A management method and system for business and financial data homologous collection and dynamic correlation mapping
By calculating the correlation coefficient of business and financial data and implementing adaptive management, the problem of ensuring the consistency of business and financial data is solved, and the efficiency of verification and validation is improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HENGYANG FINANCE ECONOMICS & IND POLYTECHNIC
- Filing Date
- 2026-01-14
- Publication Date
- 2026-06-02
Smart Images
Figure CN122134479A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data management technology, specifically a management method and system for the same-source collection and dynamic correlation mapping of business and financial data. Background Technology
[0002] Business-finance integration, also known as the fusion of business and finance, is a new concept proposed in the field of financial management in recent years. It provides enterprises with a brand-new perspective on financial management, so that business data and financial data are no longer managed separately.
[0003] Existing technologies do not guarantee the consistency of business and financial data management, and cannot adaptively change management based on changes in business data, resulting in low efficiency in the verification and validation of business and financial data. Summary of the Invention
[0004] To address the shortcomings mentioned in the background art, the present invention aims to provide a management method and system for the same-source collection and dynamic association mapping of business and financial data.
[0005] Firstly, the objective of this invention can be achieved through the following technical solution: a management method for the same-source collection and dynamic correlation mapping of business and financial data, the method comprising the following steps: Receive business data, financial data, and business-finance co-source data; and perform preprocessing to obtain processed business data, financial data, and business-finance co-source data. The business data includes sales data and procurement data, the financial data includes accounting data and cost data, and the business-finance co-source data is transaction record data. The business correlation coefficient is obtained by performing business correlation calculation on the processed business data, and the financial correlation coefficient is obtained by performing financial correlation calculation on the processed financial data. The business correlation coefficient, the financial correlation coefficient and the processed business and financial data are dynamically correlated and mapped to obtain the business and financial correlation mapping coefficient. Based on a preset threshold for business-finance correlation mapping, the calculated business-finance correlation mapping coefficient is compared with the threshold, and the business-finance data is adaptively managed according to the comparison results.
[0006] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The business correlation coefficient is obtained by performing business correlation calculation based on the processed business data. It is then calculated by cosine similarity after extracting the feature vectors of sales data and procurement data.
[0007] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The process of calculating the business relevance coefficient based on the processed business data includes: The sales data feature vector is labeled Xi, and the purchase data feature vector is labeled Ci, where i is the quantity index of the business data, and i = 1, 2, 3, ..., n, and n is the total number of business data. Cosine similarity is calculated based on the labeled sales data feature vector Xi and procurement data feature vector Ci. The calculated cosine similarity is used to indicate the direct correlation between business transactions. The formula is as follows: In the formula, Ywi is the business relevance coefficient. The modulus of the feature vector of sales data. Let be the modulus of the feature vector of the procurement data, a be the sales impact coefficient, and b be the procurement impact coefficient.
[0008] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The financial correlation coefficient is obtained by performing financial correlation calculation based on the processed financial data. It is then calculated by cosine similarity after extracting the feature vectors of accounting data and cost data.
[0009] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The process of calculating the financial correlation coefficient based on the processed financial data includes: The feature vector of accounting data is labeled as Hj, and the feature vector of cost data is labeled as Qj, where j is the quantity label of financial data, and j = 1, 2, 3, ..., m, where m is the total number of financial data. The cosine similarity is calculated based on the labeled accounting data feature vector Hj and cost data feature vector Qj, using the following formula: In the formula, Cwj is the financial correlation coefficient. The modulus of the feature vector of accounting data. Let r be the modulus of the cost data feature vector, r be the accounting impact coefficient, and f be the cost impact coefficient.
[0010] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The calculation process of dynamically mapping and associating the business correlation coefficient, the financial correlation coefficient, and the processed business-finance co-source data includes: The processed business and financial data from the same source is labeled as T; Using formula The business-finance correlation mapping coefficient Sij was calculated. In the formula, Yw0 is the preset standard business correlation coefficient, Cw0 is the preset standard financial correlation coefficient, exp() is the exponential function, ln() is the logarithmic function, α, β, and γ are all preset weight coefficients, and α+β+γ=1.
[0011] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The process of comparing the calculated business-finance correlation mapping coefficient with the business-finance correlation mapping threshold includes: When the business-finance correlation mapping coefficient is greater than or equal to the business-finance correlation mapping threshold, the business data and the corresponding business correlation will be managed and adjusted. When the business-finance correlation mapping coefficient is less than the business-finance correlation mapping threshold, management adjustments are made to the financial data and the corresponding financial correlation.
[0012] In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: The preprocessing process includes three steps: data format parsing and unification, data cleaning, and data standardization.
[0013] Secondly, in order to achieve the above objectives, this invention discloses a management system for the common-source collection and dynamic association mapping of business and financial data, comprising: The data processing module is used to receive business data, financial data, and business-finance co-source data; and to perform preprocessing to obtain processed business data, financial data, and business-finance co-source data. The business data includes sales data and procurement data, the financial data includes accounting data and cost data, and the business-finance co-source data is transaction record data. The association mapping module is used to calculate the business association coefficient based on the processed business data, calculate the financial association coefficient based on the processed financial data, and perform dynamic association mapping calculation on the business association coefficient, the financial association coefficient, and the processed business and financial data to obtain the business and financial association mapping coefficient. The adaptive management module is used to compare the calculated business-finance correlation mapping coefficient with the business-finance correlation mapping threshold based on the preset threshold, and to perform adaptive management of business-finance data according to the comparison result.
[0014] In another aspect of the present invention, in order to achieve the above-mentioned objective, a terminal device is disclosed, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The memory stores the computer program capable of running on the processor. When the processor loads and executes the computer program, it employs a management method for business and financial data homogeneous acquisition and dynamic association mapping as described above.
[0015] The beneficial effects of this invention are: This invention calculates business correlation coefficients and financial correlation coefficients by collecting and processing business data and financial data respectively. Then, it dynamically maps these coefficients with business and financial data from the same source to obtain business-finance correlation mapping coefficients. Finally, it compares these business-finance correlation mapping coefficients with business-finance correlation mapping thresholds and adaptively manages the business and financial data based on the comparison results, thereby improving the efficiency of business and financial data verification and inspection. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Example 1: like Figure 1 As shown, a management method for the same-source collection and dynamic association mapping of business and financial data includes the following steps: S101: Receive business data, financial data, and business-finance co-source data; and perform preprocessing to obtain processed business data, financial data, and business-finance co-source data. Wherein, the business data includes sales data and procurement data, the financial data includes accounting data and cost data, and the business-finance co-source data is transaction record data. Specifically, in this embodiment, the process of collecting business and financial data from the same source is as follows: data is collected by directly calling the server of the main system. Specifically, the business and financial data from the same source is obtained through transaction serial number, transaction type, transaction occurrence time, and transaction order number. The process of collecting business data involves calling specific order summary data from the business system. Sales data includes total sales volume, unit price, and sales region, while procurement data includes purchase quantity, unit price, and supplier information. The process of collecting business and financial data from the same source: by calling the data recorded in the logs of the financial system, including accounting data such as the amount and time of receipt, and cost data such as manufacturing cost data and material cost data; The preprocessing of business data, financial data, and business-finance co-source data includes: The process of unifying data format parsing, data cleaning, and data standardization; The process of unifying data format parsing includes: Perform data format parsing, field extraction, and character set conversion on business and financial data to unify the data format to a standard preset format and prevent garbled characters. The data cleaning process includes: handling missing and outlier values in business data, financial data, and business-finance co-source data. Outlier handling is based on the Z-score method. Calculate the Z-score value for each data point, and delete data points with Z-score values greater than a preset threshold as outliers. The data standardization process includes: unifying the encoding of business data, financial data, and business-finance co-source data after unifying the format; and standardizing the corresponding business data and financial data based on the business-finance co-source data. S102: Based on the processed business data, perform business correlation calculation to obtain the business correlation coefficient; based on the processed financial data, perform financial correlation calculation to obtain the financial correlation coefficient; perform dynamic correlation mapping calculation on the business correlation coefficient, financial correlation coefficient, and processed business and financial data to obtain the business and financial correlation mapping coefficient. In this embodiment, the calculation process for obtaining the business correlation coefficient based on the processed business data involves extracting feature vectors from sales data and procurement data and then calculating the cosine similarity. The specific process is as follows: Extract the feature vectors from the sales data and the purchase data respectively; thus obtaining the feature vectors for the sales data and the purchase data. The sales data feature vector is labeled Xi, and the purchase data feature vector is labeled Ci, where i is the quantity index of the business data, and i = 1, 2, 3, ..., n, and n is the total number of business data. Cosine similarity is calculated based on the labeled sales data feature vector Xi and procurement data feature vector Ci. The calculated cosine similarity is used to indicate the direct correlation between business transactions. The formula is as follows: In the formula, Ywi is the business relevance coefficient. The modulus of the feature vector of sales data. Let be the modulus of the feature vector of the procurement data, 'a' be the sales impact coefficient, and 'b' be the procurement impact coefficient. In this embodiment, the calculation process for obtaining the financial correlation coefficient based on the processed financial data involves extracting feature vectors from accounting data and cost data and then calculating the cosine similarity. The process is as follows: Extract the feature vectors from the accounting data and cost data respectively to obtain the feature vectors of the accounting data and cost data. The feature vector of accounting data is labeled as Hj, and the feature vector of cost data is labeled as Qj, where j is the quantity label of financial data, and j = 1, 2, 3, ..., m, where m is the total number of financial data. The cosine similarity is calculated based on the labeled accounting data feature vector Hj and cost data feature vector Qj, using the following formula: In the formula, Cwj is the financial correlation coefficient. The modulus of the feature vector of accounting data. Let r be the modulus of the cost data feature vector, r be the accounting impact coefficient, and f be the cost impact coefficient; In this embodiment, the sales impact coefficient, procurement impact coefficient, accounting impact coefficient, and cost impact coefficient are calculated by comprehensively evaluating the impact of external factors when the application obtains sales data, procurement data, accounting data, and cost data on a daily basis. These factors include human factors, machine detection, and environmental factors, etc. Human factors refer to those caused by improper human operation or scanning. The process of dynamically mapping and calculating the business correlation coefficient Ywi, the financial correlation coefficient Cwj, and the processed business-finance co-source data includes: The processed business and financial data from the same source is labeled as T; Using formula The business-finance correlation mapping coefficient Sij is calculated, where Yw0 is the preset standard business correlation coefficient, Cw0 is the preset standard financial correlation coefficient, exp() is the exponential function, ln() is the logarithmic function, α, β, and γ are preset weight coefficients, and α+β+γ=1. Furthermore, in the specific implementation process, the preset standard business correlation coefficient and the preset standard financial correlation coefficient are obtained by calculating multiple business correlation coefficients and financial correlation coefficients, and then by averaging the data from multiple simulation calculations. S103: Based on the preset business-finance correlation mapping threshold, the calculated business-finance correlation mapping coefficient is compared with the business-finance correlation mapping threshold, and the business-finance data is adaptively managed according to the comparison result.
[0019] The preset threshold for business-finance correlation mapping is obtained by averaging multiple simulations after collecting historical business data, financial data, and business-finance data from the same source. It is used as a standard for managing current business and financial data. The process of comparing the calculated business-finance correlation mapping coefficient with the business-finance correlation mapping threshold includes: When the business-finance correlation mapping coefficient is greater than or equal to the business-finance correlation mapping threshold, the business data and the corresponding business correlation will be managed and adjusted. When the business-finance correlation mapping coefficient is less than the business-finance correlation mapping threshold, management adjustments are made to the financial data and the corresponding financial correlation.
[0020] Example 2: A management system for the common-source collection and dynamic association mapping of business and financial data, comprising: Data processing module 11 is used to receive business data, financial data, and business-finance co-source data; and to perform preprocessing to obtain processed business data, financial data, and business-finance co-source data. The business data includes sales data and procurement data, the financial data includes accounting data and cost data, and the business-finance co-source data is transaction record data. The association mapping module 12 is used to calculate the business association coefficient based on the processed business data, calculate the financial association coefficient based on the processed financial data, and perform dynamic association mapping calculation on the business association coefficient, the financial association coefficient, and the processed business and financial data to obtain the business and financial association mapping coefficient. The adaptive management module 13 is used to compare the calculated business-finance correlation mapping coefficient with the business-finance correlation mapping threshold based on the preset business-finance correlation mapping threshold, and to perform adaptive management of business-finance data according to the comparison result.
[0021] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.
[0022] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0023] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.
[0024] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0025] The foregoing has shown and described the basic principles, main features, and advantages of this disclosure. Those skilled in the art should understand that this disclosure is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of this disclosure. Various changes and modifications can be made to this disclosure without departing from its spirit and scope, and all such changes and modifications fall within the scope of this disclosure as claimed.
Claims
1. A management method for the same-source collection and dynamic correlation mapping of business and financial data, characterized in that, The method includes the following steps: Receive business data, financial data, and data from the same source as business and finance data; The data is then preprocessed to obtain processed business data, financial data, and business-finance co-source data. The business data includes sales data and procurement data, the financial data includes accounting data and cost data, and the business-finance co-source data is transaction record data. The business correlation coefficient is obtained by performing business correlation calculation on the processed business data, and the financial correlation coefficient is obtained by performing financial correlation calculation on the processed financial data. The business correlation coefficient, the financial correlation coefficient and the processed business and financial data are dynamically correlated and mapped to obtain the business and financial correlation mapping coefficient. Based on a preset threshold for business-finance correlation mapping, the calculated business-finance correlation mapping coefficient is compared with the threshold, and the business-finance data is adaptively managed according to the comparison results.
2. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 1, characterized in that, The business correlation coefficient is obtained by performing business correlation calculation based on the processed business data. It is then calculated by cosine similarity after extracting the feature vectors of sales data and procurement data.
3. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 2, characterized in that, The process of calculating the business relevance coefficient based on the processed business data includes: The sales data feature vector is labeled Xi, and the purchase data feature vector is labeled Ci, where i is the quantity index of the business data, and i = 1, 2, 3, ..., n, and n is the total number of business data. Cosine similarity is calculated based on the labeled sales data feature vector Xi and procurement data feature vector Ci. The calculated cosine similarity is used to indicate the direct correlation between business transactions. The formula is as follows: In the formula, Ywi is the business relevance coefficient. The modulus of the feature vector of sales data. Let be the modulus of the feature vector of the procurement data, a be the sales impact coefficient, and b be the procurement impact coefficient.
4. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 1, characterized in that, The financial correlation coefficient is obtained by performing financial correlation calculation based on the processed financial data. It is then calculated by cosine similarity after extracting the feature vectors of accounting data and cost data.
5. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 4, characterized in that, The process of calculating the financial correlation coefficient based on the processed financial data includes: The feature vector of accounting data is labeled as Hj, and the feature vector of cost data is labeled as Qj, where j is the quantity label of financial data, and j = 1, 2, 3, ..., m, where m is the total number of financial data. The cosine similarity is calculated based on the labeled accounting data feature vector Hj and cost data feature vector Qj, using the following formula: In the formula, Cwj is the financial correlation coefficient. The modulus of the feature vector of accounting data. Let r be the modulus of the cost data feature vector, r be the accounting impact coefficient, and f be the cost impact coefficient.
6. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 1, characterized in that, The calculation process of dynamically mapping and associating the business correlation coefficient, the financial correlation coefficient, and the processed business-finance co-source data includes: The processed business and financial data from the same source is labeled as T; Using formula The business-finance correlation mapping coefficient Sij was calculated. In the formula, Yw0 is the preset standard business correlation coefficient, Cw0 is the preset standard financial correlation coefficient, exp() is the exponential function, ln() is the logarithmic function, α, β, and γ are all preset weight coefficients, and α+β+γ=1.
7. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 1, characterized in that, The process of comparing the calculated business-finance correlation mapping coefficient with the business-finance correlation mapping threshold includes: When the business-finance correlation mapping coefficient is greater than or equal to the business-finance correlation mapping threshold, the business data and the corresponding business correlation will be managed and adjusted. When the business-finance correlation mapping coefficient is less than the business-finance correlation mapping threshold, management adjustments are made to the financial data and the corresponding financial correlation.
8. The management method for the same-source collection and dynamic association mapping of business and financial data according to claim 1, characterized in that, The preprocessing process includes three steps: data format parsing and unification, data cleaning, and data standardization.
9. A management system for the common-source collection and dynamic association mapping of business and financial data, employing the management method for the common-source collection and dynamic association mapping of business and financial data as described in any one of claims 1 to 8, characterized in that, include: The data processing module is used to receive business data, financial data, and data from the same source as business and finance. The data is then preprocessed to obtain processed business data, financial data, and business-finance co-source data. The business data includes sales data and procurement data, the financial data includes accounting data and cost data, and the business-finance co-source data is transaction record data. The association mapping module is used to calculate the business association coefficient based on the processed business data, calculate the financial association coefficient based on the processed financial data, and perform dynamic association mapping calculation on the business association coefficient, the financial association coefficient, and the processed business and financial data to obtain the business and financial association mapping coefficient. The adaptive management module is used to compare the calculated business-finance correlation mapping coefficient with the business-finance correlation mapping threshold based on the preset threshold, and to perform adaptive management of business-finance data according to the comparison result.
10. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program that can run on the processor. When the processor loads and executes the computer program, it employs a management method for the same-source acquisition and dynamic association mapping of business and financial data as described in any one of claims 1 to 8.