Data redistribution accounting system and method based on intelligent business rules

Abstract

This invention relates to the field of enterprise financial management technology, specifically to a data redistribution accounting system and method based on intelligent business rules. The system includes: an intelligent rule configuration module, a data acquisition and preprocessing module, a dynamic adjustment module, a visualization module, and an anomaly detection and early warning module. The intelligent rule configuration module configures multi-dimensional business accounting rules according to the group's cost management needs. The data acquisition and preprocessing module collects internal transaction data, cost data, and financial data and performs standardized processing. The intelligent accounting engine performs data redistribution accounting based on preset business rules. This invention significantly improves the efficiency of group cost accounting, reduces manual workload, and shortens the accounting cycle through automated data acquisition and an intelligent accounting engine. By adopting standardized data processing procedures and intelligent accounting rules, it avoids errors in manual accounting and improves the accuracy and consistency of accounting results.

Description

Technical Field

[0001] This invention relates to the field of enterprise financial management technology, specifically to a data redistribution accounting system and method based on intelligent business rules. Background Technology

[0002] In the financial management of modern enterprise groups, cost accounting and data reallocation are crucial aspects. As enterprises grow in size and business complexity increases, traditional manual accounting methods can no longer meet their needs, primarily due to the following problems: (1) Traditional manual accounting methods require a lot of manpower and time, and cannot process massive amounts of business data, resulting in long accounting cycles and failure to provide timely support for enterprise decision-making.

[0003] (2) Manual accounting is prone to errors, especially when dealing with complex internal transactions and profit spread accounting, where calculation errors and data inconsistencies are likely to occur.

[0004] (3) Traditional accounting systems usually use fixed rules and cannot flexibly adapt to different business scenarios and management needs. When business rules change, a lot of system transformation work is required.

[0005] (4) Most existing accounting systems lack self-learning and optimization capabilities and cannot automatically optimize accounting rules based on historical data and business feedback, resulting in the inability to continuously improve accounting results.

[0006] (5) Traditional accounting systems usually only provide simple reports and lack multi-dimensional visualization and analysis capabilities, which cannot provide intuitive cost analysis and decision support for enterprise managers.

[0007] To address the aforementioned issues, we propose a data redistribution accounting system and method based on intelligent business rules. Summary of the Invention

[0008] The purpose of this invention is to provide a data redistribution accounting system and method based on intelligent business rules to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: A data redistribution accounting system based on intelligent business rules includes: The intelligent rule configuration module is used to configure multi-dimensional business accounting rules according to the group's cost management needs, including internal transaction average price calculation rules, realized profit price difference calculation rules, unrealized profit price difference calculation rules, and total cost calculation rules. The data acquisition and preprocessing module is used to collect internal transaction data, cost data, and financial data of the group, and to perform standardized processing, including data cleaning, format unification, and outlier detection. The intelligent accounting engine is used to perform data redistribution accounting based on preset business rules, including internal transaction average price calculation, realized profit price difference calculation, unrealized profit price difference calculation, and total cost calculation. The dynamic adjustment module is used to automatically optimize accounting rules based on real-time business data and historical accounting results, and to fine-tune the rule parameters using machine learning algorithms. The visualization module is used to display accounting results and cost analysis reports in chart form, and supports multi-dimensional data drill-down and analysis.

[0010] Preferably, the intelligent accounting engine specifically includes: Internal transaction average price calculation: Calculates the average transaction price between any two legal entities within the group; Realized profit price difference calculation: Calculate the average price based on the price differences of multiple revenue and gross profit statements for the same period, and determine the placement of the price difference based on the component replenishment strategy; Unrealized profit margin calculation: Unrealized profit margin is calculated based on the inventory balance sheet; Total Cost Calculation: Calculate the total cost of the group.

[0011] Preferably, the specific process of profit spread calculation implemented in the intelligent accounting engine is as follows: When the component replenishment strategy is procurement, the price difference is included in the raw material cost. When the component supply strategy is outsourcing, the price difference is included in the outsourcing fee.

[0012] Preferably, it also includes an anomaly detection and early warning module for real-time monitoring of anomalies during the accounting process, specifically including: Data anomaly detection: Detects the completeness, rationality, and consistency of input data; Anomaly detection in accounting results: By comparing historical data with preset thresholds, abnormal accounting results can be identified; Rule conflict detection: Detects conflicts and contradictions between configured business rules.

[0013] The data redistribution accounting method based on intelligent business rules includes the following steps: S1. Configure business accounting rules: Based on the group's cost management needs, configure the internal transaction average price calculation rules, realized profit price difference calculation rules, unrealized profit price difference calculation rules, and total cost calculation rules; S2. Data Acquisition and Preprocessing: Collect internal transaction data, cost data, and financial data of the group, and perform data cleaning, format standardization, and outlier detection; S3 Intelligent Accounting Processing: Data redistribution and accounting are performed based on preset business rules. The accounting method is as follows: S31. The specific formula for calculating the average price of internal transactions is as follows:

[0014] in, The average transaction price, For the first Sales revenue from this transaction For the first Sales volume of the transaction Number of transactions; S32. The specific formula for calculating realized profit margin is as follows:

[0015] in, For realized profit margin, The actual transaction price. The average transaction price; The specific formula for calculating unrealized profit margin is as follows:

[0016] in, For unrealized profit margin, For the first The actual price of the inventory The average transaction price, For the first The inventory quantity of this type of inventory, This refers to the number of inventory types. The specific formula for calculating the total cost is as follows:

[0017] in, Total group costs Operating costs, For the first The actual price of the transaction, The average transaction price, For the first Sales volume of the transaction The number of transactions involving price differences; S4. Dynamic rule optimization: Based on real-time business data and historical accounting results, machine learning algorithms are used to automatically optimize accounting rules; S5. Results Display and Analysis: Displays accounting results and cost analysis reports in chart form, supporting multi-dimensional data drill-down and analysis.

[0018] Preferably, in step S1, the cost model configuration for customizing the consolidation scope includes: A new mapping relationship table for legal entity business units has been added, which uses the relationships between entities to bring out the accounting rules. Without creating a new mapping table, enter constants in the association conditions to configure the rules.

[0019] Preferably, in step S4, the specific process of dynamic rule optimization is as follows: Establish a reward function for rule optimization, using accounting accuracy, computational efficiency, and business matching degree as evaluation indicators. The reward function is as follows:

[0020] in, , , For the weighting coefficients, satisfying , For the sake of accuracy, For computational efficiency, For business matching degree; Through multiple rounds of iterative training, the weights and thresholds of the rules are automatically adjusted; Enables manual intervention and rule locking.

[0021] Preferably, it also includes anomaly detection and early warning, employing 3D technology. The principle for anomaly detection is as follows:

[0022] when When this happens, the data is identified as abnormal. in, The mean of the data; The standard deviation of the data; For the first One data point; This represents the total number of data points. This represents the absolute deviation of the data point from the mean.

[0023] Compared with the prior art, the beneficial effects of the present invention are: This invention significantly improves the efficiency of group cost accounting, reduces manual workload, and shortens the accounting cycle through automated data acquisition and intelligent accounting engine.

[0024] This invention employs standardized data processing procedures and intelligent accounting rules, avoiding errors in manual accounting and improving the accuracy and consistency of accounting results.

[0025] This invention supports multi-dimensional business rule configuration, which can flexibly adapt to different business scenarios and management needs, and can be quickly adjusted when business rules change.

[0026] This invention, through dynamic adjustment modules and machine learning algorithms, can automatically optimize accounting rules based on historical data and business feedback, thereby continuously improving accounting performance.

[0027] This invention, through its visualization module, can display accounting results and cost analysis reports in an intuitive chart format, supporting multi-dimensional data drill-down and analysis, and providing powerful decision support for enterprise managers.

[0028] This invention, through an anomaly detection and early warning module, can monitor abnormal situations in the accounting process in real time, promptly detect and handle problems, and ensure the stability and reliability of the accounting process. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the data redistribution accounting system based on intelligent business rules according to the present invention; Figure 2 This is a flowchart illustrating the workflow of the intelligent accounting engine of this invention. Figure 3 This is a flowchart of the optimization algorithm for the dynamic adjustment module of the present invention; Figure 4 This is a flowchart of the data redistribution accounting method of the present invention. Detailed Implementation

[0030] 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.

[0031] Please see Figure 1 A data redistribution accounting system based on intelligent business rules includes: The system includes an intelligent rule configuration module, a data acquisition and preprocessing module, a dynamic adjustment module, a visualization module, and an anomaly detection and early warning module. The intelligent rule configuration module is used to configure multi-dimensional business accounting rules according to the group's cost management needs, including internal transaction average price calculation rules, realized profit spread calculation rules, unrealized profit spread calculation rules, and total cost calculation rules. The intelligent rule configuration module also enables the configuration of cost models for custom consolidation scopes, including: A new mapping relationship table for legal entity business units has been added, which uses the relationships between entities to bring out the accounting rules. Without creating a new mapping table, enter constants in the association conditions to configure the rules.

[0032] The data acquisition and preprocessing module is used to collect transaction data, cost data, and financial data within the group and perform standardized processing. The data acquisition and preprocessing module collects data from data sources such as the enterprise's ERP system, financial system, and business system through interface integration, and then performs preprocessing operations such as data cleaning, format unification, and outlier detection to ensure the quality and consistency of the input data.

[0033] The intelligent accounting engine is used to perform data redistribution accounting based on preset business rules, including internal transaction average price calculation, realized profit margin calculation, unrealized profit margin calculation, and total cost calculation. Specifically, it includes: Internal transaction average price calculation: Calculates the average transaction price between any two legal entities within the group; Realized profit price difference calculation: Calculate the average price based on the price differences of multiple revenue and gross profit statements for the same period, and determine the placement of the price difference based on the component replenishment strategy; Unrealized profit margin calculation: Unrealized profit margin is calculated based on the inventory balance sheet; Total Cost Calculation: Calculate the total cost of the group.

[0034] The dynamic adjustment module is used to automatically optimize accounting rules based on real-time business data and historical accounting results. It uses machine learning algorithms to fine-tune rule parameters. At the same time, the dynamic adjustment module enables manual intervention and rule locking. Administrators can manually adjust rules or lock certain key rules according to business needs.

[0035] The visualization module displays accounting results and cost analysis reports in chart format, supporting multi-dimensional data drill-down and analysis. It also provides a rich set of visualization components, including bar charts, line charts, pie charts, and heatmaps, which intuitively present accounting results and cost analysis data. Furthermore, this module supports multi-dimensional data drill-down and analysis, allowing users to click on different dimensions in the charts to view detailed data information.

[0036] The anomaly detection and early warning module is used to monitor anomalies in the accounting process in real time, specifically including: Data anomaly detection: Detects the completeness, rationality, and consistency of input data; Anomaly detection in accounting results: By comparing historical data with preset thresholds, abnormal accounting results can be identified; Rule conflict detection: Detects conflicts and contradictions between configured business rules.

[0037] Please see Figure 2 As shown, the workflow of the intelligent accounting engine of this invention is as follows: Data input: Receives pre-processed intra-group transaction data, cost data, and financial data; Rule matching: Matching corresponding accounting rules based on the business characteristics of the input data; Internal transaction average price calculation: The average transaction price between any two legal entities within the group, using the following formula:

[0038] in, The average transaction price, For the first Sales revenue from this transaction For the first Sales volume of the transaction Number of transactions; Realized profit spread calculation: The average price is calculated based on the price differences among multiple revenue and gross profit statements for the same period, and the placement of the price spread is determined according to the component replenishment strategy. The specific formula is as follows:

[0039] in, For realized profit margin, The actual transaction price. The average transaction price; Unrealized profit margin calculation: The unrealized profit margin is calculated based on the inventory balance sheet. The specific formula is as follows:

[0040] in, For unrealized profit margin, For the first The actual price of the inventory The average transaction price, For the first The inventory quantity of this type of inventory, This refers to the number of inventory types. Total Cost Calculation: The total cost of the group is calculated according to the formula, the specific formula is as follows:

[0041] in, Total group costs Operating costs, For the first The actual price of the transaction, The average transaction price, For the first Sales volume of the transaction The number of transactions involving price differences; Output results: Output the accounting results, including the average internal transaction price, realized profit margin, unrealized profit margin, and total group costs.

[0042] Please see Figure 3 As shown, the optimization algorithm flow of the dynamic adjustment module of this invention is as follows: Data collection: Collect historical accounting data and business feedback data; Feature extraction: Extracting key features from the collected data, including accounting accuracy, computational efficiency, and business matching degree; Reward function construction: The reward function for rule optimization is as follows:

[0043] in, , , For the weighting coefficients, satisfying , For the sake of accuracy, For computational efficiency, For business matching degree; Reinforcement learning training: The reinforcement learning algorithm is used for multiple rounds of iterative training, automatically adjusting the weights and thresholds of the rules to maximize the reward function value; Rule update: Update the accounting rules based on the training results.

[0044] Effect evaluation: Evaluate the effect of the updated rules. If the preset optimization goal is met, stop training; otherwise, continue to the next round of training.

[0045] Please see Figure 4 As shown, the data redistribution accounting method based on intelligent business rules includes the following steps: S1. Configure business accounting rules: Based on the group's cost management needs, configure the internal transaction average price calculation rules, realized profit price difference calculation rules, unrealized profit price difference calculation rules, and total cost calculation rules; S2. Data Acquisition and Preprocessing: Collect internal transaction data, cost data, and financial data of the group, and perform data cleaning, format standardization, and outlier detection; S3 Intelligent Accounting Processing: Data redistribution and accounting are performed based on preset business rules. The accounting method is as follows: S31. The specific formula for calculating the average price of internal transactions is as follows:

[0046] in, The average transaction price, For the first Sales revenue from this transaction For the first Sales volume of the transaction Number of transactions; S32. The specific formula for calculating realized profit margin is as follows:

[0047] in, For realized profit margin, The actual transaction price. The average transaction price; The specific formula for calculating unrealized profit margin is as follows:

[0048] in, For unrealized profit margin, For the first The actual price of the inventory The average transaction price, For the first The inventory quantity of this type of inventory, This refers to the number of inventory types. The specific formula for calculating the total cost is as follows:

[0049] in, Total group costs Operating costs, For the first The actual price of the transaction, The average transaction price, For the first Sales volume of the transaction The number of transactions involving price differences; S4. Dynamic rule optimization: Based on real-time business data and historical accounting results, machine learning algorithms are used to automatically optimize accounting rules; S5. Results Display and Analysis: Displays accounting results and cost analysis reports in chart form, supporting multi-dimensional data drill-down and analysis.

[0050] S6. Anomaly Detection and Early Warning: This module monitors anomalies in the accounting process in real time, employs the 3σ principle for anomaly detection, identifies abnormal data, and issues early warnings. The principle for anomaly detection is as follows:

[0051] when When this happens, the data is identified as abnormal. in, The mean of the data; The standard deviation of the data; For the first One data point; This represents the total number of data points. This represents the absolute deviation of the data point from the mean.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data redistribution accounting system based on intelligent business rules, characterized in that, include: The intelligent rule configuration module is used to configure multi-dimensional business accounting rules according to the group's cost management needs, including internal transaction average price calculation rules, realized profit price difference calculation rules, unrealized profit price difference calculation rules, and total cost calculation rules. The data acquisition and preprocessing module is used to collect internal transaction data, cost data, and financial data of the group, and to perform standardized processing, including data cleaning, format unification, and outlier detection. The intelligent accounting engine is used to perform data redistribution accounting based on preset business rules, including internal transaction average price calculation, realized profit price difference calculation, unrealized profit price difference calculation, and total cost calculation. The dynamic adjustment module is used to automatically optimize accounting rules based on real-time business data and historical accounting results, and to fine-tune the rule parameters using machine learning algorithms. The visualization module is used to display accounting results and cost analysis reports in chart form, and supports multi-dimensional data drill-down and analysis.

2. The data redistribution accounting system based on intelligent business rules according to claim 1, characterized in that, The intelligent accounting engine specifically includes: Internal transaction average price calculation: Calculates the average transaction price between any two legal entities within the group; Realized profit price difference calculation: Calculate the average price based on the price differences of multiple revenue and gross profit statements for the same period, and determine the placement of the price difference based on the component replenishment strategy; Unrealized profit margin calculation: Unrealized profit margin is calculated based on the inventory balance sheet; Total Cost Calculation: Calculate the total cost of the group.

3. The data redistribution accounting system based on intelligent business rules according to claim 1, characterized in that, The specific process of profit spread calculation implemented in the intelligent accounting engine is as follows: When the component replenishment strategy is procurement, the price difference is included in the raw material cost. When the component supply strategy is outsourcing, the price difference is included in the outsourcing fee.

4. The data redistribution accounting system based on intelligent business rules according to claim 1, characterized in that, It also includes an anomaly detection and early warning module, used to monitor abnormal situations in the accounting process in real time, specifically including: Data anomaly detection: Detects the completeness, rationality, and consistency of input data; Anomaly detection in accounting results: By comparing historical data with preset thresholds, abnormal accounting results can be identified; Rule conflict detection: Detects conflicts and contradictions between configured business rules.

5. A data redistribution accounting method based on intelligent business rules, characterized in that, Includes the following steps: S1. Configure business accounting rules: Based on the group's cost management needs, configure the internal transaction average price calculation rules, realized profit price difference calculation rules, unrealized profit price difference calculation rules, and total cost calculation rules; S2. Data Acquisition and Preprocessing: Collect internal transaction data, cost data, and financial data of the group, and perform data cleaning, format standardization, and outlier detection; S3 Intelligent Accounting Processing: Data redistribution and accounting are performed based on preset business rules. The accounting method is as follows: S31. The specific formula for calculating the average price of internal transactions is as follows: ; in, The average transaction price, For the first Sales revenue from this transaction For the first Sales volume of the transaction Number of transactions; S32. The specific formula for calculating realized profit margin is as follows: ; in, For realized profit margin, The actual transaction price. The average transaction price; The specific formula for calculating unrealized profit margin is as follows: ; in, For unrealized profit margin, For the first The actual price of the inventory The average transaction price, For the first The inventory quantity of this type of inventory, This refers to the number of inventory types. The specific formula for calculating the total cost is as follows: ; in, Total group costs Operating costs, For the first The actual price of the transaction, The average transaction price, For the first Sales volume of the transaction The number of transactions involving price differences; S4. Dynamic rule optimization: Based on real-time business data and historical accounting results, machine learning algorithms are used to automatically optimize accounting rules; S5. Results Display and Analysis: Displays accounting results and cost analysis reports in chart form, supporting multi-dimensional data drill-down and analysis.

6. The data redistribution accounting method based on intelligent business rules according to claim 5, characterized in that, In step S1, the cost model configuration for custom merging scope includes: A new mapping relationship table for legal entity business units has been added, which uses the relationships between entities to bring out the accounting rules. Without creating a new mapping table, enter constants in the association conditions to configure the rules.

7. The data redistribution accounting method based on intelligent business rules according to claim 5, characterized in that, In step S4, the specific process of dynamic rule optimization is as follows: Establish a reward function for rule optimization, using accounting accuracy, computational efficiency, and business matching degree as evaluation indicators. The reward function is as follows: ; in, , , For the weighting coefficients, satisfying , For the sake of accuracy, For computational efficiency, For business matching degree; Through multiple rounds of iterative training, the weights and thresholds of the rules are automatically adjusted; Enables manual intervention and rule locking.

8. The data redistribution accounting method based on intelligent business rules according to claim 5, characterized in that, It also includes anomaly detection and early warning, using 3D technology. The principle for anomaly detection is as follows: ; when When this happens, the data is identified as abnormal. in, The mean of the data; The standard deviation of the data; For the first One data point; This represents the total number of data points. This represents the absolute deviation of the data point from the mean.

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