Charging pile detection scheduling optimization method

Abstract

The invention discloses a charging pile detection scheduling optimization method, which comprises the following steps of: 1) constructing mapping relation table data between a charging pile detection task and detection equipment to obtain a data set; 2) taking the shortest completion time of a detection task as an optimization target, establishing corresponding constraint conditions, and forming a mathematical model for scheduling optimization; 3) generating a preliminary scheduling solution by using a heuristic algorithm; (4) carrying out optimization solution in a Gurobi solver; and 5) outputting and verifying a result. The method shows remarkable advantages in the aspect of charging pile detection task scheduling, the overall detection period is shortened by 22.5% through the optimized scheduling scheme, the task execution efficiency is remarkably improved, and according to the optimization result, the task waiting time is shortened, and the compactness of the detection process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production scheduling optimization and flexible job shop scheduling, and in particular to a charging pile detection scheduling optimization method. Background Art

[0002] With the rapid development of new energy vehicles, charging piles, as key supporting equipment, have attracted extensive attention in the industry for their quality and safety performance testing. Charging pile products on the market have diversified functions, mainly including AC charging piles and DC charging piles. However, the existing technology still has many technical difficulties in the charging pile testing process, which urgently needs to be improved and optimized.

[0003] Different types of charging piles need to complete multiple detection tasks, and the existing detection task allocation schemes are usually lacking in scientificity and rationality. Some scholars have studied the allocation problem of charging pile detection tasks and proposed a static task allocation model. However, studies have shown that the model has poor efficiency when facing dynamic task requirements. Some scholars also pointed out that due to the uncertainty of detection tasks in actual operations, traditional allocation schemes are prone to cause some detection equipment to be idle for a long time, while other equipment is overloaded. This phenomenon of uneven resource allocation not only reduces the overall utilization of equipment, but also significantly prolongs the detection cycle. In addition, some scholars have found through the study of existing detection systems that the optimization of task allocation schemes is directly related to the improvement of detection efficiency. However, their research only conducts allocation experiments on the detection tasks of a small number of charging piles, and fails to effectively solve the scheduling problem of large-scale charging pile detection tasks. Some scholars' research further pointed out that in the task allocation link, the mapping relationship between equipment and detection tasks is too simple, and the performance differences of equipment and the dependencies between tasks are not fully considered, thereby reducing the accuracy of allocation. The execution order of detection tasks is of great significance for optimizing the detection cycle. Some foreign scholars have proposed a rule-based detection task sorting method, which optimizes the execution order of some tasks through priority strategies. Despite this, their research has exposed multiple defects in practical applications, such as failure to handle the dependencies between complex tasks and neglect of equipment idle time. Some professors have developed a model for predicting task execution time based on deep learning methods to guide the sorting of detection tasks. However, experimental results show that this method has low prediction accuracy when facing multi-task parallel scenarios, resulting in unreasonable task sorting results. Foreign professors have also studied the time dependency of charging pile detection tasks and proposed a task sorting algorithm based on time windows. Although this method can reduce the waiting time of some tasks, its computational complexity is high and it is difficult to promote and apply in actual detection scenarios. Some scholars pointed out through simulation analysis that unreasonable planning of task execution order will lead to task accumulation and reduced equipment utilization, thereby further prolonging the detection cycle. This problem is still one of the key difficulties in the current research on charging pile detection optimization.

[0004] The utilization efficiency of detection equipment resources directly affects the operating costs of the detection agency. Some scholars have shown that in the process of charging pile detection, the existing methods fail to fully consider the matching problem between equipment capabilities and task requirements, resulting in low equipment utilization. In response to this, other scholars have developed an equipment scheduling method based on heuristic algorithms to try to improve the utilization efficiency of equipment. Experiments show that this method is effective under specific conditions, but performs poorly when the task volume is large or the task type is complex. In addition, more scholars have pointed out that the utilization of detection equipment is also affected by task dependencies and equipment switching time. Their research proposed an optimization model based on integer programming to reduce equipment switching time and improve the overall utilization of equipment. However, the calculation time of this method in large-scale task scenarios is long and it is difficult to meet actual needs. In response to this, some scholars have analyzed the utilization trend of different equipment in multi-task scenarios by constructing a dynamic scheduling model. The study found that dynamic scheduling of equipment resources is the key to improving utilization, but there is still a lack of efficient optimization methods for complex detection scenarios. In the field of charging pile detection scheduling optimization, the flexible job shop scheduling problem (FJSP) is a commonly used theoretical model. At present, a large number of scheduling optimization algorithms have been widely used in the manufacturing and logistics fields, but there are still relatively few studies on charging pile detection scenarios. Specifically, existing research focuses on single-objective optimization, such as minimizing the detection cycle or maximizing equipment utilization, while research on multi-objective optimization problems is still insufficient. The scheduling optimization of charging pile detection tasks needs to consider the time dependency of tasks, equipment capacity limitations, and coordination between multiple tasks. Therefore, we provide a charging pile detection scheduling optimization method. Summary of the invention

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

[0006] The technical solution adopted by the present invention is as follows:

[0007] A charging pile detection scheduling optimization method comprises the following steps:

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

[0009] 2) Constructing a mathematical model, taking the shortest completion time of the detection task as the optimization goal, and establishing corresponding constraints to form a mathematical model for scheduling optimization. The constraints include the order of execution of tasks, the number of tasks that can be executed simultaneously by the detection equipment, and the time required for the detection equipment to switch between different tasks;

[0010] 3) Based on the data set obtained in step 1), taking the mathematical model constructed in step 2) as the target, a heuristic algorithm is used to generate a preliminary scheduling solution;

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

[0012] 5) Result output and verification.

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

[0014] Furthermore, in step 1), the experimental data of the detection of the charging pile by the detection device includes the detection task data of the charging pile by the detection device, and the detection task data includes the serial number of the optional detection device and the task execution time under the selected detection device.

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

[0016] 2.1) Taking the shortest completion time of the detection task as the optimization goal:

[0017] Minimize F=C max

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

[0019] 2.2) Establish the time constraint relationship of the task, including the relationship expression of the task start time, end time, and execution time:

[0020] The completion time of the detection task must be greater than or equal to its start time plus the execution time. This is the basic constraint of the task order. It takes enough time to ensure that the task is completed. It can be described by the following formula:

[0021]

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

[0023] 2.3) At the same time, each detection device can only perform one detection task, and the detection task cannot be assigned to multiple detection devices at the same time. It can be described by the following formula:

[0024]

[0025] Among them, a m,h Assign a variable to the device, indicating 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 the execution process, that is, the execution of some tasks requires the completion of other tasks. During the scheduling process, if the detection task h1 takes precedence over the detection task h2, the start time of the detection task h2 cannot be earlier than the completion time of the detection task h1. The specific description can be described by the following formula:

[0027]

[0028] Among them, S h2,j is the start time of detection task h2 on detection device j, C h1,j is the completion time of detection task h1 on detection device j, P is the task priority relationship set, which includes the dependencies between all detection tasks. In the process of charging pile detection task scheduling, 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 frequent switching of tasks 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 is the start time of detection task h+1 on detection device j, C h,j is the completion time of detection task h on detection device j, ΔT j is the minimum switching time of detection device j. According to the formula, after the detection task h is completed, the detection device j must go through the minimum switching time ΔT j After that, task h+1 can be started. This constraint provides a reasonable time interval for the continuous task scheduling of the device.

[0031] S3.4. The detection equipment can start to execute new detection tasks only after completing the detection tasks. This is the order constraint between detection tasks, which ensures that the detection tasks will not overlap. It can be described by the following formula:

[0032] b j(h+1) ≥f jh

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

[0034] Furthermore, the specific steps of step 3) to generate the initial task allocation plan are as follows:

[0035] 3.1) Based on the data set obtained in step 1), combined with the order relationship between the execution tasks and the initial state parameters of the detection equipment, 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 target, the heuristic algorithm is used to quickly iterate and optimize the objective function, improve the task allocation, and obtain some initial detection task allocation schemes with shorter completion time for the detection tasks, that is, the initial solution;

[0036] 3.2) Verify whether the initial solution satisfies all constraints, including the order of tasks, the number of tasks that can be performed simultaneously by the detection device, and 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 plan of the mapping relationship table data between the detection tasks and the detection equipment, and convert the initial solution into a task execution Gantt chart to intuitively display the execution order of the detection tasks and the utilization of the detection equipment.

[0038] Furthermore, the specific process of step 4) optimization solution 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 through each iteration and record the optimization results. Stop the optimization when the change of the objective function value is less than the set value or reaches the maximum number of iterations to obtain an approximate global optimal solution.

[0041] Furthermore, the specific process of step 5) result output and verification is as follows:

[0042] 5.1) Finally, an optimized task equipment allocation plan is generated, including the detection task sequence of each detection equipment;

[0043] 5.2) Draw the optimized task execution status into a Gantt chart to intuitively display the completion sequence of the inspection tasks and the utilization of the inspection equipment;

[0044] 5.3) Calculate the ratio of the use time of the testing equipment to the total working time and evaluate the utilization of the testing equipment resources;

[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 and recalculate.

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

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

[0048] 2. The present invention effectively reduces the equipment idle rate through a scientific and reasonable task allocation scheme, and the equipment efficiency is improved by 15.3% compared with the previous one. The optimization of resource utilization not only improves the operating efficiency of the detection organization, but also reduces the phenomenon of uneven equipment load.

[0049] 3. The present invention performs well in task allocation balance, and by effectively alleviating the equipment bottleneck problem, the stability and fairness of the scheduling system are comprehensively improved. Experimental verification further proves the practical application value of the present invention, and its results can provide scientific support for the efficient execution of charging pile detection tasks, and also provide important reference and reference for similar scheduling optimization problems. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0051] Figure 2 A schematic diagram of the coding process of a charging pile detection scheduling optimization method of the present invention;

[0052] Figure 3 It is a schematic diagram of a convergence curve of an optimization algorithm of a charging pile detection scheduling optimization method of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] The present invention will be further described below in conjunction with the embodiments.

[0055] Example 1

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

[0057] S1. Optimization goal setting and constraint definition;

[0058] S2, data input and initialization;

[0059] S3, mathematical model construction;

[0060] S4, generating an initial task allocation plan;

[0061] S5, optimization solution;

[0062] S6. Result output and verification;

[0063] Step S1, optimization goal setting and constraint definition:

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

[0065] S1.2. Establish a logical framework including task dependency (the order of execution of tasks), equipment capacity limit (the limit on the number of tasks that can be executed simultaneously by the detection equipment), and task switching time (the time required for the detection equipment to switch between different tasks);

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

[0067] S1.4. Convert the input test task data and test equipment parameters into a format that can be recognized and processed by the optimization algorithm;

[0068] Step S2: Data input and initialization:

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

[0070] S2.2, set the initial state of the detection equipment, including parameters such as availability, maximum task execution capacity, and minimum task switching time;

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

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

[0073] Step S3, mathematical model construction:

[0074] S3.1. Taking the shortest completion time of the detection task as the optimization goal:

[0075] Minimize F=C max

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

[0077] S3.2. Establish the time constraint relationship of the task, including the relationship expression of 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 the execution time. This is the basic constraint on the task order. It ensures that sufficient time is required to complete the task. The constraints between detection tasks can be described by the following formula:

[0079]

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

[0081] S3.3. At the same time, each detection device can only perform one detection task. The detection task cannot be assigned to multiple detection devices at the same time. It can be described by the following formula:

[0082]

[0083] Among them, a m,h Assign a variable to the device, indicating 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 the execution process, that is, the execution of some tasks requires the completion of other tasks. During the scheduling process, if the detection task h1 takes precedence over the detection task h2, the start time of the detection task h2 cannot be earlier than the completion time of the detection task h1. The specific description can be described by the following formula:

[0085]

[0086] Among them, S h2,j is the start time of detection task h2 on detection device j, C h1,jis the completion time of detection task h1 on detection device j, P is the task priority relationship set, which includes the dependencies between all detection tasks. In the process of charging pile detection task scheduling, 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 frequent switching of tasks 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 is the start time of detection task h+1 on detection device j, C h,j is the completion time of detection task h on detection device j, ΔT j is the minimum switching time of detection device j. According to the formula, after the detection task h is completed, the detection device j must go through the minimum switching time ΔT j After that, task h+1 can be started. This constraint provides a reasonable time interval for the continuous task scheduling of the device.

[0089] S3.4. The detection equipment can start to execute new detection tasks only after completing the detection tasks. This is the order constraint between detection tasks, which ensures that the detection tasks will not overlap. It can be described by the following formula:

[0090] b j(h+1) ≥f jh

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

[0092] Step S4: Generate an initial task allocation plan:

[0093] S4.1, quickly generate the initial task allocation plan based on task dependencies and equipment status;

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

[0095] S4.3, using a two-dimensional array to record the initial allocation plan of tasks and equipment;

[0096] S4.4. Convert the initial solution into a task execution Gantt chart to visually display the execution sequence of tasks and the utilization of equipment;

[0097] Step S5, optimizing and solving:

[0098] S5.1. Use heuristic algorithms to quickly iterate and optimize the objective function value and improve task allocation;

[0099] S5.2, further optimize the initial solution with the help of 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 through each iteration and record the optimization results;

[0102] Step S6: Output and verification of results:

[0103] S6.1, finally generating an optimized task equipment allocation plan, including the task sequence of each equipment;

[0104] S6.2. Draw the optimized task execution status into a Gantt chart to intuitively display the task completion sequence and equipment utilization;

[0105] S6.3. Calculate the ratio of equipment usage time to total working time to evaluate 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 goal is clearly defined and the constraints are defined, the logical framework and mapping relationship between tasks and equipment are constructed, the data of 35 charging piles and 100 detection tasks are input and initialized, the accuracy of all necessary information is ensured, a mathematical model with the shortest completion time as the goal is established, the time constraints of the tasks and the use restrictions of the equipment are set, the initial task allocation plan is generated and verified to meet all constraints, the task execution order is intuitively displayed through the Gantt chart, the heuristic algorithm and the Gurobi solver are used to iteratively optimize the task allocation, gradually approach the global optimal solution, ensure the efficiency and accuracy of the optimization process, output the optimized task allocation plan, and evaluate the optimization effect by calculating the equipment utilization and verifying the feasibility of the plan. This method significantly improves the overall efficiency of charging pile detection and the utilization of equipment resources through precise goal setting, rigorous mathematical modeling and efficient optimization algorithms, ensuring the orderly progress 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 test tasks. The experimental data comes from 35 charging piles and 100 test tasks. Some of the test data are detailed in Tables 1 to 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, the initial task allocation scheme is first generated by a heuristic algorithm, and then the task allocation and equipment utilization scheme are gradually optimized using an optimization algorithm (such as a combination of a heuristic algorithm and the Gurobi tool). 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) Description of evaluation indicators

[0126] The optimization effect is evaluated by the following three main indicators:

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

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

[0129] ③Task allocation balance: Evaluate the balance of task allocation by analyzing the load of task allocation between devices;

[0130] (4) Data preprocessing and experimental environment

[0131] After preprocessing, the experimental data was converted into an input format recognizable by the optimization algorithm. This experiment was run on a computer with an Inteli7-12700 processor and 16GB of memory. The optimization algorithm was developed using Python language and Gurobi optimization tool;

[0132] (5) Experimental parameter setting

[0133] ①Experimental 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] ②Optimize 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] ③Data scale

[0145] Number of inspection tasks: 100

[0146] Number of testing equipment: 35

[0147] ④Evaluation indicators

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

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

[0150] Task allocation balance: Evaluate the balance of task allocation by analyzing the task allocation load between devices;

[0151] (6) Optimization results analysis

[0152] In order to verify the convergence performance and global search ability of the optimization algorithm, the convergence curve of the optimization algorithm was drawn ( Figure 3), it can be seen from the convergence curve that the objective function value of the optimization algorithm decreases rapidly in the first 50 iterations, and then gradually stabilizes, indicating that the algorithm can quickly find a task allocation solution close to the optimal solution;

[0153] The convergence speed of the optimization algorithm proposed in the present invention is significantly better than that of the traditional method in the early stage of iteration, which can quickly reduce the task completion time and show good global search ability;

[0154] In the later iterations, the change range of the objective function value gradually decreases and tends to be stable, indicating that the algorithm of the present invention has a strong local search capability, can effectively avoid falling into the local optimum, and at the same time ensure the convergence stability of the optimization results;

[0155] The overall performance and convergence effect of the optimization algorithm have been stable in multiple experimental verifications, further proving the efficiency and practicality of the optimization method of the present invention.

[0156] Next, we will analyze the optimized scheduling effect and the realization of the optimization goal in detail. Figure 3 (Sketch of detection task allocation) and Gantt chart of detection task execution are elaborated in detail;

[0157] (1) Schematic diagram of detection task allocation

[0158] Through the optimized task allocation scheme, we can see that the matching between the detection tasks and the detection equipment is more balanced. The optimized scheme solves the problem of uneven equipment load in the traditional method, reduces the 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 intuitively reflected in the Gantt chart. Through scientific task allocation and reasonable equipment scheduling, the optimized solution significantly shortens the task execution time and significantly improves the efficiency of task completion. The specific optimization effect analysis is as follows:

[0161] ① Optimization of task completion time

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

[0163] ②Increase in equipment utilization

[0164] The experimental results show that the idle rate of optimized equipment has been greatly reduced, and the actual utilization efficiency has been increased by 15.3%. The optimization scheme has effectively alleviated the problem of resource waste and significantly improved the comprehensive utilization rate of testing equipment by scientifically allocating tasks and balancing equipment loads.

[0165] ③Improvement of task allocation balance

[0166] The optimization algorithm effectively alleviates the equipment bottleneck problem and makes the task allocation more reasonable and balanced. The improvement of the balance of task allocation not only improves the stability of the scheduling system, but also enhances the fairness of resource allocation, providing technical guarantee for the efficient execution of detection tasks.

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

[0168] ① Simulated annealing algorithm (SA): The simulated annealing algorithm is a classic optimization algorithm that can avoid local optimality to a certain extent, but in large-scale task scenarios, it converges slowly and has unstable performance;

[0169] ② Particle Swarm Optimization (PSO): Particle Swarm Optimization has strong global search capabilities, but it is easy to fall into local optimality when the task complexity is high, and the optimization results are generally poor in multi-objective scenarios;

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

[0171] (8) Results Analysis

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

[0173] ② Equipment utilization: In terms of improving equipment utilization, the optimization range of the method of the present invention reaches 15.3%, which is significantly better than 8.2% of the simulated annealing algorithm and 10.7% of the particle swarm optimization algorithm. This gap shows 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 achieving 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 the optimization algorithm, making the task allocation more balanced. Compared with the simulated annealing algorithm and the 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] Combined with Table 6, the following is a specific comparison and analysis of the results:

[0176]

[0177]

[0178] Working principle:

[0179] like Figure 1-3 As shown in the figure: In practical application, the present invention lays a solid foundation for the optimization process by setting clear optimization goals and defining constraints. The system clearly defines the shortest completion time of the detection task as the main optimization goal, and constructs a logical framework including multiple constraints such as task dependency, equipment capacity limit and task switching time. By collecting and sorting out the execution time, equipment number and equipment usage characteristics required for the detection task, a detailed task-equipment mapping table is formed. These preparatory work ensures that the optimization algorithm can accurately identify and process the input data, thereby achieving targeted improvement of system performance.

[0180] During the data input and initialization and mathematical model construction phase, the system initialized the device status by importing detailed data of 35 charging piles and 100 detection tasks, and built a mathematical model with the shortest completion time as the goal. This model not only takes into account the start and end time of the task, but also ensures that each device can only perform one task at the same time and follows the constraints of task priority and minimum switching time. By generating an initial task allocation plan and optimizing it using heuristic algorithms and Gurobi solvers, the system can quickly iterate and approach the global optimal solution, effectively improving the scientific nature of task allocation and equipment utilization.

[0181] The optimized results are intuitively displayed through visualization tools such as Gantt charts, ensuring that the task allocation plan not only meets all constraints, but also significantly shortens the overall task completion time, improves the utilization efficiency of equipment and the balance of task allocation. Specifically, the optimization method achieved a 22.5% reduction in task completion time, a 15.3% increase in equipment utilization, and a significant balance in task allocation. This method greatly improves the overall efficiency and resource utilization of charging pile detection through precise target setting, rigorous mathematical modeling, and efficient optimization algorithms, ensuring the orderly progress 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, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements 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 charging pile detection scheduling optimization method, characterized in that: The following steps are involved: (1) Data input and initialization: setting the initial state parameters of the detection equipment, collecting experimental data of the detection equipment on the charging pile detection, constructing the mapping relationship table data between the charging pile detection task and the detection equipment, and obtaining the data set; (2) constructing a mathematical model, taking the shortest completion time of the detection task as the optimization goal, and establishing corresponding constraints to form a mathematical model for scheduling optimization. The constraints include the order of execution of tasks, the number of tasks that can be executed simultaneously by the detection equipment, and the time required for the detection equipment to switch between different tasks; (3) Based on the data set obtained in step 1), taking the mathematical model constructed in step 2) as the target, a preliminary scheduling solution is generated using a heuristic algorithm; (4) Inputting the preliminary scheduling solution generated in step 3) into the Gurobi solver for optimization; (5) Result output and verification.

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

3. A charging pile detection scheduling optimization method as claimed in claim 1, characterized in that: In step 1), the experimental data of the detection of the charging pile by the detection device includes the detection task data of the charging pile by the detection device, and the detection task data includes the number of the optional detection device and the task execution time under the selected detection device.

4. A charging pile detection scheduling optimization method as claimed in claim 1, characterized in that: The construction of the mathematical model described in step 2) is specifically as follows: (2.1) The shortest completion time of the detection task is the optimization goal: Minimize F=C max Where: F is the total completion time of the detection task; C max is the maximum completion time of all detection tasks; (2.2) Establish the time constraint relationship of the task, including the relational expressions of the task start time, end time, and execution time: The completion time of the detection task must be greater than or equal to its start time plus the execution time. This is the basic constraint of the task order. It takes enough time to ensure that the task is completed. It can be described by the following formula: Among them, f jh represents the completion time of detection task h on detection device j, b jh represents the start time of detection task h on detection device j, p jh represents the execution time of detection task h on detection device j; (2.3) At the same time, each detection device can only perform one detection task, and the detection task cannot be assigned to multiple detection devices at the same time. It can be described by the following formula: Among them, a m,h Assign a variable to the device, indicating 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 the execution process, that is, the execution of some tasks requires the completion of other tasks. During the scheduling process, if the detection task h1 takes precedence over the detection task h2, the start time of the detection task h2 cannot be earlier than the completion time of the detection task h1. The specific description can be described by the following formula: Among them, S h2,j is the start time of detection task h2 on detection device j, C h1,j is the completion time of detection task h1 on detection device j, P is the task priority relationship set, which includes the dependencies between all detection tasks. In the process of charging pile detection task scheduling, 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 frequent task switching of the equipment 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 is the start time of detection task h+1 on detection device j, C h,j is the completion time of detection task h on detection device j, ΔT j is the minimum switching time of detection device j. According to the formula, after the detection task h is completed, the detection device j must wait for the minimum switching time ΔTj before it can start to execute task h+1. This constraint provides a reasonable time interval for the continuous task scheduling of the device. S3.

4. The detection equipment can start to execute new detection tasks only after completing the detection tasks. This is the order constraint between detection tasks, which ensures that the detection tasks will not overlap. It can be described by the following formula: b j(h+1) ≥f jh Among them, b j(h+1) Indicates the start time of detection task h+1 on detection device j.

5. A charging pile detection scheduling optimization method as claimed in claim 1, characterized in that: Step 3) The specific steps of generating the initial task allocation plan are as follows: (3.1) Based on the data set obtained in step 1), combined with the order relationship between the execution tasks and the initial state parameters of the detection equipment, 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 target, the heuristic algorithm is used to quickly iterate and optimize the objective function, improve the task allocation, and obtain some initial detection task allocation schemes with shorter completion time for the detection tasks, that is, the initial solution; (3.2) Verify whether the initial solution satisfies all constraints, including the order of execution of tasks, the number of tasks that can be executed simultaneously by the detection equipment, and the time required for the detection equipment 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 plan of the mapping relationship table data between the detection tasks and the detection equipment, and convert the initial solution into a task execution Gantt chart to intuitively display the execution order of the detection tasks and the utilization of the detection equipment.

6. A charging pile detection scheduling optimization method as claimed in claim 1, characterized in that: Step 4) The specific process of optimization solution is: (4.1) Input the initial detection task allocation plan 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) The task allocation matrix is ​​adjusted in each iteration and the optimization results are recorded. When the change in the objective function value is less than the set value or the maximum number of iterations is reached, the optimization is stopped to obtain an approximate global optimal solution.

7. A charging pile detection scheduling optimization method as claimed 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 equipment allocation plan is generated, including the detection task sequence of each detection equipment; (5.2) Draw the optimized task execution status into a Gantt chart to intuitively display the completion sequence of the inspection tasks and the utilization of the inspection equipment; (5.3) Calculate the ratio of the use time of the testing equipment to the total working time and evaluate the utilization of the testing equipment resources; (5.4) Check whether the optimization scheme satisfies all constraints and verify the feasibility of the scheme through experiments. If so, output the result; otherwise, return and recalculate.

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