Quality inspection task scheduling method and system based on optimized myxobacteria plug-in
By optimizing the processing of task and equipment information in the quality inspection workshop using slime mold plugins and utilizing a pheromone concentration update mechanism, the optimal scheduling scheme is automatically generated, solving the problem of time-consuming and error-prone manual scheduling, improving scheduling efficiency and quality, and reducing production costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-20
- Publication Date
- 2026-04-10
AI Technical Summary
The existing scheduling in the quality inspection workshop relies on human experience, which makes the scheduling process time-consuming and prone to errors, making it difficult to make the best decisions quickly when there are many tasks and complex equipment.
A quality inspection task scheduling method based on optimized slime mold plugins is adopted. By collecting task and equipment information and utilizing the pheromone concentration update mechanism of the slime mold algorithm, candidate solutions are optimized and the optimal scheduling scheme is automatically generated.
In situations with numerous tasks and complex equipment, we can quickly generate efficient and high-quality scheduling solutions to reduce production time and costs.
Smart Images
Figure CN121836151A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and in particular to a quality inspection task scheduling method and system based on an optimized slime mold plugin. Background Technology
[0002] In the field of quality management in modern manufacturing, the efficient scheduling of the quality inspection workshop is crucial for ensuring product quality and improving production quality inspection efficiency. The quality inspection workshop typically handles a series of diverse inspection tasks, which need to be completed on different equipment and are subject to various complex scheduling constraints, including limitations imposed by quality inspection standards, equipment, and personnel resources. A reasonable scheduling plan can ensure that tasks are completed on time while maximizing equipment and personnel utilization and reducing inspection time and costs.
[0003] In related technologies and industry practices, the scheduling of quality inspection workshops is mainly done manually, supplemented by intelligent scheduling. It relies on the professional knowledge of the scheduler and various experience-based simplification assumptions. The scheduler needs to manually allocate tasks and equipment and arrange inspection times based on factors such as the urgency of the task, the availability of equipment, and the complexity of the task.
[0004] However, over-reliance on the dispatcher's expertise and various experience-based simplification assumptions makes the scheduling process time-consuming and error-prone. When there are many tasks and complex equipment, it is difficult to respond flexibly and accurately to complex emergencies, and the dispatcher cannot make the optimal scheduling decision quickly. Summary of the Invention
[0005] This application provides a quality inspection task scheduling method and system based on an optimized slime mold plugin. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.
[0006] In a first aspect, embodiments of this application provide a quality inspection task scheduling method based on an optimized slime mold plugin, the method comprising: Collect task information for each quality inspection task in the quality inspection workshop and equipment information for each quality inspection device. Initialize the preset optimized slime mold plugin algorithm parameters, including population size and default pheromone concentration; Input the task information, equipment information and algorithm parameters into the optimized slime mold plugin to determine the scheduling result for the quality inspection workshop; Output the scheduling results for the quality inspection workshop. The scheduling results include the final quality inspection equipment for each quality inspection task.
[0007] Optionally, the preset optimized slime mold plugin includes an allocation layer, a pheromone concentration configuration layer, a comprehensive fitness value calculation layer, a pheromone concentration update layer, and a scheduling result generation layer; Determine the scheduling results corresponding to the quality inspection workshop, including: The allocation layer performs random and heuristic allocation for each quality inspection task and each quality inspection device based on the population size, resulting in multiple candidate scheduling schemes; The pheromone concentration configuration layer configures a default pheromone concentration for each candidate scheduling scheme, thus obtaining the initial pheromone concentration for each candidate scheduling scheme. The comprehensive fitness value calculation layer calculates the comprehensive fitness value of each candidate scheduling scheme based on task information and equipment information; The pheromone concentration update layer uses the comprehensive fitness value of each candidate scheduling scheme to update the initial pheromone concentration of each candidate scheduling scheme, thereby obtaining the target pheromone concentration of each candidate scheduling scheme. The scheduling result generation layer generates the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme.
[0008] Optionally, based on population size, each quality inspection task and each quality inspection device can be randomly and heuristically assigned to obtain multiple candidate scheduling schemes, including: Each quality inspection task is randomly assigned to each quality inspection device, resulting in multiple random scheduling schemes. From the task information and equipment information, determine the priority of each quality inspection task and the efficiency parameters of each piece of equipment; Based on the priority of each quality inspection task and the efficiency parameters of each device, heuristic allocation is performed in combination with the population size to obtain multiple heuristic scheduling schemes. Multiple random scheduling schemes are combined with multiple heuristic scheduling schemes to obtain multiple candidate scheduling schemes for the initial population.
[0009] Optionally, based on task information and equipment information, calculate the comprehensive fitness value for each candidate scheduling scheme, including: From the task information and equipment information, obtain the inspection sequence of each quality inspection task, the capacity constraints of each quality inspection equipment, and the sequence constraints between each quality inspection task; For each candidate scheduling scheme, the total production time required to complete all quality inspection tasks is quantified by using inspection order, capacity constraints, and sequence constraints. From the task information and equipment information, obtain the type of quality inspection equipment assigned to each quality inspection task and the usage time, as well as the quantity and type of resources required for each quality inspection task; By using the type and usage time of quality inspection equipment, as well as the quantity and type of resources, the equipment usage cost and resource consumption cost of each candidate scheduling scheme are quantified and summed to obtain the total cost of each candidate scheduling scheme. The task completion pass rate of each candidate scheduling scheme is quantified and used as a quality indicator for each candidate scheduling scheme. The total production time, total cost, and quality indicators are weighted and summed to obtain the comprehensive fitness value of each candidate scheduling scheme.
[0010] Optionally, based on the target pheromone concentration of each candidate scheduling scheme, the scheduling result corresponding to the quality inspection workshop is generated, including: The target pheromone concentrations of each candidate scheduling scheme are sorted, and a preset number of candidate scheduling schemes are eliminated in ascending order to obtain the remaining candidate scheduling schemes. From the remaining candidate scheduling schemes, determine whether the number of quality inspection devices assigned to each quality inspection task is 1; If so, determine the unique quality inspection equipment for each quality inspection task from the remaining candidate scheduling schemes, and use it as the scheduling result for the corresponding quality inspection workshop; If not, calculate the comprehensive fitness value of each remaining candidate scheduling scheme based on the task information and equipment information; update the target pheromone concentration of each remaining candidate scheduling scheme based on the comprehensive fitness value of each remaining candidate scheduling scheme, and execute the strategy of sorting the updated pheromone concentration again until the number of quality inspection devices assigned to each quality inspection task is 1.
[0011] Optionally, based on the target pheromone concentration of each candidate scheduling scheme, the scheduling result corresponding to the quality inspection workshop is generated, including: From the target pheromone concentration of each candidate scheduling scheme, determine the target pheromone concentration of each quality inspection task and each quality inspection device; Based on the target pheromone concentration of each quality inspection task and each quality inspection equipment, calculate the probability that each quality inspection equipment will be selected by each quality inspection task. Multiple quality inspection devices are eliminated sequentially in ascending order of probability to obtain multiple candidate quality inspection devices for each quality inspection task. Each quality inspection task and multiple alternative quality inspection devices for each quality inspection task are randomly and heuristically assigned to obtain multiple target candidate scheduling schemes. For each target candidate scheduling scheme, a traversal and screening process is performed to generate the scheduling result corresponding to the quality inspection workshop.
[0012] Optionally, for each target candidate scheduling scheme, a traversal and filtering process is performed to generate the scheduling result corresponding to the quality inspection workshop, including: The target pheromone concentration of each target candidate scheduling scheme is obtained from the target pheromone concentration of each quality inspection task and each quality inspection equipment. Based on task information and equipment information, calculate the target comprehensive fitness value for each target candidate scheduling scheme; Using the target comprehensive fitness value of each target candidate scheduling scheme, the strategy of updating the target pheromone concentration of each target candidate scheduling scheme is executed again until the number of quality inspection devices after elimination is 1, thus obtaining the final quality inspection device for each quality inspection task. The final quality inspection equipment for each quality inspection task is used as the scheduling result for the corresponding quality inspection workshop.
[0013] Optionally, the probability of each quality inspection device being selected for each quality inspection task is calculated using the following formula:
[0014] For each task Can be allocated to quality inspection equipment Quality inspection equipment , It is a quality inspection equipment The target pheromone concentration, It is a quality inspection equipment The target pheromone concentration.
[0015] Optionally, the method also includes: Updated pheromone concentration = (1 - decay rate) × current pheromone concentration + fitness value.
[0016] Secondly, embodiments of this application provide a quality inspection task scheduling system based on an optimized slime mold plugin, the system comprising: The information collection module is used to collect task information for each quality inspection task in the quality inspection workshop and equipment information for each quality inspection device. The algorithm parameter initialization module is used to initialize the algorithm parameters of the preset optimized slime mold plugin. The algorithm parameters include the population size and the default pheromone concentration. The scheduling result determination module is used to input task information, equipment information and algorithm parameters into the optimized slime mold plugin to determine the scheduling result corresponding to the quality inspection workshop; The scheduling result output module is used to output the scheduling results corresponding to the quality inspection workshop. The scheduling results include the final quality inspection equipment corresponding to each quality inspection task.
[0017] The technical solutions provided in this application embodiment may include the following beneficial effects: In this embodiment, by optimizing the slime mold plugin to process task information, equipment information and algorithm parameters, the scheduling result corresponding to the quality inspection workshop can be obtained. The optimized slime mold plugin can automatically process a large amount of task information and equipment information, and gradually optimize candidate solutions by using the pheromone concentration update mechanism. Therefore, when there are many tasks and complex equipment, the system can quickly generate the optimal scheduling scheme, which not only improves the scheduling efficiency, but also ensures the quality of the scheduling scheme and reduces production time and cost.
[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description
[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0020] Figure 1 This is a flowchart illustrating a quality inspection task scheduling method based on an optimized slime mold plugin, as provided in an embodiment of this application. Figure 2 This is a schematic diagram of information received by a scheduling system according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an optimized slime mold insert provided in an embodiment of this application; Figure 4 This is a schematic block diagram illustrating a process for generating scheduling results corresponding to a quality inspection workshop, provided in an embodiment of this application. Figure 5 This is a schematic diagram illustrating the generation result of an initial population candidate scheme provided in an embodiment of this application; Figure 6 This is a schematic diagram of a scheduling result displayed on a client according to an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a quality inspection task scheduling system based on an optimized slime mold plugin, provided in an embodiment of this application; Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0021] The following description and accompanying drawings fully illustrate specific embodiments of this application to enable those skilled in the art to practice them.
[0022] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0023] In the following description, when referring to the accompanying drawings, the same numbers in different drawings denote the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.
[0024] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.
[0025] Currently, scheduling in quality inspection workshops is typically done manually. Manual scheduling relies on the experience and expertise of the scheduler, who needs to manually allocate tasks and equipment and schedule inspection times based on factors such as the urgency of the task, equipment availability, and task complexity.
[0026] The applicant of this application recognizes that manual scheduling relies on the experience and expertise of the dispatcher, and the scheduling process is time-consuming and prone to errors. When there are many tasks and complex equipment, dispatchers find it difficult to quickly make optimal scheduling decisions.
[0027] To address the aforementioned problems, this application provides a quality inspection task scheduling method and system based on an optimized slime mold plugin, thereby resolving the issues present in the related technical problems. In the embodiments of this application, by optimizing the slime mold plugin to process task information, equipment information, and algorithm parameters, the scheduling result corresponding to the quality inspection workshop can be obtained. This optimized slime mold plugin can automatically process a large amount of task and equipment information, and gradually optimize candidate solutions using a pheromone concentration update mechanism. Therefore, when the number of tasks is large and the equipment is complex, the system can quickly generate the optimal scheduling scheme, which not only improves scheduling efficiency but also ensures the quality of the scheduling scheme, reducing production time and costs. The following detailed description uses exemplary embodiments.
[0028] The following will be combined with the appendix Figure 1 - Appendix Figure 6This application provides a detailed description of the quality inspection task scheduling method based on optimized slime mold plugins, as provided in the embodiments of this application. This method can be implemented using a computer program and can run on a quality inspection task scheduling system based on the von Neumann architecture and optimized slime mold plugins. This computer program can be integrated into the application or run as a standalone utility application.
[0029] Please see Figure 1 This document presents a flowchart illustrating a quality inspection task scheduling method based on an optimized slime mold plugin, as described in an embodiment of this application. Figure 1 As shown, the method in this application embodiment includes the following steps: S101, collect task information for each quality inspection task in the quality inspection workshop and equipment information for each quality inspection device; In manufacturing, the quality inspection workshop is a dedicated area responsible for product quality inspection. Quality inspection tasks are specific inspection operations that need to be performed in the workshop; each task has its own specific inspection content and requirements. Task information refers to detailed data related to each quality inspection task, such as task number, task type, inspection time, priority, resource requirements, etc. This data is used as input to the scheduling algorithm. Quality inspection equipment refers to the machines or tools used to perform quality inspection tasks; each piece of equipment has its specific functions and capacity. Equipment information is detailed data related to each piece of quality inspection equipment, such as equipment number, equipment type, hourly cost, availability, etc. This data is used as input to the scheduling algorithm.
[0030] In some embodiments of this application, detailed information on all quality inspection tasks is obtained from the management system or database of the quality inspection workshop. Detailed information on all quality inspection equipment is also obtained from the management system or database of the quality inspection workshop. The collected task and equipment information is organized into a structured data table, and the organized task and equipment information is input into the scheduling system as input data for the algorithm.
[0031] One example of a task information table is shown in Table 1.
[0032] Table 1
[0033] One example of a device information table is shown in Table 2.
[0034] Table 2
[0035] In one possible implementation, task and equipment information is exported from the quality inspection workshop's management system and saved as a CSV file. Python is used to read the CSV file and organize the data into a Pandas DataFrame. The organized task and equipment information is then input into the scheduling system. The information received by the scheduling system includes, for example... Figure 2 As shown.
[0036] S102, Initialize the preset optimized slime mold plugin algorithm parameters, including population size and default pheromone concentration; The Predefined Optimized Slime Mold Plugin is an optimization tool based on the slime mold algorithm, used to solve scheduling problems in quality inspection workshops. Algorithm parameters are those that need to be set during the optimization algorithm's execution. Population size refers to the number of candidate solutions in the algorithm. Each candidate solution represents a possible scheduling scheme. Default pheromone concentration refers to the initial pheromone concentration on all paths (task allocation and device selection) during algorithm initialization.
[0037] In some embodiments of this application, initial values for population size and default pheromone concentration are determined. The system can automatically set these initial values when the algorithm starts. The initialized parameters are input into the slime mold optimization plugin as the basic configuration for algorithm operation.
[0038] For example, the system will generate 50 candidate solutions, each representing a possible scheduling scheme. During algorithm initialization, the pheromone concentration on all paths is set to 1.0, indicating that in the initial stage, all paths are considered equally likely.
[0039] S103, input the task information, equipment information and algorithm parameters into the optimized slime mold plugin to determine the scheduling result corresponding to the quality inspection workshop; For example Figure 3 As shown, the preset optimized slime mold plugin includes an allocation layer, a pheromone concentration configuration layer, a comprehensive fitness value calculation layer, a pheromone concentration update layer, and a scheduling result generation layer.
[0040] In some embodiments of this application, the specific process of determining the scheduling result corresponding to the quality inspection workshop includes: the allocation layer randomly and heuristically assigning each quality inspection task and each quality inspection equipment according to the population size to obtain multiple candidate scheduling schemes; the pheromone concentration configuration layer configuring a default pheromone concentration for each candidate scheduling scheme to obtain the initial pheromone concentration of each candidate scheduling scheme; the comprehensive fitness value calculation layer calculating the comprehensive fitness value of each candidate scheduling scheme according to the task information and equipment information; the pheromone concentration update layer updating the initial pheromone concentration of each candidate scheduling scheme using the comprehensive fitness value of each candidate scheduling scheme to obtain the target pheromone concentration of each candidate scheduling scheme; and the scheduling result generation layer generating the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme.
[0041] Random allocation generates task assignment schemes randomly. Heuristic allocation generates high-quality scheduling schemes based on task priority and equipment efficiency. Finally, the results of random and heuristic allocations are combined to form the initial population.
[0042] The default pheromone concentration is the initial pheromone concentration across all paths (task allocation and device selection). The initial pheromone concentration is the pheromone concentration for each candidate scheduling scheme in the initial stage.
[0043] Among them, the overall fitness value is a fitness value that takes into account the total production time, total cost and quality indicators.
[0044] In some embodiments of this application, the specific process of randomly and heuristically assigning each quality inspection task and each quality inspection device according to the population size to obtain multiple candidate scheduling schemes includes: randomly assigning each quality inspection task to each quality inspection device to obtain multiple random scheduling schemes; determining the priority of each quality inspection task and the efficiency parameter of each device from the task information and device information; performing heuristic allocation based on the priority of each quality inspection task and the efficiency parameter of each device, combined with the population size, to obtain multiple heuristic scheduling schemes; merging the multiple random scheduling schemes with the multiple heuristic scheduling schemes to obtain multiple candidate scheduling schemes for the initial population, the result being, for example... Figure 5 As shown.
[0045] In one possible implementation, each task is randomly assigned to each device, generating multiple random scheduling schemes. The priority of each task is extracted from the task information. The efficiency parameter of each device is extracted from the device information. For each task and each device, a matching score is calculated, which comprehensively considers the task priority and the device efficiency parameter. The calculation formula is: Matching Score = Quantized value of task priority × Quantized value of device efficiency parameter. For each task, devices capable of handling the task are selected. Among the selected devices, the device with the highest matching score is selected as the assignment device for that task. Based on the above selection results, a scheduling scheme is generated, which includes the devices assigned to each task. Depending on the population size, the above process is repeated to generate multiple heuristic scheduling schemes. The results of random assignment and heuristic assignment are combined to obtain multiple candidate scheduling schemes for the initial population.
[0046] In some embodiments of this application, the specific process of calculating the comprehensive fitness value of each candidate scheduling scheme based on task information and equipment information includes: obtaining the inspection sequence of each quality inspection task, the capacity constraint of each quality inspection equipment, and the sequence constraint between each quality inspection task from the task information and equipment information; for each candidate scheduling scheme, quantifying the total production time required for all quality inspection tasks to complete the inspection using the inspection sequence, capacity constraint, and sequence constraint; obtaining the type and usage time of the quality inspection equipment allocated to each quality inspection task, as well as the quantity and type of resources required for each quality inspection task from the task information and equipment information; quantifying the equipment usage cost and resource consumption cost of each candidate scheduling scheme using the type and usage time of the quality inspection equipment, and the quantity and type of resources, and summing them to obtain the total cost of each candidate scheduling scheme; quantifying the pass rate of task completion for each candidate scheduling scheme as the quality indicator of each candidate scheduling scheme; and weighting and summing the total production time, total cost, and quality indicator to obtain the comprehensive fitness value of each candidate scheduling scheme.
[0047] Specifically, the process of quantifying the total production time required to complete all quality inspection tasks is as follows: The total production time is initialized to 0 and used to accumulate the completion times of all tasks. Each quality inspection task is processed sequentially according to the inspection order. Based on capacity constraints, the availability of the quality inspection equipment at the current time is checked. If the equipment is available, the quality inspection task is assigned to that equipment, and the equipment's usage time is updated. The inspection time of the quality inspection task is added to the total production time. For tasks with sequential constraints, the next task begins only after the previous task is completed. If the previous task is not completed, the current task needs to wait until the previous task is completed. The waiting time is added to the total production time. Finally, the inspection time and waiting time of all tasks are summed to obtain the total production time.
[0048] Specifically, the process of quantifying and summing the equipment usage cost and resource consumption cost of each candidate scheduling scheme to obtain the total cost of each candidate scheduling scheme is as follows: The total cost is initialized to 0 and used to accumulate the equipment usage cost and resource consumption cost of each task. For each quality inspection task, its quality inspection equipment usage cost and resource consumption cost are calculated. The equipment usage cost is the task's inspection time multiplied by the hourly cost of the equipment. The resource consumption cost is the quantity of resources required by the task multiplied by the unit cost of the resources. The equipment usage cost and resource consumption cost of each task are summed to obtain the total cost.
[0049] Specifically, the process of quantifying the task completion pass rate for each candidate scheduling scheme includes: initializing the task completion pass rate to 1.0, indicating that under ideal conditions, all tasks can achieve the maximum pass rate. For each quality inspection task, the actual completion pass rate is calculated based on the allocated equipment and inspection time. Equipment efficiency is obtained from the equipment information table. The actual pass rate is calculated based on the equipment efficiency parameters and the task's maximum pass rate. Actual pass rate = Maximum pass rate × Equipment efficiency. The actual pass rates of all quality inspection tasks are summed to obtain the total pass rate. The total pass rate is divided by the number of quality inspection tasks to obtain the average pass rate.
[0050] In some embodiments of this application, for example Figure 4 As shown, the specific process of generating the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme includes: sorting the target pheromone concentration of each candidate scheduling scheme, and sequentially eliminating a preset number of candidate scheduling schemes in ascending order to obtain the remaining candidate scheduling schemes; determining whether the number of quality inspection equipment assigned to each quality inspection task is 1 from the remaining candidate scheduling schemes; if so, determining the unique quality inspection equipment for each quality inspection task from the remaining candidate scheduling schemes as the scheduling result corresponding to the quality inspection workshop; if not, calculating the comprehensive fitness value of each remaining candidate scheduling scheme based on task information and equipment information; updating the target pheromone concentration of each remaining candidate scheduling scheme based on the comprehensive fitness value of each remaining candidate scheduling scheme, and re-executing the strategy of sorting the updated pheromone concentration until the number of quality inspection equipment assigned to each quality inspection task is 1.
[0051] For example, there are 3 quality inspection tasks (T1, T2, T3) and 3 quality inspection devices (M1, M2, M3), and the relevant information is shown in Table 3.
[0052] Table 3
[0053] Sort the target pheromone concentrations from lowest to highest: Scheme 4 (0.5), Scheme 3 (0.6), Scheme 2 (0.7), Scheme 1 (0.8). Eliminate the scheme with the lowest pheromone concentration, thus Scheme 4 is eliminated, leaving Schemes 1, 2, and 3. In Schemes 1, 2, and 3, T1 is assigned to M1, M2, and M3; T2 is assigned to M1, M2, and M3; and T3 is also assigned to M1, M2, and M3. Each task is assigned to multiple devices, so further optimization is needed. Calculate the overall fitness value of the remaining schemes: Scheme 1: Overall fitness value = 0.9; Scheme 2: Overall fitness value = 0.85; Scheme 3: Overall fitness value = 0.8. Update the target pheromone concentrations based on the overall fitness values (updated values are as follows): Scheme 1: Target pheromone concentration = 0.9; Scheme 2: Target pheromone concentration = 0.85; Scheme 3: Target pheromone concentration = 0.8. Further sorting and elimination: Option 3 was eliminated, leaving Option 1 and Option 2. Within Option 1 and Option 2, after further optimization, it was finally determined that each task was assigned to only one device, for example: T1→M1, T2→M2, T3→M3.
[0054] In other embodiments of this application, the specific process of generating the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme includes: determining the target pheromone concentration of each quality inspection task and each quality inspection equipment from the target pheromone concentration of each candidate scheduling scheme; calculating the probability that each quality inspection equipment is selected by each quality inspection task based on the target pheromone concentration of each quality inspection task and each quality inspection equipment; sequentially eliminating a preset number of quality inspection equipment in ascending order of probability to obtain multiple candidate quality inspection equipment for each quality inspection task; randomly assigning and heuristically assigning each quality inspection task and the multiple candidate quality inspection equipment for each quality inspection task to obtain multiple target candidate scheduling schemes; and traversing and filtering each target candidate scheduling scheme to generate the scheduling result corresponding to the quality inspection workshop.
[0055] Specifically, the process of traversing and filtering each target candidate scheduling scheme to generate the scheduling result corresponding to the quality inspection workshop includes: obtaining the target pheromone concentration of each target candidate scheduling scheme from the target pheromone concentration of each quality inspection task and each quality inspection equipment; calculating the target comprehensive fitness value of each target candidate scheduling scheme based on task information and equipment information; using the target comprehensive fitness value of each target candidate scheduling scheme, re-executing the strategy of updating the target pheromone concentration of each target candidate scheduling scheme until the number of eliminated quality inspection equipment is 1, thus obtaining the final quality inspection equipment for each quality inspection task; and using the final quality inspection equipment for each quality inspection task as the scheduling result corresponding to the quality inspection workshop.
[0056] For example, suppose we have 3 quality inspection tasks (T1, T2, T3) and 3 quality inspection devices (M1, M2, M3). Based on the target pheromone concentration, calculate the probability that each quality inspection device will be selected by each quality inspection task. For task T1, the probability of selecting device M1 is: ; Similarly, calculate the selection probabilities for other tasks and devices.
[0057] A certain number of quality inspection devices are eliminated sequentially according to their probability from smallest to largest. Assuming we eliminate the device with the lowest probability, then for task T1, device M3 is eliminated, leaving M1 and M2 as candidate devices. For each quality inspection task, it is randomly and heuristically assigned to multiple candidate quality inspection devices, generating multiple target candidate scheduling schemes. For example, for task T1, the following scheme is generated: Random allocation: T1→M1; Heuristic assignment: T1→M2.
[0058] For each target candidate scheduling scheme, a traversal and screening process is performed to generate the final scheduling result. The target pheromone concentration of each target candidate scheduling scheme is obtained from the target pheromone concentration of each quality inspection task and each quality inspection device. Based on the task information and device information, the target comprehensive fitness value of each target candidate scheduling scheme is calculated. The target pheromone concentration of each target candidate scheduling scheme is updated based on the target comprehensive fitness value. This process is repeated until the number of candidate quality inspection devices for each quality inspection task is reduced to 1, obtaining the final quality inspection device for each quality inspection task. After the above process, the final determined scheduling result is as follows: T1 is assigned to M1, T2 is assigned to M2, and T3 is assigned to M3.
[0059] Specifically, the formula for calculating the probability that each quality inspection device will be selected for each quality inspection task is as follows:
[0060] For each task Can be allocated to quality inspection equipment Quality inspection equipment , It is a quality inspection equipment The target pheromone concentration, It is a quality inspection equipment The target pheromone concentration.
[0061] Specifically, the updated pheromone concentration = (1 - decay rate) × current pheromone concentration + fitness value.
[0062] S104 outputs the scheduling results corresponding to the quality inspection workshop. The scheduling results include the final quality inspection equipment corresponding to each quality inspection task.
[0063] In some embodiments of this application, detailed information on all tasks and devices is read from a task information table and a device information table. An optimization algorithm (such as an optimized slime mold algorithm) is used to generate scheduling results, determining the final device assigned to each task. The scheduling results are output in tabular or list form, including the number of each task and its corresponding final device number. In one data scenario, the final displayed scheduling results are as follows: Figure 6 As shown.
[0064] In this embodiment, by optimizing the slime mold plugin to process task information, equipment information and algorithm parameters, the scheduling result corresponding to the quality inspection workshop can be obtained. The optimized slime mold plugin can automatically process a large amount of task information and equipment information, and gradually optimize candidate solutions by using the pheromone concentration update mechanism. Therefore, when there are many tasks and complex equipment, the system can quickly generate the optimal scheduling scheme, which not only improves the scheduling efficiency, but also ensures the quality of the scheduling scheme and reduces production time and cost.
[0065] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.
[0066] Please see Figure 7 This illustration shows a schematic diagram of a quality inspection task scheduling system based on optimized slime mold plugins, provided in an exemplary embodiment of this application. This quality inspection task scheduling system based on optimized slime mold plugins can be implemented as all or part of an electronic device through software, hardware, or a combination of both. System 1 includes an information collection module 10, an algorithm parameter initialization module 20, a scheduling result determination module 30, and a scheduling result output module 40.
[0067] Information collection module 10 is used to collect task information for each quality inspection task in the quality inspection workshop and equipment information for each quality inspection device. The algorithm parameter initialization module 20 is used to initialize the algorithm parameters of the preset optimized slime mold plugin. The algorithm parameters include the population size and the default pheromone concentration. The scheduling result determination module 30 is used to input task information, equipment information and algorithm parameters into the optimized slime mold plugin to determine the scheduling result corresponding to the quality inspection workshop; The scheduling result output module 40 is used to output the scheduling results corresponding to the quality inspection workshop. The scheduling results include the final quality inspection equipment corresponding to each quality inspection task.
[0068] It should be noted that the quality inspection task scheduling system based on optimized slime mold inserts provided in the above embodiments is only illustrated by the division of the above functional modules when executing the quality inspection task scheduling method based on optimized slime mold inserts. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the quality inspection task scheduling system based on optimized slime mold inserts and the quality inspection task scheduling method based on optimized slime mold inserts provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.
[0069] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0070] In this embodiment, by optimizing the slime mold plugin to process task information, equipment information and algorithm parameters, the scheduling result corresponding to the quality inspection workshop can be obtained. The optimized slime mold plugin can automatically process a large amount of task information and equipment information, and gradually optimize candidate solutions by using the pheromone concentration update mechanism. Therefore, when there are many tasks and complex equipment, the system can quickly generate the optimal scheduling scheme, which not only improves the scheduling efficiency, but also ensures the quality of the scheduling scheme and reduces production time and cost.
[0071] This application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implement the quality inspection task scheduling method based on the optimized slime mold plugin provided in the above-described method embodiments.
[0072] This application also provides a computer program product containing instructions that, when run on a computer, cause the computer to execute the quality inspection task scheduling method based on the optimized slime mold plugin of the above-described method embodiments.
[0073] Please see Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 8 As shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.
[0074] The communication bus 1002 is used to realize the connection and communication between these components.
[0075] The user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.
[0076] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).
[0077] The processor 1001 may include one or more processing cores. The processor 1001 connects to various parts within the electronic device 1000 using various interfaces and lines. It executes various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005. Optionally, the processor 1001 may be implemented using at least one hardware form selected from Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 1001 may integrate one or more of the following: a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), and a modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed on the screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip, without being integrated into the processor 1001.
[0078] The memory 1005 may include random access memory (RAM) or read-only memory. Optionally, the memory 1005 may include a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 1005 may also be at least one storage system located remotely from the aforementioned processor 1001. Figure 8As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a quality inspection task scheduling application based on an optimized slime mold plugin.
[0079] exist Figure 8 In the illustrated electronic device 1000, the user interface 1003 is mainly used to provide an input interface for the user and to acquire user input data; while the processor 1001 can be used to call the quality inspection task scheduling application based on the optimized slime mold plugin stored in the memory 1005, and specifically perform the following operations: Collect task information for each quality inspection task in the quality inspection workshop and equipment information for each quality inspection device. Initialize the preset optimized slime mold plugin algorithm parameters, including population size and default pheromone concentration; Input the task information, equipment information and algorithm parameters into the optimized slime mold plugin to determine the scheduling result for the quality inspection workshop; Output the scheduling results for the quality inspection workshop. The scheduling results include the final quality inspection equipment for each quality inspection task.
[0080] In one embodiment, when the processor 1001 executes the scheduling result corresponding to the quality inspection workshop, it specifically performs the following operations: The allocation layer performs random and heuristic allocation for each quality inspection task and each quality inspection device based on the population size, resulting in multiple candidate scheduling schemes; The pheromone concentration configuration layer configures a default pheromone concentration for each candidate scheduling scheme, thus obtaining the initial pheromone concentration for each candidate scheduling scheme. The comprehensive fitness value calculation layer calculates the comprehensive fitness value of each candidate scheduling scheme based on task information and equipment information; The pheromone concentration update layer uses the comprehensive fitness value of each candidate scheduling scheme to update the initial pheromone concentration of each candidate scheduling scheme, thereby obtaining the target pheromone concentration of each candidate scheduling scheme. The scheduling result generation layer generates the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme.
[0081] In one embodiment, when the processor 1001 performs random and heuristic allocation of each quality inspection task and each quality inspection device according to the population size to obtain multiple candidate scheduling schemes, it specifically performs the following operations: Each quality inspection task is randomly assigned to each quality inspection device, resulting in multiple random scheduling schemes. From the task information and equipment information, determine the priority of each quality inspection task and the efficiency parameters of each piece of equipment; Based on the priority of each quality inspection task and the efficiency parameters of each device, heuristic allocation is performed in combination with the population size to obtain multiple heuristic scheduling schemes. Multiple random scheduling schemes are combined with multiple heuristic scheduling schemes to obtain multiple candidate scheduling schemes for the initial population.
[0082] In one embodiment, when the processor 1001 calculates the comprehensive fitness value of each candidate scheduling scheme based on task information and device information, it specifically performs the following operations: From the task information and equipment information, obtain the inspection sequence of each quality inspection task, the capacity constraints of each quality inspection equipment, and the sequence constraints between each quality inspection task; For each candidate scheduling scheme, the total production time required to complete all quality inspection tasks is quantified by using inspection order, capacity constraints, and sequence constraints. From the task information and equipment information, obtain the type of quality inspection equipment assigned to each quality inspection task and the usage time, as well as the quantity and type of resources required for each quality inspection task; By using the type and usage time of quality inspection equipment, as well as the quantity and type of resources, the equipment usage cost and resource consumption cost of each candidate scheduling scheme are quantified and summed to obtain the total cost of each candidate scheduling scheme. The task completion pass rate of each candidate scheduling scheme is quantified and used as a quality indicator for each candidate scheduling scheme. The total production time, total cost, and quality indicators are weighted and summed to obtain the comprehensive fitness value of each candidate scheduling scheme.
[0083] In one embodiment, when the processor 1001 generates the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme, it specifically performs the following operations: The target pheromone concentrations of each candidate scheduling scheme are sorted, and a preset number of candidate scheduling schemes are eliminated in ascending order to obtain the remaining candidate scheduling schemes. From the remaining candidate scheduling schemes, determine whether the number of quality inspection devices assigned to each quality inspection task is 1; If so, determine the unique quality inspection equipment for each quality inspection task from the remaining candidate scheduling schemes, and use it as the scheduling result for the corresponding quality inspection workshop; If not, calculate the comprehensive fitness value of each remaining candidate scheduling scheme based on the task information and equipment information; update the target pheromone concentration of each remaining candidate scheduling scheme based on the comprehensive fitness value of each remaining candidate scheduling scheme, and execute the strategy of sorting the updated pheromone concentration again until the number of quality inspection devices assigned to each quality inspection task is 1.
[0084] In one embodiment, when the processor 1001 generates the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme, it specifically performs the following operations: From the target pheromone concentration of each candidate scheduling scheme, determine the target pheromone concentration of each quality inspection task and each quality inspection device; Based on the target pheromone concentration of each quality inspection task and each quality inspection equipment, calculate the probability that each quality inspection equipment will be selected by each quality inspection task. Multiple quality inspection devices are eliminated sequentially in ascending order of probability to obtain multiple candidate quality inspection devices for each quality inspection task. Each quality inspection task and multiple alternative quality inspection devices for each quality inspection task are randomly and heuristically assigned to obtain multiple target candidate scheduling schemes. For each target candidate scheduling scheme, a traversal and screening process is performed to generate the scheduling result corresponding to the quality inspection workshop.
[0085] In one embodiment, when the processor 1001 performs a traversal and filtering of each target candidate scheduling scheme to generate a scheduling result corresponding to the quality inspection workshop, it specifically performs the following operations: The target pheromone concentration of each target candidate scheduling scheme is obtained from the target pheromone concentration of each quality inspection task and each quality inspection equipment. Based on task information and equipment information, calculate the target comprehensive fitness value for each target candidate scheduling scheme; Using the target comprehensive fitness value of each target candidate scheduling scheme, the strategy of updating the target pheromone concentration of each target candidate scheduling scheme is executed again until the number of quality inspection devices after elimination is 1, thus obtaining the final quality inspection device for each quality inspection task. The final quality inspection equipment for each quality inspection task is used as the scheduling result for the corresponding quality inspection workshop.
[0086] In this embodiment, by optimizing the slime mold plugin to process task information, equipment information and algorithm parameters, the scheduling result corresponding to the quality inspection workshop can be obtained. The optimized slime mold plugin can automatically process a large amount of task information and equipment information, and gradually optimize candidate solutions by using the pheromone concentration update mechanism. Therefore, when there are many tasks and complex equipment, the system can quickly generate the optimal scheduling scheme, which not only improves the scheduling efficiency, but also ensures the quality of the scheduling scheme and reduces production time and cost.
[0087] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program for scheduling quality inspection tasks based on the optimized slime mold plugin can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium for the program for scheduling quality inspection tasks based on the optimized slime mold plugin can be a magnetic disk, optical disk, read-only memory, or random access memory, etc.
[0088] The above-disclosed embodiments are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall still fall within the scope of this application.
Claims
1. A quality inspection task scheduling method based on optimized slime plug-in, characterized in that, The method comprises: collecting task information of each quality inspection task in a quality inspection workshop and device information of each quality inspection device; initializing algorithm parameters of a preset optimization mycoparasitism plug-in, the algorithm parameters comprising a population size and a default pheromone concentration; inputting the task information, the device information and the algorithm parameters into the optimization mycoparasitism plug-in to determine a scheduling result corresponding to the quality inspection workshop; outputting the scheduling result corresponding to the quality inspection workshop, the scheduling result comprising a final quality inspection device corresponding to each quality inspection task.
2. The method of claim 1, wherein, The preset optimization mycoparasitism plug-in comprises a distribution layer, a pheromone concentration configuration layer, a comprehensive fitness value calculation layer, a pheromone concentration updating layer and a scheduling result generation layer. The determination of the scheduling result corresponding to the quality inspection workshop comprises: the distribution layer randomly and heuristically distributes each quality inspection task and each quality inspection device according to the population size to obtain a plurality of candidate scheduling schemes; the pheromone concentration configuration layer configures the default pheromone concentration for each candidate scheduling scheme to obtain an initial pheromone concentration of each candidate scheduling scheme; the comprehensive fitness value calculation layer calculates a comprehensive fitness value of each candidate scheduling scheme according to the task information and the device information; the pheromone concentration updating layer updates the initial pheromone concentration of each candidate scheduling scheme by using the comprehensive fitness value of each candidate scheduling scheme to obtain a target pheromone concentration of each candidate scheduling scheme; the scheduling result generation layer generates the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme.
3. The method of claim 2, wherein, The random distribution of each quality inspection task to each quality inspection device to obtain a plurality of random scheduling schemes; determination of a priority of each quality inspection task and an efficiency parameter of each device from the task information and the device information; heuristic distribution of each quality inspection task according to the priority of each quality inspection task and the efficiency parameter of each device in combination with the population size to obtain a plurality of heuristic scheduling schemes; merging of the plurality of random scheduling schemes and the plurality of heuristic scheduling schemes to obtain a plurality of candidate scheduling schemes of an initial population. The calculation of a comprehensive fitness value of each candidate scheduling scheme according to the task information and the device information comprises:
4. The method of claim 2, wherein, obtaining an inspection order of each quality inspection task, a capacity constraint of each quality inspection device and an order constraint between quality inspection tasks from the task information and the device information; quantifying total production time required for all quality inspection tasks to complete inspection by using the inspection order, the capacity constraint and the order constraint for each candidate scheduling scheme; obtaining a quality inspection device type and a use time to which each quality inspection task is assigned and a resource quantity and type required by each quality inspection task from the task information and the device information; Quantify the device usage cost, the resource consumption cost of each candidate scheduling scheme by using the quality inspection equipment type and usage time, the resource quantity and type, and sum the costs to obtain the total cost of each candidate scheduling scheme; Quantify the qualified rate of task completion of each candidate scheduling scheme as a quality index of each candidate scheduling scheme; Weighted sum the total production time, the total cost and the quality index to obtain the comprehensive fitness value of each candidate scheduling scheme.
5. The method of claim 2, wherein, The generation of the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme comprises: Sort the target pheromone concentration of each candidate scheduling scheme, and sequentially remove a preset number of candidate scheduling schemes in ascending order to obtain the remaining candidate scheduling schemes; From the remaining candidate scheduling schemes, determine whether the number of quality inspection equipment allocated to each quality inspection task is 1; If yes, determine the unique quality inspection equipment of each quality inspection task from the remaining candidate scheduling schemes as the scheduling result corresponding to the quality inspection workshop; If no, calculate the comprehensive fitness value of each remaining candidate scheduling scheme according to the task information and the equipment information, update the target pheromone concentration of each remaining candidate scheduling scheme according to the comprehensive fitness value of each remaining candidate scheduling scheme, and execute again the strategy of sorting the updated pheromone concentration until the number of quality inspection equipment allocated to each quality inspection task is 1.
6. The method of claim 2, wherein, The generation of the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme comprises: Determine the target pheromone concentration of each quality inspection task and each quality inspection equipment from the target pheromone concentration of each candidate scheduling scheme; Calculate the probability of selection of each quality inspection equipment by each quality inspection task based on the target pheromone concentration of each quality inspection task and each quality inspection equipment; Sequentially remove a preset number of quality inspection equipment in ascending order of the probability to obtain multiple alternative quality inspection equipment of each quality inspection task; Randomly allocate and heuristically allocate each quality inspection task and the multiple alternative quality inspection equipment of each quality inspection task respectively to obtain multiple target candidate scheduling schemes; For each target candidate scheduling scheme, perform traversal screening to generate the scheduling result corresponding to the quality inspection workshop.
7. The method of claim 6, wherein, The generation of the scheduling result corresponding to the quality inspection workshop based on the target pheromone concentration of each candidate scheduling scheme comprises: Obtain the target pheromone concentration of each target candidate scheduling scheme from the target pheromone concentration of each quality inspection task and each quality inspection equipment; Calculate the target comprehensive fitness value of each target candidate scheduling scheme according to the task information and the equipment information; Use the target comprehensive fitness value of each target candidate scheduling scheme to execute again the strategy of updating the target pheromone concentration of each target candidate scheduling scheme until the removed quality inspection equipment is 1 to obtain the final quality inspection equipment of each quality inspection task; The final quality inspection equipment of each quality inspection task is taken as the corresponding scheduling result of the quality inspection workshop.
8. The method of claim 6, wherein, The calculation formula of the probability that each quality inspection equipment is selected by each quality inspection task is: wherein, for each task may be assigned to the quality inspection device , the quality inspection device , is a target pheromone concentration of the quality inspection device is a target pheromone concentration of the quality inspection device is a target pheromone concentration of the quality inspection device is a target pheromone concentration of the quality inspection device 9. The method of claim 2, wherein, The update expression of pheromone concentration is: The updated pheromone concentration = (1- decay rate) * current pheromone concentration + fitness value.
10. A quality inspection task scheduling system based on optimized slime plug-in, characterized in that, The system comprises: An information collection module is configured to collect task information of each quality inspection task of a quality inspection workshop and equipment information of each quality inspection equipment. An algorithm parameter initialization module is configured to initialize algorithm parameters of a preset optimization myxobacteria plug-in, wherein the algorithm parameters comprise a population size and a default pheromone concentration. A scheduling result determination module is configured to input the task information, the equipment information and the algorithm parameters into the optimization myxobacteria plug-in to determine a corresponding scheduling result of the quality inspection workshop. A scheduling result output module is configured to output the corresponding scheduling result of the quality inspection workshop, wherein the scheduling result comprises a final quality inspection equipment corresponding to each quality inspection task.