Equipment production scheduling method and device based on modular production line

Abstract

The present invention discloses a method and device for scheduling equipment production based on a modular production line, which relates to the field of data processing technology. The method includes: reading target production line tasks; performing hierarchical discrete decomposition of target production line tasks to determine a task decomposition network; using the modular configuration of the production line as a constraint, performing branch and bound and hierarchical recursive optimization to determine a task scheduling strategy; generating module adjustment instructions, and displaying the module adjustment instructions on a human-computer interaction interface; establishing a mapping between the task scheduling strategy and the target production line tasks, combining the module adjustment instructions, and combining the production line central control system to perform production scheduling control. The method solves the technical problem that the existing production line equipment production scheduling is difficult to effectively respond to complex and changeable production tasks, which leads to insufficient flexibility and adaptability of production line scheduling and low production efficiency, realizes precise control based on modularization, and achieves the technical effect of improving production scheduling flexibility, adaptability and production efficiency.

Description

Technical Field

[0001] The present application relates to the technical field related to data processing, and specifically to a method and device for scheduling equipment production based on a modular production line. Background Art

[0002] With the rapid development of the manufacturing industry, the flexibility and efficiency of production lines are crucial to the competitiveness of enterprises. The efficient scheduling and optimization of production lines have become a key link for enterprises to enhance their competitiveness. However, with the rapid changes in the market and the growth of personalized needs, traditional production line equipment scheduling often seems to be unable to cope with complex and changeable production tasks. It takes a long time to schedule the production of equipment, which makes it difficult to effectively meet production needs, thereby affecting production efficiency as well as the flexibility and adaptability of the production line, and may also increase production costs.

[0003] Therefore, at the current stage, there are technical problems in the production line equipment production scheduling related technologies, which make it difficult to effectively cope with complex and changeable production tasks, resulting in insufficient flexibility and adaptability of production line scheduling and low production efficiency. Summary of the Invention

[0004] This application provides an equipment production scheduling method and device based on a modular production line, and adopts technical means such as hierarchical discrete decomposition, building integer programming modules, establishing strategies and task mapping, to solve the technical problems of the existing production line equipment production scheduling that are difficult to effectively respond to complex and changeable production tasks, which in turn leads to insufficient flexibility and adaptability of production line scheduling and low production efficiency. It realizes precise control based on modularization and achieves the technical effect of improving the flexibility, adaptability and production efficiency of production scheduling.

[0005] The present application provides an equipment production scheduling method based on a modular production line, the method comprising: reading a target production line task, the target production line task being a single task or a mixed task; performing a hierarchical discrete decomposition on the target production line task to determine a task decomposition network, wherein each network node corresponds to a greedy constraint; building an integer programming module, traversing the task decomposition network, performing branch and bound and hierarchical recursive optimization with the modular configuration of the production line as a constraint, and determining a task scheduling strategy, wherein the integer programming module includes multiple solvers; traversing the task scheduling strategy, locating module adjustment nodes and adjustment strategy points to generate module adjustment instructions, and displaying the module adjustment instructions on a human-computer interaction interface; establishing a mapping between the task scheduling strategy and the target production line task, combining the module adjustment instructions, and performing production scheduling control in conjunction with the production line central control system.

[0006] In a possible implementation, the determination of the task decomposition network further performs the following processing: determining a task decomposition layer, wherein the task decomposition layer at least includes a task unit-process node-node minimum unit; based on the task decomposition layer, discretely decomposing and hierarchically associating the target production line task to determine the task decomposition network; traversing the task decomposition network, determining the node decision target and performing a relaxation conversion to determine the greedy constraint condition; and establishing a mapping between the greedy constraint condition and the task decomposition network.

[0007] In a possible implementation, the task scheduling strategy is determined by performing the following processing: traversing the task decomposition network, performing solver mapping and combination, and determining the solution architecture; traversing the solution architecture, performing variable relaxation, performing bottom-up hierarchical optimization decision-making, and determining the relaxed optimal solution, wherein the relaxed optimal solution is the upper bound of the conventional solution; integrating the relaxed optimal solutions to determine the task scheduling strategy.

[0008] In a possible implementation, the relaxed optimal solution is integrated to determine the task scheduling strategy, and the following processing is performed: traverse the minimum unit layer, determine multiple relaxed solutions by performing variable relaxation and decision optimization; combine the multiple relaxed solutions based on the inter-layer task decomposition relationship, and perform verification and combination optimization based on the modular configuration of the production line and the greedy constraints to determine the upper-layer solution; traverse the upper-layer solution, perform variable relaxation and decision optimization layer by layer to determine the solution architecture; and generate the task scheduling strategy based on the solution architecture.

[0009] In a possible implementation, the equipment production scheduling method based on a modular production line also performs the following processing: if the relaxed solution is the lower bound of the conventional solution, or there is invalid decision-making in the node decomposition task, the solver corresponding to the network node is pruned; as the strategy of the solution architecture is generated, the solution architecture is discretely reduced.

[0010] In a possible implementation, the production scheduling control is carried out in combination with the production line central control system, and the following processing is also performed: for the modular configuration of the production line, an independent control system mapped to each functional module is determined; based on the task scheduling strategy, an independent scheduling strategy and a superior coordination strategy are determined, the independent scheduling strategy responds to the independent control system, and the superior coordination strategy responds to the production line central control system; an auxiliary network time protocol is used to perform communication management and control under module collaborative interaction.

[0011] In a possible implementation, the production scheduling control also performs the following processing: determining whether there is a modular collision, wherein the modular collision includes module adjustment and task collision, and the module adjustment includes module replacement and module upgrade; if there is a task collision, performing task collision management based on module parallelization and task priority; if there is a module adjustment, determining the adjustment time zone based on the global impact and the occasional synergy of the modules, and performing module adjustment management.

[0012] The present application also provides an equipment production scheduling device based on a modular production line, comprising: a target production line task reading module, the target production line task reading module is used to read the target production line task, the target production line task is a single task or a mixed task; a task decomposition network determination module, the task decomposition network determination module is used to perform hierarchical discrete decomposition of the target production line task and determine the task decomposition network, wherein each network node corresponds to a greedy constraint condition; a task scheduling strategy determination module, the task scheduling strategy determination module is used to build an integer programming module, traverse the task decomposition network, and perform branch and bound and hierarchical recursive optimization with the production line modular configuration as a constraint to determine the task scheduling strategy, wherein the integer programming module includes multiple solvers; a module adjustment instruction generation module, the module adjustment instruction generation module is used to traverse the task scheduling strategy, locate module adjustment nodes and adjustment strategy points to generate module adjustment instructions, and display the module adjustment instructions on a human-computer interaction interface; a production scheduling control module, the production scheduling control module is used to establish a mapping between the task scheduling strategy and the target production line task, combine the module adjustment instructions, and combine with the production line central control system to perform production scheduling control.

[0013] The equipment production scheduling method and device based on modular production lines proposed in this application are intended to read the target production line tasks; perform hierarchical discrete decomposition of the target production line tasks to determine the task decomposition network; use the modular configuration of the production line as a constraint to perform branch and bound and hierarchical recursive optimization to determine the task scheduling strategy; generate module adjustment instructions and display the module adjustment instructions on the human-computer interaction interface; establish a mapping between the task scheduling strategy and the target production line tasks, combine the module adjustment instructions, and combine the production line central control system to perform production scheduling control. This solves the technical problem that the existing production line equipment production scheduling is difficult to effectively respond to complex and changing production tasks, which leads to insufficient flexibility and adaptability of production line scheduling and low production efficiency. It realizes precise control based on modularization and achieves the technical effect of improving production scheduling flexibility, adaptability and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present disclosure. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic diagram of a process flow for a method for scheduling equipment production based on a modular production line according to an embodiment of the present application;

[0016] Figure 2 A schematic diagram of the structure of an equipment production scheduling device based on a modular production line provided in an embodiment of the present application.

[0017] Description of the accompanying drawings: target production line task reading module 10, task decomposition network determination module 20, task scheduling strategy determination module 30, module adjustment instruction generation module 40, production scheduling control module 50. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides a method for scheduling equipment production based on a modular production line, such as Figure 1 As shown, the method includes:

[0022] Step S100 , reading a target production line task, where the target production line task is a single task or a mixed task. Reading target production line tasks refers to obtaining or identifying task information required for a specific production line from the production task database, production plan, and other sources. This refers to extracting task data related to the production line, which may include key information such as task type, quantity, priority, required materials and equipment, and completion time. Target production line tasks include single tasks and mixed tasks. Specifically, a single task is a one-step task. When the task is completed, the production line will not immediately provide subsequent related tasks. A single task has a clear start and end point, a clear goal, and usually does not require further task triggering or decomposition. After completion, no new production tasks will be triggered. For example, a simple production step may be the assembly or inspection of a part. A mixed task is a continuously triggered task. When the current task is completed, the production line will continue to provide subsequent related tasks, forming a task chain or task network. Mixed tasks typically include multiple steps and decision points, requiring the production line to perform continuous multi-stage operations. There are also dependencies between multiple tasks. When one task is completed, the next production task will be triggered, requiring more complex production task management and scheduling strategies. For example, the production process of a complex product may include multiple steps such as production, material conversion, assembly, and inspection. For single tasks, the production line can adjust modules and resources more flexibly to meet different production needs; for mixed tasks, the production line needs to manage the relationships and dependencies between tasks more finely to ensure the coordination and efficiency of the entire production process; this helps the production line better plan resources, optimize scheduling strategies, and ensure the smooth progress of the production process.

[0023] Step S200 : performing a hierarchical discrete decomposition on the target production line task to determine a task decomposition network, wherein each network node corresponds to a greedy constraint condition. Hierarchical discrete decomposition of target production line tasks involves breaking down complex production line tasks into smaller, more specific subtasks or operations. This helps better organize and manage production tasks and improves production efficiency. Specifically, the large, complex target production line tasks are first identified and then broken down into multiple levels or subtasks. Each level has clear objectives and associated variables. For example, an automobile production line might be decomposed into subtasks such as body manufacturing, engine assembly, and interior installation. Each subtask is then further refined. The task decomposition network is a graphical representation that shows the relationships and dependencies between subtasks. Each node in the network represents a subtask derived from the hierarchical discrete decomposition, and edges represent connections or dependencies between these tasks. The greedy constraints set at each node are a local optimization strategy designed to ensure that each subtask achieves optimal results. These greedy constraints may include time, cost, quality, and other aspects, and are used to guide the execution strategy of each subtask. For example, at a particular node, a greedy constraint may require the selection of raw materials with the lowest cost but satisfactory quality. However, when greedy constraints reach a local optimum at each node, they may lead to a suboptimal global solution.

[0024] In one possible implementation, step S200 further includes step S210, determining a task decomposition layer, wherein the task decomposition layer comprises at least a task unit, a process node, and a node minimum unit. A task unit is the top level of task decomposition, representing a complete, independent, large-scale task. For example, a complete production line upgrade project or a new product R&D project can both be considered a task unit. A process node is a subtask under a task unit, representing the main processes or steps required to complete the entire task unit, typically with a certain logical sequence and dependencies. For example, in a production line upgrade project, stages such as equipment procurement, equipment installation, system debugging, and trial operation are included. A process node is located below the task unit and represents the second level of task decomposition. It can be further decomposed into a node minimum unit. A node minimum unit is a specific work package or activity under the process node, representing the lowest level of task decomposition and the most specific execution unit. For example, under the process node of equipment installation, a node minimum unit may include specific activities such as dismantling old equipment, transporting new equipment, installing new equipment, and conducting preliminary testing. A node minimum unit is located below the process node and represents the lowest level of task decomposition.

[0025] Step S200 further includes step S220, performing discrete decomposition and hierarchical association on the target production line tasks based on the task decomposition layer to determine the task decomposition network. Discrete decomposition involves decomposing individual tasks layer by layer according to their internal structure or implementation process until they are decomposed into relatively independent work units. Specifically, the overall goal and scope of the target production line task are clarified. Based on the characteristics and requirements of the individual tasks, they are divided into several process nodes. For each process node, a specific node minimum unit is further refined. Hierarchical association establishes the relationships between the various levels in the task decomposition layer. Specifically, the process node to which each node minimum unit belongs and the task units to which each process node belongs are clarified, forming the basic parent-child relationship of the task decomposition layer. The dependencies between each node minimum unit and the process node are analyzed, including pre- and post-dependencies. Pre-dependencies indicate that the completion of one node or node is a prerequisite for the start of other nodes or nodes, while post-dependencies are the opposite. Logical relationships are established to describe the relationships between different nodes or node groups. For example, some nodes may be parallel (can be performed simultaneously) while others may be serial (must be performed in a certain order). Through discrete decomposition and hierarchical association, a clear task decomposition network is obtained. Starting from the individual tasks, it is gradually expanded through process nodes and node minimum units to form a tree structure, which then determines the task decomposition network.

[0026] Step S200 also includes step S230, traversing the task decomposition network, determining node decision goals, performing slack conversion, and determining the greedy constraint conditions. Each node and connection in the task decomposition network is examined, and a decision goal is determined for each node. For example, for a certain node minimum unit, its decision goal may be to minimize execution time, maximize resource utilization, or reduce costs. Slack conversion is used to handle task time uncertainty and flexibility. In the task decomposition network, some tasks may have a wide time window, that is, a certain amount of slack time. Slack time refers to the maximum amount of time a task can be delayed in starting or ending without affecting the project schedule. Performing slack conversion is to adjust the task time parameters according to actual conditions and production task needs to optimize the overall performance of the production line task. For example, some tasks with long slack times may be delayed to provide more resources and time for other critical tasks. Then, the greedy constraint conditions need to be determined, taking into account the overall goal of the production task, resource constraints, time constraints, etc. For example, the task with the shortest execution time is prioritized; tasks on the critical path are prioritized; the start time of each task is ensured to be no earlier than the end time of all its predecessor tasks; and the rational allocation and full utilization of resources are ensured.

[0027] Step S200 also includes step S240, establishing a mapping between the greedy constraint conditions and the task decomposition network. The greedy constraint conditions are mapped to each node and path in the task decomposition network to ensure that these conditions can be followed during the task execution process, thereby achieving global optimal or suboptimal decisions. Specifically, the greedy constraint conditions are mapped to the nodes in the task decomposition network, that is, one or more constraints are assigned to each node to limit the node selection decision. For example, if there is a constraint condition requiring a key task to be executed first, the node corresponding to the task should have a higher priority; if there is a constraint condition requiring certain tasks to be executed in a specific order, the paths formed by these tasks in the task decomposition network should be restricted to be executed only in that order.

[0028] Step S300: Build an integer programming module, traverse the task decomposition network, and use the modular configuration of the production line as a constraint to perform branch and bound and hierarchical recursive optimization to determine the task scheduling strategy, wherein the integer programming module includes multiple solvers. In production line task scheduling, the integer programming module can ensure that decision variables such as resource allocation and production sequence are integers, meeting actual production needs. It usually includes multiple solvers. Specifically, the solver is the core of the integer programming module and integrates multiple algorithms (such as branch and bound method, cutting plane method, implicit enumeration method, etc.) for solving integer programming problems. Based on the built integer programming module, traverse the determined task decomposition network to determine which subtasks need to be executed simultaneously and which subtasks have a sequence. The modular configuration of the production line is an important constraint condition for production line design and optimization. These constraints are converted into mathematical expressions and added to the integer programming model to ensure that the solved task scheduling strategy meets the requirements of the modular configuration of the production line. The branch-and-bound method progressively narrows the search scope by continuously decomposing the problem into smaller subproblems (i.e., branches) and calculating the upper or lower bounds of the objective function value in each subproblem (i.e., bounds), until the optimal solution is found or the termination condition is met. In a task decomposition network, subtasks at different levels have dependencies. Hierarchical recursive optimization is a method that leverages these dependencies for layer-by-layer optimization. It first optimizes the lowest-level subtasks (i.e., leaf nodes), then propagates the optimization results upwards layer by layer until the entire task decomposition network is optimized. Through these multiple steps, the integer programming module determines the task scheduling strategy. The task scheduling strategy provides information such as the execution order of each subtask and resource allocation to guide actual production operations.

[0029] In a possible implementation, step S300 further includes step S310, traversing the task decomposition network, performing solver mapping and combination, and determining a solution architecture. Solver mapping is to match tasks in the task decomposition network with appropriate solvers. Specifically, the characteristics and requirements of each task are analyzed, such as computational complexity, data type, constraints, etc. According to the characteristics of the task, a suitable solver is selected from the existing solver library and mapped to the task, that is, how the solver will be applied to the task is determined. Solver combination refers to combining multiple solvers according to a certain strategy to jointly solve a complex task or problem, such as serial combination, parallel combination or hybrid combination; the solution architecture refers to the solver combination scheme and its organizational structure used when solving the entire task decomposition network, that is, according to the structure of the task decomposition network and the characteristics of the task, a suitable solver combination strategy is selected, and the communication and collaboration mechanism between solvers is determined, such as data exchange, result merging, etc.

[0030] Step S300 also includes step S320, traversing the solution architecture, performing variable relaxation, and making bottom-up hierarchical optimization decisions to determine a relaxed optimal solution, wherein the relaxed optimal solution is an upper bound of the conventional solution. Variable relaxation refers to temporarily relaxing integer constraints, allowing variables to take any real value. Through variable relaxation, a relaxed problem related to the original problem but easier to solve is obtained. The solution to the relaxed problem is usually called a relaxed solution and serves as an upper bound (for minimization problems) or a lower bound (for maximization problems) of the original problem. Bottom-up refers to a decision-making process that starts from the lowest level (or most specific details) of the problem and gradually advances to higher levels (or more abstract concepts). This means starting with a single variable or single constraint and gradually considering how they combine to form more complex subproblems and ultimately affect the solution to the entire problem. Using the relaxed solution as a reference, more complex solutions are gradually constructed, and an attempt is made to find the best solution that satisfies all constraints. Variable relaxation and solving the relaxed problem are repeated multiple times to gradually approach the optimal solution to the original problem, and ultimately determine the relaxed optimal solution, thereby improving efficiency while ensuring that the results meet the requirements.

[0031] Step S300 also includes step S330, integrating the relaxed optimal solution and determining the task scheduling strategy. The relaxed optimal solution is combined with the actual problem, considering the limitations and constraints in actual production task scheduling, and the relaxed optimal solution is adjusted and optimized according to the actual situation to ensure its feasibility and effectiveness in actual task scheduling. Based on the integrated relaxed optimal solution, a specific task scheduling strategy is formulated, including determining the execution order of tasks, resource allocation method, time plan formulation, etc., to ensure that tasks can be completed efficiently and accurately.

[0032] In one possible implementation, step S330 further includes step S331, traversing the minimum unit layer and determining multiple relaxed solutions by performing variable relaxation and decision optimization. Specifically, a task point is broken down into multiple points, and the optimal relaxed solutions for the multiple points are determined based on the corresponding greedy decision directions. The process also includes step S332, combining the multiple relaxed solutions based on the inter-layer task decomposition relationship, performing verification and combinatorial optimization based on the production line modular configuration and greedy constraints, and determining an upper-level solution. Based on the inter-layer task decomposition relationship, the relaxed solutions at different levels and under different conditions are combined to form a complete, cross-level solution. The combined relaxed solutions are then verified based on the production line modular configuration and greedy constraints, for example, to determine their feasibility, compatibility with the production line configuration, and compliance with the greedy constraints. Based on the verification results, the combined relaxed solutions are optimized to improve their quality (e.g., reduce cost, increase efficiency), satisfy more constraints, or better adapt to the production line modular configuration. Ultimately, a high-quality upper-level solution is obtained that satisfies all constraints and matches the production line modular configuration.

[0033] Step S330 also includes step S333, traversing the upper layer solution, performing variable relaxation and decision optimization layer by layer, and determining the solution architecture. At each layer, the decision variables of that layer are relaxed, that is, these variables are allowed to take real values ​​rather than integer values, thereby obtaining a relaxed problem that is easier to solve. Then, the optimal solution or suboptimal solution of that layer is found through an optimization algorithm, such as using a branch and bound method, a genetic algorithm, a simulated annealing algorithm, etc., searching in the solution space of the relaxed problem to find the optimal solution or suboptimal solution that meets the constraints of that layer, determining the values ​​of the decision variables of each layer, and combining these values ​​according to the hierarchical structure of the task decomposition network to obtain the solution architecture of the entire system. This not only takes into account the optimal solution or suboptimal solution of each layer, but also takes into account the dependencies between layers and the optimization goal of the entire system, thus being a globally optimal or near-globally optimal solution. It also includes step S334, generating the task scheduling strategy based on the solution architecture. Determine the order of task execution based on the priority values ​​of each task in the solution architecture; based on the resource allocation results in the solution architecture, determine the human, material and other resources required for each task and make reasonable allocations; based on the time schedule in the solution architecture, formulate a specific time plan for each task, including start time, end time, etc.; and guide the actual execution of tasks based on the generated task scheduling strategy.

[0034] In one possible implementation, step S330 further includes step S335: if the relaxed solution is a lower bound of the conventional solution, or if the node decomposition task has invalid decision-making, pruning the solver corresponding to the network node is performed. In minimization problems, if the relaxed solution is a lower bound of the conventional solution, it means that the known optimal solution cannot be worse than this lower bound. Invalid decision-making in a node decomposition task means that the solution of a node or subtask has little or no contribution to the optimal solution of the overall problem, and may even lead to overall performance degradation. Pruning is intended to reduce unnecessary search or computation and improve solution efficiency. For nodes with invalid decisions, the search for their child nodes or subsequent states is discontinued, and the node and its subtree are directly removed from the search space. Pruning can significantly reduce the search space and improve solution speed and efficiency. The solution architecture also includes step S336: following the strategy generation of the solution architecture, the solution architecture is subjected to discrete reduction processing. Discrete reduction processing maps the optimized solution from the continuous or relaxed solution space to the original discrete solution space to meet specific discrete requirements or constraints, such as time units (minutes, hours, etc.) in task scheduling or integer restrictions on resource allocation.

[0035] Step S400 , traversing the task scheduling strategy, locating module adjustment nodes and adjustment strategy points to generate module adjustment instructions, and displaying the module adjustment instructions on a human-computer interaction interface. A module adjustment node refers to a specific node or task in a task scheduling strategy that requires adjustment or optimization. This involves traversing the task scheduling strategy, analyzing the performance metrics (such as time, cost, and efficiency) of each node in the strategy, and identifying nodes that require adjustment based on preset thresholds or conditions. An adjustment strategy point refers to the specific optimization or adjustment measures proposed for a module adjustment node. Based on the specific circumstances of the module adjustment node (such as insufficient resources or time conflicts), a corresponding adjustment strategy is formulated, such as resource reallocation, task sequence adjustment, or parallel processing. A module adjustment instruction is a specific, executable instruction generated based on the adjustment strategy point. It may include a task ID, adjustment type (such as time adjustment or resource adjustment), and adjustment value (such as a new completion time or new resource allocation). Module adjustment instructions are clear and executable, allowing operators to directly execute them. The human-computer interaction interface (HCI) is a window or platform for human-computer interaction. Generated module adjustment instructions are displayed intuitively on the HCI, such as a task list, a comparison chart before and after the adjustment, and operation buttons. Operators can use the interface to understand the adjustment content and perform the corresponding operations. By traversing the task scheduling strategy, locating the module adjustment node, determining the adjustment strategy point, generating the module adjustment instruction, and displaying it on the human-computer interaction interface, dynamic optimization and adjustment of the production line tasks can be achieved, thereby improving production efficiency and resource utilization.

[0036] Step S500: Establish a mapping between the task scheduling strategy and the target production line task, combine the module adjustment instructions, and perform production scheduling control in conjunction with the production line central control system. Establishing a mapping between the task scheduling strategy and the target production line task aims to clarify the correspondence between each task or subtask in the task scheduling strategy and the actual target production line task. Specifically, the specific content and requirements of the target production line task, including product type, production quantity, and delivery date, are analyzed. The tasks or subtasks in the task scheduling strategy are matched with the actual production line task to ensure that each production line task has a corresponding scheduling strategy guidance. For example, the task scheduling strategy is refined or adjusted to meet the requirements of the actual production line task. After determining the mapping relationship between the task scheduling strategy and the production line task, it is necessary to combine the previously generated module adjustment instructions to specifically adjust and optimize the production line task. That is, according to the specific content and requirements of the module adjustment instructions, the corresponding production line task is adjusted, such as changing the execution order of tasks, adjusting the execution time of tasks, and allocating or reallocating resources. When executing the adjustment instructions, it is necessary to ensure consistency with the task scheduling strategy while taking into account the production capacity and constraints of the actual production line. The production line central control system is the core module responsible for the operation and scheduling of the entire production line. It integrates task scheduling strategies and module adjustment instructions with the production line central control system to achieve scheduling and control of actual production. Specifically, task scheduling strategies and module adjustment instructions are input into the production line central control system to ensure that the system can understand and execute these adjustment instructions. The production line's operating status is monitored and evaluated using the production line central control system's real-time monitoring and data analysis capabilities. Based on the monitoring results and actual needs, the production line is scheduled and controlled in real time through the production line central control system, including starting or stopping tasks, adjusting production speeds, and allocating or reallocating resources. During the scheduling and control process, consistency with the task scheduling strategies and module adjustment instructions must be maintained, and necessary adjustments and optimizations must be made based on actual conditions. By establishing a mapping between task scheduling strategies and target production line tasks, combining module adjustment instructions, and integrating production scheduling and control with the production line central control system, comprehensive, efficient, and precise control of actual production can be achieved.

[0037] In one possible implementation, step S500 further includes step S510, in which, for the modular configuration of the production line, an independent control system mapped to each functional module is determined. The modular configuration of the production line refers to the modular design of each functional unit (such as an assembly unit, a processing unit, etc.) on the production line, where each module has relatively independent functions and control systems. The control system refers to a module capable of controlling production scheduling tasks and is generally composed of three basic parts: an input, a processor, and an output. Specifically, the specific requirements and functions of each functional module are analyzed to determine the parameters, indicators, and ranges required for its control. Based on the results of the requirements analysis, an appropriate control system type and hardware configuration are selected to ensure that the control system can meet the performance requirements and operational convenience of the functional module. A mapping relationship between the functional module and the independent control system is established, and the specific functional modules and parameters corresponding to each control system are clarified to ensure that each functional module can be independently, efficiently, and reliably controlled, thereby improving the operating efficiency and stability of the entire production line.

[0038] Step S500 also includes step S520, which determines an independent scheduling strategy and a higher-level coordination strategy based on the task scheduling strategy, wherein the independent scheduling strategy responds to the independent control system, and the higher-level coordination strategy responds to the production line central control system. The independent scheduling strategy refers to the task scheduling strategy for each independent control system (mapped to each functional module), which determines the execution order, priority and resource allocation of tasks according to the requirements and functions of each independent control system, that is, dynamically adjusts the scheduling of tasks according to the real-time data, status and task requirements of the independent control system to ensure that each functional module can complete the task efficiently and accurately; the higher-level coordination strategy refers to the strategy for coordinated scheduling of each independent control system at the level of the entire production line central control system, which mainly focuses on the global optimization and collaborative work of the entire production line, ensuring that the tasks between the various functional modules can be coordinated to achieve the overall optimal effect, that is, coordinate the task scheduling of each independent control system according to the global data, status and task requirements of the production line central control system to ensure that the entire production line can operate efficiently and orderly.

[0039] Step S500 also includes step S530, assisting the network time protocol to perform communication control under the collaborative interaction of modules. The NTP protocol is used to ensure time synchronization between different modules or systems, and on this basis, to achieve inter-module collaboration and communication management. Time synchronization is key to ensuring system collaboration and the correct execution of timing-dependent operations. The NTP protocol enables the system to synchronize with one or more reliable time sources to obtain accurate time information. Specifically, using the NTP protocol, all modules that need to interact can be connected to the same NTP server, or each connected to a different NTP server. All modules are synchronized with a reliable time source (such as an atomic clock, GPS, etc.), providing high-precision time correction. Different modules or systems can perform their respective tasks and operations based on a unified time standard. Collaboration between modules can be based on shared timestamps, scheduled task triggering, event synchronization, etc. For example, one module sends a request to another module at a specific time, or multiple modules start a task at the same time. Communication control refers to the monitoring, scheduling, and management of communication during module collaboration to ensure reliability, security, and efficiency. For example, time-based communication windows can be set to avoid communication congestion, or the integrity and authenticity of messages can be verified based on timestamps.

[0040] In a possible implementation, step S500 further includes step S540, determining whether there is a modular collision, wherein the modular collision includes module adjustment and task collision, and the module adjustment includes module replacement and module upgrade. Modular collision refers to the interaction or adjustment between modules in the production line scheduling task, which leads to functional conflicts, performance degradation or task execution errors in the system, including module adjustment and task collision. Specifically, module adjustment refers to replacing or upgrading modules in the system to adapt to new requirements or improve system performance. Module replacement refers to replacing a module in the system with a new module to replace the old module that no longer meets the requirements. Module upgrade refers to improving or enhancing the internal implementation of the module while keeping the module interface and data format unchanged, so as to improve module performance or add new functions. Task collision refers to the interaction or adjustment between modules in the modular system, which leads to conflicts or interference in the task execution process. Different modules may compete for shared resources or destroy the dependencies between tasks when executing tasks, resulting in some tasks failing to execute correctly or producing erroneous results.

[0041] Step S500 also includes step S550, if there is a task collision, task collision management is performed based on module parallelization and task priority. Reasonable task scheduling and priority allocation are used to ensure that tasks between parallel modules can be carried out in coordination, reduce or avoid task conflicts, and thus improve the overall system efficiency. Specifically, module parallelization refers to the decomposition of tasks in a system or application so that different modules can perform their respective tasks simultaneously or in parallel; task priority refers to the order in which tasks are sorted according to factors such as the urgency, importance, and time requirements of the tasks. Tasks with high priority will receive more resources and attention to ensure that they can be completed on time; if there is a task collision, task collision management is performed. For example, before the task begins to execute, a detailed scheduling plan is formulated based on the priority of the task and the dependency between modules; during the task execution process, the execution order and priority of the task are dynamically adjusted according to the real-time status of the system and resource usage; through effective resource management, it is ensured that each module and task can obtain sufficient resource support.

[0042] Step S500 also includes step S560. If there is module adjustment, the adjustment time zone is determined based on the global impact and the inter-module synergy, and the module adjustment management and control is carried out. Global impact refers to the scope and degree of impact of module adjustment on the entire system, and the inter-module synergy refers to the mutual dependence and cooperation relationship between modules in terms of functions, data, communication, etc. Determining the adjustment time zone means determining the best time and method for module adjustment based on comprehensive consideration of the global impact and the inter-module synergy. Specifically, according to the urgency and importance of the adjustment, the modules that need to be adjusted are prioritized. According to the priority ranking results, system resources are reasonably allocated to ensure that the adjustment of key modules can be carried out first. According to the system's operating status and business needs, a suitable time window is selected for module adjustment. During the module adjustment process, the system's operating status and performance indicators need to be monitored in real time to ensure that the adjustment process does not cause too much impact on the system.

[0043] In the above, refer to Figure 1 The equipment production scheduling method based on the modular production line according to the embodiment of the present invention is described in detail. Figure 2 The following describes an equipment production scheduling device based on a modular production line according to an embodiment of the present invention.

[0044] The equipment production scheduling device based on a modular production line according to an embodiment of the present invention is used to solve the technical problem that the existing equipment production scheduling of production lines is difficult to effectively respond to complex and changeable production tasks, which in turn leads to insufficient flexibility and adaptability of production line scheduling and low production efficiency. It realizes precise control based on modularization and achieves the technical effect of improving the flexibility, adaptability and production efficiency of production scheduling. The equipment production scheduling device based on a modular production line includes: a target production line task reading module 10, a task decomposition network determination module 20, a task scheduling strategy determination module 30, a module adjustment instruction generation module 40, and a production scheduling control module 50.

[0045] A target production line task reading module 10 is used to read a target production line task, where the target production line task is a single task or a mixed task;

[0046] A task decomposition network determination module 20 is used to perform a hierarchical discrete decomposition of the target production line task to determine a task decomposition network, wherein each network node corresponds to a greedy constraint condition;

[0047] A task scheduling strategy determination module 30 is used to build an integer programming module, traverse the task decomposition network, perform branch and bound and hierarchical recursive optimization based on the production line modular configuration as a constraint, and determine the task scheduling strategy, wherein the integer programming module includes multiple solvers;

[0048] A module adjustment instruction generation module 40 is used to traverse the task scheduling strategy, locate module adjustment nodes and adjustment strategy points to generate module adjustment instructions, and display the module adjustment instructions on a human-computer interaction interface;

[0049] The production scheduling control module 50 is used to establish a mapping between the task scheduling strategy and the target production line task, combine the module adjustment instructions, and perform production scheduling control in conjunction with the production line central control system.

[0050] The specific configuration of the task decomposition network determination module 20 will be described in detail below. The task decomposition network determination module 20 may further include: determining a task decomposition layer, wherein the task decomposition layer includes at least a task unit, a process node, and a node minimum unit; based on the task decomposition layer, discretely decomposing and hierarchically associating the target production line tasks to determine the task decomposition network; traversing the task decomposition network, determining node decision targets and performing relaxation conversions to determine the greedy constraints; and establishing a mapping between the greedy constraints and the task decomposition network.

[0051] The specific configuration of the task scheduling strategy determination module 30 will be described in detail below. The task scheduling strategy determination module 30 further includes: traversing the task decomposition network, performing solver mapping and combination, and determining a solution architecture; traversing the solution architecture, performing variable relaxation, and performing a bottom-up hierarchical optimization decision to determine a relaxed optimal solution, wherein the relaxed optimal solution is the upper bound of the conventional solution; and integrating the relaxed optimal solutions to determine the task scheduling strategy.

[0052] The specific configuration of the task scheduling strategy determination module 30 will be described in detail below. The task scheduling strategy determination module 30 may further include: traversing the minimum unit layer, determining multiple relaxed solutions by performing variable relaxation and decision optimization; combining the multiple relaxed solutions based on the inter-layer task decomposition relationship, and performing verification and combination optimization based on the modular configuration of the production line and the greedy constraints to determine the upper-layer solution; traversing the upper-layer solution, performing variable relaxation and decision optimization layer by layer to determine the solution architecture; and generating the task scheduling strategy based on the solution architecture.

[0053] The specific configuration of the task scheduling strategy determination module 30 will be described in detail below. The task scheduling strategy determination module 30 may further include: if the relaxed solution is a lower bound of the conventional solution, or if the node decomposition task has invalid decision-making, pruning the solver corresponding to the network node; and performing discrete reduction processing on the solution architecture as the strategy of the solution architecture is generated.

[0054] The specific configuration of the production scheduling control module 50 will be described in detail below. The production scheduling control module 50 may further include: determining an independent control system mapped to each functional module based on the modular configuration of the production line; determining an independent scheduling strategy and a higher-level coordination strategy based on the task scheduling strategy, wherein the independent scheduling strategy responds to the independent control system and the higher-level coordination strategy responds to the production line central control system; and assisting the network time protocol to perform communication control under the collaborative interaction of modules.

[0055] The specific configuration of the production scheduling control module 50 will be described in detail below. The production scheduling control module 50 further includes: determining whether there is a module collision, wherein the module collision includes module adjustment and task collision, and the module adjustment includes module replacement and module upgrade; if there is a task collision, performing task collision management based on module parallelization and task priority; if there is a module adjustment, determining the adjustment time zone based on global impact and occasional module coordination, and performing module adjustment management.

[0056] The equipment production scheduling device based on a modular production line provided in an embodiment of the present invention can execute the equipment production scheduling method based on a modular production line provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0057] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0058] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. The equipment production scheduling method based on modular production line is characterized by: The method comprises: Reading a target production line task, where the target production line task is a single task or a mixed task; Performing a hierarchical discrete decomposition of the target production line task to determine a task decomposition network, wherein each network node corresponds to a greedy constraint condition; Building an integer programming module, traversing the task decomposition network, using the modular configuration of the production line as a constraint, performing branch and bound and hierarchical recursive optimization to determine the task scheduling strategy, wherein the integer programming module includes multiple solvers; Traversing the task scheduling strategy, locating the module adjustment node and the adjustment strategy point to generate a module adjustment instruction, and displaying the module adjustment instruction on a human-computer interaction interface; Establish a mapping between the task scheduling strategy and the target production line task, combine the module adjustment instructions, and combine with the production line central control system to perform production scheduling control; The step of determining the task decomposition network includes: Determine a task decomposition layer, wherein the task decomposition layer at least includes a task unit-process node-node minimum unit; Based on the task decomposition layer, discretely decompose and hierarchically associate the target production line tasks to determine the task decomposition network; Traversing the task decomposition network, determining the node decision target and performing relaxation conversion, and determining the greedy constraint condition; Establishing a mapping between the greedy constraint condition and the task decomposition network; The step of determining the task scheduling strategy includes: Traversing the task decomposition network, performing solver mapping and combination, and determining the solution architecture; Traversing the solution architecture, performing variable relaxation, performing hierarchical optimization decision-making from bottom to top, and determining the relaxed optimal solution, wherein the relaxed optimal solution is the upper bound of the conventional solution; Integrating the relaxed optimal solutions to determine the task scheduling strategy; Integrating the relaxed optimal solution to determine the task scheduling strategy includes: Traverse the minimum unit layer, determine multiple relaxed solutions by performing variable relaxation and decision optimization; Based on the inter-layer task decomposition relationship, the multiple relaxed solutions are combined, and verification and combination optimization are performed based on the modular configuration of the production line and the greedy constraints to determine the upper-layer solution; Traversing the upper layer solution, performing variable relaxation and decision optimization layer by layer, and determining the solution architecture; Based on the solution architecture, the task scheduling strategy is generated.

2. The equipment production scheduling method based on a modular production line according to claim 1, characterized in that: If the relaxed solution is the lower bound of the conventional solution, or the node decomposition task has invalid decision-making, the solver corresponding to the network node is pruned; As the strategy of the solution architecture is generated, the solution architecture is subjected to a discrete reduction process.

3. The equipment production scheduling method based on a modular production line according to claim 1, characterized in that: The production scheduling control in combination with the production line central control system includes: Based on the modular configuration of the production line, determine the independent control system mapped to each functional module; Based on the task scheduling strategy, an independent scheduling strategy and a higher-level coordination strategy are determined, wherein the independent scheduling strategy responds to the independent control system, and the higher-level coordination strategy responds to the production line central control system; Assists the network time protocol to carry out communication control under the collaborative interaction of modules.

4. The equipment production scheduling method based on a modular production line according to claim 1, characterized in that: The production scheduling control includes: Determining whether there is a modular collision, wherein the modular collision includes module adjustment and task collision, and the module adjustment includes module replacement and module upgrade; If there is a task collision, the task collision control will be carried out based on module parallelization and task priority; If there is a module adjustment, the adjustment time zone will be determined based on the global impact and the occasional synergy between modules, and module adjustment management will be carried out.

5. The equipment production scheduling device based on modular production line is characterized by: The device is used to implement the equipment production scheduling method based on a modular production line according to any one of claims 1 to 4, and the device includes: A target production line task reading module, wherein the target production line task reading module is used to read a target production line task, wherein the target production line task is a single task or a mixed task; A task decomposition network determination module, the task decomposition network determination module is used to perform a hierarchical discrete decomposition of the target production line task to determine a task decomposition network, wherein each network node corresponds to a greedy constraint condition; A task scheduling strategy determination module is used to build an integer programming module, traverse the task decomposition network, perform branch and bound and hierarchical recursive optimization based on the production line modular configuration as a constraint, and determine the task scheduling strategy, wherein the integer programming module includes multiple solvers; A module adjustment instruction generation module, the module adjustment instruction generation module is used to traverse the task scheduling strategy, locate the module adjustment node and the adjustment strategy point to generate module adjustment instructions, and display the module adjustment instructions on a human-computer interaction interface; The production scheduling control module is used to establish a mapping between the task scheduling strategy and the target production line task, combine the module adjustment instructions, and perform production scheduling control in conjunction with the production line central control system.

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