Supply chain service rule verification system and method, medium and equipment
By using dynamic form parsing and an asynchronous computing engine to generate field dependency graphs for parallel processing, the problems of response latency and low resource utilization in existing technologies are solved, enabling efficient and flexible supply chain business rule validation.
Patent Information
- Application Number
- CN202511681473.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-17
- Publication Date
- 2026-02-24
AI Technical Summary
Existing supply chain business rule verification systems suffer from high response latency, low resource utilization, insufficient flexibility, and high maintenance costs, making them unable to adapt to rapidly changing supply chain business needs.
The system uses a dynamic form parsing module to generate a field dependency graph, and combines it with an asynchronous computing engine and a rule visualization and intervention module to achieve low-code configuration, parallel processing, and visual monitoring, and dynamically adjust business rules.
It significantly improves response speed, resource utilization and flexibility, reduces system maintenance costs, and adapts to complex and ever-changing supply chain management scenarios.
Smart Images
Figure CN121560431A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a supply chain business rule verification system, method, medium, and device based on dynamic form dependency graphs and asynchronous computation, belonging to the field of supply chain management technology. Background Technology
[0002] Existing supply chain business rule verification systems mostly adopt a static rule configuration and synchronous execution architecture, which has the following main drawbacks: High response latency, synchronous verification requires sequential execution of rule chains, and remote API calls and other I / O operations cause accumulated latency, resulting in a poor user experience. Insufficient flexibility, rule changes require developers to modify code and redeploy, which cannot adapt to the rapidly changing needs of supply chain business. Low resource utilization, computing resources are idle during synchronization waiting, failing to fully utilize the parallel processing capabilities of multi-core CPUs. High maintenance costs, complex business logic leads to bloated code, and problems are difficult to locate. Summary of the Invention
[0003] The purpose of this invention is to provide a supply chain business rule verification system, method, medium, and device. Through the collaboration of multiple modules, the efficiency of the supply chain business rule verification system is significantly improved, supporting real-time verification, dynamic adjustment, and efficient resource utilization.
[0004] To achieve the above objectives, the present invention is implemented using the following technical solution.
[0005] On one hand, the present invention provides a supply chain business rule verification system, comprising:
[0006] The dynamic form parsing module allows you to configure business rules through a visual interface, deduce dependencies based on form field attributes, and generate a machine-executable field dependency graph.
[0007] An asynchronous computing engine is used to receive field dependency graphs, distribute tasks to the main thread and Web Worker thread pool for parallel processing, push results and status to the rule visualization and intervention module, and update the user interface.
[0008] The rule visualization and intervention module is used to monitor the node status and data flow of the field dependency graph, and provides a manual intervention interface to dynamically operate on tasks in execution.
[0009] Optionally, the dynamic form parsing module includes:
[0010] A zero-code / low-code form designer that provides a graphical component drag-and-drop and property configuration interface for configuring business rules;
[0011] A form configuration parser is used to parse business rules in a JSON Schema.
[0012] The automatic dependency inference unit is used to analyze the dependencies between fields and construct a field dependency graph.
[0013] With zero-code / low-code form design, business users can quickly customize and modify rules without development intervention, greatly shortening the response cycle for business requirements.
[0014] Optionally, the asynchronous computing engine includes a task scheduler, which dynamically calculates task priorities based on the topological sorting results of the field dependency graph and manages the task execution queue.
[0015] The race condition control unit generates a unique identifier (UUID) for each asynchronous task, ensuring that subsequent requests do not overwrite the results of previously incomplete requests.
[0016] The debouncing merging unit merges the same field validation requests that are triggered consecutively within a short period of time, and only performs the last validation, which can reduce redundant calculations.
[0017] Optionally, the debouncing merging unit performs debouncing processing when the field value changes;
[0018] The set of affected fields is calculated based on the field dependency graph, and the race control unit creates a task for each field and generates a unique identifier to achieve race control.
[0019] Optionally, the main thread performs lightweight synchronous validation, including required field validation, format validation, or range validation; the Web Worker thread pool performs heavyweight asynchronous validation, including API calls or complex calculations.
[0020] Asynchronous parallel verification increased system data throughput several times, greatly optimizing the user experience. Intelligent task scheduling and Web Worker thread pools fully utilized the computing power of modern multi-core CPUs, avoiding resource idleness caused by synchronous waiting.
[0021] Optionally, the rule visualization and intervention module adopts multimodal visual presentation, using nodes of different colors and shapes to represent field status, and highlighting lines to show dependencies and data flow; the field status includes pending verification, verification in progress, success, failure, and waiting for dependencies.
[0022] Dependency graph visualization makes complex business logic clear at a glance, simplifying the debugging and problem localization process.
[0023] Optionally, dynamic operations can be performed on tasks in execution, and intervention instructions can be fed back to the task scheduler; the dynamic operations include retry, skipping, and priority adjustment, which enhances the controllability of the system.
[0024] Secondly, the present invention provides a supply chain business rule verification method based on the supply chain business rule verification system according to any one of the first aspects, comprising:
[0025] The dynamic form parsing module receives form configurations, parses them, and generates a field dependency graph.
[0026] Listen for changes in form field values and determine the set of affected fields based on the field dependency graph;
[0027] The asynchronous computing engine creates a validation task for each affected field and adds it to a priority task queue.
[0028] Schedule and execute tasks in the task queue, performing debouncing and race condition control during the process;
[0029] Update the user interface based on the task execution results and provide verification information.
[0030] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the supply chain business rule verification method of the second aspect.
[0031] Fourthly, the present invention provides a computer device, comprising:
[0032] Memory, used to store computer programs;
[0033] A processor for executing the computer program to implement the supply chain business rule verification method of the second aspect.
[0034] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:
[0035] Compared to existing technologies, the supply chain business rule verification system provided by this invention has advantages such as low response latency, high flexibility, and high resource utilization, while also having lower system maintenance costs. It is suitable for modern supply chain management scenarios with diverse data sources, complex business logic, and ever-changing requirements. By introducing low-code configuration, automatic dependency derivation, parallel computing, and visual monitoring, this invention significantly improves the efficiency and accuracy of business rule verification, providing a solid technical foundation for the intelligentization and automation of supply chain management systems. Attached Figure Description
[0036] Figure 1 This is a flowchart illustrating the overall system architecture and data processing of an embodiment of the present invention. Detailed Implementation
[0037] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.
[0038] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.
[0039] Example 1
[0040] This embodiment introduces a supply chain business rule verification system, such as... Figure 1 As shown, the system comprises three main modules: a dynamic form parsing module, an asynchronous computing engine, and a rule visualization and intervention module.
[0041] Specifically, the dynamic form parsing module configures business rules through a visual interface, derives dependencies based on form field attributes, and generates a machine-executable directed acyclic graph (DAG), where nodes represent fields or external data sources, and edges represent dependencies between fields.
[0042] The asynchronous computing engine receives the field dependency graph, distributes tasks to the main thread and Web Worker thread pool for parallel processing, summarizes the results and updates the status, and pushes the results and status to the rule visualization and intervention module and updates the user interface.
[0043] The rule visualization and intervention module is used to monitor the node status and data flow of the field dependency graph, and provides a manual intervention interface to dynamically operate on tasks in execution.
[0044] The dynamic form parsing module includes the following sections:
[0045] A zero-code / low-code form designer that provides a graphical component drag-and-drop and property configuration interface to configure business rules. It allows business users to define field types, validation rules, and display logic without the need for code intervention.
[0046] A form configuration parser is used to parse business rules in a JSON Schema.
[0047] The dependency inference unit analyzes the dependencies (explicit and implicit dependencies) between fields based on field configuration attributes and automatically constructs a field dependency graph.
[0048] Specifically, the asynchronous computing engine includes a task scheduler, which dynamically calculates task priorities based on the topological sorting results of the field dependency graph and manages the task execution queue.
[0049] The race condition control unit generates a unique identifier (UUID) for each asynchronous task, ensuring that subsequent requests do not overwrite the results of previously incomplete requests.
[0050] The debouncing merging unit merges the same field validation requests that are triggered consecutively within a short period of time, and only performs the last validation, which can reduce redundant calculations.
[0051] Specifically, when a field value changes, the debouncing merging unit performs debouncing processing; the set of affected fields is calculated based on the field dependency graph, and the race control unit creates a task for each field and generates a unique identifier to achieve race control.
[0052] The asynchronous computing engine enables intelligent task allocation:
[0053] Lightweight synchronous validations (such as required fields, format, and range) are assigned to the main thread for execution, while heavyweight asynchronous validations (such as calling backend APIs to verify inventory and verify supplier qualifications) are assigned to the Web Worker thread pool for parallel execution.
[0054] The rule visualization and intervention module uses nodes of different colors and shapes to represent field status (waiting, validating, success, failure), and highlights the dependencies and data flow with connected lines.
[0055] Operators are allowed to dynamically intervene in the execution process, such as manually retrying failed tasks, temporarily skipping a check, or adjusting task priorities to deal with emergency business scenarios.
[0056] Based on the above system, this embodiment provides a JSON Schema configuration example, defining a supplier quality inspection form:
[0057] { "formId": "supplier_quality_check", "fields": [ { "key": "defect_rate", "label": "product defect rate", "type": "number", "rules": ["required", { "max": 0.05, "message": "defect rate exceeds 5% threshold"}], "dataSource": "api: / / quality / system / statistic?supplierId={supplierId}"}, { "key": "penalty_action", "label": "default handling plan", "type": "select", "showWhen": "defect_rate > 0.05", "options": [ { "value": "fine", "label": "economic compensation"}, { "value": "suspend", "label": "suspension of cooperation"} ]} ]};
[0058] The form defines two fields: defect_rate and penalty_action.
[0059] The `defect_rate` field contains a valid rules array (`rules`) that includes required fields. These rules state that the maximum defect rate cannot exceed 0.05; if it does, an error message will be displayed. The data for this field comes from an API located at `quality / system / statistic`. When the form needs to retrieve defect values, this API is called, and the returned data is used as the defect rate. This field is a core business metric used to monitor the supplier's product quality level.
[0060] The penalty_action field will only be displayed when the defect rate is greater than 0.05, and users can choose one of two options: financial compensation or suspension of cooperation.
[0061] This embodiment illustrates the system's execution scheme in a scenario where a supplier's product defect rate reaches 7%, or 0.07. The system then performs the following operations:
[0062] The system automatically retrieves the 7% defect rate data from the API. Since 7% > 5%, the "Breach of Contract Handling Plan" field is automatically displayed. Quality management personnel can choose "Economic Compensation" or "Suspension of Cooperation" based on the situation. The system records the handling decision and triggers subsequent business processes.
[0063] Example 2
[0064] Based on the same inventive concept as Embodiment 1, this embodiment provides relevant code to illustrate the asynchronous computation process:
[0065] Dynamic form parsing and dependency graph construction, code as follows:
[0066] const schema = await fetchFormSchema('supplier_quality_check'); constdependencyGraph = buildAndSortDependencyGraph(schema);
[0067] Retrieve the form schema from the server, construct and sort the dependency graph based on the schema, and use the dependency graph to determine the word...
[0068] The dependencies between sections can be identified, and when a certain field changes, all affected fields can be found.
[0069] Monitor field changes and schedule validation tasks:
[0070] function onFieldChange(changedFieldKey, newValue) { constaffedFields = findAffectedFields(dependencyGraph, changedFieldKey);affectedFields.forEach(field => { const validationTask = createValidationTask(field, newValue); if (isHeavyTask(validationTask)) { scheduleToWebWorker('. / validation-worker.js', validationTask) .then(result => updateFieldUI(field.key, result)) .catch(error => handleValidationError(field.key,error));} else { const result = executeLightweightValidation(validationTask); updateFieldUI(field.key, result);}});}
[0071] When a field value changes, all affected fields are identified based on the dependency graph. A validation task is created for each affected field, and the task's importance is determined by whether it is executed on the main thread or scheduled to a Web Worker.
[0072] Performing complex validations in Web Workers:
[0073] self.onmessage = function(e) { const task = e.data; let result;switch (task.type) { case 'API_VALIDATION': result = fetch(task.dataSource).then(response => response.json()); break; case 'COMPLEX_CALCULATION':result = performComplexCalculation(task.data); break; default: result = {isValid: false, message: 'Unknown task type'};} Promise.resolve(result).then(data => self.postMessage(data));};
[0074] In Web Worker, different validation logic is executed based on the task type, such as API validation or complex calculations, and the results are returned to the main thread.
[0075] Example 3
[0076] Based on the above embodiments, this embodiment introduces a supply chain business rule verification method:
[0077] The system receives form configurations through a dynamic form parsing module, parses and generates a field dependency graph; listens for form field value change events, and determines the set of affected fields based on the field dependency graph; an asynchronous computing engine creates a validation task for each affected field and adds it to a priority task queue; it schedules and executes tasks in the task queue, performing debouncing and race conditions control during the process; and it updates the user interface and provides validation information based on the task execution results.
[0078] Example 4
[0079] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the supply chain business rule verification method provided in Embodiment 3.
[0080] Example 5
[0081] Fourthly, the present invention provides a computer device, characterized in that it comprises:
[0082] Memory, used to store computer programs;
[0083] A processor is used to execute the computer program to implement the supply chain business rule verification method of Embodiment 3.
[0084] In summary, this invention proposes a highly efficient supply chain business rule validation solution through the deep integration of a dynamic form dependency graph and an asynchronous computing engine. The system effectively overcomes the shortcomings of traditional solutions in terms of response latency, flexibility, and resource utilization, making it particularly suitable for modern supply chain management scenarios characterized by diverse data sources, complex business logic, and ever-changing requirements. By introducing low-code configuration, automatic dependency derivation, parallel computing, and visual monitoring, the efficiency and accuracy of business rule validation are significantly improved, providing a solid technical foundation for the intelligentization and automation of supply chain management systems.
[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0086] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0087] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0089] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.
Claims
1. A supply chain business rule verification system, characterized in that, include: The dynamic form parsing module allows you to configure business rules through a visual interface, deduce dependencies based on form field attributes, and generate a machine-executable field dependency graph. An asynchronous computing engine is used to receive field dependency graphs, distribute tasks to the main thread and Web Worker thread pool for parallel processing, push results and status to the rule visualization and intervention module, and update the user interface. The rule visualization and intervention module is used to monitor the node status and data flow of the field dependency graph, and provides a manual intervention interface to dynamically operate on tasks in execution.
2. The supply chain business rule verification system according to claim 1, characterized in that, The dynamic form parsing module includes: A zero-code / low-code form designer that provides a graphical component drag-and-drop and property configuration interface for configuring business rules; A form configuration parser is used to parse business rules in a JSON Schema. The automatic dependency inference unit is used to analyze the dependencies between fields and construct a field dependency graph.
3. The supply chain business rule verification system according to claim 2, characterized in that, The asynchronous computing engine includes a task scheduler, which dynamically calculates task priorities based on the topological sorting results of the field dependency graph and manages the task execution queue. The race condition control unit generates a unique identifier for each asynchronous task; The debouncing merging unit merges identical field validation requests that are triggered consecutively within a short period of time, and only performs the last validation.
4. The supply chain business rule verification system according to claim 3, characterized in that, When the field value changes, the debouncing merging unit performs debouncing processing; The set of affected fields is calculated based on the field dependency graph, and the race control unit creates a task for each field and generates a unique identifier to achieve race control.
5. The supply chain business rule verification system according to claim 4, characterized in that, The main thread performs lightweight synchronous checks, including mandatory field checks, format checks, or range checks. Web Worker thread pools perform heavyweight asynchronous checks, including API calls or complex calculations.
6. The supply chain business rule verification system according to claim 5, characterized in that, The rule visualization and intervention module adopts a multimodal visual presentation, using nodes of different colors and shapes to represent field status, and highlighting lines to show dependencies and data flow. The field status includes pending verification, verification in progress, success, failure, and waiting for dependencies.
7. The supply chain business rule verification system according to claim 6, characterized in that, Dynamically manage tasks in progress and feed back intervention commands to the task scheduler. The dynamic operations include retry, skip, and priority adjustment.
8. A supply chain business rule verification method based on the supply chain business rule verification system according to any one of claims 1 to 7, characterized in that, include: The dynamic form parsing module receives form configurations, parses them, and generates a field dependency graph. Listen for changes in form field values and determine the set of affected fields based on the field dependency graph; The asynchronous computing engine creates a validation task for each affected field and adds it to a priority task queue. Schedule and execute tasks in the task queue, performing debouncing and race condition control during the process; Update the user interface based on the task execution results and provide verification information.
9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the supply chain business rule verification method as described in claim 8.
10. A computer device, characterized in that, include: Memory, used to store computer programs; A processor is used to execute the computer program to implement the supply chain business rule verification method of claim 8.