MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering

Abstract

The invention discloses an MES work order dynamic scheduling and resource self-adaption system based on real-time event triggering, and relates to the technical field of intelligent manufacturing and industrial automation, equipment states and material events are monitored in real time through interfaces such as a PLC and an OPCUA, event types and validity are analyzed through hardware filtering, feature code recognition and double verification, and the real-time event triggering is realized. Event data, MES batch information and equipment load data are integrated to generate standardized scheduling input, a preset strategy is matched, an optimal dispatching scheme is generated through a priority algorithm, material, equipment and logistics resource allocation and dispatching operation are completed, synchronization and MES interaction ensure that page logic is consistent, full-process automation of dynamic work order scheduling is achieved, and work order scheduling efficiency is improved. The equipment state and the material event are responded in real time, the production cooperation efficiency is improved by more than 30%, the resource adaptation accuracy rate reaches 95% or above, manual intervention is reduced, and the consistency of data and MES is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing and industrial automation, and in particular to an MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering. Background Art

[0002] With the digital transformation of the manufacturing industry, traditional MES work order scheduling relies on manual intervention and has poor real-time performance, making it difficult to adapt to the needs of multi-device collaboration and complex material flow. The development of industrial automation technology and the popularization of interfaces such as PLC and OPC UA provide a basis for real-time perception of equipment status and material events, giving rise to the demand for efficient dynamic scheduling and resource self-adaptation systems. MES work order dynamic scheduling and resource self-adaptation systems based on real-time event triggering have emerged.

[0003] The existing or traditional MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering has at least the following technical problems: 1. The existing MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering lacks hardware filtering and precise time threshold control mechanism, which makes the event signal susceptible to jitter interference and cannot quickly identify equipment status changes within 50 milliseconds. The trigger signal false alarm rate of key indicators such as buffer idle rate is high, affecting the timeliness and accuracy of scheduling decisions. At the same time, the lack of standardized event classification rules based on binary feature codes will cause event type identification to rely on fuzzy logic or manual presets, making it difficult to accurately distinguish between equipment status events and material events. In complex production scenarios, event classification errors will occur, thereby misleading subsequent scheduling processes.

[0004] 2. The existing MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering lacks a double-verification event trigger condition evaluation system, which will cause invalid events to frequently enter the scheduling process. It is impossible to combine historical data for secondary verification, making it difficult to ensure the effectiveness of the triggered events, increasing the burden of the system to process redundant information and reducing scheduling efficiency. In the data integration link, there is a lack of quantitative correlation calculation and integrity verification mechanism for multi-dimensional data, which will lead to the inability to effectively integrate event data, MES batch information and equipment load data, missing data cannot be filled in time, and data timestamp deviation cannot be corrected.

[0005] 3. The traditional MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering lacks a dispatch priority algorithm that comprehensively considers priority, load rate and policy weight in generating dispatch plans. This will result in the dispatch plan being unable to balance the urgency of the task and the status of equipment resources. The lack of a resource availability verification mechanism will lead to the generation of plans with path conflicts or resource waiting timeouts, and the feasibility and efficiency of the dispatch plan cannot be guaranteed. Summary of the Invention

[0006] The present invention aims to provide a MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering, which solves the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering, including: a real-time event perception and triggering module for real-time monitoring of specified equipment status and material events, analyzing event types, and evaluating whether the events meet the triggering conditions.

[0008] The multi-dimensional data fusion processing module is used to integrate the multi-dimensional impact parameters after the event is triggered when the event meets the triggering conditions, and then generate standardized scheduling decision input.

[0009] The dynamic work order scheduling decision module is used to analyze and match preset strategies based on event types and data characteristics, and then generate the optimal work dispatch plan through a priority algorithm.

[0010] The resource adaptive execution module is used to allocate materials, equipment and logistics resources and execute dispatch operations based on the optimal dispatch plan.

[0011] The MES system's two-way interaction module is used to interact with MES in real time through a standardized interface, combining data synchronization requirements with card control conditions to ensure that dispatch operations are consistent with the MES page logic.

[0012] The beneficial effects of the present invention are: 1. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering provided by the embodiment of the present invention directly connects to the device through the PLC interface and uses hardware filtering to eliminate signal jitter during the event perception and data processing process, combines the preset feature code rules to accurately classify event types, and uses a double verification mechanism to evaluate trigger conditions, which is conducive to avoiding invalid signal interference, quickly and accurately identifying and screening valid events, and providing a reliable data basis for subsequent scheduling.

[0013] 2. In the multi-dimensional data integration and standardization process, the embodiment of the present invention uses the device ID as the core of association, integrates event data, MES batch information and equipment load data, fills in missing data, corrects timestamp deviations, and converts data into unified and comparable quantitative indicators. This helps eliminate data dimension differences, ensures data integrity, synchronization and standardization, and provides strong support for scientific scheduling decisions.

[0014] 3. In the process of strategy matching and work dispatching plan generation, the embodiment of the present invention adopts weighted matching degree calculation and priority secondary screening strategy, combines event priority, equipment load rate and strategy weight, and verifies resource availability at the same time, which is conducive to screening out the best plan from multiple strategies, generating efficient and feasible work dispatching plans, and ensuring the rationality of scheduling decisions and the efficiency of execution.

[0015] 4. In the process of resource execution and system interaction, the embodiment of the present invention monitors resource status in real time, triggers emergency dispatch in a timely manner, and synchronizes data with the MES system in a high-frequency and high-precision manner. This is conducive to ensuring the smooth progress of dispatch operations and rapid response to abnormal situations. At the same time, it ensures that the MES page is consistent with the actual operation, thereby improving the coordination and visualization level of production management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention provides an MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering, which includes: a real-time event perception and triggering module, a multi-dimensional data fusion processing module, a dynamic work order scheduling decision module, a resource self-adaptation execution module, an MES system two-way interaction module and a database.

[0020] The real-time event perception and triggering module is connected to the multidimensional data fusion processing module, the multidimensional data fusion processing module is respectively connected to the dynamic work order scheduling decision module and the database, the dynamic work order scheduling decision module is connected to the resource self-adaptive execution module, the resource self-adaptive execution module is connected to the MES system two-way interaction module, and the MES system two-way interaction module is connected to the database.

[0021] The real-time event perception and triggering module is used to monitor the status of specified equipment and material events in real time, analyze the event type, and evaluate whether the event meets the trigger conditions.

[0022] In a specific embodiment, the real-time monitoring of specified equipment status and material events is as follows: directly connect to the equipment hardware through the programmable logic controller interface, monitor equipment signals such as loading port status and buffer occupancy in real time, eliminate jitter through the hardware filtering circuit, and transmit to the edge computing node using the Modbus transmission control protocol. The status change identification is completed within the set 50 milliseconds, and a valid signal is triggered when the buffer idle rate exceeds 70% and lasts for 150 milliseconds.

[0023] The cleaning time is monitored by a timing module implanted in the machine control system. An event is generated when the remaining time is less than or equal to 300 seconds. For virtual batch requirements, the machine signal is subscribed to in real time through the open platform communication unified architecture interface. The demand signal is parsed and events are generated within 3 seconds. All events are encapsulated as JavaScript object representation data with timestamps and priorities, and pushed to the multi-dimensional data fusion processing module within 200 milliseconds via the Kafka message queue.

[0024] It should be noted that the Modbus transmission control protocol is an industrial communication protocol used to transmit device status data, such as loading port status and buffer occupancy, between programmable logic controllers and edge computing nodes. In this solution, it is used to reliably transmit real-time device signals to edge nodes, enabling state change recognition within 50 milliseconds.

[0025] The Kafka message queue is a high-throughput distributed messaging system used to asynchronously transmit event data between different modules. In this solution, it is used to receive event data encapsulated in JSON format, including timestamps and priorities, and push the data to the multidimensional data fusion module with a low latency of 200 milliseconds, supporting high-concurrency processing.

[0026] In a specific embodiment, the specific process of analyzing the event type is as follows: the binary feature code of the original signal is collected through the PLC interface, and the type is identified based on the preset mapping rules. The device status event feature code rule is: when the current 4 bits are 0010, it is determined to be a semiconductor device loading port status change event.

[0027] The material event feature code rule is: when the first four bits are 0101, it is determined to be a front-opening wafer box cleaning-related event.

[0028] It should be noted that when the first four bits are 0010, the specific meaning of determining it as a semiconductor device loading port status change event is as follows: if the device status signal is a 16-bit binary number, the first four bits represent the event category, and the subsequent eight bits represent the status value, such as "10000000" represents idle.

[0029] In a specific embodiment, the specific process of evaluating whether the event meets the trigger condition is as follows: after collecting the original signals of the equipment status and material events through the hardware interface, they are first classified and processed based on the event type. For equipment status events, the loading port status changes and buffer occupancy rate are monitored in real time according to the set time point. When the buffer idle rate exceeds the preset threshold and the state lasts for a specific period of time, the validity of the state change is confirmed through dual verification of hardware filtering and software anti-shake. For the scenario where the device has no buffer, when the loading port state changes from occupied to idle and maintains for a specified time, the trigger condition evaluation process is started.

[0030] For material events, thresholds are determined through a timing module and a status subscription mechanism. Foup cleaning events are triggered when the remaining time reaches a preset countdown threshold, and a secondary verification is performed based on historical cleaning cycle data. Dummy batch demand events are evaluated based on the duration of the machine's continuous demand signal. A trigger signal is generated when the signal duration exceeds the set value and the corresponding virtual batch inventory status is available. All qualifying events are processed in order using a priority algorithm (combining event type and business impact). Finally, they are packaged into structured data objects with timestamps and trigger flags and pushed to subsequent processing modules.

[0031] It should be noted that the preset threshold is determined based on the equipment operation history data and production process standards. For example, the buffer idle rate threshold refers to the average proportion of the buffer idle state when the equipment is continuously running, and is set to a value that can avoid waste of buffer resources and ensure production continuity in combination with the production rhythm and material turnover efficiency. The preset countdown threshold is set according to the material handling process requirements and equipment maintenance specifications. For example, the Foup cleaning countdown threshold needs to comprehensively consider factors such as the upper limit of Foup usage time, the time required for cleaning, and the safe interval time for waiting for use after cleaning to determine the specific value.

[0032] In the multi-dimensional data integration and standardization process, the embodiment of the present invention takes the device ID as the correlation core, integrates event data, MES batch information and equipment load data, fills in missing data, corrects timestamp deviations, and converts data into unified and comparable quantitative indicators, which is conducive to eliminating data dimension differences, ensuring data integrity, synchronization and standardization, and providing strong support for scientific scheduling decisions.

[0033] The multi-dimensional data fusion processing module is used to integrate the multi-dimensional impact parameters after the event is triggered when the event meets the triggering conditions, and then generate standardized scheduling decision input.

[0034] In a specific embodiment, the multi-dimensional impact parameters after the event is triggered are integrated, and the specific process is as follows: the multi-dimensional impact parameters include event data, MES batch information and equipment load data.

[0035] Event data is obtained through the PLC interface and message queue. The event data includes millisecond timestamps and event type codes. After preprocessing by the edge node, the transmission timeout data is filtered with a threshold of 200 milliseconds. MES batch information is simultaneously pulled through the WebService interface. MES batch information includes batch ID and level 1-5 priority. Equipment load data is collected through the OPCUA protocol. Equipment load data includes equipment overall efficiency (OEE) and task queue length. All data is sorted by timestamp and awaits fusion.

[0036] Using the device ID in the event data as the association key, the MES batch information is matched through database query, and the number of missing fields is divided by the total number of fields to obtain completeness. When the missing rate is greater than 10%, the default priority is filled in. The overall efficiency of the equipment and the length of the task queue are weighted to obtain the overall efficiency of the equipment. The time difference between the event and the equipment data is synchronously verified. 1 minus the time difference is divided by 50 milliseconds to generate a consistency score. Finally, the three types of data are integrated into a structured record containing priority, load rate, and consistency score, and the in-memory database is written within 300 milliseconds.

[0037] It should be noted that the OPC UA protocol is a cross-platform industrial automation communication protocol. In this solution, it is used to collect load data such as equipment overall efficiency (OEE) and task queue length, and synchronize it with other data to provide real-time equipment status basis for dynamic work order scheduling and resource allocation.

[0038] In a specific embodiment, the generation of standardized scheduling decision input is carried out as follows: the integrated event data, MES batch information and equipment load data are standardized, the 1 to 5 priority levels of the MES batch and the basic priority corresponding to the event type are extracted, the batch priority and the event basic priority are weighted to obtain the comprehensive priority, and the result is rounded to an integer level. At the same time, the OEE value in the equipment load data and the task queue length are weighted to obtain the load rate, that is, the equipment load rate indicator in the range of 0-1.

[0039] It should be noted that the scheduling decision score is generated based on the comprehensive priority and equipment load rate. The calculation formula is: scheduling score equals comprehensive priority × (1-load rate). The score range is 0 to 5, with 5 being the highest priority. The fields such as event type, comprehensive priority, equipment load rate, scheduling score and timestamp are encapsulated into a standardized input structure. It is necessary to ensure that the mapping accuracy between event type and preset policy library is ≥

[0040] 98%, the timestamp deviation from the system clock is ≤50ms. The final generated structured data is pushed to the dynamic scheduling decision module after distributed verification, supporting the decision input processing demand of 200 times per second.

[0041] In the process of strategy matching and work dispatching plan generation, the embodiment of the present invention adopts weighted matching degree calculation and priority secondary screening strategy, combines event priority, equipment load rate and strategy weight, and verifies resource availability at the same time, which is conducive to screening out the optimal solution from multiple strategies, generating an efficient and feasible work dispatching plan, and ensuring the rationality of scheduling decisions and the efficiency of execution.

[0042] The dynamic work order scheduling decision module is used to analyze and match preset strategies based on event types and data characteristics, and then generate the optimal work dispatch plan through a priority algorithm.

[0043] In a specific embodiment, the analysis matches the preset strategy, and the specific process is as follows: extract the event type, comprehensive priority and equipment load rate from the standardized scheduling decision input, and perform conditional mapping with the 10 types of basic strategies preset in the strategy library. Each strategy in the strategy library contains trigger conditions and parameter thresholds, and Boolean operations are used to verify whether the event characteristics meet the basic conditions.

[0044] Candidate matching strategies are quantitatively scored and calculated using a weighted matching formula, where the number of satisfied conditions is the number of event features that meet the strategy conditions. When the matching degree of multiple strategies is greater than or equal to 0.8, a secondary screening is performed based on strategy priority, and the strategy with the highest matching degree and highest priority is finally selected as the execution plan, ensuring that the strategy matching accuracy is greater than or equal to 95%, and the single matching time is controlled within 150ms.

[0045] It should be noted that the 10 basic policies include the no-buffer scheduling policy and the empty Foup switching policy. For example, when a Foup cleaning event occurs with an overall priority ≥ 3 and a device load rate ≤ 0.7, the empty Foup switching policy is triggered as a candidate match. The weighted matching degree formula is: matching degree equals the number of conditions met / the total number of conditions × 0.6 + (1 - device load rate) × 0.4.

[0046] In a specific embodiment, the optimal dispatching plan is generated by the priority algorithm, and the specific process is as follows: extract the event type, comprehensive priority, equipment load rate and policy matching result from the standardized scheduling decision input, apply the candidate plan with dispatching priority equal to comprehensive priority × (1-equipment load rate) + policy weight coefficient, arrange the candidate plans in descending order of dispatching priority, and synchronously verify resource availability. When the highest priority plan has a resource conflict, it is automatically skipped and the suboptimal plan is selected until the optimal plan is generated that meets the requirements of "path conflict rate less than or equal to 3% and resource waiting time less than or equal to 60 seconds". The time consumption of plan generation is controlled within 200ms.

[0047] In the process of resource execution and system interaction, the embodiment of the present invention monitors resource status in real time, triggers emergency dispatch in a timely manner, and synchronizes data with the MES system in a high-frequency and high-precision manner. This is conducive to ensuring the smooth progress of dispatch operations and rapid response to abnormal situations. At the same time, it ensures that the MES page is consistent with actual operations, thereby improving the coordination and visualization level of production management.

[0048] The resource adaptive execution module is used to allocate materials, equipment, and logistics resources and execute dispatch operations based on the optimal dispatch plan;

[0049] In a specific embodiment, the allocation of materials, equipment and logistics resources and the execution of dispatching operations are as follows: according to the optimal dispatching plan, the type and quantity of required materials, the number and status requirements of the target equipment, and the path planning of logistics resources are extracted. First, the material status, equipment idle rate and logistics path conflict index are verified. After confirming that the resources are available, the corresponding resources are locked, and dispatching instructions are sent to the equipment through the OPCUA protocol. At the same time, path planning data is issued to the logistics system. During the execution process, changes in resource status are monitored in real time. If equipment failure or material position deviation occurs, the emergency dispatch process is automatically triggered, and spare resources are reallocated and dispatching tasks are adjusted to ensure that the dispatching operation completes the initial response within 90 seconds. The entire process data is synchronized to the MES system to form an operation log.

[0050] It should be noted that the required materials extracted are such as Foup and Dummy batches, the target equipment are such as LoadPort and FoupClean machines, logistics resources are such as overhead cranes and transmission tracks, and equipment failures are such as LoadPort abnormal signals.

[0051] The MES system's two-way interaction module is used to interact with MES in real time through a standardized interface, combining data synchronization requirements with card control conditions to ensure that dispatch operations are consistent with the MES page logic.

[0052] In a specific embodiment, the process of ensuring that the dispatching operation is consistent with the logic of the MES page is as follows: after the dispatching plan is generated, the equipment number, material type and dispatching time are structured and converted according to the MES system interface specification, and synchronized to the MES database in real time at intervals of 500ms through the WebService interface. When the dispatching is executed, the equipment status changes and the MES page display status are monitored synchronously. When the deviation between the two exceeds 100ms, the data verification process is triggered. By comparing the dispatching instruction timestamp with the MES page record timestamp, the display status is corrected. After the dispatching is completed, the operation log is automatically generated and sent back to MES, and the page task status is updated to ensure the full process consistency of the page display and the actual dispatching operation. The data synchronization delay is controlled within 200ms.

[0053] The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering provided by the embodiment of the present invention directly connects to the device through the PLC interface and uses hardware filtering to eliminate signal jitter during the event perception and data processing process. It accurately classifies event types in combination with preset feature code rules, and uses a double verification mechanism to evaluate trigger conditions. This is conducive to avoiding invalid signal interference, quickly and accurately identifying and screening valid events, and providing a reliable data basis for subsequent scheduling.

[0054] The database is used to store batch information matching MES, and also stores three types of data fused into structured records containing priority, load rate, and consistency score. It also stores equipment number, material type, and dispatch time according to the MES system interface specifications.

[0055] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering, characterized by: include: Real-time event perception and triggering module, used to monitor the status of specified equipment and material events in real time, analyze event types, and evaluate whether the events meet the trigger conditions; The multi-dimensional data fusion processing module is used to integrate the multi-dimensional impact parameters after the event is triggered when the event meets the trigger conditions, and then generate standardized scheduling decision input; A dynamic work order scheduling decision module is used to analyze and match preset strategies based on event types and data characteristics, and then generate the optimal work dispatch plan through a priority algorithm; The resource adaptive execution module is used to allocate materials, equipment, and logistics resources and execute dispatch operations based on the optimal dispatch plan; The MES system's two-way interaction module is used to interact with MES in real time through a standardized interface, combining data synchronization requirements with card control conditions to ensure that dispatch operations are consistent with the MES page logic.

2. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 1 is characterized in that: The specific process of real-time monitoring of specified equipment status and material events is as follows: Through a direct connection to the device hardware via a programmable logic controller interface, it monitors device signals such as loading port status and buffer occupancy in real time, eliminates jitter through hardware filtering circuits, and transmits these signals to the edge computing node using the Modbus transmission control protocol. It identifies status changes within a set 50 milliseconds and triggers an effective signal when the buffer idle rate exceeds 70% and persists for 150 milliseconds. The cleaning time is monitored by a timing module implanted in the machine control system. An event is generated when the remaining time is less than or equal to 300 seconds. For virtual batch requirements, the machine signal is subscribed to in real time through the open platform communication unified architecture interface. The demand signal is parsed and events are generated within 3 seconds. All events are encapsulated as JavaScript object representation data with timestamps and priorities, and pushed to the multi-dimensional data fusion processing module within 200 milliseconds via the Kafka message queue.

3. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 2 is characterized in that: The specific process of analyzing event types is as follows: The binary signature code of the original signal is collected through the PLC interface, and the type is identified based on the preset mapping rules. The device status event signature code rule is: when the current 4 bits are 0010, it is determined to be a semiconductor device loading port status change event; The material event feature code rule is: when the first four bits are 0101, it is determined to be a front-opening wafer box cleaning-related event.

4. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 3 is characterized in that: The specific process of evaluating whether an event meets the triggering conditions is as follows: After collecting raw signals of equipment status and material events through the hardware interface, they are first classified and processed based on the event type. For equipment status events, the system monitors loading port status changes and buffer occupancy rates in real time at set time points. When the buffer idle rate exceeds a preset threshold and this state persists for a specific duration, the validity of the state change is confirmed through dual verification using hardware filtering and software anti-shake. For equipment without a buffer, the trigger condition evaluation process is initiated when the loading port status changes from occupied to idle and remains idle for a specified period of time. For material events, thresholds are determined through a timing module and a status subscription mechanism. Foup cleaning events are triggered when the remaining time reaches a preset countdown threshold, and a secondary verification is performed based on historical cleaning cycle data. Dummy batch demand events are evaluated based on the duration of the machine's continuous demand signal. A trigger signal is generated when the signal duration exceeds the set value and the corresponding virtual batch inventory status is available. All qualifying events are processed in order using a priority algorithm (combining event type and business impact). Finally, they are packaged into structured data objects with timestamps and trigger flags and pushed to subsequent processing modules.

5. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 4 is characterized in that: The multi-dimensional impact parameters after the integration event is triggered are as follows: Multi-dimensional influencing parameters include event data, MES batch information, and equipment load data; Event data is acquired through the PLC interface and message queue. The event data includes millisecond timestamps and event type codes. After pre-processing by the edge node, transmission timeout data is filtered with a threshold of 200 milliseconds. MES batch information is simultaneously pulled through the WebService interface. MES batch information includes batch ID and level 1-5 priority. Equipment load data is collected via the OPCUA protocol. Equipment load data includes equipment overall efficiency (OEE) and task queue length. All data is sorted by timestamp and awaits integration. Using the device ID in the event data as the association key, the MES batch information is matched through database query, and the number of missing fields is divided by the total number of fields to obtain completeness. When the missing rate is greater than 10%, the default priority is filled in. The overall efficiency of the equipment and the length of the task queue are weighted to obtain the overall efficiency of the equipment. The time difference between the event and the equipment data is synchronously verified. 1 minus the time difference is divided by 50 milliseconds to generate a consistency score. Finally, the three types of data are integrated into a structured record containing priority, load rate, and consistency score, and the in-memory database is written within 300 milliseconds.

6. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 5 is characterized in that: The specific process of generating standardized scheduling decision input is as follows: The integrated event data, MES batch information and equipment load data are standardized, and the 1 to 5 priority levels of the MES batch and the basic priority corresponding to the event type are extracted. The batch priority and the event basic priority are weighted to obtain the comprehensive priority, and the result is rounded to the integer level. At the same time, the OEE value in the equipment load data and the task queue length are weighted to obtain the load rate, that is, the equipment load rate indicator in the range of 0-1.

7. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 6 is characterized in that: The specific process of analyzing and matching the preset strategy is as follows: The event type, comprehensive priority, and equipment load rate are extracted from the standardized scheduling decision input and conditionally mapped with the 10 basic policies preset in the policy library. Each policy in the policy library contains trigger conditions and parameter thresholds. Boolean operations are used to verify whether the event characteristics meet the basic conditions. Candidate matching strategies are quantitatively scored and calculated using a weighted matching formula, where the number of satisfied conditions is the number of event features that meet the strategy conditions. When the matching degree of multiple strategies is greater than or equal to 0.8, a secondary screening is performed based on strategy priority, and the strategy with the highest matching degree and highest priority is finally selected as the execution plan, ensuring that the strategy matching accuracy is greater than or equal to 95%, and the single matching time is controlled within 150ms.

8. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 7 is characterized in that: The optimal work dispatching plan is generated by the priority algorithm. The specific process is as follows: The system extracts event type, comprehensive priority, equipment load rate, and policy matching results from standardized scheduling decision inputs. It then applies candidate plans whose dispatch priority equals comprehensive priority × (1-equipment load rate) + policy weight coefficient. These plans are sorted in descending order of dispatch priority and simultaneously verify resource availability. If the highest-priority plan has a resource conflict, it automatically skips it and selects the next-best plan. This process continues until an optimal plan is generated, satisfying the criteria of "path conflict rate less than or equal to 3% and resource waiting time less than or equal to 60 seconds." This process takes less than 200 milliseconds to generate.

9. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 8 is characterized in that: The specific process of allocating materials, equipment, and logistics resources and executing dispatch operations is as follows: According to the optimal dispatch plan, the type and quantity of required materials, the number and status requirements of the target equipment, and the path planning of logistics resources are extracted. First, the material status, equipment idle rate and logistics path conflict index are verified. After confirming that the resources are available, the corresponding resources are locked, and the dispatch instructions are sent to the equipment through the OPCUA protocol. At the same time, the path planning data is issued to the logistics system. During the execution process, the changes in resource status are monitored in real time. If there is an equipment failure or material position deviation, the emergency dispatch process is automatically triggered, the spare resources are reallocated and the dispatch tasks are adjusted to ensure that the dispatch operation completes the initial response within 90 seconds. The entire process data is synchronized to the MES system to form an operation log.

10. The MES work order dynamic scheduling and resource self-adaptation system based on real-time event triggering according to claim 9 is characterized in that: The specific process of ensuring that the dispatching operation is consistent with the MES page logic is as follows: After the dispatch plan is generated, the equipment number, material type and dispatch time are structured and converted according to the MES system interface specifications, and synchronized to the MES database in real time at 500ms intervals through the WebService interface. When the dispatch is executed, the equipment status changes and the MES page display status are monitored synchronously. When the deviation between the two exceeds 100ms, the data verification process is triggered. By comparing the dispatch instruction timestamp with the MES page record timestamp, the display status is corrected. After the dispatch is completed, the operation log is automatically generated and sent back to the MES to update the page task status to ensure the full process consistency of the page display and the actual dispatch operation. The data synchronization delay is controlled within 200ms.

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