Multi-system fused wooden door manufacturing enterprise information system integration architecture and method

Abstract

The invention discloses a multi-system fused wooden door manufacturing enterprise information system integration architecture and method, which adopts a layered loose coupling design, and comprises an enterprise operation layer which comprises an enterprise resource planning system for order and financial management and a product life cycle management system for product model and process management; the planning and scheduling layer comprises an advanced planning and scheduling system which serves as a planning decision center and carries out limited productivity simulation and optimized scheduling based on multi-source real-time data, and the manufacturing operation layer comprises a manufacturing execution system used for production execution and monitoring and a warehouse management system used for fine management of materials. By constructing a unified data model, adopting an event-driven loose coupling collaboration mechanism and establishing a middle platform for converging real-time manufacturing data, full-process digital communication and intelligent linkage from customer orders, product design, material supply, intelligent scheduling, manufacturing execution to warehouse logistics are realized.

Description

Technical Field

[0001] This invention relates to the field of enterprise information system technology, specifically to an integrated architecture and method for a multi-system integrated information system for a wooden door manufacturing enterprise. Background Technology

[0002] Currently, the wooden door manufacturing industry is transforming from a traditional mass production model to a customized intelligent manufacturing model characterized by small batches, multiple varieties, and personalization. In this process, many companies have successively introduced information systems based on Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), and Warehouse Management System (WMS) to improve management levels. However, existing technological solutions generally suffer from the following prominent problems in practical applications:

[0003] (1) The phenomenon of "information silos" between systems is serious. Each system is deployed independently, the data standards are not uniform, and the interfaces are mostly hard-coded point-to-point, which makes it impossible for data to flow automatically and accurately. For example, order changes in ERP cannot be synchronized to MES in real time, and the production performance data of MES also needs to be manually entered before it can be fed back to ERP for cost accounting, which is inefficient and prone to errors.

[0004] (2) Production planning and on-site execution are seriously disconnected. ERP, based on macro material requirements planning (MRP) with unlimited capacity, is unable to cope with the dynamic and complex situations on the production site (such as sudden equipment failure, material supply delays, and temporary process changes). APS system, which lacks real-time data input, often has production scheduling results that do not match the actual capacity and have poor executability.

[0005] (3) The production process is opaque, forming a "black box". Management has difficulty grasping the order progress, work-in-process distribution, overall equipment efficiency (OEE) and key quality parameters in real time and accurately, and decision-making lacks effective data support, making it difficult to trace problems.

[0006] (4) Insufficient overall flexibility and responsiveness. Faced with frequent changes in customer orders, emergency orders, or design adjustments, the existing rigid system architecture is unable to achieve rapid coordination and linkage from design, planning, materials to production. The overall adjustment cycle is long and cannot meet the agility requirements of the customized market.

[0007] The root cause lies in the lack of a top-level integration architecture and methodology capable of achieving unified data semantics, automated business collaboration, and supporting real-time decision-making. Existing system integration technologies often employ point-to-point hard-coded interfaces, resulting in high coupling between systems, poor scalability, and high maintenance costs. This fails to support the core requirements of customized intelligent wooden door manufacturing for end-to-end integration, real-time operation, transparency, and flexibility. Summary of the Invention

[0008] (a) Technical problems to be solved

[0009] To address the shortcomings of existing technologies, this invention provides an integrated architecture and method for a multi-system deep integration of information systems in wooden door manufacturing enterprises. The purpose of this invention is to achieve full-process digital connectivity and intelligent linkage from customer orders, product design, material supply, intelligent scheduling, manufacturing execution to warehousing and logistics by constructing a unified data model, adopting an event-driven loosely coupled collaborative mechanism, and establishing a central platform that aggregates real-time manufacturing data. This effectively breaks down information silos, achieves precise matching between planning and execution, improves the transparency and traceability of the production process, and significantly enhances the enterprise's overall flexible manufacturing capabilities to meet customized and variable production demands.

[0010] (II) Technical Solution

[0011] To achieve the above objectives, the present invention provides the following technical solution: an integrated information system architecture for a wooden door manufacturing enterprise that integrates multiple systems, adopting a layered loosely coupled design, comprising:

[0012] The enterprise operations layer includes an Enterprise Resource Planning (ERP) system for order and financial management, and a Product Lifecycle Management (PLM) system for product model and process management.

[0013] The planning and scheduling layer includes the Advanced Planning and Scheduling (APS) system, which serves as the planning decision-making center and performs finite capacity simulation and optimized scheduling based on multi-source real-time data.

[0014] The manufacturing operations layer includes the Manufacturing Execution System (MES) for production execution and monitoring, and the Warehouse Management System (WMS) for refined material management.

[0015] The field perception layer includes a Supervisory Control and Data Acquisition (SCADA) system for monitoring critical equipment and Internet of Things (IoT) acquisition terminals for collecting data on personnel, materials, and the environment.

[0016] The core integration layer includes:

[0017] Enterprise Service Bus (ESB) or Message Queuing (MQ) is used to enable asynchronous, standardized message passing based on events between systems;

[0018] The Unified Data Model and Master Data Management (MDM) module is used to define and maintain master data standards and a unified coding system covering all domains of materials, equipment, processes, and orders, providing a semantic foundation for data interaction across the entire system.

[0019] The manufacturing data platform is used to collect, clean, integrate and store process data from the manufacturing operations layer and the field perception layer in real time, forming a manufacturing data lake.

[0020] The core integration layer connects and coordinates the enterprise operation layer, planning and scheduling layer, manufacturing operation layer, and field perception layer through the event-driven mechanism based on the unified data model and the ESB / MQ, to achieve automatic data flow and intelligent business collaboration throughout the entire process from order receipt to product delivery.

[0021] As a preferred embodiment, the dynamic data input sources required for the scheduling calculation of the APS system include: order demand data from ERP, process constraint data from PLM, real-time capacity and work-in-process status data from MES, material inventory data from WMS, and real-time equipment status data from SCADA, thereby achieving accurate scheduling based on global real-time information.

[0022] As a preferred solution, the manufacturing data lake built by the manufacturing data platform covers all elements of "people, machines, materials, methods, environment, and measurement", and provides unified data services for the scheduling optimization, event-triggered dynamic rescheduling, production big data analysis, and visual monitoring of the APS system.

[0023] As a preferred option, the core integration layer also includes a security and operation and maintenance module, which provides cross-layer data encryption transmission, fine-grained access control, monitoring of the operating status of each system, and full operation log auditing functions to ensure the overall security and stability of the system.

[0024] As a preferred embodiment, the event-driven mechanism is specifically driven by a series of business events, which include at least order creation / modification events, process data release events, production anomaly events, and equipment status events. Each system subscribes to the events it is interested in through the ESB / MQ and automatically triggers predefined business processes when an event is released, thereby achieving loosely coupled collaboration across systems.

[0025] A method for integrating information systems in a wooden door manufacturing enterprise, characterized by the following steps:

[0026] S1: Master data initialization and synchronization. In the MDM module, the coding and data model of materials, equipment and processes are uniformly defined, and synchronized to various business systems through the core integration layer to ensure data consistency.

[0027] S2: Order-driven design responds to order creation or change events in the ERP system. Through an event-driven mechanism, it automatically triggers the PLM system to perform product configuration and process design, and publishes structured bill of materials (BOM) and process route (BOP) data to the manufacturing data platform.

[0028] S3: Dynamic Advanced Planning and Scheduling. The APS system obtains real-time data from the manufacturing data platform and related systems based on preset cycles or event triggers, executes limited capacity simulation scheduling under multiple constraints, and generates optimized work plans.

[0029] S4: Precise issuance from planning to execution. The work plan generated in S3 is synchronously issued to MES and WMS through the core integration layer. MES converts it into specific work orders and dispatches them to workstations. WMS converts it into material delivery instructions.

[0030] S5: Full-process execution and real-time data collection. During the production process, MES guides on-site operations, WMS executes material distribution, and SCADA and IoT devices collect production, equipment, and quality data in real time and upload them to MES and the manufacturing data platform.

[0031] S6: Real-time feedback and closed-loop dynamic adjustment. MES provides real-time feedback on production status to APS and ERP. When an abnormal production event is detected, APS is triggered to dynamically reschedule, generate and issue the adjusted work plan, forming an agile closed loop of planning and execution.

[0032] S7: Order completion and business closure. After production is completed, MES triggers finished product reporting, WMS executes finished product warehousing, and sends an order completion event to ERP. ERP automatically completes cost accounting and closes the order status.

[0033] As a preferred embodiment, the events in S2 and S3 include, but are not limited to, order events, process data release events, equipment malfunction events, and material shortage events. All of these events are published and subscribed to through ESB / MQ to achieve automatic triggering and response of cross-system business flows.

[0034] As a preferred embodiment, the production anomaly events in S6 include equipment failure, defective materials, exceeding quality parameters, and time deviations. The Advanced Planning and Scheduling (APS) system subscribes to these events and triggers rescheduling calculations to generate adjusted work plans and reissue them.

[0035] As a preferred embodiment, in step S5, the data collected by the SCADA system and IoT devices includes the spindle speed, operating power, coordinate position, vibration value, temperature, material RFID information, process scanning information, and quality inspection results. This data is aggregated in real time on the manufacturing data platform, providing a data foundation for production process transparency and full lifecycle traceability.

[0036] As a preferred solution, all data interactions across the entire system are based on the unified data model, ensuring that core business entities have unique identifiers and consistent semantics throughout the system, eliminating data ambiguity from the source, and enabling accurate, seamless, and automatic cross-system data flow, quality inspection data, etc., which are then uploaded to the MES and manufacturing data platform in real time.

[0037] S6: Real-time Feedback and Dynamic Adjustment. The MES system provides real-time feedback on the start, completion, and scrapping status of work orders to the APS and ERP systems. When the MES or SCADA system detects anomalies such as equipment failure, defective materials, or quality exceeding standards, it immediately generates a "production anomaly event" and publishes it via ESB / MQ. APS systems subscribed to these events automatically trigger rescheduling calculations, generate adjusted plans, and reissue them, forming an agile closed loop of "perception-decision-adjustment."

[0038] S7: Finished Goods Receiving and Order Closure. After all processes are completed, MES triggers a finished goods reporting process. WMS guides the finished goods to be scanned and put into storage, updating the inventory status. Subsequently, MES sends an "order completion event" to ERP, which automatically performs cost accounting and closes the order, completing the entire business loop.

[0039] (III) Beneficial Effects

[0040] Compared with existing technologies, this invention provides an integrated architecture and method for a multi-system information system in a wooden door manufacturing enterprise, which has the following beneficial effects:

[0041] (1) Achieved semantically unified deep data collaboration: By constructing an enterprise-level unified data model and MDM, the core problems of inconsistent data standards and semantic ambiguity among multiple systems were solved from the source, and the accurate and unambiguous automatic flow of cross-system data was realized, laying a solid foundation for deep business integration.

[0042] (2) An event-driven flexible business collaboration was constructed: an event-driven architecture with loose coupling was implemented using ESB / MQ, replacing the rigid point-to-point hard-coded interface. By defining a series of business events, automatic triggering and intelligent linkage of the entire process from order to delivery were realized, which greatly improved the system's agility and flexibility in responding to changes (such as order changes, emergency order insertions, and anomalies).

[0043] (3) It supports dynamic intelligent decision-making based on real-time data: By aggregating data from the entire manufacturing process in real time through the manufacturing data platform, it provides APS with real, multi-source, and dynamic decision input. This enables APS scheduling to be upgraded from static prediction to dynamic response based on real-time status, and supports second-level rescheduling triggered by events, which significantly improves the executability of production plans and the overall system's anti-disturbance capability.

[0044] (4) Achieved full transparency and traceability of the production process: Based on real-time and comprehensive data collection and the data aggregation capability of the middle platform, it realized full-scale and visual monitoring of order progress, equipment status, process execution and quality data, and supported the forward tracking and reverse traceability of the entire life cycle of the product from raw materials to finished products.

[0045] (5) Reduced system integration complexity and long-term maintenance costs: The standardized layered architecture and loosely coupled integration method make it easy for each business system to be upgraded, expanded and replaced independently, which greatly improves the flexibility, maintainability and investment protection of the entire enterprise IT architecture. Attached Figure Description

[0046] Figure 1 Overview diagram of the integrated architecture of the information system for digital and intelligent manufacturing of wooden doors;

[0047] Figure 2 Flowchart of the main steps in the integration of a digital and intelligent manufacturing information system for wooden doors;

[0048] Figure 3 A sequence diagram of collaborative response to "order changes" driven by events in the digital and intelligent manufacturing of wooden doors;

[0049] Figure 4 Schematic diagram of the data acquisition and quality feedback control principle of the digital intelligent manufacturing workstation for wooden doors. Detailed Implementation

[0050] To better understand the purpose, structure, and function of this invention, specific embodiments of the invention are now described with reference to the accompanying drawings, but the scope of protection of this invention is not limited to the following description.

[0051] Example 1: Implementation of a Smart Production Line System for Customized Wooden Doors

[0052] As attached Figure 1 As shown, this integrated architecture is deployed in an actual wooden door factory as follows: ERP, PLM, APS and manufacturing data platform are deployed in the enterprise data center (cloud or local); MES and WMS servers are deployed in the workshop computer room; an industrial ring network is deployed in the workshop to connect the SCADA server, operation terminal, RFID reader, sensor and other data acquisition equipment at each workstation.

[0053] The specific implementation steps are detailed below:

[0054] (1) Master data preparation: In the MDM module, define the material code of "Solid Wood Composite Door Leaf - European Style - Model A" as "SMFH-OS-A" and associate it with the sub-item codes of all its boards, hardware, etc. Define the equipment code of "CNC Machining Center - No. 01" as "CNC-01". Through ESB, initialize and synchronize these basic data to all systems including ERP, PLM, APS, MES, and WMS in one go.

[0055] (2) Order Trigger Design: Sales personnel create a sales order "ORD202310001" in the ERP system, ordering 10 sets of "SMFH-OS-A" doors with specifications of 2150×900×45 mm. After the order is confirmed, the ERP system publishes the "NewOrder Event" to the entire system through the ESB. The PLM system that subscribes to the event responds automatically, calls the parametric 3D model of "SMFH-OS-A", drives the model update according to the order size, automatically generates the instantiated BOM, the corresponding CNC machining program (G code) and paint spraying parameters, and publishes them to the manufacturing data platform after review.

[0056] (3) Intelligent Scheduling: The APS system is set to run scheduling automatically every hour. When scheduling is triggered, APS obtains the process route (material cutting → milling → sanding → spraying → assembly) of ORD202310001 from the manufacturing data platform, and at the same time obtains the real-time load of each equipment from MES (e.g., CNC-01 is currently at 70% load), and checks the WMS to see if the required oak veneer panels are in stock. APS simulates based on constraints such as delivery time, process sequence, and equipment capacity, and schedules the "milling" process of this order from 14:00 to 15:00 on the same day, occupying the CNC-01 equipment, and simultaneously generates an instruction to deliver the specified materials to the CNC station during this time period to the WMS.

[0057] (4) Task execution and data acquisition: At 13:50, the WMS delivers the oak veneer panels to the cutting station on time according to the instructions. The MES displays the work order on the screen at the cutting station, and the worker scans the code to start work. After the cutting is completed, the boards are tagged with RFID tags. When the boards are transferred to the CNC station, the RFID reader automatically identifies them, and the MES immediately retrieves the corresponding processing program and sends it to the CNC-01 controller. The SCADA system begins to collect and monitor the status data of the equipment, such as operating power, spindle speed, and coordinate position, in real time.

[0058] (5) Abnormal Response and Dynamic Adjustment: Suppose that when CNC-01 is machining the 5th door, SCADA detects that the spindle vibration value exceeds the threshold and immediately generates an "Equipment Abnormal Alarm Event". After receiving the event, MES automatically sets the current work order to "Interrupted" status and notifies maintenance. At the same time, MES publishes an "Equipment Fault Event" through ESB. The APS system subscribed to this event is immediately triggered, and the "milling" task for the remaining 5 doors is rescheduled, assigned to the backup equipment CNC-02, and the planned time for all subsequent processes is recalculated. The updated work plan and expected delivery time are synchronized to MES, WMS and fed back to ERP.

[0059] (6) Order closure: After all processes are completed, the finished product is packaged and a unique identification code is affixed. After scanning at the warehouse entrance, the WMS updates the inventory status to "completed". The MES sends an "Order Completed Event" to the ERP system, and the ERP system automatically triggers operations such as cost aggregation and accounts receivable update for the order, thus completing the order status closure.

[0060] Through the above implementation methods, the present invention effectively realizes the full-process, multi-system, and in-depth collaborative and intelligent management of customized wooden door production from order receipt to product delivery, verifying the feasibility and superiority of the architecture and method.

[0061] Example 2: Scenario of Customized Wooden Door Order Specification Change

[0062] The company has completed the definition of material, equipment, and process codes in the MDM and synchronized them to the ERP, PLM, APS, MES, and WMS systems through the core integration layer. Business personnel created a custom wooden door order with order number "ORD202311015" in the ERP system, with initial dimensions of 2100×900×45 mm, totaling 8 sets. After the order entered the production preparation stage ( Figure 2 The customer requested that the door height be adjusted to 2150 mm. The sales staff completed the order change operation in the ERP system. The ERP system then generated an order change event and published it to the core integration layer via the Enterprise Service Bus. After subscribing to this order change event, the PLM system automatically invoked the corresponding parametric product model, regenerated the door structure data based on the new dimensional parameters, formed an updated bill of materials (BOM) and process route, and published the BOM and process data to the manufacturing data platform (…). Figure 3After the APS system subscribes to the same order change event, it retrieves updated order data and process constraint information from the manufacturing data platform, simultaneously obtains the current work-in-process status and equipment load information from the MES system, and the relevant material inventory status from the WMS system. Based on limited capacity constraints, it re-executes the scheduling calculation to generate an adjusted work plan. This work plan is synchronously issued to the MES and WMS systems through the core integration layer. The MES system converts it into a new work order and issues it to the corresponding workstation, while the WMS system generates corresponding material delivery instructions. Figure 2 During production execution, the MES system guides operators to perform tasks according to the updated process parameters, while the SCADA system collects real-time data on spindle speed, machining time, and vibration at the CNC machining station, and uploads this data, along with material information identified by RFID, to the manufacturing data platform. Figure 4 If no abnormalities occur during the production process, the order is completed according to the adjusted plan. The MES system triggers a finished product reporting event, the WMS completes the finished product warehousing, and the ERP system completes the order status closure and cost settlement, forming a complete business loop. Figure 2 ).

[0063] Example 3: Emergency Order Placement and Collaborative Production Scheduling Scenario

[0064] The company is currently executing order "ORD202311020," with production tasks partially assigned to the cutting and milling processes. During the execution of this order, sales personnel added an urgent supplementary order "ORD202311021" in the ERP system, requiring the production of 3 sets of the same model of wooden doors to be completed without significantly delaying the original delivery schedule. After confirming the supplementary order, the ERP system publishes a new order event through the core integration layer. Figure 2 , Figure 3 After the PLM system subscribes to this event, it directly reuses existing product models and process templates based on the inserted order parameters to generate the corresponding BOM and process route, and writes the data into the manufacturing data platform. Figure 3After subscribing to a new order event, the APS system retrieves the process routes and work-in-process status data for two orders from the manufacturing data platform. Simultaneously, it combines this data with equipment occupancy information from the MES and inventory information from the WMS to recalculate the existing production plan. During scheduling, the APS system marks the inserted order as high priority and adjusts the execution order of some processes in the original order without affecting the continuity of key processes, generating a new work plan. This work plan is then distributed to the MES system via the core integration layer. The MES system simultaneously displays the inserted order task on the workstation terminal and guides operators to prioritize its execution. The WMS system simultaneously completes the picking and delivery of materials required for the inserted order. During production execution, the SCADA system continuously collects equipment operating status, and the MES system updates the execution progress of the two orders in real time and uploads it to the manufacturing data platform. Figure 4 Once the emergency order is completed, the MES system first triggers the completion event for the emergency order and sends feedback to the ERP system. Subsequently, the original order continues to be executed according to the adjusted plan until completion, thereby achieving collaborative production between emergency orders and work-in-progress orders.

[0065] Example 4: Dynamic rescheduling scenario triggered by equipment malfunction

[0066] Order "ORD202311030" has completed design and scheduling and entered the milling production stage. The MES system dispatches the work order to the CNC machining center "CNC-02" according to the work plan issued by the APS, and the SCADA system begins real-time monitoring of the machine's machining status. During production, the SCADA system detects that the spindle vibration value of "CNC-02" continuously exceeds the preset threshold and determines it to be an equipment malfunction. Figure 4 The SCADA system uploads the anomaly information to the MES system, which then marks the current work order status as interrupted and publishes the device anomaly event through the core integration layer. Figure 2 After the APS system subscribes to the equipment malfunction event, it retrieves the remaining processes for the order, the status of other available equipment, and the current production load information from the manufacturing data platform, and initiates dynamic rescheduling calculations for the affected processes. Figure 2 , Figure 3The APS system transfers the remaining milling operations to the backup equipment "CNC-03" in the scheduling results and adjusts the execution time of subsequent grinding and painting operations accordingly. The adjusted work plan is reissued to the MES system through the core integration layer. The MES system dispatches the updated work order to the "CNC-03" workstation and notifies the operator to switch jobs. After the equipment malfunction is resolved and the equipment returns to normal, the SCADA system updates the equipment status to the MES system and the manufacturing data platform. The order continues to be executed under the adjusted plan. Finally, the MES system triggers the completion event, the WMS completes the finished product warehousing, and the ERP system completes the order settlement, thus forming a closed-loop control process based on equipment malfunction detection, dynamic plan adjustment, and execution feedback.

[0067] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. An integrated information system architecture for a multi-system wooden door manufacturing enterprise, employing a layered, loosely coupled design, characterized in that: include: The enterprise operations layer includes an Enterprise Resource Planning (ERP) system for order and financial management, and a Product Lifecycle Management (PLM) system for product model and process management. The planning and scheduling layer includes the Advanced Planning and Scheduling (APS) system, which serves as the planning decision-making center and performs finite capacity simulation and optimized scheduling based on multi-source real-time data. The manufacturing operations layer includes the Manufacturing Execution System (MES) for production execution and monitoring, and the Warehouse Management System (WMS) for refined material management. The field perception layer includes a Supervisory Control and Data Acquisition (SCADA) system for monitoring critical equipment and Internet of Things (IoT) acquisition terminals for collecting data on personnel, materials, and the environment. The core integration layer includes: Enterprise Service Bus (ESB) or Message Queuing (MQ) is used to enable asynchronous, standardized message passing based on events between systems; The Unified Data Model and Master Data Management (MDM) module is used to define and maintain master data standards and a unified coding system covering all domains of materials, equipment, processes, and orders, providing a semantic foundation for data interaction across the entire system. The manufacturing data platform is used to collect, clean, integrate and store process data from the manufacturing operations layer and the field perception layer in real time, forming a manufacturing data lake. The core integration layer connects and coordinates the enterprise operation layer, planning and scheduling layer, manufacturing operation layer, and field perception layer through the event-driven mechanism based on the unified data model and the enterprise service bus / message queue, to achieve automatic data flow and intelligent business collaboration throughout the entire process from order receipt to product delivery.

2. The integrated information system architecture for a wooden door manufacturing enterprise integrating multiple systems as described in claim 1, characterized in that, The advanced planning and scheduling system requires dynamic data input sources for scheduling calculations, including: order demand data from enterprise resource planning, process constraint data from product lifecycle management, real-time capacity and work-in-process status data from manufacturing execution system, material inventory data from warehouse management system, and real-time equipment status data from data acquisition and monitoring control system, thereby achieving accurate scheduling based on global real-time information.

3. The integrated information system architecture for a wooden door manufacturing enterprise integrating multiple systems as described in claim 1, characterized in that, The manufacturing data lake built by the manufacturing data platform covers all elements of "people, machines, materials, methods, environment, and measurement," and provides unified data services for scheduling optimization, event-triggered dynamic rescheduling, production big data analysis, and visual monitoring of the advanced planning and scheduling system.

4. The integrated information system architecture for a wooden door manufacturing enterprise that integrates multiple systems according to claim 1, characterized in that, The core integration layer also includes a security and operation and maintenance module, which provides cross-layer data encryption transmission, fine-grained access control, monitoring of the operating status of each system, and full operation log auditing functions to ensure the overall security and stability of the system.

5. The integrated information system architecture for a wooden door manufacturing enterprise integrating multiple systems as described in claim 1, characterized in that, The event-driven mechanism is specifically driven by a series of business events, which include at least order creation / modification events, process data release events, production anomaly events, and equipment status events. Each system subscribes to events it is interested in through the enterprise service bus / message queue, and automatically triggers predefined business processes when an event is published, thereby achieving loosely coupled collaboration across systems.

6. The method for integrating a multi-system information system architecture for a wooden door manufacturing enterprise according to any one of claims 1 to 5, characterized in that, Includes the following steps: S1: Master data initialization and synchronization. In the master data management module, the codes and data models of materials, equipment and processes are uniformly defined, and synchronized to various business systems through the core integration layer to ensure data consistency. S2: Order-driven design responds to order creation or change events in the enterprise resource planning system. Through an event-driven mechanism, it automatically triggers the PLM system to perform product configuration and process design, and publishes structured bill of materials (BOM) and process route (BOP) data to the manufacturing data platform. S3: Dynamic Advanced Planning and Scheduling. The advanced planning and scheduling system obtains real-time data from the manufacturing data platform and related systems based on preset cycles or event triggers, executes limited capacity simulation scheduling under multiple constraints, and generates optimized work plans. S4: Precise issuance from planning to execution. The work plan generated in S3 is synchronously issued to the Manufacturing Execution System and the Warehouse Management System through the core integration layer. The Manufacturing Execution System converts it into specific work orders and dispatches them to workstations. The Warehouse Management System converts it into material delivery instructions. S5: End-to-end execution and real-time data acquisition. During the production execution process, the Manufacturing Execution System guides on-site operations, the Warehouse Management System executes material distribution, and SCADA and IoT devices collect production, equipment, and quality data in real time and upload them to the Manufacturing Execution System and the Manufacturing Data Platform. S6: Real-time feedback and closed-loop dynamic adjustment. The Manufacturing Execution System provides real-time feedback on the production status to the Advanced Planning and Scheduling System and the Enterprise Resource Planning System. When an abnormal production event is detected, the Advanced Planning and Scheduling System is triggered to perform dynamic rescheduling, generate and issue the adjusted work plan, and form an agile closed loop of planning and execution. S7: Order completion and business closure. After production is completed, the Manufacturing Execution System triggers the finished product reporting, the Warehouse Management System executes the finished product warehousing, and sends an order completion event to the Enterprise Resource Planning System. The Enterprise Resource Planning System automatically completes cost accounting and closes the order status.

7. The method for integrating a multi-system information system for a wooden door manufacturing enterprise according to claim 6, characterized in that, The events in S2 and S3 include, but are not limited to, order events, process data release events, equipment malfunction events, and material shortage events. All of these events are published and subscribed to through the enterprise service bus / message queue to achieve automatic triggering and response of cross-system business flows.

8. The method for integrating a multi-system information system for a wooden door manufacturing enterprise according to claim 6, characterized in that, The production anomaly events mentioned in S6 include equipment failure, defective materials, exceeding quality parameters, and time deviation. The advanced planning and scheduling system subscribes to these events and triggers rescheduling calculations to generate adjusted work plans and reissue them.

9. The method for integrating a multi-system information system for a wooden door manufacturing enterprise according to claim 6, characterized in that, In S5, the data collected by the SCADA system and IoT devices includes the spindle speed, operating power, coordinate position, vibration value, temperature, material RFID information, process scanning information, and quality inspection results. The data is aggregated in real time on the manufacturing data platform, providing a data foundation for production process transparency and full life cycle traceability.

10. The method for integrating a multi-system information system for a wooden door manufacturing enterprise according to claim 6, characterized in that, All data interactions across the system are based on the unified data model, ensuring that core business entities have unique identifiers and consistent semantics throughout the system, eliminating data ambiguity from the source, and achieving accurate, seamless, and automatic data flow across systems.

Images