Salary allocation system and method based on rule engine

Abstract

The invention discloses a salary allocation system and method based on a rule engine, and the system comprises a rule configuration layer which is used for providing a visual interface and supporting a user to define, modify and manage an allocation rule; the rule engine layer is used for analyzing and executing rules and driving apportionment calculation; the data service layer is used for integrating enterprise human resources, financial accounting and project management data; and the execution control layer is used for scheduling task flows, monitoring execution states and processing exceptions. According to the invention, accurate and automatic allocation of costs such as employee salary to different research and development projects and company projects is realized through an accurate business rule engine and data processing capability in the whole process from man-hour data and project acknowledgement data to financial vouchers, so that the financial accounting efficiency is improved, the accuracy of cost analysis is enhanced, and the economic benefit is improved. And a reliable data basis is provided for project profit analysis in enterprise management.

Description

Technical Field

[0001] This invention relates to the field of financial information management technology, and in particular to a payroll allocation system and method based on a rule engine. Background Technology

[0002] Currently, enterprises (especially medium and large-sized groups, R&D-oriented enterprises, and project-based operating companies) generally face complex, ever-changing, and precise business needs when allocating salary costs. Salary allocation not only involves simple departmental expense collection, but also requires precise allocation of labor costs based on multi-dimensional management perspectives (such as projects, product lines, cost centers, regions, and special budgets) to meet the needs of management accounting, project accounting, tax compliance, and internal performance evaluation.

[0003] However, existing technologies have the following problems: difficulty in changing rules: each adjustment of the allocation strategy requires code modification, resulting in long development cycles and high risks; high system coupling: different allocation scenarios (such as by department, by project, by working hours) are interdependent and difficult to expand; lack of unified management: rules are scattered in multiple modules, resulting in high maintenance and auditing costs.

[0004] In summary, existing technical solutions generally suffer from core pain points when addressing the sophisticated, dynamic, and compliant wage cost allocation needs of modern enterprises: long development cycles, inflexible changes, high maintenance costs, and significant audit risks. Therefore, there is an urgent need for a dedicated system architecture that can completely decouple financial allocation rules from the code, enable visual management by business personnel, and possess strong scalability and reliable auditing capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide a wage allocation system and method based on a rule engine to solve the technical problems of rigid allocation rules, difficulty in expansion, and complex maintenance in the prior art.

[0006] This invention is implemented using the following technical solution: a wage allocation system based on a rule engine, comprising: The rule configuration layer provides a visual interface and supports users in defining, modifying, and managing allocation rules. The rules engine layer is used to parse and execute rules and drive amortization calculations. The data service layer is used to integrate enterprise human resources, financial accounting, and project management data; The execution control layer is used to schedule task flows, monitor execution status, and handle exceptions.

[0007] Furthermore, the rule configuration layer includes: The rule modeling module is used to abstract the allocation rules into a combination of conditions and actions, and supports parameterized configuration of formulas, weights, and priorities; The version management module is used to store, compare, and roll back historical versions of rules; The access control module is used for editing, publishing, and defining the scope of role control rules.

[0008] Furthermore, the rule engine layer includes: The policy loading module is used to dynamically load rules and policies through a unified interface; The context management module is used to build the execution environment and inject payroll data, organizational structure, and cost center information; The rule routing module is used to automatically match applicable rules based on data characteristics and supports the parallel or serial execution of multiple rules.

[0009] Furthermore, the data service layer includes a data preprocessing unit, which includes: The data validation module is used to verify the integrity of salary data; The data conversion module is used to convert heterogeneous data into a standard format that the rule engine can recognize. The cache management module is used to optimize the caching of frequently accessed baseline data.

[0010] Furthermore, the data service layer also includes a cost-sharing calculation unit, which includes: The multi-dimensional allocation module is used to support multi-dimensional allocation by department, project, product line, and region; The dynamic weight calculation module is used to calculate the weight coefficients in real time according to preset rules; The cost tracking module is used to record the original data, rules, and results of each allocation, and to generate a complete audit trail.

[0011] Furthermore, the execution control layer includes a conflict handling and fault tolerance unit, which includes: The rule conflict detection module is used to identify overlapping or contradictory rules and prompt the user to intervene. The abnormal circuit breaker module is used to automatically switch to backup rules or transfer to manual processing. The result verification module is used to verify the reasonableness of the allocation results.

[0012] Furthermore, the execution control layer also includes an interface and integration unit, which includes: The unified API gateway module provides RESTful interfaces for HR systems, ERP systems, and financial software to use. The message queue adaptation module is used to support receiving asynchronous distributed tasks via Kafka and RabbitMQ; The file parsing module supports batch data import in Excel and CSV formats.

[0013] A rule-engine-based payroll allocation method, implemented based on the aforementioned rule-engine-based payroll allocation system, includes the following steps: Step 1: Define the wage allocation rule strategy interface; Step 2: Create a specific allocation rule class and implement the strategy interface; Step 3: Configure the Spring Bean for salary allocation rules; Step 4: Build a wage allocation rule engine, sort the injected rules by priority, and then execute them. Step 5: Design a business component generation interface to convert the allocation calculation results into structured business component detail data, providing standardized input for subsequent workflow approval; Step Six: Introduce a multi-threaded mechanism to batch process business component workflow initiation tasks, and use status flags to avoid duplicate processing of already generated business components, ensuring data consistency and processing efficiency; Step 7: Establish a retry fallback mechanism to automatically retry business components that fail to start the workflow at exponentially increasing intervals. Cases that still fail are then handled manually, forming a complete closed loop for exception handling.

[0014] The beneficial effects of this invention are as follows: This invention employs a strategy pattern to construct the core engine, modularizing and organizing complex allocation logic, supporting flexible configuration of various allocation rules, and possessing a highly scalable architecture that facilitates rapid integration of new rules in the future. Furthermore, this invention intelligently adapts differentiated business component generation strategies for departments with different functions such as front-end, middle-end, and back-end operations, with particular optimization of hierarchical relationship processing for special departments (such as the "New Business Group") to ensure accurate mapping. In addition, this invention integrates a retry strategy, which can effectively improve the success rate of task processing. For business components that ultimately fail, the system accurately records their detailed information and generates complete failure logs and statistical reports, providing a clear basis for manual intervention and problem tracing.

[0015] This invention, through a precise business rules engine and data processing capabilities, enables the accurate and automated allocation of costs such as employee salaries to different R&D projects and company projects throughout the entire process, from working hour data and project receipt data to financial vouchers. It also generates "allocation business documents" that comply with financial regulations for approval, thereby improving financial accounting efficiency, enhancing the accuracy of cost analysis, and providing a reliable data foundation for project profitability analysis in enterprise management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0020] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0021] See Figure 1 A rule engine-based pay allocation system includes: a rule configuration layer, which provides a visual interface and supports users in defining, modifying and managing allocation rules; a rule engine layer, which parses and executes rules and drives allocation calculations; a data service layer, which integrates enterprise human resources, financial accounting and project management data; and an execution control layer, which schedules task flows, monitors execution status and handles exceptions.

[0022] In this embodiment, the rule configuration layer includes: a rule modeling module, used to abstract the allocation rule into a combination of conditions and actions, and support parameterized configuration of formulas, weights and priorities; a version management module, used to store, compare and roll back historical versions of rules; and an access control module, used to control the editing, publishing and effective scope of rules by role.

[0023] Specifically, the rule modeling module allows for atomic rule definition: it provides a graphical or script-based editor, allowing users to break down a complete allocation rule into multiple atomic components. For example, a rule can be broken down into a "condition judgment component," a "weight calculation component," and a "target allocation component." It also supports various rule types: Formula rules: users can define mathematical formulas (e.g., (department hours / total hours) * total salary). Decision table rules: defined in tabular form; for example, when the "project type" is "R&D" and the "personnel level" is "senior," the allocation coefficient is 1.5. Script rules: support embedding lightweight scripts (such as Groovy) to handle complex logic. Percentage / fixed value rules: directly specify a fixed percentage or amount for allocation. Parameterized configuration is also possible: all variables in the rule (such as department, cost center, project code) are parameterized and bound to the actual data model, achieving one-time configuration for multiple applications.

[0024] The version management module enables full lifecycle tracking: recording the entire status of each rule from "draft to approval to effective to ineffective." Version comparison and traceability: supporting graphical comparison of any two historical versions, clearly recording the operator, time, and reason for each change. Scheduled effective / ineffective: supporting setting future effective times and automatic ineffective times for rules, facilitating annual rule switching or project cycle management.

[0025] The access control module allows for fine-grained access control: permissions can be controlled down to the level of "create, delete, modify, query, publish, and execute" for "rule sets," "individual rules," or even "a parameter within a rule." Simulated execution permissions allow specific roles (such as financial analysts) to simulate the execution of unpublished rules, verifying results without affecting production data.

[0026] In this embodiment, the rule engine layer includes: a policy loading module, used to dynamically load rule policies through a unified interface; a context management module, used to build an execution environment and inject salary data, organizational structure and cost center information; and a rule routing module, used to automatically match applicable rules according to data characteristics and support the parallel or serial execution of multiple rules.

[0027] The policy loading module features a hot-reload mechanism: it supports dynamically loading, unloading, or updating published rule policy packages (JAR files or script files) without restarting system services, enabling seamless rule updates without the business being aware of the changes. A unified interface specification is provided: standardized interfaces (such as `execute(RuleContext context)`) that all policy components must implement are defined, ensuring the engine can consistently invoke various rules. Dependency management is implemented: it automatically manages the third-party libraries that policy components depend on, avoiding conflicts.

[0028] The context management module performs environment construction: creating an independent, thread-safe "RuleContext" object for each allocation task. Data injection and encapsulation: encapsulating preprocessed salary data, organizational trees, project information, historical allocation results, etc., into standardized data objects (Facts) within the context object for rule invocation. Result storage area: providing a temporary storage area within the context object for passing intermediate calculation results between multiple rules executed in a chain.

[0029] The rule routing module can generate intelligent rule chains: automatically selecting a preset rule execution chain based on the attributes of the input data (such as "employee type = outsourced"). For example, the "outsourced personnel rule chain" might include "work hour verification rules," "tax rate matching rules," and "fixed proportion apportionment rules." Execution mode control: supports sequential execution, parallel execution (for rules with no dependencies), and priority-based execution. Short-circuit optimization: when the condition of a rule is "false," it can be configured to skip subsequent associated rules, improving execution efficiency.

[0030] In this embodiment, the data service layer includes a data preprocessing unit, which includes: a data verification module for verifying the integrity of salary data; a data conversion module for converting heterogeneous data into a standard format recognizable by the rule engine; and a cache management module for caching and optimizing frequently accessed baseline data.

[0031] The data validation module can perform structured validation: checking whether the data table structure, field types, and required fields are compliant. Business logic validation: implementing cross-field logic checks, such as "total individual working hours ≤ monthly standard working days * 8" and "the sum of allocation ratios must be 100%". Abnormal data marking and isolation: marking and isolating records that fail validation, generating clear error reports, and preventing "dirty data" from entering the core calculation process.

[0032] The data transformation module can perform standardized mapping: heterogeneous codes from different source systems (such as "DeptID" in HRM and "Department_Code" in ERP) are uniformly converted into the system's internal standard codes through a mapping table. Data derivation and aggregation: derived indicators are calculated in real time based on raw data, such as aggregating "effective project hours" based on attendance records, or calculating "dynamic allocation weight base" based on sales revenue.

[0033] The cache management module supports multi-level caching strategies: employing a multi-level caching architecture that combines local caching (such as Guava Cache) and distributed caching (such as Redis). Cache update monitoring: when the master data of the source system (such as an organizational structure system) changes, the relevant cache is proactively refreshed or invalidated through a message queue notification mechanism.

[0034] In this embodiment, the data service layer further includes an allocation calculation unit, which includes: a multi-dimensional allocation module for supporting multi-dimensional allocation by department, project, product line and region; a dynamic weight calculation module for calculating weight coefficients in real time according to preset rules; and a cost tracking module for recording the original data, rule basis and results of each allocation, and generating a complete audit trail.

[0035] The multi-dimensional allocation module allows for cross-dimensional calculations: supporting cross-allocation based on multiple dimensions such as "department × project × cost type". For example, a research and development expense can be first allocated by project, and then its portion under each project can be further allocated by participating departments. Hierarchical allocation: supports hierarchical and recursive allocation based on organizational structure trees or project WBS structures, either top-down or bottom-up.

[0036] The dynamic weight calculation module can obtain weights in real time: weight values ​​not only support fixed configurations but can also be obtained in real time by calling external interfaces or executing sub-rules. For example, the "current project completion progress" can be obtained in real time from the project management system API as a weight coefficient. Weight constraints and adjustments: It supports setting upper and lower limits for weights and automatically performs normalization adjustments after allocation calculation to ensure that the allocation result is reasonable and the total is consistent.

[0037] The cost tracking module enables end-to-end traceability: for each final allocation item, it records all upstream data source IDs (such as the original payslip ID), triggered rule IDs and versions, and intermediate calculation values ​​at each step. Visual audit trails: It provides an interface that allows auditors to click on allocation results to trace the complete calculation path and its basis.

[0038] In this embodiment, the execution control layer includes a conflict handling and fault tolerance unit, which includes: a rule conflict detection module for identifying overlapping or contradictory rules and prompting the user to intervene; an anomaly circuit breaker module for automatically switching to backup rules or transferring to manual processing; and a result verification module for verifying the reasonableness of the allocation results.

[0039] The rule conflict detection module can perform static conflict detection: before a rule is published, static analysis technology is used to detect whether there are rule pairs in the rule base that have "completely overlapping conditions but contradictory action results". Dynamic conflict warning: during rule execution, when the same data is matched by multiple rules and the difference in results exceeds a preset threshold, a warning is issued and execution is paused, submitting the case to manual arbitration.

[0040] The exception circuit breaker module can implement failure degradation strategies: when a rule times out or encounters an error, a degradation strategy is automatically triggered, such as using the result of the last successful execution or executing a simplified backup rule. Circuit breaker mode: for rules that frequently call external services (such as weighted APIs), the circuit breaker mode is adopted, and the system automatically breaks the circuit when the failure rate reaches a threshold to prevent system resources from being overwhelmed.

[0041] The result verification module can automatically balance the verification: calculate the difference in the total amount before and after allocation, and automatically issue an alarm for tasks that exceed the allowable error range (e.g., 0.01 yuan). Reasonableness rule verification: apply a set of post-verification rules (e.g., "the amount allocated to a project by manager-level personnel should not be less than X yuan") to perform a second check on the calculation results.

[0042] In this embodiment, the execution control layer further includes an interface and integration unit, which includes: a unified API gateway module for providing RESTful interfaces for HR systems, ERP, and financial software to call; a message queue adaptation module for supporting the reception of asynchronous distributed tasks via Kafka and RabbitMQ; and a file parsing module for supporting batch data import in Excel and CSV formats.

[0043] The unified API gateway module provides an asynchronous task interface: offering a complete asynchronous API for "submitting allocation tasks - querying task status - obtaining allocation results," suitable for batch processing of large datasets. A real-time query interface is also provided: offering an API for querying real-time allocation results based on dimensions such as employee, department, and time.

[0044] The message queue adaptation module can perform event-driven consumption: subscribing to the "monthly payroll calculation completed" event from the HR system or the "project phase closed" event from the project management system, automatically triggering the corresponding allocation tasks. Result publishing: publishing the allocation completion event and key result summaries to the message queue to notify downstream consumers such as the financial system (generating vouchers) and the budget system (updating actual costs).

[0045] The file parsing module supports template import: users can download predefined templates, populate them with data, and upload them; the system will automatically parse the data based on the template configuration. It also supports large data file chunking: automatically chunking and processing extremely large CSV / Excel files to avoid memory overflow.

[0046] A rule engine-based method for wage allocation includes the following steps: Step 1: Define the wage allocation rule strategy interface, which includes three core methods: determine whether the rule applies to the current context, perform specific allocation calculations and return the result object, and obtain the rule priority value.

[0047] Step 2: Create specific allocation rule classes and implement the strategy interface, and complete the following logic respectively: write the rule matching condition judgment logic, implement the specific allocation algorithm and construct the calculation result, and set the rule priority value to support sorting and matching.

[0048] Step 3: Configure the Spring Bean for salary allocation rules. Define four specific rule implementations through a visual parameter configuration interface. The ApplicationContext manages and assembles these rules into a rule list for easy centralized maintenance and future expansion.

[0049] Step 4: Build a wage allocation rule engine. Sort the injected rules by priority and execute them. Match the first rule that meets the conditions and return its calculation result. If there is no match, return an empty result object. The rule chain pattern is used to realize a flexible and scalable allocation calculation process.

[0050] Step 5: Design a business component generation interface to convert the allocation calculation results into structured business component detail data, providing standardized input for subsequent workflow approvals.

[0051] Step Six: Introduce a multi-threaded mechanism to batch process business component workflow initiation tasks. Use status flags to avoid duplicate processing of already generated business components, ensuring data consistency and processing efficiency.

[0052] Step 7: Establish a retry fallback mechanism to automatically retry business components that fail to start the workflow at exponentially increasing intervals. Cases that still fail are then handled manually, forming a complete closed loop for exception handling.

[0053] This invention employs a rule engine architecture, abstracting allocation rules into independent strategy components. These components are managed and executed through a unified interface. Different allocation algorithms are encapsulated into independent strategy classes, achieving standardized management based on a unified rule interface, ensuring high cohesion and functional independence of each rule module. By constructing a context object containing all allocation data and employing data prefetching and caching mechanisms, duplicate queries are effectively avoided, improving data processing efficiency. Based on multi-level priority evaluation and condition matching algorithms, the optimal allocation rule is accurately identified and automatically selected. A visual rule parameter configuration interface is provided, allowing business personnel to dynamically adjust allocation strategies according to actual needs.

[0054] Furthermore, this invention flexibly supports differentiated resource allocation needs across multiple departments and scenarios through rule combinations and condition configurations. It employs a rule isolation design to ensure that modifications and adjustments to individual rules do not affect the normal operation of other functional modules. Through contextual data aggregation and computational pipeline optimization, it significantly improves the execution efficiency of large-scale data processing. Based on a unified interface specification, it guarantees seamless integration of new rules and high system scalability. An automatic retry mechanism is further introduced, with retry intervals increasing exponentially to effectively handle temporary failures. Ultimately, manual intervention is used as a fallback, improving problem-solving efficiency while preventing data duplication and ensuring data consistency.

[0055] For the foregoing embodiments, in order to simplify the description, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0056] The above embodiments describe the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Modifications and variations made by those skilled in the art without departing from the spirit and scope of the invention should be within the protection scope of the appended claims.

Claims

1. A wage allocation system based on a rules engine, characterized in that, include: The rule configuration layer provides a visual interface and supports users in defining, modifying, and managing allocation rules. The rules engine layer is used to parse and execute rules and drive amortization calculations. The data service layer is used to integrate enterprise human resources, financial accounting, and project management data; The execution control layer is used to schedule task flows, monitor execution status, and handle exceptions.

2. The wage allocation system based on a rule engine as described in claim 1, characterized in that, The rule configuration layer includes: The rule modeling module is used to abstract the allocation rules into a combination of conditions and actions, and supports parameterized configuration of formulas, weights, and priorities; The version management module is used to store, compare, and roll back historical versions of rules; The access control module is used for editing, publishing, and defining the scope of role control rules.

3. The wage allocation system based on a rule engine as described in claim 1, characterized in that, The rule engine layer includes: The policy loading module is used to dynamically load rules and policies through a unified interface; The context management module is used to build the execution environment and inject payroll data, organizational structure, and cost center information; The rule routing module is used to automatically match applicable rules based on data characteristics and supports the parallel or serial execution of multiple rules.

4. The wage allocation system based on a rule engine as described in claim 1, characterized in that, The data service layer includes a data preprocessing unit, which includes: The data validation module is used to verify the integrity of salary data; The data conversion module is used to convert heterogeneous data into a standard format that the rule engine can recognize. The cache management module is used to optimize the caching of frequently accessed baseline data.

5. A rule-based salary allocation system as described in claim 4, characterized in that, The data service layer further includes a cost-sharing calculation unit, which includes: The multi-dimensional allocation module is used to support multi-dimensional allocation by department, project, product line, and region; The dynamic weight calculation module is used to calculate the weight coefficients in real time according to preset rules; The cost tracking module is used to record the original data, rules, and results of each allocation, and to generate a complete audit trail.

6. The wage allocation system based on a rule engine as described in claim 1, characterized in that, The execution control layer includes a conflict handling and fault tolerance unit, which includes: The rule conflict detection module is used to identify overlapping or contradictory rules and prompt the user to intervene. The abnormal circuit breaker module is used to automatically switch to backup rules or transfer to manual processing. The result verification module is used to verify the reasonableness of the allocation results.

7. A rule-based salary allocation system as described in claim 6, characterized in that, The execution control layer further includes an interface and integration unit, which includes: The unified API gateway module provides RESTful interfaces for HR systems, ERP systems, and financial software to use. The message queue adaptation module is used to support receiving asynchronous distributed tasks via Kafka and RabbitMQ; The file parsing module supports batch data import in Excel and CSV formats.

8. A rule-engine-based wage allocation method, implemented based on the rule-engine-based wage allocation system described in any one of claims 1 to 7, characterized in that, Includes the following steps: Step 1: Define the wage allocation rule strategy interface; Step 2: Create a specific allocation rule class and implement the strategy interface; Step 3: Configure the Spring Bean for salary allocation rules; Step 4: Build a wage allocation rule engine, sort the injected rules by priority, and then execute them. Step 5: Design a business component generation interface to convert the allocation calculation results into structured business component detail data, providing standardized input for subsequent workflow approval; Step Six: Introduce a multi-threaded mechanism to batch process business component workflow initiation tasks, and use status flags to avoid duplicate processing of already generated business components, ensuring data consistency and processing efficiency; Step 7: Establish a retry fallback mechanism to automatically retry business components that fail to start the workflow at exponentially increasing intervals. Cases that still fail are then handled manually, forming a complete closed loop for exception handling.

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