Intelligent scheduling method for workshop production tasks based on MES system

Abstract

The present invention discloses an intelligent scheduling method for workshop production tasks based on an MES system, and relates to the technical field of scheduling management. The method comprises: decomposing workshop production tasks, obtaining a list of production subtasks, extracting the task features of each subtask for clustering identification, outputting the first, second and third production subtask lists, and connecting the personnel scheduling management system and the machine scheduling management system by the MES system to obtain resource information, generating a personnel scheduling strategy, a machine scheduling strategy and a human-machine collaborative scheduling strategy, and performing intelligent scheduling of workshop production tasks. The present invention solves the technical problems in the prior art that resource conflicts, production efficiency and product quality are affected due to the complicated trend of production processes and the limited and shared nature of production resources, and achieves the technical effect of intelligent scheduling of production resources, optimizing resource utilization, and improving production efficiency and product quality through task decomposition and subtask feature analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of scheduling management, and in particular to an intelligent scheduling method for workshop production tasks based on a MES system. Background Art

[0002] Workshop production task scheduling is a key link in the manufacturing industry, which involves the reasonable allocation of production tasks to various resources in the workshop to ensure that the production process is carried out efficiently and stably. With the continuous development of the manufacturing industry, especially the advancement of Industry 4.0, the complexity and requirements of production task scheduling are also increasing. In the modern manufacturing environment, workshop production task scheduling needs to cope with diversified products, complex process flows, changing market demands and increasingly high quality standards. In the production process, the limited and shared nature of resources (such as equipment, personnel, materials, etc.) will lead to resource conflicts. How to reasonably schedule tasks and avoid resource conflicts under limited resources is an important challenge. Summary of the invention

[0003] The present application provides an intelligent scheduling method for workshop production tasks based on the MES system, which is used to solve the technical problems in the prior art caused by the trend of complex production processes and the limited and shared nature of production resources, resulting in resource conflicts and affecting production efficiency and product quality.

[0004] The present application provides a method for intelligent scheduling of workshop production tasks based on an MES system, the method comprising: connecting to an MES system to decompose workshop production tasks and obtain a production subtask list; extracting task features of each subtask in the production subtask list, wherein the task features include task repetition requirements, task complexity requirements, task flexibility requirements, and task accuracy requirements; clustering and identifying the production subtask list according to the task features of each subtask, and outputting a first production subtask list, a second production subtask list, and a third production subtask list, wherein the first production subtask list is a task list that can be completed independently by machines and equipment, the second production subtask list is a task list that can be completed independently by humans, and the third production subtask list is a task list that requires human-machine collaboration to complete production; the MES system is connected to a personnel scheduling management system and a machine scheduling management system, and according to the first production subtask list, the second production subtask list, and the third production subtask list, obtains a personnel scheduling strategy, a machine scheduling strategy, and a human-machine collaborative scheduling strategy; and performs intelligent scheduling of workshop production tasks according to the personnel scheduling strategy, the machine scheduling strategy, and the human-machine collaborative scheduling strategy.

[0005] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0006] The intelligent scheduling method for workshop production tasks based on the MES system provided in the present application relates to the field of scheduling management technology. By decomposing the workshop production tasks, a list of production subtasks is obtained, and the task characteristics of each subtask are extracted for clustering identification, and the first, second and third production subtask lists are output. Then, the MES system connects the personnel scheduling management system and the machine scheduling management system to obtain resource information, and combines the first, second and third production subtask lists to generate personnel, machine and human-machine collaborative scheduling strategies to perform intelligent scheduling of workshop production tasks. The method solves the technical problems in the prior art that resource conflicts, production efficiency and product quality are affected due to the complexity trend of production processes and the limited and shared nature of production resources, and realizes the technical effect of intelligent scheduling of production resources, optimizing resource utilization, and improving production efficiency and product quality through task decomposition and subtask characteristic analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1 A flow chart of a method for intelligent scheduling of workshop production tasks based on an MES system provided in an embodiment of the present application;

[0009] Figure 2 A flow chart of obtaining personnel scheduling strategy, machine scheduling strategy and human-machine collaborative scheduling strategy in the intelligent scheduling method for workshop production tasks based on the MES system provided in the embodiment of the present application;

[0010] Figure 3 A schematic diagram of the process of switching management of abnormal production lines in the intelligent scheduling method for workshop production tasks based on the MES system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The present application provides an intelligent scheduling method for workshop production tasks based on the MES system, which is used to solve the technical problems in the prior art caused by the trend of complex production processes and the limited and shared nature of production resources, resulting in resource conflicts and affecting production efficiency and product quality.

[0012] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0013] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0014] Example

[0015] like Figure 1 As shown, the present application provides a method for intelligent scheduling of workshop production tasks based on an MES system, the method comprising:

[0016] P10: Connect to the MES system to decompose workshop production tasks and obtain a list of production subtasks.

[0017] First, by connecting to the MES system, decompose the workshop production tasks and obtain the list of production subtasks. Specifically, MES (Manufacturing Execution System) is an integrated information management system used to monitor, control and optimize the production process of the workshop. In this step, the MES system obtains detailed information on the current production tasks by exchanging data with the workshop's production equipment and information system. The connection method of the MES system usually includes data communication with production equipment and enterprise resource planning system (ERP) through network protocols (such as HTTP, OPC, etc.).

[0018] The workshop production task is usually a complex overall task, including multiple operation steps and subtasks. In the MES system, the task decomposition algorithm is used to break down the overall production task into specific production subtasks. These subtasks are the smallest work units that can be executed independently, ensuring that each subtask has clear start and end conditions. The task decomposition process needs to consider the production process, equipment capabilities and task dependencies.

[0019] By decomposing the tasks, a detailed production subtask list is generated. The subtask list contains the specific information of each subtask, such as task ID, task name, task description, required resources, expected completion time, etc. This information will serve as the basic data for subsequent task scheduling and management.

[0020] P20: Extracting the task characteristics of each subtask in the production subtask list, wherein the task characteristics include task repetition requirements, task complexity requirements, task flexibility requirements, and task accuracy requirements.

[0021] Optionally, after obtaining the list of production subtasks, the next step is to extract the task features of each subtask. These features are the key basis for intelligent scheduling and determine how tasks are classified and assigned. First, the MES system uses feature extraction algorithms to extract key task features from multiple dimensions such as the description of each subtask, process requirements, and resource requirements. These task features include task repetition requirements, task complexity requirements, task flexibility requirements, and task accuracy requirements.

[0022] The task repetition requirement refers to the frequency with which a task needs to be repeated multiple times during the production process. The repeatability of a task can be determined by analyzing historical production data and task logs. For example, in the automobile manufacturing process, the bolt tightening task needs to be repeated in multiple assembly processes, so it has a high repetition requirement. Tasks with high repetition requirements are usually suitable for automated equipment to improve efficiency and consistency.

[0023] The task complexity requirement refers to the complexity and technical difficulty of the task, which can be determined by analyzing the process steps, operation complexity and skill requirements required for the task. For example, the processing task of precision parts requires complex operation steps and a high level of technical ability, so it has a high complexity requirement. Tasks with high complexity requirements usually require skilled workers or high-precision equipment to complete.

[0024] The task flexibility requirement refers to the flexibility of the task completion time. The flexibility of the task can be determined by evaluating the time requirements of the task and the flexibility of the production plan. For example, some tasks can be completed in a longer time frame and have a higher flexibility requirement, while some tasks must be completed before a strict deadline and have a lower flexibility requirement. Tasks with high flexibility requirements can be adjusted in the production plan to optimize resource utilization.

[0025] The task precision requirement refers to the task's requirements for precision and accuracy. The task's precision requirement can be determined by analyzing the task's requirements for product quality and specifications. For example, the task of soldering electronic components requires highly precise operations to ensure the normal operation of the circuit, so it has high precision requirements. Tasks with high precision requirements usually require precision equipment and skilled operators to complete to ensure product quality.

[0026] Through the detailed extraction and analysis of the characteristics of production subtasks, it can provide important basis for subsequent intelligent scheduling and ensure the smooth progress of the production process.

[0027] P30: Cluster and identify the production subtask list according to the task characteristics of each subtask, and output a first production subtask list, a second production subtask list and a third production subtask list, wherein the first production subtask list is a task list that can be completed independently by machine equipment, the second production subtask list is a task list that can be completed independently by manual labor, and the third production subtask list is a task list that requires human-machine collaboration to complete production.

[0028] Furthermore, step P30 of the embodiment of the present application further includes:

[0029] P31: Initialize ternary clustering centers; P32: Iterate the production subtask list according to the ternary clustering centers, calculate the distance between each task feature and the three centers, and assign each task in the production subtask list according to the distance, until the production subtask list is traversed, and the first production subtask list, the second production subtask list and the third production subtask list are output.

[0030] It should be understood that after extracting the task features of the production subtasks, it is necessary to cluster and identify these tasks. That is, according to the extracted task features, the production subtask list is clustered, and tasks with similar features are classified into one category and identified by category, so as to facilitate more effective management and scheduling of tasks with a first production subtask list, a second production subtask list and a third production subtask list. Among them, the first production subtask list contains tasks that can be completed independently by machines and equipment. These tasks usually have high repetition requirements and low complexity requirements, and are suitable for execution by automated equipment. The second production subtask list contains tasks that can be completed independently by humans. These tasks usually require manual operation and have high complexity requirements and flexibility requirements. The third production subtask list contains tasks that require human-machine collaboration. These tasks usually have high precision requirements and require humans and machines to work together.

[0031] The specific clustering process can be, first initialize the ternary cluster center, the cluster center is the core point in the clustering algorithm, used to represent the center position of each class. In this method, three cluster centers are initialized, corresponding to the cluster centers of machine tasks, manual tasks and human-machine collaborative tasks respectively. The initialization of these center points can be set by analyzing historical data or by predefined standards. In addition, the initialization of the ternary cluster center is based on task characteristics, including task repetition requirements, task complexity requirements, task flexibility requirements and task accuracy requirements. Each cluster center has a different characteristic value to reflect the type of task it represents.

[0032] Furthermore, the production subtask list is iterated according to the ternary cluster center, that is, for each task in the production subtask list, the distance from the three cluster centers is calculated according to its task characteristics, for example, the Euclidean distance formula or other suitable measurement methods can be used. Then, according to the calculated distance, each task is assigned to the task list to which the nearest cluster center belongs, so that tasks with similar characteristics are classified into one category. And so on, the position of the cluster center is iteratively calculated. According to the allocation result, the position of each cluster center is recalculated, and the characteristic value of the cluster center is updated, that is, for each task list, the average value of all task characteristics is calculated and used as the new cluster center. This process will continue until the task allocation is stable, that is, the cluster center no longer changes significantly.

[0033] After multiple iterations, the production subtask list is finally divided into three categories, and the first production subtask list, the second production subtask list and the third production subtask list are output respectively. Each list contains a specific type of task, which is convenient for subsequent scheduling and management.

[0034] P40: The MES system is connected to the personnel scheduling management system and the machine scheduling management system, and obtains the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy according to the first production subtask list, the second production subtask list and the third production subtask list.

[0035] Further, such as Figure 2 As shown, step P40 of the embodiment of the present application also includes:

[0036] P41: The MES system connects the personnel scheduling management system and the machine scheduling management system, and updates the personnel resource status and the machine resource status in real time; P42: According to the task execution relationship of the first production subtask list, the second production subtask list and the third production subtask list, the allocation sequence code of each task is obtained; P43: According to the personnel resource status and the machine resource status, and the allocation sequence code, an objective function is established to maximize resource utilization; P44: According to the objective function, resources are cyclically allocated for each task to obtain personnel scheduling strategy, machine scheduling strategy and human-machine collaborative scheduling strategy.

[0037] Optionally, the personnel scheduling management system and the machine scheduling management system are connected through the MES system to formulate specific scheduling strategies, which will ensure that the production tasks in the workshop can be carried out efficiently and orderly. First, establish a connection between the MES system and the personnel scheduling management system and the machine scheduling management system. The personnel scheduling management system is used to manage and schedule the personnel resources in the workshop, including the workers' skill levels, working hours and current working status. Through real-time connection, the MES system can obtain the latest status of personnel resources. The machine scheduling management system is used to manage and schedule the machines and equipment in the workshop, including the operating status, maintenance status and availability of the equipment. The MES system updates the status of machine resources in real time by connecting to this system.

[0038] Further, according to the task execution relationship in the first, second and third production subtask lists, an execution order code is assigned to each task. The task execution relationship refers to the dependency relationship and execution order between tasks. For example, some tasks can only be executed after other tasks are completed. By analyzing the task execution relationship, the allocation order code of each task is determined. The allocation order code represents its execution order in the overall production plan, which helps to clarify the order of tasks in the scheduling process.

[0039] Furthermore, in order to maximize resource utilization, the MES system constructs a multi-objective optimization function based on the current human resource status, machine resource status, and task allocation sequence coding. The objective function takes into account the human resource status, machine resource status, and task allocation sequence coding, aiming to maximize resource utilization and minimize production cycle and cost.

[0040] Finally, resources are allocated cyclically according to the objective function. During the resource allocation process, the priority of the task and the availability of resources need to be considered. Through iterative calculation and optimization adjustment, the most suitable personnel and machine resources are allocated to each task, and the specific scheduling time and sequence are determined. In this way, the MES system can generate detailed personnel scheduling strategies, machine scheduling strategies, and human-machine collaborative scheduling strategies. Among them, the personnel scheduling strategy can allocate the most suitable personnel for each task according to the skills of the personnel and the complexity of the task. The machine scheduling strategy can allocate the most suitable machine equipment for each task according to the capabilities of the machine and the requirements of the task. The human-machine collaborative scheduling strategy can reasonably arrange the collaborative work of personnel and machines for tasks that require human-machine collaboration to ensure the smooth completion of the task. These strategies not only guide the specific operations of the production site, but also ensure the smooth completion of production tasks and the smooth realization of production goals.

[0041] P50: Intelligent scheduling of workshop production tasks is performed according to the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy.

[0042] Specifically, after formulating the personnel scheduling strategy, machine scheduling strategy and human-machine collaborative scheduling strategy, the MES system will perform intelligent scheduling of workshop production tasks according to these strategies. For example, first based on the scheduling strategy generated in the previous step, the MES system will make detailed execution plans for each production subtask. Including determining the start time, end time, required resources (personnel, machines) and the order of tasks. In order to achieve this goal, classic or modern scheduling techniques such as priority sorting algorithm, critical path method (CPM), resource constrained project scheduling (RCPSP) can be used.

[0043] During the scheduling process, the MES system needs to continuously receive data feedback from the production site through real-time data processing technology, such as equipment status changes, personnel attendance, task completion, etc., and dynamically adjust the scheduling plan based on these data. This real-time performance ensures the flexibility and adaptability of the scheduling strategy, and can effectively respond to various emergencies and uncertain factors in the production process.

[0044] At the same time, for tasks that require human-machine collaboration, the MES system must not only ensure a reasonable division of labor and close cooperation between people and machines, but also monitor each link in the collaborative process in real time to ensure that the task can proceed smoothly according to the established plan. In order to achieve this goal, it can be combined with technologies such as the Industrial Internet of Things (IIoT), machine vision, and intelligent robots to achieve comprehensive perception and intelligent control of the production site.

[0045] Ultimately, through the implementation of the intelligent scheduling process, the MES system can ensure the efficient and orderly progress of workshop production tasks, improve production efficiency and product quality, reduce production costs and energy consumption, and bring significant economic and social benefits to the enterprise.

[0046] Furthermore, the embodiment of the present application further includes step P60, and step P60 further includes:

[0047] P61: Setting a conflict detection module, which performs conflict detection on the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy, and obtains a strategy conflict index, wherein the conflict detection includes resource conflict detection, task priority conflict detection, time conflict detection and human-machine collaborative conflict detection; P62: According to the strategy conflict index, the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy are updated, and the optimized personnel scheduling strategy, the optimized machine scheduling strategy and the optimized human-machine collaborative scheduling strategy are output.

[0048] The expression of resource conflict detection includes: ,in, is the resource conflict indicator, is the total demand for resource r at time t, is the total supply of resource r at time t.

[0049] The expression for task priority conflict detection includes ,in, is the task priority conflict indicator, is the resource requirement set of task i, is the resource requirement set of task j, is the maximum value function, Indicates that the priority of task i is less than that of task j, which is used to filter out those task pairs that need to be executed first. is the intersection of the resource requirements of task i and task j.

[0050] The expression of the time conflict detection includes ,in, is the time conflict indicator, is the resource usage of task i at time t, is the available resources for task i at time t.

[0051] The expression of the human-machine collaborative conflict detection includes ,in, As the human-machine collaborative conflict indicator, represents the human resource demand for task k, is the machine resource requirement for task k, is the sum of human-machine collaborative resources for task k.

[0052] Optionally, in order to further improve the accuracy and efficiency of workshop production scheduling, the embodiment of the present application introduces a conflict detection and optimization mechanism, which can prevent and solve potential problems that may exist in the scheduling strategy through pre-conflict detection, thereby ensuring the smooth progress of the production process. Specifically, first, a special conflict detection module is set up, which integrates a variety of detection algorithms and logics, including resource conflict detection, task priority conflict detection, time conflict detection and human-machine collaboration conflict detection, which can comprehensively cover conflicts in resources, task priority, time and human-machine collaboration.

[0053] Specifically, resource conflict detection is used to ensure that different tasks will not be blocked from executing due to competing for the same resources (such as equipment and personnel) in the same time period; task priority conflict detection uses intelligent analysis to prevent low-priority tasks from improperly occupying the resources required by high-priority tasks, and to ensure the priority execution of key tasks; time conflict detection focuses on the overlap of task execution time, ensuring that each task can be carried out without conflict within the predetermined time period; finally, human-machine collaboration conflict detection is specifically aimed at tasks that require human-machine collaboration, ensuring that the required manpower and equipment are available simultaneously and in sufficient quantities when the task is executed. By setting up a conflict detection module in the MES system, conflict detection can be performed on personnel scheduling strategies, machine scheduling strategies, and human-machine collaborative scheduling strategies, and strategy conflict indicators can be generated. These indicators quantify the degree and scope of various conflicts, providing data support for subsequent strategy optimization.

[0054] Furthermore, the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy are optimized in a targeted manner according to the policy conflict index. Exemplarily, according to the policy conflict index, the conflict points in the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy are identified. For example, if the conflict index generated by the system shows that there are many equipment resource conflicts for a certain task, the equipment allocation problem of the task needs to be solved first. Through repeated iterations and optimization, the optimized personnel scheduling strategy, machine scheduling strategy and human-machine collaborative scheduling strategy are finally output. These optimized strategies not only solve the original conflict problems, but may also bring further improvements in efficiency, cost, quality and other aspects.

[0055] At the same time, when further refining the conflict detection mechanism, a specific quantitative expression can be defined for each conflict type to more accurately evaluate the degree of conflict and perform corresponding optimization. Among them, the quantitative expression of resource conflict detection is ,in, is the resource conflict indicator, is the total demand for resource r at time t, is the total supply of resource r at time t. Resource conflict detection determines whether there is a resource conflict by comparing the total demand for a resource with the total supply in a specific time period. When the demand is greater than the supply, a resource conflict occurs.

[0056] The expression for task priority conflict detection includes ,in, is the task priority conflict indicator, is the resource requirement set of task i, is the resource requirement set of task j, is the maximum value function, Indicates that the priority of task i is less than that of task j, which is used to filter out those task pairs that need to be executed first. is the intersection of the resource requirements of task i and task j. Task priority conflict detection compares the priorities of two tasks and their resource requirement sets to filter out situations where a task with a lower priority occupies the resources required by a task with a higher priority. The conflict indicator is the amount of conflict in shared resources among the tasks that need to be executed first.

[0057] The expression of the time conflict detection includes ,in, is the time conflict indicator, is the resource usage of task i at time t, is the available amount of resources for task i at time t. Time conflict detection determines whether there is a time conflict by comparing the resource usage and available amount of resources of a task in a specific time period. When the usage is greater than the available amount, a time conflict occurs.

[0058] The expression of the human-machine collaborative conflict detection includes ,in, As the human-machine collaborative conflict indicator, represents the human resource demand for task k, is the machine resource requirement for task k, is the sum of human-machine collaborative resources for task k. Human-machine collaborative conflict detection determines whether there is a collaborative conflict by comparing the total amount of human and machine resources required for the task. When the required total amount exceeds the sum of human-machine collaborative resources, a collaborative conflict occurs.

[0059] Further, such as Figure 3 As shown, the embodiment of the present application further includes step P70, and step P70 further includes:

[0060] P71: Obtain the first workshop production line set corresponding to the first production subtask list; P72: Monitor the production status corresponding to the first workshop production line set according to the MES system, and obtain the production line status data set; P73: Perform state abnormality identification on the production line status data set, and obtain state abnormality indicators; P74: If the state abnormality indicator is greater than the first tolerance area, generate the first early warning scheduling information, and the MES system switches the abnormal production line according to the first early warning scheduling information.

[0061] Specifically, after completing conflict detection and strategy optimization, in order to ensure the smooth progress of production tasks, it is also necessary to monitor the status of the production line and issue warnings and dispatch when anomalies occur. First, according to the current production plan, that is, the first production subtask list, identify all workshop production lines that need to participate in the execution of these subtasks to form the first workshop production line set. Then, using sensor networks, Internet of Things technologies and other means, collect the operating data of each production line in real time, including but not limited to equipment operating status, production efficiency, energy consumption, etc., to form a production line status data set, which provides a basis for subsequent anomaly identification.

[0062] Furthermore, advanced technologies such as machine learning and data mining are used to conduct in-depth analysis of the collected production line status data set. By comparing the preset normal operating state range or mode, the production line whose state deviates from the normal track, that is, the production line with abnormalities, is identified. These abnormalities may manifest in various forms such as equipment failure, reduced production efficiency, abnormally increased energy consumption, etc. The state abnormality index can be calculated based on the identification results, which quantifies the severity of the abnormality.

[0063] In order to distinguish different degrees of abnormalities and take corresponding countermeasures, the first tolerance area is set as the abnormal threshold according to the historical abnormal records. When the status abnormality index of a production line exceeds the first tolerance area, it is considered that the production line is in a more serious abnormal state and immediate measures need to be taken. At this time, the system will automatically generate the first warning dispatch information, which describes in detail the specific situation of the abnormal production line, the type of abnormality, the degree of abnormality and the recommended countermeasures.

[0064] After receiving the early warning dispatch information, the MES system will immediately start the emergency response mechanism. According to the specific content of the information, the operator will automatically or assist the operator to switch the abnormal production line, including transferring the production task to other spare production lines, suspending the current production for troubleshooting and repair, and adjusting the production plan to adapt to new production conditions. This process ensures the continuity of production tasks and the effective use of production resources, and minimizes the impact of abnormalities on production progress and quality.

[0065] Furthermore, the embodiment of the present application also includes step P75: if the abnormal status indicator is in the first tolerance area, second warning scheduling information is generated, and the MES system schedules and manages the abnormal production line according to the second warning scheduling information.

[0066] Step P75 also includes: P75-1: the MES system places the abnormal production line in the first working mode according to the second early warning scheduling information, and generates an auxiliary personnel scheduling indicator, wherein the first working mode is an inefficient production mode; P75-2: generating an auxiliary personnel scheduling strategy according to the auxiliary personnel scheduling indicator; P75-3: performing personnel-assisted production on the abnormal production line according to the auxiliary personnel scheduling strategy.

[0067] It should be understood that when the status abnormality indicator is within the set first tolerance area, it indicates that although the production line has not experienced a serious failure, its operating status has deviated from the normal range and there is a risk of further deterioration. At this time, the MES system will generate the second early warning dispatch information based on this status, which is intended to warn in advance and take preventive measures.

[0068] After receiving the second warning dispatch information, the MES system will first switch the abnormal production line to the first working mode, that is, the inefficient production mode. This mode can reduce the load of the production line by adjusting production parameters, reducing production speed, etc., to avoid the aggravation of abnormal conditions due to continuous efficient operation. At the same time, the system will generate auxiliary personnel dispatch indicators based on the specific conditions of the current production line, such as the degree of equipment aging, fault history, etc., and clarify the number and professional requirements of auxiliary personnel who need reinforcement. Based on the auxiliary personnel dispatch indicators, a detailed auxiliary personnel dispatch strategy is further generated. This strategy includes determining the source of auxiliary personnel (such as internal deployment, temporary recruitment, etc.), the specific time of allocation to the abnormal production line, task division, and necessary training arrangements. Through a scientific and reasonable dispatch strategy, it is ensured that auxiliary personnel can be in place quickly and effectively support the normal operation of the production line.

[0069] Finally, according to the auxiliary personnel scheduling strategy, the MES system guides relevant personnel to assist in production of abnormal production lines. In this process, the auxiliary personnel will monitor the production line in real time, perform equipment maintenance, troubleshooting, and necessary production operations according to the established division of labor. Through human-machine collaboration, the pressure on the production line can be effectively relieved, production efficiency and quality can be improved, and the risk of production interruption caused by abnormal conditions can be reduced. In addition, the MES system will continue to monitor the status of the production line and adjust the auxiliary personnel scheduling strategy in a timely manner according to the actual situation to ensure the smooth progress of the entire production process.

[0070] In summary, the embodiments of the present application have at least the following technical effects:

[0071] This application decomposes the workshop production tasks, obtains the production subtask list, extracts the task characteristics of each subtask, clusters and identifies the production subtask list, and outputs the first, second and third production subtask lists. The MES system then connects the personnel scheduling management system and the machine scheduling management system to obtain resource information, and combines the first, second and third production subtask lists to generate personnel scheduling strategies, machine scheduling strategies and human-machine collaborative scheduling strategies to perform intelligent scheduling of workshop production tasks.

[0072] The technical effect of intelligently scheduling production resources, optimizing resource utilization, and improving production efficiency and product quality has been achieved through task decomposition and sub-task feature analysis.

[0073] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0074] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0075] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. The intelligent scheduling method for workshop production tasks based on MES system is characterized by: The method comprises: Connect to the MES system to decompose workshop production tasks and obtain a list of production subtasks; Extracting task features of each subtask in the production subtask list, wherein the task features include task repetition requirements, task complexity requirements, task flexibility requirements, and task accuracy requirements; The production subtask list is clustered and identified according to the task characteristics of each subtask, and a first production subtask list, a second production subtask list and a third production subtask list are output, wherein the first production subtask list is a task list that can be completed independently by machines and equipment, the second production subtask list is a task list that can be completed independently by humans, and the third production subtask list is a task list that requires human-machine collaboration to complete production; The MES system is connected to the personnel scheduling management system and the machine scheduling management system, and acquires the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy according to the first production subtask list, the second production subtask list and the third production subtask list; Intelligently schedule workshop production tasks according to the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy; The method further includes: setting a conflict detection module, the conflict detection module performs conflict detection on the personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy, and obtains a policy conflict index, wherein the conflict detection includes resource conflict detection, task priority conflict detection, time conflict detection and human-machine collaborative conflict detection; The personnel scheduling strategy, the machine scheduling strategy and the human-machine collaborative scheduling strategy are updated according to the strategy conflict index, and an optimized personnel scheduling strategy, an optimized machine scheduling strategy and an optimized human-machine collaborative scheduling strategy are output; The expression of resource conflict detection includes ,in, is the resource conflict indicator, is the total demand for resource r at time t, is the total supply of resource r at time t; The expression for task priority conflict detection includes ,in, is the task priority conflict indicator, is the resource requirement set of task i, is the resource requirement set of task j, is the maximum value function, Indicates that the priority of task i is less than that of task j, which is used to filter out those task pairs that need to be executed first. is the intersection of the resource requirements of task i and task j; The expression of the time conflict detection includes ,in, is the time conflict indicator, is the resource usage of task i at time t, is the available resources for task i at time t; The expression of the human-machine collaborative conflict detection includes ,in, As the human-machine collaborative conflict indicator, represents the human resource demand for task k, is the machine resource requirement for task k, is the sum of human-machine collaborative resources for task k.

2. The method according to claim 1, characterized in that The method further comprises: Obtaining a first workshop production line set corresponding to the first production subtask list; Monitoring the production status corresponding to the first workshop production line set according to the MES system to obtain a production line status data set; Performing state abnormality identification on the production line state data set to obtain state abnormality indicators; If the status abnormality index is greater than the first tolerance area, first warning scheduling information is generated, and the MES system performs switch management on the abnormal production line according to the first warning scheduling information.

3. The method according to claim 2, characterized in that If the abnormal state indicator is in the first tolerance area, second warning scheduling information is generated, and the MES system performs scheduling management on the abnormal production line according to the second warning scheduling information, including: The MES system places the abnormal production line in a first working mode according to the second early warning scheduling information, and generates an auxiliary personnel scheduling indicator, wherein the first working mode is an inefficient production mode; Generating an auxiliary personnel scheduling strategy according to the auxiliary personnel scheduling indicator; The abnormal production line is provided with personnel-assisted production according to the auxiliary personnel scheduling strategy.

4. The method according to claim 1, characterized in that Clustering and identifying the production subtask list according to the task characteristics of each subtask, and outputting a first production subtask list, a second production subtask list, and a third production subtask list, the method comprising: Initialize the ternary cluster centers; The production subtask list is iterated according to the ternary clustering centers, the distance between each task feature and the three centers is calculated, and the tasks in the production subtask list are allocated according to the distances, until the production subtask list is traversed, and the first production subtask list, the second production subtask list and the third production subtask list are output.

5. The method according to claim 1, characterized in that Obtain personnel scheduling strategy, machine scheduling strategy and human-machine collaborative scheduling strategy, including: The MES system is connected to the personnel scheduling management system and the machine scheduling management system to update the personnel resource status and the machine resource status in real time; According to the task execution relationship among the first production subtask list, the second production subtask list and the third production subtask list, obtaining the allocation sequence code of each task; Establishing an objective function according to the human resource status and the machine resource status and the allocation sequence encoding to maximize resource utilization; According to the objective function, resources are cyclically allocated for each task to obtain personnel scheduling strategy, machine scheduling strategy and human-machine collaborative scheduling strategy.

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