Business full-link dynamic salary calculation method and system based on AI rule self-adaption

Abstract

The application discloses a kind of based on AI rule self-adapting business full-link dynamic salary calculation method and system.The application obtains full-link data by two-way linkage data acquisition module and detects integrity reversely;Using feature extraction model, orders are classified into existing scene or cluster new scene;Through AI rule self-adapting engine, scene and rule association is established, and scene-based salary calculation sub-rule is autonomously matched;After fingerprint and logic cross verification by multi-source heterogeneous module, dynamic salary calculation is carried out by AI engine;For the result of exceeding abnormal threshold, reverse backtracking self-correction or hierarchical early warning is started, and rule autonomous iteration is completed.The application breaks through static solidified salary calculation framework, realizes business scene intelligent adaptation, cross-domain data automatic verification and full-process closed-loop self-correction, and significantly improves salary calculation accuracy and automation level.

Description

Technical Field

[0001] This invention belongs to the field of enterprise information management technology, and more specifically, relates to a dynamic payroll calculation method and system for the entire business chain based on AI rule adaptation. Background Technology

[0002] In the current field of enterprise business collaboration and payroll data processing, most mainstream IT-based payroll solutions adopt a technical model of static rule presets plus manual intervention adjustments. The usual practice is that the human resources department first presets payroll rules in the payroll system based on the company's fixed product lines and business types, and then enters them into the system. The system can only perform simple mathematical calculations on fixed business and financial data according to these static preset rules.

[0003] However, business models are highly dynamic. Existing technologies suffer from the following core technical deficiencies, which are issues at the underlying system architecture level and cannot be resolved through simple management process optimization or system module assembly:

[0004] 1. The payroll calculation rules are static and fixed, lacking self-adaptability: When enterprises encounter dynamic scenarios such as the addition or adjustment of product lines, temporary business incentives, or special order types (such as large customized orders, cross-regional joint orders, etc.), the system cannot respond, and each adjustment requires manual reconfiguration. This not only leads to low system response efficiency, but also makes it extremely easy for human error to cause rule configuration errors, resulting in serious deviations in the final payroll calculation results.

[0005] Second, the payroll rules are not precisely linked to the business scenarios: no dedicated payroll templates have been designed for different business scenarios, and mismatches between business scenarios and payroll rules are very likely to occur during manual configuration.

[0006] Third, the payroll data verification method is single-source and single-dimensional: the existing system only checks the correctness of the data entry format, without cross-validating the logical relationships and authenticity between cross-domain business data, delivery data, and financial data. Once logically mutually exclusive errors occur in the underlying business, such as delivery not being completed but payment being received by finance, or an order type being a renewal but payroll being calculated according to new contract rules, the system cannot effectively identify and intercept them, and can only output incorrect payroll results.

[0007] IV. Lack of self-detection and early warning correction mechanism for payroll results: The existing system directly outputs the results after the calculation is completed. If the payroll is abnormal due to errors in the underlying data or deviations in the rule configuration, it is necessary to rely entirely on manual verification and investigation of each item, which is very costly.

[0008] 5. The data linkage across the entire business chain is in a one-way passive manner: The existing architecture only pushes data from the business and financial systems to the payroll system in one direction. The payroll system lacks a reverse detection mechanism. If there is a delay in data push or data is missing, the system will still perform calculations based on incomplete data, resulting in distorted final payroll results.

[0009] In summary, there is an urgent need for a data processing method and system that can break through the traditional static framework and integrate machine learning, multi-source data cross-validation, and intelligent feature extraction adaptive mechanisms to solve the technical pain points of existing payroll calculation solutions that are highly dependent on manual labor and have low fault tolerance from the underlying technical architecture level. Summary of the Invention

[0010] To address the core technical shortcomings of existing payroll calculation technologies, such as lack of adaptive capabilities, multi-dimensional data verification, and self-correction warnings, this invention provides a dynamic payroll calculation method and system based on AI rule-based adaptive calculation across the entire business chain. This invention breaks through the traditional static payroll calculation framework, integrating deep learning, feature clustering, and data fingerprinting technologies to construct a fully automated technical closed loop. This enables intelligent matching of business scenarios with payroll calculation rules, automatic data verification, and real-time self-correction of payroll results.

[0011] According to a first aspect of the present invention, a dynamic payroll calculation method for the entire business chain based on AI rule adaptation is provided, the method comprising the following steps:

[0012] S1: Collect full-chain business data from the business system, financial system and delivery system through the two-way linkage data acquisition module, and perform reverse integrity checks on the collected full-chain business data. If data missing or format error is detected, send a completion reminder to the corresponding data source terminal to obtain compliant full-chain business data.

[0013] S2: Input the compliant full-link business data into the pre-trained business scenario feature extraction model for real-time feature extraction, classify the current order into the matching existing business scenario, and use the feature clustering algorithm to automatically cluster the current orders that have not been successfully matched to mark them as new scenarios;

[0014] S3: Utilize a pre-built AI rule adaptive engine to match the corresponding scenario-based salary calculation rules for the current order based on the business scenario classification results of the current order;

[0015] S4: The compliant business end-to-end data is cross-validated through the multi-source heterogeneous data cross-validation module, and abnormal data that fails the cross-validation is intercepted. The business end-to-end data that passes the cross-validation is used as the data to be calculated. The cross-validation includes data fingerprint verification and logical association verification.

[0016] S5: The AI ​​rule adaptive engine performs dynamic salary calculation based on the matched scenario-based salary calculation rules and the salary data to be calculated, and obtains the initial salary calculation result;

[0017] S6: Perform real-time anomaly detection on the initial salary calculation result. If the initial salary calculation result exceeds the preset anomaly threshold, activate the self-correction mechanism to backtrack and locate the cause of the anomaly. If the cause of the anomaly is a rule matching deviation, trigger the AI ​​rule adaptive engine to rematch the salary calculation rules and recalculate the salary. If it is determined that there is an anomaly that cannot be corrected autonomously, send a graded warning message to the warning terminal.

[0018] S7: Output the final salary calculation result and control the AI ​​rule adaptive engine to learn autonomously using the new data and rules of this salary calculation to complete the dynamic iteration of the salary calculation rules.

[0019] Optionally, step S2 specifically includes:

[0020] Using a business scenario feature extraction model built on a convolutional neural network, the core features of the business scenario in the compliant full-link business data are extracted. The core features include product line type, order amount, order type, business model, and order type.

[0021] The extracted core features are quantized and calibrated to obtain feature vectors, and the similarity between the feature vectors and the corresponding feature vectors of existing business scenarios is calculated.

[0022] If the similarity is greater than or equal to the preset matching threshold, the current order will be classified into a matching existing business scenario.

[0023] If the similarity is less than the preset matching threshold, the feature vector is clustered using the feature clustering algorithm to generate a new scene, and a new scene reminder message is sent to the user's terminal.

[0024] Optionally, step S3 specifically includes:

[0025] Obtain the basic payroll calculation rules initially defined by the human team, as well as the company's historical business data and historical payroll calculation data;

[0026] The AI ​​rule adaptive engine is based on reinforcement learning and gradient descent algorithms. By learning from the historical business data and historical payroll data, it autonomously establishes a correlation model between business scenarios and payroll rules, and solidifies the mined implicit business rules into scenario-based payroll sub-rules for each business scenario.

[0027] Based on the existing business scenario to which the current order belongs, the association model is used to automatically and accurately match the corresponding scenario-based salary calculation rules for the current order.

[0028] For current orders belonging to the new scenario, the basic salary calculation rules will be used for matching for the time being;

[0029] When there are adjustments to enterprise business rules or new business scenarios, the AI ​​rule adaptive engine learns from the new data and autonomously iterates the salary calculation rules, compares the iterated salary calculation rules with the original rules, and generates a rule change prompt.

[0030] Optionally, step S4 specifically includes:

[0031] Establish a logical relationship model between data from the business system, delivery system, and financial system in advance, and configure multi-source data association rules;

[0032] Data fingerprint verification: A unique feature identifier is generated for the compliant business end-to-end data using a hash digest algorithm to verify the authenticity and integrity of the data during transmission and prevent data tampering.

[0033] Logical association verification: Based on the logical association model and the multi-source data association rules, cross-domain detection is performed to check the logical consistency of business data, delivery data and financial data across different dimensions;

[0034] When a data fingerprint mismatch or a logical conflict between multiple data sources is detected, the abnormal data will be automatically classified as missing data, logical error, or data tampering. The dynamic payroll calculation for the order corresponding to the abnormal data will be suspended, and a precise correction reminder will be sent to the data source terminal that generated the abnormal data.

[0035] Optionally, step S6 specifically includes:

[0036] Determine whether the difference rate of salary calculation results for the same type of orders and the fluctuation rate of commission ratio in the initial salary calculation results exceed the preset abnormal threshold;

[0037] If the abnormal threshold is exceeded, a reverse backtracking mechanism will be automatically triggered to check the data collection, rule matching, and cross-validation processes in sequence in order to locate the abnormal cause that caused the salary calculation anomaly.

[0038] If the cause of the anomaly is a rule matching deviation, the AI ​​rule adaptive engine is controlled to adjust the parameters autonomously and rematch the scenario-based salary calculation rules, and then the salary calculation is performed again.

[0039] If the cause of the anomaly is an error in the underlying multi-source data collection, then the current payroll calculation operation will be paused and a data correction reminder will be pushed to the corresponding data source terminal;

[0040] If the cause of the anomaly is a new business scenario with no matching rules or a multi-source data logic conflict that cannot be resolved autonomously, it is determined to be a serious anomaly that cannot be corrected autonomously. A graded early warning message containing anomaly cause analysis and correction suggestions is generated and sent to the early warning terminal. The final calculation is completed after obtaining a manual confirmation instruction.

[0041] Optionally, after step S7, the dynamic payroll calculation method for the entire business chain further includes an exception archiving step:

[0042] Abnormal data, self-correction results, hierarchical early warning information, and iteration records of payroll calculation rules throughout the entire payroll calculation process are classified, stored, and archived in the database to generate a tamper-proof ledger of abnormal payroll data for business process traceability and auditing.

[0043] According to a second aspect of the present invention, a dynamic payroll system for the entire business chain based on AI rule adaptation is provided. The system includes a data acquisition layer, a core algorithm layer, and a result output layer that are interconnected.

[0044] The data acquisition layer includes a two-way linkage data acquisition module, which is used to collect full-link business data from the business system, financial system and delivery system through a lightweight API interface, and to perform reverse integrity checks on the full-link business data. If data missing or format error is detected, a completion reminder is sent to the corresponding data source terminal to obtain compliant full-link business data.

[0045] The core algorithm layer includes a business scenario feature extraction module, an AI rule adaptive engine, a multi-source heterogeneous data cross-validation module, and a salary calculation result self-early warning correction module. The business scenario feature extraction module performs real-time feature extraction, scenario matching, and automatic clustering of new scenarios on the compliant full-link business data. The AI ​​rule adaptive engine autonomously establishes a correlation model between business scenarios and rules, matches scenario-based salary calculation sub-rules, performs dynamic salary calculation, and enables autonomous learning and iteration of salary calculation rules. The multi-source heterogeneous data cross-validation module performs data fingerprint verification and logical association verification. The salary calculation result self-early warning correction module performs anomaly detection on the initial salary calculation result, reverse-tracks to locate the cause of the anomaly, initiates a self-correction mechanism, and sends tiered early warning information.

[0046] The result output layer includes a salary calculation result visualization module, a rule iteration recording module, and an abnormal data archiving module, which are used to output the final salary calculation result for multi-terminal visualization, record the iteration process of the salary calculation rules, and classify and archive abnormal data.

[0047] Optionally, the dynamic payroll system across the entire business chain also includes:

[0048] The hardware terminal layer includes a data acquisition terminal that connects to the target enterprise's existing business system, financial system, and delivery system; a cloud server equipped with a GPU computing module and deploying the core algorithm layer; a user operation terminal; and an early warning terminal.

[0049] The beneficial effects of this invention are as follows:

[0050] I. Achieved dynamic and autonomous adaptation of payroll rules, breaking through the limitations of static rule frameworks: Innovatively applied reinforcement learning algorithms to the underlying logic of enterprise-wide payroll calculation. The AI ​​rule adaptive engine can autonomously build a correlation model between scenarios and rules by learning from historical data, discovering implicit rules, and achieving precise matching of one rule per scenario. The system's response time to dynamic business adjustments has been shortened from the original 1-2 working days of manual processing to millisecond-level real-time response, completely reducing the error rate of manual rule configuration to 0.

[0051] Second, it significantly improves the accuracy and authenticity of multi-source heterogeneous payroll data: A bidirectional linkage data acquisition module is designed to break down the barrier of one-way passive push and achieve reverse integrity detection; a pioneering dual verification mechanism integrating data fingerprint verification and multi-dimensional logical association verification can successfully identify deep cross-domain logical errors that are difficult to detect in isolated systems. The error identification rate of payroll data reaches 100%, and compliance integrity is improved to over 99.9%.

[0052] Third, a closed-loop system for self-detection and full-process self-correction of payroll calculation results has been established: an innovative anomaly threshold backtracking model has been constructed. When a result triggers the threshold red line, the system can automatically backtrack to locate the problem, intelligently distinguish whether it is a rule deviation of the AI ​​engine or an error in the underlying data, and achieve automatic reconciliation and recalculation, or output graded early warning suggestions. This improves the efficiency of anomaly detection by more than 90%, and the anomaly rate of payroll calculation results has been rapidly reduced from the current 5%-8% to below 0.1%.

[0053] Fourth, it boasts extremely high compatibility and low implementation and transformation costs, achieving full automation of payroll calculation: forming a complete technical closed loop of eight steps, the manual intervention rate in the payroll process has plummeted from over 80% in traditional technologies to below 5%, requiring only initial calibration and confirmation of serious conflicts. The combination of hardware and software and lightweight interface technology allows enterprises to avoid overhauling their existing business and financial system infrastructure, reducing transformation costs by over 70%, and providing a flexible and agile underlying data engine for enterprise digital transformation.

[0054] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0055] The present invention can be better understood by referring to the following description taken in conjunction with the accompanying drawings, in which the same or similar reference numerals are used throughout the drawings to denote the same or similar parts.

[0056] Figure 1 A flowchart illustrating the implementation of an AI-based rule-adaptive dynamic payroll calculation method across the entire business chain, according to an embodiment of the present invention, is shown.

[0057] Figure 2 A schematic diagram of the architecture of a dynamic payroll system based on AI rule adaptation for the entire business chain, according to an embodiment of the present invention, is shown. Detailed Implementation

[0058] To enable those skilled in the art to more fully understand the technical solutions of the present invention, exemplary embodiments of the present invention will be described more comprehensively and in detail below with reference to the accompanying drawings. Obviously, the one or more embodiments of the present invention described below are merely one or more specific ways to implement the technical solutions of the present invention, and are not exhaustive. It should be understood that other ways belonging to a general inventive concept can be used to implement the technical solutions of the present invention, and should not be limited to the embodiments described exemplary. Based on one or more embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0059] Example: Figure 1 This document illustrates a flowchart of the implementation of an AI-based rule-adaptive dynamic payroll calculation method across the entire business chain, according to an embodiment of the present invention. (Refer to...) Figure 1 The AI-rule-adaptive dynamic payroll calculation method for the entire business chain, according to embodiments of the present invention, includes the following steps:

[0060] S1: Collect full-chain business data from the business system, financial system and delivery system through the two-way linkage data acquisition module, and perform reverse integrity checks on the collected full-chain business data. If data missing or format error is detected, send a completion reminder to the corresponding data source terminal to obtain compliant full-chain business data.

[0061] S2: Input the compliant full-link business data into the pre-trained business scenario feature extraction model for real-time feature extraction, classify the current order into the matching existing business scenario, and use the feature clustering algorithm to automatically cluster the current orders that have not been successfully matched to mark them as new scenarios;

[0062] S3: Utilize a pre-built AI rule adaptive engine to match the corresponding scenario-based salary calculation rules for the current order based on the business scenario classification results of the current order;

[0063] S4: The compliant business end-to-end data is cross-validated through the multi-source heterogeneous data cross-validation module, and abnormal data that fails the cross-validation is intercepted. The business end-to-end data that passes the cross-validation is used as the data to be calculated. The cross-validation includes data fingerprint verification and logical association verification.

[0064] S5: The AI ​​rule adaptive engine performs dynamic salary calculation based on the matched scenario-based salary calculation rules and the salary data to be calculated, and obtains the initial salary calculation result;

[0065] S6: Perform real-time anomaly detection on the initial salary calculation result. If the initial salary calculation result exceeds the preset anomaly threshold, activate the self-correction mechanism to backtrack and locate the cause of the anomaly. If the cause of the anomaly is a rule matching deviation, trigger the AI ​​rule adaptive engine to rematch the salary calculation rules and recalculate the salary. If it is determined that there is an anomaly that cannot be corrected autonomously, send a graded warning message to the warning terminal.

[0066] S7: Output the final salary calculation result and control the AI ​​rule adaptive engine to learn autonomously using the new data and rules of this salary calculation to complete the dynamic iteration of the salary calculation rules.

[0067] Furthermore, step S2 in this embodiment of the invention specifically includes:

[0068] Using a business scenario feature extraction model built on a convolutional neural network, the core features of the business scenario in the compliant full-link business data are extracted. The core features include product line type, order amount, order type, business model, and order type.

[0069] The extracted core features are quantized and calibrated to obtain feature vectors, and the similarity between the feature vectors and the corresponding feature vectors of existing business scenarios is calculated.

[0070] If the similarity is greater than or equal to the preset matching threshold, the current order will be classified into a matching existing business scenario.

[0071] If the similarity is less than the preset matching threshold, the feature vector is clustered using the feature clustering algorithm to generate a new scene, and a new scene reminder message is sent to the user's terminal.

[0072] Furthermore, step S3 in this embodiment of the invention specifically includes:

[0073] Obtain the basic payroll calculation rules initially defined by the human team, as well as the company's historical business data and historical payroll calculation data;

[0074] The AI ​​rule adaptive engine is based on reinforcement learning and gradient descent algorithms. By learning from the historical business data and historical payroll data, it autonomously establishes a correlation model between business scenarios and payroll rules, and solidifies the mined implicit business rules into scenario-based payroll sub-rules for each business scenario.

[0075] Based on the existing business scenario to which the current order belongs, the association model is used to automatically and accurately match the corresponding scenario-based salary calculation rules for the current order.

[0076] For current orders belonging to the new scenario, the basic salary calculation rules will be used for matching for the time being;

[0077] When there are adjustments to enterprise business rules or new business scenarios, the AI ​​rule adaptive engine learns from the new data and autonomously iterates the salary calculation rules, compares the iterated salary calculation rules with the original rules, and generates a rule change prompt.

[0078] Furthermore, step S4 in this embodiment of the invention specifically includes:

[0079] Establish a logical relationship model between data from the business system, delivery system, and financial system in advance, and configure multi-source data association rules;

[0080] Data fingerprint verification: A unique feature identifier is generated for the compliant business end-to-end data using a hash digest algorithm to verify the authenticity and integrity of the data during transmission and prevent data tampering.

[0081] Logical association verification: Based on the logical association model and the multi-source data association rules, cross-domain detection is performed to check the logical consistency of business data, delivery data and financial data across different dimensions;

[0082] When a data fingerprint mismatch or a logical conflict between multiple data sources is detected, the abnormal data will be automatically classified as missing data, logical error, or data tampering. The dynamic payroll calculation for the order corresponding to the abnormal data will be suspended, and a precise correction reminder will be sent to the data source terminal that generated the abnormal data.

[0083] Furthermore, step S6 in this embodiment of the invention specifically includes:

[0084] Determine whether the difference rate of salary calculation results for the same type of orders and the fluctuation rate of commission ratio in the initial salary calculation results exceed the preset abnormal threshold;

[0085] If the abnormal threshold is exceeded, a reverse backtracking mechanism will be automatically triggered to check the data collection, rule matching, and cross-validation processes in sequence in order to locate the abnormal cause that caused the salary calculation anomaly.

[0086] If the cause of the anomaly is a rule matching deviation, the AI ​​rule adaptive engine is controlled to adjust the parameters autonomously and rematch the scenario-based salary calculation rules, and then the salary calculation is performed again.

[0087] If the cause of the anomaly is an error in the underlying multi-source data collection, then the current payroll calculation operation will be paused and a data correction reminder will be pushed to the corresponding data source terminal;

[0088] If the cause of the anomaly is a new business scenario with no matching rules or a multi-source data logic conflict that cannot be resolved autonomously, it is determined to be a serious anomaly that cannot be corrected autonomously. A graded early warning message containing anomaly cause analysis and correction suggestions is generated and sent to the early warning terminal. The final calculation is completed after obtaining a manual confirmation instruction.

[0089] Furthermore, in this embodiment of the invention, after step S7, an exception archiving step is also included:

[0090] Abnormal data, self-correction results, hierarchical early warning information, and iteration records of payroll calculation rules throughout the entire payroll calculation process are classified, stored, and archived in the database to generate a tamper-proof ledger of abnormal payroll data for business process traceability and auditing.

[0091] Specifically, the implementation process of the AI ​​rule-adaptive dynamic payroll calculation method for the entire business chain in this embodiment of the invention is as follows:

[0092] S100, bidirectional linkage data acquisition and initial calibration:

[0093] During the system's cold start phase, HR managers only need to input the most basic payroll boundaries, such as base commission rates and base salary / bonus levels, into the engine and import historical business ledgers to complete the initial training of the model. In normal operation, unlike traditional payroll systems that passively receive data "whatever comes in," this system's data acquisition layer possesses reverse detection and error-detection capabilities. The collector pulls end-to-end data from the business side (CRM system), finance side, and delivery side simultaneously through a lightweight API interface. If empty fields are detected in the data packets (such as empty order signing time or abnormal payment voucher fields), the system will immediately reject the data and push the abnormal message back to the corresponding front-end data source terminal, sending a completion reminder to purify the data from the source and ensure the compliance of the data entering the payroll pool.

[0094] S200, Business Scenario Feature Extraction and Matching:

[0095] Once the data is ready, the business scenario feature extraction module, based on a combination of convolutional neural networks and feature clustering algorithms, intervenes. It extracts and quantifies core feature vectors from complex structured / unstructured data in real time, such as product line type, order amount, order type (new / renewal / customized), and business model (single-region / cross-regional joint order). If the current order feature vector matches an existing scenario, it is categorized; if the system identifies a new combination never before seen in the multi-dimensional feature space (e.g., a new business model of cross-regional after-sales secondary orders), the clustering algorithm automatically generates a new scenario cluster label and immediately sends a notification to the operator via the terminal.

[0096] S300 and AI-based payroll calculation rules are automatically matched:

[0097] As the computing power brain of this invention, the AI ​​rule adaptive engine operates on the underlying basis of reinforcement learning and gradient descent algorithms. In the analysis of massive amounts of payroll data, the engine can uncover hidden patterns. For example, it autonomously learns and deduces the implicit rule that "due to the difficulty of collaboration in cross-regional joint orders, the actual commission rate usually increases by 10%", and solidifies this into scenario-based payroll sub-rules. The system then automatically and seamlessly attaches matching scenario-based sub-rules to the orders categorized in step S200, achieving a precise mapping of one rule per scenario. For new scenarios, the basic boundary rules are temporarily applied for transition. When enterprises release temporary incentive policies that cause data changes, the engine can autonomously capture these changes and iterate through incremental learning, without requiring manual modification of the underlying code.

[0098] S400, cross-validation of multi-source heterogeneous data:

[0099] To prevent data tampering and deep-seated business logic fallacies, a double-layered defense is in place:

[0100] Data fingerprint verification: Data fingerprints are generated using a hash encryption digest algorithm to verify the authenticity and integrity of data during cross-system transmission and prevent malicious modification by terminals.

[0101] Logical association verification: A knowledge graph logical model is pre-built based on business common sense among multi-source data. For example, the rule engine will strongly verify: "The high commission ratio of the first signed new order must not be applied to the renewal of old orders," and "The corresponding commission bonus pool calculation must not be triggered before the delivery status node shows 100% acceptance." When a conflict occurs, the module will accurately classify the exception into missing, logical error, and tampering categories, suspend the calculation of the abnormal document, and reversely pinpoint the responsible terminal to report the error.

[0102] S500, Dynamic Salary Calculation:

[0103] After cross-validation and approval, the clean and compliant data is substituted into the salary calculation rules matched in step S300. The cloud GPU cluster is then used to instantly complete the complex concurrent dynamic calculation of commissions and bonuses, and the initial salary calculation results are output.

[0104] S600, Self-early warning and graded self-correction of salary calculation results:

[0105] This module employs an anomaly threshold algorithm for fallback inspection. If a salesperson's commission amount exceeds the historical average difference rate of similar positions by ≥15%, the algorithm immediately triggers a full-chain reverse backtracking.

[0106] The system retrospectively investigates the three key stages: data collection, rule matching, and data verification. If the cause is found to be an occasional weight deviation in the AI ​​model's matching strategy, the AI ​​engine will automatically reduce dimensionality or readjust parameters, reconfigure sub-rules, and recalculate. If the cause is a hidden error in the underlying multi-source data collection, the calculation is paused and pushed back to the front end for correction. If a deep-seated data logic deadlock is found, such as irreconcilable system conflicts or a lack of rule references, the system will issue a tiered warning and send a diagnostic report to the human operator's terminal. The calculation can only continue after the human operator has reviewed and confirmed the report.

[0107] S700, Result Output, Rule-Based Closed-Loop Iteration, and Anomaly Archiving:

[0108] The final, accurate payroll calculation result is then displayed on the UI. Simultaneously, the AI ​​rule-adaptive engine maps the new, error-free data set to the new rules, using this as a reward for reinforcement learning, thus feeding it back to the network for self-iteration and cognitive evolution. Finally, all interception logs, verification conflicts, and self-correcting parameter records are categorized and archived in the database, generating tamper-proof ledgers to meet the enterprise's full-process traceability and financial system auditing needs.

[0109] Accordingly, based on the AI ​​rule-adaptive dynamic payroll calculation method for the entire business chain in this embodiment of the invention, this embodiment of the invention also proposes an AI rule-adaptive dynamic payroll calculation system for the entire business chain.

[0110] Figure 2 This diagram illustrates the architecture of an AI-based rule-adaptive dynamic payroll system across the entire business chain, according to an embodiment of the present invention. (Refer to...) Figure 2 The AI-based rule-adaptive dynamic salary calculation system for the entire business chain in this invention includes a data acquisition layer, a core algorithm layer, and a result output layer that are interconnected.

[0111] The data acquisition layer includes a two-way linkage data acquisition module, which is used to collect full-link business data from business systems, financial systems and delivery systems through a lightweight API interface, and to perform reverse integrity checks on the full-link business data. If data is missing or formatted incorrectly, a completion reminder is sent to the corresponding data source terminal to obtain compliant full-link business data.

[0112] The core algorithm layer includes a business scenario feature extraction module, an AI rule adaptive engine, a multi-source heterogeneous data cross-validation module, and a salary calculation result self-early warning and correction module. The business scenario feature extraction module is used to perform real-time feature extraction, scenario matching, and automatic clustering of new scenarios on compliant business end-to-end data. The AI ​​rule adaptive engine is used to autonomously establish a correlation model between business scenarios and rules, match scenario-based salary calculation sub-rules, perform dynamic salary calculation, and autonomously learn and iterate the salary calculation rules. The multi-source heterogeneous data cross-validation module is used to perform data fingerprint verification and logical association verification. The salary calculation result self-early warning and correction module is used to detect anomalies in the initial salary calculation results, trace back to locate the cause of anomalies, activate the self-correction mechanism, and send tiered early warning information.

[0113] The output layer includes a salary calculation result visualization module, a rule iteration recording module, and an anomaly data archiving module. These modules are used to output the final salary calculation result for multi-terminal visualization, record the iteration process of the salary calculation rules, and classify and archive anomaly data.

[0114] Furthermore, the dynamic payroll system for the entire business chain in this embodiment of the invention also includes:

[0115] The hardware terminal layer includes a data acquisition terminal that connects to the target enterprise's existing business system, financial system, and delivery system; a cloud server equipped with a GPU computing module and deploying the core algorithm layer; a user operation terminal; and an early warning terminal.

[0116] Specifically, the dynamic payroll calculation system for the entire business chain in this embodiment of the invention features a software and hardware decoupling and highly compatible architecture, mainly divided into four layers:

[0117] Hardware terminal layer: includes various industrial-grade data collectors distributed within the enterprise network, cloud servers with powerful computing capabilities to support large-matrix deep learning operations, PC / mobile user terminals, and early warning receivers such as WeChat for enterprises.

[0118] Data Acquisition Layer: Relying on the open APIs of the enterprise's underlying architecture, the system adopts a lightweight, bypass-monitoring mode to integrate with the enterprise's existing platform. The system does not fundamentally refactor the underlying code of the original basic data, process management, order management, and financial management modules; it only collects data in real time and monitors it bidirectionally, resulting in extremely low implementation and modification costs.

[0119] Core Algorithm Layer: Integrated and running in the cloud. Internally, it includes a business scenario feature extraction module, a core AI rule adaptive engine, a multi-source cross-validation module, and a self-correcting early warning module. It completely replaces the tedious work of HR and finance personnel who previously relied heavily on massive manual parameter configurations, VLOOKUP comparisons, and manual risk assessment using Excel spreadsheets.

[0120] Results Output Layer: Presented intuitively on the front end as a dashboard for the CEO, financial dashboard, process dashboard, and results display interface. This helps management gain a macro-level understanding of the accuracy of payroll calculations, the number of process anomaly interceptions, and the dynamic feedback results of business incentives.

[0121] While one or more embodiments of the present invention have been described above, those skilled in the art will recognize that the present invention can be implemented in any other form without departing from its spirit and scope. Therefore, the embodiments described above are illustrative and not restrictive, and many modifications and substitutions will be apparent to those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A dynamic payroll calculation method for the entire business chain based on AI rule adaptation, characterized in that, Includes the following steps: S1: Collect full-chain business data from the business system, financial system and delivery system through the two-way linkage data acquisition module, and perform reverse integrity checks on the collected full-chain business data. If data missing or format error is detected, send a completion reminder to the corresponding data source terminal to obtain compliant full-chain business data. S2: Input the compliant full-link business data into the pre-trained business scenario feature extraction model for real-time feature extraction, classify the current order into the matching existing business scenario, and use the feature clustering algorithm to automatically cluster the current orders that have not been successfully matched to mark them as new scenarios; S3: Utilize a pre-built AI rule adaptive engine to match the corresponding scenario-based salary calculation rules for the current order based on the business scenario classification results of the current order; S4: The compliant business end-to-end data is cross-validated through the multi-source heterogeneous data cross-validation module, and abnormal data that fails the cross-validation is intercepted. The business end-to-end data that passes the cross-validation is used as the data to be calculated. The cross-validation includes data fingerprint verification and logical association verification. S5: The AI ​​rule adaptive engine performs dynamic salary calculation based on the matched scenario-based salary calculation rules and the salary data to be calculated, and obtains the initial salary calculation result; S6: Perform real-time anomaly detection on the initial salary calculation result. If the initial salary calculation result exceeds the preset anomaly threshold, activate the self-correction mechanism to backtrack and locate the cause of the anomaly. If the cause of the anomaly is a rule matching deviation, trigger the AI ​​rule adaptive engine to rematch the salary calculation rules and recalculate the salary. If it is determined that there is an anomaly that cannot be corrected autonomously, send a graded warning message to the warning terminal. S7: Output the final salary calculation result and control the AI ​​rule adaptive engine to learn autonomously using the new data and rules of this salary calculation to complete the dynamic iteration of the salary calculation rules.

2. The AI-rule-adaptive dynamic payroll calculation method for the entire business chain according to claim 1, characterized in that, Step S2 specifically includes: Using a business scenario feature extraction model built on a convolutional neural network, the core features of the business scenario in the compliant full-link business data are extracted. The core features include product line type, order amount, order type, business model, and order type. The extracted core features are quantized and calibrated to obtain feature vectors, and the similarity between the feature vectors and the corresponding feature vectors of existing business scenarios is calculated. If the similarity is greater than or equal to the preset matching threshold, the current order will be classified into a matching existing business scenario. If the similarity is less than the preset matching threshold, the feature vector is clustered using the feature clustering algorithm to generate a new scene, and a new scene reminder message is sent to the user's terminal.

3. The AI-rule-adaptive dynamic payroll calculation method for the entire business chain according to claim 1, characterized in that, Step S3 specifically includes: Obtain the basic payroll calculation rules initially defined by the human team, as well as the company's historical business data and historical payroll calculation data; The AI ​​rule adaptive engine is based on reinforcement learning and gradient descent algorithms. By learning from the historical business data and historical payroll data, it autonomously establishes a correlation model between business scenarios and payroll rules, and solidifies the mined implicit business rules into scenario-based payroll sub-rules for each business scenario. Based on the existing business scenario to which the current order belongs, the association model is used to automatically and accurately match the corresponding scenario-based salary calculation rules for the current order. For current orders belonging to the new scenario, the basic salary calculation rules will be used for matching for the time being; When there are adjustments to enterprise business rules or new business scenarios, the AI ​​rule adaptive engine learns from the new data and autonomously iterates the salary calculation rules, compares the iterated salary calculation rules with the original rules, and generates a rule change prompt.

4. The AI-rule-adaptive dynamic payroll calculation method for the entire business chain according to claim 1, characterized in that, Step S4 specifically includes: Establish a logical relationship model between data from the business system, delivery system, and financial system in advance, and configure multi-source data association rules; Data fingerprint verification: A unique feature identifier is generated for the compliant business end-to-end data using a hash digest algorithm to verify the authenticity and integrity of the data during transmission and prevent data tampering. Logical association verification: Based on the logical association model and the multi-source data association rules, cross-domain detection is performed to check the logical consistency of business data, delivery data and financial data across different dimensions; When a data fingerprint mismatch or a logical conflict between multiple data sources is detected, the abnormal data will be automatically classified as missing data, logical error, or data tampering. The dynamic payroll calculation for the order corresponding to the abnormal data will be suspended, and a precise correction reminder will be sent to the data source terminal that generated the abnormal data.

5. The AI-rule-adaptive dynamic payroll calculation method for the entire business chain according to claim 1, characterized in that, Step S6 specifically includes: Determine whether the difference rate of salary calculation results for the same type of orders and the fluctuation rate of commission ratio in the initial salary calculation results exceed the preset abnormal threshold; If the abnormal threshold is exceeded, a reverse backtracking mechanism will be automatically triggered to check the data collection, rule matching, and cross-validation processes in sequence in order to locate the abnormal cause that caused the salary calculation anomaly. If the cause of the anomaly is a rule matching deviation, the AI ​​rule adaptive engine is controlled to adjust the parameters autonomously and rematch the scenario-based salary calculation rules, and then the salary calculation is performed again. If the cause of the anomaly is an error in the underlying multi-source data collection, then the current payroll calculation operation will be paused and a data correction reminder will be pushed to the corresponding data source terminal; If the cause of the anomaly is a new business scenario with no matching rules or a multi-source data logic conflict that cannot be resolved autonomously, it is determined to be a serious anomaly that cannot be corrected autonomously. A graded early warning message containing anomaly cause analysis and correction suggestions is generated and sent to the early warning terminal. The final calculation is completed after obtaining a manual confirmation instruction.

6. The AI-based rule-adaptive dynamic payroll calculation method for the entire business chain according to claim 1, characterized in that, Following step S7, an exception archiving step is also included: Abnormal data, self-correction results, hierarchical early warning information, and iteration records of payroll calculation rules throughout the entire payroll calculation process are classified, stored, and archived in the database to generate a tamper-proof ledger of abnormal payroll data for business process traceability and auditing.

7. A dynamic payroll calculation system for the entire business chain based on AI rule adaptation, characterized in that, It includes a data acquisition layer, a core algorithm layer, and a result output layer that are interconnected. The data acquisition layer includes a two-way linkage data acquisition module, which is used to collect full-link business data from the business system, financial system and delivery system through a lightweight API interface, and to perform reverse integrity checks on the full-link business data. If data missing or format error is detected, a completion reminder is sent to the corresponding data source terminal to obtain compliant full-link business data. The core algorithm layer includes a business scenario feature extraction module, an AI rule adaptive engine, a multi-source heterogeneous data cross-validation module, and a salary calculation result self-early warning correction module. The business scenario feature extraction module performs real-time feature extraction, scenario matching, and automatic clustering of new scenarios on the compliant full-link business data. The AI ​​rule adaptive engine autonomously establishes a correlation model between business scenarios and rules, matches scenario-based salary calculation sub-rules, performs dynamic salary calculation, and enables autonomous learning and iteration of salary calculation rules. The multi-source heterogeneous data cross-validation module performs data fingerprint verification and logical association verification. The salary calculation result self-early warning correction module performs anomaly detection on the initial salary calculation result, reverse-tracks to locate the cause of the anomaly, initiates a self-correction mechanism, and sends tiered early warning information. The result output layer includes a salary calculation result visualization module, a rule iteration recording module, and an abnormal data archiving module, which are used to output the final salary calculation result for multi-terminal visualization, record the iteration process of the salary calculation rules, and classify and archive abnormal data.

8. The AI-based rule-adaptive dynamic payroll system for the entire business chain according to claim 7, characterized in that, Also includes: The hardware terminal layer includes a data acquisition terminal that connects to the target enterprise's existing business system, financial system, and delivery system; a cloud server equipped with a GPU computing module and deploying the core algorithm layer; a user operation terminal; and an early warning terminal.

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