Electric power communication multi-route automatic planning method coupled with power grid constraint conditions

Abstract

The invention discloses an electric power communication multi-route automatic planning method coupled with a power grid constraint condition, and the method comprises the following steps: S1, node data acquisition: obtaining node data in network coverage, and drawing a network topology graph; s2, priority ranking: ranking the priorities of the transmitted data services according to the data service information, determining an allocation sequence according to the priority sequence, and enabling the higher the priority is, the higher the allocation sequence is; s3, acquiring a shortest path; s4, acquiring a secondary path; s5, performing dynamic adjustment; s6, optimizing the path; and S7, outputting a result: outputting the electric power communication multi-routing path meeting the constraint condition and the optimization target. According to the electric power communication multi-route automatic planning method coupled with the power grid constraint conditions, the shortest path data calculated according to the multi-constraint conditions is screened, and the recommended path is obtained by automatically planning and combining with the optimization service requirements and the shortest path data through an algorithm, so that the efficiency and accuracy of route planning can be remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent route planning, and in particular to a method for automatic planning of multiple routes of power communication coupled with power grid constraint conditions. Background Art

[0002] With the development of smart grids, power communication services have higher and higher requirements for network reliability and stability. The traditional single route planning method has risks. Once the main route fails, it may cause service interruption. Therefore, multi-route automatic planning came into being. By configuring multiple routing paths for key power services, redundant backup and fast switching of services are achieved, thereby improving the reliability and stability of service transmission.

[0003] The existing public technical solution announcement number CN106506357A discloses a dual routing configuration method and device for a power communication network, which belongs to the field of power communication technology. Based on the maximum disjoint algorithm, the method uses delay and risk as weights to perform routing planning for existing services in the network topology of the power communication network, select the main path and backup path of the service, and add delay and risk constraints to obtain two disjoint paths, with the main path having the shortest delay and the backup path having the lowest risk, thereby ensuring the normal transmission of the power communication network service and making it more suitable for the power communication network service routing configuration.

[0004] However, the current automatic planning of multiple routes for power communication needs to comprehensively consider the constraints such as safe relay protection and safe and stable control of the power grid, as well as optimization indicators such as service reliability, transmission delay, economy and overload avoidance, to achieve automatic planning of multiple routes for power communication. A single constraint will lead to congestion caused by excessive concentration of services on some links, thus affecting the normal operation of communication services. Summary of the invention

[0005] The purpose of the present invention is to provide a method for automatic planning of multiple routes for power communication coupled with power grid constraints, so as to solve the problem that the single constraint condition for automatic planning of multiple routes for power communication proposed in the above background technology will lead to excessive concentration of services carried by some links, resulting in congestion and affecting the normal operation of communication services.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for automatic planning of multiple routes of power communication coupled with power grid constraints, comprising the following steps:

[0007] S1. Node data acquisition: obtain node data in the network coverage and draw a network topology map;

[0008] S2. Priority sorting: sort the priorities of the transmitted data services according to the data service information, and determine the allocation order according to the priority order. The higher the priority, the higher the allocation order.

[0009] S3, shortest path acquisition: obtain the shortest path based on the Dijkstra algorithm, calculate the shortest path between nodes in the network to achieve efficient data transmission and routing selection, and store the path data in the memory after obtaining the shortest path;

[0010] S4, secondary path acquisition: select different starting points and calculate the shortest path length from the current starting point to each node;

[0011] S5, dynamic adjustment: constraint analysis, adjusting path data according to the constraints, and obtaining the initial routing path of the basic constraints;

[0012] S6, path optimization: based on the initial path, further optimize the path according to the optimization target. If the path does not meet the requirements, return to step S5 and re-acquire the routing path.

[0013] S7. Result output: output the multi-routing path of power communication that meets the constraints and optimization objectives, and evaluate the output path.

[0014] Preferably, the step S3, obtaining the shortest path, further comprises the following steps:

[0015] S301, create an auxiliary array D1, used to store the shortest path length from the starting point to each node. Initially, except for the value corresponding to the starting point in D1 which is 0, the values ​​of the remaining nodes are set to infinity. At the same time, create a set S1, used to store the visited nodes. Initially, S1 only contains the starting point.

[0016] S302, among the unvisited nodes, select the node U1 closest to the starting point, add it to the set S1, update the shortest path length of the node V1 adjacent to the node U1, if the path from the node U1 to the node V1 is shorter than the currently recorded shortest path length, update D1[V1] to the new shorter path length, repeat the above steps until all nodes are visited or the termination condition is reached;

[0017] S303. When the algorithm ends, the auxiliary array D1 stores the shortest path length from the starting point to each node.

[0018] Preferably, the node data in the step S1, node data acquisition, includes connection relationships and parameters of nodes, lines, and transformers.

[0019] Preferably, the data service information in the priority sorting in step S2 includes service priority, source node, destination node, bandwidth requirement and delay requirement.

[0020] Preferably, the step S4, obtaining the secondary path, comprises the following steps:

[0021] S401, create an auxiliary array Dn for storing the shortest path length from the current starting point to each node. Initially, except for the value corresponding to the current starting point in Dn being 0, the values ​​of the remaining nodes are all set to infinity. At the same time, create a set Sn for storing the visited nodes. Initially, Sn only contains the current starting point.

[0022] S402. Select the node Un that is closest to the current starting point from the unvisited nodes, add it to the set Sn, and update the shortest path length of the node Vn adjacent to the node Un. If the path from the node Un to the node Vn is shorter than the currently recorded shortest path length, update Dn[Vn] to the new shorter path length. Repeat the above steps until all nodes are visited or the termination condition is reached.

[0023] S403. When the algorithm ends, the auxiliary array Dn stores the shortest path length from the current starting point to each node.

[0024] Preferably, the step S5, dynamic adjustment, comprises the following steps:

[0025] S501, screening unqualified paths: screening unqualified routing paths according to the node data and the data service information;

[0026] S502, maximum disjoint path: select the best routing path from the remaining routing paths, and find the maximum disjoint path in the network through the Bhandri maximum disjoint routing algorithm;

[0027] S503, obtaining recommended paths: searching for two or more paths such that there are no common nodes or links between the paths.

[0028] Preferably, the constraints in the step S5 and the dynamic adjustment include minimizing transmission delay, maximizing service reliability, and reducing equipment cost, and determining the optimization target of route planning according to service requirements.

[0029] Preferably, the shortest path data in the step S3, shortest path acquisition, is stored and preset, and in subsequent routing path queries, the stored shortest path data is used preferentially, and the shortest path data includes source node data, destination node data, transmission time, link reliability, bandwidth utilization, and usage cost.

[0030] Preferably, in the step S6, path optimization, a path with the shortest path length, the highest bandwidth utilization, or the highest node and link reliability can be selected as the final route.

[0031] Preferably, in the step S7, in the result output, optimization suggestions for specific scenarios are provided according to the evaluation results, such as adding redundant paths and adjusting device configuration.

[0032] Compared with the prior art, the present invention provides a method for automatic planning of multiple routes of power communication coupled with grid constraints, which has the following beneficial effects:

[0033] 1. By screening the shortest path data calculated by multiple constraints, using the algorithm to automatically plan and optimize business needs and the shortest path data, the recommended path can be obtained, which can significantly improve the efficiency and accuracy of route planning and reduce planning costs. Multiple constraints greatly enrich the automatic planning conditions of multiple routes for power communication and improve the reliability of communication services.

[0034] 2. Use the memory to preset the shortest path data. During the routing path calculation process, by comparing the source node data and the destination node data, quickly list the adapted routing paths, and select the optimal routing path in the set, which greatly shortens the routing path planning time and improves planning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0036] Figure 2 This is a schematic flow chart of step S5, a method for dynamic adjustment of the present invention;

[0037] Figure 3 This is a schematic diagram of the shortest path data of the present invention;

[0038] Figure 4 It is a schematic diagram of the working process of the memory of the present invention;

[0039] Figure 5 This is a schematic diagram of data service information of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention.

[0041] like Figure 1 and Figure 5 As shown, a method for automatic planning of multiple routes of power communication coupled with power grid constraints comprises the following steps:

[0042] S1. Node data acquisition: Obtain node data in the network coverage and draw a network topology diagram. The node data in step S1. Node data acquisition includes connection relationships and parameters of nodes, lines, and transformers.

[0043] S2, priority sorting: Sort the priority of the transmitted data services according to the data service information, and determine the allocation order according to the priority order. The higher the priority, the higher the allocation order. The data service information in step S2, priority sorting includes service priority, source node, destination node, bandwidth requirement and delay requirement.

[0044] like Figure 1 , Figure 3 and Figure 4 As shown, a method for automatic planning of multiple routes of power communication coupled with power grid constraints comprises the following steps:

[0045] S3, shortest path acquisition: obtain the shortest path based on Dijkstra algorithm, calculate the shortest path between nodes in the network to achieve efficient data transmission and routing selection, and store the path data in the memory after obtaining the shortest path. S301, create an auxiliary array D1 to store the shortest path length from the starting point to each node. Initially, except for the value corresponding to the starting point in D1 which is 0, the values ​​of the remaining nodes are set to infinity. At the same time, create a set S1 to store the visited nodes. Initially, S1 only contains the starting point.

[0046] S302, among the unvisited nodes, select the node U1 closest to the starting point, add it to the set S1, update the shortest path length of the node V1 adjacent to the node U1, if the path from the node U1 to the node V1 is shorter than the currently recorded shortest path length, update D1[V1] to the new shorter path length, repeat the above steps until all nodes are visited or the termination condition is reached;

[0047] S303, when the algorithm ends, the auxiliary array D1 stores the shortest path length from the starting point to each node. The shortest path data in step S3, the shortest path acquisition is stored and preset. In subsequent routing path queries, the stored shortest path data is used first. The shortest path data includes source node data, destination node data, transmission time, link reliability, bandwidth utilization, and usage cost. The shortest path data is preset using the memory. In the routing path calculation process, by comparing the source node data and the destination node data, the adapted routing paths are quickly listed, and the optimal routing path is selected in the set, which greatly shortens the routing path planning time and improves the planning efficiency.

[0048] S4, secondary path acquisition: select different starting points, and calculate the shortest path length from the current starting point to each node, S401, create an auxiliary array Dn, used to store the shortest path length from the current starting point to each node, initially, except for the value corresponding to the current starting point in Dn is 0, the values ​​of the remaining nodes are set to infinity, and at the same time create a set Sn, used to store the visited nodes, initially, Sn only contains the current starting point;

[0049] S402. Select the node Un that is closest to the current starting point from the unvisited nodes, add it to the set Sn, and update the shortest path length of the node Vn adjacent to the node Un. If the path from the node Un to the node Vn is shorter than the currently recorded shortest path length, update Dn[Vn] to the new shorter path length. Repeat the above steps until all nodes are visited or the termination condition is reached.

[0050] S403. When the algorithm ends, the auxiliary array Dn stores the shortest path length from the current starting point to each node. Through step S3, the shortest path acquisition and step S4, the secondary path acquisition, the pre-set shortest path data stored in the memory is obtained and can be directly output. If there is a conflict in the data results, the calculation can be repeated.

[0051] like Figure 1 , Figure 2 and Figure 4 As shown, a method for automatic planning of multiple routes of power communication coupled with power grid constraints comprises the following steps:

[0052] S5. Dynamic adjustment: analyzing constraints, adjusting path data according to constraints, and obtaining the initial routing path of basic constraints. S501. Screening unqualified paths: screening unqualified routing paths according to node data and data service information;

[0053] S502, maximum disjoint path: select the best routing path from the remaining routing paths, and find the maximum disjoint path in the network through the Bhandri maximum disjoint routing algorithm;

[0054] S503, obtain recommended paths: find two or more paths so that there are no common nodes or links between these paths; step S5, the constraints in the dynamic adjustment include minimizing transmission delay, maximizing business reliability, and reducing equipment costs. According to business needs, the optimization goal of route planning is determined, and the shortest path data calculated is screened by multiple constraints. The algorithm is used for automatic planning combined with optimization of business needs and shortest path data to obtain the recommended path, which can significantly improve the efficiency and accuracy of route planning and reduce planning costs. Multiple constraints greatly enrich the automatic planning conditions of multiple routes of power communication and improve the reliability of communication services.

[0055] S6, path optimization: Based on the initial path, the path is further optimized according to the optimization target. If the path does not meet the requirements, return to step S5 and re-acquire the routing path. In step S6, path optimization, the path with the shortest path length, the highest bandwidth utilization, or the highest node and link reliability can be selected as the final route.

[0056] S7, result output: output the multi-routing paths of power communication that meet the constraints and optimization goals, and evaluate the output paths. In step S7, result output, provide optimization suggestions for specific scenarios based on the evaluation results, such as adding redundant paths and adjusting equipment configurations. The result output is divided into two parts: one is the evaluation result, including the optimization suggestions after evaluation, and the other is the output of the shortest path to achieve automatic route planning. When the result is output, the pre-set shortest path data can be directly obtained to obtain and output the shortest path, or the shortest path data can be obtained based on recalculation.

[0057] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatic planning of multiple routes for power communication coupled with grid constraints, characterized in that: The following steps are involved: S1. Node data acquisition: obtain node data in the network coverage and draw a network topology map; S2. Priority sorting: sort the priorities of the transmitted data services according to the data service information, and determine the allocation order according to the priority order. The higher the priority, the higher the allocation order. S3, shortest path acquisition: obtain the shortest path based on the Dijkstra algorithm, calculate the shortest path between nodes in the network to achieve efficient data transmission and routing selection, and store the path data in the memory after obtaining the shortest path; S4, secondary path acquisition: select different starting points and calculate the shortest path length from the current starting point to each node; S5, dynamic adjustment: constraint analysis, adjusting path data according to the constraints, and obtaining the initial routing path of the basic constraints; S6, path optimization: based on the initial path, further optimize the path according to the optimization target. If the path does not meet the requirements, return to step S5 and re-acquire the routing path. S7. Result output: output the multi-routing path of power communication that meets the constraints and optimization objectives, and evaluate the output path.

2. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 1, characterized in that: The step S3, shortest path acquisition, further comprises the following steps: S301, create an auxiliary array D1, used to store the shortest path length from the starting point to each node. Initially, except for the value corresponding to the starting point in D1 which is 0, the values ​​of the remaining nodes are set to infinity. At the same time, create a set S1, used to store the visited nodes. Initially, S1 only contains the starting point. S302, among the unvisited nodes, select the node U1 closest to the starting point, add it to the set S1, update the shortest path length of the node V1 adjacent to the node U1, if the path from the node U1 to the node V1 is shorter than the currently recorded shortest path length, update D1[V1] to the new shorter path length, repeat the above steps until all nodes are visited or the termination condition is reached; S303. When the algorithm ends, the auxiliary array D1 stores the shortest path length from the starting point to each node.

3. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 2, characterized in that: The node data in the step S1, node data acquisition, includes connection relationships and parameters of nodes, lines, and transformers.

4. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 3 is characterized in that: The data service information in the priority sorting in step S2 includes service priority, source node, destination node, bandwidth requirement and delay requirement.

5. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 4, characterized in that: The step S4, secondary path acquisition, comprises the following steps: S401, create an auxiliary array Dn for storing the shortest path length from the current starting point to each node. Initially, except for the value corresponding to the current starting point in Dn being 0, the values ​​of the remaining nodes are all set to infinity. At the same time, create a set Sn for storing the visited nodes. Initially, Sn only contains the current starting point. S402. Select the node Un that is closest to the current starting point from the unvisited nodes, add it to the set Sn, and update the shortest path length of the node Vn adjacent to the node Un. If the path from the node Un to the node Vn is shorter than the currently recorded shortest path length, update Dn[Vn] to the new shorter path length. Repeat the above steps until all nodes are visited or the termination condition is reached. S403. When the algorithm ends, the auxiliary array Dn stores the shortest path length from the current starting point to each node.

6. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 5, characterized in that: The step S5, dynamic adjustment, comprises the following steps: S501, screening unqualified paths: screening unqualified routing paths according to the node data and the data service information; S502, maximum disjoint path: select the best routing path from the remaining routing paths, and find the maximum disjoint path in the network through the Bhandri maximum disjoint routing algorithm; S503, obtaining recommended paths: searching for two or more paths such that there are no common nodes or links between the paths.

7. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 6, characterized in that: The constraints in the step S5 and dynamic adjustment include minimizing transmission delay, maximizing service reliability, and reducing equipment costs, and determining the optimization target of route planning according to service requirements.

8. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 7, characterized in that: The shortest path data in the step S3, shortest path acquisition, is stored and preset, and in subsequent routing path inquiries, the stored shortest path data is used preferentially, and the shortest path data includes source node data, destination node data, transmission time, link reliability, bandwidth utilization, and usage cost.

9. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 8, characterized in that: In the step S6, in the path optimization, the path with the shortest path length, the highest bandwidth utilization rate or the highest node and link reliability can be selected as the final route.

10. The method for automatic planning of multiple routes of power communication coupled with grid constraints according to claim 9, characterized in that: In the step S7, in the result output, optimization suggestions for specific scenarios are provided according to the evaluation results, such as adding redundant paths and adjusting device configuration.

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