A charging pile detection and scheduling optimization method

Abstract

This invention discloses a method for optimizing the scheduling of charging pile detection, comprising: 1) constructing a mapping table of charging pile detection tasks and detection equipment to obtain a dataset; 2) taking the shortest completion time of the detection task as the optimization objective and establishing corresponding constraints to form a mathematical model for scheduling optimization; 3) generating a preliminary scheduling solution using a heuristic algorithm; 4) performing optimization solving using the Gurobi solver; and 5) outputting and verifying the results. This invention demonstrates significant advantages in the scheduling of charging pile detection tasks. The optimized scheduling scheme shortens the overall detection cycle by 22.5%, significantly improving task execution efficiency. This optimization not only reduces task waiting time but also enhances the compactness of the detection process.

Description

Technical Field

[0001] This invention relates to the fields of production scheduling optimization and flexible workshop scheduling technology, specifically to a method for optimizing the detection and scheduling of charging piles. Background Technology

[0002] With the rapid development of new energy vehicles, charging piles, as key supporting equipment, have received widespread attention in the industry regarding their quality and safety performance testing. Charging pile products on the market offer diverse functions, mainly including AC charging piles and DC charging piles. However, existing technologies still present many technical challenges in the charging pile testing process, requiring urgent improvement and optimization.

[0003] Different types of charging piles require multiple testing tasks, but existing task allocation schemes often lack scientific rigor and rationality. Some scholars have studied the allocation of charging pile testing tasks and proposed a static task allocation model. However, research shows that this model performs poorly when faced with dynamic task demands. Other scholars have pointed out that due to the inherent uncertainty of testing tasks in actual operation, traditional allocation schemes easily lead to some testing equipment remaining idle for extended periods while others operate under overload. This uneven resource allocation not only reduces the overall utilization rate of the equipment but also significantly prolongs the testing cycle. Furthermore, some scholars, through research on existing testing systems, have found that optimizing the task allocation scheme directly impacts testing efficiency. However, their research only conducted allocation experiments on a small number of charging pile testing tasks, failing to effectively address the scheduling problem of large-scale charging pile testing tasks. Still other scholars have further pointed out that the mapping relationship between equipment and testing tasks in the task allocation process is too simplistic, failing to fully consider differences in equipment performance and dependencies between tasks, thus reducing the accuracy of the allocation. The execution order of testing tasks is crucial for optimizing the testing cycle. Some international scholars have proposed a rule-based method for ranking detection tasks, optimizing the execution order of some tasks through a priority strategy. However, this research has revealed several shortcomings in practical applications, such as failing to handle dependencies between complex tasks and neglecting device idle time. Some professors have developed a model based on deep learning to predict task execution time and guide the ranking of detection tasks. However, experimental results show that this method has low prediction accuracy in multi-task parallel scenarios, leading to unreasonable task ranking results. Other international professors have studied the time dependencies of charging pile detection tasks and proposed a task ranking algorithm based on time windows. Although this method can reduce the waiting time of some tasks, its high computational complexity makes it difficult to promote its application in practical detection scenarios. Some scholars have pointed out through simulation analysis that unreasonable planning of task execution order can lead to task backlog and decreased device utilization, further lengthening the detection cycle. This problem remains one of the key challenges in current research on charging pile detection optimization.

[0004] The utilization efficiency of testing equipment resources directly affects the operating costs of testing organizations. Some scholars have shown that existing methods in charging pile testing fail to adequately consider the matching between equipment capabilities and task requirements, leading to low equipment utilization. In response, other scholars have developed a heuristic algorithm-based equipment scheduling method to improve equipment utilization efficiency. Experiments show that this method is effective under specific conditions, but performs poorly when the workload is large or the task types are complex. Furthermore, other scholars have pointed out that the utilization rate of testing equipment is also affected by task dependence and equipment switching time. Their research proposes an optimization model based on integer programming to reduce equipment switching time and improve overall equipment utilization. However, this method has a long computation time in large-scale task scenarios, making it difficult to meet practical needs. In response, some scholars have analyzed the utilization trends of different equipment in multi-task scenarios by constructing dynamic scheduling models. Research has found that dynamic scheduling of equipment resources is key to improving utilization, but currently, there is still a lack of efficient optimization methods for complex testing scenarios. In the field of charging pile testing scheduling optimization, the Flexible Job Shop Scheduling Problem (FJSP) is a commonly used theoretical model. Currently, numerous scheduling optimization algorithms have been widely applied in manufacturing and logistics, but research specifically on charging pile detection scenarios remains relatively limited. Specifically, existing research largely focuses on single-objective optimization, such as minimizing the detection cycle or maximizing equipment utilization, while research on multi-objective optimization problems is insufficient. Scheduling optimization for charging pile detection tasks requires simultaneous consideration of task time dependencies, equipment capacity limitations, and coordination among multiple tasks. Therefore, we propose a scheduling optimization method for charging pile detection. Summary of the Invention

[0005] In view of the above-mentioned shortcomings of the existing technology, the first objective of the present invention is to provide a charging pile detection and scheduling optimization method to solve the problems in the background technology.

[0006] The technical solution adopted in this invention is as follows:

[0007] A method for optimizing the detection and scheduling of charging piles includes the following steps:

[0008] 1) Data input and initialization: Set the initial state parameters of the detection equipment, collect experimental data of the detection equipment on the charging pile, construct a mapping table between the charging pile detection task and the detection equipment, and obtain the dataset;

[0009] 2) Construct a mathematical model with the shortest completion time of the detection task as the optimization objective, and establish corresponding constraints to form a mathematical model for scheduling optimization. The constraints include the sequential relationship between the execution tasks, the limit on the number of tasks that the detection device can execute at the same time, and the time required for the detection device to switch between different tasks.

[0010] 3) Based on the dataset obtained in step 1), and with the mathematical model constructed in step 2) as the target, a preliminary scheduling solution is generated using a heuristic algorithm;

[0011] 4) Input the preliminary scheduling solution generated in step 3) into the Gurobi solver for optimization;

[0012] 5) Results output and verification.

[0013] Further, in step 1), the initial state parameters of the detection device include the availability of the detection device, the maximum capacity to execute tasks, and the minimum task switching time.

[0014] Further, in step 1), the experimental data of the testing equipment for the charging pile includes the testing task data of the testing equipment for the charging pile, wherein the testing task data includes the number of the selectable testing equipment and the task execution time under the selected testing equipment.

[0015] Furthermore, the construction of the mathematical model described in step 2) is as follows:

[0016] 2.1) The optimization objective is to minimize the completion time of the detection task:

[0017] Minimize F = C max

[0018] Where: F is the total completion time of the detection task; C max This is the maximum completion time for all detection tasks;

[0019] 2.2) Establish the time constraints for the task, including the relational expressions for the task start time, end time, and execution time:

[0020] The completion time of a detection task must be greater than or equal to its start time plus its execution time. This is a fundamental constraint on task order, ensuring that sufficient time is required for task completion. This can be described by the following formula:

[0021]

[0022] Among them, f jh b represents the completion time of detection task h on detection equipment j. jh p represents the start time of detection task h on detection device j. jh This indicates the execution time of detection task h on detection device j;

[0023] 2.3) At any given time, each testing device can only perform one testing task. Testing tasks cannot be assigned to multiple testing devices simultaneously. This can be described by the following formula:

[0024]

[0025] Among them, a m,h Assign a variable to the device to indicate whether the detection task h is executed by the detection device m. If it is 1, it means that the task is assigned to the detection device m for execution; if it is 0, it means that it is not assigned to the device.

[0026] Task priority refers to the requirement that a specific order must be met during execution. That is, the execution of some tasks depends on the completion of other tasks. During scheduling, if detection task h1 takes precedence over detection task h2, then the start time of detection task h2 cannot be earlier than the completion time of detection task h1. This can be described using the following formula:

[0027]

[0028] Among them, S h2,j C is the start time of detection task h2 on detection device j. h1,j Let h1 be the completion time of detection task h1 on detection device j, and P be the set of task priority relationships, containing the dependencies between all detection tasks. During the scheduling of charging pile detection tasks, in order to ensure the normal operation of the equipment and the smooth connection of detection tasks, the equipment needs to wait for a certain minimum switching time after completing the current detection task before starting the next detection task. This constraint can effectively avoid the overload problem caused by the equipment switching frequently and ensure the rationality of task execution. It can be described by the following formula:

[0029] S h+1,j ≥C h,j +ΔT j

[0030] Among them, S h+1,j It is the start time of detection task h+1 on detection device j, C h,j ΔT is the completion time of detection task h on detection equipment j. j This is the minimum switching time for detection equipment j. According to the formula, after detection task h is completed, detection equipment j must undergo a minimum switching time ΔT. j Only after this can task h+1 be executed. This constraint provides a reasonable time interval for the continuous task scheduling of the device.

[0031] S3.4. The testing equipment can only start executing a new testing task after completing the previous one. This is a constraint on the sequence of testing tasks, ensuring that testing tasks do not overlap. It can be described by the following formula:

[0032] b j(h+1) ≥f jh

[0033] Among them, b j(h+1) This indicates the start time of detection task h+1 on detection device j.

[0034] Furthermore, the specific steps for generating the initial task allocation scheme in step 3) are as follows:

[0035] 3.1) Based on the dataset obtained in step 1), combined with the sequential relationship between the execution tasks and the initial state parameters of the detection device, a series of detection task allocation schemes are randomly generated using the Python random function of the heuristic algorithm. Then, taking the mathematical model constructed in step 2) as the optimization objective, the objective function is rapidly iterated and optimized using the heuristic algorithm to improve the task allocation and obtain some initial detection task allocation schemes with shorter completion times for detection tasks, which is the initial solution.

[0036] 3.2) Verify whether the initial solution satisfies all constraints, including the sequential relationship between tasks, the limit on the number of tasks that the detection device can execute simultaneously, and the constraint on the time required for the detection device to switch between different tasks; if the initial solution satisfies all constraints, proceed to the next step; otherwise, return to regenerate the initial solution.

[0037] 3.3) Use a two-dimensional array to record the initial allocation scheme of the mapping relationship table data between detection tasks and detection equipment, and transform the initial solution into a task execution Gantt chart to intuitively show the execution order of detection tasks and the utilization of detection equipment.

[0038] Furthermore, the specific process of optimizing the solution in step 4) is as follows:

[0039] 4.1) Input the initial detection task allocation scheme generated by the heuristic algorithm into the gurobi solver for further iterative optimization, with the mathematical model constructed in step 2) as the optimization target;

[0040] 4.2) Adjust the task allocation matrix in each iteration and record the optimization results. Stop the optimization when the change in the objective function value is less than the set value or the maximum number of iterations is reached, and obtain an approximate global optimal solution.

[0041] Furthermore, the specific process for outputting and validating the results in step 5) is as follows:

[0042] 5.1) Finally, an optimized task and equipment allocation scheme is generated, including the detection task sequence for each detection device;

[0043] 5.2) The optimized task execution status is plotted as a Gantt chart to visually display the completion sequence of the detection tasks and the utilization of the detection equipment;

[0044] 5.3) Calculate the ratio of the usage time of the testing equipment to the total working time, and evaluate the resource utilization of the testing equipment;

[0045] 5.4) Check whether the optimization scheme meets all constraints and verify the feasibility of the scheme through experiments. If it meets the constraints, output the results; otherwise, return to recalculate.

[0046] Compared with known public technologies, the technical solution provided by this invention has the following beneficial effects:

[0047] 1. This invention demonstrates significant advantages in scheduling charging pile testing tasks. The optimized scheduling scheme shortens the overall testing cycle by 22.5%, significantly improving task execution efficiency. This optimization not only reduces task waiting time but also enhances the compactness of the testing process.

[0048] 2. This invention, through a scientific and reasonable task allocation scheme, effectively reduces equipment idle rate and improves equipment efficiency by 15.3% compared to before. The optimized resource utilization not only improves the operational efficiency of the testing organization but also reduces uneven equipment load.

[0049] 3. This invention demonstrates excellent task allocation balance, effectively alleviating equipment bottlenecks and comprehensively improving the stability and fairness of the scheduling system. Experimental verification further proves the practical application value of this invention, and its results can provide scientific support for the efficient execution of charging pile detection tasks, while also providing important reference and guidance for similar scheduling optimization problems. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the charging pile detection and scheduling optimization process of the charging pile detection and scheduling optimization method of the present invention;

[0051] Figure 2 This is a schematic diagram of the coding process for a charging pile detection and scheduling optimization method according to the present invention;

[0052] Figure 3 This is a schematic diagram of the convergence curve of the optimization algorithm for the charging pile detection and scheduling optimization method of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0054] The present invention will be further described below with reference to embodiments.

[0055] Example 1

[0056] like Figures 1-3 As shown, the present invention provides a technical solution: a charging pile detection and scheduling optimization method, comprising the following steps:

[0057] S1. Optimize target setting and constraint definition;

[0058] S2, Data Input and Initialization;

[0059] S3. Mathematical model construction;

[0060] S4. Generate the initial task allocation scheme;

[0061] S5. Optimize the solution;

[0062] S6. Result Output and Verification;

[0063] In step S1, during the optimization of target setting and constraint definition:

[0064] S1.1 By clearly defining the objectives, the optimization process can improve system performance in a targeted manner;

[0065] S1.2 Establish a logical framework that includes constraints such as task dependencies (the order of execution between tasks), device capacity limits (the number of tasks that a detection device can execute simultaneously), and task switching time (the time required for a detection device to switch between different tasks).

[0066] S1.3 Collect the execution time required for the testing task, the required testing equipment number, and the usage characteristics of the testing equipment to form a task equipment mapping table;

[0067] S1.4. Convert the input detection task data and detection equipment parameters into a format that the optimization algorithm can recognize and process;

[0068] Step S2, Data Input and Initialization:

[0069] S2.1 Input data from 35 charging piles and 100 testing tasks in the experiment, ensuring that the data includes information such as task execution time and optional testing equipment number;

[0070] S2.2 Set the initial state of the testing equipment, including parameters such as availability, maximum capacity for executing tasks, and minimum task switching time;

[0071] S2.3 Construct a task-equipment mapping table using experimental data to describe the many-to-many relationship between detection tasks and detection equipment;

[0072] S2.4 Initialize the parameters required for the heuristic method.

[0073] Step S3, Mathematical model construction in progress:

[0074] S3.1 Optimization objective: Minimize the completion time of the detection task.

[0075] Minimize F = C max

[0076] Where: F is the total completion time of the detection task; C max This is the maximum completion time for all detection tasks;

[0077] S3.2 Establish the time constraints for the task, including the relationship expressions for the task start time, end time, and execution time:

[0078] The completion time of a detection task must be greater than or equal to its start time plus its execution time. This is a fundamental constraint on task order, ensuring that sufficient time is required for task completion. The constraints between detection tasks can be described by the following formula:

[0079]

[0080] Among them, f jh b represents the completion time of detection task h on detection equipment j. jh p represents the start time of detection task h on detection device j. jh This indicates the execution time of detection task h on detection device j;

[0081] S3.3 At any given time, each testing device can only perform one testing task. Testing tasks cannot be assigned to multiple testing devices simultaneously. This can be described by the following formula:

[0082]

[0083] Among them, a m,h Assign a variable to the device to indicate whether the detection task h is executed by the detection device m. If it is 1, it means that the task is assigned to the detection device m for execution; if it is 0, it means that it is not assigned to the detection device.

[0084] Task priority refers to the requirement that a specific order must be met during execution. That is, the execution of some tasks depends on the completion of other tasks. During scheduling, if detection task h1 takes precedence over detection task h2, then the start time of detection task h2 cannot be earlier than the completion time of detection task h1. This can be described using the following formula:

[0085]

[0086] Among them, S h2,j C is the start time of detection task h2 on detection device j. h1,jLet h1 be the completion time of detection task h1 on detection device j, and P be the set of task priority relationships, containing the dependencies between all detection tasks. During the scheduling of charging pile detection tasks, in order to ensure the normal operation of the equipment and the smooth connection of detection tasks, the equipment needs to wait for a certain minimum switching time after completing the current detection task before starting the next detection task. This constraint can effectively avoid the overload problem caused by the equipment switching frequently and ensure the rationality of task execution. It can be described by the following formula:

[0087] S h+1,j ≥C h,j +ΔT j

[0088] Among them, S h+1,j It is the start time of detection task h+1 on detection device j, C h,j ΔT is the completion time of detection task h on detection equipment j. j This is the minimum switching time for detection equipment j. According to the formula, after detection task h is completed, detection equipment j must undergo a minimum switching time ΔT. j Only after this can task h+1 be executed. This constraint provides a reasonable time interval for the continuous task scheduling of the device.

[0089] S3.4. The testing equipment can only start executing a new testing task after completing the previous one. This is a constraint on the sequence of testing tasks, ensuring that testing tasks do not overlap. It can be described by the following formula:

[0090] b j(h+1) ≥f jh

[0091] Among them, b j(h+1) This indicates the start time of detection task h+1 on detection device j.

[0092] In step S4, generating the initial task allocation scheme:

[0093] S4.1 Quickly generate an initial task allocation scheme by combining task dependencies and device status;

[0094] S4.2 Verify whether the initial solution satisfies all constraints, including time dependency, device exclusivity, and switching time constraints.

[0095] S4.3 Use a two-dimensional array to record the initial allocation scheme of tasks and devices;

[0096] S4.4. Transform the initial solution into a task execution Gantt chart to visually display the task execution order and equipment utilization.

[0097] Step S5, Optimization in progress:

[0098] S5.1 Utilize heuristic algorithms to rapidly iteratively optimize the objective function value and improve task allocation;

[0099] S5.2. Further optimize the initial solution using the Gurobi solver to obtain an approximate global optimal solution;

[0100] S5.3 When the change in the objective function value is less than e -5 Or stop optimization when the maximum number of iterations (500) is reached;

[0101] S5.4 Adjust the task allocation matrix in each iteration and record the optimization results;

[0102] Step S6, Result Output and Verification:

[0103] S6.1 Finally, generate the optimized task and device allocation scheme, including the task sequence for each device;

[0104] S6.2. Draw a Gantt chart to visually display the task completion sequence and equipment utilization.

[0105] S6.3 Calculate the ratio of equipment usage time to total working time to assess equipment resource utilization;

[0106] S6.4 Check whether the optimization scheme meets all constraints and verify the feasibility of the scheme through experiments.

[0107] In this embodiment, the optimization objective and constraints are clearly defined, a logical framework and mapping relationship between tasks and equipment are constructed, data from 35 charging piles and 100 detection tasks are input and initialized to ensure the accuracy of all necessary information, a mathematical model with the shortest completion time as the objective is established, time constraints for tasks and usage limitations for equipment are set, an initial task allocation scheme is generated and verified to meet all constraints, a Gantt chart is used to visually display the task execution order, and a heuristic algorithm and Gurobi solver are used to iteratively optimize the task allocation, gradually approaching the global optimum to ensure the efficiency and accuracy of the optimization process, outputting the optimized task allocation scheme, and evaluating the optimization effect by calculating equipment utilization and verifying the feasibility of the scheme. This method, through precise objective setting, rigorous mathematical modeling, and efficient optimization algorithms, significantly improves the overall efficiency of charging pile detection and the utilization rate of equipment resources, ensuring the orderly execution of detection tasks and the optimization of system performance.

[0108] Among them: Experimental Design and Performance Comparison

[0109] (1) Data Description: The experimental data comes from 35 charging piles and 100 testing tasks. Some test data are detailed in Tables 1-5.

[0110] Table 1 Charging Mode 2 - Connection Method: Type B AC Charging Pile

[0111]

[0112]

[0113] Table 2 Charging Mode 2 - Connection Method: Type C AC Charging Pile

[0114]

[0115] Table 3 Charging Mode 3 - Connection Method: Type B AC Charging Pile

[0116]

[0117]

[0118] Table 4 Charging Mode 3 - Connection Method: Type C AC Charging Pile

[0119]

[0120] Table 5 Charging Mode 4 - Connection Method: Type C DC Charging Pile

[0121]

[0122] (2) Optimization algorithm implementation steps

[0123] During the optimization process, an initial task allocation scheme is first generated using a heuristic algorithm. Then, an optimization algorithm (such as combining a heuristic algorithm with the Gurobi tool) is used to gradually optimize the task allocation and equipment utilization scheme. The objective function value is evaluated after each iteration until convergence or the maximum number of iterations is reached.

[0124] The specific implementation is as follows:

[0125] (3) Evaluation Indicator Description

[0126] The optimization effect is evaluated using the following three main metrics:

[0127] ①Task completion time: refers to the shortest time required to complete all detection tasks;

[0128] ② Equipment utilization rate: refers to the ratio of the actual usage time of the testing equipment to the total working time;

[0129] ③Task distribution balance: The degree of balance in task distribution is evaluated by analyzing the load distribution among devices.

[0130] (4) Data preprocessing and experimental environment

[0131] The experimental data was preprocessed and converted into an input format that the optimization algorithm could recognize. This experiment was run on a computer with an Intel i7-12700 processor and 16GB of memory. The optimization algorithm was developed using Python and the Gurobi optimization tool.

[0132] (5) Experimental parameter settings

[0133] ① Experimental hardware environment

[0134] Processor: Intel i7-12700

[0135] Memory: 16GB

[0136] Operating System: Windows 10

[0137] Optimization tools: Python language and Gurobi optimization tool

[0138] ② Optimize algorithm parameters

[0139] Initial population size: 100

[0140] Maximum number of iterations: 500

[0141] Convergence threshold: The change in the objective function value is less than 1e -5

[0142] Crossover probability: 0.8

[0143] Mutation probability: 0.1

[0144] ③ Data scale

[0145] Number of testing tasks: 100

[0146] Number of testing devices: 35 units

[0147] ④ Evaluation indicators

[0148] Task completion time: The total completion time of the detection task, in minutes;

[0149] Equipment utilization rate: The ratio of actual equipment usage time to total working time;

[0150] Task distribution balance: By analyzing the task distribution load among devices, the degree of balance in task distribution is evaluated;

[0151] (6) Optimization Result Analysis

[0152] To verify the convergence performance and global search capability of the optimization algorithm, the convergence curve of the optimization algorithm was plotted. Figure 3As can be seen from the convergence curve, the objective function value of the optimization algorithm drops rapidly in the first 50 iterations and then gradually stabilizes, indicating that the algorithm can quickly find a task allocation scheme close to the optimal solution.

[0153] The optimization algorithm proposed in this invention has a significantly faster convergence speed than traditional methods in the early stages of iteration, which can quickly reduce the task completion time and demonstrates good global search capabilities.

[0154] In later iterations, the magnitude of the objective function value changes gradually decreases and tends to stabilize, indicating that the algorithm of this invention has a strong local search capability, can effectively avoid getting trapped in local optima, and at the same time ensure the convergence stability of the optimization results.

[0155] The overall performance and convergence of the optimization algorithm remained stable in multiple experimental verifications, further demonstrating the efficiency and practicality of the optimization method of this invention.

[0156] Next, we will analyze the optimized scheduling effect and the achievement of the optimization goals in detail. The following will combine... Figure 3 The diagram of task allocation and the Gantt chart of task execution are explained in detail.

[0157] (1) Detection task allocation diagram

[0158] The optimized task allocation scheme shows that the matching between detection tasks and detection equipment is more balanced. The optimized scheme solves the problem of uneven equipment load in the traditional method, reduces equipment idle time, and significantly improves resource utilization.

[0159] (2) Gantt chart of detection task execution

[0160] The optimized execution sequence and equipment utilization are visually represented in the Gantt chart. Through scientific task allocation and reasonable equipment scheduling, the optimized solution significantly shortens task execution time and significantly improves task completion efficiency. Specific optimization effect analysis:

[0161] ① Optimize task completion time

[0162] After optimization, the total detection time was reduced by 22.5%. This result shows that scientific task allocation and equipment scheduling strategies can significantly shorten the overall task execution cycle, thereby improving scheduling efficiency.

[0163] ② Improved equipment utilization

[0164] Experimental results show that the optimized equipment idle rate was significantly reduced and the actual utilization efficiency was increased by 15.3%. The optimization scheme effectively alleviated the problem of resource waste and significantly improved the overall utilization rate of the testing equipment by scientifically allocating tasks and balancing equipment load.

[0165] ③ Improved task allocation balance

[0166] The optimized algorithm effectively alleviated the equipment bottleneck problem, making task allocation more rational and balanced. The improved balance of task allocation not only improved the stability of the scheduling system, but also enhanced the fairness of resource allocation, providing technical support for the efficient execution of detection tasks.

[0167] (7) Algorithm comparison: The experiment selected the traditional method and the algorithm of the present invention for performance comparison.

[0168] ①Simulated Annealing (SA): Simulated annealing is a classic optimization algorithm that can avoid local optima to some extent, but it has a slow convergence speed and unstable performance in large-scale task scenarios.

[0169] ② Particle Swarm Optimization (PSO): Particle Swarm Optimization has strong global search capabilities, but it is prone to getting trapped in local optima when the task complexity is high, and the optimization results are generally poor in multi-objective scenarios.

[0170] ③ Optimization method of the present invention: The method of the present invention combines heuristic algorithms with the Gurobi tool, and utilizes a flexible job shop scheduling model and optimization algorithm to significantly improve computational efficiency and the globality of optimization results;

[0171] (8) Results Analysis

[0172] ①Task completion time: In terms of optimizing task completion time, the method of this invention shows significant advantages, with the optimized task completion time reduced by 22.5%. In comparison, the optimization effect of simulated annealing algorithm is 12.8%, and particle swarm optimization algorithm is 17.6%. This result shows that the optimization efficiency of the method of this invention in terms of task completion time is significantly better than the comparison algorithms, and it can more effectively improve task execution efficiency.

[0173] ② Equipment utilization rate: In terms of improving equipment utilization rate, the optimization range of the method of the present invention reaches 15.3%, which is significantly better than the 8.2% of the simulated annealing algorithm and the 10.7% of the particle swarm optimization algorithm. This gap indicates that the method of the present invention is more scientific and reasonable in the allocation strategy of tasks and equipment, effectively reducing the idle rate of equipment, thereby realizing the full utilization of resources.

[0174] ③Task allocation balance: In terms of task allocation balance, the method of the present invention effectively alleviates the equipment bottleneck problem through optimization algorithm, making task allocation more balanced. Compared with simulated annealing algorithm and particle swarm optimization algorithm, the method of the present invention has significant advantages in the balanced processing of equipment load, and further improves the stability and fairness of the scheduling system.

[0175] Based on Table 6, the following is a detailed comparison and analysis of the results:

[0176]

[0177]

[0178] Working principle:

[0179] like Figure 1-3 As shown, in practical applications, this invention lays a solid foundation for the optimization process by setting clear optimization objectives and defining constraints. The system clearly defines the shortest completion time of the detection task as the main optimization objective and constructs a logical framework that includes multiple constraints such as task dependencies, device capacity limitations, and task switching time. By collecting and organizing the execution time, device number, and device usage characteristics required for the detection task, a detailed task-device mapping table is formed. These preparatory works ensure that the optimization algorithm can accurately identify and process the input data, thereby achieving targeted improvements in system performance.

[0180] During the data input, initialization, and mathematical model construction phases, the system imported detailed data from 35 charging piles and 100 detection tasks to initialize the device status and construct a mathematical model with the shortest completion time as its objective. This model not only considered the start and end times of tasks but also ensured that each device could only execute one task at a time, adhering to constraints of task priority and minimum switching time. By generating an initial task allocation scheme and optimizing it using heuristic algorithms and the Gurobi solver, the system can rapidly iterate and approach the global optimum, effectively improving the scientific nature of task allocation and device utilization.

[0181] The optimized results are visually displayed using tools such as Gantt charts, ensuring that the task allocation scheme not only meets all constraints but also significantly shortens the overall task completion time, improves equipment utilization efficiency, and enhances the balance of task allocation. Specifically, the optimization method achieves a 22.5% reduction in task completion time, a 15.3% increase in equipment utilization, and a significant improvement in task allocation balance. This method, through precise target setting, rigorous mathematical modeling, and efficient optimization algorithms, greatly improves the overall efficiency and resource utilization of charging pile detection, ensuring the orderly execution of detection tasks and comprehensive optimization of system performance.

[0182] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the detection and scheduling of charging piles, characterized in that, Includes the following steps: 1) Data input and initialization: Set the initial state parameters of the detection equipment, collect experimental data of the detection equipment on the charging pile, construct a mapping table between the charging pile detection task and the detection equipment, and obtain the dataset; 2) Construct a mathematical model with the shortest completion time of the detection task as the optimization objective, and establish corresponding constraints to form a mathematical model for scheduling optimization. The constraints include the sequential relationship between the execution tasks, the limit on the number of tasks that the detection device can execute at the same time, and the time required for the detection device to switch between different tasks. 3) Based on the dataset obtained in step 1), and with the mathematical model constructed in step 2) as the target, a preliminary scheduling solution is generated using a heuristic algorithm; 4) Input the preliminary scheduling solution generated in step 3) into the Gurobi solver for optimization; 5) Result output and verification; The construction of the mathematical model described in step 2) is as follows: 2.1) The optimization objective is to minimize the completion time of the detection task: Minimize F=C max Where: F is the total completion time of the detection task; C max This is the maximum completion time for all detection tasks; 2.2) Establish the time constraints for the task, including the relational expressions for the task start time, end time, and execution time: The completion time of a detection task must be greater than or equal to its start time plus its execution time. This is a fundamental constraint on task order, ensuring that sufficient time is required for task completion. This can be described by the following formula: Among them, f jh b represents the completion time of detection task h on detection equipment j. jh p represents the start time of detection task h on detection device j. jh This indicates the execution time of detection task h on detection device j; 2.3) At any given time, each testing device can only perform one testing task. Testing tasks cannot be assigned to multiple testing devices simultaneously. This can be described by the following formula: Among them, a m, h is a device assignment variable that indicates whether the detection task h is executed by the detection device m. If it is 1, it means that the task is assigned to the detection device m for execution; if it is 0, it means that it is not assigned to the device. Task priority refers to the requirement that a specific order must be met during execution. That is, the execution of some tasks depends on the completion of other tasks. During scheduling, if detection task h1 takes precedence over detection task h2, then the start time of detection task h2 cannot be earlier than the completion time of detection task h1. This can be described using the following formula: Among them, S h2,j C is the start time of detection task h2 on detection device j. h1,j Let h1 be the completion time of detection task h1 on detection device j, and P be the set of task priority relationships, containing the dependencies between all detection tasks. During the scheduling of charging pile detection tasks, in order to ensure the normal operation of the equipment and the smooth connection of detection tasks, the equipment needs to wait for a certain minimum switching time after completing the current detection task before starting the next detection task. This constraint can effectively avoid the overload problem caused by the equipment switching frequently and ensure the rationality of task execution. It can be described by the following formula: S h+1,j ≥C h,j +ΔT j Among them, S h+1,j It is the start time of detection task h+1 on detection device j, C h,j ΔT is the completion time of detection task h on detection equipment j. j This is the minimum switching time for detection equipment j. According to the formula, after detection task h is completed, detection equipment j must undergo a minimum switching time ΔT. j Only after this can task h+1 be executed. This constraint provides a reasonable time interval for the continuous task scheduling of the device. S3.

4. The testing equipment can only start executing a new testing task after completing the previous one. This is a constraint on the sequence of testing tasks, ensuring that testing tasks do not overlap. It can be described by the following formula: b j (h+1)≥f jh Among them, b j (h+1) represents the start time of detection task h+1 on detection device j; Step 3) The specific steps for generating the initial task allocation scheme are as follows: 3.1) Based on the dataset obtained in step 1), combined with the sequential relationship between the execution tasks and the initial state parameters of the detection device, a series of detection task allocation schemes are randomly generated using the Python random function of the heuristic algorithm. Then, taking the mathematical model constructed in step 2) as the optimization objective, the objective function is rapidly iterated and optimized using the heuristic algorithm to improve the task allocation and obtain some initial detection task allocation schemes with shorter completion times for detection tasks, which is the initial solution. 3.2) Verify whether the initial solution satisfies all constraints, including the sequential relationship between tasks, the limit on the number of tasks that the detection device can execute simultaneously, and the constraint on the time required for the detection device to switch between different tasks; if the initial solution satisfies all constraints, proceed to the next step; otherwise, return to regenerate the initial solution. 3.3) Use a two-dimensional array to record the initial allocation scheme of the mapping relationship table data between detection tasks and detection equipment, and transform the initial solution into a task execution Gantt chart to intuitively show the execution order of detection tasks and the utilization of detection equipment.

2. The charging pile detection and scheduling optimization method as described in claim 1, characterized in that, In step 1), the initial state parameters of the detection device include the availability of the detection device, the maximum capacity to execute tasks, and the minimum task switching time.

3. The charging pile detection and scheduling optimization method as described in claim 1, characterized in that, In step 1), the experimental data of the testing equipment for the charging pile includes the testing task data of the testing equipment for the charging pile. The testing task data includes the number of the selectable testing equipment and the task execution time under the selected testing equipment.

4. The charging pile detection and scheduling optimization method as described in claim 1, characterized in that, Step 4) The specific process of optimization and solution is as follows: 4.1) Input the initial detection task allocation scheme generated by the heuristic algorithm into the gurobi solver for further iterative optimization, with the mathematical model constructed in step 2) as the optimization target; 4.2) Adjust the task allocation matrix in each iteration and record the optimization results. Stop the optimization when the change in the objective function value is less than the set value or the maximum number of iterations is reached, and obtain an approximate global optimal solution.

5. The charging pile detection and scheduling optimization method as described in claim 1, characterized in that, Step 5) The specific process of result output and verification is as follows: 5.1) Finally, an optimized task and equipment allocation scheme is generated, including the detection task sequence for each detection device; 5.2) The optimized task execution status is plotted as a Gantt chart to visually display the completion sequence of the detection tasks and the utilization of the detection equipment; 5.3) Calculate the ratio of the usage time of the testing equipment to the total working time, and evaluate the resource utilization of the testing equipment; 5.4) Check whether the optimization scheme meets all constraints and verify the feasibility of the scheme through experiments. If it meets the constraints, output the results; otherwise, return to recalculate.

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