A material detection line simulation method and device based on time sequence

Abstract

The present invention discloses a material detection line simulation method and device based on time sequence, the material detection line simulation method includes: pre-establishing a material detection line simulation unit according to the actual process route and equipment layout of the material detection line, wherein the material detection line simulation unit includes a process logic model and a logistics logic model; setting constraints in the material detection line simulation unit, iteratively optimizing the simulation result data, the iterative optimization includes iterative optimization of multi-order optimization for adjusting serial and parallel relationships and / or inter-station adjustments; iterative optimization until convergence, obtaining the final optimization result, and simulating output based on the final optimization result. It can reduce the idle and waiting time of the equipment, accelerate the detection and transportation process of materials, and improve the overall production efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a material detection line simulation method and device based on timing. Background Art

[0002] In the power industry, the design and operation of material testing lines are crucial to ensure the reliability and safety of power equipment. Power materials must undergo a rigorous testing process to meet high standards of safety and performance. However, traditional power material testing lines face many challenges, especially in terms of efficiency, flexibility, and resource optimization.

[0003] In the past, the design and management of power material detection lines mainly relied on fixed process flows and manual experience. This method may be sufficient when dealing with static production needs, but with the development of the power industry towards intelligence and automation, as well as the rapid changes in market demand, the original method can no longer meet the needs of modern power detection systems. For example, when new power equipment is introduced or the detection standards are updated, traditional material detection lines often take a long time to adjust. In addition, the allocation of resources in power material detection lines is often not scientific enough, resulting in idle or overloaded equipment, long detection cycles, and low efficiency.

[0004] Therefore, it is necessary to implement simulation methods so that the power material detection line can discover potential bottlenecks and problems in advance and achieve continuous optimization of the process, thereby significantly improving detection efficiency, shortening product time to market, and ultimately achieving the goal of reducing costs and improving the overall safety and reliability of the power system. Summary of the invention

[0005] In view of the defects existing in the above-mentioned prior art, the present invention provides a material detection line simulation method and device based on timing simulation, which can reduce the idle and waiting time of equipment, accelerate the detection and transportation process of materials, and improve the overall production efficiency.

[0006] In a first aspect, the present invention provides a material detection line simulation method based on timing simulation, the method comprising:

[0007] According to the actual process route and equipment layout of the material detection line, a material detection line simulation unit is pre-established, wherein the material detection line simulation unit includes a process logic model and a logistics logic model;

[0008] Set the constraints in the material detection line simulation unit and iteratively optimize the simulation result data. The iterative optimization includes multi-order optimization iterations to adjust the serial and parallel relationships and / or the adjustments between workstations.

[0009] Iterate the optimization until convergence to obtain the final optimization result, and perform simulation output based on the final optimization result.

[0010] The process of establishing the material inspection line simulation unit includes layout import and modeling of the process logic model based on the imported layout. The layout import includes the factory layout diagram, workstation area division and production line system import.

[0011] The logical elements in the process logic model are obtained to model the logistics logic model, including at least determining the inspection process, operation rhythm and logistics process. The inspection process is the order of material inspection, the operation rhythm is the inspection time of each inspection equipment at each workstation, and the logistics process is the flow of materials between each inspection equipment at each workstation; then the logistics logic model is modeled, and the logistics logic model includes the inspection process of loading and unloading, and the routing method of material artifacts between different inspection equipment; finally, the logistics logic model and the process logic model are logically connected, including the resource selection and scheduling for controlling the movement of parts or moving to complete the inspection.

[0012] Among them, set the effective time of simulation and start the simulation;

[0013] The simulation result data is obtained and output, wherein the simulation result data includes the scheduling stability of logistics transportation, the average production cycle of products, the average inventory quantity, the equipment utilization rate, the average utilization rate of logistics resources, and the detection equipment data and the working status of AGV.

[0014] Wherein, obtaining the optimal material detection line simulation unit based on multi-stage optimization includes: in each iteration process, running a first stage optimization and / or at least one second stage optimization.

[0015] Among them, in the first stage of optimization, the serial and parallel relationship of the detection materials is optimized;

[0016] In the second phase, the positional relationships of the inspection machine stations are optimized.

[0017] Among them, the first stage optimizes the serial and parallel relationship of the detection materials, including defining the serial and parallel operation modes of the detection materials;

[0018] The simulated annealing algorithm is used to evaluate the results of each iteration based on the objective function of the first stage, and the optimal solution is found through algorithm iteration. The serial and parallel relationship of the detection materials is adjusted in each iteration;

[0019] Run the simulation after the first stage of adjustment and record the optimal solution and loss function results after the first stage of optimization.

[0020] Among them, the positional relationship between the detection machine stations is optimized in the second stage, including applying the optimization algorithm to evaluate and optimize the station layout based on the second stage objective function, and recording the results of the loss function after the second stage optimization.

[0021] Among them, the result of the loss function after the second stage optimization is compared with the result of the loss function after the first stage optimization;

[0022] If the difference between the result of the loss function after the second stage optimization and the result of the loss function after the first stage optimization is greater than the threshold, the second stage optimization result is rejected; and the second stage optimization is performed again.

[0023] The optimization goal of the first stage is to minimize the value of the following first stage objective function L1, which is expressed as:

[0024]

[0025] Where N is the total number of products that leave the production system during the simulation cycle; T is the simulation cycle, which is the total time length set in the simulation; CT i represents the inspection time of the i-th product in the production system; X it It represents the number of inspection items on inspection machine i at time t.

[0026] Among them, the purpose of the second stage optimization is to minimize the value of the second stage objective function L2, which is expressed as:

[0027]

[0028] Among them, UT is the total working time of all AGVs in full load state, which refers to the time that AGV performs tasks; L is the total amount of materials carried by all AGVs in full load state; D is the total driving distance of all AGVs; DT is the total working time of all AGVs in no-load state; IT is the total time of all AGVs in idle and idle docking state; α, β and γ are weight coefficients.

[0029] In a second aspect, the present invention further provides a material detection line simulation device based on time sequence, which adopts the material detection line simulation method based on time sequence, and specifically includes:

[0030] A construction module is used to pre-establish a material detection line simulation unit according to the actual process route and equipment layout of the material detection line, wherein the material detection line simulation unit includes a process logic model and a logistics logic model;

[0031] The optimization module is used to set the constraint conditions in the material detection line simulation unit, iteratively optimize the simulation result data, iteratively optimize until convergence, and obtain the final optimization result. The iterative optimization includes the iteration of multi-order optimization for adjusting the series-parallel relationship and / or the adjustment between workstations;

[0032] The output module is used to perform simulation output based on the final optimization results.

[0033] The building blocks include:

[0034] The process of establishing the material inspection line simulation unit includes layout import and modeling of the process logic model based on the imported layout. The layout import includes the factory layout diagram, workstation area division and production line system import.

[0035] The logical elements in the process logic model are obtained to model the logistics logic model, including at least determining the inspection process, operation rhythm and logistics process. The inspection process is the order of material inspection, the operation rhythm is the inspection time of each inspection equipment at each workstation, and the logistics process is the flow of materials between each inspection equipment at each workstation; then the logistics logic model is modeled, and the logistics logic model includes the inspection process of loading and unloading, and the routing method of material artifacts between different inspection equipment; finally, the logistics logic model and the process logic model are logically connected, including the resource selection and scheduling for controlling the movement of parts or moving to complete the inspection.

[0036] The optimization modules include:

[0037] During each iteration, run the first stage optimization and / or at least one second stage optimization;

[0038] In the first stage of optimization, the serial and parallel relationship of the testing materials is optimized;

[0039] In the second stage of optimization, the positional relationship of the inspection machine stations is optimized.

[0040] The optimization modules include:

[0041] The first phase involves optimizing the serial and parallel relationships of the test materials, including defining the serial and parallel operation modes of the test materials;

[0042] The simulated annealing algorithm is used to evaluate the results of each iteration based on the objective function of the first stage, and the optimal solution is found through algorithm iteration. The serial and parallel relationship of the detection materials is adjusted in each iteration;

[0043] Run the simulation after the first stage of adjustment and record the optimal solution and loss function results after the first stage of optimization.

[0044] The optimization modules include:

[0045] In the second stage, the positional relationship between the inspection machine stations is optimized, including applying the optimization algorithm to evaluate and optimize the station layout based on the second stage objective function, and recording the results of the loss function after the second stage optimization.

[0046] The optimization module also includes:

[0047] Compare the results of the loss function after the second stage optimization with the results of the loss function after the first stage optimization;

[0048] If the difference between the result of the loss function after the second stage optimization and the result of the loss function after the first stage optimization is greater than the threshold, the second stage optimization result is rejected; and the second stage optimization is performed again.

[0049] The present invention reduces the idle and waiting time of equipment and accelerates the inspection and transportation process of materials by optimizing the process logic model and logistics logic model, thereby improving the overall production efficiency. Through the multi-stage optimization method, it is not limited to a single process step or equipment, but a comprehensive optimization of the entire production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0051] Figure 1 is a flow chart showing a material detection line simulation method based on timing simulation according to an embodiment of the present invention;

[0052] Figure 2 is a flow chart showing modeling of a material detection line simulation method based on timing simulation according to an embodiment of the present invention;

[0053] Figure 3 is a schematic diagram showing a timing-based material detection line simulation device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0056] It should be understood that although the terms first, second, third, etc. may be used to describe ... in the embodiments of the present invention, these ... should not be limited to these terms. These terms are only used to distinguish .... For example, without departing from the scope of the embodiments of the present invention, the first ... may also be referred to as the second ..., and similarly, the second ... may also be referred to as the first ....

[0057] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0058] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0059] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0060] In the operation of material testing lines, there are various efficiency problems, such as idle equipment, long waiting time, logistics bottlenecks, etc. Traditional material testing lines may not fully consider the optimal layout and process between each process step, resulting in resource waste and low production efficiency. When faced with changes in production demand or different material testing requirements, the existing material testing lines may not be able to adapt quickly, and a method that can be dynamically adjusted is needed to optimize the production process.

[0061] like Figure 1 As shown, the present invention discloses a material detection line simulation method based on timing, the method comprising:

[0062] According to the actual process route and equipment layout of the material detection line, a material detection line simulation unit is pre-established, wherein the material detection line simulation unit includes a process logic model and a logistics logic model;

[0063] Set the constraints in the material detection line simulation unit and iteratively optimize the simulation result data. The iterative optimization includes multi-order optimization iterations to adjust the serial and parallel relationships and / or the adjustments between workstations.

[0064] Iterate the optimization until convergence to obtain the final optimization result, and perform simulation output based on the final optimization result.

[0065] The simulation output includes outputting the final processing utilization rate of all equipment and the utilization rate of logistics equipment such as automatic guided vehicles (AGVs) in the form of a table.

[0066] The present invention reduces the idle and waiting time of equipment and accelerates the inspection and transportation process of materials by optimizing the process logic model and the logistics logic model, thereby improving the overall production efficiency. Through the multi-stage optimization method, it is not limited to a single process step or equipment, but a comprehensive optimization of the entire production line.

[0067] Among them, in the simulation environment, according to the actual process route and equipment layout of the material inspection line, a process logic model including process steps, time settings, and inspection sequence is established; then, a logistics logic model is established to set logistics parameters, including the inspection time, transportation time and logistics equipment operation parameters of each material.

[0068] First, collect and analyze the process route and equipment layout of the material testing line, including obtaining detailed process drawings, equipment layout drawings and operation manuals.

[0069] Among them, the collection layout includes the collection of process layout data in the factory, including the 2D map of the process planning in the factory, the reference picture data of the process layout in the factory, the layout of the inspection line station, etc. The collected data is used to simulate the three-dimensional spatial layout of all resources on the three-dimensional visualization platform. The collected data includes the inspection content, equipment and tooling auxiliary consumables, and the transportation method data of materials and parts. The inspection content includes the content of the inspection process route of different materials, the serial and parallel of the process, the time required for each process and the number of parts and components, and parameter settings are performed to achieve the effect of real-time simulation. Equipment and tooling auxiliary consumables include the equipment and tooling required for each step on this inspection line, as well as when the tooling and equipment enter and exit, and the name and quantity of auxiliary consumables at the station. The transportation method data of materials and parts includes the transportation method of the inspection materials on the entire inspection line, the transportation tools of parts, and the running speed of the equipment.

[0070] Then the model is established, which includes two parts: process model building and logistics logic modeling.

[0071] According to the collected process routes, the process logic model is constructed using simulation software. Among them, process logic modeling includes establishing a logical model of the process on the inspection line in a simulation environment, including process steps, time settings, inspection sequence, component constraints, etc. Specifically, it includes defining the start and end events for each inspection step and setting the time required for the operation. According to the process flow, set the dependencies between the processes to ensure the logic of the process and the consistency of the actual operation. Enter the specific operation time and waiting time for each process.

[0072] Logistics logic modeling, set logistics parameters, including the inspection time and transportation time of each material, the time occupied by crane hoisting or robot handling, the time of crane or robot participation in work, the crane hoisting speed, AGV transportation speed, and the operation time of other logistics tools. The logistics model focuses on the flow, storage and processing of materials. It is established in the same simulation environment and is interconnected with the process logic model. In order to facilitate the simulation experiment of the logistics model, the operating parameters of the model are set to the table through the chart and method in the table, and the method is written to call and read, making inspection and modification more convenient and quick.

[0073] Specifically, Figure 2 As shown, the modeling process includes layout import, including the factory layout diagram, workstation area division and production line system import, as well as physical modeling of the process logic model, specifically the modeling of equipment (A and B vehicles), inspection equipment machine and tooling equipment (hoisting, borescope and other equipment). Quest supports the import of actual models such as inspection lines, tooling, equipment, resources, etc., or you can directly use the models that come with Quest.

[0074] After that, the logical elements in the process logic model are obtained to model the logistics logic model, including at least determining the inspection process, that is, the order of material inspection, the operation rhythm, that is, the inspection time of each inspection equipment at each workstation, and the logistics process, that is, the flow of materials between each inspection equipment at each workstation. This is completed in Quest through built-in logic and program writing, such as A vehicle scheduling, parallel assembly waiting, etc. Then, the logistics logic model is modeled, including inspection processes such as loading and unloading, and the routing method of material workpieces between different inspection equipment. Finally, the logistics logic model and the process logic model are connected, that is, the movement of parts is controlled or moved to complete the inspection resource selection and scheduling.

[0075] Taking the inspection process of the workshop as the research object, a model is established according to the inspection process flow and micro-logistics transportation process in the workshop, the constraints in the simulation are set, the fitted data collected from the workshop production are used as simulation parameters, and the simulation output data is used to analyze the optimization task of the actual production process.

[0076] Also, improvements can be made by adjusting the series and parallel relationships or the adjustments between workstations; and the improved logistics plan can be re-simulated to generate new simulation analysis data, and the adjustment can be continuously repeated to achieve the ideal logistics effect.

[0077] In establishing the material inspection line simulation unit, especially involving the workshop inspection process and micro-logistics transportation, the constraints include time constraints, dependency constraints, resource constraints and logistics constraints.

[0078] Among them, time constraints include:

[0079] Process time: The specific time required for each inspection step, including the time for equipment startup, operation, inspection and shutdown.

[0080] Transfer time: The time required for materials to move between different processes, which depends on the speed and distance of logistics equipment.

[0081] The dependency constraints include:

[0082] Process dependency: Some processes can only start after other processes are completed. This logical dependency needs to be explicitly set in the model.

[0083] Resource dependency: Specific tests may require specific equipment or tools.

[0084] Among them, resource constraints include: Equipment availability: the operating status and occupancy of each device.

[0085] Among them, logistics constraints include:

[0086] Storage space: The capacity limits of storage devices and regions.

[0087] Logistics equipment capabilities: such as load capacity and speed limits of equipment such as AGVs and cranes.

[0088] Transportation path: The flow path of materials within the workshop needs to avoid intersections and congestion.

[0089] Specifically, setting time constraints in Quest mainly involves defining the duration of various operations, including processing time, waiting time, and transfer time. When creating or editing a process (such as an inspection step), you can set the duration of each process directly in the properties. When defining a material handling system (such as an AGV), you can set the speed and distance, and Quest will automatically calculate the transfer time.

[0090] In Quest, dependency constraints are implemented by configuring the logical relationships between processes.

[0091] The process dependency setting needs to be set in the process diagram by drawing connecting lines from one process to another. These connecting lines can be set as conditional triggers, indicating that the completion of one process triggers the start of the next process.

[0092] In resource allocation, a process is specified to use specific equipment or tools to ensure that the resource is available when the process is executed.

[0093] Resource management is a core function in Quest. In resource configuration, the number, work shifts and possible failure rates of each resource are defined. The availability at different times can be defined using the calendar function.

[0094] Logistics constraint settings involve the handling and storage of materials, and require the configuration of the operating parameters of logistics equipment and its path selection.

[0095] Set capacity limits for warehouses and other storage facilities. For handling equipment, such as AGVs (Automated Guided Vehicle System), set their load capacity and operating speed.

[0096] When setting up transport routes, use Quest's path planning tool to define the movement path of materials from one location to another, set the path selection logic, and avoid congestion.

[0097] Set the effective time of the simulation and start the simulation.

[0098] The simulation result data are obtained and output, wherein the simulation result data includes the scheduling stability of logistics transportation, the average production cycle of products, the average inventory quantity, the equipment utilization rate, the average utilization rate of logistics resources, and the detection equipment data and the working condition of the AGV.

[0099] The detection equipment data and the result data of the simulation software running on the working condition of the automatic guided vehicle AGV are as described in Tables 1 and 2 below.

[0100] Table 1 Simulation results

[0101]

[0102] Table 2 AGV working status table

[0103]

[0104] The present invention utilizes simulation output data to analyze the optimization task of the actual production process.

[0105] The present invention obtains the optimal material detection line simulation unit based on multi-stage optimization. In each iteration process, the first stage optimization is run once, and optionally, the second stage optimization can also be run at least once.

[0106] In the first stage optimization process, the first stage objective function minimizes the product production cycle and the minimum number of inspection items on the production line as the goal, and optimizes the series and parallel relationship of the inspection materials to maximize production efficiency and equipment utilization.

[0107] If the user needs to adjust the positional relationship of the machine stations during the simulation, the second stage optimization needs to be run at least once in each iteration. Optionally, the user can set whether to automatically optimize the positional relationship of the machine stations during the simulation in the simulation objective. In the second stage, the objective function of the second stage establishes an optimization function based on the logistics subsystem, and optimizes the positional relationship of the machine stations to further reduce production time and material handling costs.

[0108] Also, when optimizing the inspection line, if it is necessary to enter the second stage, it is necessary to ensure that the optimization of the second stage (position adjustment between inspection machine stations) does not negatively affect the optimization results of the first stage (optimization of the serial and parallel relationship of the inspection materials).

[0109] Among them, the serial and parallel relationship of the detection materials in the first stage is optimized, including defining the serial and parallel operation modes of the equipment.

[0110] Set an initial configuration, possibly based on historical data.

[0111] Apply optimization algorithms (such as simulated annealing), evaluate the results of each iteration based on the objective function of the first stage, find the optimal solution through algorithm iteration, and adjust the serial and parallel relationship of the detection materials in each iteration.

[0112] Run the simulation after the first stage of adjustment and record the optimal solution and loss function results after the first stage of optimization.

[0113] In the second stage, the positional relationship between the inspection machine stations is optimized, including determining the preliminary position optimization plan and applying the optimization algorithm to evaluate and optimize the station layout based on the second stage objective function.

[0114] Record the optimized loss function results and compare them with the loss function results after optimization in the first stage.

[0115] If the second stage results in a significant increase in loss results, this indicates that the optimization in the second stage may have harmed the optimization effect of the first stage.

[0116] If the optimization result of the second stage is not satisfactory (i.e. the increase in loss exceeds the threshold), reject the optimization result of the second stage and re-perform the second stage optimization, including adjusting the parameters of the optimization algorithm to ensure that the new second stage optimization does not reduce the performance indicators of the first stage.

[0117] Final evaluation and implementation:

[0118] The present invention uses this phased optimization strategy with feedback to ensure that all important factors are comprehensively considered during the optimization process and avoid overall performance degradation caused by local optimal solutions.

[0119] For the objective function of the first stage, the following evaluation indicators are defined based on the scheduling stability of logistics transportation, the average production cycle of products, the average inventory quantity, equipment utilization, and the average utilization rate of logistics resources:

[0120] The average production cycle time (MCT) of a product is the average production time taken by N products leaving the production system in a unit time period [0, T]. Its mathematical expression is:

[0121] (CT i represents the average processing time of the i-th product)

[0122] Mean work in process MWIP is the average number of inspected products in the production process within a unit time period [0, T). Its function expression is:

[0123] (T is the unit time observation period, X it is the number of products produced by detection machine i at time t).

[0124] Equipment utilization rate. The utilization rate of machine equipment reflects the use of processing equipment. A high utilization rate means that the machine idle time is short and the utilization rate of equipment resources is good. Conversely, the utilization rate of machine equipment is poor.

[0125] The objective function of the first stage is the combined effect of minimizing the product production cycle and the minimum number of inspected products on the production line, as well as the overall shortest transportation path. The optimization purpose is also to minimize the value of the following first-stage objective function L1. The objective function L1 is.

[0126]

[0127] in,

[0128] N: represents the total number of products that leave the production system during the shock protection period.

[0129] T: represents the simulation cycle, which is the total time length set in the simulation.

[0130] CT i : represents the detection time taken by the i-th product in the production system.

[0131] X it : represents the number of inspection items on inspection machine i at time t.

[0132] n: represents the total number of testing machines, that is, the total number of testing machines.

[0133] Among them, in the second stage, the objective function of the second stage establishes an optimization function based on the logistics subsystem, including the utilization rate of the AGV vehicle, which is reflected by the working time and idle time of the AGV vehicle. For the handling tasks of a given logistics handling system and different scheduling schemes with the same number of logistics vehicles, the scheduling scheme that meets the system's handling needs and has the lowest vehicle utilization rate is the optimal one.

[0134] Among them, the objective function L2 of the second stage is expressed as:

[0135]

[0136] The parameters are defined as follows:

[0137] UT: The total working time of all AGVs under full load, which refers to the time the AGV performs tasks (such as moving materials).

[0138] L: The total amount of materials moved by all AGVs when fully loaded.

[0139] D: The total driving distance of all AGVs, which can be calculated from the running speed and time of the AGV, or directly provided by the logistics management system.

[0140] DT: The total working time of all AGVs in an empty state, that is, the time when the AGV has no handling task but is moving (for example, from one workstation to another).

[0141] IT: The total time that all AGVs are in idle and idle docked states, that is, the time when the AGV is not performing any tasks and is not moving.

[0142] α, β, and γ: These parameters are weight coefficients used to adjust the relative importance of each item in the objective function and can be adjusted according to actual needs.

[0143] After multiple iterations of multi-stage optimization, the final optimization result will be obtained. In each iteration of multi-stage optimization, the optimal solution and loss function results after the first stage optimization are compared with the loss results after the second stage calculation adjustment. If the second stage causes a significant increase in the loss results, this indicates that the optimization of the second stage may have damaged the optimization effect of the first stage. In this multi-stage optimization iteration, it is necessary to reject this second stage optimization result and re-perform the second stage optimization.

[0144] The loss function for the first and second phases is defined as follows:

[0145]

[0146] The parameters are defined as follows:

[0147] Idle_Time_k: The total time that the kth device is idle during the entire simulation period T.

[0148] Wait_Time_k: The total time that the kth device is in the waiting state during the entire simulation cycle T.

[0149] Utilization_Rate_k: The utilization rate of the kth device, defined as the total time the device is in the busy state divided by the entire simulation period T.

[0150] Total_Items_k: The total number of items that the kth device needs to process during the entire simulation cycle T.

[0151] Inspected_Items_k: The number of items actually inspected by the kth device during the entire simulation period T.

[0152] Loaded_Items_k: The number of items actually loaded by the kth device during the entire simulation period T.

[0153] The above loss function is used to evaluate the quality of each second-stage optimization. If the new loss function value is too high compared to the loss function value that has not been optimized in the second stage, the second-stage optimization result in this iteration can be rejected, and then the second-stage optimization can be performed again. In this way, it can be ensured that the second-stage optimization in each multi-stage iteration will not cause the performance of the first-stage indicators to deteriorate, thereby avoiding falling into a local optimal state.

[0154] The present invention adjusts the serial and parallel relationship in the first stage optimization and adjusts the positions between machine stations in the second stage optimization.

[0155] Optionally, a simulated annealing algorithm SA is used to optimize the workpiece detection task with a limited number of devices. Simulated annealing is a randomized search algorithm that simulates the annealing process in the physical process, starting from a high temperature and gradually cooling down to find an approximate global optimal solution to the problem.

[0156] The inspection step arrangement of each workpiece is regarded as the state of a solution, and the optimal inspection step arrangement is searched by simulated annealing. The solution is represented as an array, where each element is a triple (workpiece ID, equipment ID, time period). For example, if there are three workpieces, each of which requires two different inspection equipment, a possible solution array is as follows: [(1,A,1), (1,B,2), (2,A,1), (2,C,3), (3,B,4), (3,C,5)].

[0157] This means that the inspection of workpiece 1 uses device A in time period 1 and device B in time period 2; workpiece 2 uses device A in time period 1 and device C in time period 3; and so on.

[0158] The specific implementation of the simulated annealing algorithm includes the initialization phase where an initial solution is chosen (starting randomly within the constraints). The initial temperature and cooling rate are set. During the iteration process, at each temperature, multiple iterations are performed to try to update the solution, randomly selecting an element in the solution and trying to modify it (e.g. changing the device used or the time period).

[0159] Calculate the first-stage objective function solution of the new solution and compare it with the current solution. Use the Metropolis criterion to decide whether to accept the new solution. If the new solution is better, accept the new solution. If the new solution is not as good as the current solution, accept the new solution with a certain probability, and this probability decreases as the "temperature" decreases. Then reduce the temperature (for example, multiply by the cooling rate). Until the termination condition, the termination condition includes reaching the minimum temperature or the number of iterations reaching the preset limit.

[0160] Since there are many constraints in serial-parallel adjustment, a constraint detection function is needed to perform serial-parallel constraint detection, including using the constraint detection function to determine whether a given solution meets the number restrictions of equipment and whether it meets the constraints of the serial-parallel order of the same workpiece.

[0161] The constraint check function counts the number of times each device is used for each time period in the solution. It checks whether any device has been used more than the available number of devices. It returns True if all devices are used within the limit; otherwise, it returns False.

[0162] The constrained detection function constrains the detection order of each workpiece to ensure that the detection steps that need to be serialized start after the previous serial detection is completed, that is, the time periods of the workpiece detection steps that need to be serialized do not overlap.

[0163] In the second stage, the default method is also to use the simulated annealing algorithm to optimize the layout of the inspection equipment stations, including determining the initial solution, temperature and cooling rate during initialization and parameter setting. The initial solution starts from the current station layout.

[0164] Temperature (T): Setting a high initial temperature allows the algorithm to escape from the local optimum at the beginning.

[0165] Cooling rate (r): Set a cooling rate to gradually reduce the temperature.

[0166] The simulated annealing process is an iterative process that selects a neighbor solution, that is, randomly selects one or more stations and moves them to other locations, or exchanges the positions of two stations.

[0167] Calculate the value of the new layout under the second-stage objective function and compare it with the value of the current layout. If the new layout is better than the current layout, accept the new layout. If the new layout is worse, it is also accepted with a certain probability, which is exp(Δf / T), where Δf is the cost increase and T is the current temperature.

[0168] Update the temperature and reduce the temperature according to the cooling rate, T1 = r*T.

[0169] When the maximum number of iterations is reached, the temperature drops below a preset threshold, and the improvement of the solution is less than a predetermined threshold, the iteration process is terminated.

[0170] Finally, after obtaining the final optimization results, the final processing utilization rate of all equipment and the utilization rate of logistics equipment such as AGV are output in the form of a table.

[0171] The present invention reduces the idle and waiting time of equipment and accelerates the inspection and transportation process of materials by optimizing the process logic model and the logistics logic model, thereby improving the overall production efficiency. Through the multi-stage optimization method, it is not limited to a single process step or equipment, but a comprehensive optimization of the entire production line.

[0172] like Figure 3 As shown, the present invention also provides a material detection line simulation device based on time sequence, which adopts the material detection line simulation method based on time sequence, and specifically includes:

[0173] A construction module is used to pre-establish a material detection line simulation unit according to the actual process route and equipment layout of the material detection line, wherein the material detection line simulation unit includes a process logic model and a logistics logic model;

[0174] The optimization module is used to set the constraint conditions in the material detection line simulation unit, iteratively optimize the simulation result data, iteratively optimize until convergence, and obtain the final optimization result. The iterative optimization includes the iteration of multi-order optimization for adjusting the series-parallel relationship and / or the adjustment between workstations;

[0175] The output module is used to perform simulation output based on the final optimization results.

[0176] The building blocks include:

[0177] The modeling process includes layout import, including plant layout diagram, workstation area division and production line system import, as well as physical modeling;

[0178] Obtain the logical elements in the model to perform logistics logic modeling, including at least determining the inspection process, i.e., the material inspection order, the operation rhythm, i.e., the inspection time of each inspection equipment at each workstation, and the logistics process, i.e., the flow direction of materials between each inspection equipment at each workstation;

[0179] Then, we will carry out logical modeling, including the inspection process of loading and unloading, and the routing of materials and workpieces between different inspection equipment;

[0180] Finally, the logical connection is made, that is, the movement of parts is controlled or completed to complete the inspection resource selection and scheduling.

[0181] The optimization modules include:

[0182] Obtaining the optimal detection line based on multi-stage optimization, in each iteration process, running the first stage optimization and / or at least one second stage optimization;

[0183] In the first stage of optimization, the serial and parallel relationship of the testing materials is optimized;

[0184] In the second stage of optimization, the positional relationship of the inspection machine stations is optimized.

[0185] The optimization modules include:

[0186] The first phase involves optimizing the serial and parallel relationships of the test materials, including defining the serial and parallel operation modes of the test materials;

[0187] The simulated annealing algorithm is used to evaluate the results of each iteration based on the objective function of the first stage, and the optimal solution is found through algorithm iteration. The serial and parallel relationship of the detection materials is adjusted in each iteration;

[0188] Run the simulation after the first stage of adjustment and record the optimal solution and loss function results after the first stage of optimization.

[0189] The optimization modules include:

[0190] In the second stage, the positions of the inspection machine stations are adjusted, including applying the optimization algorithm to evaluate and optimize the station layout based on the objective function of the second stage, and calculating the results of the loss function after the adjustment in the second stage.

[0191] The optimization module also includes:

[0192] Calculate the results of the adjusted loss function in the second stage and compare them with the results of the optimized loss function in the first stage;

[0193] If the difference between the loss result of the second stage and the result of the loss function after optimization in the first stage is greater than the threshold, the second stage optimization result is rejected; and the second stage optimization is performed again.

[0194] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0195] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0196] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0197] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0198] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0199] The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, but is not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

Claims

1. A material detection line simulation method based on time sequence, the method comprising: According to the actual process route and equipment layout of the material detection line, a material detection line simulation unit is pre-established, wherein the material detection line simulation unit includes a process logic model and a logistics logic model; Set the constraints in the material detection line simulation unit and iteratively optimize the simulation result data. The iterative optimization includes multi-order optimization iterations to adjust the serial and parallel relationships and / or the adjustments between workstations. Iterate and optimize until convergence, obtain the final optimization result, and perform simulation output based on the final optimization result; Wherein, obtaining the optimal material detection line simulation unit based on multi-stage optimization includes: in each iteration process, running the first stage optimization and / or at least one second stage optimization; In the first phase of optimization, the serial and parallel relationship of the testing materials is optimized, including: Define the serial and parallel operation modes of the inspection materials; apply the simulated annealing algorithm to evaluate the results of each iteration based on the first-stage objective function, find the optimal solution through algorithm iteration, and adjust the serial and parallel relationship of the inspection materials in each iteration; the first-stage objective function minimizes the product production cycle and the minimum number of inspection products on the production line as the goal; run the simulation after the first-stage adjustment, and record the optimal solution after the first-stage optimization and the results of the loss function; In the second stage of optimization, the positional relationship of the machine workstations is optimized and detected, including the application of the optimization algorithm to evaluate and optimize the workstation layout based on the second stage objective function, and recording the results of the loss function after the second stage optimization; among them, the second stage objective function establishes an optimization function based on the logistics subsystem, including the utilization rate based on AGV, which is reflected by the working time and idle time of AGV.

2. A method for simulating a material detection line based on timing as claimed in claim 1, characterized in that: The process of establishing the material inspection line simulation unit includes layout import and modeling of the process logic model based on the imported layout. The layout import includes the factory layout diagram, workstation area division and production line system import. Acquire the logical elements in the process logic model to model the logistics logic model, including at least determining the inspection process, operation rhythm and logistics process. The inspection process is the material inspection order, the operation rhythm is the inspection time of each inspection equipment at each workstation, and the logistics process is the flow direction of materials between each inspection equipment at each workstation; Then the logistics logic model is built, which includes the inspection process of loading and unloading, and the routing of materials and workpieces between different inspection equipment; Finally, the logistics logic model and the process logic model are logically connected, including the resource selection and scheduling to control the movement of parts or to complete the inspection.

3. A method for simulating a material detection line based on timing as claimed in claim 1, characterized in that: Set the effective time of the simulation and start the simulation; The simulation result data is obtained and output, wherein the simulation result data includes the scheduling stability of logistics transportation, the average production cycle of products, the average inventory quantity, the equipment utilization rate, the average utilization rate of logistics resources, and the detection equipment data and the working status of AGV.

4. A method for simulating a material detection line based on timing as claimed in claim 1, characterized in that: Compare the results of the loss function after the second stage optimization with the results of the loss function after the first stage optimization; If the difference between the result of the loss function after the second stage optimization and the result of the loss function after the first stage optimization is greater than the threshold, the second stage optimization result is rejected; Re-perform the second stage of optimization.

5. A method for simulating a material detection line based on timing as claimed in claim 1, characterized in that: The purpose of the first stage optimization is to minimize the value of the following first stage objective function L1, which is expressed as: Where N is the total number of products that leave the production system during the simulation cycle; T is the simulation cycle, which is the total time length set in the simulation; CT i represents the detection time of the i-th product in the production system; X it It represents the number of inspection items on inspection machine i at time t.

6. A method for simulating a material detection line based on timing as claimed in claim 1, characterized in that: The purpose of the second stage optimization is to minimize the value of the second stage objective function L2, which is expressed as: Among them, UT is the total working time of all AGVs in full load state, which refers to the time that AGV performs tasks; L is the total amount of materials carried by all AGVs in full load state; D is the total driving distance of all AGVs; DT is the total working time of all AGVs in no-load state; IT is the total time of all AGVs in idle and idle docking state; α, β and γ are weight coefficients.

7. A material detection line simulation device based on time sequence, characterized in that: The material detection line simulation method based on timing as claimed in any one of claims 1 to 6 is adopted, specifically comprising: A construction module is used to pre-establish a material detection line simulation unit according to the actual process route and equipment layout of the material detection line, wherein the material detection line simulation unit includes a process logic model and a logistics logic model; The optimization module is used to set the constraint conditions in the material detection line simulation unit, iteratively optimize the simulation result data, iteratively optimize until convergence, and obtain the final optimization result. The iterative optimization includes the iteration of multi-order optimization for adjusting the series-parallel relationship and / or the adjustment between workstations; The output module is used to perform simulation output based on the final optimization results.

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