Intelligent control and optimization method and device for production process based on MES system

Abstract

The present application discloses a method and device for intelligent control and optimization of production processes based on an MES system, which belongs to the field of production process optimization. The present application obtains the bill of materials and process parameters from the MES system, and constructs a process knowledge graph based on this. The process nodes are associated with resource categories, estimated durations and priorities, and the logical edges represent the direct pre-dependencies and indirect resource constraints between processes. After receiving the order adjustment information, the affected process nodes and logical relationships in the graph are updated. Based on the updated graph, a set of discrete decision units is established, and each unit corresponds to a process node to be scheduled or its associated physical equipment instance to be allocated. Then, based on the dependencies and constraints in the updated graph, the execution order for the process to be scheduled is determined by the decision unit set, and the working period is planned for the equipment instance to be allocated, and the process rescheduling and resource allocation information is obtained. Based on this, production scheduling instructions are generated and transmitted to the MES system, which can effectively improve production efficiency when orders are changed.

Description

Technical Field

[0001] The present invention relates to the field of production process optimization, and in particular to a method and device for intelligent control and optimization of production processes based on an MES system. Background Art

[0002] In the era of intelligent manufacturing, intelligent control and optimization of production processes have become key to enhancing the core competitiveness of modern manufacturing enterprises. These methods aim to achieve automated, refined, and intelligent management of production processes through advanced technologies, thereby improving production efficiency, reducing operating costs, and enhancing rapid response to market changes. These methods have broad application prospects and significant economic value.

[0003] Currently, existing methods for controlling and optimizing production processes primarily rely on Manufacturing Execution Systems (MES) for data collection and basic scheduling. Some existing technologies incorporate traditional rule-based scheduling logic. While these methods are effective for relatively fixed production plans and resource allocation, their adaptability and efficiency are often limited when dealing with dynamic disruptions such as frequent order changes and urgent order insertions.

[0004] Existing technologies often struggle to quickly and comprehensively assess the impact of changes on the entire process chain when handling production plan adjustments triggered by sudden order changes, and they also struggle to efficiently reschedule under changing constraints. Consequently, when order changes trigger dynamic process chain reorganization and resource conflict resolution, existing technologies face technical challenges such as delayed production scheduling adjustments, poor adaptability to complex process dependencies and resource constraints, and difficulty achieving globally optimized production plans, resulting in limited improvements in overall production efficiency. Summary of the Invention

[0005] This application actually provides a production process intelligent control and optimization method, system, equipment and computer storage medium based on the MES system, which can effectively improve production efficiency.

[0006] In a first aspect, the present application provides a method for intelligently controlling and optimizing a production process based on an MES system, the method comprising:

[0007] Obtain bill of materials data and production process operation parameters through the MES system;

[0008] Based on the bill of materials data and production process operation parameters, a process knowledge graph is constructed. The process knowledge graph includes multiple process nodes and multiple logical relationship edges connecting the process nodes. Each process node represents a production process and is associated with a corresponding production resource category, estimated operation duration, and preset process execution priority. The logical relationship edges represent the direct pre-dependencies and indirect resource usage constraints between different process nodes.

[0009] Receive order adjustment information, and update the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph;

[0010] Based on the updated process knowledge graph, a set of discrete decision-making units is established. Each discrete decision-making unit in the discrete decision-making unit set corresponds to an updated process node to be scheduled or a production resource instance to be allocated. The production resource instance to be allocated is a physical device under the production resource category associated with the updated process node to be scheduled.

[0011] Based on the direct pre-dependencies and indirect resource usage constraints in the updated process knowledge graph, a set of discrete decision units is used to determine the execution order for multiple updated process nodes to be scheduled, and working periods for multiple production resource instances to be allocated, thereby obtaining process rescheduling information and corresponding production resource allocation information.

[0012] Generate production scheduling instructions based on process rescheduling information and production resource allocation information, and transmit the production scheduling instructions to the MES system to complete intelligent control and optimization of the production process.

[0013] In one feasible embodiment, after updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph, the method further includes:

[0014] Obtain real-time status data of production resources and work-in-process progress data related to the process nodes to be scheduled through the MES system;

[0015] Based on the real-time status data of production resources and the progress data of work-in-process processing, the estimated operation duration of the updated process node to be scheduled in the updated process knowledge graph and the availability status of the physical equipment under the production resource category associated with the updated process node to be scheduled are calibrated.

[0016] In one possible implementation, the order adjustment information includes product specifications, required quantity, and agreed delivery date;

[0017] Before updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph, the method further includes:

[0018] Based on changes in product specifications, the increase or decrease in demand quantity, and the degree of adjustment to the agreed delivery date, the impact of order adjustment information is assessed to obtain disturbance level information;

[0019] Based on the order adjustment information, the nodes of the process to be scheduled and the corresponding logical relationship edges in the process knowledge graph are updated to obtain the updated process knowledge graph, including:

[0020] According to the order adjustment information and disturbance level information, the associated process nodes to be scheduled and the corresponding logical relationship edges in the process knowledge graph are updated to obtain the updated process knowledge graph.

[0021] In one feasible implementation, based on the order adjustment information and the disturbance level information, the nodes of the process to be scheduled and the corresponding logical relationship edges in the process knowledge graph are updated to obtain an updated process knowledge graph, including:

[0022] Based on changes in product specifications, increases or decreases in required quantities, and adjustments to agreed delivery dates, directly related initial process nodes are extracted from the process knowledge graph.

[0023] In the process knowledge graph, the associated process nodes of a predetermined range are expanded along the logical relationship edges of the initial process nodes. The set of process nodes to be scheduled and the corresponding logical relationship edges that need to be modified are determined in the process knowledge graph. The depth of the predetermined range is determined by the disturbance level information.

[0024] For each to-be-scheduled process node in the to-be-scheduled process node set, if the order adjustment information includes adjustment instruction information corresponding to the estimated operation duration and the preset process execution priority of the to-be-scheduled process node, modify the estimated operation duration and the preset process execution priority of the corresponding to-be-scheduled process node according to the adjustment instruction information;

[0025] Based on the modified nodes of the process to be scheduled, the logical relationship edges between the modified nodes of the process to be scheduled are updated through the order adjustment information about the change instruction information of the preset process execution priority and production resource category to obtain the updated process knowledge graph.

[0026] In one possible implementation, a process knowledge graph is constructed based on bill of materials data and production process operation parameters, including:

[0027] Extract all the production processes required to produce the target product from the bill of materials data, and create a corresponding process node for each production process;

[0028] Extract the production resource category, estimated operation duration, and preset operation execution priority corresponding to each process node from the production process operation parameters, and associate the extracted production resource category, estimated operation duration, and preset operation execution priority as attributes to the corresponding process node;

[0029] According to the preset processing order of each production process recorded in the production process operation parameters, a logical relationship edge is established between two process nodes with a direct processing sequence;

[0030] The process nodes are screened to obtain multiple groups of process node groups that share the same production resource category, and logical relationship edges are established between different process nodes in the process node groups that have not established logical relationship edges.

[0031] In one possible implementation, the discrete decision-making unit includes a first type of discrete decision-making unit and a second type of discrete decision-making unit;

[0032] Based on the updated process knowledge graph, a set of discrete decision-making units is established, including:

[0033] Traverse each updated process node to be scheduled in the updated process knowledge graph, and create a corresponding first-class discrete decision unit for each updated process node to be scheduled;

[0034] Extracting physical devices whose availability status meets preset conditions under the production resource category associated with the updated process node to be scheduled, and determining each physical device as a production resource instance to be allocated;

[0035] In the case that the production resource instance to be allocated does not have a corresponding second-type discrete decision-making unit, a corresponding second-type discrete decision-making unit is created for each production resource instance to be allocated.

[0036] In one feasible implementation, based on the direct pre-dependencies and indirect resource usage constraints in the updated process knowledge graph, a discrete decision unit set is used to determine the execution order for multiple updated process nodes to be scheduled, and to determine the working time periods for multiple production resource instances to be allocated, thereby obtaining process rescheduling information and corresponding production resource allocation information, including:

[0037] Assign an execution time interval to each first-category discrete decision-making unit in the discrete decision-making unit set. The length of the execution time interval is determined by the estimated operation duration of the updated process node to be scheduled, and the starting position of the execution time interval is determined by the preset process execution priority ranking of the updated process node to be scheduled.

[0038] Traversing each pair of first-class discrete decision units with a direct preceding dependency in the discrete decision unit set, when the end position of the execution time interval of the preceding first-class discrete decision unit is after the start position of the execution time interval of the succeeding first-class discrete decision unit, moving the start position of the execution time interval of the succeeding first-class discrete decision unit to after the end position of the execution time interval of the preceding first-class discrete decision unit;

[0039] Traversing each second-type discrete decision-making unit in the discrete decision-making unit set, when multiple first-type discrete decision-making units are associated with the same second-type discrete decision-making unit, and the execution time intervals of the associated first-type discrete decision-making units overlap, reallocating the starting position of the execution time interval of the associated first-type discrete decision-making units according to the updated preset process execution priority order of the process node to be scheduled, and generating the working period information of the second-type discrete decision-making units;

[0040] The execution time intervals of all first-type discrete decision-making units and the working period information of all second-type discrete decision-making units are merged to form process rescheduling information and production resource allocation information.

[0041] In a second aspect, the present application provides a production process intelligent control and optimization device based on an MES system, the system comprising:

[0042] The acquisition module is used to obtain the bill of materials data and production process operation parameters through the MES system;

[0043] A construction module is used to construct a process knowledge graph based on bill of materials data and production process operating parameters. The process knowledge graph includes multiple process nodes and multiple logical relationship edges connecting the process nodes. Each process node represents a production process and is associated with a corresponding production resource category, estimated operation duration, and preset process execution priority. The logical relationship edges represent the direct pre-dependencies and indirect resource usage constraints between different process nodes.

[0044] A receiving module is used to receive order adjustment information and update the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph;

[0045] A module is established to establish a set of discrete decision-making units based on the updated process knowledge graph. Each discrete decision-making unit in the discrete decision-making unit set corresponds to an updated process node to be scheduled or a production resource instance to be allocated. The production resource instance to be allocated is a physical device under the production resource category associated with the updated process node to be scheduled.

[0046] The determination module is used to determine the execution order for multiple updated process nodes to be scheduled based on the direct pre-dependencies and indirect resource usage constraints in the updated process knowledge graph through a set of discrete decision units, and to determine the working time periods for multiple production resource instances to be allocated, thereby obtaining process rescheduling information and corresponding production resource allocation information;

[0047] The generation module is used to generate production scheduling instructions based on process rearrangement information and production resource allocation information, and transmit the production scheduling instructions to the MES system to complete intelligent control and optimization of the production process.

[0048] In a third aspect, the present application provides an electronic device comprising: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement a method for intelligent control and optimization of production processes based on an MES system as in any embodiment of the first aspect.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a method for intelligent control and optimization of production processes based on an MES system as in any one of the embodiments of the first aspect is implemented.

[0050] The present application implements an intelligent control and optimization method, system, device and computer storage medium for production processes based on an MES system. By constructing a process knowledge graph containing process nodes, logical relationship edges and node attributes, it first provides a global, structured process and resource association view for a dynamically changing production environment. When order adjustment information is received, the knowledge graph is updated in a targeted manner, which can quickly and accurately identify the changes in the process scope and its dependencies affected by the order change, overcoming the defects of the existing technology in that the impact of the change is not comprehensive and timely. On this basis, a set of discrete decision units corresponding to the updated process nodes to be scheduled and the production resource instances to be allocated is established, and the execution order and working period are determined for these decision units based on the direct pre-dependencies and indirect resource usage constraints clearly defined in the updated knowledge graph, effectively solving the problems of low rescheduling efficiency and poor adaptability to complex dependencies in the existing technology under variable constraint conditions. Therefore, the present invention can achieve rapid response and intelligent optimization of production scheduling schemes in scenarios where order changes trigger dynamic reorganization of process chains and resolution of resource conflicts, thereby improving the adaptability and execution efficiency of production plans.

[0051] Furthermore, after the order is adjusted and the process knowledge graph is updated, the real-time status data of production resources and work-in-progress processing progress data obtained by the MES system are further used to calibrate the estimated operation time of the process node to be scheduled in the knowledge graph and the availability parameters of the associated physical equipment. This step enables the process knowledge graph to not only reflect the logical structure adjustment brought about by the order change, but also dynamically integrate the real-time working condition information of the production site. This real-time calibration mechanism ensures that the subsequent process rescheduling and resource allocation decisions based on this knowledge graph are based on the latest and most accurate production factor data, thereby significantly improving the realism and executability of the scheduling plan, and effectively avoiding the problem of disconnection between the scheduling plan and the actual execution due to deviations in working time estimation or sudden changes in resource status. Therefore, the present invention further enhances the response sensitivity and decision-making accuracy of the production process control method in response to order changes and on-site dynamics through real-time data calibration, and improves the reliability and optimization effect of the scheduling results. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0053] Figure 1 This is a flowchart of a method for intelligent control and optimization of production processes based on an MES system provided by one embodiment of the present application;

[0054] Figure 2 This is a flowchart of a method for updating a process knowledge graph provided by an embodiment of the present application;

[0055] Figure 3 This is a flowchart of a method for determining process rescheduling information and production resource allocation information provided by an embodiment of the present application;

[0056] Figure 4 This is a schematic diagram of the structure of a production process intelligent control and optimization device based on an MES system provided by an embodiment of the present application;

[0057] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0059] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0060] Existing technologies often struggle to quickly and comprehensively assess the impact of changes on the entire process chain when handling production plan adjustments triggered by sudden order changes, and they also struggle to efficiently reschedule under changing constraints. Consequently, when order changes trigger dynamic process chain reorganization and resource conflict resolution, existing technologies face technical challenges such as delayed production scheduling adjustments, poor adaptability to complex process dependencies and resource constraints, and difficulty achieving globally optimized production plans, resulting in limited improvements in overall production efficiency.

[0061] In order to solve the problems of the prior art, the embodiments of the present application provide a method, system, device and computer storage medium for intelligent control and optimization of production processes based on an MES system. The following first introduces the method for intelligent control and optimization of production processes based on an MES system provided by the embodiments of the present application.

[0062] Figure 1 The figure shows a flow chart of a production process intelligent control and optimization method based on an MES system provided by an embodiment of the present application. Figure 1 As shown, the method includes steps S110 to S160.

[0063] S110: Obtain bill of materials data and production process operation parameters through the MES system.

[0064] MES, short for Manufacturing Execution System, is used to manage and control real-time operations at the production site, including data collection, process monitoring, resource scheduling, and instruction execution. Bill of Materials (BOM) data refers to information describing the structural composition of a product, encompassing all required raw materials, components, subassemblies, and their quantity relationships. This data originates from the product design system or enterprise resource planning system. Production process operating parameters refer to dynamic or preset parameters involved in the production process, including the estimated duration of the process, the type of production resources required, and the preset execution priority of the process. These parameters are based on historical production records or standard process configurations.

[0065] First, access the bill of materials repository through the MES system's database query interface, using structured query language or an application programming interface to retrieve the complete structural information of the target product. Parameter sources include product design databases, such as retrieving component lists and quantity relationships based on product models to ensure that the data covers all materials required for production. The MES system's real-time data acquisition module then connects to production monitoring equipment or process configuration files to retrieve process-related parameters. Parameter sources include production logs or preset process libraries, such as extracting the estimated operation time, resource category, and priority for each process. These parameters are based on equipment performance indicators or standardized work instructions.

[0066] For example, the MES system connects to the enterprise resource planning database through a standard interface to obtain the complete bill of materials data of the current product, such as the motherboard, housing, display screen and other components of a certain model of electronic product and their assembly quantity relationships. At the same time, the MES system collects the actual production process operation parameters from the workshop control terminal, including the standard operation time of each assembly process, the required equipment type such as automatic placement machines or testing instruments, and the preset process priority sequence. After format conversion, these data are formed into a structured data set. For example, the parameters of the placement process are recorded as resource category surface mount technology (SMT) equipment, estimated duration of 120 seconds, and priority level 1. This data set fully describes the basic configuration of the current production process and provides a benchmark reference for subsequent order change responses.

[0067] S120: Based on the bill of materials data and production process operating parameters, a process knowledge graph is constructed. The process knowledge graph includes multiple process nodes and multiple logical relationship edges connecting the process nodes. Each process node represents a production process. Each process node is associated with a corresponding production resource category, estimated operation time and preset process execution priority. The logical relationship edges represent the direct pre-dependencies and indirect resource usage constraints between different process nodes.

[0068] The process knowledge graph is a topological network model that structuredly represents the production process. The process node is the basic unit of the graph, mapping specific production process entities. Its associated attributes include production resource category, estimated operation duration, and preset process execution priority. Production resource category refers to the type of equipment required for the process, estimated operation duration refers to the standard amount of time required to complete the process, and preset process execution priority refers to the ranking weight of the process in the global scheduling sequence. Logical relationship edges are directed or undirected edges connecting nodes. They contain two types of constraints: direct predecessor dependencies and indirect resource usage constraints. Direct predecessor dependencies represent a mandatory precedence order between processes, while indirect resource usage constraints represent mutually exclusive relationships between processes that share the same type of production resources.

[0069] First, all the process names required to produce the target product are extracted from the bill of materials data, and a unique node is created for each process. Then, the attribute values ​​corresponding to the nodes are retrieved from the production process operation parameters, and the resource category, estimated operation time and preset process execution priority are bound to the corresponding nodes. Then, directed edges representing direct pre-dependencies are added between nodes with direct precedence relationships according to the preset processing order. Finally, node groups sharing the same resource category are screened, and undirected edges representing indirect resource usage constraints are added between nodes in the group that do not have direct dependencies, ultimately obtaining a structured process knowledge graph representing the production process.

[0070] S130: Receive order adjustment information, and update the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph.

[0071] Order adjustment information refers to external instruction data that triggers changes to production plans, including product specification change parameters, required quantity increases or decreases, and agreed delivery date offsets. Process nodes to be scheduled refer to the subset of process nodes in the process knowledge graph affected by order adjustments, whose attributes or relationships require reconfiguration. Logical edge updates refer to the reconstruction of direct pre-dependencies and indirect resource usage constraints between nodes, such as adding or removing dependency edges or modifying resource competition relationships.

[0072] First, order adjustment information is received through the message queue, and the product specifications, required quantity and delivery date change instructions are parsed; then, the affected initial process nodes are located in the process knowledge graph, for example, matching the relevant process nodes according to the product specification changes; then, based on the scope of the change impact, the associated nodes are expanded along the logical relationship edge to form a set of process nodes to be scheduled; for each node in the set, if the order adjustment information contains its attribute adjustment instructions, the estimated operation time or the preset process execution priority is updated; finally, the logical relationship edge is reconstructed according to the node attribute changes, such as rebuilding the resource competition edge due to the change of priority, or adjusting the pre-dependency edge due to the increase or decrease of the process, and finally an updated process knowledge graph is generated.

[0073] For example, in an electronic product assembly scenario, a customer changes the motherboard specification from type A to type B and shortens the delivery cycle. After receiving the order adjustment information, the system locates the motherboard patch node as the initial node in the knowledge graph constructed by S120; expands along the dependency edge to the firmware burning node, and expands along the resource constraint edge to the display patch node to form a set of nodes to be scheduled. According to the specification change instruction, the estimated operation time of the motherboard patch node is updated from 150 seconds to 180 seconds; based on the delivery urgency, the priority is increased from level 2 to level 1. Due to the change in priority, the resource competition edge weights of the motherboard patch node and the display patch node are reconstructed; due to the increase in operation time, the pre-dependency time window of the subsequent firmware burning node is extended. The updated graph accurately reflects the process chain relationship after the change.

[0074] S140: Based on the updated process knowledge graph, a set of discrete decision units is established, where each discrete decision unit in the discrete decision unit set corresponds to an updated process node to be scheduled or a production resource instance to be allocated. The production resource instance to be allocated is a physical device under the production resource category associated with the updated process node to be scheduled.

[0075] Discrete decision-making units (DDUs) are the smallest decision-making units for scheduling optimization and are divided into two categories: the first type maps process nodes to be scheduled and carries the process execution logic; the second type maps production resource instances to be allocated and carries the resource occupation logic. Process nodes to be scheduled refer to all process nodes that need to be rescheduled in the updated process knowledge graph. Production resource instances to be allocated refer to specific physical devices available under the production resource category associated with a process node, such as placement machine device number 001 associated with a placement process node.

[0076] First, traverse each to-be-scheduled process node in the updated process knowledge graph, create a unique first-class discrete decision-making unit for each node and inherit its attributes; then, extract all available physical equipment under the node-associated production resource category, and filter out equipment instances whose status meets the preset conditions; then check whether the equipment instance already has a corresponding second-class discrete decision-making unit. If not, create a new second-class discrete decision-making unit and associate it with the equipment identifier; finally, integrate all first-class and second-class units to form a discrete decision-making unit set, and establish a mapping relationship between process units and resource units.

[0077] S150: Based on the direct predecessor dependencies and indirect resource usage constraints in the updated process knowledge graph, a set of discrete decision units is used to determine the execution order for multiple updated process nodes to be scheduled, and working time periods are determined for multiple production resource instances to be allocated, thereby obtaining process rescheduling information and corresponding production resource allocation information.

[0078] Process rescheduling information refers to the updated execution order and timing of the process nodes to be scheduled, including the start and end times of each process node. Production resource allocation information refers to the working period allocation plan for the production resource instances to be allocated, including the activation time and working hours of the physical equipment.

[0079] First, an execution time interval is assigned to each first-class discrete decision-making unit in the discrete decision-making unit set. The interval length is determined by the estimated duration of the associated operation. The starting position of the interval is generated based on the preset process execution priority, with higher-priority nodes assigned earlier start times. Next, each pair of first-class discrete decision-making units with a direct predecessor dependency is traversed. If the execution time interval end position of the predecessor unit is later than the start position of the successor unit, the start position of the successor unit is moved after the end position of the predecessor unit to ensure that the process execution order satisfies the dependency constraints. Next, each second-class discrete decision-making unit is traversed. If the execution time intervals of multiple first-class units associated with the same second-class discrete decision-making unit overlap, the starting positions of the overlapping units are reallocated based on the preset process execution priority order. The higher-priority unit remains in its original position, while the lower-priority unit is moved back until the overlap is eliminated. The equipment's operating time period information is then generated simultaneously. Finally, the adjusted execution time intervals of all first-class units are combined to form process rescheduling information, and the operating time information of all second-class units is combined to form production resource allocation information.

[0080] S160: Generate production scheduling instructions based on process rearrangement information and production resource allocation information, and transmit the production scheduling instructions to the MES system to complete intelligent control and optimization of the production process.

[0081] Production scheduling instructions are a set of operational instructions that drive MES scheduling. These instructions include process execution instructions and resource allocation instructions. Process execution instructions are generated based on process rescheduling information to start and stop sequence commands for process nodes. They include the process node ID, executing equipment ID, and planned start and end times. Resource allocation instructions are generated based on production resource allocation information to configure equipment operating periods. They include the physical equipment ID, activation time, release time, and associated process ID.

[0082] First, the process node execution time intervals in the process rescheduling information are converted into process execution instructions. For example, a structured instruction unit containing the equipment ID, start time, and end time is generated for each process node. Simultaneously, the equipment operating periods in the production resource allocation information are converted into resource allocation instructions. For example, activation and release time points and a list of bound processes are generated for each piece of equipment. These two types of instructions are then encapsulated into message packets through a predefined MES system interface using standard industrial communication protocols. These packets are then pushed to the MES system's scheduling engine in real time, triggering equipment control and task dispatching on the production site.

[0083] For example, in the scenario of electronic product assembly order changes, the process rescheduling information output by step S150 specifies that the motherboard patch process be executed on the patch machine device 001 in the time interval T1 to T2, and the resource allocation information sets the working period of the device to T1 to T3. The system generates process execution instructions: process ID motherboard patch, device ID patch machine 001, start time T1, end time T2; resource allocation instructions: device ID patch machine 001, enable time T1, release time T3. The instruction set is pushed to the MES system via the OPC UA protocol. After parsing, the MES drives the patch machine 001 to start the motherboard patch operation at time T1, and after completion at T2, it remains in the ready state until the resources are released at T3, thereby achieving precise scheduling and execution after the order change.

[0084] This embodiment first provides a global, structured view of process and resource associations for a dynamically changing production environment by constructing a process knowledge graph containing process nodes, logical relationship edges, and node attributes. When order adjustment information is received, the knowledge graph is updated in a targeted manner, which can quickly and accurately identify changes in the process scope and its dependencies affected by the order change, overcoming the defects of the existing technology in that the impact of the change is not comprehensive and timely. On this basis, a set of discrete decision units corresponding to the updated process nodes to be scheduled and the production resource instances to be allocated is established, and the execution order and working period are determined for these decision units based on the direct pre-dependencies and indirect resource usage constraints clearly defined in the updated knowledge graph, effectively solving the problems of low rescheduling efficiency and poor adaptability to complex dependencies in the existing technology under variable constraint conditions. Therefore, the present invention can achieve rapid response and intelligent optimization of production scheduling solutions in scenarios where order changes trigger dynamic reorganization of process chains and resolution of resource conflicts, thereby improving the adaptability and execution efficiency of production plans.

[0085] Because the existing technology has problems such as delayed static parameter updates and lack of awareness of real-time production status, the rescheduling plan may be out of touch with the actual production environment, thereby causing resource conflicts or invalid progress predictions. In order to solve the above problems, this application solves them in the following way.

[0086] In one feasible embodiment, in step S130, after updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information and obtaining the updated process knowledge graph, the method further includes:

[0087] The MES system is used to obtain real-time status data of production resources and work-in-process processing progress data related to the process nodes to be scheduled.

[0088] Real-time production resource status data refers to a collection of dynamic operating indicators for physical equipment, including instantaneous parameters such as equipment failure status, maintenance status, real-time load rate, and energy consumption. This data originates from equipment sensors or control systems connected to the MES system, such as operating codes collected by PLCs or temperature sensor readings. Work-in-process progress data refers to a chronological record of the completion status of currently executing process tasks, including elapsed time, remaining workload, and quality inspection results. This data is obtained through the MES system's production monitoring module, such as work-in-process location tracked by RFID or process execution percentages reported by industrial computers.

[0089] After the process knowledge graph is updated, a data request is first sent to the MES system, specifying the physical device ID and WIP batch number associated with the process node to be scheduled. Next, the real-time status code and load indicator of the physical device are retrieved from the device control layer via the OPC UA protocol. The WIP database is simultaneously queried and progress records are filtered by process node ID. Finally, the device status data and progress data are linked by process node ID to form a structured calibration input dataset. For example, the fault flag for device 001 associated with the motherboard patch process node is 0, the load rate is 70%, and the WIP completion rate is 80%.

[0090] Based on the real-time status data of production resources and the progress data of work-in-process processing, the estimated operation duration of the updated process node to be scheduled in the updated process knowledge graph and the availability status of the physical equipment under the production resource category associated with the updated process node to be scheduled are calibrated.

[0091] The availability status refers to a Boolean flag that indicates whether a physical device can be scheduled, and is determined by a combination of the device's fault status, maintenance plan, and real-time load threshold. First, the sliding window weighted average method is used to process the work-in-process progress data. The actual operation duration of the last five similar work-in-process products is used as a sample, and the calibration value is calculated by assigning exponential decay weights in reverse order of completion time. Afterwards, the availability status is determined based on the real-time status data of production resources: if the real-time status code of the device is faulty or scheduled for maintenance, it is marked as unavailable; if the real-time load rate exceeds the threshold set for the model's historical peak performance, it is marked as overloaded and cannot be assigned; otherwise, it is marked as available. Finally, the calibrated estimated operation duration is written into the corresponding process node attribute field, and the availability status label of the associated physical device is updated.

[0092] For example, in the electronic product assembly scenario, after the motherboard specification change triggers the update of the process knowledge graph, the system immediately executes the calibration process. The MES system reads the status code 0 and the load rate of 70% from the PLC of the placement machine 001, and at the same time finds that the completion rates of the three work-in-progress batches associated with the motherboard placement process are 75%, 80%, and 85%, respectively. Using the sliding window weighted average method, the actual operation time of the three most recent batches is 125 seconds, 130 seconds, and 128 seconds, and the calibration time of 127 seconds is calculated with weights of 0.5, 0.3, and 0.2, covering the original value of 150 seconds. Since the load rate of 70% is lower than the threshold of 85%, the placement machine 001 is marked as available. The calibrated knowledge graph provides an accurate parameter basis for subsequent scheduling.

[0093] This embodiment, after adjusting and updating the process knowledge graph based on the order, further utilizes the real-time status data of production resources and work-in-progress processing progress data obtained by the MES system to calibrate the estimated operation time of the process node to be scheduled and the availability parameters of the associated physical equipment in the knowledge graph. This step enables the process knowledge graph to not only reflect the logical structure adjustment brought about by the order change, but also dynamically integrate the real-time working condition information of the production site. This real-time calibration mechanism ensures that the subsequent process rescheduling and resource allocation decisions based on this knowledge graph are based on the latest and most accurate production factor data, thereby significantly improving the realism and executability of the scheduling plan, and effectively avoiding the problem of disconnection between the scheduling plan and the actual execution due to deviations in working time estimation or sudden changes in resource status. Therefore, the present invention further enhances the response sensitivity and decision-making accuracy of the production process control method in response to order changes and on-site dynamics through real-time data calibration, and improves the reliability and optimization effect of the scheduling results.

[0094] In one possible implementation, the order adjustment information includes product specifications, required quantity, and agreed delivery date;

[0095] In step S130, the method further includes updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information, and obtaining the updated process knowledge graph.

[0096] Based on changes in product specifications, increases or decreases in demand quantities, and the degree of adjustment in agreed delivery dates, the impact range of order adjustment information is evaluated to obtain disturbance level information.

[0097] Disturbance level information is a categorized indicator that quantifies the impact of order changes on the production process. It is calculated based on three dimensions: the degree of change in product specifications, the increase or decrease in required quantity, and the degree of adjustment to the agreed delivery date. The degree of change in product specifications is measured by the ratio of process steps that differ between the new and old bills of materials. The increase or decrease in required quantity is calculated as the percentage change in the order quantity. The degree of adjustment to the agreed delivery date is assessed based on the lead time compression ratio.

[0098] First, the product specification change parameters in the order adjustment information are analyzed, and the proportion of affected process nodes is calculated by comparing the old and new bills of materials. Next, the increase or decrease in the required quantity is calculated as the percentage difference between the changed quantity and the original quantity. The degree of adjustment to the agreed delivery date is also calculated as the ratio of the shortened planned delivery cycle to the original cycle. Finally, a weighted summation formula is used to integrate the three indicators. Pre-set thresholds are used to map these indicators to high, medium, and low disturbance levels. The weight coefficients are set based on historical order impact analysis: 0.5 for specification changes, 0.3 for quantity increases or decreases, and 0.2 for delivery adjustments.

[0099] In step S130, the nodes of the process to be scheduled and the corresponding logical relationship edges in the process knowledge graph are updated according to the order adjustment information to obtain an updated process knowledge graph, including:

[0100] According to the order adjustment information and disturbance level information, the associated process nodes to be scheduled and the corresponding logical relationship edges in the process knowledge graph are updated to obtain the updated process knowledge graph.

[0101] First, the initial set of process nodes is identified based on product specification changes. The expansion depth is then determined based on the disturbance level. A breadth-first search is performed along direct predecessor dependency edges and indirect resource usage constraint edges to generate a set of process nodes to be scheduled. For each node in this set, if the order adjustment information includes instructions for adjusting the estimated operation duration or the preset process execution priority, the original attribute values ​​are overwritten. Finally, based on the production resource category change instructions, the logical relationship edges between nodes are reconstructed: if the resource category changes, the original resource constraint edge is deleted and a new edge is established; if the priority changes, the dependency edge weights are adjusted.

[0102] For example, in an electronic product assembly scenario, a customer changes the motherboard specification from type A to type B, increases the demand quantity by 50%, and compresses the delivery cycle by 40%. The system calculates the degree of specification change as 30%, the quantity increase by 50%, and the delivery compression rate by 40%. The weighted sum is 0.47, which is determined to be a high disturbance level. In the process knowledge graph, the motherboard patch node is located as the initial node, and 15 nodes such as patch, firmware burning, and detection are expanded at three levels to form a set to be scheduled. According to the order instructions, the estimated operation time of the motherboard patch node is changed from 150 seconds to 180 seconds, and the priority is raised from level 2 to level 1. Due to the addition of a laser engraving process, a new direct preceding dependency edge is established between the patch node and the detection node, and the resource constraint edge between the original patch node and the shell assembly node is deleted.

[0103] Figure 2 FIG1 shows a flow chart of a method for updating a process knowledge graph provided by an embodiment of the present application. Figure 2 As shown, the method includes steps S210 to S240.

[0104] In one feasible implementation, based on the order adjustment information and the disturbance level information, the nodes of the process to be scheduled and the corresponding logical relationship edges in the process knowledge graph are updated to obtain an updated process knowledge graph, including:

[0105] S210: Based on changes in product specifications, increases or decreases in required quantities, and the degree of adjustment of the agreed delivery date, directly related initial process nodes are extracted from the process knowledge graph.

[0106] Initial process nodes refer to the first set of process nodes directly affected by order adjustments. Product specification changes are identified by the process ratio of the bill of materials. The magnitude of the change in the required quantity reflects the magnitude of the change in order size. The degree of adjustment to the agreed delivery date reflects time pressure. These three parameters collectively determine the source process nodes that require priority modification.

[0107] First, product specification change parameters are extracted from the order adjustment information and compared with the original bill of materials to identify added, deleted, or modified material items. The increase or decrease in the required quantity is then calculated as the percentage difference between the changed quantity and the original quantity. The degree of delivery date adjustment is also evaluated as the planned delivery cycle compression rate. Finally, the process nodes directly associated with the material change, parallel process nodes strongly related to the increase or decrease in required quantity, and critical path nodes subject to delivery cycle constraints are located in the process knowledge graph and merged to form the initial process node set.

[0108] S220: Expand the associated process nodes within a predetermined range along the logical relationship edges of the initial process nodes in the process knowledge graph, determine the set of process nodes to be scheduled and the corresponding logical relationship edges that need to be modified in the process knowledge graph, and the depth of the predetermined range is determined by the disturbance level information.

[0109] Expanding a predetermined range of associated process nodes involves performing a breadth-first traversal along the logical relationship edges, starting with the initial node. The set of process nodes to be scheduled includes the initial node and all nodes within its expanded range, and the corresponding logical relationship edges include the associated edges between these nodes.

[0110] First, the expansion depth threshold is set based on the disturbance level information: 3 for high disturbance level, 2 for medium level, and 1 for low level. Next, starting from the initial node, a breadth-first search is performed along two types of logical relationship edges: the first type is direct predecessor dependency edges, which traverse successor nodes as directed edges; the second type is indirect resource usage constraint edges, which traverse nodes sharing the same resource category as undirected edges. A depth counter is incremented at each expansion level, and the search terminates when the threshold is reached. Finally, all visited nodes are integrated to generate a set of nodes to be scheduled, and all logical relationship edges between nodes are recorded.

[0111] S230: For each process node to be scheduled in the set of process nodes to be scheduled, when the order adjustment information includes adjustment instruction information corresponding to the estimated operation duration and preset process execution priority of the process node to be scheduled, modify the estimated operation duration and preset process execution priority of the corresponding process node to be scheduled according to the adjustment instruction information.

[0112] Adjustment instructions refer to the process attribute modifications explicitly specified in the order adjustment information, including updated estimated operation durations and reset priority levels for pre-set processes. Attribute overwrite operations are performed only if the order adjustment information contains instructions corresponding to this node.

[0113] First, iterate over each node in the set of process nodes to be scheduled. Then, it searches the order adjustment information to see if there are any adjustment instructions for that node. If there are any estimated operation duration adjustment instructions, the original duration in the node attribute is replaced with the new value. If there are any preset process execution priority adjustment instructions, the original priority is replaced with the new level. Finally, the original attribute values ​​of nodes without adjustment instructions are retained.

[0114] S240: Based on the modified nodes of the process to be scheduled, the logical relationship edges between the modified nodes of the process to be scheduled are updated through the order adjustment information on the change instruction information of the preset process execution priority and the production resource category to obtain the updated process knowledge graph.

[0115] Change instructions refer to modification requirements regarding process dependencies and resource constraints within order adjustment information, including adding / deleting dependency edges and changing resource allocation relationships. Logical edge updates must ensure that the topological constraints between nodes are consistent with actual production requirements.

[0116] First, for the modified set of process nodes to be scheduled, the change instructions in the order adjustment information are retrieved. Then, two types of changes are processed. For changes in the preset process execution priority, the weights of the dependency edges between nodes are recalculated. For example, the urgency factor of the dependency edge from a high-priority node to a low-priority node is increased. For changes in the production resource category, the indirect constraint edges corresponding to the original resource category are deleted and constraint edges are established under the new resource category. Finally, invalid edges are removed and new edges are added to form a complete updated logical relationship network.

[0117] For example, in an electronic product assembly scenario, a change in motherboard specifications affects the patch process, a 50% increase in demand affects the batch testing process, and a 40% reduction in lead time affects the final inspection process. Based on this, the system extracts three initial nodes: motherboard patch, batch testing, and final inspection. Due to the high disturbance level, a three-level deep expansion is performed: from the motherboard patch node, two levels of dependency edges are expanded to the firmware burning and soldering nodes, and one level of resource constraint edges are expanded to the display patch node. The batch testing node is expanded to the pre-assembly node, and the final inspection node is expanded to the pre-packaging node, resulting in a total of 15 nodes to be scheduled. Based on the order instructions, the operation duration of the motherboard patch node is updated to 180 seconds and the priority is updated to level 1. Due to the addition of a laser engraving process, a new dependency edge is established between the motherboard patch node and the final inspection node. Due to a change in test equipment, the resource constraint edge between the batch testing node and the housing assembly node is deleted, and a new constraint edge is established between the batch testing node and the laser engraving node.

[0118] In one possible implementation, a process knowledge graph is constructed based on bill of materials data and production process operation parameters, including:

[0119] Extract all the production processes required to produce the target product from the bill of materials data, and create a corresponding process node for each production process.

[0120] A process node is the fundamental unit of the process knowledge graph, representing an independent production process required to produce a target product. Each node has a unique identifier, generated by parsing the product structure information in the bill of materials data, and represents the atomic task unit in the product manufacturing process.

[0121] First, the hierarchical structure of the bill of materials (BOM) data is parsed to identify all assembly-level steps. A depth-first traversal algorithm is then used to extract leaf node processes. For example, in the electronics assembly scenario, this includes end-of-line processes such as motherboard soldering, firmware burning, and final testing. Finally, a node with a unique ID is created for each process step, with the number of nodes fully corresponding to the process steps in the BOM.

[0122] The production resource category, estimated operation duration and preset process execution priority corresponding to each process node are extracted from the production process operation parameters, and the extracted production resource category, estimated operation duration and preset process execution priority are associated with the corresponding process node as attributes.

[0123] Node attributes include production resource category, estimated operation duration, and preset process execution priority. First, using the process node ID as the key, the corresponding attributes are retrieved from the production process operation parameter library. Then, the resource category string, estimated operation duration value, and priority level value are bound to the node as structured data. For example, the attributes bound to the placement process node are: resource category SMT equipment, estimated operation duration 120 seconds, and priority level 1. Finally, the attribute integrity is verified to ensure that no nodes are missing key parameters.

[0124] According to the preset processing order of each production process recorded in the production process operation parameters, a logical relationship edge is established between two process nodes with a direct processing sequence.

[0125] The direction of the edge is determined by the preset processing order, starting from the preceding process node and ending at the succeeding process node. First, the process route table in the production process operation parameters is loaded, and the list of predecessor processes for each process is parsed. Then, all process nodes are traversed. If node A is the predecessor of node B, a directed edge is established between A and B. Finally, the dependency network is verified to be free of loops. For example, the firmware burning node must rely on the motherboard patch node for completion.

[0126] The process nodes are screened to obtain multiple groups of process node groups that share the same production resource category, and logical relationship edges are established between different process nodes in the process node groups that have not established logical relationship edges.

[0127] A process node group is a collection of nodes with the same resource category attribute. Constraint edges are generated between nodes within the group that do not have direct dependencies. First, group the nodes by resource category. For example, all nodes using SMT equipment are grouped together. Then, traverse each node group and establish undirected edges for pairs of nodes not connected by direct dependency edges. For example, if the placement process and the dispensing process share placement machine resources but have no prioritization, resource constraint edges are established. Finally, resource category labels are added to the constraint edges to identify the source of the competition.

[0128] For example, in an electronic product assembly scenario, the system first extracts 20 processes from the bill of materials and creates corresponding nodes. The following attributes are bound to the placement node: resource category placement machine, estimated duration 120 seconds, priority 1; and the following attributes are bound to the detection node: resource category tester, estimated duration 60 seconds, priority 2. Dependency edges are established based on the process route: motherboard placement - firmware burning - functional testing. After grouping by resource category, resource constraint edges are established between the placement node and the dispensing node of the shared placement machine, and constraint edges are established between the detection node and the calibration node of the shared tester, ultimately forming a process knowledge graph containing 15 edges.

[0129] In one possible implementation, the discrete decision-making unit includes a first type of discrete decision-making unit and a second type of discrete decision-making unit;

[0130] Based on the updated process knowledge graph, a set of discrete decision-making units is established, including:

[0131] Traverse each updated process node to be scheduled in the updated process knowledge graph, and create a corresponding first-class discrete decision unit for each updated process node to be scheduled.

[0132] The first-class discrete decision-making unit (DDU) is a decision-making entity that maps to the process nodes to be scheduled in the process knowledge graph and carries the temporal logic of process execution. Each DDU inherits the key attributes of the corresponding process node, including production resource category, estimated operation duration, and preset process execution priority, serving as the basic unit for subsequent scheduling optimization. First, all process nodes to be scheduled in the updated process knowledge graph are traversed. Then, a unique first-class discrete decision-making unit (DDU) is generated for each node, replicating its associated production resource category, estimated operation duration, and preset process execution priority attributes. Finally, a mapping relationship between the process node ID and the DDU ID is established to ensure traceability.

[0133] Physical devices whose availability status meets preset conditions under the production resource category associated with the updated process node to be scheduled are extracted, and each physical device is determined as a production resource instance to be allocated.

[0134] A production resource instance to be allocated refers to a specific physical device available under the production resource category associated with a process node. A device whose availability status meets preset conditions is in an available state, such as idle or standby, or undergoing troubleshooting, maintenance, or operation. First, for each first-category discrete decision-making unit, its production resource category attributes are extracted. Then, the process knowledge graph is queried for all physical devices under that resource category, filtering for device instances with an availability status label of "available." Finally, a resource instance identifier is generated for each available device, such as placement machine device number 001.

[0135] In the case that the production resource instance to be allocated does not have a corresponding second-type discrete decision-making unit, a corresponding second-type discrete decision-making unit is created for each production resource instance to be allocated.

[0136] The second-type discrete decision-making unit (DDU) is a decision-making entity that maps to physical device resources and carries the logic for resource occupancy periods. Creation requires that the resource instance is not already associated with an existing DDU to avoid duplicate creation and resource allocation conflicts. First, a check is performed to see if a corresponding DDU already exists for the production resource instance to be allocated. If not, a new DDU is created and associated with the physical device identifier, device model, and maximum load parameters. Finally, a binding relationship is established between the resource instance and the DDU, for example, binding D002 to device 001.

[0137] For example, in an electronic product assembly scenario, the updated process knowledge graph is traversed to obtain nine nodes to be scheduled. A first-class unit, T001, is created for the placement process node, inheriting the following attributes: resource category placement machine, estimated duration 180 seconds, and priority level 1. Available devices under the placement machine category are screened to obtain placement machine 001 (idle) and placement machine 002 (idle). Since there are no existing second-class discrete decision-making units, units D001 and D002 are created for each device, resulting in a set of 15 discrete decision-making units.

[0138] Figure 3 FIG. 1 shows a flow chart of a method for determining process rearrangement information and production resource allocation information provided by an embodiment of the present application. Figure 3 As shown, the method includes steps S310 to S340.

[0139] In one practicable embodiment, step S150: based on the direct pre-dependencies and indirect resource usage constraints in the updated process knowledge graph, a discrete decision unit set is used to determine the execution order for the multiple updated process nodes to be scheduled, and a working period is determined for the multiple production resource instances to be allocated, thereby obtaining process rescheduling information and corresponding production resource allocation information, including:

[0140] S310: Allocate an execution time interval for each first-category discrete decision-making unit in the discrete decision-making unit set. The length of the execution time interval is determined by the estimated operation duration of the updated process node to be scheduled, and the starting position of the execution time interval is determined by the preset process execution priority ranking of the updated process node to be scheduled.

[0141] An execution time interval is a continuous time period allocated to a first-class discrete decision-making unit, consisting of two time points: a start point and an end point. The length of the interval is determined by the estimated duration of the process node and represents the standard amount of time required to complete the process. The starting point is determined based on the preset process execution priority. High-priority nodes are assigned earlier start times to ensure that critical processes are executed first.

[0142] First, the estimated job duration attribute inherited by each first-class discrete decision-making unit is read. For example, for a motherboard patch unit, it is 180 seconds, and this is used directly as the length of the execution time interval. All first-class units are then sorted in descending order according to the preset process execution priority, for example, priority level 1 > level 2. Based on the global scheduling start time, the highest-priority unit is assigned the first time interval start position. The starting positions of subsequent units are connected in the order of arrangement. Ultimately, each unit is assigned an interval from the start time to the end time, where the end time is equal to the start time plus the estimated job duration.

[0143] S320: Traverse each pair of first-class discrete decision units that have a direct preceding dependency in the discrete decision unit set. When the end position of the execution time interval of the preceding first-class discrete decision unit is after the start position of the execution time interval of the succeeding first-class discrete decision unit, move the start position of the execution time interval of the succeeding first-class discrete decision unit to after the end position of the execution time interval of the preceding first-class discrete decision unit.

[0144] Traverse all direct predecessor dependency edges in the process knowledge graph and locate each pair of associated first-class discrete decision units, such as from the motherboard patch unit to the firmware burn unit. Compare the end position of the predecessor unit's execution time interval with the start position of the successor unit. If the predecessor unit's end position is later than the successor unit's start position, for example, the motherboard patch end time T2 = 10:05 and the firmware burn start time T1 = 10:03, then move the successor unit's start position to after the predecessor unit's end position, such as the new firmware burn start time T1' = 10:06, and simultaneously update its end position, where the new end time = the new start time + the estimated job duration. This process is recursively executed until all dependencies meet the timing constraints.

[0145] S330: traverse each second-type discrete decision unit in the discrete decision unit set. When multiple first-type discrete decision units are associated with the same second-type discrete decision unit and the execution time intervals of the associated first-type discrete decision units overlap, reallocate the starting position of the execution time interval of the associated first-type discrete decision units according to the updated preset process execution priority order of the process node to be scheduled, and generate working period information of the second-type discrete decision units.

[0146] Working period information refers to the set of consecutive time periods occupied by second-category units. It is generated by merging the adjusted execution time intervals of associated first-category units. Each second-category discrete decision-making unit, such as unit D001 corresponding to placement machine 001, is traversed. All associated first-category discrete decision-making units, such as motherboard placement unit T001 and display screen placement unit T002, are retrieved. The execution time intervals of these first-category discrete decision-making units are checked for overlap. If there is overlap, such as when the T001 interval 10:00-10:03 overlaps with the T002 interval 10:02-10:05, the starting positions are reallocated according to the preset process execution priority order. That is, high-priority units remain in their original positions, while low-priority units are moved back until the overlap is eliminated. For example, T002 is moved back to 10:04-10:07. The execution time intervals of all associated first-category units are then merged to generate the working period information for the second-category units. For example, the working periods of D001 are 10:00-10:03 and 10:04-10:07.

[0147] S340: Merging the execution time intervals of all first-type discrete decision-making units and the working period information of all second-type discrete decision-making units to form process rescheduling information and production resource allocation information.

[0148] The process rescheduling information integrates the adjusted execution time intervals of all first-category units to form the final execution order and timing plan of the process nodes. The production resource allocation information integrates the working period information of all second-category units to define the task allocation plan for physical equipment.

[0149] Collect the execution time intervals of all first-category discrete decision-making units after dependency and resource conflicts have been resolved, sort them by starting position, and generate a process rescheduling information table. This table can include the process ID, start time, and end time. Synchronously aggregate the work period information of all second-category units and integrate it by equipment ID into a resource allocation information table. This table can include the equipment ID, work period start and end times, and the bound process ID.

[0150] For example, if an order change causes the estimated operation time of the motherboard patch process to increase to 180 seconds and its priority to level 1, and a new laser engraving process with a priority of level 2 and a duration of 60 seconds is added, and this laser engraving process and the patch process share the patch machine 001 device, the discrete decision units are first assigned initial execution time intervals: the patch unit is assigned an interval of 0 seconds to 180 seconds due to its highest priority, the laser engraving unit is assigned an interval of 150 seconds to 210 seconds, and the firmware burning unit is assigned an interval of 180 seconds to 270 seconds because it depends on the patch completion. Then, the direct predecessor dependencies are traversed. Since the start time of the firmware burning unit is 180 seconds later than the end time of the patch unit, the dependency constraints are satisfied and no adjustment is required. Next, the resource constraints were traversed, revealing that the SMT unit and the laser engraving unit shared SMT machine 001, with overlapping time intervals. Based on the preset process execution priority, the SMT unit remained in place, while the laser engraving unit's starting position was moved back 180 seconds to the SMT unit's end time. The new interval was adjusted to 180 to 240 seconds, and SMT machine 001's operating time periods were generated: 0 to 180 seconds and 180 to 240 seconds. The firmware burning unit, which relied on the laser engraving process for completion, had its start time moved back to 240 seconds and its end time extended to 330 seconds. Finally, the adjusted execution time intervals of all units were merged, resulting in process rescheduling information: 0 to 180 seconds for the SMT unit, 180 to 240 seconds for the laser engraving unit, and 240 to 330 seconds for the firmware burning unit. The equipment operating time periods were also merged, generating production resource allocation information: 0 to 240 seconds for SMT machine 001 and 240 to 330 seconds for burning device 002.

[0151] Based on the same concept, the embodiment of the present application provides a production process intelligent control and optimization device based on the MES system. Figure 4 The intelligent control and optimization device for production processes based on the MES system provided in the embodiment of the present application is described in detail.

[0152] Figure 4 This is a structural block diagram of a production process intelligent control and optimization device based on the MES system shown in an embodiment of the present application.

[0153] like Figure 4 As shown, the production process intelligent control and optimization device based on the MES system may include:

[0154] The acquisition module 410 is used to obtain the bill of materials data and production process operation parameters through the MES system;

[0155] Construction module 420 is used to construct a process knowledge graph based on the bill of materials data and production process operation parameters. The process knowledge graph includes multiple process nodes and multiple logical relationship edges connecting the process nodes. Each process node represents a production process and is associated with a corresponding production resource category, estimated operation duration, and preset process execution priority. The logical relationship edges represent direct pre-dependencies and indirect resource usage constraints between different process nodes.

[0156] A receiving module 430 is configured to receive order adjustment information and update the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph;

[0157] Establishing module 440, for establishing a set of discrete decision units based on the updated process knowledge graph, where each discrete decision unit in the set corresponds to an updated process node to be scheduled or a production resource instance to be allocated, where the production resource instance to be allocated is a physical device under the production resource category associated with the updated process node to be scheduled;

[0158] Determination module 450 is used to determine the execution order for multiple updated process nodes to be scheduled and the working time period for multiple production resource instances to be allocated based on the direct predecessor dependencies and indirect resource usage constraints in the updated process knowledge graph through a set of discrete decision units, thereby obtaining process rescheduling information and corresponding production resource allocation information;

[0159] The generation module 460 is used to generate production scheduling instructions based on the process rearrangement information and the production resource allocation information, and transmit the production scheduling instructions to the MES system to complete the intelligent control and optimization of the production process.

[0160] In one embodiment, the receiving module 430 is also used to update the to-be-scheduled process nodes and corresponding logical relationship edges associated in the process knowledge graph according to the order adjustment information, and after obtaining the updated process knowledge graph, obtain the real-time status data of production resources and work-in-process processing progress data related to the to-be-scheduled process nodes through the MES system; based on the real-time status data of production resources and work-in-process processing progress data, calibrate the estimated operation duration of the updated to-be-scheduled process nodes in the updated process knowledge graph, as well as the availability status of the physical equipment under the production resource category associated with the updated to-be-scheduled process nodes.

[0161] In one embodiment, the order adjustment information includes product specifications, required quantities and agreed delivery dates; the receiving module 430 is also used to update the to-be-scheduled process nodes and corresponding logical relationship edges associated in the process knowledge graph according to the order adjustment information, and before obtaining the updated process knowledge graph, evaluate the impact range of the order adjustment information based on the changes in product specifications, the increase or decrease in required quantities and the degree of adjustment of the agreed delivery date to obtain disturbance level information; based on the order adjustment information and the disturbance level information, update the to-be-scheduled process nodes and corresponding logical relationship edges associated in the process knowledge graph to obtain an updated process knowledge graph.

[0162] In one embodiment, the receiving module 430 is specifically used to extract directly related initial process nodes in the process knowledge graph based on changes in product specifications, increases or decreases in required quantities, and the degree of adjustment of agreed delivery dates; expand related process nodes within a predetermined range along the logical relationship edges of the initial process nodes in the process knowledge graph, and determine the set of process nodes to be scheduled and the corresponding logical relationship edges that need to be modified in the process knowledge graph, and the depth of the predetermined range is determined by the disturbance level information; for each process node to be scheduled in the set of process nodes to be scheduled, when the order adjustment information includes the estimated operation duration of the process node to be scheduled and the adjustment instruction information corresponding to the preset process execution priority, the estimated operation duration and the preset process execution priority of the corresponding process node to be scheduled are modified according to the adjustment instruction information; based on the modified process nodes to be scheduled, the logical relationship edges between the modified process nodes to be scheduled are updated through the change instruction information on the preset process execution priority and the production resource category in the order adjustment information to obtain an updated process knowledge graph.

[0163] In one embodiment, the construction module 420 is specifically used to extract all production processes required to produce the target product from the bill of materials data, and create a corresponding process node for each production process; extract the production resource category, estimated operation time and preset process execution priority corresponding to each process node from the production process operation parameters, and associate the extracted production resource category, estimated operation time and preset process execution priority as attributes to the corresponding process node; establish a logical relationship edge between two process nodes with a direct processing sequence according to the preset processing order of each production process recorded in the production process operation parameters; screen the process nodes to obtain multiple groups of process node groups sharing the same production resource category, and establish logical relationship edges between different process nodes in the process node group that have not established logical relationship edges.

[0164] In one embodiment, the discrete decision units include first-type discrete decision units and second-type discrete decision units; the establishment module 440 is specifically used to traverse each updated process node to be scheduled in the updated process knowledge graph, and create a corresponding first-type discrete decision unit for each updated process node to be scheduled; extract physical devices whose availability status meets preset conditions under the production resource category associated with the updated process node to be scheduled, and determine each physical device as a production resource instance to be allocated; if there is no corresponding second-type discrete decision unit for the production resource instance to be allocated, create a corresponding second-type discrete decision unit for each production resource instance to be allocated.

[0165] In one embodiment, the determination module 450 is specifically configured to allocate an execution time interval to each first-class discrete decision unit in the discrete decision unit set, wherein the length of the execution time interval is determined by the estimated operation duration of the updated process node to be scheduled, and the starting position of the execution time interval is determined by the preset process execution priority ranking of the updated process node to be scheduled; traverse each pair of first-class discrete decision units with a direct preceding dependency relationship in the discrete decision unit set, and when the end position of the execution time interval of the leading first-class discrete decision unit is after the starting position of the execution time interval of the trailing first-class discrete decision unit, the starting position of the execution time interval of the trailing first-class discrete decision unit is moved back to the starting position of the execution time interval of the leading first-class discrete decision unit. After the end position of the execution time interval of the second type discrete decision unit; traverse each second type discrete decision unit in the discrete decision unit set, when there are multiple first type discrete decision units associated with the same second type discrete decision unit, and the execution time intervals of the associated first type discrete decision units overlap, reallocate the starting position of the execution time interval of the associated first type discrete decision unit according to the updated preset process execution priority order of the process node to be scheduled, and generate the working period information of the second type discrete decision unit; merge the execution time intervals of all first type discrete decision units and the working period information of all second type discrete decision units to form process rescheduling information and production resource allocation information.

[0166] Figure 4 Each module in the system shown has the function of implementing Figures 1 to 3 The functions of each step in the embodiment can achieve the corresponding technical effects, which will not be described in detail here for the sake of brevity.

[0167] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present application is shown.

[0168] The electronic device may include a processor 510 and a memory 520 storing computer program instructions.

[0169] Specifically, the processor 510 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0170] The memory 520 may include a large-capacity memory for data or instructions. By way of example and not limitation, the memory 520 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 520 may include removable or non-removable (or fixed) media. Where appropriate, the memory 520 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 520 is a non-volatile solid-state memory.

[0171] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to the first aspect of the present disclosure.

[0172] The processor 510 reads and executes computer program instructions stored in the memory 520 to implement any one of the production process intelligent control and optimization methods based on the MES system in the above embodiments.

[0173] In one example, the electronic device may further include a communication interface 530 and a bus 540. Figure 5 As shown, the processor 510 , the memory 520 , and the communication interface 530 are connected via a bus 540 and communicate with each other.

[0174] Communication interface 530 is primarily used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of the present application. Bus 540, which includes hardware, software, or both, couples the components of the online data traffic metering device to one another. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industrial Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 540 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0175] The electronic device can execute the production process intelligent control and optimization method based on the MES system in the embodiment of the present application, thereby realizing the combination of Figures 1 to 3 Described is the intelligent control and optimization method of production process based on MES system.

[0176] In addition, in conjunction with the MES system-based production process intelligent control and optimization method in the above embodiments, embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the MES system-based production process intelligent control and optimization methods in the above embodiments is implemented.

[0177] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0178] The functional blocks shown in the block diagrams described above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they may be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments may be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave over a transmission medium or communication link. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memory, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. Code segments may be downloaded via a computer network such as the Internet or an intranet.

[0179] Aspects of the present application have been described above with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each block in the flowcharts and / or block diagrams, as well as combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine such that execution of these instructions by the processor of the computer or other programmable data processing device enables the implementation of the functions / actions specified in one or more blocks in the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, as well as combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware that performs the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0180] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A production process intelligent control and optimization method based on MES system, characterized by: The method comprises: Obtain bill of materials data and production process operation parameters through the MES system; Constructing a process knowledge graph based on the bill of materials data and the production process operating parameters. The process knowledge graph includes multiple process nodes and multiple logical relationship edges connecting the process nodes. Each process node represents a production process. Each process node is associated with a corresponding production resource category, estimated operation duration, and preset process execution priority. The logical relationship edges represent direct pre-dependencies and indirect resource usage constraints between different process nodes. Receive order adjustment information, and update the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph, wherein the order adjustment information includes product specifications, required quantity, and agreed delivery date; Based on the updated process knowledge graph, a set of discrete decision units is established, where each discrete decision unit in the set corresponds to an updated process node to be scheduled or a production resource instance to be allocated, and the production resource instance to be allocated is a physical device under the production resource category associated with the updated process node to be scheduled; According to the direct predecessor dependency and the indirect resource usage constraint in the updated process knowledge graph, the discrete decision unit set is used to determine the execution order for the multiple updated process nodes to be scheduled, and the working time period for the multiple production resource instances to be allocated, thereby obtaining process rescheduling information and corresponding production resource allocation information; Generate production scheduling instructions based on the process rearrangement information and the production resource allocation information, and transmit the production scheduling instructions to the MES system to complete intelligent control and optimization of the production process; Before updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph, the method further includes: Based on the changes in the product specifications, the increase or decrease in the required quantity, and the degree of adjustment of the agreed delivery date, the impact range of the order adjustment information is evaluated to obtain disturbance level information; The updating of the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph includes: Extracting directly related initial process nodes from the process knowledge graph based on changes in the product specifications, the increase or decrease in the required quantity, and the degree of adjustment of the agreed delivery date; Expanding a predetermined range of associated process nodes along the logical relationship edges of the initial process nodes in the process knowledge graph, determining a set of process nodes to be scheduled and corresponding logical relationship edges that need to be modified in the process knowledge graph, wherein the depth of the predetermined range is determined by the disturbance level information; For each to-be-scheduled process node in the set of to-be-scheduled process nodes, if the order adjustment information includes adjustment instruction information corresponding to the estimated operation duration and the preset process execution priority of the to-be-scheduled process node, modifying the estimated operation duration and the preset process execution priority of the corresponding to-be-scheduled process node according to the adjustment instruction information; Based on the modified nodes of the process to be scheduled, the logical relationship edges between the modified nodes of the process to be scheduled are updated through the order adjustment information about the preset process execution priority and the change instruction information of the production resource category to obtain the updated process knowledge graph.

2. The method according to claim 1, characterized in that After updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph, the method further includes: Acquiring real-time status data of production resources and work-in-progress processing progress data related to the process node to be scheduled through the MES system; Based on the real-time status data of the production resources and the work-in-process processing progress data, calibrate the estimated operation duration of the updated process node to be scheduled in the updated process knowledge graph, and the availability status of the physical equipment under the production resource category associated with the updated process node to be scheduled.

3. The method according to claim 1, characterized in that The step of constructing a process knowledge graph based on the bill of materials data and the production process operation parameters includes: Extracting all production processes required to produce the target product from the bill of materials data, and creating a corresponding process node for each production process; Extracting the production resource category, the estimated operation duration, and the preset operation execution priority corresponding to each process node from the production process operation parameters, and associating the extracted production resource category, the estimated operation duration, and the preset operation execution priority as attributes to the corresponding process node; According to the preset processing order of each production process recorded in the production process operation parameters, establishing the logical relationship edge between two process nodes having a direct processing order; The process nodes are screened to obtain a plurality of process node groups that share the same production resource category, and the logical relationship edges are established between different process nodes in the process node groups that have not established the logical relationship edges.

4. The method according to claim 1, wherein The discrete decision-making units include a first type of discrete decision-making unit and a second type of discrete decision-making unit; The step of establishing a set of discrete decision-making units based on the updated process knowledge graph includes: Traversing each of the updated to-be-scheduled process nodes in the updated process knowledge graph, and creating a corresponding first-type discrete decision unit for the updated to-be-scheduled process node; Extracting each physical device whose availability status meets a preset condition under the production resource category associated with the updated process node to be scheduled, and determining each physical device as an instance of the production resource to be allocated; In the case that the production resource instance to be allocated does not have a corresponding second-type discrete decision-making unit, a corresponding second-type discrete decision-making unit is created for each production resource instance to be allocated.

5. The method according to claim 4, characterized in that The step of determining an execution order for a plurality of updated process nodes to be scheduled and determining working time periods for a plurality of production resource instances to be allocated based on the direct predecessor dependency and the indirect resource usage constraint in the updated process knowledge graph through the discrete decision unit set, thereby obtaining process rescheduling information and corresponding production resource allocation information, including: Allocating an execution time interval to each discrete decision-making unit of the first type in the discrete decision-making unit set, where the length of the execution time interval is determined by the estimated operation duration of the updated process node to be scheduled, and the starting position of the execution time interval is determined by the preset process execution priority ranking of the updated process node to be scheduled; Traversing each pair of first-type discrete decision units in the discrete decision unit set that have the direct preceding dependency relationship, and when the end position of the execution time interval of the preceding first-type discrete decision unit is after the start position of the execution time interval of the succeeding first-type discrete decision unit, moving the start position of the execution time interval of the succeeding first-type discrete decision unit to after the end position of the execution time interval of the preceding first-type discrete decision unit; Traversing each second-type discrete decision-making unit in the discrete decision-making unit set, when multiple first-type discrete decision-making units are associated with the same second-type discrete decision-making unit, and when the execution time intervals of the associated first-type discrete decision-making units overlap, reallocating the starting positions of the execution time intervals of the associated first-type discrete decision-making units according to the updated preset process execution priority order of the process node to be scheduled, and generating working period information of the second-type discrete decision-making units; The execution time intervals of all the first-type discrete decision-making units and the working period information of all the second-type discrete decision-making units are combined to form the process rescheduling information and the production resource allocation information.

6. A production process intelligent control and optimization device based on the MES system, characterized in that: The device comprises: The acquisition module is used to obtain the bill of materials data and production process operation parameters through the MES system; a construction module, configured to construct a process knowledge graph based on the bill of materials data and the production process operating parameters, wherein the process knowledge graph includes a plurality of process nodes and a plurality of logical relationship edges connecting the process nodes, each process node representing a production process, each process node being associated with a corresponding production resource category, an estimated operation duration, and a preset process execution priority, and the logical relationship edges representing direct pre-dependencies and indirect resource usage constraints between different process nodes; a receiving module, configured to receive order adjustment information, and update the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain an updated process knowledge graph, wherein the order adjustment information includes product specifications, required quantity, and agreed delivery date; An establishment module is used to establish a set of discrete decision units based on the updated process knowledge graph, each discrete decision unit in the discrete decision unit set corresponds to an updated process node to be scheduled or a production resource instance to be allocated, and the production resource instance to be allocated is a physical device under the production resource category associated with the updated process node to be scheduled; a determination module, configured to determine, based on the direct predecessor dependency and the indirect resource usage constraint in the updated process knowledge graph, an execution order for a plurality of the updated process nodes to be scheduled, and a working period for a plurality of the production resource instances to be allocated, through the discrete decision unit set, to obtain process rescheduling information and corresponding production resource allocation information; A generation module for generating a production scheduling instruction based on the process rearrangement information and the production resource allocation information, and transmitting the production scheduling instruction to the MES system to complete intelligent control and optimization of the production process; The receiving module is further configured to, before updating the to-be-scheduled process nodes and corresponding logical relationship edges associated with the process knowledge graph according to the order adjustment information to obtain the updated process knowledge graph, evaluate the impact scope of the order adjustment information based on changes in product specifications, increases or decreases in the required quantity, and the degree of adjustment of the agreed delivery date to obtain disturbance level information; The receiving module is specifically configured to extract directly associated initial process nodes from the process knowledge graph based on changes in the product specifications, increases or decreases in the required quantity, and the degree of adjustment of the agreed delivery date; expand associated process nodes within a predetermined range along the logical relationship edges of the initial process nodes in the process knowledge graph, and determine a set of process nodes to be scheduled and corresponding logical relationship edges that need to be modified in the process knowledge graph, where the depth of the predetermined range is determined by the disturbance level information; for each process node to be scheduled in the set of process nodes to be scheduled, when the order adjustment information includes adjustment instruction information corresponding to the estimated operation duration and preset process execution priority of the process node to be scheduled, modify the estimated operation duration and preset process execution priority of the corresponding process node to be scheduled according to the adjustment instruction information; based on the modified process nodes to be scheduled, update the logical relationship edges between the modified process nodes to be scheduled through the change instruction information regarding the preset process execution priority and the production resource category in the order adjustment information to obtain an updated process knowledge graph.

7. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the production process intelligent control and optimization method based on the MES system as described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the intelligent control and optimization method for production processes based on the MES system according to any one of claims 1 to 5.

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