CN-122023045-A - Industry and financial fusion management method and system based on intelligent data analysis

CN122023045ACN 122023045 ACN122023045 ACN 122023045ACN-122023045-A

Abstract

The application relates to the technical field of industry and property fusion management, and discloses an intelligent data analysis-based industry and property fusion management method and system, the method comprises the steps of firstly capturing order state change event data and voucher generation event data from a key business system and a financial system through an automatic means, and ensuring timeliness and accuracy of original data. These data are then cleaned and normalized, format differences are eliminated and data standards are unified, and then the cleaned and normalized data are processed with a financial event correlation engine to identify and establish associations between business events and financial credentials. And marking the successfully associated data as associated and generating a fusion event record, and marking the data which are not successfully associated as pending. Therefore, not only is the accuracy and the comprehensiveness of industry and financial data integration improved, but also the flexibility and the adaptability of data processing are enhanced through an intelligent means, and more powerful decision support is provided for enterprises.

Inventors

ZHANG LEI
BAO YUZHE
WANG JIANYING

Assignees

中国计量大学现代科技学院

Dates

Publication Date: 20260512
Application Date: 20260202

Claims (10)

1. The business fusion management method based on intelligent data analysis is characterized by comprising the following steps of: capturing order state change event data from a critical business system; capturing credential generation event data from a financial system; Carrying out data cleaning and standardization on the order state change event data and the voucher generation event data to obtain order state change event data after cleaning standardization and voucher generation event data after cleaning standardization; inputting the cleaning standardized order state change event data and the cleaning standardized voucher generation event data into a financial event correlation engine to obtain a correlation analysis result; If the association analysis result is successful association, marking the cleaning standardized order state change event data and the cleaning standardized voucher generation event data with associated marks, and generating a fusion event record containing the cleaning standardized order state change event data and the cleaning standardized voucher generation event data; if the association analysis result is that successful association cannot be achieved, marking to-be-processed marks are made on the order state change event data after washing standardization and the certificate generation event data after washing standardization.
2. The intelligent data analysis-based financial fusion management method of claim 1, wherein inputting the clean standardized order status change event data and the clean standardized voucher generation event data into a financial event correlation engine to obtain correlation analysis results, comprising: and inputting the cleaning standardized order state change event data and the cleaning standardized voucher generation event data into a strong key association analysis unit of the financial event association engine to obtain a primary association analysis result.
3. The business fusion management method based on intelligent data analysis according to claim 2, wherein inputting the cleaning normalized order status change event data and the cleaning normalized voucher generation event data into a strong key correlation analysis unit of the business event correlation engine to obtain a primary correlation analysis result, comprises: The strong key association analysis unit checks whether a predefined strong association key exists in the order state change event data after the cleaning standardization and the certificate generation event data after the cleaning standardization at the same time; If so, the primary association analysis result is a successful association; And if the order state change event data after the cleaning standardization and the certificate generation event data after the cleaning standardization are input into an intelligent association analysis unit of an industrial and financial event association engine to obtain a secondary association analysis result.
4. The intellectual data analysis based financial fusion management method of claim 3, wherein inputting the cleaning standardized order status change event data and the cleaning standardized voucher generation event data into the intellectual association analysis unit of the financial event association engine to obtain a secondary association analysis result comprises: Judging the time adjacency between the order state change event data after washing standardization and the certificate generation event data after washing standardization to obtain a time adjacency score value; Judging the amount similarity between the order state change event data after washing standardization and the certificate generation event data after washing standardization to obtain an amount similarity score value; judging the entity relevance between the order state change event data after washing standardization and the certificate generation event data after washing standardization to obtain an entity relevance score value; Judging the consistency of the measurement units between the order state change event data after the cleaning standardization and the certificate generation event data after the cleaning standardization so as to obtain a consistency score value of the measurement units; judging the text similarity between the order state change event data after washing standardization and the voucher generation event data after washing standardization to obtain a text similarity score value; And generating the secondary association analysis result based on the time proximity score value, the sum similarity score value, the entity association score value, the measurement unit consistency score value and the text similarity score value.
5. The intelligent data analysis-based financial fusion management method of claim 4, wherein generating the secondary association analysis result based on the temporal proximity score value, the monetary similarity score value, the entity association score value, the unit of measure consistency score value, and the text similarity score value comprises: inputting the time proximity score value, the monetary amount similarity score value, the entity relevance score value, the measurement unit consistency score value and the text similarity score value into a scoring model to obtain an inter-data relevance comprehensive score value; if the comprehensive grading value of the inter-data association is greater than or equal to a preset threshold value, the secondary association analysis result is successful association; and if the comprehensive score value of the inter-data association is smaller than a preset threshold value, the secondary association analysis result is that the association cannot be successfully carried out.
6. The business fusion management method based on intelligent data analysis of claim 5, further comprising weighting the temporal proximity score, the monetary amount similarity score, the entity association score, the unit of measure consistency score, and the unit of measure consistency score to be easily conformed based on the text similarity score before inputting the temporal proximity score, the monetary amount similarity score, the entity association score, and the unit of measure consistency score to a scoring model to obtain a post-conformed temporal proximity score, a post-conformed monetary amount similarity score, a post-conformed entity association score, and a post-conformed unit of measure consistency score.
7. The business fusion management method based on intelligent data analysis according to claim 6, wherein weighting the temporal proximity score value, the monetary similarity score value, the entity relevance score value, and the unit of measure consistency score value based on the text similarity score value is subject to co-ordination to obtain a post-co-temporal proximity score, a post-co-monetary similarity score value, a post-co-entity relevance score value, and a post-co-metering unit consistency score value, comprising: determining a fractional order guide operator for the parameterized representation of the weight differences; The time proximity score value, the monetary similarity score value, the entity relevance score value and the measurement unit consistency score value are subjected to monomodal distribution modulation to obtain a modulated time proximity score value, a modulated monetary similarity score value, a modulated entity relevance score value and a modulated measurement unit consistency score value; defining a conformal space under the combined action of the modulated weight states; And performing unimodal distribution modulation on the modulated time proximity score, the modulated amount similarity score, the modulated entity relevance score and the modulated measurement unit consistency score based on the modulated weight co-action conformal space and the text similarity score value, and performing conformal mapping under a unimodal score order to obtain the conformal time proximity score, the conformal amount similarity score, the conformal post-entity relevance score and the conformal measurement unit consistency score.
8. The intelligent data analysis-based financial fusion management method of claim 4 wherein determining a text similarity between the clean normalized order status change event data and the clean normalized voucher generation event data to obtain a text similarity score value comprises: Carrying out semantic embedded coding on the order state change event data after washing standardization and the voucher generation event data after washing standardization to obtain an order state change event semantic embedded coding vector and a voucher generation event semantic embedded coding vector; And calculating cosine similarity between the order state change event semantic embedded coding vector and the voucher generation event semantic embedded coding vector as the text similarity score value.
9. The intelligent data analysis based financial fusion management method of claim 1 wherein the order status change event data comprises an order ID, a customer ID, a product ID, an amount, a time stamp, and an event type, and the voucher generation event data comprises a voucher ID, a subject code, an amount, a lending direction, and a time stamp.
10. A business fusion management system based on intelligent data analysis for performing the business fusion management method based on intelligent data analysis as set forth in any one of claims 1 to 9, comprising: an order state change event capture module for capturing order state change event data from the critical business system; A credential generation event capture module for capturing credential generation event data from a financial system; The data cleaning module is used for cleaning and normalizing the order state change event data and the voucher generation event data to obtain cleaned and normalized order state change event data and cleaned and normalized voucher generation event data; the association analysis result generation module is used for inputting the order state change event data after the cleaning standardization and the voucher generation event data after the cleaning standardization into an industrial and financial event association engine to obtain an association analysis result; the fusion event record generation module is used for marking the cleaning standardized order state change event data and the cleaning standardized voucher generation event data with associated marks if the association analysis result is successful association, and generating a fusion event record containing the cleaning standardized order state change event data and the cleaning standardized voucher generation event data; and the data marking module is used for marking the cleaning standardized order state change event data and the cleaning standardized voucher generation event data with to-be-processed marks if the association analysis result is that successful association cannot be achieved.

Description

Industry and financial fusion management method and system based on intelligent data analysis Technical Field The application relates to the technical field of industry and property fusion management, in particular to an intelligent data analysis-based industry and property fusion management method and system. Background Under the background of rapid development of informatization, efficient integration of enterprise business and financial management becomes one of key factors for improving enterprise competitiveness. However, in the prior art, real-time association and mapping of business data with financial data still faces many challenges. Traditionally, business systems (e.g., sales, purchasing, etc.) and financial systems (e.g., accounting, funds management, etc.) of an enterprise are often operated independently, which results in a hysteresis in data interaction between the two systems, and thus, immediate data sharing and synchronous updating cannot be achieved. The information island phenomenon not only affects the timeliness and accuracy of the decision, but also can cause the problems of repeated input errors, inconsistent data and the like. Specifically, various order status change events generated during execution of a business process, such as order creation, shipping, return, etc., typically need to be reflected in a financial system in time for corresponding accounting. However, due to the lack of efficient mechanisms to ensure that these business events are automatically and accurately mapped onto financial credentials, manual intervention is often required to check and adjust, which undoubtedly increases the effort and the likelihood of errors. Meanwhile, as the scale of enterprises increases and the business complexity increases, the manual matching becomes more and more difficult due to massive business data and financial data, and an intelligent method is urgently needed to solve the problem. Thus, an optimized business fusion management scheme based on intelligent data analysis is desired. Disclosure of Invention The application is provided for solving the problems of low data processing efficiency, easy error and information island between business and financial systems in the prior art. The embodiment of the application provides a business fusion management method and system based on intelligent data analysis. According to the financial fusion management method based on intelligent data analysis, order state change event data are captured from a key business system, voucher generation event data are captured from a financial system, data cleaning and standardization are conducted on the order state change event data and the voucher generation event data to obtain cleaned standardized order state change event data and cleaned standardized voucher generation event data, the cleaned standardized order state change event data and the cleaned standardized voucher generation event data are input into a financial event correlation engine to obtain a correlation analysis result, if the correlation analysis result is successful correlation, the cleaned standardized order state change event data and the cleaned standardized voucher generation event data are marked with correlation marks, fusion event records containing the cleaned standardized order state change event data and the cleaned standardized voucher generation event data are generated, and if the correlation analysis result is unsuccessful correlation, the cleaned standardized order state change event data and the cleaned standardized voucher generation event data are marked with processing marks. With reference to the first aspect, in a possible implementation manner, inputting the order state change event data after washing standardization and the voucher generation event data after washing standardization into an industrial and financial event association engine to obtain an association analysis result includes inputting the order state change event data after washing standardization and the voucher generation event data after washing standardization into a strong key association analysis unit of the industrial and financial event association engine to obtain a primary association analysis result. With reference to the first aspect, in one possible implementation manner, the step of inputting the order state change event data after washing standardization and the credential generation event data after washing standardization into a strong key association analysis unit of the financial event association engine to obtain a primary association analysis result includes the step of checking whether a predefined strong association key exists in the order state change event data after washing standardization and the credential generation event data after washing standardization at the same time by the strong key association analysis unit, if so, the primary association analysis result is successful association, and if not, the step of inp