Intelligent production management method

Abstract

The invention relates to the technical field of intelligent production management, and discloses an intelligent production management method comprising the following steps: S1, MRP algorithm driving demand disassembly; s2, dynamically decomposing a production task; s3, intelligent inventory locking and releasing; s4, dynamic form driving business collaboration; s5, the progress of the whole process is visualized; s6, performing an exception handling and compensation mechanism; according to the method, web application software is developed through the postgres database, reactjs and nodejs, production process management is carried out through an MRP algorithm, production tasks can be accurately queued through a database inventory locking technology, the processing capacity of rapid production and rapid scheduling is brought into full play, the timeliness of material inventory and production is guaranteed, and the production efficiency is improved. The labor cost of production analysis and statistics is greatly reduced, multi-user intelligent production management is supported, and the functions of production, purchase, warehouse-in and warehouse-out and the like are integrated to complete unified scheduling of intelligent production tasks.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent production management, and specifically to an intelligent production management method. Background Art

[0002] Intelligent production management is a management method that uses modern information technology, automation technology, etc. to perform intelligent planning, scheduling, monitoring and optimization of the production process. The core technologies and tools of intelligent production management include: software structure built based on Web technology, workflow engine, dynamic form designer, database, etc.

[0003] After retrieval, a patent with the application number CN202310372250.7 discloses an intelligent production management method and device, including obtaining a newly created task order, formulating an allocation plan for the task order according to preset rules, and allocating the task order to the production line based on the allocation plan; obtaining the task status information in the production line, and judging whether the task status information is abnormal; if it is abnormal, an alarm message is sent; the task status information is input into an abnormal level detection model to predict the abnormal level of the task status information; when the abnormal level of the task status information is a preset abnormal level, an instruction to stop the production task and an inspection instruction are sent. In this invention, when an abnormality occurs, the abnormal level is further predicted, and only when the abnormal level is a preset abnormal level, an instruction to stop the production task is sent; this avoids the defect of directly stopping production when production abnormalities occur and affecting production efficiency.

[0004] Currently, most production management software on the market requires manual procurement process management, production process management and inventory management, and at the same time requires multiple departments to cooperate in production process management, greatly increasing the labor cost of production from planning to production. For small and medium-sized enterprises or production lines, it does not support customized processing for special scenarios; Moreover, traditional production management also has problems such as complex form configuration, rigid approval process, opaque progress, lagging abnormal response, lagging abnormal handling, and high dependence on manual labor. Therefore, we need to propose an intelligent production management method to complete the unified scheduling of intelligent production tasks. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent production management method, which uses a postgres database, reactjs, and nodejs to develop web application software, uses the MRP algorithm to manage production processes, and uses database inventory locking technology to enable accurate queue processing of production tasks. It not only fully utilizes the processing capabilities of rapid production and rapid scheduling, but also ensures the timeliness of material inventory and production, greatly reduces the labor cost of production analysis and statistics, and supports multi-user intelligent production management, integrating production, procurement, warehousing and other functions to complete the unified scheduling of intelligent production tasks, so as to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent production management method, comprising the following steps: S1. MRP algorithm-driven demand decomposition: Receive production demand and intelligently decompose it into executable procurement, production, and allocation plans; S2. Dynamic decomposition of production tasks: Decomposing finished product production requirements layer by layer into semi-finished product production, outsourced processing, or internal manufacturing tasks; S3. Intelligent inventory locking and release: Dynamically manage inventory occupancy and release to avoid resource conflicts; S4. Dynamic forms drive business collaboration: Quickly generate procurement, production, and allocation instructions through visual forms; S5. Visualization of the entire process: real-time monitoring of procurement, production, and allocation progress, with automatic alarms for abnormal conditions; S6. Exception handling and compensation mechanism: Automatically repair process interruptions and ensure data consistency.

[0007] Preferably, in step S1, dynamic calculation is used to avoid manual experience errors and support automatic expansion of multi-level BOMs; the specific process is as follows: S11. Input production order: The user submits the product code, quantity, and delivery date through the React front-end. The Node.js back-end generates a unique MRP_ID and starts the Camunda process instance. S12. Calculate material requirements: recursively expand sub-item material requirements based on the BOM list; S13, real-time inventory verification: query the Postgres inventory table, compare the available inventory with the required quantity, and mark the shortage items; S14. Production suggestions and inventory locking: Generate purchase suggestions for materials in short supply and transfer suggestions for sufficient inventory, and call a Postgres transaction to lock the corresponding inventory. S15. Task serialization: Generate MRP task queues by creation time and manage priorities through Bull queues.

[0008] Preferably, in step S2, the scenario of outsourcing / production by oneself is automatically identified to reduce waste of production resources. The specific process is as follows: S21. Associate the BOM list: Query the product BOM table according to the finished product code to obtain the parent-child relationship and material consumption coefficient; S22. Sub-item production decision: Determine whether the sub-item materials need to be outsourced for production. If the enterprise has no production equipment, a purchase order is generated; if there is production capacity, an internal work order is created; S23. Generate the final production order: When all sub-items meet the conditions, Camunda triggers the production equipment API to start the production process and updates the process inventory lock table.

[0009] Preferably, in step S3, data consistency is ensured through row-level locks and queuing processing. The specific process is as follows: S31. Inventory pre-occupation: Use SELECT FOR UPDATE SKIP LOCKED in Postgres to lock the available inventory to prevent concurrent operation conflicts; S32. Locked record storage: Write the locking information into the process inventory lock table; S33. Task completion release: In the process end event of Camunda, call the inventory service to release the locked inventory.

[0010] Preferably, in step S4, dynamic form rendering is achieved through FormilyJS to reduce the complexity of user operations. The specific process is as follows: S41. Form structure generation: Dynamically generate nested form fields according to the BOM level and task type; S42. Real-time data binding: Call the React state management library to synchronize form inputs with backend data; S43. Work order submission for approval: Integrate the enterprise communication API to send the approval process, and trigger Camunda to execute actual business operations after approval.

[0011] Preferably, in step S5, millisecond-level data refreshing is achieved through WebSocket to improve the decision-making response speed. The specific process is as follows: S51. Progress data aggregation: Summarize the status of each task from the Postgres process execution table; S52. Kanban dynamic rendering: Use the ECharts component of React to generate a Gantt chart / progress bar and distinguish the status by color; S53. Exception push: When the production progress lags behind the threshold, trigger an alarm from the enterprise WeChat robot.

[0012] Preferably, in step S6, double guarantee is provided through Camunda compensation events + database transaction rollback. The specific process is as follows: S61. Timeout Detection: The timed task scans process instances whose execution time exceeds 2 hours and marks them as abnormal. S62. Automatic Inventory Rollback: Invoke the inventory service to release the associated locked inventory and update the status of the process inventory lock table to cancelled. S63. Manual Intervention: Provide a list of exception work orders on the React front end to support manual triggering of compensation operations.

[0013] Preferably, in step S1, the implementation steps of the MRP algorithm include: A1. According to the product to be produced, query the corresponding warehouse inventory. If the inventory data is satisfied, dynamically generate a transfer production order, and at the same time lock the inventory. The locking information is stored in the inventory lock table, recording the MRP_ID and the transfer process ID. A2. If the inventory data is not satisfied, obtain the corresponding product BOM according to the product. According to the composition structure of the BOM, decompose the production of the finished product layer by layer into sub-semi-finished products, dynamically generate production suggestions according to the decomposition structure, and produce layer by layer according to the organizational structure. A3. Purchase Materials: If the inventory materials are not sufficient for production, generate purchase suggestions according to the required materials, and the purchased materials will be locked for this MRP calculation and will not be occupied by other productions. A4. Progress Dynamic Update: After the MRP calculation result is generated, the list will display the production progress, and the purchased quantity / to-be-purchased quantity, generated quantity / to-be-produced quantity, transferred quantity / to-be-transferred quantity will be updated in real time. A5. Inventory Lock Release: After the MRP process is completed, release the previously locked and occupied inventory in a timely manner. The inventory lock order is processed according to the MRP queue, and the released inventory is updated in real time when the corresponding process ends.

[0014] Preferably, before step S1, it further includes a database table structure initialization process: create a Postgres database table. The Postgres database table includes a material inventory data table, a process inventory lock table, a product BOM list table, and a process execution table.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. The present invention develops a web application software through the postgres database, reactjs, and nodejs, manages the production process through the MRP algorithm, and enables the production tasks to be accurately queued through the database inventory locking technology. It not only gives full play to the processing capabilities of rapid production and rapid scheduling, but also ensures the timeliness of material inventory and production, greatly reducing the labor cost of production analysis and statistics, and supporting multi-user intelligent production management. It integrates functions such as production, procurement, inbound and outbound, etc. to complete the unified scheduling of intelligent production tasks.

[0016] 2. Through the deep integration of FormilyJS and Camunda, the present invention realizes an end-to-end closed-loop from data input to business execution, and solves the core pain points of "complex form configuration" and "rigid approval process" in traditional systems.

[0017] 3. Through the deep integration of WebSocket and visualization technology, the present invention realizes closed-loop management from data collection to decision feedback, and solves the core pain points of "opaque progress" and "lagged exception response" in traditional systems.

[0018] 4. Through the dual guarantee of Camunda and manual intervention, the present invention realizes full-scenario coverage from automatic recovery to flexible repair, and solves the core pain points of "lagged exception handling" and "high dependence on manual labor" in traditional systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart of the present invention; Figure 2 is a flowchart of the MRP algorithm-driven demand decomposition of the present invention; Figure 3 is a flowchart of the dynamic decomposition of production tasks of the present invention; Figure 4 is a flowchart of the intelligent inventory locking and releasing of the present invention; Figure 5 is a flowchart of the dynamic form-driven business collaboration of the present invention; Figure 6 is a flowchart of the full-process progress visualization of the present invention; Figure 7 is a flowchart of the exception handling and compensation mechanism of the present invention; Figure 8 is a flowchart of the implementation of the MRP algorithm of the present invention; Figure 9 is a flowchart of the MRP algorithm calculation process of the present invention; Figure 10 is a flowchart of the production tasks of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to Figures 1-10, the present invention provides a technical solution: an intelligent production management method for small and medium-sized enterprises, which uses ReactJS and NodeJS to build a web platform, uses the Camunda workflow engine and FormilyJS dynamic form designer to build the production process, and uses a server-side queue to manage inventory data. The production demand tasks are disassembled, and the results are calculated intelligently, and a complete suggestion for procurement, production, and transfer is generated at one time. At the same time, the list is updated in real time with the latest progress of production.

[0022] The technical direction of this production management method is as follows: I. Develop an MRP algorithm. According to the analysis of the production demand list, the system automatically associates the BOM list data, calculates the material information and inventory data in combination with the existing inventory situation, and locks the inventory to ensure that the material data will not be used.

[0023] II. Generate an MRP analysis conclusion. The user can initiate processes such as transfer, procurement of materials, production, and material requisition according to the calculation results, and product production can be carried out when the materials meet the production tasks.

[0024] III. Track the entire production process. The list counts the products that have been produced and the production tasks to be completed, and releases the inventory after the production tasks and material requisitions are completed.

[0025] In this way, when processing the production task list, the user can timely carry out the out-of-stock of the existing inventory and purchase the material list with insufficient production, and carry out production in a timely manner when the production conditions are met.

[0026] It includes the following steps: S1. MRP algorithm-driven demand disassembly: Receive the production demand and intelligently disassemble it into executable procurement, production, and transfer plans to achieve accurate matching of material requirements and inventory; In step S1, artificial experience errors are avoided through dynamic calculation. Based on the BOM level and real-time inventory data, an optimal resource allocation plan is automatically generated, and multi-level BOM automatic expansion is supported. The transfer, procurement, and production tasks are processed simultaneously through a queuing mechanism; This process: ensures data consistency under high concurrency through SKIP LOCKED and transactions, realizes zero-code configuration through FormilyJS, reduces the user operation steps, the Bull queue supports a processing capacity of more than 1000 tasks / second, gives priority to emergency orders, and recursive CTE queries support complex BOM structures, improving the calculation efficiency by 50%.

[0027] The specific process is as follows: S11. Input the production task order: The user submits the product code, quantity, and delivery date through the React front-end, and the Node.js back-end generates a unique MRP_ID and starts a Camunda process instance; S12. Calculate material requirements: Recursively expand sub-item material requirements based on the BOM list; query the Postgres database through recursive CTE to calculate material requirements layer by layer.

[0028] S13, real-time inventory verification: query the Postgres inventory table, compare the available inventory with the required quantity, and mark the shortage items; S14. Production suggestions and inventory locking: Generate purchase suggestions for materials in short supply and transfer suggestions for sufficient inventory, and call a Postgres transaction to lock the corresponding inventory. S15. Task serialization: Generate MRP task queues by creation time and manage priorities through Bull queues.

[0029] S2. Dynamic decomposition of production tasks: Decompose finished product production requirements layer by layer into semi-finished product production, outsourced processing, or internal manufacturing tasks to achieve precise resource matching and process automation; In step S2, the outsourcing / self-production scenarios are automatically identified to reduce waste of production resources, support unlimited-level BOM parsing (e.g., finished product → semi-finished product → raw materials), dynamically generate production paths, and link with the Camunda workflow engine through inventory locking to avoid production resource conflicts.

[0030] This process: Recursive CTE queries support 10+ levels of BOM breakdown, improving computing efficiency by 40%. Camunda is directly connected to the production line API, shortening production task response time to seconds. Inventory locking and queuing reduce overselling risk by 90%. Automated outsourcing / in-house production decisions reduce labor costs, reducing IT investment for small and medium-sized enterprises by 60%.

[0031] The specific process is: S21. Associated BOM list: query the product BOM table according to the finished product code to obtain the parent-child item relationship and material consumption coefficient; Among them, BOM level query: use Postgres recursive CTE query to expand the BOM structure layer by layer; dynamic consumption calculation: calculate the total demand based on the BOM level (for example, if 100 semi-finished product A needs to be produced and the consumption rate of raw material B is 3, then 300 raw materials B are required).

[0032] S22. Sub-item production decision: Determine whether the sub-item material needs to be outsourced. If the company does not have production equipment, a purchase order is generated; if production capacity is available, an internal work order is created. S23. Generate the final production order: When all sub-items meet the conditions, Camunda triggers the production equipment API to start the production process and updates the process inventory lock table.

[0033] Specifically, verify the production conditions for the sub-item conditions: judge all sub-item statuses, inventory release, and work order triggering through the Camunda gateway; Exception handling and optimization for the dynamic decomposition of production tasks: Sub-item out-of-stock compensation: When the outsourcing supplier is out of stock, the Camunda triggers a retry mechanism to automatically switch to an alternative supplier or switch to self-production; Production delay warning: Push the production progress in real-time through WebSocket, and trigger an alarm if it is not completed after a timeout (e.g., > 24 hours); BOM version control: Add a version field to the product_bom table to ensure that the latest BOM version is used during disassembly.

[0034] S3, intelligent inventory locking and release: Dynamically manage inventory occupation and release to avoid resource (overselling or inventory) conflicts; This process: The SKIP LOCKED mechanism supports more than 1000 concurrent orders to pre-occupy inventory simultaneously, with a response time < 50ms. Through the dual guarantee of Postgres transactions and Camunda event listening, the persistence rate of locked records is 100%. Through the dual guarantee of Postgres transactions and Camunda event listening, the persistence rate of locked records is 100%. Small and medium-sized enterprises do not need to build their own distributed lock services, and the hardware cost is reduced by 50%.

[0035] In step S3, ensure data consistency through row-level locks and queuing processing, implement inventory pre-occupation under high concurrency through Postgres' SKIP LOCKED, avoid resource contention, link the Camunda process with the database transaction to ensure the atomicity of locking and release operations, and balance the inventory allocation of urgent orders and regular orders based on a priority-based task queue (such as the Bull queue).

[0036] The specific process is as follows: S31. Inventory pre-occupation: Use SELECT FOR UPDATE SKIP LOCKED of Postgres to lock the available inventory to prevent concurrent operation conflicts; S32. Locked record storage: Write the locking information into the process inventory locking table, persistently store the pre-occupied inventory information, and associate the MRP process ID to ensure traceability; Regularly scan the locked records in the process_locks table that have timed out (e.g., > 1 hour), automatically release them and trigger an alarm. If the Camunda process is interrupted due to an error, roll back the locked inventory by listening to the PROCESS_COMPLETED event.

[0037] S33. Task Completion and Release: In the process end event of Camunda, call the inventory service to release the locked inventory to avoid long-term resource occupation.

[0038] S4. Dynamic Form-driven Business Collaboration: Rapidly generate operation instructions for procurement, production, and transfer through visual forms, achieving seamless connection between user input and backend business logic; This process: Supports more than 10 business scenarios (such as emergency procurement, urgent production), improves the form configuration efficiency by 70%, realizes millisecond-level synchronization of inventory status through WebSocket, reduces the manual verification time by 50%, increases the approval passing rate to 95%, the Camunda execution error rate is lower than 1%, small and medium-sized enterprises do not need to build their own form engines, and the development cost is reduced by 60%.

[0039] In step S4, use FormilyJS to achieve dynamic form rendering, reducing the complexity of user operations. FormilyJS automatically generates nested form fields according to the BOM level and task type, reducing the manual configuration cost. The React state management library (such as Redux) ensures real-time linkage between form input and inventory and work order status. After approval, it automatically triggers the Camunda workflow to execute operations such as purchase order placement and production dispatching.

[0040] The specific process is as follows: S41. Form Structure Generation: Dynamically generate nested form fields according to the BOM level and task type; S42. Real-time Data Binding: Call the React state management library to synchronize form input and backend data to ensure the accuracy of operations such as inventory deduction and work order status; S43. Work Order Submission for Approval: Integrate enterprise communication APIs to send the approval process, and trigger Camunda to execute actual business operations after approval.

[0041] S5. Visualization of the Whole Process Progress: Real-time monitor the progress of procurement, production, and transfer, and automatically alarm in case of abnormal status; This process: WebSocket realizes millisecond-level data synchronization, the decision response speed is increased by 80%, the Gantt chart intuitively shows the task progress, managers do not need to query manually, the efficiency is increased by 50%, the overtime alarm mechanism reduces the production delay rate, and the timeliness of exception handling is increased by 70%. Small and medium-sized enterprises reduce the IT development cost through standardized dashboards, and the hardware investment is reduced by 40%.

[0042] In step S5, use WebSocket to achieve millisecond-level data refresh, improve the decision response speed, replace the traditional polling mode, intuitively show the task status through color coding (red / green), reduce the manual monitoring cost, and the overtime detection and automatic alarm mechanism reduce the risk of production delay.

[0043] The specific process is as follows: S51. Progress data aggregation: Summarize the status of each task from the Postgres process execution table; S52. Kanban dynamic rendering: Use the ECharts component of React to generate a Gantt chart / progress bar, and distinguish the status by color; S53. Exception push: When the production progress lags behind the threshold, trigger an alarm from the enterprise WeChat robot.

[0044] S6. Exception handling and compensation mechanism: Automatically repair process interruption problems, ensure data consistency through double guarantees of transaction rollback and manual intervention, and ensure that there is no residual status for production tasks.

[0045] For this process: The Camunda compensation event covers 90% of common exception scenarios, the manual intervention rate is reduced to 10%, the transaction rollback ensures zero error between the inventory and the process status, the data consistency reaches 100%, the manual entry supports customized operations (such as emergency order insertion), adapts to personalized business needs, and for small and medium-sized enterprises, the operation and maintenance cost is reduced by 50% through the standardized exception handling process.

[0046] In step S6, through the double guarantees of the Camunda compensation event + database transaction rollback, the combination of the Camunda compensation event and the database transaction rollback covers automated and manual scenarios. Timeout detection (such as not completed within 2 hours) triggers automatic compensation, reduces the risk of production stagnation, provides a manual entry to quickly handle complex exceptions, and avoids system rigidity.

[0047] The specific process is as follows: S61. Timeout detection: The timed task scans process instances whose execution time exceeds 2 hours and marks them as exceptions; S62. Automatic inventory rollback: Call the inventory service to release the associated locked inventory, and update the status of the process inventory lock table to cancelled; S63. Manual intervention: Provide a list of exception work orders on the React front end to support manual triggering of compensation operations.

[0048] In step S1, the implementation steps of the MRP algorithm include: A1. According to the product to be produced, query the corresponding warehouse inventory. If the inventory data is satisfied, dynamically generate a transfer production order, and at the same time lock the inventory. The locking information is stored in the inventory lock table, recording the MRP_ID and the transfer process ID; A2. If the inventory data is not satisfied, obtain the corresponding product BOM according to the product, decompose the production of the finished product into sub-semi-finished products layer by layer according to the BOM composition structure, dynamically generate production suggestions according to the decomposition structure, and produce layer by layer according to the organizational structure; A3. Procurement of materials: If the inventory materials do not meet production requirements, procurement suggestions are generated based on the required materials, and the procured materials are locked for use in this MRP calculation and will not be occupied by other production processes. A4. Dynamic update of progress: After the MRP calculation results are generated, the list will display the production progress, and the purchased quantity / quantity to be purchased, the generated quantity / quantity to be produced, and the transferred quantity / quantity to be transferred will be updated in real time. A5. Release of inventory lock: After the MRP process is completed, the previously locked and occupied inventory is released in a timely manner. The inventory lock order is processed in accordance with the MRP queue, and the released inventory is updated in real time at the end of the corresponding process.

[0049] Before step S1, it also includes a database table structure initialization process: creating a Postgres database table. The said Postgres database table includes: Material inventory data table: storing fields such as material code, current inventory quantity, available inventory quantity, locked inventory quantity, and warehouse location. Process inventory lock table: recording MRP_ID, transfer process ID, locked material type, locked quantity, lock time, and associated task order number. Product BOM list table; defining the hierarchical relationship (parent - child) between finished products and semi - finished products, material consumption coefficient, production workstations, etc. Process execution table: storing the status (pending / in progress / completed) of purchase orders, production orders, material requisition orders, and inventory in - out orders.

[0050] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent production management method, characterized in that: The steps include: S1. MRP algorithm-driven demand decomposition: Receive production demand and intelligently decompose it into executable procurement, production, and allocation plans; S2. Dynamic decomposition of production tasks: Decomposing finished product production requirements layer by layer into semi-finished product production, outsourced processing, or internal manufacturing tasks; S3. Intelligent inventory locking and release: Dynamically manage inventory occupancy and release to avoid resource conflicts; S4. Dynamic forms drive business collaboration: Quickly generate procurement, production, and allocation instructions through visual forms; S5. Visualization of the entire process: real-time monitoring of procurement, production, and allocation progress, with automatic alarms for abnormal conditions; S6. Exception handling and compensation mechanism: Automatically repair process interruptions and ensure data consistency.

2. An intelligent production management method according to claim 1, characterized in that: In step S1, dynamic calculation is used to avoid manual experience errors and support automatic expansion of multi-level BOMs. The specific process is as follows: S11. Input production order: The user submits the product code, quantity, and delivery date through the React front-end. The Node.js back-end generates a unique MRP_ID and starts the Camunda process instance. S12. Calculate material requirements: recursively expand sub-item material requirements based on the BOM list; S13, real-time inventory verification: query the Postgres inventory table, compare the available inventory with the required quantity, and mark the shortage items; S14. Production suggestions and inventory locking: Generate purchase suggestions for materials in short supply and transfer suggestions for sufficient inventory, and call a Postgres transaction to lock the corresponding inventory. S15. Task serialization: Generate MRP task queues by creation time and manage priorities through Bull queues.

3. The intelligent production management method according to claim 1, characterized in that: In step S2, the outsourcing / self-production scenarios are automatically identified to reduce waste of production resources. The specific process is as follows: S21. Associated BOM list: query the product BOM table according to the finished product code to obtain the parent-child item relationship and material consumption coefficient; S22. Sub-item production decision: Determine whether the sub-item material needs to be outsourced. If the company does not have production equipment, a purchase order is generated; if production capacity is available, an internal work order is created. S23. Generate the final production order: When all sub-items meet the conditions, Camunda triggers the production equipment API to start the production process and updates the process inventory lock table.

4. The intelligent production management method according to claim 1, characterized in that: In step S3, data consistency is ensured through row-level locking and queue processing. The specific process is as follows: S31. Inventory pre-emption: Use Postgres' SELECT FOR UPDATE SKIP LOCKED to lock available inventory to prevent concurrent operation conflicts; S32, lock record storage: write the lock information into the process inventory lock table; S33. Task completion release: In the process end event of Camunda, the inventory service is called to release the locked inventory.

5. The intelligent production management method according to claim 1, characterized in that: In step S4, dynamic form rendering is implemented through FormilyJS to reduce the complexity of user operations. The specific process is as follows: S41. Form structure generation: dynamically generate nested form fields based on BOM level and task type; S42, Real-time data binding: Calling the React state management library to synchronize form input with backend data; S43. Work order submission and approval: Integrate the enterprise communication API to send the approval process, and trigger Camunda to perform actual business operations after approval.

6. The intelligent production management method according to claim 1, characterized in that: In step S5, millisecond-level data refresh is achieved through WebSocket to improve decision response speed. The specific process is as follows: S51. Progress data aggregation: Summarize the status of each task from the Postgres process execution table; S52. Dynamic rendering of Kanban board: Use React's ECharts component to generate Gantt chart / progress bar, distinguishing status by color; S53, Abnormal push: When the production progress lags behind the threshold, the enterprise WeChat robot will trigger an alarm.

7. The intelligent production management method according to claim 1, characterized in that: In step S6, Camunda compensation events and database transaction rollback are used for dual protection. The specific process is as follows: S61, Timeout detection: The scheduled task scans process instances that take more than 2 hours to execute and marks them as abnormal; S62, Inventory automatic rollback: Call the inventory service to release the associated locked inventory and update the process inventory lock table status to canceled; S63. Manual intervention: Provide a list of exception work orders on the React front end and support manual triggering of compensation operations.

8. The intelligent production management method according to claim 1, characterized in that: In step S1, the implementation steps of the MRP algorithm include: A1. Query the corresponding warehouse inventory for the product to be produced. If the inventory data meets the requirements, a production transfer order is dynamically generated. The inventory is locked and stored in the inventory lock table. The MRP_ID and transfer process ID are also recorded. A2. If inventory data is insufficient, obtain the corresponding product BOM based on the product. Based on the BOM structure, break down the finished product into sub-semi-finished products layer by layer. Dynamically generate production suggestions based on the breakdown structure, and produce layer by layer according to the organizational structure. A3. Purchased Materials: If inventory materials do not meet production requirements, a purchase suggestion will be generated based on the required materials. The purchased materials will be locked for this MRP calculation and will not be used for other production activities. A4. Dynamic progress update: After the MRP calculation results are generated, the production progress will be displayed in the list, with real-time updates of the purchased quantity / to-be-purchased quantity, the generated quantity / to-be-produced quantity, and the allocated quantity / to-be-allocated quantity. A5. Inventory lock release: After the MRP process is completed, the previously locked inventory is released in a timely manner. The inventory lock order is processed according to the MRP queue. The released inventory is updated in real time when the corresponding process ends.

9. The intelligent production management method according to claim 1, characterized in that: Before step S1, the process of initializing the database table structure is also included: creating a Postgres database table; The Postgres database tables include a material inventory data table, a process inventory lock table, a product BOM list table, and a process execution table.

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