Intelligent calculation method for receivable debts based on dynamic data aggregation

Abstract

The invention relates to an intelligent calculation method for receivable debts based on dynamic data aggregation. The method comprises the following steps: calling a dynamic data acquisition and integration module, obtaining various preset types of receipts in real time, establishing a data association relationship, and aggregating dispersed receipt data; calling a dynamic algorithm scheduling engine, and dynamically calling an adaptive calculation model according to the real-time state of the document; and calling a mixed data processing module, and when it is detected that the preset rule is updated, recalculating the historical data of the document to ensure the continuity and integrity of the data. According to the invention, real-time collection and processing of multi-type document data are realized, real-time calculation of receivable debts is carried out according to a document state accurate scheduling algorithm, and timeliness and accuracy of the data are ensured; hybrid calculation of historical data and optimized data is supported, data coherence and integrity are guaranteed, and powerful support is provided for long-term financial analysis; and the overall operation management efficiency can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of Enterprise Resource Planning (ERP) technology, specifically a method for intelligent calculation of accounts receivable based on dynamic data aggregation. Background Technology

[0002] In current Enterprise Resource Planning (ERP) systems, the accounts receivable module faces several pressing technical challenges. First, business document data is scattered across different systems or modules, resulting in significant data inconsistencies and low query efficiency. This data fragmentation not only increases the difficulty of data integration but also severely impacts subsequent data analysis and decision support. Sales orders, return orders, advance payment orders, and receipt orders, among other documents, operate independently, requiring the integration of this multi-source data for accounts receivable calculation, making the calculation logic exceptionally complex.

[0003] Secondly, the existing accounts receivable processing flow lacks coherence, with poor information flow between different business stages, making it prone to data loss or errors, further increasing the complexity of accounts receivable calculation. This disjointed process not only reduces work efficiency but also increases the risk of errors.

[0004] Secondly, accounts receivable information is scattered across multiple business documents and systems, lacking centralized display and management. This makes it difficult for finance personnel to have a comprehensive and accurate grasp of the outstanding balances, causing significant challenges to the company's financial management.

[0005] Furthermore, the existing accounts receivable reconciliation mechanism is not clear enough, and errors and omissions are prone to occur during the reconciliation process, seriously affecting the accuracy and timeliness of accounts receivable. This unclear reconciliation mechanism not only increases the workload of finance personnel, but may also lead to an increase in corporate financial risk.

[0006] Currently, relying on manual operation for accounts receivable management is not only inefficient but also prone to errors. With the rapid development of business operations, this traditional manual calculation method can no longer meet the needs of modern enterprises for fast and accurate accounts receivable management.

[0007] As business continues to grow, integrating accounts receivable reconciliation data from multiple channels becomes increasingly inconvenient. The calculation rules for different types of documents vary across different business stages and are interconnected, further increasing the computational complexity. Simultaneously, the lack of unified standards for processing historical and optimized data hinders a smooth transition and effective utilization of data, severely impacting the company's accurate analysis and decision-making regarding financial data.

[0008] The information disclosed in this background section is intended only to enhance the understanding of the general background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0009] To address the shortcomings of existing technologies, the present invention aims to provide an intelligent accounts receivable calculation method based on dynamic data aggregation. This method enables real-time collection and processing of data from multiple types of documents, and performs real-time accounts receivable calculation based on a precise scheduling algorithm according to document status, ensuring the timeliness and accuracy of the data. It also supports mixed calculation of historical and optimized data, ensuring data continuity and integrity, and providing strong support for long-term financial analysis. This method can effectively improve overall operational management efficiency.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0011] A method for intelligent calculation of accounts receivable based on dynamic data aggregation, characterized in that it includes:

[0012] The system calls the dynamic data acquisition and integration module to acquire various preset types of documents in real time. Through data cleaning and conversion technology, it unifies the data format, establishes data relationships, and aggregates scattered document data into an organic whole, providing a comprehensive and consistent data foundation for accounts receivable calculation.

[0013] The dynamic algorithm scheduling engine is invoked to obtain the status identifier in the document to determine the current business stage. Corresponding accounts receivable calculation models are built for different business stages. The appropriate calculation model is dynamically invoked according to the real-time status of the document to ensure the accuracy and timeliness of accounts receivable calculation.

[0014] The system calls the hybrid data processing module to obtain historical data from the input documents. It then performs data transformation and calculation processing according to preset rules for different business stages. When an update to the preset rules is detected, a recalculation operation is triggered to recalculate the historical data of the documents according to the updated preset rules, ensuring the continuity and integrity of the data and providing long-term and reliable data support for accounts receivable calculation.

[0015] Based on the above technical solution, the various preset document types include: sales order, return order, advance payment order, payment order, customer expense order, and customer accounts receivable initialization order.

[0016] Based on the above technical solution, status indicators are included in both sales orders and return orders;

[0017] The business stages of the sales order are divided into: not yet shipped, shipped but not signed for, signed for but not paid for, and paid for.

[0018] The business stages of the return order are divided into: unapproved, approved but not returned, returned but not put into storage, and put into storage.

[0019] Based on the above technical solution, preset rules are created for data conversion and calculation processing for sales orders that have been shipped but not signed for, signed for but not paid for, and for return orders that have not been reviewed, have been recovered but not put into storage, and have been put into storage.

[0020] Based on the above technical solutions, it also includes:

[0021] The system invokes the overdue payment risk control and early warning module to obtain the customer file corresponding to the document. Based on the maximum overdue payment limit, cumulative overdue payment amount, and overdue payment information in the file, an early warning mechanism is triggered when the overdue payment reaches or exceeds the risk threshold, and early warning information is pushed to relevant personnel.

[0022] Based on the above technical solution, the risk threshold in the debt risk control and early warning module is set according to the company's financial policy and historical data;

[0023] Outstanding payments are obtained by real-time tracking of uncollected amounts for customers' unsigned orders;

[0024] The aforementioned early warning mechanism adopts a tiered early warning strategy, classifying early warnings into mild, moderate, and severe levels based on the degree of debt risk. Early warning information is pushed to relevant personnel using different color indicators and message reminders.

[0025] Based on the above technical solutions, it also includes:

[0026] Build a universal cashier system that integrates multiple payment methods and maintains preferential policy information to achieve unified payment for collection business;

[0027] Build an accounts receivable data center to centrally display the following key information: customer profiles, documents, amount receivable, amount received, amount outstanding, and settlement status.

[0028] Based on the above technical solutions, key information is displayed centrally in the form of visual charts or reports.

[0029] Based on the above technical solution, the various payment methods include: cash, bank card, and online payment;

[0030] The discount strategy information includes: full order discount strategy and permissive discount strategy.

[0031] Based on the above technical solutions, it also includes:

[0032] The dynamic algorithm scheduling engine checks whether there is a correlation between sales orders or return orders and advance payment orders or receipts. When a correlation exists, the dynamic algorithm scheduling engine performs the following comprehensive calculation based on the status and amount of the related documents:

[0033] When a sales order is in the stage of signed but not yet paid, if there are approved advance payment orders, the advance payments will be written off first, and then the outstanding amount will be calculated.

[0034] When the business stage of a return order is "received but not yet put into storage", if there is a corresponding prepayment order, it will be processed according to the rules for prepayment to refund.

[0035] The intelligent calculation method for accounts receivable based on dynamic data aggregation described in this invention has the following beneficial effects:

[0036] 1. Through dynamic data aggregation, it can collect and process multi-type document data in real time. Based on different document statuses (such as different payment stages of sales orders or different processing stages of return orders), it can schedule corresponding algorithm frameworks to achieve real-time intelligent calculation of accounts receivable. After a sales order is signed for, it can accurately calculate the outstanding amount based on real-time data such as received amount and amount receivable, providing enterprises with timely and accurate financial status information.

[0037] 2. Supports mixed calculation logic for historical and optimized data. Clear processing rules have been established for historical data such as sales orders and return orders in different states, ensuring that historical data can be integrated into the new calculation system and guaranteeing data consistency and integrity. After system upgrades or changes in business rules, historical data can be recalculated according to the new rules, providing support for enterprises to analyze long-term financial conditions.

[0038] 3. Build a universal cashier system, optimize settlement functions, and enhance the continuity of operational processes. This makes it more convenient and efficient for employees to handle business, reducing operational errors. A unified accounts receivable reconciliation function and clear data display facilitate the analysis and management of financial data, helping companies to adjust business strategies in a timely manner, rationally plan funds, and improve overall operational management efficiency. Attached Figure Description

[0039] The present invention includes the following figures:

[0040] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein:

[0041] Figure 1 The flowchart is a first embodiment of the intelligent calculation method for accounts receivable based on dynamic data aggregation described in this invention. Detailed Implementation

[0042] The present invention will be further described in detail below with reference to the accompanying drawings. This detailed description is an illustration in conjunction with exemplary embodiments of the invention, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0043] like Figure 1 As shown, the intelligent calculation method for accounts receivable based on dynamic data aggregation according to the present invention includes:

[0044] The system calls the dynamic data acquisition and integration module to acquire various preset types of documents in real time. Through data cleaning and conversion technology, it unifies the data format, establishes data relationships, and aggregates scattered document data into an organic whole, providing a comprehensive and consistent data foundation for accounts receivable calculation.

[0045] For example, the various preset document types include: sales order, return order, advance payment order, payment receipt, customer expense order, and customer accounts receivable initialization order;

[0046] For example, the data cleaning includes: calculating the hash value of the document, finding duplicate documents by comparing the hash values ​​and deleting them;

[0047] For example, the data conversion includes: mapping field information with the same meaning but different field names to preset standard field names; converting monetary data in different formats into standard currency cells and using preset currency precision.

[0048] For example, the establishment of data associations uses the customer ID, which uniquely identifies the customer, as the key field to perform first-level data association for various preset types of documents; and uses the core identifier field in the documents to perform second-level data association for various preset types of documents; for example:

[0049] The sales order number is used as the core identifier field to perform a second-level data association between sales orders, advance payment orders, and receipt orders;

[0050] The dynamic algorithm scheduling engine is invoked to obtain the status identifier in the document to determine the current business stage. Corresponding accounts receivable calculation models are built for different business stages. The appropriate calculation model is dynamically invoked according to the real-time status of the document to ensure the accuracy and timeliness of accounts receivable calculation.

[0051] For example, both sales orders and return orders include status indicators;

[0052] The business stages of the sales order are divided into: not yet shipped, shipped but not signed for, signed for but not paid for, and paid for.

[0053] The business stages of the return order are divided into: unapproved, approved but not returned, returned but not put into storage, and put into storage.

[0054] For example, for the pre-shipment stage, the calculation model of "accounts receivable = order amount - full order discount" is used; for the pre-shipment stage, the calculation model of "accounts receivable = shipment amount" is used; for the pre-shipment stage, the calculation model of "accounts receivable = signed amount" is used; and for the pre-shipment stage, the calculation model of calculating the outstanding amount based on the amount received and the accounts receivable is used.

[0055] For example, for the unapproved business stage, the calculation model is "Refundable amount = Order amount (absolute value) + Refundable discount"; for the approved but not yet recovered business stage, the calculation model is "Refundable amount = Order amount (absolute value) + Refundable discount"; for the recovered but not yet put into inventory stage, the calculation model is "Refundable amount = Recovery amount"; for the inventory stage, the calculation model is "Refundable amount = Inventory amount" and the unrefunded amount is calculated accordingly.

[0056] The following is another example of an accounts receivable calculation model that can be built for different business stages:

[0057] During the pre-shipment stage, the accounts receivable calculation model is the order amount minus the whole order discount, while also taking into account the impact of changes in the quantity and unit price of goods in the order on the order amount;

[0058] During the stage of goods being shipped but not yet signed for, the outbound cost and quantity of the goods are obtained by combining the inventory management system, the outbound amount is calculated, and the impact of possible losses or price adjustments on the outbound amount is considered.

[0059] During the stage of signed but unpaid goods, the signed amount is calculated based on the quantity of signed goods and the actual sales price, while also taking into account the impact of possible discounts or promotional activities on the signed amount;

[0060] When calculating the outstanding amount after payment has been received, in addition to considering the amount received and the amount due, it is also necessary to consider the impact of possible refunds, handling fees and other factors on the outstanding amount.

[0061] During the unapproved stage, the refund amount calculation model considers the order amount (absolute value) plus the refundable discount, while also taking into account the impact of the reason for return on the discount policy;

[0062] In the stage where the goods have been reviewed but not yet recovered, the recovery amount is estimated by combining logistics information and the expected recovery value of the goods, taking into account the impact of damage to the goods during transportation on the recovery amount.

[0063] During the stage of recycling but not yet put into storage, the recycling amount is assessed based on the actual quantity and quality of the recycled goods, taking into account the impact of depreciation or damage of the recycled goods on the recycling amount;

[0064] Once the goods have been received, the receiving amount is calculated by combining warehouse receiving records and product costs, taking into account the impact of changes in inventory costs on the receiving amount.

[0065] The system calls the hybrid data processing module to obtain the historical data of the input documents. It performs data transformation and calculation processing according to preset rules for different business stages. When the preset rules are updated, a recalculation operation is triggered to recalculate the historical data of the documents according to the updated preset rules, ensuring the continuity and integrity of the data and providing long-term and reliable data support for accounts receivable calculation.

[0066] For example, preset rules are created for data transformation and calculation processing for sales orders that are outbound but not signed for, signed for but not paid for, and for return orders that are unapproved, collected but not put into storage, and put into storage; for example:

[0067] Pre-defined rules for the "Outbound but Not Signed For" stage: During this stage, if product quality issues arise that prevent some items from being sold normally, the outbound amount will be adjusted based on the severity of the quality problem and relevant agreements. For minor quality issues, a certain percentage discount will be given, and the adjusted outbound amount = original outbound amount * (1 - discount percentage). If the problem seriously affects sales, the outbound amount will be deducted from the outbound amount. Simultaneously, relevant information about the quality problem, such as problem description and handling method, will be recorded for subsequent querying and statistical analysis.

[0068] Pre-set rules for the "Signed but Not Paid" stage: If there is any overdue payment after the customer has signed for the package, overdue interest will be calculated based on the number of overdue days and the overdue interest rate stipulated in the contract, and the overdue interest will be included in the amount receivable; Amount receivable = Amount signed for + Overdue interest; At the same time, a credit assessment will be conducted on overdue customers, and if the number of overdue payments exceeds a certain threshold, the credit limit and payment policy for the customer's subsequent orders will be adjusted;

[0069] Pre-defined rules for the unreviewed stage: During the unreviewed stage, if the return order information is found to be incomplete or does not comply with the return policy, such as if the return time exceeds the specified period, the system will automatically prompt the user to supplement the information or reject the return application; if the return order meets the basic requirements, but the product has special circumstances, such as the product being customized, the system will determine whether the return is allowed and how to calculate the refund amount according to the customization contract; if the return is allowed, the refund amount may need to deduct certain customization costs.

[0070] Pre-set rules for the "recovered but not yet put into storage" stage: After the goods are recovered, the recovered goods will be inspected for quality; if the quality of the goods meets the requirements, the refund amount will be calculated according to the original return order; if the goods are damaged or show signs of use, the refund amount will be adjusted accordingly based on the degree of damage and market value assessment; the adjusted refund amount = original refund amount * (1 - damage degree coefficient); at the same time, the damage of the goods will be recorded for subsequent traceability and analysis.

[0071] Pre-defined rules for the warehousing stage: If the actual quantity received does not match the return order record during warehousing, it will be handled according to the inventory count results and relevant procedures; if the actual quantity received is less than the return order record, the reason must be investigated. If the goods are lost or damaged during transportation, the warehousing amount will be calculated based on the actual quantity received, and relevant responsibilities will be pursued; if the actual quantity received is more than the return order record, the source of the excess goods must be verified, and the warehousing amount and relevant records will be adjusted after confirmation.

[0072] For example, historical data of documents is stored in a historical database table. When the system detects an update to the preset rules, it writes a data migration script to perform batch calculations and updates on the historical data according to the new rules.

[0073] For example, when new real-time data is inserted into the data warehouse, it is processed according to the currently valid preset rules to achieve mixed calculation of historical data and real-time data.

[0074] Based on the above technical solution, data acquisition triggers are set in the database tables of each business system. By capturing the addition, modification and deletion operations of document data in real time, the raw data is extracted using ETL (Extract, Transform, Load) tools.

[0075] According to the preset data cleaning rules, remove duplicate data and correct erroneous data formats;

[0076] Perform first-level data association on various preset document types based on customer number;

[0077] Second-level data association is performed on various preset data types of documents based on the core identifier fields in the documents;

[0078] Store the data in a unified data warehouse to achieve document data aggregation.

[0079] Based on the above technical solutions, it also includes:

[0080] The system invokes the overdue payment risk control and early warning module to obtain the customer file corresponding to the document. Based on the maximum overdue payment limit, cumulative overdue payment amount, and overdue payment information in the file, an early warning mechanism is triggered when the overdue payment reaches or exceeds the risk threshold, and early warning information is pushed to relevant personnel.

[0081] For example, in the debt risk control and early warning module, the risk threshold is set according to the company's financial policies and historical data;

[0082] For example, outstanding payments are obtained by real-time statistics of the uncollected amount of customers' unsigned orders;

[0083] For example, the early warning mechanism adopts a tiered early warning strategy, which divides the risk of default into mild, moderate and severe warnings, and pushes early warning information to relevant personnel with different color codes and message reminders.

[0084] Based on the above technical solutions, it also includes:

[0085] Build a universal cashier system that integrates multiple payment methods and maintains preferential policy information to achieve unified payment for collection business;

[0086] For example, the various payment methods include: cash, bank cards, and online payments;

[0087] For example, the discount strategy information includes: full order discount strategy and discount strategy;

[0088] Build an accounts receivable data center to centrally display the following key information: customer profiles, documents, amount receivable, amount received, amount outstanding, and settlement status;

[0089] For example, key information can be centrally displayed in the form of visual charts or reports; for instance, bar charts can be used to compare the amount of accounts receivable from different customers, line charts can be used to show the changing trend of accounts receivable from a single customer, and reports can be used to list in detail the source of each account receivable, amount, settlement status, and other information, making it easier for enterprises to conduct data analysis and decision-making.

[0090] Furthermore, by connecting with a payment gateway, secure processing of the payment process can be achieved.

[0091] Furthermore, the payment details and promotional information are displayed on the checkout interface, allowing users to clearly understand their payment status.

[0092] The general cashier interacts with the dynamic data collection and integration module. After the payment operation is completed, the payment information (such as payment order number, payment amount, payment time, payment method, etc.) is transmitted to the dynamic data collection and integration module in real time. This module associates and stores this data with the corresponding sales order, return order, etc.

[0093] The universal cashier obtains information such as the current order's due amount and discount amount from the dynamic algorithm scheduling engine, which is used to display to the user and perform payment calculations. When calculating the discount strategy, the universal cashier sends the order information to the dynamic algorithm scheduling engine, which calculates the actual discount amount according to the discount strategy rules, and then returns it to the universal cashier for display and payment processing.

[0094] The universal checkout also interacts with the accounts receivable data center, synchronizing successful payment information to the data center to update data such as the amount receivable, the amount received, and the amount not yet received.

[0095] Based on the above technical solutions, it also includes:

[0096] The dynamic algorithm scheduling engine checks whether there is a correlation between sales orders or return orders and advance payment orders or receipts. When a correlation exists, the dynamic algorithm scheduling engine performs the following comprehensive calculation based on the status and amount of the related documents:

[0097] When a sales order is in the stage of signed but not yet paid, if there are approved advance payment orders, the advance payments will be written off first, and then the outstanding amount will be calculated.

[0098] When the business stage of a return order is "received but not yet put into storage", if there is a corresponding prepayment order, it will be processed according to the rules for prepayment to refund.

[0099] As one possible implementation plan, when a sales order is in the pre-shipment stage, if a prepayment note exists and its amount exceeds the current order amount, the excess amount can be frozen according to business rules for subsequent orders or other business. If the prepayment note amount is less than the current order amount, the usage of the prepayment note is recorded, and the used amount is deducted from the accounts receivable when calculating the receivable amount later. Once a sales order has been paid for, if an error in the payment amount is found and a refund is needed, it is first determined whether the sales order is associated with a prepayment note and a payment receipt note. If they are associated, the refund is first deducted from the prepayment note; if the prepayment note amount is insufficient, the refund is then deducted from the received payments. After the refund, the dynamic algorithm scheduling engine recalculates the outstanding amount and updates the relevant data.

[0100] As one of the alternative implementation schemes, during the unapproved stage of the return order, if a prepayment note exists and its amount is sufficient to cover the refund amount due for the return, the prepayment can be directly converted into a refund, and the status and amount information of the prepayment note and the return order will be updated. If the prepayment note amount is insufficient, its usage will be recorded, and further processing will be carried out according to the actual situation after the return is approved. Once the return order has been received into inventory, if a further refund is required (e.g., if other issues are found with the goods requiring a further refund), first check if there is a corresponding prepayment note or receipt. If a prepayment note exists, the refund amount will be deducted from it first; if no prepayment note exists, the refund will be deducted from settled payments or subsequent receivables. Simultaneously, the dynamic algorithm scheduling engine will recalculate the outstanding amount and the refunded amount, and update the relevant records.

[0101] Based on the above technical solution, it also includes: a special business scenario calculation module, which is used to combine the amounts of sales orders and return orders when combining them into one order. The calculation is performed according to the rule of "Amount receivable / Amount refundable = Sales amount - Whole order discount - Return amount" to ensure that the calculation results are consistent with the actual business situation.

[0102] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0103] The above description is only a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those skilled in the art based on the content disclosed in the present invention should be included in the scope of protection set forth in the claims.

Claims

1. A method for intelligent calculation of accounts receivable based on dynamic data aggregation, characterized in that, include: The system calls the dynamic data acquisition and integration module to acquire various preset types of documents in real time. Through data cleaning and conversion technology, it unifies the data format, establishes data relationships, and aggregates scattered document data into an organic whole, providing a comprehensive and consistent data foundation for accounts receivable calculation. The dynamic algorithm scheduling engine is invoked to obtain the status identifier in the document to determine the current business stage. Corresponding accounts receivable calculation models are built for different business stages. The appropriate calculation model is dynamically invoked according to the real-time status of the document to ensure the accuracy and timeliness of accounts receivable calculation. The system calls the hybrid data processing module to obtain historical data from the input documents. It then performs data transformation and calculation processing according to preset rules for different business stages. When an update to the preset rules is detected, a recalculation operation is triggered to recalculate the historical data of the documents according to the updated preset rules, ensuring the continuity and integrity of the data and providing long-term and reliable data support for accounts receivable calculation.

2. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 1, characterized in that, The various preset document types include: sales order, return order, advance payment order, payment receipt, customer expense order, and customer accounts receivable initialization order.

3. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 2, characterized in that, Both sales orders and return orders include status indicators; The business stages of the sales order are divided into: not yet shipped, shipped but not signed for, signed for but not paid for, and paid for. The business stages of the return order are divided into: unapproved, approved but not returned, returned but not put into storage, and put into storage.

4. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 2, characterized in that, For sales orders that have been shipped but not signed for, or signed for but not paid for, and for return orders that have not been approved, have been recovered but not put into storage, or have been put into storage, create preset rules for data transformation and calculation processing respectively.

5. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 1, characterized in that, Also includes: The system invokes the overdue payment risk control and early warning module to obtain the customer file corresponding to the document. Based on the maximum overdue payment limit, cumulative overdue payment amount, and overdue payment information in the file, an early warning mechanism is triggered when the overdue payment reaches or exceeds the risk threshold, and early warning information is pushed to relevant personnel.

6. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 5, characterized in that, In the aforementioned debt risk control and early warning module, the risk threshold is set based on the company's financial policies and historical data; Outstanding payments are obtained by real-time tracking of uncollected amounts for customers' unsigned orders; The aforementioned early warning mechanism adopts a tiered early warning strategy, classifying early warnings into mild, moderate, and severe levels based on the degree of debt risk. Early warning information is pushed to relevant personnel using different color indicators and message reminders.

7. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 1, characterized in that, Also includes: Build a universal cashier system that integrates multiple payment methods and maintains preferential policy information to achieve unified payment for collection business; Build an accounts receivable data center to centrally display the following key information: customer profiles, documents, amount receivable, amount received, amount outstanding, and settlement status.

8. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 7, characterized in that, Key information is presented in a centralized manner using visual charts or reports.

9. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 7, characterized in that, The various payment methods include: cash, bank cards, and online payments; The discount strategy information includes: full order discount strategy and permissive discount strategy.

10. The intelligent calculation method for accounts receivable based on dynamic data aggregation as described in claim 2, characterized in that, Also includes: The dynamic algorithm scheduling engine checks whether there is a correlation between sales orders or return orders and advance payment orders or receipts. When a correlation exists, the dynamic algorithm scheduling engine performs the following comprehensive calculation based on the status and amount of the related documents: When a sales order is in the stage of signed but not yet paid, if there are approved advance payment orders, the advance payments will be written off first, and then the outstanding amount will be calculated. When the business stage of a return order is "received but not yet put into storage", if there is a corresponding prepayment order, it will be processed according to the rules for prepayment to refund.

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