A collaborative control system for printed circuit board production based on manufacturing execution system

Abstract

This invention discloses a collaborative control system for printed circuit board (PCB) production based on a manufacturing execution system. The invention breaks down production orders into task units with process-related relationships and aggregates equipment, materials, work-in-process (WIP), and quality status in real time to establish a multi-constraint production collaboration model. Based on this, dynamic scheduling is performed according to order urgency, waiting time, equipment switchover impact, and downstream load to generate collaborative execution plans. Quality feedback is linked to real-time scheduling; in case of anomalies, the scope of impact is identified and local rescheduling is triggered; tasks are automatically replaced when materials are limited; consistency verification and resource locking are performed before execution; and a complete traceability chain is built for each batch. This invention effectively solves the problems of WIP backlog and uneven production capacity caused by shared equipment, material fluctuations, and quality anomalies in multi-variety, small-batch PCB production, improving production collaboration, response efficiency, and full-process traceability.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and manufacturing execution systems, and more specifically, to a collaborative control system for printed circuit board production based on a manufacturing execution system. Background Technology

[0002] The production process of printed circuit boards (PCBs) typically includes multiple stages such as material preparation, inner layer circuit fabrication, inner layer etching, automated optical inspection, browning or blackening, lamination, drilling, copper plating, electroplating, outer layer circuit fabrication, pattern electroplating, outer layer etching, solder mask application, character etching, surface treatment, molding, electrical testing, and final inspection. Different types of PCBs vary in terms of material type, number of layers, board thickness, circuit precision, hole diameter, surface treatment methods, and quality grades, resulting in significant differences in their corresponding process routes, equipment types, production parameters, and quality control requirements.

[0003] As printed circuit board (PCB) products increasingly move towards higher variety, smaller batches, higher precision, and faster delivery, production sites typically handle multiple orders, product models, and production batches simultaneously, with these batches flowing between different processes. Some processes also experience issues such as shared equipment, long changeover times, long work-in-process (WIP) waiting times, and imbalances in capacity between upstream and downstream processes. Therefore, relying solely on static production plans can easily lead to localized equipment congestion, large backlogs of WIP, or material shortages in subsequent processes.

[0004] Existing manufacturing execution systems (MES) can perform functions such as production order management, process management, equipment status data collection, and product traceability. However, in practical applications, MES typically focus on recording production information and issuing production tasks. When equipment malfunctions, production schedules are delayed, materials are not fully stocked, or quality inspections are abnormal, production managers often need to manually adjust the production sequence and resources based on their experience.

[0005] Therefore, there is a need for a printed circuit board production collaborative control system that can rely on the manufacturing execution system to uniformly associate production tasks, equipment, processes, materials, work-in-process and quality status, and implement dynamic collaborative control according to changes in the production site. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a collaborative control system for printed circuit board production based on a manufacturing execution system, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The production task parsing module is used to obtain production orders and process routes based on the manufacturing execution system, and to break down the production orders into production task units with process relationships.

[0009] The manufacturing status aggregation module is used to obtain the equipment, materials, work-in-process, and quality status corresponding to the production task unit;

[0010] The process constraint modeling module is used to establish a production coordination constraint model based on process relationships and manufacturing resource adaptation relationships;

[0011] The collaborative scheduling decision module is used to determine the target production resources and execution order of production task units based on the manufacturing status and production collaborative constraint model, and to generate a production collaborative execution plan.

[0012] The material coordination module is used to verify the material availability status of the production task unit.

[0013] The production execution control module is used to issue production execution instructions according to the production collaborative execution plan and update the production status based on execution feedback;

[0014] The quality feedback module is used to associate quality inspection results with corresponding production task units and generate quality risk information;

[0015] The abnormal closed-loop control module is used to identify the affected production task units when a production abnormality occurs, and to trigger the collaborative scheduling decision module to reschedule the affected tasks.

[0016] The production traceability module is used to record production execution, materials, quality, and anomaly handling information for production task units, forming production traceability data for corresponding product batches.

[0017] Preferably, the production task parsing module is used to split the production order into production batches and process tasks according to the process route corresponding to the printed circuit board product, and to configure product code, order code, batch code, process type, process version, planned start time, planned completion time and preceding task information for each production task unit;

[0018] When the same production order corresponds to multiple process versions, the production task parsing module determines the valid process version corresponding to each production task unit based on the product version status, engineering change status, and production batch status, so as to avoid the mixing of production tasks with different process versions in the same production batch.

[0019] Preferably, the manufacturing status aggregation module aggregates the production site status according to the dimensions of equipment, process, material, and work-in-process.

[0020] The equipment operating status includes at least the equipment idle status, production status, line changeover status, maintenance status, and fault status; the process execution status includes at least the pending execution status, execution status, waiting for inspection status, rework status, and completed status; the material status includes at least the inventory status, locked status, delivery status, online status, and remaining quantity status; and the work-in-process status includes at least the process in which the work-in-process is located, dwell time, waiting time, batch quantity, and availability status for subsequent processes.

[0021] Preferably, the process constraint modeling module uses production task units as task nodes and equipment, production lines, or work units capable of executing the production task units as resource nodes, and establishes process sequence constraints between task nodes and resource adaptation constraints between task nodes and resource nodes; the process constraint modeling module is also used to establish at least two of the following constraint relationships: non-parallel production batch constraints, equipment capacity constraints, process parameter adaptation constraints, material kitting constraints, production switching constraints, and quality release constraints; when the constraint state of any production task unit changes, the executable state of the task nodes and resource nodes associated with that production task unit is updated.

[0022] Preferably, the collaborative scheduling decision module is used to establish a set of candidate tasks for production task units that meet the conditions for completion of previous processes, material availability, equipment compatibility, and quality release.

[0023] For each production task unit in the candidate task set, the corresponding order delivery urgency, cumulative waiting time of the process, expected idle time of the equipment, impact of equipment changeover, congestion of subsequent processes, and quality risk are obtained as multi-dimensional evaluation indicators. After normalizing each evaluation indicator, a weighted summation model is used to calculate and determine the comprehensive task scheduling priority of each production task unit.

[0024] Based on task scheduling priority, the target production resource is determined from the candidate production resources, and a production collaborative execution plan is generated that includes the production task, the target production resource, the planned start time, the planned completion time, and the task execution order.

[0025] Preferably, the material coordination module is used to obtain the corresponding bill of materials based on the product model, production batch, process type and process version of the production task unit, and to verify the material model, material batch, material quantity, material validity status and material location based on the bill of materials;

[0026] When all necessary materials for a production task unit meet the online conditions, the corresponding production task unit is set to the material executable state; when there is a shortage of materials, incorrect materials, frozen materials, or delayed material delivery, the corresponding production task unit is set to the material restricted state, and the material restricted information is sent to the collaborative scheduling decision module to reselect an executable production task.

[0027] Preferably, the quality feedback module is used to associate the test results generated by online testing equipment, automatic optical testing equipment, electrical testing equipment or manual quality inspection station with the corresponding production task unit;

[0028] When the quality inspection results meet the preset release conditions, a quality release status is generated and the corresponding production task is allowed to enter the next process.

[0029] When the quality inspection results do not meet the preset release conditions, quality risk information is generated based on the defect type, defect quantity, defect location and corresponding production process parameters, and the production task is set to the status of pending re-inspection, rework or isolation.

[0030] The collaborative scheduling decision module adjusts the scheduling status of other production tasks that have the same equipment, the same batch of materials, or the same process parameters as the abnormal production tasks based on the quality risk information.

[0031] Preferably, the abnormal closed-loop control module is used to perform a breadth-first search traversal downstream in the production collaboration constraint model after an abnormal event occurs, starting from the task node corresponding to the abnormal production task unit, and matching parallel task nodes with the same material batch identifier, and including the task nodes covered by the traversal into the scope of abnormal influence.

[0032] For production task units that are outside the scope of the aforementioned anomaly and whose execution conditions have not changed, the original production collaborative execution plan shall be maintained.

[0033] For production task units that are within the scope of the anomaly and have not yet started execution, the original resource allocation results are canceled or frozen, and the collaborative scheduling decision module re-determines the production task execution order and target production resources to form a partially updated production collaborative execution plan.

[0034] Preferably, before sending production execution instructions to production resources, the production execution control module performs a consistency check on the current task status, equipment working mode, process program version, and production task lock status of the target production resources.

[0035] After the consistency check passes, the production task is marked as execution locked and a production execution instruction is sent to the corresponding production resource.

[0036] Upon receiving task completion or task termination information from production resources, the execution lock status of the corresponding production task is released, and the actual start time, actual end time, actual processing quantity, and equipment execution results are synchronized to the manufacturing execution system.

[0037] When the consistency check fails, the production execution instruction is prevented from being issued and the collaborative scheduling decision module is triggered to re-match resources.

[0038] Preferably, the production traceability module establishes a production traceability chain according to product batches, and stores production order information, process version information, process execution information, equipment information, material batch information, process parameter information, quality inspection information, anomaly handling information, and scheduling change information in sequence according to production time. When a traceability request for a specified printed circuit board product or a specified production batch is received, the corresponding production traceability chain is obtained according to the product code or batch code, and the production execution status of each process from material input to completion and the correlation of abnormal events are determined.

[0039] The technical effects and advantages of this invention are as follows:

[0040] This invention further transforms production orders in a manufacturing execution system into production task units with process-related relationships, and links them in real time with production equipment, materials, work-in-process, and quality status, enabling production plans to dynamically change according to the on-site situation. Simultaneously, by establishing a production coordination constraint model between production tasks and production resources, it achieves unified management of upstream and downstream processes, production equipment, materials, and quality release conditions, effectively reducing downstream work-in-process backlog caused by excessively rapid local production. In scheduling decisions, this invention uses equipment load, material availability, work-in-process backlog status, and quality risk status as a combined basis, replacing the method of simply scheduling tasks based on order priority or equipment idleness, thereby significantly improving overall production coordination. Online detection and quality inspection results can be fed back to the scheduling process in real time. When anomalies occur, production tasks related to the same equipment, material batches, or process parameters can be linked for control to prevent the quality anomaly from escalating. Furthermore, by employing anomaly impact range identification and local dynamic rescheduling strategies, only affected tasks are adjusted after equipment, material, or quality status anomalies occur, while unaffected tasks continue to execute according to the original plan, ensuring both production plan stability and response efficiency. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the system structure of the present invention.

[0042] Figure 2 This is a schematic diagram of the production collaborative control process of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The system uses the Manufacturing Execution System as the main management platform for production business data and production execution information, and interacts with automated equipment, production lines, warehousing systems, testing equipment and manual production stations in the production site.

[0045] The Manufacturing Execution System (MES) pre-stores production orders, product process routes, bills of materials, process parameters, equipment resource information, and quality standards.

[0046] When a new printed circuit board production order enters the manufacturing execution system, the production task parsing module first reads the production order.

[0047] Suppose a production order requires the production of a batch of multilayer printed circuit boards. The main processes include, in sequence, material cutting, inner layer circuitry, inner layer testing, lamination, drilling, copper plating, electroplating, outer layer circuitry, solder mask, surface treatment, molding, and electrical testing.

[0048] The production task analysis module divides orders into several production batches based on the production quantity of the production order and the workshop production batch requirements.

[0049] For each production batch, multiple production task units are established according to the corresponding process route.

[0050] Each production task unit stores the preceding and subsequent production tasks for that task.

[0051] Therefore, during the production control process, it is possible to clearly determine whether any production task has met the conditions for proceeding to the next process.

[0052] The manufacturing status aggregation module continuously collects the status of production equipment and production tasks.

[0053] For example, there are multiple drilling machines in the workshop.

[0054] Some of the drilling equipment is in operation, some is under maintenance, and some is idle.

[0055] The manufacturing status aggregation module can obtain the current products being produced, the current production batch, the estimated completion time, and the current operating mode of different equipment.

[0056] At the same time, the completion status of the pre-pressing process of the production task to be drilled and the location of the corresponding plate are obtained.

[0057] By bringing together all of the above information, we can determine the tasks that can actually be scheduled for production, rather than simply starting production based on the original planned start time.

[0058] The process constraint modeling module constructs a production coordination constraint model through a process directional hierarchical network and an association matrix. Specifically, the system defines production task units as sets of task nodes. Define production equipment as a set of resource nodes. .

[0059] For the first type of process sequence constraint, the system in Internally, a directed edge is established. If the task It is a task The preceding process has a directed edge. .

[0060] For the second type of equipment adaptation constraints and the third type of material constraints, the system adopts a bipartite graph correlation matrix. To express. If the task node Satisfy the requirements of resource nodes The matrix elements are determined by the equipment capacity and material availability conditions of the equipment being executed. Otherwise .

[0061] The production collaboration constraint model is established using the graph structure and the association matrix described above. When the on-site state changes, the system dynamically maintains the constraint model by updating the directed edge states and the element values ​​of the association matrix in real time.

[0062] During the production process, the collaborative scheduling decision module initiates production scheduling periodically or when the production status changes.

[0063] First, select production tasks that meet the basic execution conditions from all pending production tasks.

[0064] Production tasks that have not completed the preceding processes will not be included in the candidate set.

[0065] Tasks with incomplete or missing materials will not be included in the candidate set.

[0066] Production tasks that have not been released for quality will also not be included in the candidate set.

[0067] For candidate production tasks that meet the execution conditions, the system configuration includes the urgency of order delivery. Cumulative waiting time for each process Expected idle time of equipment The extent of the impact of equipment line replacement Congestion level in subsequent processes and the degree of quality risk The multidimensional evaluation vector.

[0068] If an order is close to its planned delivery time, the priority of the corresponding production task will be increased.

[0069] First, the metrics of each dimension are subjected to min-max normalization and uniformly mapped to... The interval is used to obtain the normalized index vector. Subsequently, the system uses a weighted summation model to calculate the overall scheduling priority of each production task unit. :

[0070]

[0071] in, The pre-defined weight coefficients for each evaluation dimension, and satisfying the following conditions: For example, when an order faces the risk of default, the system can dynamically adjust the risk level. The weight value can be dynamically increased when there are large fluctuations in workshop quality. The weight values ​​are determined by the priority. The collaborative scheduling decision module ultimately prioritizes the tasks according to their importance. Arrange the candidate tasks in descending order to generate an execution sequence.

[0072] After comprehensive evaluation, a production task execution sequence corresponding to each piece of equipment is formed.

[0073] Take the automated optical inspection process as an example.

[0074] After the printed circuit board with the inner layer circuitry completed enters the automatic optical inspection equipment, the system obtains the inspection results.

[0075] If the test results are normal, the corresponding production batch will be released and allowed to proceed to the next process.

[0076] If the test results indicate the presence of abnormal defects, the system will first set the current batch to a quality-limited state.

[0077] At the same time, the corresponding circuit processing equipment, process procedures, and material batches are located based on the production traceability information.

[0078] If the same processing equipment is found to have processed other batches to be inspected within a similar time period, the system can increase the inspection priority of these production batches.

[0079] If the anomaly reaches the preset risk conditions, subsequent similar production tasks on the relevant production equipment can be suspended, pending confirmation of equipment status or process parameters.

[0080] The above methods enable real-time linkage between quality information and production scheduling.

[0081] Suppose that a drilling machine malfunctions while performing a production task.

[0082] After receiving a device failure event, the anomaly closed-loop control module triggers the anomaly impact range tracking algorithm:

[0083] First, the device node that is currently experiencing a failure and the abnormal production task unit it is processing are designated as the root node;

[0084] Secondly, starting from the root node, the system performs a topological search of related tasks downstream along the process hierarchy path in the production collaboration constraint model. During the tracking and verification process, the system automatically marks downstream tasks awaiting production that are affected by the interruption of the preceding workstation, following the logical flow of the process; at the same time, it extracts the material batch identifiers associated with the abnormal tasks and matches other parallel task nodes with the same batch identifier in the database.

[0085] The system will traverse all covered task nodes and classify them into the scope of the anomaly. For production tasks that are being processed in the equipment and cannot be transferred, they will be marked as an abnormal pending state; for production tasks that are within the scope of the anomaly and have been allocated resources but have not yet started processing, the original equipment allocation results will be canceled and their status will be frozen for scheduling.

[0086] Subsequently, it was determined whether other drilling equipment in the workshop met the processing conditions for the corresponding production task.

[0087] If other eligible equipment exists, the corresponding production task will be added back to the candidate task set.

[0088] The collaborative scheduling decision module redetermines the target equipment based on the workload of the remaining equipment and the priority of production tasks.

[0089] At the same time, based on the expected delays of these production tasks, it can be determined whether the production plans for subsequent processes such as copper plating and electroplating need to be adjusted accordingly.

[0090] For other production tasks that are not related to the faulty equipment and whose execution conditions have not changed, maintain the original production schedule.

[0091] Therefore, there is no need to completely reschedule the entire production workshop due to a partial equipment failure.

[0092] Specific example of scheduling data simulation:

[0093] Suppose that a certain copper plating process has three candidate tasks that meet the execution conditions: Task A (expedited batch, ERP system reports 12 hours remaining delivery time, already in production for 120 minutes, process parameters need to be reset), Task B (regular batch, 48 hours remaining delivery time, already in production for 300 minutes, parameter matching does not require reset), and Task C (regular batch, 72 hours remaining delivery time, already in production for 60 minutes, parameter matching does not require reset).

[0094] System retrieves weight configuration: delivery urgency Waiting time Line replacement impact .

[0095] The system reads engineering parameters from various dimensions and automatically completes the normalization mapping:

[0096] For task A, the system determines its urgency to be extremely high (normalized value 0.95), its delay characteristic to be moderate (normalized value 0.40), and it involves line-switching losses (matching degree 0). Its overall priority is... ;

[0097] For task B, the urgency is normal (normalized value 0.40), the delay is severe (normalized value 0.95), and the parameters do not require switching (matching degree 1.0). Its overall priority is... ;

[0098] For task C, the urgency is low (normalized value 0.25), the delay is slight (normalized value 0.10), and parameter switching is unnecessary (matching degree 1.0). Its overall priority... .

[0099] The system compares the priority computing results of each task, automatically issues control commands, and controls the underlying devices to perform processing in the order of: Task B → Task A → Task C.

[0100] This example demonstrates that the system successfully uses an algorithm to automatically balance the relationship between waiting time, line change costs, and order priority.

[0101] When the material coordination module detects that a specific sheet material required for a production batch has not been delivered to the production station as planned, it sets the production task to a material-restricted state.

[0102] The collaborative scheduling decision module will no longer assign this task to the target production equipment.

[0103] If the target production equipment is about to become idle, then select an alternative task from other production tasks that have completed material preparation and meet the equipment compatibility requirements.

[0104] Once the corresponding material arrives at the production station, the material coordination module re-verifies the material status.

[0105] Once the verification is successful, the corresponding production task is restored to a schedulable state and enters the next round of collaborative scheduling.

[0106] Therefore, equipment downtime caused by material abnormalities in a single order can be reduced.

[0107] Throughout the entire production process, the production traceability module continuously records production data.

[0108] For each production batch, the actual production equipment, production start time, production end time, actual quantity processed, material batch, process version, key process parameters, and quality inspection results can be recorded sequentially.

[0109] When equipment failure, quality abnormality, or production plan adjustment occurs, record the time of occurrence, type of abnormality, affected production tasks, original scheduling plan, and adjusted scheduling plan.

[0110] Once the final product is completed, a complete production traceability chain can be formed based on the production batch.

[0111] If an anomaly is subsequently discovered in a certain batch of printed circuit boards, the relevant equipment, material batches, and production parameters can be quickly identified based on the production traceability chain, providing data support for production problem analysis.

[0112] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative control system for printed circuit board production based on a manufacturing execution system, characterized in that, include: The production task parsing module is used to obtain production orders and process routes based on the manufacturing execution system, and to break down the production orders into production task units with process relationships. The manufacturing status aggregation module is used to obtain the equipment, materials, work-in-process, and quality status corresponding to the production task unit; The process constraint modeling module is used to establish a production coordination constraint model based on process relationships and manufacturing resource adaptation relationships; The collaborative scheduling decision module is used to determine the target production resources and execution order of production task units based on the manufacturing status and production collaborative constraint model, and to generate a production collaborative execution plan. The material coordination module is used to verify the material availability status corresponding to the production task unit; The production execution control module is used to issue production execution instructions according to the production collaborative execution plan and update the production status based on execution feedback; The quality feedback module is used to associate quality inspection results with corresponding production task units and generate quality risk information; The abnormal closed-loop control module is used to identify the affected production task units when a production abnormality occurs, and to trigger the collaborative scheduling decision module to reschedule the affected tasks. The production traceability module is used to record production execution, materials, quality, and anomaly handling information for production task units, forming production traceability data for corresponding product batches.

2. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The production task parsing module is used to split the production order into production batches and process tasks according to the process route corresponding to the printed circuit board product, and to configure product code, order code, batch code, process type, process version, planned start time, planned completion time and preceding task information for each production task unit; When the same production order corresponds to multiple process versions, the production task parsing module determines the valid process version corresponding to each production task unit based on the product version status, engineering change status, and production batch status, so as to avoid the mixing of production tasks with different process versions in the same production batch.

3. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The manufacturing status aggregation module aggregates the production site status according to the dimensions of equipment, process, material, and work-in-process. The equipment operating status includes at least the equipment idle status, production status, line changeover status, maintenance status, and fault status; the process execution status includes at least the pending execution status, execution status, waiting for inspection status, rework status, and completed status; the material status includes at least the inventory status, locked status, delivery status, online status, and remaining quantity status; and the work-in-process status includes at least the process in which the work-in-process is located, dwell time, waiting time, batch quantity, and availability status for subsequent processes.

4. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The process constraint modeling module uses production task units as task nodes and equipment, production lines, or work units capable of executing the production task units as resource nodes. It establishes process sequence constraints between task nodes and resource adaptation constraints between task nodes and resource nodes. The process constraint modeling module is also used to establish at least two of the following constraint relationships: non-parallel production batch constraints, equipment capacity constraints, process parameter adaptation constraints, material kitting constraints, production switching constraints, and quality release constraints. When the constraint state of any production task unit changes, the executable state of the task nodes and resource nodes associated with that production task unit is updated.

5. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The collaborative scheduling decision module is used to establish a set of candidate tasks for production task units that meet the conditions for completion of previous processes, material availability, equipment compatibility and quality release. For each production task unit in the candidate task set, the corresponding order delivery urgency, cumulative waiting time of the process, expected idle time of the equipment, impact of equipment changeover, congestion of subsequent processes, and quality risk are obtained as multi-dimensional evaluation indicators. After normalizing each evaluation index, a weighted summation model is used to calculate and determine the comprehensive task scheduling priority of each production task unit. Based on task scheduling priority, the target production resource is determined from the candidate production resources, and a production collaborative execution plan is generated that includes the production task, the target production resource, the planned start time, the planned completion time, and the task execution order.

6. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The material coordination module is used to obtain the corresponding bill of materials based on the product model, production batch, process type and process version of the production task unit, and to verify the material model, material batch, material quantity, material validity status and material location based on the bill of materials. When all necessary materials for a production task unit meet the online conditions, the corresponding production task unit is set to the material executable state; when there is a shortage of materials, incorrect materials, frozen materials, or delayed material delivery, the corresponding production task unit is set to the material restricted state, and the material restricted information is sent to the collaborative scheduling decision module to reselect an executable production task.

7. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The quality feedback module is used to associate the test results generated by online testing equipment, automatic optical testing equipment, electrical testing equipment or manual quality inspection station with the corresponding production task unit. When the quality inspection results meet the preset release conditions, a quality release status is generated and the corresponding production task is allowed to enter the next process. When the quality inspection results do not meet the preset release conditions, quality risk information is generated based on the defect type, defect quantity, defect location and corresponding production process parameters, and the production task is set to the status of pending re-inspection, rework or isolation. The collaborative scheduling decision module adjusts the scheduling status of other production tasks that have the same equipment, the same batch of materials, or the same process parameters as the abnormal production tasks based on the quality risk information.

8. The printed circuit board production collaborative control system based on a manufacturing execution system according to claim 1, characterized in that: The abnormal closed-loop control module is used to perform a breadth-first search traversal downstream in the production collaboration constraint model after an abnormal event occurs, starting from the task node corresponding to the abnormal production task unit, and matching parallel task nodes with the same material batch identifier, and including the task nodes covered by the traversal into the scope of abnormal impact. For production task units that are outside the scope of the aforementioned anomaly and whose execution conditions have not changed, the original production collaborative execution plan shall be maintained. For production task units that are within the scope of the anomaly and have not yet started execution, the original resource allocation results are canceled or frozen, and the collaborative scheduling decision module re-determines the production task execution order and target production resources to form a partially updated production collaborative execution plan.

9. A collaborative control system for printed circuit board production based on a manufacturing execution system according to claim 1, characterized in that: Before sending production execution instructions to production resources, the production execution control module performs a consistency check on the current task status, equipment working mode, process program version, and production task lock status of the target production resources. After the consistency check passes, the production task is marked as execution locked and a production execution instruction is sent to the corresponding production resource. Upon receiving task completion or task termination information from production resources, the execution lock status of the corresponding production task is released, and the actual start time, actual end time, actual processing quantity, and equipment execution results are synchronized to the manufacturing execution system. When the consistency check fails, the production execution instruction is prevented from being issued and the collaborative scheduling decision module is triggered to re-match resources.

10. A collaborative control system for printed circuit board production based on a manufacturing execution system according to claim 1, characterized in that: The production traceability module establishes a production traceability chain according to product batches, and stores production order information, process version information, process execution information, equipment information, material batch information, process parameter information, quality inspection information, anomaly handling information, and scheduling change information in sequence according to production time. When a traceability request for a specified printed circuit board product or a specified production batch is received, the corresponding production traceability chain is obtained according to the product code or batch code, and the production execution status of each process from material input to completion and the correlation of abnormal events are determined.

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