Cable production line self-adaptive production scheduling method based on AI driving

Abstract

The invention relates to a cable production line adaptive production scheduling method based on AI driving. The method comprises the following steps: S1, collecting and preprocessing production line data; s2, establishing a data-driven production process model based on the preprocessed production line data; s3, based on the constructed production process model, generating an initial scheduling scheme by adopting a particle swarm optimization algorithm; s4, based on the initial scheduling scheme, tasks are distributed to specific stations and equipment, and the production line executes the scheme; s5, monitoring the execution process of the task, and collecting process deviation; and in combination with deep reinforcement learning, adjusting scheduling according to a real-time production state. According to the method, an efficient scheduling scheme can be generated in a complex multi-constraint environment, and dynamic self-adaptive scheduling optimization is realized by monitoring task execution and feeding back process deviation in real time, so that production requirements of emergencies and rapid changes in a cable production line are effectively met.

Description

Technical Field

[0001] The present invention relates to the field of intelligent scheduling, and in particular to an AI-driven adaptive production scheduling method for cable production lines. Background Art

[0002] In modern manufacturing, production scheduling is one of the core links to ensure the efficient operation of the production system, especially in cable production lines, which have complex process flows, limited equipment resources, and diversified customer needs (such as multiple varieties, small batches, and fast delivery), which puts forward higher scheduling optimization requirements. In the actual production process, there are many challenges, such as fluctuations in equipment operating status, uncertainty in task execution time, dynamic changes in inventory usage, and the impact of environmental factors (such as temperature and humidity) on production line performance. Therefore, traditional static scheduling methods are often difficult to adapt to these dynamic changes, which can easily lead to low equipment utilization, production delays, or waste of resources. Summary of the invention

[0003] In order to solve the above problems, the purpose of the present invention is to provide an AI-driven adaptive production scheduling method for cable production lines, which can generate efficient scheduling plans in complex multi-constraint environments, and realize dynamic adaptive scheduling optimization by real-time monitoring of task execution and feedback of process deviations, thereby effectively adapting to emergencies and rapidly changing production needs in cable production lines.

[0004] To achieve the above object, the present invention adopts the following technical solutions: An AI-driven adaptive production scheduling method for a cable production line includes the following steps: S1: Collect production line data, including equipment operation data, production task data, inventory data and environmental data, and pre-process them; S2: Establish a data-driven production process model based on the preprocessed production line data; The production process model is a two-layer model architecture, including a task flow model and a resource status model, as follows: The task flow model dynamically describes the execution order, process requirements and scheduling constraints of tasks. It uses a directed acyclic graph to model each task. Each task is a node T in the graph. a , through the edge e ab Represents dependencies between tasks; Resource status model, which describes the status of materials and equipment in the production process through resource consumption and allocation rules; it regards resources as constraints to limit the allocation and execution of tasks; S3: Based on the constructed production process model, the particle swarm optimization algorithm is used to generate the initial scheduling plan; S4: Based on the initial scheduling plan, tasks are assigned to specific workstations and equipment, and the production line execution plan; S5: Monitor the execution process of tasks and collect process deviations; combine deep reinforcement learning to adjust scheduling according to real-time production status.

[0005] Furthermore, production line data is collected, including equipment operation data, production task data, inventory data and environmental data, as follows: Through the intelligent sensor integrated PLC interface, the equipment operation data is collected, including equipment status, extrusion temperature, tension, line speed, equipment speed and power; Integrate with the production management database through the MES system interface to obtain real-time production task data, including current order requirements, progress and priority; Integrate the smart warehousing system with the MES system to synchronize inventory data regularly, including raw material inventory and remaining consumables; Environmental data, including workshop temperature, humidity and air data, are collected in real time through environmental IoT sensors.

[0006] Further, preprocessing is as follows: sliding average filtering is used to filter out instantaneous abnormal fluctuations, and rule detection is introduced to find erroneous values; the mean filling interpolation method is used to repair missing values; the periodic data collected by the PLC is aligned to avoid errors caused by different sampling rates, and the NTP protocol is used to ensure time consistency, ensuring that all types of data are synchronized based on a unified timestamp.

[0007] Furthermore, the task process model is as follows: Model all production tasks as a directed acyclic graph G=(V,E), where: is a set of tasks, each task represents a node; It's a task With the task The set of dependencies between them; Indicates the task It's a task Prerequisites ; Acyclicity: That is, avoid circular dependencies and ensure the topological order of the task flow is legal; Among them, task T a Corresponding to a node in the DAG graph, its attribute set includes: ,in Respectively for tasks task end time, task start time, task duration, task resource set and task priority; Task constraints include time constraints and resource constraints, as follows: , that is, the start time of the successor task must be later than or equal to the end time of its predecessor task; , that is, task T a The start time must be at its earliest start time and the latest start time within the scope; , that is, at any time t, for the kth resource R k , the total resource usage of running tasks cannot exceed the maximum resource capacity ; active(t) is the set of tasks being executed at time t; For the task For resources R k consumption; If the resource supply decreases due to real-time changes, the inventory surplus R k (t) is lower than the demand, then adjust the start time of the affected tasks: ; where t replenish (k) represents resource R k Supplementary time point.

[0008] Further, S3 is as follows: Based on the constructed production process model, the task set, resource set, dependency set and constraint conditions are obtained as the input of PSO; The fitness function is to minimize the duration and minimize the total delay: ; in, It is task T a Delivery time; C max is the total processing time; n represents the maximum task volume; Each particle represents a scheduling scheme, including task execution order and resource allocation; particle X is represented by the vector [X T ,X R ] indicates that X T is the task order, X R It is a resource allocation plan; Initialize particle swarm size N, maximum number of iterations i ter , speed range V min ,V max, learning factors c1, c2, and inertia weight ω; Randomly initialize the particle position X i (t) and speed V i (t) Ensure that the initial values ​​of task sequence and resource allocation are reasonable; Traverse each particle and calculate the fitness function; Decode particle position X i (t) Scheduling plan, including task execution order and resource allocation plan; After sorting X T Translated into task priority order; Check whether resource constraints and task time window constraints are met; Calculate the fitness function and add a penalty term if the constraint is violated; Update the state of each particle using the velocity and position update formula: Speed ​​update formula: ; Position update formula: ; in, is the historical optimal solution of particle i; is the global optimal solution of the population; r1, r2 are random factors; Constraint checks are performed on the updated particles for correction; For the fitness of the current particle, if it is better than the historical optimal solution , then update the current particle to the historical optimal solution ; If the fitness of the current particle is better than the global optimal solution , update the current particle to the global optimal solution ; If the maximum number of iterations is reached or the change in the global optimal value is less than the threshold, the iteration ends and the generated scheduling plan, i.e., the initial scheduling plan, is output.

[0009] Furthermore, S4 is specifically: Based on the initial scheduling plan, tasks are decomposed into specific workstations and equipment according to resource allocation results; ensuring that tasks meet process requirements, equipment capabilities, and process constraints; The MES system is used to decompose the scheduling plan and send it to each workstation, equipment or operator. The MES system is also used to monitor task progress, resource usage, abnormal status, and dynamically adjust equipment and plans.

[0010] Furthermore, the S5 is specifically: S51: Track the execution status of tasks in real time and collect process deviations as feedback data for the production system; S52: Based on real-time monitoring data and combined with the PPO algorithm, dynamically optimize the scheduling plan.

[0011] Furthermore, S51 is specifically: Through the MES system and factory IoT devices, the following data is collected in real time to track the execution status of tasks in real time: Actual start time , actual end time , actual processing time , device M k Real-time status S k (t), the real-time status includes idle, running, fault, and equipment failure probability ; Inventory remaining R k (t); Resource consumption rate ; Collect process deviations as feedback data for the production system, including: Processing time deviation , Task Delay And equipment failure time ,in To plan the processing time, The start time of the scheduled task.

[0012] Furthermore, S52 is specifically: In scheduling optimization, the reinforcement learning problem is modeled as a Markov decision process, which includes the following elements: State space S, including: current task state , Device Status , Resource Status , current task queue and dependencies ; Action space A, including task allocation, task sequence adjustment and task insertion processing; Reward function R: ; ; ; ; ; Among them, R1, R2, R3, and R4 are minimizing the total construction period, minimizing task delays, maximizing equipment utilization, and punishing equipment failures, respectively, and w1, w2, w3, and w4 are weight coefficients; For equipment working efficiency; Initialize the policy network π θ (a|s) and value function ; Take the initial scheduling plan as the basic input; In the current strategy π θ Under the simulation scheduling optimization process, the state s is collected t 、Action a t and reward r t The policy network is updated according to the following objective function: ; Among them, θ represents the parameters of the current policy network; is the importance sampling ratio; is the advantage function; is the cutoff threshold; is the cropping operation, indicating cropping r t The value of (θ) is such that within the interval; is the expected function; is the objective function; ; Among them, Q(s t ,a t ) is in state s t Take action a t Total return after t ) is in state s t The state value of the following; Loss function of value function : ; in, is the current value function network for state s t The estimated value of R t represents the actual target return value at time step t; Repeat sampling, policy updating, and value function updating until the model converges and obtains the adjusted scheduling plan.

[0013] The present invention has the following beneficial effects: 1. The present invention can generate efficient scheduling solutions in complex multi-constraint environments, and realize dynamic adaptive scheduling optimization by real-time monitoring of task execution and feedback of process deviations, thereby effectively adapting to emergencies and rapidly changing production needs in cable production lines; 2. The present invention uses a double-layer model, combines mathematical formulas and optimization goals, builds a data-driven production process model, can dynamically adapt to real-time changes, improve production efficiency, and provide scheduling optimization support for the cable production process. The PSO scheduling algorithm is optimized based on the production process model, and task logic (task flow model) and resource constraints (resource allocation model) information are provided for PSO. The legitimacy and fitness evaluation criteria of the PSO particle solution are defined. At the same time, dynamic feedback is used to ensure that the production scheduling plan is updated in real time. Therefore, the PSO scheduling algorithm based on the production process model can generate efficient, flexible and feasible scheduling plans; 3. The present invention can dynamically adjust the production scheduling plan by real-time monitoring of the task execution process and combining the PPO algorithm to solve the problems of processing time delay and equipment failure. It utilizes the adaptability of reinforcement learning and the real-time feedback capability of the MES system to realize the intelligent and dynamic optimization of production scheduling, effectively improve production efficiency and resource utilization, and enhance the system's response capability to emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0015] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments: refer to Figure 1 In this embodiment, an AI-driven adaptive production scheduling method for a cable production line is provided, comprising the following steps: S1: Collect production line data, including equipment operation data, production task data, inventory data and environmental data, and pre-process them; S2: Establish a data-driven production process model based on the preprocessed production line data; S3: Based on the constructed production process model, the particle swarm optimization algorithm is used to generate the initial scheduling plan; S4: Based on the initial scheduling plan, tasks are assigned to specific workstations and equipment, and the production line execution plan; S5: Monitor the execution process of tasks and collect process deviations (such as processing time delays or equipment status changes); combine deep reinforcement learning (such as the PPO algorithm) to adjust scheduling according to real-time production status.

[0016] In this embodiment, production line data is collected, including equipment operation data, production task data, inventory data and environmental data, as follows: Through the intelligent sensor integrated PLC interface, the equipment operation data is collected, including equipment status (start, work, stop, fault), extrusion temperature, tension, line speed, equipment speed and power; Integrate with the production management database through the MES system interface to obtain real-time production task data, including current order requirements (cable type, specification, length), progress (current completion quantity, time consumption) and priority; Integrate the smart warehousing system with the MES system to synchronize inventory data regularly, including the inventory of raw materials (copper wire, PVC sheath material, etc.) and the remaining status of consumables (such as lubricating oil and cooling water); Environmental data, including workshop temperature, humidity, and air data, are collected in real time through environmental IoT sensors.

[0017] In this embodiment, the preprocessing is as follows: a sliding average filter is used to filter out instantaneous abnormal fluctuations (such as abnormal tension values), and rule detection is introduced (such as temperature exceeding the limit, extrusion line speed fluctuations) to find erroneous values; the mean filling interpolation method is used to repair missing values; the periodic data collected by the PLC is aligned to avoid errors caused by different sampling rates, and the NTP protocol is used to ensure time consistency, ensuring that all types of data are synchronized based on a unified timestamp.

[0018] In this embodiment, the production process model is a two-layer model architecture, including a task flow model and a resource status model, as follows: The task flow model dynamically describes the execution order, process requirements and scheduling constraints of tasks. It uses a directed acyclic graph (DAG) to model each task. Each task is a node T in the graph. a , through the edge e ab Represents dependencies between tasks; The resource status model describes the status of materials and equipment in the production process through resource consumption and allocation rules; resources are regarded as constraints to limit the allocation and execution of tasks.

[0019] In this embodiment, the resource status model is as follows: Describes the dynamic status of resources: including the availability and consumption of equipment, materials, and manpower, and represents the dynamic distribution and use of resources in the production process.

[0020] Constraint task allocation and execution: Each task must meet its resource requirements. If resources are insufficient, the task cannot be executed. Ensure optimal use of resource allocation. Support dynamic adjustment and optimization: Based on the real-time status of production, optimize resource allocation and task scheduling, and coordinate the order of process execution.

[0021] In this embodiment, the task process model is as follows: Model all production tasks as a directed acyclic graph G=(V,E), where: is a set of tasks, each task represents a node; It's a task With the task The set of dependencies between them; Indicates the task It's a task Prerequisites ; Acyclicity: That is, avoid circular dependencies and ensure the topological order of the task flow is legal; Among them, task T a Corresponding to a node in the DAG graph, its attribute set includes: ,in Respectively for tasks task end time, task start time, task duration, task resource set and task priority; Task constraints include time constraints and resource constraints, as follows: , that is, the start time of the successor task must be later than or equal to the end time of its predecessor task; , that is, task T a The start time must be at its earliest start time and the latest start time within the scope; , that is, at any time t, for the kth resource R k , the total resource usage of running tasks cannot exceed the maximum resource capacity ; active(t) is the set of tasks being executed at time t; For the task For resources R k consumption; If the resource supply decreases due to real-time changes, the inventory surplus R k (t) is lower than the demand, then adjust the start time of the affected tasks: ; where t replenish (k) represents resource R k Supplementary time point.

[0022] In this embodiment, S3 is specifically as follows: Based on the constructed production process model, the task set, resource set, dependency set and constraint conditions are obtained as the input of PSO; The fitness function is to minimize the duration and minimize the total delay: ; in, It is task T a Delivery time; C max is the total processing time; n represents the maximum task volume; Each particle represents a scheduling scheme, including task execution order and resource allocation; particle X is represented by the vector [X T ,X R ] indicates that X T is the task order, X R It is a resource allocation plan; Initialize particle swarm size N, maximum number of iterations i ter , speed range V min ,V max , learning factors c1, c2, and inertia weight ω; Randomly initialize the particle position X i (t) and speed V i (t) Ensure that the initial values ​​of task sequence and resource allocation are reasonable; Traverse each particle and calculate the fitness function; Decode particle position X i (t) Scheduling plan, including task execution order and resource allocation plan; After sorting X T Translated into task priority order; Check whether resource constraints and task time window constraints are met; Calculate the fitness function and add a penalty term if the constraint is violated; Update the state of each particle using the velocity and position update formula: Speed ​​update formula: ; Position update formula: ; in, is the historical optimal solution of particle i; is the global optimal solution of the population; r1, r2 are random factors; Check constraints on updated particles and make corrections if necessary (e.g., dependency conflicts in task order, resource overload handling); For the fitness of the current particle, if it is better than the historical optimal solution , then update the current particle to the historical optimal solution ; If the fitness of the current particle is better than the global optimal solution , update the current particle to the global optimal solution .

[0023] If the maximum number of iterations is reached or the change in the global optimal value is less than the threshold, the iteration ends and the generated scheduling plan, i.e., the initial scheduling plan, is output.

[0024] In this embodiment, S4 is specifically: Based on the initial scheduling plan, tasks are decomposed into specific workstations and equipment according to resource allocation results; ensuring that tasks meet process requirements, equipment capabilities, and process constraints; The MES system is used to decompose the scheduling plan and send it to each workstation, equipment or operator. The MES system is also used to monitor task progress, resource usage, abnormal status, and dynamically adjust equipment and plans.

[0025] In this embodiment, S5 is specifically: S51: Track the execution status of tasks in real time and collect process deviations (such as processing time delays and equipment status changes) as feedback data for the production system; S52: Based on real-time monitoring data and combined with the PPO algorithm, dynamically optimize the scheduling plan.

[0026] In this embodiment, S51 is specifically: Through the MES system and factory IoT devices, the following data is collected in real time to track the execution status of tasks in real time: Actual start time , actual end time , actual processing time , device M k Real-time status S k (t), the real-time status includes idle, running, fault, and equipment failure probability ; Inventory remaining R k (t); Resource consumption rate ; Collect process deviations as feedback data for the production system, including: Processing time deviation , Task Delay And equipment failure time ,in To plan the processing time, The start time of the scheduled task.

[0027] In this embodiment, S52 is specifically: In scheduling optimization, the reinforcement learning problem is modeled as a Markov decision process, which includes the following elements: State space S, including: current task state , Device Status , Resource Status , current task queue and dependencies ; Action space A, including task allocation, task sequence adjustment and task insertion processing; Reward function R: ; ; ; ; ; Among them, R1, R2, R3, and R4 are minimizing the total construction period, minimizing task delays, maximizing equipment utilization, and punishing equipment failures, respectively, and w1, w2, w3, and w4 are weight coefficients; For equipment working efficiency; Initialize the policy network π θ (a|s) and value function ; Take the initial scheduling plan as the basic input; In the current strategy π θ Under the simulation scheduling optimization process, the state s is collected t 、Action a t and reward r t The policy network is updated according to the following objective function: ; Among them, θ represents the parameters of the current policy network; is the importance sampling ratio; is the advantage function; is the cutoff threshold; is the cropping operation, indicating cropping r t The value of (θ) is such that within the interval; is the expected function; is the objective function; ; Among them, Q(s t ,a t ) is in state s t Take action a t Total return after t ) is in state s t The state value of the following; Loss function of value function : ; in, is the current value function network for state s t The estimated value of R t represents the actual target return value at time step t.

[0028] Repeat sampling, policy updating, and value function updating until the model converges and obtains the adjusted scheduling plan.

[0029] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0030] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0031] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0032] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0033] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any technician familiar with the profession may use the above disclosed technical content to change or modify it into an equivalent embodiment with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention still belongs to the protection scope of the technical solution of the present invention.

Claims

1. An AI-driven adaptive production scheduling method for cable production lines, characterized in that: The following steps are involved: S1: Collect production line data, including equipment operation data, production task data, inventory data and environmental data, and pre-process them; S2: Establish a data-driven production process model based on the preprocessed production line data; The production process model is a two-layer model architecture, including a task flow model and a resource status model, as follows: The task flow model dynamically describes the execution order, process requirements and scheduling constraints of tasks. It uses a directed acyclic graph to model each task. Each task is a node T in the graph. a , through the edge e ab Represents dependencies between tasks; Resource status model, which describes the status of materials and equipment in the production process through resource consumption and allocation rules; it regards resources as constraints to limit the allocation and execution of tasks; S3: Based on the constructed production process model, the particle swarm optimization algorithm is used to generate the initial scheduling plan; S4: Based on the initial scheduling plan, tasks are assigned to specific workstations and equipment, and the production line execution plan; S5: Monitor the execution process of tasks and collect process deviations; combine deep reinforcement learning to adjust scheduling according to real-time production status.

2. The AI-driven adaptive production scheduling method for cable production lines according to claim 1 is characterized in that: The collected production line data includes equipment operation data, production task data, inventory data and environmental data, as follows: Through the intelligent sensor integrated PLC interface, the equipment operation data is collected, including equipment status, extrusion temperature, tension, line speed, equipment speed and power; Integrate with the production management database through the MES system interface to obtain real-time production task data, including current order requirements, progress and priority; Integrate the smart warehousing system with the MES system to synchronize inventory data regularly, including raw material inventory and remaining consumables; Environmental data, including workshop temperature, humidity and air data, are collected in real time through environmental IoT sensors.

3. The AI-driven adaptive production scheduling method for cable production lines according to claim 2 is characterized in that: The preprocessing is specifically as follows: using sliding average filtering to filter out instantaneous abnormal fluctuations, and introducing rule detection to find erroneous values; using mean filling interpolation method to repair missing values; aligning the periodic data collected by PLC to avoid errors caused by different sampling rates, and using NTP protocol to ensure time consistency, ensuring that all types of data are synchronized based on a unified timestamp.

4. The AI-driven adaptive production scheduling method for cable production lines according to claim 1 is characterized in that: The task process model is as follows: Model all production tasks as a directed acyclic graph G=(V,E), where: is a set of tasks, each task represents a node; It's a task With the task The set of dependencies between them; Indicates the task It's a task Prerequisites ; Acyclicity: That is, avoid circular dependencies to ensure the topological order of the task flow is legal; Among them, task T a Corresponding to a node in the DAG graph, task T a The properties collection includes: ,in Respectively for tasks task end time, task start time, task duration, task resource set and task priority; Task constraints include time constraints and resource constraints, as follows: , the start time of the successor task must be later than or equal to the end time of its predecessor task; , Task T a The start time must be at its earliest start time and the latest start time within the scope; , at any time t, for the kth resource R k , the total resource usage of running tasks cannot exceed the maximum resource capacity ; active(t) is the set of tasks being executed at time t; For the task For resources R k consumption; If the resource supply decreases due to real-time changes, the inventory surplus R k (t) is lower than the demand, then adjust the start time of the affected tasks: ； where t replenish (k) represents resource R k Supplementary time point.

5. The AI-driven adaptive production scheduling method for cable production lines according to claim 1 is characterized in that: The S3 is specifically as follows: Based on the constructed production process model, the task set, resource set, dependency set and constraint conditions are obtained as the input of PSO; The fitness function is to minimize the duration and minimize the total delay: ； in, It is task T a Delivery time; C max is the total processing time; n represents the maximum task volume; Each particle represents a scheduling scheme, including task execution order and resource allocation; particle X is represented by the vector [X T ,X R ] indicates that X T is the task order, X R It is a resource allocation plan; Initialize particle swarm size N, maximum number of iterations i ter , speed range V min ,V max , learning factors c1, c2, and inertia weight ω; Randomly initialize the particle position X i (t) and speed V i (t) Ensure that the initial values ​​of task sequence and resource allocation are reasonable; Traverse each particle and calculate the fitness function; Decode particle position X i (t) Scheduling plan, including task execution order and resource allocation plan; After sorting X T Translated into task priority order; Check whether resource constraints and task time window constraints are met; Calculate the fitness function and add a penalty term if the constraint is violated; Update the state of each particle using the velocity and position update formula: Speed ​​update formula: ； Position update formula: ； in, is the historical optimal solution of particle i; is the global optimal solution of the population; r1, r2 are random factors; Constraint checks are performed on the updated particles for correction; For the fitness of the current particle, if it is better than the historical optimal solution , then update the current particle to the historical optimal solution ; If the fitness of the current particle is better than the global optimal solution , update the current particle to the global optimal solution ; If the maximum number of iterations is reached or the change in the global optimal value is less than the threshold, the iteration ends and the generated scheduling plan, i.e., the initial scheduling plan, is output.

6. The AI-driven adaptive production scheduling method for cable production lines according to claim 5 is characterized in that: The S4 is specifically: Based on the initial scheduling plan, tasks are decomposed into specific workstations and equipment according to resource allocation results; ensuring that tasks meet process requirements, equipment capabilities, and process constraints; The MES system is used to decompose the scheduling plan and send it to each workstation, equipment or operator. The MES system is also used to monitor task progress, resource usage, abnormal status, and dynamically adjust equipment and plans.

7. The AI-driven adaptive production scheduling method for cable production lines according to claim 1 is characterized in that: The S5 is specifically: S51: Track the execution status of tasks in real time and collect process deviations as feedback data for the production system; S52: Based on real-time monitoring data and combined with the PPO algorithm, dynamically optimize the scheduling plan.

8. The AI-driven adaptive production scheduling method for cable production lines according to claim 7 is characterized in that: The S51 is specifically: Through the MES system and factory IoT devices, the following data is collected in real time to track the execution status of tasks in real time: Actual start time , actual end time , actual processing time , device M k Real-time status S k (t), the real-time status includes idle, running, fault, and equipment failure probability ; Inventory remaining R k (t); Resource consumption rate ; Collect process deviations as feedback data for the production system, including: Processing time deviation , Task Delay And equipment failure time ,in To plan the processing time, The start time of the scheduled task.

9. The AI-driven adaptive production scheduling method for cable production lines according to claim 8 is characterized in that: The S52 is specifically: In scheduling optimization, the reinforcement learning problem is modeled as a Markov decision process, which includes the following elements: State space S, including: current task state , Device Status , Resource Status , current task queue and dependencies ; Action space A, including task allocation, task sequence adjustment and task insertion processing; Reward function R: ; ； ； ； ； Among them, R1, R2, R3, and R4 are minimizing the total construction period, minimizing task delays, maximizing equipment utilization, and punishing equipment failures, respectively, and w1, w2, w3, and w4 are weight coefficients; For equipment working efficiency; Initialize the policy network π θ (a|s) and value function ; Take the initial scheduling plan as the basic input; In the current strategy π θ Under the simulation scheduling optimization process, the state s is collected t 、Action a t and reward r t The policy network is updated according to the following objective function: ； Among them, θ represents the parameters of the current policy network; is the importance sampling ratio; is the advantage function; is the cutoff threshold; is the cropping operation, indicating cropping r t The value of (θ) is such that within the interval; is the expected function; is the objective function; ； Among them, Q(s t ,a t ) is in state s t Take action a t Total return after t ) is in state s t The state value of the following; Loss function of value function : ； in, is the current value function network for state s t The estimated value of R t represents the actual target return value at time step t; Repeat sampling, policy updating, and value function updating until the model converges and obtains the adjusted scheduling plan.

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