A data processor for financial management based on big data system

Abstract

The present invention relates to the field of financial data management technology, and specifically to a data processor for financial management based on a big data system, including an edge data anomaly verification module, wherein the edge data anomaly verification module controls each edge to verify the financial data submitted by the corresponding department, extracts the anomaly verification results, and generates an edge anomaly verification data set. When the present invention is aimed at the financial reimbursement of an enterprise, it not only takes into account the relationship between the amount of the reimbursement and the invoice voucher, but also takes into account the correlation between different financial data, thereby realizing dynamic verification of financial data; at the same time, when abnormal financial data is monitored, cross-departmental correlation of abnormal financial data is realized, and more accurate and comprehensive early warning information is generated to assist the corresponding person in charge of the enterprise to make secondary verification decisions on financial data, thereby realizing effective supervision of financial data.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data management, and in particular to a data processor for financial management based on a big data system. Background Art

[0002] With the rapid development of economy and technology, more and more companies are choosing financial software. Compared with the more primitive manual control of financial data, the current financial software performs better in both process control and account verification results.

[0003] However, as the scale of enterprises continues to expand, the demand for financial analysis is also increasing. In the existing technology, for financial reimbursement, it is often just a comparison of the amount relationship between the reimbursement amount and the invoice voucher. However, in real life, there are often multiple reimbursement items corresponding to one invoice voucher or one reimbursement item corresponding to multiple invoice vouchers. For this situation, the existing data processors used for financial management often directly determine it as an abnormal situation, and cannot perform dynamic verification, and misjudgment often occurs; at the same time, when the existing technology issues warnings for abnormal financial data, it is often an independent analysis, and it is impossible to make an effective judgment based on the correlation between the financial data, and thus the warning data often has large deviations and omissions; therefore, the existing data processors used for financial management have major defects. Summary of the invention

[0004] The object of the present invention is to provide a data processor for financial management based on a big data system to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a data processor for financial management based on a big data system, the data processor comprising:

[0006] An edge data acquisition module, which controls the edge of each department of the enterprise to obtain the financial data submitted by the corresponding department;

[0007] An edge data anomaly checking module, which controls each edge to check the financial data submitted by the corresponding department, extracts the anomaly checking results, and generates an edge anomaly checking data set;

[0008] A data association analysis module, which performs association analysis on the financial data collected by the data acquisition module to generate associated financial information sets corresponding to different financial data;

[0009] A terminal abnormality data optimization module, which summarizes the various edge abnormality verification data sets obtained by the edge data abnormality verification module, and optimizes the various edge abnormality verification data sets obtained according to the associated financial information set corresponding to each element in the obtained edge abnormality verification data set to generate a risk verification demand data set;

[0010] A verification demand analysis module, which visualizes the financial anomalies of each department of the enterprise based on the elements in the risk verification demand data set, generates a focus sequence for the inspection department, and evaluates the verification complexity of the corresponding finances of each department of the enterprise in combination with the financial data obtained from each department of the enterprise;

[0011] The risk warning recommendation management module combines the inspection department's attention sequence and the corresponding verification complexity of each department to generate a secondary verification recommendation warning sequence; and feeds back the combination of the secondary verification recommendation warning sequence and the risk verification requirement data set as warning information to the corresponding person in charge of the enterprise.

[0012] Furthermore, the edge data acquisition module corresponds to one or more edge terminals, each edge terminal is bound to a department of a corresponding enterprise, and each edge terminal receives financial data of the enterprise department bound thereto;

[0013] The financial data includes the project name, the expenditure index required for the project task and the submitted invoice voucher;

[0014] The number of expense indicators of each project task in each financial data is unique; each project includes one or more project tasks, and the expense indicators required for each project task correspond to one or more submitted invoice vouchers, or the expense indicators required for one or more project tasks correspond to one submitted invoice voucher;

[0015] The cost indicator required for the project task is the amount of expenditure for executing the corresponding project task reported by the personnel executing the corresponding project task to their respective departments.

[0016] In the present invention, each financial data contains one or more invoice vouchers, but each financial data contains only one expenditure indicator required for the project.

[0017] Furthermore, when the edge data anomaly checking module generates an edge anomaly checking data set, each edge corresponds to an edge anomaly checking data set, and each element in each edge anomaly checking data set corresponds to a financial data;

[0018] The edge data anomaly checking module controls each edge to perform image recognition on the invoice voucher when checking the financial data submitted by the corresponding department, and extracts the bill information corresponding to each invoice voucher, wherein the bill information includes the bill amount and the business scope involved in the issuer of the corresponding bill; the edge data anomaly checking module marks each financial data with the same corresponding bill information submitted by the corresponding department in the edge;

[0019] Calculate the minimum value of the combination deviation values ​​corresponding to the marked financial data. In the process of obtaining the combination deviation value, combine the financial data and the invoice voucher according to the combination number corresponding to the financial data to obtain different combination results, and obtain the task feature information and the bill feature information corresponding to each combination element in each combination result; the combination number is equal to the minimum value of the total number of corresponding invoices and the total number of corresponding project tasks in the marked financial data; the number of combination elements in each combination result is equal to the obtained combination number;

[0020] If the minimum value of the combined deviation values ​​respectively corresponding to the marked financial data is equal to 0, then the verification result of the marked financial data is determined to be normal; otherwise, the verification result of the marked financial data is determined to be abnormal;

[0021] The present invention can realize the joint verification of multiple financial data by means of combined deviation values, and the verification process is dynamic. Different combined results correspond to different combined deviation values ​​(and the verification results are also different). The minimum value of the combined deviation values ​​is selected as the basis for judgment because the minimum value of the combined deviation values ​​is the most representative. Under normal circumstances, the correct combined result is unique. When the financial data are all normal, the combined deviation value corresponding to the correct combined result is 0, and it is not 0 in other cases.

[0022] The task characteristic information includes first task characteristic information and second task characteristic information,

[0023] The first task characteristic information is the sum of the expenditure indicators required for the corresponding project tasks in the project names in the marked financial data corresponding to the corresponding combination elements; the second task characteristic information is the union of the business scopes required for the corresponding project tasks in the project names in the marked financial data corresponding to the corresponding combination elements in the prefabricated tables in the database;

[0024] The bill characteristic information includes first bill characteristic information and second bill characteristic information,

[0025] The first bill characteristic information is the sum of the bill amounts in the bill information corresponding to each invoice voucher corresponding to the corresponding combination element; the second bill characteristic information is the intersection of the business scope involved in the billing party in the bill information corresponding to each invoice voucher corresponding to the corresponding combination element;

[0026] The combined deviation value corresponding to the combined result is recorded as R.

[0027] ,

[0028] Wherein, A1r represents the first task feature information in the task feature information corresponding to the rth combination element in the corresponding combination result;

[0029] B1r represents the first bill feature information in the bill feature information corresponding to the rth combination element in the corresponding combination result;

[0030] A2r represents the second task feature information in the task feature information corresponding to the rth combination element in the corresponding combination result;

[0031] B2r represents the second bill feature information in the bill feature information corresponding to the rth combination element in the corresponding combination result;

[0032] g represents the deviation coefficient, and g is a constant preset in the database; r1 represents the total number of combination elements in the corresponding combination result;

[0033] H{} represents the decision function,

[0034] When A2r is a subset of B2r, H{A2r, B2r}=0 is determined; otherwise, H{A2r, B2r}=1 is determined.

[0035] When obtaining the combined deviation value corresponding to the combined result in the present invention, two perspectives, namely, bill feature information and task feature information, are considered to quantify the deviation corresponding to the combined result, thus providing data support for selecting marked abnormal financial data in subsequent steps.

[0036] Further, each element in the associated financial information set generated by the data association analysis module corresponds to a financial data;

[0037] When the data association analysis module performs association analysis on the financial data collected by the data acquisition module, any two financial data are recorded as first financial data and second financial data respectively.

[0038] Respectively obtain data association features in the first financial data and the second financial data, wherein the data association features include the department to which the personnel corresponding to the corresponding financial data belong, the department involved in the project task, the issuance time of the bill, and the department personnel corresponding to the corresponding financial data, which are recorded as {C1, C2, C3, C4};

[0039] The departments involved in the project tasks are preset in the database;

[0040] When C1 in the first financial data belongs to C2 in the second financial data, C1 in the second financial data belongs to C2 in the first financial data, and the interval between C3 in the first financial data and C3 in the second financial data is less than a first preset value, it is determined that there is a cross-departmental correlation between the first financial data and the second financial data; otherwise, it is determined that there is no cross-departmental correlation between the first financial data and the second financial data;

[0041] When C4 in the first financial data is the same as C4 in the second financial data, it is determined that the first financial data and the second financial data have the same department association; otherwise, it is determined that the first financial data and the second financial data do not have the same department association;

[0042] The associated financial demand data set corresponding to each financial data is a collection of various financial data that have cross-departmental or intra-departmental associations with the corresponding financial data.

[0043] Furthermore, when the terminal abnormal data optimization module generates a risk verification requirement data set,

[0044] Obtain the associated financial information sets corresponding to each element in each edge anomaly check data set, and take the union of each edge anomaly check data set and all the associated financial information sets corresponding to its elements as the optimization result of the corresponding edge anomaly check data set; take the union of the optimization results of each edge anomaly check data set as the risk check demand data set.

[0045] Furthermore, the verification demand analysis module obtains the risk verification demand data set generated by the terminal abnormal data optimization module, divides the financial data belonging to each department in the risk verification demand data set, and visualizes the division result of each department on the display;

[0046] Calculate the abnormal concern evaluation value corresponding to each department, and record the abnormal concern evaluation value corresponding to the j-th department as Qj.

[0047] Qj=Ej·SEj,

[0048] Among them, Ej represents the ratio of the number of financial data belonging to the jth department in the risk verification demand data set to the total number of financial data in the jth department;

[0049] SEj represents the sum of the cost indicators required for the project tasks corresponding to each financial data belonging to the j-th department in the risk verification demand data set, divided by the sum of the cost indicators required for the project tasks corresponding to each financial data in the j-th department;

[0050] When the total amount of financial data in the jth department is 0, it is determined that Ej=0 and SEj=0;

[0051] The departments are sorted in descending order according to the corresponding abnormal attention evaluation values ​​to obtain the inspection department attention sequence.

[0052] Furthermore, when the verification demand analysis module evaluates the verification complexity of each department of the enterprise corresponding to the financial

[0053] The edge data anomaly verification module controls each edge to verify the financial data submitted by the corresponding department. The minimum value of the corresponding combination deviation value in the same department is 0. The combination call ratio corresponding to each combination result is calculated, and the average value of each combination call ratio is used as the verification complexity of the corresponding finance of the corresponding department of the enterprise.

[0054] The combination call ratio corresponding to the combination result is equal to the quotient of the number of financial data corresponding to the corresponding combination result divided by the total number of elements in the corresponding combination result.

[0055] Furthermore, when the risk warning recommendation management module generates a secondary verification recommendation warning sequence,

[0056] Sort each department according to the corresponding financial verification complexity to obtain the verification complexity assessment sequence;

[0057] Calculate the secondary verification priority evaluation value corresponding to each department, where the secondary verification priority evaluation value is equal to the quotient of the sequence number value of the corresponding department in the inspection department attention sequence and the sequence number value of the corresponding department in the verification complexity evaluation sequence;

[0058] The secondary verification recommendation warning sequence is a result of sorting each department in ascending order according to the corresponding secondary verification priority evaluation value.

[0059] Compared with the prior art, the beneficial effects achieved by the present invention are: when dealing with the financial reimbursement of an enterprise, the present invention not only takes into account the relationship between the amount of the reimbursement and the invoice voucher, but also takes into account the correlation between different financial data, thereby realizing dynamic verification of the financial data; at the same time, when abnormal financial data is monitored, cross-departmental correlation of the abnormal financial data is realized, and more accurate and comprehensive early warning information is generated, which assists the corresponding person in charge of the enterprise to make a secondary verification decision on the financial data, thereby realizing effective supervision of the financial data. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 It is a structural schematic diagram of a data processor for financial management based on a big data system according to the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] See also Figure 1 The present invention provides a technical solution: a data processor for financial management based on a big data system, the data processor comprising:

[0064] An edge data acquisition module, which controls the edge of each department of the enterprise to obtain the financial data submitted by the corresponding department;

[0065] The edge data acquisition module corresponds to one or more edge terminals, each edge terminal is bound to a department of a corresponding enterprise, and each edge terminal receives the financial data of the enterprise department bound thereto;

[0066] The financial data includes the project name, the expenditure index required for the project task and the submitted invoice voucher;

[0067] The number of expense indicators of each project task in each financial data is unique; each project includes one or more project tasks, and the expense indicators required for each project task correspond to one or more submitted invoice vouchers, or the expense indicators required for one or more project tasks correspond to one submitted invoice voucher;

[0068] The cost indicator required for the project task is the amount of expenditure for executing the corresponding project task reported by the personnel executing the corresponding project task to their respective departments.

[0069] An edge data anomaly checking module, which controls each edge to check the financial data submitted by the corresponding department, extracts the anomaly checking results, and generates an edge anomaly checking data set;

[0070] When the edge data anomaly checking module generates an edge anomaly checking data set, each edge corresponds to an edge anomaly checking data set, and each element in each edge anomaly checking data set corresponds to a financial data;

[0071] The edge data anomaly checking module controls each edge to perform image recognition on the invoice voucher when checking the financial data submitted by the corresponding department, and extracts the bill information corresponding to each invoice voucher, wherein the bill information includes the bill amount and the business scope involved in the issuer of the corresponding bill; the edge data anomaly checking module marks each financial data with the same corresponding bill information submitted by the corresponding department in the edge;

[0072] Calculate the minimum value of the combination deviation values ​​corresponding to the marked financial data. In the process of obtaining the combination deviation value, combine the financial data and the invoice voucher according to the combination number corresponding to the financial data to obtain different combination results, and obtain the task feature information and the bill feature information corresponding to each combination element in each combination result; the combination number is equal to the minimum value of the total number of corresponding invoices and the total number of corresponding project tasks in the marked financial data; the number of combination elements in each combination result is equal to the obtained combination number;

[0073] If the minimum value of the combined deviation values ​​respectively corresponding to the marked financial data is equal to 0, then the verification result of the marked financial data is determined to be normal; otherwise, the verification result of the marked financial data is determined to be abnormal;

[0074] In this embodiment, if there are three financial data with the same marked bill information, namely L1, L2 and L3,

[0075] If L1, L2 and L3 all have and only have two invoices, both M1 and M2;

[0076] Since the total number of invoices in the marked financial data is 2, the total number of project tasks corresponding to the marked financial data is 3, and 3>2,

[0077] The corresponding number of combinations is 2.

[0078] The resulting combinations are as follows: {[L1, M1], [(L2, L3), M2]}, {[L2, M1], [(L1, L3), M2]}, {[L3, M1], [(L1, L2), M2]}, {[L1, M2], [(L2, L3), M1]}, {[L2, M2], [(L1, L3), M1]}, {[L3, M2], [(L1, L2), M1]}.

[0079] The task characteristic information includes first task characteristic information and second task characteristic information,

[0080] The first task characteristic information is the sum of the expenditure indicators required for the corresponding project tasks in the project names in the marked financial data corresponding to the corresponding combination elements; the second task characteristic information is the union of the business scopes required for the corresponding project tasks in the project names in the marked financial data corresponding to the corresponding combination elements in the prefabricated tables in the database;

[0081] The bill characteristic information includes first bill characteristic information and second bill characteristic information,

[0082] The first bill characteristic information is the sum of the bill amounts in the bill information corresponding to each invoice voucher corresponding to the corresponding combination element; the second bill characteristic information is the intersection of the business scope involved in the billing party in the bill information corresponding to each invoice voucher corresponding to the corresponding combination element;

[0083] The combined deviation value corresponding to the combined result is recorded as R.

[0084] ,

[0085] Wherein, A1r represents the first task feature information in the task feature information corresponding to the rth combination element in the corresponding combination result;

[0086] B1r represents the first bill feature information in the bill feature information corresponding to the rth combination element in the corresponding combination result;

[0087] A2r represents the second task feature information in the task feature information corresponding to the rth combination element in the corresponding combination result;

[0088] B2r represents the second bill feature information in the bill feature information corresponding to the rth combination element in the corresponding combination result;

[0089] g represents the deviation coefficient, and g is a constant preset in the database; r1 represents the total number of combination elements in the corresponding combination result;

[0090] H{} represents the decision function,

[0091] When A2r is a subset of B2r, H{A2r, B2r}=0 is determined; otherwise, H{A2r, B2r}=1 is determined.

[0092] A data association analysis module, which performs association analysis on the financial data collected by the data acquisition module to generate associated financial information sets corresponding to different financial data;

[0093] Each element in the associated financial information set generated by the data association analysis module corresponds to a financial data;

[0094] When the data association analysis module performs association analysis on the financial data collected by the data acquisition module, any two financial data are recorded as first financial data and second financial data respectively.

[0095] Respectively obtain data association features in the first financial data and the second financial data, wherein the data association features include the department to which the personnel corresponding to the corresponding financial data belong, the department involved in the project task, the issuance time of the bill, and the department personnel corresponding to the corresponding financial data, which are recorded as {C1, C2, C3, C4};

[0096] The departments involved in the project tasks are preset in the database;

[0097] When C1 in the first financial data belongs to C2 in the second financial data, C1 in the second financial data belongs to C2 in the first financial data, and the interval between C3 in the first financial data and C3 in the second financial data is less than a first preset value, it is determined that there is a cross-departmental correlation between the first financial data and the second financial data; otherwise, it is determined that there is no cross-departmental correlation between the first financial data and the second financial data;

[0098] When C4 in the first financial data is the same as C4 in the second financial data, it is determined that the first financial data and the second financial data have the same department association; otherwise, it is determined that the first financial data and the second financial data do not have the same department association;

[0099] The associated financial demand data set corresponding to each financial data is a collection of various financial data that have cross-departmental or intra-departmental associations with the corresponding financial data.

[0100] A terminal abnormality data optimization module, which summarizes the various edge abnormality verification data sets obtained by the edge data abnormality verification module, and optimizes the various edge abnormality verification data sets obtained according to the associated financial information set corresponding to each element in the obtained edge abnormality verification data set to generate a risk verification demand data set;

[0101] When the terminal abnormal data optimization module generates a risk verification requirement data set,

[0102] Obtain the associated financial information sets corresponding to each element in each edge anomaly check data set, and take the union of each edge anomaly check data set and all the associated financial information sets corresponding to its elements as the optimization result of the corresponding edge anomaly check data set; take the union of the optimization results of each edge anomaly check data set as the risk check demand data set.

[0103] A verification demand analysis module, which visualizes the financial anomalies of each department of the enterprise based on the elements in the risk verification demand data set, generates a focus sequence for the inspection department, and evaluates the verification complexity of the corresponding finances of each department of the enterprise in combination with the financial data obtained from each department of the enterprise;

[0104] The verification demand analysis module obtains the risk verification demand data set generated by the terminal abnormal data optimization module, divides the financial data belonging to each department in the risk verification demand data set, and visualizes the division results of each department on the display;

[0105] Calculate the abnormal concern evaluation value corresponding to each department, and record the abnormal concern evaluation value corresponding to the j-th department as Qj.

[0106] Qj=Ej·SEj,

[0107] Among them, Ej represents the ratio of the number of financial data belonging to the jth department in the risk verification demand data set to the total number of financial data in the jth department;

[0108] SEj represents the sum of the cost indicators required for the project tasks corresponding to each financial data belonging to the j-th department in the risk verification demand data set, divided by the sum of the cost indicators required for the project tasks corresponding to each financial data in the j-th department;

[0109] When the total amount of financial data in the jth department is 0, it is determined that Ej=0 and SEj=0;

[0110] The departments are sorted in descending order according to the corresponding abnormal attention evaluation values ​​to obtain the inspection department attention sequence.

[0111] When the verification demand analysis module evaluates the verification complexity of each department of the enterprise,

[0112] The edge data anomaly verification module controls each edge to verify the financial data submitted by the corresponding department. The minimum value of the corresponding combination deviation value in the same department is 0. The combination call ratio corresponding to each combination result is calculated, and the average value of each combination call ratio is used as the verification complexity of the corresponding finance of the corresponding department of the enterprise.

[0113] The combination call ratio corresponding to the combination result is equal to the quotient of the number of financial data corresponding to the corresponding combination result divided by the total number of elements in the corresponding combination result.

[0114] The risk warning recommendation management module generates a secondary verification recommendation warning sequence based on the inspection department's attention sequence and the verification complexity corresponding to each department; and feeds back the combination of the secondary verification recommendation warning sequence and the risk verification requirement data set as warning information to the corresponding person in charge of the enterprise.

[0115] When the risk warning recommendation management module generates a secondary verification recommendation warning sequence,

[0116] Sort each department according to the corresponding financial verification complexity to obtain the verification complexity assessment sequence;

[0117] Calculate the secondary verification priority evaluation value corresponding to each department, where the secondary verification priority evaluation value is equal to the quotient of the sequence number value of the corresponding department in the inspection department attention sequence and the sequence number value of the corresponding department in the verification complexity evaluation sequence;

[0118] The secondary verification recommendation warning sequence is a result of sorting each department in ascending order according to the corresponding secondary verification priority evaluation value.

[0119] This embodiment generates a secondary verification recommendation warning sequence, considering that both the inspection department's focus sequence and the verification complexity assessment sequence are one-sided, and it is necessary to comprehensively consider the two to integrate the verification recommendation sequence.

[0120] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0121] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data processor for financial management based on a big data system, characterized in that: The data processor comprises: An edge data acquisition module, which controls the edge of each department of the enterprise to obtain the financial data submitted by the corresponding department; An edge data anomaly checking module, which controls each edge to check the financial data submitted by the corresponding department, extracts the anomaly checking results, and generates an edge anomaly checking data set; A data association analysis module, which performs association analysis on the financial data collected by the data acquisition module to generate associated financial information sets corresponding to different financial data; A terminal abnormality data optimization module, which summarizes the various edge abnormality verification data sets obtained by the edge data abnormality verification module, and optimizes the various edge abnormality verification data sets obtained according to the associated financial information set corresponding to each element in the obtained edge abnormality verification data set to generate a risk verification demand data set; A verification demand analysis module, which visualizes the financial anomalies of each department of the enterprise based on the elements in the risk verification demand data set, generates a focus sequence for the inspection department, and evaluates the verification complexity of the corresponding finances of each department of the enterprise in combination with the financial data obtained from each department of the enterprise; A risk warning recommendation management module, which generates a secondary verification recommendation warning sequence based on the inspection department's attention sequence and the verification complexity of each department; and feeds back the combination of the secondary verification recommendation warning sequence and the risk verification requirement data set as warning information to the corresponding person in charge of the enterprise; When the edge data anomaly checking module generates an edge anomaly checking data set, each edge corresponds to an edge anomaly checking data set, and each element in each edge anomaly checking data set corresponds to a financial data; The edge data anomaly checking module controls each edge to perform image recognition on the invoice voucher when checking the financial data submitted by the corresponding department, and extracts the bill information corresponding to each invoice voucher, wherein the bill information includes the bill amount and the business scope involved in the issuer of the corresponding bill; the edge data anomaly checking module marks each financial data with the same corresponding bill information submitted by the corresponding department in the edge; Calculate the minimum value of the combination deviation values ​​corresponding to the marked financial data. In the process of obtaining the combination deviation value, combine the financial data and the invoice voucher according to the combination number corresponding to the financial data to obtain different combination results, and obtain the task feature information and the bill feature information corresponding to each combination element in each combination result; the combination number is equal to the minimum value of the total number of corresponding invoices and the total number of corresponding project tasks in the marked financial data; the number of combination elements in each combination result is equal to the obtained combination number; If the minimum value of the combined deviation values ​​respectively corresponding to the marked financial data is equal to 0, then the verification result of the marked financial data is determined to be normal; otherwise, the verification result of the marked financial data is determined to be abnormal; The task characteristic information includes first task characteristic information and second task characteristic information, The first task characteristic information is the sum of the expenditure indicators required for the corresponding project tasks in the project names in the marked financial data corresponding to the corresponding combination elements; the second task characteristic information is the union of the business scopes required for the corresponding project tasks in the project names in the marked financial data corresponding to the corresponding combination elements in the prefabricated tables in the database; The bill characteristic information includes first bill characteristic information and second bill characteristic information, The first bill characteristic information is the sum of the bill amounts in the bill information corresponding to each invoice voucher corresponding to the corresponding combination element; the second bill characteristic information is the intersection of the business scope involved in the billing party in the bill information corresponding to each invoice voucher corresponding to the corresponding combination element; The combined deviation value corresponding to the combined result is recorded as R. Wherein, A1r represents the first task feature information in the task feature information corresponding to the rth combination element in the corresponding combination result; B1r represents the first bill feature information in the bill feature information corresponding to the rth combination element in the corresponding combination result; A2r represents the second task feature information in the task feature information corresponding to the rth combination element in the corresponding combination result; B2r represents the second bill feature information in the bill feature information corresponding to the rth combination element in the corresponding combination result; g represents the deviation coefficient, and g is a constant preset in the database; r1 represents the total number of combination elements in the corresponding combination result; H{} represents the decision function, When A2r is a subset of B2r, H{A2r, B2r}=0 is determined; otherwise, H{A2r, B2r}=1 is determined; Each element in the associated financial information set generated by the data association analysis module corresponds to a financial data; When the data association analysis module performs association analysis on the financial data collected by the data acquisition module, any two financial data are recorded as first financial data and second financial data respectively. Respectively obtain data association features in the first financial data and the second financial data, wherein the data association features include the department to which the personnel corresponding to the corresponding financial data belong, the department involved in the project task, the issuance time of the bill, and the department personnel corresponding to the corresponding financial data, which are recorded as {C1, C2, C3, C4}; The departments involved in the project tasks are preset in the database; When C1 in the first financial data belongs to C2 in the second financial data, C1 in the second financial data belongs to C2 in the first financial data, and the interval between C3 in the first financial data and C3 in the second financial data is less than a first preset value, it is determined that there is a cross-departmental correlation between the first financial data and the second financial data; otherwise, it is determined that there is no cross-departmental correlation between the first financial data and the second financial data; When C4 in the first financial data is the same as C4 in the second financial data, it is determined that the first financial data and the second financial data have the same department association; otherwise, it is determined that the first financial data and the second financial data do not have the same department association; The associated financial demand data set corresponding to each financial data is a collection of various financial data that have cross-departmental or intra-departmental associations with the corresponding financial data.

2. A data processor for financial management based on a big data system according to claim 1, characterized in that: The edge data acquisition module corresponds to one or more edge terminals, each edge terminal is bound to a department of a corresponding enterprise, and each edge terminal receives the financial data of the enterprise department bound thereto; The financial data includes the project name, the expenditure index required for the project task and the submitted invoice voucher; The number of expense indicators of each project task in each financial data is unique; each project includes one or more project tasks, and the expense indicators required for each project task correspond to one or more submitted invoice vouchers, or the expense indicators required for one or more project tasks correspond to one submitted invoice voucher; The cost indicator required for the project task is the amount of expenditure for executing the corresponding project task reported by the personnel executing the corresponding project task to their respective departments.

3. The data processor for financial management based on a big data system according to claim 1, characterized in that: When the terminal abnormal data optimization module generates a risk verification requirement data set, Obtain the associated financial information sets corresponding to each element in each edge abnormality check data set, and take the union of each edge abnormality check data set and all associated financial information sets corresponding to its elements as the optimization result of the corresponding edge abnormality check data set; The union of the optimization results of each edge abnormality check data set is used as the risk check requirement data set.

4. The data processor for financial management based on a big data system according to claim 1, characterized in that: The verification demand analysis module obtains the risk verification demand data set generated by the terminal abnormal data optimization module, divides the financial data belonging to each department in the risk verification demand data set, and visualizes the division results of each department on the display; Calculate the abnormal concern evaluation value corresponding to each department, and record the abnormal concern evaluation value corresponding to the j-th department as Qj. Qj=Ej·SEj, Among them, Ej represents the ratio of the number of financial data belonging to the jth department in the risk verification demand data set to the total number of financial data in the jth department; SEj represents the sum of the cost indicators required for the project tasks corresponding to each financial data belonging to the j-th department in the risk verification demand data set, divided by the sum of the cost indicators required for the project tasks corresponding to each financial data in the j-th department; When the total amount of financial data in the jth department is 0, it is determined that Ej = 0 and SEj = 0; The departments are sorted in descending order according to the corresponding abnormal attention evaluation values ​​to obtain the inspection department attention sequence.

5. A data processor for financial management based on a big data system according to claim 4, characterized in that: When the verification demand analysis module evaluates the verification complexity of each department of the enterprise, The edge data anomaly verification module controls each edge to verify the financial data submitted by the corresponding department. The minimum value of the corresponding combination deviation value in the same department is 0. The combination call ratio corresponding to each combination result is calculated, and the average value of each combination call ratio is used as the verification complexity of the corresponding finance of the corresponding department of the enterprise. The combination call ratio corresponding to the combination result is equal to the quotient of the number of financial data corresponding to the corresponding combination result divided by the total number of elements in the corresponding combination result.

6. A data processor for financial management based on a big data system according to claim 5, characterized in that: When the risk warning recommendation management module generates a secondary verification recommendation warning sequence, Sort each department according to the corresponding financial verification complexity to obtain the verification complexity assessment sequence; Calculate the secondary verification priority evaluation value corresponding to each department, where the secondary verification priority evaluation value is equal to the quotient of the sequence number value of the corresponding department in the inspection department attention sequence and the sequence number value of the corresponding department in the verification complexity evaluation sequence; The secondary verification recommendation warning sequence is a result of sorting each department in ascending order according to the corresponding secondary verification priority evaluation value.

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