Network performance optimization method and system in remote calibration system based on ant colony algorithm

Abstract

The invention provides a network performance optimization method and system in a remote calibration system based on an ant colony algorithm, and the method comprises the following steps: forming basic data based on a basic model of the remote calibration system; scheduling a central network node and an edge network node by using an ant colony algorithm, and obtaining an optimal solution of a network performance optimization scheme of the remote calibration system through an iteration process; and adjusting and optimizing network performance parameters of the remote calibration system according to the optimal solution to realize optimization of the network performance of the remote calibration system. According to the method, the synchronous dependency relationship of each node is fully considered, and self-adaptive and dynamic network performance optimization is realized by applying the ant colony algorithm. According to the method, the network delay of the total task is reduced under the network load constraint condition of the edge network nodes, the situations of task congestion and resource waste are reduced, the method is high in convergence speed and high in efficiency, the obtained result has good stability, and the performance and efficiency of the remote network are improved on the whole.

Description

Technical Field

[0001] The present invention belongs to the technical field of network performance optimization in a remote calibration system structure, and in particular relates to a network performance optimization method and system in a remote calibration system based on an ant colony algorithm. Background Art

[0002] Remote calibration is to calibrate the object at a distance, that is, remote automatic control of the calibration process, remote data transmission, remote processing of calibration data, and remote comprehensive judgment of the results. As a vital link in the power system, remote calibration is becoming more and more advanced and automated. With the widespread popularization of network applications and the increase in remote devices in remote calibration systems, the network traffic has also increased. How to effectively manage and optimize remote network performance has become an important challenge.

[0003] At present, general network optimization methods are usually based on static rules or manual configuration, which are difficult to adapt to the dynamically changing network environment and the diversity of remote devices. The existing network optimization methods have limitations and cannot effectively realize adaptive and dynamic network performance optimization, which leads to poor performance and management of remote networks. They do not consider the synchronization dependencies between network nodes, which leads to network congestion in remote calibration systems and greatly increases the network delay in remote calibration tasks. In summary, the current network optimization methods are prone to network congestion, which increases the network delay in remote calibration tasks and cannot effectively realize efficient optimization of adaptive and dynamic network performance. Summary of the invention

[0004] The present invention provides a network performance optimization method and system in a remote calibration system based on an ant colony algorithm, aiming to solve the problems that the current network optimization method is prone to network congestion, increases network delay in remote calibration tasks, and cannot effectively achieve efficient optimization of adaptive and dynamic network performance.

[0005] In order to achieve the above object, the present invention adopts the following technical scheme: The present invention provides a method for optimizing network performance in a remote calibration system based on an ant colony algorithm, comprising the following steps: S1. Based on the basic model of the remote calibration system, define a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of the central network nodes and edge network nodes to form basic data; S2. Use the ant colony algorithm to schedule the central network nodes and the edge network nodes, and obtain the optimal solution of the remote calibration system network performance optimization solution through an iterative process under the condition of minimizing the total delay of the network tasks in the remote calibration system and satisfying the network load constraints of the central network nodes and the edge network nodes; S3. According to the optimal solution of the remote calibration system network performance optimization solution, adjust and optimize the network performance parameters of the remote calibration system, thereby realizing the optimization of the remote calibration system network performance.

[0006] In some embodiments, in S1 , a basic model of the remote calibration system is constructed by a mathematical model.

[0007] In some implementations, in S1, the basic model of the remote calibration system further includes: defining logical addresses between network nodes, total data packet size, data transmission rate, transmission delay and type of network processes.

[0008] In some implementations, in S2, the condition for minimizing the total delay of the network task in the remote calibration system includes: (1); (2); (3); (4); (5); (6); (7); in, represents a collection of central network nodes with different locations, Represents a collection of edge network nodes at different locations, It is a collection of task types that can be processed between network nodes. Represents the set of network processes between nodes, that is, , Represents the central network node and edge network nodes The network process between Represents a network process The total size of packets transmitted, Represents a network process The data transfer rate, Indicates the network process Transmission delay, Indicates the network process Type, binary variable Indicates the task At the central network node and edge network nodes The first Run in order, i.e. tasks Is the central network node and edge network nodes The first tasks, binary variables and Indicates whether the central network node and edge network node can transmit and receive this type of network process.

[0009] In some implementations, in S2, the network load constraint conditions of the central network node and the edge network node include: defining the network bandwidth as , then at any time, the network bandwidth and throughput should satisfy the constraints of the following formula: (8); in, is the network bandwidth, Network throughput for remote calibration systems.

[0010] In some implementations, in S2, the optimal solution of the remote calibration system network performance optimization solution is obtained by using the ant colony algorithm: S21, initializing pheromone according to the importance of each network node; S22, constructing a pseudo-random transfer probability of the ant colony based on the initialized pheromone, and initializing the initial task allocation of the network task process; S23, after completing the initial matching between the central network node and the edge network node using the pseudo-random transfer probability, locally updating the pheromone; S24, after completing one iteration, the optimal matching solution is obtained, and the currently optimal global matching solution is obtained by comparing the previously obtained solution, and then the pheromone on the mapping path between its network process and the network node is updated; S25. If the current number of iterations is less than the maximum number of iterations, jump to S22. If it is greater, end the iteration and output the optimal solution for the remote calibration system network performance optimization solution.

[0011] Further, in S21, the pheromone is initialized specifically as follows: the pheromone is initialized by evaluating the importance index of each network node, and the specific calculation process formula of the initial pheromone and the importance of the network node is as follows: (9); in, is the importance of the central network node, for The number of network processes of the type, For the central network node to be able to handle The number of nodes of type process.

[0012] Furthermore, in S21, the importance calculation formula of the edge network node is as follows: (10); in, is the importance of edge network nodes, for The number of network processes of the type, For edge network nodes to be able to process The number of nodes of type process.

[0013] Furthermore, in S23, the local update pheromone is specifically calculated as follows: (11); in, Indicates pheromone volatility factor, Indicates the degree of pheromone residue, It represents the amount of pheromone on the mapping path. The expression of the amount of pheromone on the mapping path is: (12); in, For the The ant is in the Iterate to find the solution, It is the execution time of the network task of the objective function after a solution is completed.

[0014] The present invention also provides a network performance optimization system in a remote calibration system based on an ant colony algorithm, the system comprising a basic data definition module, an ant colony scheduling optimization module and a performance parameter adjustment module; wherein: The basic data definition module is used to define a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of the central network nodes and edge network nodes based on a basic model of the remote calibration system to form basic data; The ant colony scheduling optimization module is used to schedule the central network nodes and edge network nodes. Under the condition of minimizing the total delay of network tasks in the remote calibration system and satisfying the network load constraints of the central network nodes and edge network nodes, the optimal solution of the remote calibration system network performance optimization scheme is obtained through an iterative process. The performance parameter adjustment module is used to adjust and optimize the network performance parameters of the remote calibration system according to the optimal solution of the remote calibration system network performance optimization solution output by the ant colony scheduling optimization module, so as to optimize the network performance of the remote calibration system.

[0015] Compared with the prior art, the network performance optimization method and system in the remote calibration system based on the ant colony algorithm of the present invention have the following beneficial effects: The present invention provides a network performance optimization method in a remote calibration system based on an ant colony algorithm, comprising the following steps: S1, based on the basic model of the remote calibration system, defining a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of the central network nodes and edge network nodes, to form basic data; S2, using the ant colony algorithm to schedule the central network nodes and edge network nodes, and obtaining the optimal solution of the network performance optimization scheme of the remote calibration system through an iterative process under the condition of satisfying the minimum total delay of network tasks in the remote calibration system and the network load constraints of the central network nodes and edge network nodes; S3, according to the optimal solution of the network performance optimization scheme of the remote calibration system, adjusting and optimizing the network performance parameters of the remote calibration system, thereby realizing the optimization of the network performance of the remote calibration system. Based on the above, the present invention initializes pheromones by calculating the importance of network nodes, and selects the most suitable server client processing tasks in each iteration through pseudo-random probability selection, and finally obtains a global optimal solution under limited time and cost. According to the characteristics of the problem, the present invention fully considers the synchronization dependency of each node. Under the network load constraint of the edge network node, the network delay of the total task is greatly reduced, and the occurrence of task congestion and resource waste is reduced. From the performance point of view, the method of the present invention has fast convergence speed, high efficiency, and the obtained results have good stability. The present invention solves the limitations of existing network optimization methods and improves the performance and efficiency of remote networks. By applying the ant colony algorithm to the network structure in the remote calibration system, adaptive and dynamic network performance optimization can be achieved, thereby better meeting the needs of remote network management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 A schematic diagram of a network performance optimization method in a remote calibration system based on an ant colony algorithm and a model of a system application scenario in the system according to the present invention; Figure 2 The present invention is a flow chart of a method for optimizing network performance in a remote calibration system based on an ant colony algorithm and a method for obtaining an optimal solution for a network performance optimization scheme of the remote calibration system through an ant colony algorithm in the system. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0020] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0021] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0022] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0023] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0024] How to solve the limitations of existing network optimization methods and improve the performance and efficiency of remote networks. How to achieve adaptive and dynamic network performance optimization to better meet the needs of remote network management.

[0025] like Figure 1 As shown, the present invention provides a network performance optimization method in a remote calibration system based on an ant colony algorithm. S1. Based on the basic model of the remote calibration system, define a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of the central network nodes and edge network nodes to form basic data; S2. Use the ant colony algorithm to schedule the central network nodes and the edge network nodes, and obtain the optimal solution of the remote calibration system network performance optimization solution through an iterative process under the condition of minimizing the total delay of the network tasks in the remote calibration system and satisfying the network load constraints of the central network nodes and the edge network nodes; S3. According to the optimal solution of the remote calibration system network performance optimization solution, adjust and optimize the network performance parameters of the remote calibration system, thereby realizing the optimization of the remote calibration system network performance.

[0026] The present invention uses an ant colony algorithm to simulate the behavior of ants in the process of searching for food, so as to optimize the performance of network equipment in a remote calibration system. First, the network topology is modeled to determine the relationship between network nodes and connections; then, the ant colony algorithm is used to iterate, and the optimal solution of the network performance optimization scheme is obtained through each iterative optimization scheme, thereby optimizing the network performance of the remote calibration system. According to the characteristics of the remote calibration system, the present invention fully considers the synchronization dependency relationship between each network node. Under the condition of constraints between remote calibration systems, the remote calibration system network optimization method based on the ant colony algorithm has the characteristics of simplicity, high efficiency, strong adaptability, etc., which alleviates the network congestion in the remote calibration system and greatly reduces the network delay in the remote calibration task, thereby improving the network performance of the remote calibration system.

[0027] The ant colony algorithm adopted by the present invention, as an optimization algorithm for simulating the behavior of biological groups, has been widely used in many fields and has shown good performance in solving complex optimization problems. The algorithm simulates the behavior of ants when searching for food. Through collective collaboration and information exchange, ants can find the optimal food source. Drawing on the idea of ​​the ant colony algorithm, the present invention achieves the purpose of dynamically adjusting and optimizing network performance by designing an optimization method suitable for network performance in a remote calibration system and simulating the behavior of ants in a network communication path. The present invention mathematically models the network performance optimization problem in the remote calibration system, and uses the ant colony algorithm to reasonably schedule the central network nodes and edge network nodes. Under the condition of satisfying the minimum total delay of network tasks in the remote calibration system and the network load constraints of the central network nodes and edge network nodes, the optimal solution of the network performance optimization scheme of the remote calibration system is obtained, and the network performance of the remote calibration system is optimized. The present invention greatly reduces the occurrence of task congestion and realizes load balancing of the server, while ensuring the reduction of server idling, minimizing the total completion time of the network task.

[0028] The network performance optimization method and system in the remote calibration system based on the ant colony algorithm of the present invention are further described in detail below through specific embodiments.

[0029] In order to obtain the best network performance optimization solution, the present invention models the remote calibration system. represents the remote calibration network, where A collection of central network nodes with different locations is shown. Represents a collection of edge network nodes at different locations. It is worth noting that not all network tasks can be transmitted between any two network nodes. Define central network nodes and edge network nodes The executable task type attribute is used Specifically, It is a set of task types that can be processed between network nodes. 0 and 1 are used to indicate whether each task can be executed, where 0 indicates executable and 1 indicates not executable. It is defined that only one network process can be executed between network nodes at a time, and multiple network processes cannot be responded to at the same time, otherwise it will cause network congestion. Represents the logical address between each network node. All distance measurements in this article are measured using straight-line measurements. For example, the central network node and edge network nodes The distance between them is calculated by the following formula: ; use Represents the set of network processes between nodes, that is, .in Represents the central network node and edge network nodes The network process between. Represents a network process The total size of packets transmitted; Represents a network process Data transfer rate; Indicates the network process Transmission delay; Indicates the network process Type.

[0030] Definition: Any network process generated in the remote calibration system network must determine that its corresponding edge network node has successfully received all data packets before the corresponding central node can send a new data packet to it again. Therefore, clear decision variables are needed to indicate the processing position and order of the network process. Using binary variables Representative tasks At the central network node and edge network nodes The first Run in order, i.e. tasks Is the central network node and edge network nodes The first Finally, because different central network nodes and edge network nodes will perform specific network process types. Therefore, define the binary variable and To indicate whether the central network node and the edge network node can transmit and receive this type of network process. The conditions for the shortest total execution delay of the network process are as follows: (1); (2); (3); (4); (5); (6); (7); in A collection of central network nodes with different locations is shown. Represents a collection of edge network nodes at different locations. It is a set of task types that can be processed between network nodes. 0 and 1 are used to indicate whether each task can be executed, where 0 indicates executable and 1 indicates not executable. Represents the set of network processes between nodes, that is, .in Represents the central network node and edge network nodes The network process between. Represents a network process The total size of packets transmitted; Represents a network process Data transfer rate; Indicates the network process Transmission delay; Indicates the network process Type of binary variable. Representative tasks At the central network node and edge network nodes The first Run in order, i.e. tasks Is the central network node and edge network nodes The first Tasks. Binary variables and Indicates whether the central network node and edge network node can transmit and receive this type of network process.

[0031] The constraints require that in the same pair of network central network nodes and edge network nodes, only one network process can be transmitted at the same time, and the network process in the later order must be executed after the network process in the previous order is completed; the network process can only run between a pair of central network nodes and edge network nodes with matching process types; the same network process cannot be executed repeatedly; the task type must be able to be sent by the central network node and received by the edge network node; the network process can only run on the node to which it belongs.

[0032] Define the network bandwidth as , then at any time, the network bandwidth and throughput should also meet the following constraints: (8); The formula indicates that at any time, the network throughput of the entire remote calibration system is less than the total network bandwidth, that is, the ratio of the two, the network load, is less than 1.

[0033] like Figure 2 As shown, the present invention uses an ant colony heuristic algorithm for iteration, which is as follows: S21, initializing pheromone according to the importance of each network node; S22, constructing the pseudo-random transfer probability of the ant colony based on the initialized pheromone, and initializing the initial task allocation of each network task process; S23, after completing the initial matching between the central network node and the edge network node using the pseudo-random transfer probability, locally updating the pheromone rule; S24, after completing one iteration, the optimal matching solution is obtained, and the currently optimal global matching solution is obtained by comparing the previously obtained solution, and then the pheromone on the mapping path between its network process and the network node is updated; S25, compare and judge, if the current number of iterations is less than the maximum number of iterations, jump to S22, if it is greater, end the iteration and output the optimal matching solution.

[0034] In S21, the initialization pheromone is evaluated using the importance index of each network node. The specific calculation process of the initial pheromone and the importance of the network node is as follows: For any pair of network nodes, their current task set is known, define the variable for The number of network processes of the type, define variables The central network node is able to process The number of nodes of type process. Definition is the importance of the central network node, calculated as follows: (9); The more important the central network node is, the more network processes it needs to send, so its working time should be minimized as much as possible, and other central network nodes should be used first. Client use in As the initial pheromone of the client The definition of edge network node importance is similar to that of central network nodes. The definition variable Within the coverage area of ​​the edge network node The number of tasks of the type, define variables For edge network nodes to be able to process The number of nodes of type process.

[0035] The importance calculation formula of edge network nodes is as follows: (10); Also use the inverse of server importance As the initial pheromone of the server , thus completing the pheromone initialization.

[0036] In S22 of the present invention, the specific method of constructing the pseudo-random transfer probability of the ant colony is as follows: ; express Mission from Node transferred to The probability of a node. and Represent the heuristic factor and expected heuristic factor, respectively, representing the weights of pheromone and heuristic function, where , the reciprocal of the execution time is taken as the desire value of the pair of nodes to execute the network process. When the execution time of the process increases, the desire value of the central network node or the edge network node to execute the task decreases; when the execution time of the process decreases, the desire value of the central network node or the edge network node to execute the task increases.

[0037] In S23 of the present invention, the local renewal pheromone is specifically as follows: The update rules are as follows: (11); Pheromone volatile factors are express, The larger the pheromone is, the faster it evaporates. express, The value range is (0,1]. The larger the value, the smaller the influence of the previous empirical solution on the current solution selection. Represents the amount of pheromone on the mapping path, which is expressed as: (12); in, For the The ant is in the Iterate to find the solution, It is the value of the objective function after a solution is completed.

[0038] In S24 of the present invention, the pheromone on the mapping path between the task and the network node is updated as follows: After all ants complete an iteration, they obtain an optimal matching solution. By comparing it with the previously obtained solution, they obtain the current optimal global matching solution. After that, they update the pheromone on the mapping path between their tasks and the client server. The update rules are as follows: , where pheromone volatility factor , pheromone residue level , The value range is (0,1], and its meaning is the same as the local pheromone factor.

[0039] ; in, is the optimal solution of the iterative objective function so far.

[0040] The present invention also provides a network performance optimization system in a remote calibration system based on an ant colony algorithm, the system comprising a basic data definition module, an ant colony scheduling optimization module and a performance parameter adjustment module; wherein: Basic data definition module: used to define a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of central network nodes and edge network nodes based on the basic model of the remote calibration system to form basic data; the basic model of the remote calibration system is constructed through a mathematical model, and further defines the logical addresses between network nodes, as well as the total data packet size, data transmission rate, transmission delay and type of the network process.

[0041] Ant colony scheduling optimization module: Use the ant colony algorithm to schedule the central network nodes and edge network nodes. Under the condition of minimizing the total delay of network tasks in the remote calibration system and satisfying the network load constraints of the central network nodes and edge network nodes, the optimal solution of the network performance optimization scheme of the remote calibration system is obtained through an iterative process. The condition of minimizing the total delay includes the delay constraint on task processing between network nodes at different locations, and the network load constraint defines the constraint relationship between network bandwidth and throughput. The module is also responsible for initializing pheromones, constructing the pseudo-random transfer probability of the ant colony, initializing the initial task allocation of the network task process, locally updating and globally updating pheromones, and outputting the optimal solution after reaching the maximum number of iterations.

[0042] Performance parameter adjustment module: According to the optimal solution of the remote calibration system network performance optimization plan output by the ant colony scheduling optimization module, the network performance parameters of the remote calibration system are adjusted and optimized, including data transmission rate, transmission delay, etc., to achieve the optimization of the remote calibration system network performance.

[0043] In summary, the network performance optimization method in the remote calibration system based on the ant colony algorithm proposed in the present invention is used to optimize the remote calibration system of the smart grid, and the optimal solution of the network performance optimization scheme of the remote calibration system is obtained, thereby obtaining the optimal network performance optimization scheme, thereby reducing network delay, solving the problem of network congestion, and realizing the optimization of the network performance of the remote calibration system.

[0044] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the specification and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A network performance optimization method in a remote calibration system based on ant colony algorithm, characterized in that: The steps include: S1. Based on the basic model of the remote calibration system, define a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of the central network nodes and edge network nodes to form basic data; S2. Use the ant colony algorithm to schedule the central network nodes and the edge network nodes, and obtain the optimal solution of the remote calibration system network performance optimization solution through an iterative process under the condition that the total delay of the network tasks in the remote calibration system is minimized and the network load constraints of the central network nodes and the edge network nodes are met; S3. According to the optimal solution of the remote calibration system network performance optimization solution, adjust and optimize the network performance parameters of the remote calibration system, thereby realizing the optimization of the remote calibration system network performance.

2. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 1, characterized in that: In S1, the basic model of the remote calibration system is constructed by a mathematical model.

3. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 1, characterized in that: In S1, the basic model of the remote calibration system also includes: defining the logical addresses between network nodes, the total size of data packets, data transmission rate, transmission delay and type of network processes.

4. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 1, characterized in that: In S2, the conditions for minimizing the total delay of the network task in the remote calibration system include: （1）； （2）； （3）； （4）； （5）； （6）； （7）； in, represents a collection of central network nodes with different locations, Represents a collection of edge network nodes at different locations, It is a collection of task types that can be processed between network nodes. Represents the set of network processes between nodes, that is, , Represents the central network node and edge network nodes The network process between Represents a network process The total size of packets transmitted, Represents a network process The data transfer rate, Indicates the network process Transmission delay, Indicates the network process Type, binary variable Representation Task At the central network node and edge network nodes The first Run in order, i.e. tasks Is the central network node and edge network nodes The first tasks, binary variables and Indicates whether the central network node and edge network node can transmit and receive this type of network process.

5. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 1, characterized in that: In S2, the network load constraints of the central network node and the edge network node include: defining the network bandwidth as , then at any time, the network bandwidth and throughput should satisfy the constraints of the following formula: （8）； in, is the network bandwidth, Network throughput for remote calibration systems.

6. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 1, characterized in that: In S2, the optimal solution of the remote calibration system network performance optimization scheme is obtained by using the ant colony algorithm: S21, initializing pheromone according to the importance of each network node; S22, constructing a pseudo-random transfer probability of the ant colony based on the initialized pheromone, and initializing the initial task allocation of the network task process; S23, after completing the initial matching between the central network node and the edge network node using the pseudo-random transfer probability, locally updating the pheromone; S24, after completing one iteration, the optimal matching solution is obtained, and the currently optimal global matching solution is obtained by comparing the previously obtained solution, and then the pheromone on the mapping path between its network process and the network node is updated; S25. If the current number of iterations is less than the maximum number of iterations, jump to S22. If it is greater, end the iteration and output the optimal solution for the remote calibration system network performance optimization solution.

7. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 6, characterized in that: In the S21, the initialization pheromone is specifically: the initialization pheromone is evaluated by using the importance index of each network node, and the specific calculation process formula of the initial pheromone and the importance of the network node is as follows: （9）； in, is the importance of the central network node, for The number of network processes of the type, For the central network node to be able to handle The number of nodes of type process.

8. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 6, characterized in that: In S21, the importance calculation formula of the edge network node is as follows: （10）； in, is the importance of edge network nodes, for The number of network processes of the type, For edge network nodes to be able to process The number of nodes of type process.

9. The method for optimizing network performance in a remote calibration system based on ant colony algorithm according to claim 6, characterized in that: In S23, the local update pheromone is specifically expressed as follows: （11）； in, Indicates pheromone volatility factor, Indicates the degree of pheromone residue, It represents the amount of pheromone on the mapping path. The expression of the amount of pheromone on the mapping path is: （12）； in, For the The ant is in the Iterate to find the solution, It is the execution time of the network task of the objective function after a solution is completed.

10. A system based on which the method for optimizing network performance in a remote calibration system based on an ant colony algorithm according to any one of claims 1 to 9 is based, characterized in that: The network performance optimization system includes a basic data definition module, an ant colony scheduling optimization module and a performance parameter adjustment module; wherein: The basic data definition module is used to define a set of central network nodes and edge network nodes, as well as executable task type attributes and network process sets of the central network nodes and edge network nodes based on a basic model of the remote calibration system to form basic data; The ant colony scheduling optimization module is used to schedule the central network nodes and edge network nodes. Under the condition of minimizing the total delay of network tasks in the remote calibration system and satisfying the network load constraints of the central network nodes and edge network nodes, the optimal solution of the remote calibration system network performance optimization scheme is obtained through an iterative process. The performance parameter adjustment module is used to adjust and optimize the network performance parameters of the remote calibration system according to the optimal solution of the remote calibration system network performance optimization solution output by the ant colony scheduling optimization module, so as to optimize the network performance of the remote calibration system.

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