CN117707745B - Metering task synchronous scheduling method based on self-adaptive tabu search algorithm - Google Patents
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Abstract
The invention relates to a metering task synchronous scheduling method based on a self-adaptive tabu search algorithm, which comprises the following steps: setting constraint conditions, including: the shortest total execution time of the metering task and the distance between the client and the server meet the service coverage requirement; obtaining an initial solution of a metering task synchronous scheduling scheme by using a triple greedy algorithm; initial solution based on self-adaptive tabu search algorithmOptimizing, enhancing the searching capability of the algorithm through special amnesty rules, and searching to obtain a better solution; selecting a better solution corresponding to the minimum task execution total time as an output result after the iteration condition is met; according to the characteristics of the distributed remote metering system, the synchronous dependency relationship between each client and the server is fully considered, and under the condition of synchronous scheduling constraint, the metering task synchronous scheduling method based on the self-adaptive tabu search algorithm greatly reduces the processing time of the total task and solves the problems of task congestion and resource waste in the distributed remote scheduling system.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a metering task synchronous scheduling method based on a self-adaptive tabu search algorithm.
Background
The metering technology plays a very important role in the power system, and is the accurate and unified foundation and guarantee of equipment. However, in the conventional power system field, the operation and maintenance of the equipment requires a lot of manpower and material resources. With the development of industrial Internet, the distributed technology is fused with the traditional operation technology, so that the online coordination capability of industrial products is greatly improved, and the intellectualization of a metering system is promoted. Distributed remote detection is a technology for collecting and processing data by utilizing a plurality of client nodes, and can realize the whole coverage of a target area, thereby greatly improving the efficiency and the reliability of the system. The distributed remote detection has important application value in the fields of smart grids, environment monitoring, traffic management and the like.
In daily operation and maintenance, the power network system often faces the problems of unbalanced supply and demand, energy waste, environmental pressure and the like. Specifically, the maintenance, measurement and data collection of the power equipment generate a great deal of industrial metering demands, and special metering personnel are usually required to be arranged to disassemble and send out the inspection on site, so that the labor and time cost is greatly consumed. Fortunately, the automation and the high efficiency of the power network can be effectively realized through a remote measurement technology. However, the traditional remote metering system is often small in scale, cannot rapidly process large-scale metering tasks in parallel, and is difficult to meet the calibration work requirements of large-scale electric power meters in industrial scenes. There is a need for an efficient distributed remote sensing system for distribution and handling of power meter calibration tasks. In particular, the scheduling problem of tasks in a distributed remote detection system involves strict constraints and conflicting optimization objectives. Therefore, how to effectively distribute and execute the distributed remote detection task to improve the network function integration efficiency is a problem to be solved.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a distributed remote detection system model, and performs mathematical modeling on the task synchronous scheduling problem of a distributed remote detection system, thereby solving the problems of task congestion and calculation resource waste in a large-scale metering task scene.
According to a first aspect of the present invention, there is provided a metering task synchronous scheduling method based on an adaptive tabu search algorithm, including:
Step 1, setting constraint conditions, including: the shortest total execution time of the metering task and the distance between the client and the server meet the service coverage requirement;
step 2, obtaining an initial solution of a metering task synchronous scheduling scheme by using a triple greedy algorithm ；
Step 3, the initial solution is calculated based on an adaptive tabu search algorithmOptimizing, enhancing the searching capability of the algorithm through special amnesty rules, and searching to obtain a better solution;
Step 4, repeating the step 2 and the step 3 until the iteration condition is met, and selecting the total task execution time The more optimal solution corresponding to the smallest time is taken as an output result/>。
On the basis of the technical scheme, the invention can also make the following improvements.
Optionally, the constraint condition that the total execution time of the metering task is the shortest includes:
Where Time represents the total Time for task execution, Representing task/>I, j, p, q, m and n represent the number of sequences;
Set of detection points representing different geographical locations, detection points/> By multiple clients/>And heterogeneous equipment to be detected; /(I)Representing a set of all tasks,/>；/>Representing task/>Is set to be equal to the actual start time of the (c); /(I)Representing task/>The type of (2); /(I)Representing task/>Is required for the processing time;
Representing a central laboratory set remote from the detection point; each central laboratory/> Comprises a plurality of processing servers/>，/>Representing a server/>Service coverage limitation of (a); /(I)Representing task/>In a Central laboratory/>/>Number server with the/>Sequentially running; /(I)Representing task/>At the detection point/>Number client with the/>Sequentially running;
Representing client/> A set of processable task types; Representing a server/> A set of processable task types;
and/> Respectively representing whether the client and the server can process the task of the type;
Representing task/> Whether it belongs to the detection point.
Optionally, the constraint that the distance between the client and the server meets the service coverage requirement includes:
；
；
Wherein, Representation/>Detection point/>Distance between clients,/>Representation/>The coordinates of the detection point,Representation/>Coordinates of client,/>Representing a server/>Is a service coverage distance of (a).
Optionally, the step 2 includes:
step 201, counting the number of available clients of a detection point where a metering task is located, and arranging task sets in an ascending order by taking the number as a first priority; calculating a limited resource index of the metering task, and using the limited resource index as a second priority to arrange the task sets in an ascending order; with processing time required by the task For the third priority, descending order of task sets is arranged; obtaining the task priority sequence of the current task set;
step 202, calculating importance indexes of each detection point client and each laboratory server; for any detection point, define a variable For/>The number of tasks of a type, defining the variable/>Can process/>, for the detection pointThe number of clients for the type task; definition/>For the importance degree of the client resource, the calculation formula is as follows:；
Definition of variables Is within the coverage area of the server/>The number of tasks of a type, defining variablesFor being able to handle/>, in a central laboratoryThe number of servers for the type task; the importance calculation formula of the server is as follows: /(I)；
Step 203, calculating the priority of the task in the current metering task set, and selecting the task with the highest priority for scheduling; selecting the task with the lowest importance from the client and the server in the idle state to process the task, and randomly selecting the task when the importance degree of the client is the same or the importance degree of the server is the same; greedy scheduling is carried out on all tasks of the task set to obtain an initial solution of a metering task synchronous scheduling scheme。
Optionally, the step 3 includes:
Step 301, for said initial solution Performing neighborhood searching to obtain a domain solution;
step 302, selecting an objective function value from the neighborhood solution Comparing the minimum solution with the tabu solutions in the tabu list, and judging whether special amnesty rules are met; if the tabu solution corresponds to a smaller Time value, special amnesty is used as the optimal solution of the round of iteration;
step 303, comparing the optimal solution of the current round with the historical optimal solution, and storing A solution corresponding to a smaller time;
Step 304, updating the tabu list, and accelerating the early convergence speed of the algorithm for adding the self-adaptive step length corresponding to the solution matching of the tabu list.
Optionally, the step 301 performs a neighborhood search to obtain a domain solution through a task location exchange and task sequence exchange process.
Optionally, the task location exchange process includes:
Randomly selecting a client, a server and a task, carrying out task position exchange of three connection relations of the client, the server and the server, and carrying out execution position exchange of two tasks on different clients and servers under the condition that the constraint condition of task execution is met;
the task sequence exchanging process comprises the following steps:
And randomly selecting a client, a server and tasks, and exchanging execution sequences of the two tasks on the same client or the same server.
Optionally, in the step 304, the solution added to the tabu table in each round is calculated based on the adaptive tabu search algorithm as follows:
Wherein, For updated tabu step size,/>Is the minimum value of tabu step length,/>The calculation process of (1) is as follows:；
in the above-mentioned method, the step of, ，/>Is of a constant value/>Representing historical optimal solution,/>Representing the optimal solution of the iteration of the round.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer management class program which, when executed by a processor, implements the steps of a metering task synchronous scheduling method based on an adaptive tabu search algorithm.
The metering task synchronous scheduling method based on the self-adaptive tabu search algorithm has the beneficial effects that:
1. the invention enhances the searching capability of the algorithm through special amnesty rules, solves the problem that the neighborhood searching is sunk into the local optimal solution too early, avoids invalid searching and calculating, and improves the convergence rate of the algorithm.
2. According to the characteristics of the distributed remote metering system, the synchronous dependency relationship between each client and the server is fully considered, and under the condition of synchronous scheduling constraint, the metering task synchronous scheduling method based on the self-adaptive tabu search algorithm greatly reduces the processing time of the total task and solves the problems of task congestion and resource waste in the distributed remote scheduling system. From the aspect of algorithm expression, the algorithm has high convergence rate and high efficiency, and the obtained result has good stability.
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Fig. 1 is a flowchart of an embodiment of an adaptive tabu search algorithm in a metering task synchronous scheduling method based on the adaptive tabu search algorithm;
fig. 2 is a schematic diagram of an embodiment of an application scenario related to a metering task synchronous scheduling method based on an adaptive tabu search algorithm.
Detailed Description
The principles and features of the present invention are described below with reference to the drawings, the examples are illustrated for the purpose of illustrating the invention and are not to be construed as limiting the scope of the invention.
Fig. 1 is a flowchart of an embodiment of an adaptive tabu search algorithm in a metering task synchronous scheduling method based on the adaptive tabu search algorithm, and as can be seen in conjunction with fig. 1, the synchronous scheduling method includes:
Step 1, setting constraint conditions, including: the total execution time of the metering tasks is shortest, and the distance between the client and the server meets the service coverage requirement.
Step 2, obtaining an initial solution of a metering task synchronous scheduling scheme by using a triple greedy algorithm。
Step 3, based on the self-adaptive tabu search algorithm, the initial solution is calculatedOptimizing, enhancing the searching capability of the algorithm through special amnesty rules, and searching to obtain a better solution.
The search capability of the algorithm is enhanced through special amnesty rules, the problem that the neighborhood search is sunk into a local optimal solution too early is solved, invalid search and calculation are avoided, and the convergence rate of the algorithm is improved.
Step 4, repeating the step 2 and the step 3 until the iteration condition is met, and selecting the total task execution timeThe best solution corresponding to the smallest time is taken as the output result/>。
In the specific implementation, each iteration result is stored in the memory D, the memory D is initialized before the step 2 is executed, the better solution obtained in the step 3 is stored in the memory D in the step 4, and finally the output result is selected from the memory D. The iteration condition can be set for reaching the set iteration times。
The invention provides a metering task synchronous scheduling method based on a self-adaptive tabu search algorithm, provides a distributed remote detection system model, carries out mathematical modeling on task synchronous scheduling problems of a distributed remote detection system, carries out iteration by using an initial solution construction-local search two-stage self-adaptive tabu search algorithm to obtain an optimal solution of a metering task synchronous scheduling scheme, realizes optimization of the metering task synchronous scheduling scheme, solves the problems of task congestion and calculation resource waste in a large-scale metering task scene, and can be applied to a distributed remote detection system of a smart grid.
Example 1
An embodiment 1 provided by the present invention is an embodiment of a metering task synchronous scheduling method based on an adaptive tabu search algorithm provided by the present invention, and fig. 2 is a schematic diagram of an embodiment of an application scenario related to the metering task synchronous scheduling method based on the adaptive tabu search algorithm provided by the present invention, and as can be known from fig. 1 and fig. 2, the embodiment of the synchronous scheduling method includes:
Step 1, setting constraint conditions, including: the total execution time of the metering tasks is shortest, and the distance between the client and the server meets the service coverage requirement.
In order to obtain an optimal task synchronous scheduling scheme, the intelligent power grid distributed remote detection system is modeled.
All tasks generated in the system need to be processed simultaneously by the server and the client, so that explicit decision variables are needed to indicate the processing position and sequence of the tasks. Using binary variablesRepresenting task/>In a central laboratory/>Number server with the/>Order running, i.e. task/>Is a server/>Treatment of/>Tasks; similarly, with binary variable/>Representing task/>Is a client/>Treatment of the first/>And (3) tasks. Finally, because each server and client can only handle specific task types. Thus, a binary variable/>, is definedAnd/>To indicate whether the client and server can handle this type of task, respectively.
In one possible embodiment, the constraint that the total execution time of the metering task is the shortest includes:
Constraint conditions require that a detection task can only be generated by the device to be detected in one detection point; the server or the client can only process one detection task at the same time, namely, the tasks in the later order must be executed after the tasks in the former order are completed; detecting that the task is only running on the client or the server conforming to the task type; a task can only be jointly executed by an execution client and a server once; the task type must be processable by the server; the task can only run at the detection point to which it belongs.
Where Time represents the total Time for task execution,Representing task/>I, j, p, q, m and n represent the number of sequence numbers.
In order to obtain an optimal task synchronous scheduling scheme, the intelligent power grid distributed remote detection system is modeled. UsingRepresenting a distributed remote detection network,/>Set of detection points representing different geographical locations, detection points/>By multiple clients/>And heterogeneous equipment to be detected; /(I)Representing a set of all tasks, i.e./>，/>Representing task/>Is set to be equal to the actual start time of the (c); /(I)Representing task/>The type of (2); /(I)Representing task/>Is required for the processing time.
Representing a central laboratory set remote from the detection point; each central laboratoryMay comprise a plurality of processing servers/>And has service coverage limitation,/>Representation serverService coverage limitation of (a); /(I)Representing task/>In a Central laboratory/>/>Whether to use the first/>The sequence operation is that the value is 1, otherwise the value is 0; /(I)Representing task/>At the detection point/>/>Whether to go to the/>The sequence runs, if yes, the value is 1, otherwise the value is 0.
Representing client/>A set of processable task types, each element in the set being represented by 0 (processable) and 1 (non-processable); /(I)Representing a server/>A set of processable task types, each element in the set being represented by 0 (processable) and 1 (non-processable).
And/>The method and the system respectively indicate whether the client and the server can process the task of the type, the energy value is 1, and the energy value is 0.
Representing task/>Whether it belongs to the detection point; if/>Belongs to the detection point/>1, Otherwise 0.
Definition Each detection point and central laboratory can be mapped to a logical address with a two-dimensional coordinateAnd (3) representing. All distance measurements herein use a straight line metric. In one possible embodiment, the constraint that the distance between the client and the server satisfies the service coverage requirement includes:
. Indicating that the location of the client at which the task is located must be within the service distance of the server to which the task is synchronously scheduled.
。
Wherein,Representation/>Detection point/>And/>Client/>Distance between/>Representation ofCoordinates of detection point/>Representation/>Coordinates of client,/>Representing a server/>Is a service coverage distance of (a).
Step 2, obtaining an initial solution of a metering task synchronous scheduling scheme by using a triple greedy algorithm。
In one possible embodiment, step 2 includes:
step 201, counting the number of available clients of a detection point where a metering task is located, and arranging task sets in an ascending order by taking the number as a first priority; calculating a limited resource index of the metering task, and using the limited resource index as a second priority to arrange the task sets in an ascending order; with processing time required by the task For the third priority, descending order of task sets is arranged; and obtaining the task priority sequence of the current task set.
Step 202, calculating importance indexes of each detection point client and each laboratory server; for any detection point, its current task setIs known, define the variable/>For/>The number of tasks of a type, defining variablesCan process/>, for the detection pointThe number of clients for the type task; definition/>For the importance degree of the client resource, the calculation formula is as follows: /(I)。
If the importance of a client is greater, and the more tasks that the client needs to process are represented, the working time of the client should be occupied as little as possible, and other clients are preferentially used. Thus, according toFor detection point/>The clients of (a) are arranged in ascending order.
Definition of variablesIs within the coverage area of the server/>The number of tasks of a type, defining variablesFor being able to handle/>, in a central laboratoryThe number of servers for the type task; the importance calculation formula of the server is as follows: /(I)。
Step 203, calculating the priority of the task in the current metering task set, and selecting the task with the highest priority for scheduling; selecting the task with the lowest importance from the client and the server in the idle state to process the task, and randomly selecting the task when the importance degree of the client is the same or the importance degree of the server is the same; greedy scheduling is carried out on all tasks of the task set to obtain an initial solution of a metering task synchronous scheduling scheme。
Step 3, based on the self-adaptive tabu search algorithm, the initial solution is calculatedOptimizing, enhancing the searching capability of the algorithm through special amnesty rules, and searching to obtain a better solution.
In one possible embodiment, step 3 includes:
Step 301, for initial solution And carrying out neighborhood search to obtain a domain solution.
In one possible embodiment, step 301 performs a neighborhood search to obtain a domain solution through a task location exchange and task order exchange process.
In one possible embodiment, the task location exchange procedure includes:
And randomly selecting a client, a server and a task, carrying out task position exchange of three connection relations of the client, the server and the server, and carrying out execution position exchange of two tasks on different clients and servers under the condition that the task execution constraint condition is met.
The task sequence exchanging process comprises the following steps:
And randomly selecting a client, a server and tasks, and exchanging execution sequences of the two tasks on the same client or the same server.
Step 302, selecting an objective function value from a neighborhood solutionComparing the minimum solution with the tabu solutions in the tabu list, and judging whether special amnesty rules are met; if the tabu solution corresponds to a smaller Time value, special amnesty is taken as the optimal solution for the present round of iteration.
Step 303, comparing the optimal solution of the current round with the historical optimal solution, and storingSmaller corresponding solutions.
Step 304, updating the tabu list, and accelerating the early convergence speed of the algorithm for adding the self-adaptive step length corresponding to the solution matching of the tabu list.
In one possible embodiment, the calculation of the tabu step size for each round of solutions added to the tabu table based on the adaptive tabu search algorithm in step 304 is:
Wherein, For updated tabu step size,/>For the minimum of tabu steps, rand represents a random function that generates random numbers,/>The calculation process of (1) is as follows: /(I)。
In the above-mentioned method, the step of,，/>Is of a constant value/>Representing historical optimal solution,/>Representing the optimal solution of the iteration of the round.
In the initial stage of searching, the difference between the historical optimal solution and the optimal solution in each iteration is large, so that the tabu step size is avoidedMainly receive/>Influence of the value. In this case, a solution with a larger gap from the history optimal solution has a larger probability of being prohibited from searching for more rounds. As the algorithm iterates continuously, the difference between the historical optimal solution and the optimal solution in each iteration becomes smaller,/>Will become the main factor affecting the tabu step size. By the method, the algorithm can search in a relatively small neighborhood, so that the algorithm can be converged rapidly, and the efficiency is improved.
Step 4, repeating the step 2 and the step 3 until the iteration condition is met, and selecting the total task execution timeThe best solution corresponding to the smallest time is taken as the output result/>。
According to the metering task synchronous scheduling method based on the self-adaptive tabu search algorithm, provided by the embodiment of the invention, the search capability of the algorithm is enhanced through special amnesty rules, the problem that the neighborhood search falls into a local optimal solution too early is solved, invalid search and calculation are avoided, and the convergence rate of the algorithm is improved. According to the characteristics of the distributed remote metering system, the synchronous dependency relationship between each client and the server is fully considered, and under the condition of synchronous scheduling constraint, the metering task synchronous scheduling method based on the self-adaptive tabu search algorithm greatly reduces the processing time of the total task and solves the problems of task congestion and resource waste in the distributed remote scheduling system. From the aspect of algorithm expression, the algorithm has high convergence rate and high efficiency, and the obtained result has good stability.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. The metering task synchronous scheduling method based on the self-adaptive tabu search algorithm is characterized by comprising the following steps of:
Step 1, setting constraint conditions, including: the constraint condition that the total execution time of the metering task is shortest and the constraint condition that the distance between the client and the server meets the service coverage requirement are met;
step 2, obtaining an initial solution of a metering task synchronous scheduling scheme by using a triple greedy algorithm ；
Step 3, the initial solution is calculated based on an adaptive tabu search algorithmOptimizing, enhancing the searching capability of the algorithm through special amnesty rules, and searching to obtain a better solution;
Step 4, repeating the step 2 and the step 3 until the iteration condition is met, and selecting the total task execution time The more optimal solution corresponding to the smallest time is taken as an output result/>；
The step 2 comprises the following steps:
step 201, counting the number of available clients of a detection point where a metering task is located, and arranging task sets in an ascending order by taking the number as a first priority; calculating a limited resource index of the metering task, and using the limited resource index as a second priority to arrange the task sets in an ascending order; with processing time required by the task For the third priority, descending order of task sets is arranged; obtaining the task priority sequence of the current task set;
step 202, calculating importance indexes of each detection point client and each laboratory server; for any detection point, define a variable For/>The number of tasks of a type, defining the variable/>Can process/>, for the detection pointThe number of clients for the type task; definition/>For the importance degree of the client resource, the calculation formula is as follows:；
Definition of variables Is within the coverage area of the server/>The number of tasks of a type, defining variablesFor being able to handle/>, in a central laboratoryThe number of servers for the type task; the importance calculation formula of the server is as follows: /(I)；
Step 203, calculating the priority of the task in the current metering task set, and selecting the task with the highest priority for scheduling; selecting the task with the lowest importance from the client and the server in the idle state to process the task, and randomly selecting the task when the importance degree of the client is the same or the importance degree of the server is the same; greedy scheduling is carried out on all tasks of the task set to obtain an initial solution of a metering task synchronous scheduling scheme。
2. The synchronous scheduling method according to claim 1, wherein the constraint that the total time for executing the metering task is the shortest includes:
；
；
；
；
；
；
；
；
；
；
；
Where Time represents the total Time for task execution, Representing task/>I, j, p, q, m and n represent the number of sequences;
Set of detection points representing different geographical locations, detection points/> By multiple clients/>And heterogeneous equipment to be detected; /(I)Representing a set of all tasks,/>；/>Representing task/>Is set to be equal to the actual start time of the (c); /(I)Representing task/>The type of (2); /(I)Representing task/>Is required for the processing time;
Representing a central laboratory set remote from the detection point; each central laboratory/> Comprises a plurality of processing servers/>，/>Representing a server/>Service coverage limitation of (a); /(I)Representing task/>In a Central laboratory/>/>Whether to use the first/>The sequence operation is that the value is 1, otherwise the value is 0; /(I)Representing task/>At the detection point/>/>Whether to go to the/>The sequence operation is that the value is 1, otherwise the value is 0;
Representing client/> A set of processable task types; /(I)Representing a server/>A set of processable task types;
and/> Respectively representing whether the client and the server can process the task of the type, wherein the energy value is 1, and the energy value is 0;
Representing task/> Whether or not it belongs to the detection point,/>Belongs to the detection point/>1 Otherwise/>Is 0.
3. The synchronous scheduling method according to claim 1, wherein the distance between the client and the server satisfies a constraint condition of service coverage requirement comprises:
；
；
Wherein, Representation/>Detection point/>Distance between clients,/>Representation/>Coordinates of detection point/>Representation/>Coordinates of client,/>Representing a server/>Is a service coverage distance of (a).
4. The synchronous scheduling method according to claim 1, wherein the step 3 comprises:
Step 301, for said initial solution Carrying out neighborhood searching to obtain a neighborhood solution;
step 302, selecting an objective function value from the neighborhood solution Comparing the minimum solution with the tabu solutions in the tabu list, and judging whether special amnesty rules are met; if the tabu solution corresponds to a smaller Time value, special amnesty is used as the optimal solution of the round of iteration;
step 303, comparing the optimal solution of the current round with the historical optimal solution, and storing A solution corresponding to a smaller time;
Step 304, updating the tabu list, and accelerating the early convergence speed of the algorithm for adding the self-adaptive step length corresponding to the solution matching of the tabu list.
5. The synchronous scheduling method according to claim 4, wherein the step 301 performs a neighborhood search to obtain a neighborhood solution through a task location exchange and task sequence exchange process.
6. The synchronous scheduling method of claim 5, wherein,
The task position exchange process comprises the following steps:
Randomly selecting a client, a server and a task, carrying out task position exchange of three connection relations of the client, the server and the server, and carrying out execution position exchange of two tasks on different clients and servers under the condition that the constraint condition of task execution is met;
the task sequence exchanging process comprises the following steps:
And randomly selecting a client, a server and tasks, and exchanging execution sequences of the two tasks on the same client or the same server.
7. The synchronized scheduling method of claim 4, wherein the step 304 calculates a tabu step length of each round of solution added to the tabu table based on an adaptive tabu search algorithm as follows:
Wherein, For updated tabu step size,/>For the minimum of tabu steps, rand represents a random function that generates random numbers,/>The calculation process of (1) is as follows: /(I)；
In the above-mentioned method, the step of,，/>Is of a constant value/>Representing historical optimal solution,/>Representing the optimal solution of the iteration of the round.
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