternary comprehensive expenditure amount of the current r registered enterprise in the j-th operation category, wherein DeltaT, deltaL and DeltaQ are preset permissible enterprise expenditure amount difference, permissible enterprise sales product difference and permissible enterprise tax amount difference of the same operation category, beta 1, beta 2 and beta 3 are preset coefficients, and H j r And the product quantity is expressed as the quarter integrated sales corresponding to the current r registered enterprise in the j-th operation class.
Preferably, the specific analysis process for analyzing the key early warning category based on the corresponding tax abnormal enterprise number in each operation category is as follows: and ordering the corresponding tax abnormal enterprises in each operation category according to a ordering mode from large to small, extracting the operation category with the first rank, and recording the operation category as the key operation category.
The second aspect of the invention provides a tax data collection and analysis system, which comprises:
the industry basic information acquisition module is used for extracting basic information corresponding to the appointed manufacturing industry in the target area from the enterprise tax declaration platform;
the tax declaration data acquisition module of the registered enterprises is used for positioning tax declaration data corresponding to each registered enterprise of the specified manufacturing industry from the background of the enterprise tax declaration platform based on basic information corresponding to each registered enterprise of the specified manufacturing industry;
the tax declaration data processing and analyzing module of the registered enterprise comprises a preliminary processing and analyzing unit of the tax declaration data of the registered enterprise and a deep processing and analyzing unit of the tax declaration data of the registered enterprise, which are used for processing and analyzing the tax declaration data corresponding to the current registered enterprise;
And the tax declaration data analysis result feedback terminal is used for feeding back the processing and analysis results of the tax declaration data corresponding to the current registered enterprise to the enterprise tax manager in the target area.
A third aspect of the present invention provides a computer storage medium having a computer program recorded thereon, the computer program implementing the method of the present invention when running in a memory of a server.
The invention has the beneficial effects that:
according to the tax declaration data analysis method and system, tax declaration data corresponding to each registered enterprise in the target area appointed manufacturing industry are collected, preliminary analysis and deep analysis are carried out on the tax declaration data corresponding to each registered enterprise in the target area appointed manufacturing industry, the problem that the tax declaration data of each registered enterprise is too single is solved, multiple analysis on the tax data of each registered enterprise in the target area appointed industry is achieved, objectivity and authenticity of the tax declaration data of each registered enterprise are guaranteed to the greatest extent, rationality and accuracy of tax declaration data analysis of each registered enterprise in the appointed manufacturing industry are effectively improved, meanwhile, when deep analysis is carried out on the tax declaration data of each registered enterprise in the target area appointed manufacturing industry, referential analysis is carried out on tax conditions of similar enterprises, reference of tax declaration data analysis results of the registered enterprises and pertinence of tax management of the registered enterprises are greatly improved, and when the registered enterprises carry out preliminary analysis, management efficiency and management effect of the registered enterprises are greatly improved through category division on the registered enterprises, and meanwhile, tax management direction of each registered enterprise is provided for the registered enterprise accurately.
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 method steps of the present invention;
FIG. 2 is a schematic diagram of the connection of the modules of the system of the present invention;
FIG. 3 is a schematic diagram of a system for processing and analyzing tax declaration data of a registered enterprise according to 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 invention provides a tax data acquisition and analysis method, which comprises the following steps:
Step 1, acquiring industry basic information: extracting basic information corresponding to a specified manufacturing industry in a target area from an enterprise tax declaration platform, wherein the basic information corresponding to the specified manufacturing industry comprises the number of registered enterprises corresponding to the specified manufacturing industry currently and basic information corresponding to each registered enterprise of the specified manufacturing industry currently, each registered enterprise corresponding to the specified manufacturing industry currently is numbered according to a preset sequence, the number is marked as 1, 2.
Step 2, collecting tax declaration data of a registered enterprise: positioning tax declaration data corresponding to each registered enterprise in the appointed manufacturing industry from an enterprise tax declaration platform background based on basic information corresponding to each registered enterprise in the appointed manufacturing industry, wherein the tax declaration data comprises enterprise basic declaration information, enterprise quarter operation income data and enterprise quarter operation expenditure data;
specifically, the enterprise basic declaration information is enterprise current quarter tax payment amount, enterprise quarter expenditure data comprise enterprise quarter basic operation cost data and enterprise quarter staff expenditure data, the enterprise quarter income data are enterprise quarter comprehensive sales product amount and enterprise quarter sales product total amount, the enterprise basic operation cost data specifically comprise enterprise quarter equipment cost amount, enterprise quarter material cost amount and enterprise quarter site cost amount, and the enterprise quarter staff expenditure data are expenditure payroll amount and expenditure welfare amount corresponding to each staff in the current quarter of an enterprise.
Step 3, preliminary analysis of tax declaration data of registered enterprises: processing tax declaration data corresponding to each current registered enterprise based on tax declaration data corresponding to each current registered enterprise in the appointed manufacturing industry, further dividing operation categories of each current registered enterprise, carrying out preliminary analysis on each current registered enterprise in each operation category, and counting tax anomaly coefficients corresponding to each current registered enterprise in each operation category;
the processing is performed on tax declaration data corresponding to each current registered enterprise, so that category classification is performed on each current registered enterprise, the quaternary sales product amount corresponding to each registered enterprise is extracted from the tax declaration data corresponding to each registered enterprise, the quaternary sales product amount corresponding to each registered enterprise is matched and compared with a preset sales unit interval corresponding to each operation category, the operation categories corresponding to each registered enterprise are obtained through screening, each registered enterprise is classified into the operation categories to which each registered enterprise belongs, and each operation category is numbered according to a preset sequence, and the operation categories are marked as 1, 2.
In another exemplary embodiment, the preliminary analysis is performed on each registered enterprise currently in each operation category, and the specific statistical process of calculating the tax anomaly coefficient corresponding to each registered enterprise currently in each operation category is as follows:
The method comprises the steps of firstly, counting the number of current registered enterprises corresponding to each operation category based on the operation category to which each registered enterprise corresponds, and extracting the number corresponding to each current registered enterprise in each operation category;
step two, positioning tax declaration data corresponding to historical declaration quarters corresponding to each registered enterprise in each operation category from an enterprise tax declaration platform based on numbers corresponding to each registered enterprise in each operation category;
thirdly, acquiring tax declaration data corresponding to each current registered enterprise in each operation category, matching and comparing the tax declaration data corresponding to each current registered enterprise in each operation category with tax declaration data corresponding to each historic declaration quarter, and counting the matching degree of the tax declaration data corresponding to each current registered enterprise in each operation category and each historic declaration quarter, wherein the method specifically comprises the following steps:
a1, numbering each corresponding historical reporting quarter in each current registered enterprise according to the historical reporting time, and marking the historical reporting quarters as 1, 2;
a2, comparing the current quarter expense data of each registered enterprise in each operation category with the quarter expense data corresponding to each historical declaration, counting the similarity of the current quarter expense data of each registered enterprise in each operation category and the quarter expense data corresponding to each historical declaration, and recording as 
Indicating the matching degree of the quarter expense data of the current r registered enterprise in the j-th operation class and the quarter expense data corresponding to the historical x-th reporting quarter, wherein x indicates the number of the historical reporting quarter, and x=1, 2.
It should be noted that, the specific calculation process of the matching degree of the quarter expense data of each registered enterprise in each operation category and the quarter expense data corresponding to each historical declaration is as follows:
a2-1, acquiring basic operation cost data corresponding to each registered enterprise currently in each operation category, and substituting the basic operation cost data into an enterprise basic operation cost data similarity calculation formula
In the method, the similarity between the basic operation cost data of each registered enterprise in each operation category and the basic operation cost data of each reporting quarter in the history of the basic operation cost data is obtained>
Representing the similarity of the basic operation cost data of the current nth registered enterprise quarter in the jth operation class and the basic operation cost data corresponding to the historical xth declaration quarter of the current mth registered enterprise quarter, S j r ,C j r ,D j r Respectively representing the quaternary equipment cost amount, the quaternary material cost amount and the quaternary site cost amount corresponding to the current r registered enterprise in the j-th operation category, < >>
Respectively representing the equipment cost amount, the material cost amount and the site cost amount corresponding to the present xth reporting quarter of the history xth registered enterprise in the jth operation class, wherein alpha 1, alpha 2 and alpha 3 are preset equipment market compensation coefficients, material market compensation coefficients and site market compensation coefficients, and e is a natural base number;
A2-2, obtaining the total payroll and the total payroll of each employee in the current quarter of each registered enterprise in each operation category, counting the total payroll and the total welfare of each employee in the current quarter of each registered enterprise in each operation category, and similarly, calculating the total payroll and the total welfare of each employee in each operation category in each historical quarter, substituting the total payroll and the total welfare of each employee in each operation category into a calculation formula
Obtaining the employee expenditure of each registered enterprise currently in each operation categorySimilarity of data and historical employee spending data in each reporting quarter>
Representing the similarity of employee expenditure data of the current (r) registered enterprise in the jth operation class and employee expenditure data corresponding to the (x) historic reporting quarter, wherein epsilon 1 and epsilon 2 are respectively represented as preset coefficients, G j r ,L j r Respectively expressed as the total payroll of the comprehensive expenditure staff and the total welfare of the comprehensive expenditure staff in the current quarter of the current r registered enterprise in the j-th operation category,/->
Representing the total payroll of the comprehensive expenditure staff corresponding to the current (r) registered enterprise history (x) declaration quarter in the j-th operation class as the total payroll of the comprehensive expenditure staff welfare;
A2-3, calculating the similarity of the current quarter expense data of each registered enterprise in each operation category and the quarter expense data corresponding to each historical reporting time based on the similarity of the current employee expense data of each registered enterprise in each operation category and the historical reporting quarter employee expense data of each registered enterprise in each operation category and the similarity of the current basic operation cost data of each registered enterprise in each operation category and the historical basic operation cost data of each reporting quarter, wherein the calculation formula is as follows
η1, η2 are the impact weights corresponding to the basic operation cost and the employee expense data, η1+η2=1, +.>
Representing the similarity between the basic operation cost data of the current r registered enterprise in the j-th operation class and the basic operation cost data of the historical x-th reporting quarter;
a3, comparing the quarter income data of the current registered enterprises in each operation category with the quarter income data corresponding to each time of reporting of the histories thereof, and counting the current notes in each operation categoryThe similarity of the enterprise quarter income data and the quarter income data corresponding to each declaration of the history thereof is recorded as
The specific statistical formula is
H j r ,X j r Respectively expressed as the total quantity of the product sold in the quarter and the total quantity of the product sold in the quarter of the current r registered enterprises in the j-th operation category,/o >
Respectively representing the total sales product quantity and the sales product total amount corresponding to the x-th declaration quarter of the current r registered enterprise history in the j-th operation category.
A4, calculating and obtaining the matching degree of tax declaration data corresponding to each registered enterprise and each declaration quarter of the history in each operation category by using a calculation formula, wherein the specific calculation formula is as follows
The matching degree of tax declaration data corresponding to the current r registered enterprise and the historical x declaration quarter of the j-th operation class is represented, mu 1 and mu 2 are preset coefficients, and k represents a set constant;
step four, sorting the matching degree of tax declaration data corresponding to the current registered enterprises in each operation category and the historical declaration quarters of the current registered enterprises according to the sequence from big to small, taking the historical declaration quarter with the first sequence as the reference quarter of the current registered enterprises in each operation category, and extracting the tax payment amount corresponding to the current registered enterprises in each operation category in the reference quarter;
fifthly, substituting the tax payment amount corresponding to each registered enterprise in each operation category in the reference quarter into a calculation formula
In (1) obtaining eachTax anomaly coefficient, NS, corresponding to each registered enterprise currently in the operation category j r Indicating the tax amount paid in the current quarter of the current r registered enterprise in the j-th operation category,/for>
The method comprises the steps that (1) the current r registered enterprises in the j operation categories pay tax amount corresponding to the current r registered enterprises in the reference quarter, deltaS is expressed as a preset license paying tax amount difference value, sigma is expressed as a compensation coefficient, r is expressed as a number corresponding to each registered enterprise in each operation category, r is expressed as an operation category number, j is expressed as an operation category number, j=1, 2.
And 4, deeply analyzing tax declaration data of the registered enterprises: performing deep analysis on each registered enterprise in each operation category based on tax anomaly coefficients corresponding to each registered enterprise in each operation category, counting comprehensive tax anomaly coefficients corresponding to each registered enterprise in each operation category, matching and comparing the comprehensive tax anomaly coefficients corresponding to each registered enterprise in each operation category with pre-set pre-alarm tax anomaly coefficients corresponding to enterprises, if the tax anomaly coefficient corresponding to a certain registered enterprise in a certain operation category is greater than the pre-set pre-alarm tax anomaly coefficients corresponding to enterprises, recording the registered enterprise in the operation category as tax anomaly enterprises, counting the number of tax anomaly enterprises corresponding to each operation category, and analyzing key pre-alarm categories based on the number of tax anomaly enterprises corresponding to each operation category;
The specific statistical process of the comprehensive tax anomaly coefficient corresponding to each registered enterprise currently in each operation category is as follows:
1) Based on the enterprise quarter expense data corresponding to each registered enterprise in each operation category, calculating to obtain the quarter comprehensive expense amount corresponding to each registered enterprise in each operation category, calculating to obtain the quarter average comprehensive expense amount corresponding to each registered enterprise in each operation category by using an average calculation mode, setting expense amount reference data corresponding to each registered enterprise in each operation category based on the average comprehensive expense amount corresponding to each registered enterprise in each operation category, and recording as T j ，
τ is a set constant, ">
Representing the average comprehensive expenditure amount of the quarter corresponding to the current registered enterprise in the j-th operation enterprise category;
2) Based on the quarterly integrated sales product quantity corresponding to each registered enterprise in each operation category, calculating to obtain the quarterly average integrated sales product quantity corresponding to each registered enterprise in each operation category by means of average calculation, setting the reference product sales quantity corresponding to each registered enterprise in each operation category, and marking as R j ，
Representing the average integrated sales product quantity in the quarter corresponding to the current registered enterprise in the j-th operation enterprise class, wherein kappa is a set constant;
3) Based on the tax payment amount of each registered enterprise in each operation category in the current quarter, calculating the average tax payment amount corresponding to the current registered enterprise in each operation category by using an average calculation method, setting the reference tax payment amount of the corresponding enterprise in each operation category, and recording as Q j ，
Representing the average tax payment amount corresponding to the current registered enterprise in the j-th operation category, wherein omega is a set constant;
4) The abnormal information coefficient corresponding to each registered enterprise in each operation category is obtained through recognition of the abnormal information recognition algorithm of the declaration data and is recorded as YC j r ，YC j r Representing an abnormal coefficient corresponding to the current r registered enterprise declaration information in the j-th operation enterprise class;
the specific recognition process of the declaration data abnormality recognition algorithm is as follows: comprehensive expenditure amount and comprehensive sales of the current registered enterprises in each operation categoryAbnormal identification algorithm for import declaration data of sales product quantity and current tax payment amount
In the method, the reporting information anomaly coefficient corresponding to each registered enterprise in each operation category is identified and obtained, and the ZC j r Representing the quaternary comprehensive expenditure amount of the current r registered enterprise in the j-th operation category, wherein DeltaT, deltaL and DeltaQ are preset permissible enterprise expenditure amount difference, permissible enterprise sales product difference and permissible enterprise tax amount difference of the same operation category, beta 1, beta 2 and beta 3 are preset coefficients, and H j r And the product quantity is expressed as the quarter integrated sales corresponding to the current r registered enterprise in the j-th operation class.
5) Substituting the reporting information abnormal coefficient corresponding to each registered enterprise in each operation category and the tax abnormal coefficient corresponding to each registered enterprise in each operation category into a calculation formula
Obtaining comprehensive tax anomaly coefficients corresponding to each registered enterprise currently in each operation category, and adding ∈>
Representing the comprehensive tax anomaly coefficient corresponding to the current r registered enterprise in the j-th operation class,/->
For the preset coefficient, ++>
Still another exemplary embodiment of the present invention provides a specific analysis process for analyzing a key early warning category based on the number of tax abnormal enterprises corresponding to each operation category, wherein the specific analysis process includes: and ordering the corresponding tax abnormal enterprises in each operation category according to a ordering mode from large to small, extracting the operation category with the first rank, and recording the operation category as the key operation category.
According to the embodiment of the invention, the tax declaration data corresponding to each registered enterprise in the target area appointed manufacturing industry is collected, the problem that the tax declaration data of each registered enterprise is too single at present is effectively solved based on the preliminary analysis and the deep analysis of the tax declaration data corresponding to each registered enterprise in the target area appointed manufacturing industry, the multiple analysis of the tax declaration data of each registered enterprise in the target area appointed industry is realized, the objectivity and the authenticity of the tax declaration data of each registered enterprise are ensured to the greatest extent, the rationality and the accuracy of the tax declaration data analysis of each registered enterprise in the appointed manufacturing industry are effectively improved, meanwhile, the referential analysis of the tax declaration data analysis results of each registered enterprise based on similar enterprises is greatly improved, the category classification of the registered enterprise is greatly improved, the management effect of the registered tax is greatly improved, and the accuracy of the tax management of each registered enterprise is provided for the subsequent enterprise when the registered enterprise is subjected to the preliminary analysis.
Step 5, tax declaration data analysis result feedback: and the system is used for feeding back the tax abnormal enterprises and the key early warning categories corresponding to the operation categories to the enterprise tax manager in the target area.
According to the embodiment of the invention, the corresponding tax abnormal enterprise number and key early warning category in each operation category are fed back, so that an alarm direction is provided for enterprise tax management personnel in a target area, and the subsequent analysis efficiency of each registered enterprise is effectively improved.
Referring to fig. 2, the present invention provides a tax data collection and analysis system, which includes: the system comprises an industry basic information acquisition module, a registration enterprise tax declaration data processing and analysis module and a tax declaration data analysis result feedback terminal; the system comprises a registration enterprise tax declaration data processing and analyzing module, a registration enterprise tax declaration data acquisition module and a tax declaration data analysis result feedback terminal, wherein the registration enterprise tax declaration data processing and analyzing module is respectively connected with the registration enterprise tax declaration data acquisition module and the tax declaration data analysis result feedback terminal; wherein, the liquid crystal display device comprises a liquid crystal display device,
the industry basic information acquisition module is used for extracting basic information corresponding to the appointed manufacturing industry in the target area from the enterprise tax declaration platform;
The tax declaration data acquisition module of the registered enterprises is used for positioning tax declaration data corresponding to each registered enterprise of the specified manufacturing industry from the background of the enterprise tax declaration platform based on basic information corresponding to each registered enterprise of the specified manufacturing industry;
referring to fig. 3, the module for processing and analyzing tax declaration data of a registered enterprise includes a unit for primarily processing and analyzing tax declaration data of the registered enterprise and a unit for deeply processing and analyzing tax declaration data of the registered enterprise, which are used for processing and analyzing tax declaration data corresponding to the registered enterprise;
the tax declaration data preliminary processing and analyzing unit of the registered enterprises is used for processing the tax declaration data corresponding to each registered enterprise based on the tax declaration data corresponding to each registered enterprise in the appointed manufacturing industry, further dividing the operation categories of each registered enterprise, carrying out preliminary analysis on each registered enterprise in each operation category, and counting tax anomaly coefficients corresponding to each registered enterprise in each operation category;
the advanced processing and analyzing unit is used for carrying out advanced analysis on each current registered enterprise in each operation category based on tax anomaly coefficients corresponding to each current registered enterprise in each operation category;
And the tax declaration data analysis result feedback terminal is used for feeding back the processing and analysis results of the tax declaration data corresponding to the current registered enterprise to the enterprise tax manager in the target area.
Specifically, the tax declaration data analysis result feedback terminal is used for feeding back the deep analysis result of each registered enterprise currently in each operation category to the tax manager of the enterprise in the target area.
The invention also provides a computer storage medium, which is burnt with a computer program, and the computer program realizes the method of the invention 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 (7)
1. The tax data acquisition and analysis method is characterized by comprising the following steps of:
step 1, acquiring industry basic information: extracting basic information corresponding to a specified manufacturing industry in a target area from an enterprise tax declaration platform, wherein the basic information corresponding to the specified manufacturing industry comprises the number of registered enterprises corresponding to the specified manufacturing industry currently and basic information corresponding to each registered enterprise of the specified manufacturing industry currently, and numbering each registered enterprise corresponding to the specified manufacturing industry currently according to a preset sequence, wherein the number is marked as 1, 2.
Step 2, collecting tax declaration data of a registered enterprise: positioning tax declaration data corresponding to each registered enterprise in the appointed manufacturing industry from an enterprise tax declaration platform background based on basic information corresponding to each registered enterprise in the appointed manufacturing industry, wherein the tax declaration data comprises enterprise basic declaration information, enterprise quarter operation income data and enterprise quarter operation expenditure data;
step 3, preliminary analysis of tax declaration data of registered enterprises: processing tax declaration data corresponding to each current registered enterprise based on tax declaration data corresponding to each current registered enterprise in the appointed manufacturing industry, further dividing operation categories of each current registered enterprise, carrying out preliminary analysis on each current registered enterprise in each operation category, and counting tax anomaly coefficients corresponding to each current registered enterprise in each operation category;
the primary analysis is carried out on each registered enterprise currently in each operation category, and the specific statistical process for calculating the tax anomaly coefficient corresponding to each registered enterprise currently in each operation category is as follows:
the method comprises the steps of firstly, counting the number of current registered enterprises corresponding to each operation category based on the operation category to which each registered enterprise corresponds, and extracting the number corresponding to each current registered enterprise in each operation category;
Step two, positioning tax declaration data corresponding to historical declaration quarters corresponding to each registered enterprise in each operation category from an enterprise tax declaration platform based on numbers corresponding to each registered enterprise in each operation category;
thirdly, acquiring tax declaration data corresponding to each current registered enterprise in each operation category, matching and comparing the tax declaration data corresponding to each current registered enterprise in each operation category with tax declaration data corresponding to each historic declaration quarter, and counting the matching degree of the tax declaration data corresponding to each current registered enterprise in each operation category and each historic declaration quarter;
step four, sorting the matching degree of tax declaration data corresponding to the current registered enterprises in each operation category and the historical declaration quarters of the current registered enterprises according to the sequence from big to small, taking the historical declaration quarter with the first sequence as the reference quarter of the current registered enterprises in each operation category, and extracting the tax payment amount corresponding to the current registered enterprises in each operation category in the reference quarter;
fifthly, substituting the tax payment amount corresponding to each registered enterprise in each operation category in the reference quarter into a calculation formula 
Obtaining tax anomaly coefficients corresponding to each registered enterprise currently in each operation category, and +.>
Indicating that the current quarter of the current r registered business in the j-th operation category pays tax amount,
the current r registered enterprise in the j-th operation category pays tax amount corresponding to the reference quarter of the registered enterprise,/-in>
Expressed as a difference in the amount of tax paid by the preset license,/->
Representing compensation coefficient, r representing the number corresponding to the registered enterprise in each operation category, < + >>
J represents an operation class number, j=1, 2,..;
and 4, deeply analyzing tax declaration data of the registered enterprises: performing deep analysis on each registered enterprise in each operation category based on tax anomaly coefficients corresponding to each registered enterprise in each operation category, counting comprehensive tax anomaly coefficients corresponding to each registered enterprise in each operation category, matching and comparing the comprehensive tax anomaly coefficients corresponding to each registered enterprise in each operation category with pre-set pre-alarm tax anomaly coefficients corresponding to enterprises, if the tax anomaly coefficient corresponding to a certain registered enterprise in a certain operation category is greater than the pre-set pre-alarm tax anomaly coefficients corresponding to enterprises, recording the registered enterprise in the operation category as tax anomaly enterprises, counting the number of tax anomaly enterprises corresponding to each operation category, and analyzing key pre-alarm categories based on the number of tax anomaly enterprises corresponding to each operation category;
The specific statistical process of the comprehensive tax anomaly coefficient corresponding to each registered enterprise in each operation category comprises the following steps:
based on enterprise quarter expenditure data corresponding to current registered enterprises in each operation category, calculating to obtain quarter comprehensive expenditure amount corresponding to the current registered enterprises in each operation category, calculating to obtain quarter average comprehensive expenditure amount corresponding to the current registered enterprises in each operation category by using an average calculation mode, and setting each operation category based on the average comprehensive expenditure amount corresponding to the current registered enterprises in each operation categoryThe corresponding expenditure amount reference data of each registered enterprise is recorded as
，
，/>
To set constant +.>
Representing the average comprehensive expenditure amount of the quarter corresponding to the current registered enterprise in the j-th operation enterprise category;
based on the quarterly integrated sales product quantity corresponding to each registered enterprise in each operation category, calculating to obtain the quarterly average integrated sales product quantity corresponding to each registered enterprise in each operation category by means of average calculation, setting the reference product sales quantity corresponding to each registered enterprise in each operation category, and recording as
，/>
，/>
Representing the average integrated sales product quantity in the quarter corresponding to the currently registered business in the j-th business class,/business class >
Setting a constant;
based on the tax payment amount of each registered enterprise in each operation category in the current quarter, calculating the average tax payment amount corresponding to the current registered enterprise in each operation category by using an average calculation method, setting the reference tax payment amount of the corresponding enterprise in each operation category, and recording as
，/>
，/>
Representing the average tax payment amount corresponding to the current registered enterprise in the j-th operation category,/>
Setting a constant;
the abnormality coefficient of the declaration information corresponding to each registered enterprise in each operation category is obtained through recognition of the abnormality recognition algorithm of the declaration data and is recorded as
，/>
Representing an abnormal coefficient corresponding to the current r registered enterprise declaration information in the j-th operation enterprise class;
substituting the reporting information abnormal coefficient corresponding to each registered enterprise in each operation category and the tax abnormal coefficient corresponding to each registered enterprise in each operation category into a calculation formula
Obtaining comprehensive tax anomaly coefficients corresponding to each registered enterprise currently in each operation category, and adding ∈>
Representing the comprehensive tax anomaly coefficient corresponding to the current r registered enterprise in the j-th operation class,/->
For the preset coefficient, ++>
；
The specific recognition process of the declaration data abnormality recognition algorithm is as follows: importing and reporting data anomaly identification algorithm for quaternary comprehensive expenditure amount, comprehensive sales product amount and current tax payment amount corresponding to current registered enterprises in each operation category 
In the operation category, identifying and obtaining the abnormality coefficient of the declaration information corresponding to each registered enterprise currently in each operation category,/->
Indicating the current quaternary integrated expense amount of the r registered enterprises in the j-th operation category,/->
Differential value of the expenditure of the licensed enterprises for the same preset operation category, differential value of the sales products of the licensed enterprises, differential tax payment of the licensed enterprises, < ->
For the preset coefficient, ++>
Representing the product quantity of the integrated sales in the quarter corresponding to the current r registered enterprise in the j-th operation class;
step 5, tax declaration data analysis result feedback: and the system is used for feeding back the tax abnormal enterprises and the key early warning categories corresponding to the operation categories to the enterprise tax manager in the target area.
2. The tax data collection and analysis method according to claim 1, wherein: the basic information corresponding to each current registered enterprise specifically comprises the number of staff corresponding to each current registered enterprise and the organization code corresponding to each current registered enterprise.
3. The tax data collection and analysis method according to claim 1, wherein: the enterprise basic declaration information is tax amount paid by the enterprise in the current quarter, enterprise quarter expenditure data comprise enterprise quarter basic operation cost data and enterprise quarter staff expenditure data, the enterprise quarter income data are enterprise quarter comprehensive sales product amount and enterprise quarter sales product amount, the enterprise basic operation cost data specifically comprise enterprise quarter equipment cost amount, enterprise quarter material cost amount and enterprise quarter site cost amount, and the enterprise quarter staff expenditure data are expenditure payroll amount and expenditure welfare amount corresponding to each staff in the current quarter of the enterprise.
4. The tax data collection and analysis method according to claim 1, wherein: the tax declaration data corresponding to each current registered enterprise is processed, the current registered enterprises are classified and used for extracting the quaternary sales product amount corresponding to each current registered enterprise from the tax declaration data corresponding to each current registered enterprise, the quaternary sales product amount corresponding to each current registered enterprise is matched and compared with the sales unit interval corresponding to each preset operation category, the operation category corresponding to each current registered enterprise is obtained through screening, each current registered enterprise is classified in the operation category to which the current registered enterprise belongs, the operation categories are numbered according to the preset sequence, and the operation categories are marked as 1, 2.
5. The tax data collection and analysis method according to claim 1, wherein: based on the corresponding tax abnormal enterprise number in each operation category, the specific analysis process for analyzing the key early warning category is as follows: and ordering the corresponding tax abnormal enterprises in each operation category according to a ordering mode from large to small, extracting the operation category with the first rank, and recording the operation category as the key operation category.
6. A tax data collection and analysis system for performing the tax data collection and analysis method of any one of claims 1-5, the system comprising:
the industry basic information acquisition module is used for extracting basic information corresponding to the appointed manufacturing industry in the target area from the enterprise tax declaration platform;
the tax declaration data acquisition module of the registered enterprises is used for positioning tax declaration data corresponding to each registered enterprise of the specified manufacturing industry from the background of the enterprise tax declaration platform based on basic information corresponding to each registered enterprise of the specified manufacturing industry;
the tax declaration data processing and analyzing module of the registered enterprise comprises a preliminary processing and analyzing unit of the tax declaration data of the registered enterprise and a deep processing and analyzing unit of the tax declaration data of the registered enterprise, which are used for processing and analyzing the tax declaration data corresponding to the current registered enterprise;
and the tax declaration data analysis result feedback terminal is used for feeding back the processing and analysis results of the tax declaration data corresponding to the current registered enterprise to the enterprise tax manager in the target area.
7. A computer storage medium, characterized by: the computer storage medium has a computer program recorded thereon, which when run in the memory of a server implements the method of any of the preceding claims 1-5.
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