Metering task synchronous scheduling method and system based on improved simulated annealing algorithm

Abstract

The invention provides a metering task synchronous scheduling method and system based on an improved simulated annealing algorithm, and the method comprises the steps: adding a metering task condition in a mathematical model of a distributed remote detection system of a smart power grid; under the condition of a metering task, initializing a memory of the distributed remote detection system of the intelligent power grid, and constructing an initial feasible solution; carrying out iteration by using an improved simulated annealing algorithm based on the initial feasible solutions, and accepting a relatively bad solution in the initial feasible solutions through a Metropolis criterion probability; and meanwhile, a global optimal solution is obtained by taking the condition that an accepted solution is not changed at several continuous temperatures in the simulated annealing algorithm as an ending criterion. The improved simulated annealing algorithm is used for iterating the initial solution, local optimum is avoided, optimization of the measurement task synchronous scheduling method is achieved by setting the effective ending criterion, the processing time of the total task is shortened to a large extent, and the scheduling efficiency and the resource utilization rate of the system are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of dispatching methods of power grid systems, and in particular relates to a method and system for synchronous dispatching of metering tasks based on an improved simulated annealing algorithm. Background Art

[0002] With the growth of energy demand and the widespread application of renewable energy, the power grid system is facing more and more challenges, which may include demand management, optimization of energy distribution, and maintaining the reliability and safety of power supply. The concept of smart grid has therefore emerged. Smart grid is to make the power grid more efficient and reliable by introducing intelligent technology, and better adapt to current and future energy needs. Distributed remote detection system is a technology that uses multiple client nodes to collect and process data, which can achieve full coverage of the target area and greatly improve the efficiency and reliability of the system. The distributed remote calibration system is combined with the smart grid. By carrying out real-time and remote measurement of various working metering instruments in the power grid, it can better manage various links in the power grid system and has important application value.

[0003] The execution of tasks is a crucial part of remote calibration business. In the traditional metrology system, scheduling various metrology tasks to meet metrology needs usually requires a huge amount of manpower, material resources and time costs. The design of remote metrology technology effectively realizes the automatic scheduling of various metrology tasks, but due to its small scale, it is difficult to meet current industrial needs. In summary, when facing complex metrology tasks, the current scheduling method may have a long processing time for the total task and is prone to falling into the problem of local optimality in the process. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for synchronous scheduling of metering tasks based on an improved simulated annealing algorithm, so as to solve the problem that the current scheduling method may have a long processing time for the total task when facing complex metering tasks and is prone to falling into local optimality in the process.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: The present invention provides a method for synchronously scheduling metering tasks based on an improved simulated annealing algorithm, which specifically comprises the following steps: S1. In the mathematical model of the distributed remote detection system of the smart grid, metering task conditions are added; the metering task conditions include: the metering task satisfies the condition of the shortest total execution time, and the metering task satisfies the distance constraint condition between the client and the server; S2. Under the condition of metering task, initialize the memory of the smart grid distributed remote detection system and construct an initial feasible solution; S3. Based on the initial feasible solution, an improved simulated annealing algorithm is used for iteration, and the inferior solution in the initial feasible solution is accepted by probability through the Metropolis criterion; at the same time, the simulated annealing algorithm is used as the ending criterion when the accepted solution does not change at several consecutive temperatures to obtain the global optimal solution of the synchronous scheduling method of metering tasks.

[0006] In some implementations, in S1, the condition for the total execution time of the metering task to be the shortest is specifically the following formula: (1); (2); (3); (4); (5); (6); (7); (8); (9); (10); (11); in, For the tasks, is the set of all tasks, that is , For the task The actual start time of The actual end time of the task; For the task type; is the processing time required for the task, and are the set of edge detection points and the set of central laboratories in the system, respectively. is the edge detection point, For multiple clients, Central Laboratory, For multiple servers; is the detection point, for Is it A binary variable, for exist of On the server A binary variable that runs sequentially. for yes Processing tasks, A collection of server executable task type attributes. It is the client executable task type attribute. and Whether the client and server can handle this type of task.

[0007] Furthermore, in S1, the distance constraint between the client and the server is as follows: (12); (13); in, for Checkpoints and The distance between clients, for The coordinates of the detection points, for The coordinates of the client, For Server The service coverage distance.

[0008] Furthermore, S2 is specifically: S21, based on the initial feasible solution, a new solution is generated by perturbation, and the new solution generated by the perturbation is optimized by an improved simulated annealing algorithm to obtain an optimized solution; S22, storing the optimized solution in the memory, and repeating step S21 until the algorithm meets the end criteria; S23. Among the optimized solutions in the memory, select the solution with the shortest total task execution time as the output result.

[0009] Further, in S21, the disturbance mode includes: Disturbance method 1: swap the execution order of two tasks on the same client or the same server, and regenerate the scheduled tasks for the next period based on the distribution of the scheduled tasks; Perturbation method 2: Based on different tasks at the three task positions of client-client, client-server, and server-server, while satisfying the distance constraints between the client and the server, the execution positions of the three tasks are interchanged, and the scheduled tasks for the subsequent time period are regenerated based on the distribution of the scheduled tasks.

[0010] Furthermore, in S21, the client, server and task are all randomly selected.

[0011] Furthermore, in S21, both the disturbance mode 1 and the disturbance mode 2 regenerate the scheduling tasks for the subsequent time period by adopting a greedy strategy.

[0012] Furthermore, in S21, the new solution generated by the perturbation is optimized by an improved simulated annealing algorithm, and the specific steps are as follows: S211. Define the initial temperature and the temperature drop coefficient; S212, the perturbation generates a new solution, and the objective function value generated by the neighborhood solution is subtracted from the objective function value before the perturbation to obtain the difference of the objective function; S213, using the Metropolis criterion to determine the difference of the objective function, if the difference of the objective function is less than zero, then accepting the new solution as the current solution; otherwise, accepting the new solution; S214, determine whether the number of iterations has been reached, if so, update the temperature, if not, return to S213; S215, determine whether there are several consecutive temperatures at which no solution state change occurs, if so, stop the calculation, otherwise return to S212 to continue.

[0013] Further, in S213, when the Metropolis criterion is used to determine the objective function difference, if the objective function difference is greater than zero, it is determined whether to accept the new solution as the current solution based on the determination of the current temperature and the objective function difference.

[0014] The present invention also provides a metrology task synchronization scheduling system based on an improved simulated annealing algorithm, the metrology task synchronization scheduling system includes a task condition configuration module, an initial solution generation module, a solution optimization module and an iterative control module, wherein: The task condition configuration module is used to configure the measurement task conditions, including the minimum total execution time condition of the measurement task and the distance constraint condition between the client and the server; The initial solution generation module is used to initialize the system's memory and construct an initial feasible solution according to the configured measurement task conditions; The solution optimization module is used to probabilistically accept inferior solutions based on the Metropolis criterion and continuously optimize the quality of the solution; The iterative control module is used to take the condition that the accepted solution does not change at several consecutive temperatures as the end of iteration, thereby obtaining the global optimal solution for the synchronous scheduling of metering tasks.

[0015] Compared with the prior art, the method and system for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm of the present invention have the following beneficial technical effects.

[0016] The present invention is based on the improved simulated annealing algorithm of the metering task synchronization scheduling method, specifically including the following steps: S1, in the mathematical model of the smart grid distributed remote detection system, add the metering task conditions; the metering task conditions include: the metering task meets the condition of the shortest total execution time, and the metering task meets the distance constraint condition between the client and the server; S2, under the condition of the metering task conditions, initialize the memory of the smart grid distributed remote detection system, and construct an initial feasible solution; S3, based on the initial feasible solution, use the improved simulated annealing algorithm to iterate, and accept the inferior solution in the initial feasible solution by Metropolis criterion probability; at the same time, the simulated annealing algorithm does not change the accepted solution at several consecutive temperatures as the end criterion, and obtain the global optimal solution of the metering task synchronization scheduling method. Based on the above, the present invention constructs an initial feasible solution when the metering task meets the condition of the shortest total execution time and the distance constraint between the client and the server, and uses the improved simulated annealing algorithm to iterate based on the initial feasible solution, and accepts the "inferior solution" in the feasible solution by Metropolis criterion probability to avoid falling into the local optimum. At the same time, the present invention sets an effective termination criterion, obtains the global optimal solution of the synchronous scheduling scheme of the metering task, and realizes the optimization of the synchronous scheduling scheme of the metering task. Under the condition of synchronous scheduling constraints, the present invention adopts a remote calibration task synchronous scheduling method based on an improved simulated annealing algorithm to reduce the processing time of the total task to a large extent and improve the efficiency of the system scheduling.

[0017] Furthermore, the strategy proposed in the present invention introduces the Metropolis criterion, accepts the scheme with poor scheduling effect within a certain probability, prevents the algorithm from falling into the local optimal problem, and modifies the end criterion of the algorithm, effectively balancing the relationship between the number of iterations of the method and the number of iterations required to find the global optimal solution. The present invention fully considers the synchronization dependency between each client and server. Under the condition of synchronous scheduling constraints, the remote calibration task synchronous scheduling method based on the improved simulated annealing algorithm reduces the processing time of the total task to a large extent, thereby improving the overall efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings in the specification are used to provide further understanding of the present invention and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0019] Figure 1 It is a flow chart of a method for synchronously scheduling metering tasks and a method in a system based on an improved simulated annealing algorithm of the present invention; Figure 2 It is a schematic diagram of an application scenario of the method and system for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] How to propose a remote task synchronization scheduling method based on the existing mathematical model of task synchronization scheduling problem in distributed remote detection systems, so as to dynamically adjust remote detection tasks and solve the problems of task congestion and waste of computing resources in scenarios with large-scale metering tasks.

[0023] Based on this, Figure 1 As shown, the present invention provides a method for synchronous scheduling of metering tasks based on an improved simulated annealing algorithm, which is performed through the following steps: S1. In the mathematical model of the distributed remote detection system of the smart grid, metering task conditions are added; the metering task conditions include: the metering task satisfies the condition of the shortest total execution time, and the metering task satisfies the distance constraint condition between the client and the server; S2. Under the condition of metering task, initialize the memory of the smart grid distributed remote detection system and construct an initial feasible solution; S3. Based on the initial feasible solution, an improved simulated annealing algorithm is used for iteration, and the inferior solution in the initial feasible solution is accepted by probability through the Metropolis criterion; at the same time, the simulated annealing algorithm is used as the ending criterion when the accepted solution does not change at several consecutive temperatures to obtain the global optimal solution of the synchronous scheduling method of metering tasks.

[0024] like Figure 2As shown, an application scenario of a metering task synchronization scheduling method based on an improved simulated annealing algorithm of the present invention is shown. In some embodiments, the application scenario is a distributed remote detection system in a smart grid.

[0025] As a preferred embodiment, the strategy proposed by the method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm of the present invention introduces the Metropolis criterion. By introducing the Metropolis criterion, the method will probabilistically accept the neighborhood solution with poor effect, and avoid the problem of falling into the local optimum to a certain extent. At the same time, the present invention reasonably sets the end criterion of the method, effectively balancing the relationship between the number of algorithm iterations and the number of iterations required to find the global optimal solution.

[0026] In some embodiments, the present invention provides a method for synchronously scheduling metering tasks based on an improved simulated annealing algorithm. First, an existing mathematical model of a distributed remote detection system for a smart grid is summarized. In some existing mathematical models of a distributed remote detection system for a smart grid, the following constraints are proposed: (1); (2); (3); (4); (5); (6); (7); (8); (9); (10); (11).

[0027] in Indicates tasks, Represents the set of all tasks. .in Indicates the task The actual start time of Indicates the actual end time of the task; Indicates the task type; Indicates the processing time required for the task. and They represent the set of edge detection points in the system and the set of central laboratories, respectively. By multiple clients and heterogeneous equipment to be tested, the central laboratory There may be multiple processing servers in . Binary variable Indicates the task Is it a detection point? .if Belong to the detection point ,but 1 if the variable is set to 1, otherwise it is set to 0. Binary variable Representative tasks In the central laboratory of On the server Run in order, i.e. tasks Is the server The processing of task; similarly, using binary variables Representative tasks Is the client Processing task. Representation Server A collection of executable task type attributes. Each element in the collection 0 and 1 are used to indicate whether each task can be executed, where 0 indicates executable and 1 indicates not executable. Similarly, the client also has an executable task type attribute, which is Represents a binary variable. and Indicates whether the client and server can handle this type of task.

[0028] The constraints require that a detection task can only be generated by the device to be detected in one detection point; the server or client can only process one detection task at the same time, that is, the task in the later order must be executed after the task in the previous order is completed; the detection task can only be run on the client or server that meets its task type; a task can only be executed once by the client and server together; the task type must be handled by the server; the task can only be run at the detection point to which it belongs. As a preferred embodiment, the distance constraint between the client and the server of the present invention is specifically as follows: (12); (13); in, for Checkpoints and The distance between clients, for The coordinates of the detection points, for The coordinates of the client, For Server The service coverage distance.

[0029] In a method for synchronously scheduling metering tasks based on an improved simulated annealing algorithm of the present invention, the above formula indicates that the location of the client where the task is located must be within the service distance range of the server to which the task is synchronously scheduled.

[0030] In one embodiment, the first step of the present invention is to initialize the memory of the smart grid distributed remote detection system. , and then randomly construct an initial feasible solution. It should be noted that the randomly constructed initial feasible solution must satisfy the constraints of task execution.

[0031] The second step is to generate a new solution based on the perturbation of the initial feasible solution, and optimize the new solution generated by the perturbation through an improved simulated annealing algorithm to obtain the optimized solution.

[0032] Step 3: Store the optimized solution obtained in step 2 into memory In the process, the second step is repeated until the algorithm meets the termination criteria.

[0033] Step 4: The present invention uses memory to store Select the solution with the smallest total task execution time and use it as the output result.

[0034] Since the dynamic scheduling problem needs to determine the scheduling amount for the next period based on the current distribution, exchanging the order of the scheduling task nodes in a certain period may make the tasks in the later periods infeasible, so the scheduling tasks in the later periods need to be regenerated after the disturbance to ensure the feasibility of the solution. The disturbance mentioned in the second step has the following two specific disturbance methods: Perturbation method 1: Exchange the execution order of two tasks on the same client or the same server, and regenerate the scheduled tasks for the subsequent period based on the greedy strategy according to the distribution of the scheduled tasks.

[0035] Perturbation method 2: swap the three task positions of client-client, client-server, and server-server, that is, for different tasks on different clients and servers, swap the execution positions of the three tasks while satisfying the task execution constraints. And according to the distribution of the scheduled tasks, regenerate the scheduled tasks for the subsequent time period based on the greedy strategy. It should be noted that the client, server, and task are randomly selected in this operation.

[0036] As a specific embodiment, in a method for synchronous scheduling of metering tasks based on an improved simulated annealing algorithm of the present invention, in the above second step, the new solution generated by the disturbance is optimized by the improved simulated annealing algorithm, specifically according to the following steps: 1) Define the initial temperature as , the temperature drop coefficient is ; 2) The perturbation generates a new solution, and the objective function value generated by the neighborhood solution Subtract the objective function value before the disturbance to get the difference of the objective function, which is recorded as .

[0037] 3) Introduce the Metropolis criterion to determine the difference of the objective function. , then accept the new solution as the current solution; otherwise, with probability Accept new interpretations, including is the current temperature.

[0038] 4) Determine whether the number of iterations has been reached, and if so, update the temperature If not reached, return to step 2).

[0039] 5) Determine whether it is continuous If no state change of the solution occurs at the temperature, stop the calculation, otherwise return to step 2).

[0040] The present invention also provides a metering task synchronization scheduling system based on an improved simulated annealing algorithm, the metering task synchronization scheduling system includes a task condition configuration module, an initial solution generation module, a solution optimization module and an iteration control module, wherein: the task condition configuration module is used to configure the metering task conditions, including the shortest total execution time condition of the metering task and the distance constraint condition between the client and the server; the initial solution generation module is used to initialize the system's memory according to the configured metering task conditions, and construct an initial feasible solution; the solution optimization module is used to probabilistically accept inferior solutions based on the Metropolis criterion, and continuously optimize the quality of the solution; the iteration control module is used to use the condition that the accepted solution does not change at several consecutive temperatures as the condition for ending the iteration, thereby obtaining the global optimal solution for the metering task synchronization scheduling. The metering task synchronization scheduling system based on the improved simulated annealing algorithm of the present invention realizes efficient optimization of task execution through an integrated metering task synchronization scheduling module, reduces the total execution time, ensures a reasonable distance constraint between the client and the server, improves resource utilization and the flexibility of task scheduling, and improves the efficiency of obtaining the global optimal solution for the metering task synchronization scheduling.

[0041] In summary, the present invention is a synchronous scheduling method for metering tasks based on an improved simulated annealing algorithm, which aims to solve the problems of low efficiency and uneven resource allocation of traditional scheduling methods when facing complex metering tasks. Based on this, the present invention optimizes the synchronous scheduling of metering tasks by introducing an improved simulated annealing algorithm, thereby achieving efficient and fair resource allocation. The present invention defines the distance constraints between the client and the server and the condition that the total execution time of the metering task is the shortest; then the tasks are initialized and sorted, and the initial temperature and the cooling coefficient are determined at the same time; finally, the task scheduling scheme is continuously optimized through the iterative process of the simulated annealing algorithm to generate the optimal solution; when the present invention generates a neighborhood solution that is worse than the original solution, it will still accept it with probability, thereby effectively avoiding the problem of falling into a local optimal solution. The present invention changes the ending criterion from the general set temperature reaching a smaller temperature value to the state not changing at several consecutive temperatures, which not only avoids the difficulty of the former in finding a more reasonable ending temperature value, but also improves the convergence speed of the algorithm due to its dynamic adjustment characteristics. The present invention fully considers the synchronization dependency between each client and server, and under the condition of synchronization scheduling constraints, reduces the processing time of the total task to a large extent, and improves the scheduling efficiency and resource utilization of the system.

[0042] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for synchronous scheduling of metering tasks based on an improved simulated annealing algorithm, characterized in that: The specific steps include: S1. Adding metering task conditions to the mathematical model of the smart grid distributed remote detection system; the metering task conditions include: the metering task satisfies the condition of the shortest total execution time, and the metering task satisfies the distance constraint between the client and the server; S2. Under the condition of metering task, initialize the memory of the smart grid distributed remote detection system and construct an initial feasible solution; S3. Based on the initial feasible solution, an improved simulated annealing algorithm is used for iteration, and the inferior solution in the initial feasible solution is accepted by probability through the Metropolis criterion; at the same time, the simulated annealing algorithm is used as the ending criterion when the accepted solution does not change at several consecutive temperatures to obtain the global optimal solution of the synchronous scheduling method of metering tasks.

2. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 1 is characterized in that: In S1, the condition for the shortest total execution time of the measurement task is as follows: （1）； （2）； （3）； （4）； （5）； （6）； （7）； （8）； （9）； （10）； （11）； in, For the tasks, is the set of all tasks, that is ; For the task The actual start time of The actual end time of the task; For the task type; The processing time required for the task; and are the set of edge detection points and the set of central laboratories in the system, respectively. is the edge detection point, For multiple clients, Central Laboratory, For multiple servers; is the detection point, for Is it A binary variable, for exist of On the server A binary variable that runs sequentially. for yes Processing tasks, A collection of server executable task type attributes. It is the client executable task type attribute. and Whether the client and server can handle this type of task.

3. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 1 is characterized in that: In S1, the distance constraint between the client and the server is as follows: （12）； （13）； in, for Checkpoints and The distance between clients, for The coordinates of the detection points, for The coordinates of the client, For Server The service coverage distance.

4. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 1 is characterized in that: The S2 is specifically: S21, based on the initial feasible solution, a new solution is generated by perturbation, and the new solution generated by the perturbation is optimized by an improved simulated annealing algorithm to obtain an optimized solution; S22, storing the optimized solution in the memory, and repeating step S21 until the algorithm meets the end criteria; S23. Among the optimized solutions in the memory, select the solution with the shortest total task execution time as the output result.

5. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 4 is characterized in that: In the S21, the disturbance mode includes: Disturbance method 1: swap the execution order of two tasks on the same client or the same server, and regenerate the scheduled tasks for the next period based on the distribution of the scheduled tasks; Perturbation method 2: Based on different tasks at the three task positions of client-client, client-server, and server-server, while satisfying the distance constraints between the client and the server, the execution positions of the three tasks are interchanged, and the scheduled tasks for the subsequent time period are regenerated based on the distribution of the scheduled tasks.

6. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 5 is characterized in that: In S21, the client, server and task are all randomly selected.

7. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 5 is characterized in that: In S21, both the disturbance mode 1 and the disturbance mode 2 regenerate the scheduling tasks for the subsequent time period by adopting a greedy strategy.

8. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 7 is characterized in that: In S21, the new solution generated by the disturbance is optimized by an improved simulated annealing algorithm, and the specific steps are as follows: S211. Define the initial temperature and the temperature drop coefficient; S212, the perturbation generates a new solution, and the objective function value generated by the neighborhood solution is subtracted from the objective function value before the perturbation to obtain the difference of the objective function; S213, using the Metropolis criterion to determine the difference of the objective function, if the difference of the objective function is less than zero, accepting the new solution as the current solution; Otherwise, accept the new solution; S214, determine whether the number of iterations has been reached, if so, update the temperature, if not, return to S213; S215, determine whether there are several consecutive temperatures at which no solution state change occurs, if so, stop the calculation, otherwise return to S212 to continue.

9. The method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to claim 8, characterized in that: In S213, when the Metropolis criterion is used to determine the objective function difference, if the objective function difference is greater than zero, it is determined whether to accept the new solution as the current solution based on the current temperature and the objective function difference.

10. The system based on which the method for synchronous scheduling of metering tasks based on the improved simulated annealing algorithm according to any one of claims 1 to 9 is characterized in that: The metering task synchronization scheduling system includes a task condition configuration module, an initial solution generation module, a solution optimization module and an iterative control module, wherein: The task condition configuration module is used to configure the measurement task conditions, including the total shortest execution time condition of the measurement task and the distance constraint condition between the client and the server; The initial solution generation module is used to initialize the system's memory and construct an initial feasible solution according to the configured metering task conditions; The solution optimization module is used to probabilistically accept inferior solutions based on the Metropolis criterion and continuously optimize the quality of the solutions; The iterative control module is used to take the condition that the accepted solution does not change at several consecutive temperatures as the condition for terminating the iteration, thereby obtaining the global optimal solution for the synchronous scheduling of the metering tasks.

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