Power communication service route planning method and system based on particle swarm optimization

Abstract

The invention discloses an electric power communication service route planning method and system based on a particle swarm optimization algorithm, and the method comprises the steps: building an electric power communication network topology model and an electric power communication service model, and enabling the electric power communication network topology model to be used for describing node and link information; the electric power communication service model is used for describing requirements and importance degrees of different services on communication network performance; defining constraint conditions for ensuring that the route planning scheme meets service requirements and reducing network risks at the same time; using an improved Dijkstra algorithm to generate K shortest paths between a source node and a destination node as an initial path set; and solving a multi-objective optimization problem by using a particle swarm optimization algorithm, and finding an optimal path meeting a constraint condition through iterative search. According to the invention, the problems of unbalanced service importance distribution and high local network risk of the existing electric power communication service route planning method are solved.

Description

Technical Field

[0001] The present invention belongs to the field of power communication networks, and in particular relates to a method and system for planning power communication service routing based on a particle swarm algorithm. Background Art

[0002] As the infrastructure for carrying information exchange between power systems, the power communication network is spread throughout all aspects of the production and management of the power system. As the information exchange between power systems becomes more and more frequent, a large number of power communication services are densely distributed in the network. Once a network failure occurs, it will pose a threat to the operation of the power grid. Therefore, how to reduce the operation risk of the power communication network service channel has become a top priority in the research work in the field of power communication. However, since the power communication service has inherent properties that are different from traditional telecommunications services, the power communication network cannot directly apply relevant research results in the telecommunications field. Therefore, it is necessary to study the routing planning method that reduces the channel operation risk based on the characteristics of the power communication service. Summary of the invention

[0003] In order to solve the problems of uneven distribution of business importance and high local network risk in the existing power communication service routing planning methods, the present invention provides a power communication service routing planning method based on particle swarm algorithm, which outputs the top K shortest paths that meet the constraints through an improved Dijkstar algorithm, and then uses the particle swarm optimization algorithm to solve the multi-objective optimization problem, comprehensively considering the characteristics of the power business and the risk requirements of different businesses to plan the optimal path, thereby achieving the technical effect of balancing business importance and reducing network risks.

[0004] According to one aspect of the present invention, there is provided a method for planning a route for a power communication service based on a particle swarm algorithm, comprising:

[0005] Establishing a power communication network topology model and a power communication business model, wherein the power communication network topology model is used to describe node and link information, and the power communication business model is used to describe the requirements and importance of different businesses on communication network performance;

[0006] Define constraints to ensure that routing plans meet business requirements while reducing network risks;

[0007] The improved Dijkstra algorithm is used to generate K shortest paths between the source node and the destination node as the initial path set;

[0008] The particle swarm optimization algorithm is used to solve the multi-objective optimization problem, and the optimal path that meets the constraints is found through iterative search. The optimal path outputs the optimal routing solution that meets the constraints, providing guidance for the service configuration of the power communication network.

[0009] As a further technical solution, the power communication business model includes business importance, latency requirements and bandwidth requirements. The establishment of the power communication business model uses the business importance model, considers factors such as business type (internal / external, production control / enterprise management), service type (latency, bandwidth, etc.), quantifies business importance, and assigns different priorities to different businesses.

[0010] As a further technical solution, the constraint conditions include overall network risk, network bandwidth, latency and service channel availability. The constraint condition definition uses risk indicators to define node risk and edge risk, comprehensively considers factors such as service importance, node availability, link availability, etc., and evaluates the network risk level.

[0011] The above technical solutions can be used as independent optimization targets, and the risk calculation is directly related to the business importance, thus ensuring that the routing planning solution meets business needs while reducing network risks.

[0012] As a further technical solution, the overall network risk includes node risk and edge risk.

[0013] As a further technical solution, an improved Dijkstra algorithm is used to generate K shortest paths between the source node and the destination node as an initial path set, including:

[0014] initialization:

[0015] Let d(v 1 )=0, indicating that from the starting node v 1 The weight of the shortest path to itself is 0; let d(v 1 )=w 1j (j=2,3,…p), indicating that from v 1 To other nodes v j The shortest path weight is the weight of the link between them; the vertex set S of the determined shortest path = {v 1}; The node set R of the undetermined shortest path = VS = {v 2 , v 3 …v p};

[0016] Select the next node:

[0017] Find a node v in the set R k , so that v k Add to the node set S of the determined shortest path; at the same time, update the node set R = VS of the undetermined shortest path; determine whether R is an empty set, if R is an empty set, proceed to the next step;

[0018] Update the shortest path weight:

[0019] For each node in the set R, update d(v j ) by comparing the value of the newly added node v k Arrival j The shorter path of the current record is updated to d(v j ); After the update is completed, return to the node selection step to continue the cycle;

[0020] Get the shortest path:

[0021] After |V|-1 cycles, all nodes have been processed. At this time, d(v j The final value of the node where )(j=1,2,3,…p) is the node v 1 The shortest path length to the remaining nodes; output K shortest paths.

[0022] The Dijkstra algorithm improved by the above technical solution dynamically adjusts the weight of the shortest path, preferentially allocates high-importance services to low-risk paths, and outputs the top K shortest paths that meet the constraints through the improved Dijkstar algorithm.

[0023] As a further technical solution, the particle swarm optimization algorithm is used to solve the multi-objective optimization problem, including:

[0024] Initialize a group of random particles as candidate solutions;

[0025] Evaluate the objective function value for each particle;

[0026] Particles update their speed and position according to individual optimal and global optimal;

[0027] Repeat the above steps until the termination condition is met.

[0028] In the above technical solution, the particle swarm optimization uses the initial path to generate the objective function, uses the improved Dijkstra algorithm to generate K shortest paths between the source node and the destination node as the initial path set, and takes the minimization of the overall network risk as the objective function, and considers constraints such as network bandwidth, latency, and service channel availability. The particle swarm optimization parameters of this technical solution are dynamically adjusted with the importance of the service to achieve differentiated optimization.

[0029] As a further technical solution, the termination condition includes the number of iterations reaching a preset value or the objective function value reaching a preset accuracy.

[0030] According to one aspect of the present invention, there is provided a power communication service routing planning system based on a particle swarm algorithm, comprising:

[0031] A model building module, used to establish a power communication network topology model and a power communication business model, wherein the power communication network topology model is used to describe node and link information, and the power communication business model is used to describe the requirements and importance of different businesses on communication network performance;

[0032] Indicator building blocks for defining constraints to ensure that routing plans meet business needs while reducing network risks;

[0033] An initial path generation module is used to generate K shortest paths between a source node and a destination node as an initial path set using an improved Dijkstra algorithm;

[0034] The optimal path solving module is used to solve multi-objective optimization problems using the particle swarm optimization algorithm and find the optimal path that meets the constraints through iterative search.

[0035] According to one aspect of the present invention, there is provided a device for routing planning of electric power communication services based on a particle swarm algorithm, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the method for routing planning of electric power communication services based on a particle swarm algorithm.

[0036] According to one aspect of the present specification, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the method for planning power communication service routing based on particle swarm algorithm.

[0037] The present invention provides a method for planning routing of power communication services based on particle swarm algorithm, aiming to effectively reduce the risk level of power optical communication network and provide strong guarantee for the safe operation of power grid. Compared with the prior art, the present invention has the following beneficial effects:

[0038] (1) Reduce network risks: By considering the distribution of business importance, we can avoid excessive concentration of business on certain links or nodes, thereby reducing local network risks and improving the overall reliability of the network.

[0039] (2) Balance business importance: Plan different transmission paths according to the importance of different businesses to ensure that key businesses are given priority while taking into account the needs of other businesses to achieve a balance in business importance.

[0040] (3) Meeting business needs: Considering parameters such as business latency requirements and bandwidth requirements, planning routing solutions that meet business communication needs and ensuring the stability and reliability of business transmission.

[0041] (4) Improve routing planning efficiency: Utilize the fast convergence characteristics of the particle swarm optimization algorithm to efficiently find the best routing solution that meets the constraints and improve routing planning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A schematic flow chart of a method for routing planning of power communication services based on a particle swarm algorithm is provided in an embodiment of the present invention.

[0044] Figure 2 A schematic diagram of a business importance evaluation system provided in an embodiment of the present invention.

[0045] Figure 3 A schematic diagram of a power communication route planning process based on a particle swarm algorithm provided in an embodiment of the present invention.

[0046] Figure 4 A schematic diagram of a simulation network topology structure provided by an embodiment of the present invention.

[0047] Figure 5 A schematic diagram of edge risk comparison provided by an embodiment of the present invention.

[0048] Figure 6 A schematic diagram for comparing the overall risks of a network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0050] 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. Based on the embodiments in 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. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0051] The embodiment of the present invention provides a method for planning routing of power communication services based on particle swarm algorithm, such as Figure 1 As shown, including:

[0052] Network model establishment: Establish the power communication network topology model and the power communication business model. The network topology model describes the node and link information, and the business model includes parameters such as business importance, latency requirements, and bandwidth requirements.

[0053] Constraint definition: Define constraints such as overall network risk, network bandwidth, latency, and service channel availability to ensure that routing planning solutions meet business requirements and reduce network risks.

[0054] Particle swarm optimization: Use the particle swarm optimization algorithm to solve multi-objective optimization problems, find the best path that meets the constraints through iterative search, achieve balanced distribution of business importance, and reduce network risks.

[0055] Optimal path output: Output the optimal routing solution that meets the constraints and provide guidance for the business configuration of the power communication network.

[0056] The embodiment of the present invention first performs service routing modeling. When executing the routing algorithm, key variables are implicitly included in the network topology model, the service model and the evaluation index model. The evaluation index model can be regarded as a defined constraint condition.

[0057] Power communication network topology model: Based on complex network theory, the power communication network is first abstracted into a graph G = (V, E), where V = {v 1 ,v 2 …,v n} represents the node set in the network topology, i.e., the equipment in the power communication optical transmission network, E = {e 12 ,e 13 ,…,e(n-1)n} represents the set of links connecting two transmission devices in the network topology. ij ∈E represents node v i and node v j If link e 25 Too much business is distributed on v 2 or v 5 If an abnormal situation occurs, the communication service of the network will be adversely affected. The problem studied by the embodiment of the present invention is how to avoid excessive concentration of service distribution when there are many communication services, so as to prevent some links from having too high risks and reduce the serious impact on the transmission network.

[0058] Power communication business model: Let S = {s 1 ,s 2 ,…,s k} is the service set that needs to configure routing in the network, s k represents the kth business in the set, let is the forwarding route for the kth service. There are many types of power communication services, and different services have different requirements for communication network performance and different importance. For example, the priority of production control-related services is much higher than that of information management and other services. In order to clarify the priority of different services, it is necessary to calculate the importance of different types of services W. k Quantification. The importance evaluation system takes into account the business type and communication factors. Business types include internal business and external business. Internal business is power business and corporate office business related to production, control, and maintenance. External business is mainly customer business. It can be seen that the importance of internal business is often higher than that of external business. The service types of business can be divided into production control business and corporate management business, among which production control business is the most important. In the case of the same business type, the importance is differentiated according to the difference in network requirements of the business, including latency, bandwidth, etc. The importance evaluation system is as follows: Figure 2 shown.

[0059] Related indicator model: The overall risk of the communication network consists of two parts: edge risk and node risk, as shown in formula (1):

[0060]

[0061] Where F represents the overall risk of the network; V(v i ) represents node v i The risk degree of node v i When an abnormality occurs and the service is interrupted, the severity of the impact is expressed by formula (2); E(e ij ) represents edge e ij The risk level of e ijThe impact of a failure on business interruption is shown in formula (3).

[0062]

[0063] Among them, W k For node v i and edge ij The important quantification result of the k-th business, A(v i ) represents node v i Availability, A(e ij ) represents edge e ij availability.

[0064] Service link availability function:

[0065]

[0066] This function refers to the availability of edges and nodes that carry services. The higher the better in routing planning.

[0067] Delay requirement: The delay requirement of the service should be met in the forwarding route, that is, the delay of the service in the path should be less than the required maximum delay.

[0068] Delay(R k )≤D(k) max (5)

[0069] Bandwidth requirement: When allocating routes for services, the remaining bandwidth of the link should be greater than the bandwidth requirement of the service.

[0070] B(R k )≥B(k) (6)

[0071] In summary, the routing planning problem can be described as maximizing the availability S of the power communication network while reducing the overall risk F of the network when the routing delay and bandwidth meet the business communication requirements. The mathematical model of the business-oriented risk balance problem is obtained:

[0072]

[0073] Research on particle swarm algorithm for power communication business: Particle Swarm Optimization (PSO) is a heuristic optimization algorithm based on swarm intelligence. It simulates the foraging behavior of bird flocks or fish schools and uses information sharing between individuals in the group to find the optimal solution. The PSO algorithm has a faster convergence speed and has strong adaptability to the dimension and complexity of the problem. Therefore, the embodiment of the present invention uses the PSO algorithm to realize the selection of routes. Each particle represents a path, and the optimal position of the particle is the global optimal route planning required. Initialize a group of random particles as candidate solutions, each particle has its own position and speed. The particles search in the solution space and evaluate the objective function value of each particle. The particles remember the optimal position they have searched for, and also know the optimal position of the entire particle swarm. The particles update their speed and position according to the individual optimum and the global optimum, and move towards a better direction. Repeat the above steps until the termination condition is met. The mathematical model is as follows:

[0074]

[0075]

[0076] in, and are the position and velocity of the ith particle at the kth iteration, ω is the inertia weight for the particle to maintain its velocity, and are the individual historical optimal position of the particle and the historical optimal position of the entire particle group, c 1 and c 2 is the acceleration factor pointing to the optimal position, r 1 and r 2 is a random number between 0 and 1.

[0077] The PSO algorithm adjusts the current position and speed according to the information of the particle itself and the surrounding particles, and then finds the optimal solution through iterative search. The algorithm requires an initialized random solution as a candidate solution, so the improved Dijkstra algorithm is first used to generate K shortest paths between the source node and the destination node to serve as a candidate solution.

[0078] The improved Dijkstra algorithm is mainly optimized in two aspects: data structure and path generation logic. The specific steps are as follows:

[0079] 1. Initialization:

[0080] (1) Let d(v 1 )=0, indicating that from the starting node v 1 The weight of the shortest path to itself is 0;

[0081] (2) Let d(v1 )=w 1j (j=2,3,…p), that is, initially it is considered that from v 1 To other nodes v j The shortest path weight is the weight of the link between them;

[0082] (3) The vertex set S of the determined shortest path = {v 1};

[0083] (4) The node set R = VS = {v 2 , v 3 …v p};

[0084] 2. Select the next node:

[0085] (1) Find a node v in the set R k , so that That is, select the node with the smallest current shortest path weight in R;

[0086] (2) v k Add to the node set S to determine the shortest path, that is, S = S∪{v K};

[0087] (3) Simultaneously update the node set R=VS for which the shortest path is not determined;

[0088] (4) Determine whether R is an empty set. If R is not an empty set, it means that the shortest paths of all nodes have been determined and the algorithm terminates; otherwise, proceed to the next step;

[0089] 3. Update the shortest path weight

[0090] (1) For each node in the set R, update d(v j ) value, the formula is d(v j )=min{d(v j ),d(v k )+w kj}, by comparing the newly added node v k Arrival j The shorter path of the current record is updated to d(v j );

[0091] (2) After the update is completed, return to step 2 and continue the cycle;

[0092] 4. Get the shortest path

[0093] (1) After |V|-1 cycles, all nodes have been processed. At this time, d(v jThe final value of the node where )(j=1,2,3,…p) is the node v 1 The shortest path length to the remaining nodes;

[0094] (2) Output K shortest paths;

[0095] The overall algorithm flow is as follows Figure 3 shown.

[0096] In order to verify the effectiveness of the algorithm, Figure 4 The communication network topology shown in the figure is simulated, and a comparative experiment is conducted with a load-balanced routing allocation algorithm (Load Banlancing, LB) and an availability-aware routing allocation algorithm (AAR). Services are randomly generated between nodes, and the importance of the services is randomly allocated using the data in Table 1. The number of services is in the range of [10, 50]. The availability of each node is random in the range of [0.9, 1.0]. Before the experiment, 5 paths are generated for each service using the improved Dijkstra algorithm as the initial path set, which is used for the initial particles of the PSO algorithm.

[0097] Figure 5 It is the edge risk distribution diagram. The horizontal axis is the importance of the business undertaken on the network, and the vertical axis represents the amount of business carried by the edge. As can be seen from the figure, when the PSO algorithm selects the planned route, the edge in the network topology diagram carries the most business with an importance of 0.6 to 0.8, followed by businesses with an importance below 0.6, which are communication businesses with low or medium importance. It carries fewer businesses with a value greater than 0.8. On the other hand, although most of the other two algorithms carry communication businesses with low or medium importance, the business with a value greater than 0.8 also accounts for a non-negligible proportion and has a higher risk. By comparison, it can be found that the algorithm used in this paper can more effectively balance the distribution of risks, and its effect is better than the other two algorithms.

[0098] like Figure 6 The overall network risk comparison chart shows that the vertical axis is the overall network risk (*1000). As can be seen from the figure, the overall network risk of the PSO algorithm is 1.1, which is much lower than the network risk under the AAR and LB algorithm planning. The network risk is reduced by more than 20%, proving that the risk balance strategy of this method effectively reduces the overall risk of the network and is better than the traditional method.

[0099] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides a power communication service routing planning system based on a particle swarm algorithm, which is used to execute a power communication service routing planning method based on a particle swarm algorithm in the above method embodiment.

[0100] The system includes: a model building module, which is used to establish a power communication network topology model and a power communication business model, wherein the power communication network topology model is used to describe node and link information, and the power communication business model is used to describe the requirements and importance of different businesses for communication network performance; an indicator building module, which is used to define constraints to ensure that the routing planning solution meets business needs while reducing network risks; an initial path generation module, which is used to generate K shortest paths between a source node and a destination node as an initial path set using an improved Dijkstra algorithm; and an optimal path solving module, which is used to solve a multi-objective optimization problem using a particle swarm optimization algorithm and find the optimal path that meets the constraints through iterative search.

[0101] The embodiment of the present invention provides a power communication service routing planning system based on a particle swarm algorithm. Aiming at the problems of uneven distribution of service importance and high local network risk in the existing power communication service routing planning method, the above-mentioned several modules are adopted, and the first K shortest paths that meet the constraints are output through an improved Dijkstar algorithm. Then, the particle swarm optimization algorithm is used to solve the multi-objective optimization problem, and the characteristics of the power service and the risk requirements of different services are comprehensively considered to plan the optimal path, thereby achieving the technical effect of balancing the service importance and reducing the network risk.

[0102] It should be noted that the system embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference lies in the setting of corresponding functional modules, and the principles thereof are basically the same as the principles of the above-mentioned system embodiments provided by the present invention. As long as technical personnel in this field refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned system embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, they will improve the modules in the above-mentioned system embodiments to obtain corresponding system class embodiments for implementing the methods in other method class embodiments.

[0103] Based on the same inventive concept as the above-mentioned embodiment, an embodiment of the present invention also provides a power communication service routing planning device based on a particle swarm algorithm, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the power communication service routing planning method based on a particle swarm algorithm.

[0104] In an embodiment of the present invention, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or a volatile memory (volatile memory), such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in an embodiment of the present invention may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0105] In the embodiments of the present invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the method disclosed in the embodiments of the present invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0106] Based on the same inventive concept as the above-mentioned embodiment, an embodiment of the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the power communication service routing planning method based on particle swarm algorithm.

[0107] In summary, the present invention provides a method for routing planning of electric power communication services based on a particle swarm algorithm, comprising the following steps: establishing an electric power communication network topology model and an electric power communication service model; defining indicator models such as overall network risk, network bandwidth, latency, and service channel availability; using an improved Dijkstra algorithm to generate K shortest paths between a source node and a destination node as an initial path set; using a particle swarm optimization algorithm to solve a multi-objective optimization problem, comprehensively considering the characteristics of the electric power service and the risk requirements of different services, and planning the optimal path.

[0108] The present invention aims to effectively reduce the risk level of the electric power optical communication network and provide a strong guarantee for the safe operation of the power grid. The main functions of the present invention are: (1) Reduce network risks: By considering the distribution of business importance, avoid excessive concentration of business on certain links or nodes, thereby reducing local network risks and improving the overall reliability of the network. (2) Achieve business importance balance: According to the importance of different businesses, different transmission paths are planned to ensure that key businesses are given priority, while taking into account the needs of other businesses to achieve business importance balance. (3) Meet business needs: Consider parameters such as business delay requirements and bandwidth requirements, plan routing solutions that meet business communication needs, and ensure the stability and reliability of business transmission. (4) Improve routing planning efficiency: Utilize the fast convergence characteristics of the particle swarm optimization algorithm to efficiently find the best routing solution that meets the constraints and improve routing planning efficiency.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for routing planning of power communication services based on particle swarm algorithm, characterized in that: include: Establishing a power communication network topology model and a power communication business model, wherein the power communication network topology model is used to describe node and link information, and the power communication business model is used to describe the requirements and importance of different businesses on communication network performance; Define constraints to ensure that routing plans meet business requirements while reducing network risks; The improved Dijkstra algorithm is used to generate K shortest paths between the source node and the destination node as the initial path set; The particle swarm optimization algorithm is used to solve the multi-objective optimization problem, and the best path that meets the constraints is found through iterative search.

2. According to claim 1, a method for planning power communication service routing based on particle swarm algorithm, characterized in that: The electric power communication service model includes service importance, delay requirement and bandwidth requirement.

3. According to claim 1, a method for planning power communication service routing based on particle swarm algorithm, characterized in that: The constraints include overall network risk, network bandwidth, latency and service channel availability.

4. According to claim 3, a method for planning power communication service routing based on particle swarm algorithm, characterized in that: The overall network risk includes node risk and edge risk.

5. The method for planning routing of power communication services based on particle swarm algorithm according to claim 1, characterized in that: The improved Dijkstra algorithm is used to generate K shortest paths between the source node and the destination node as the initial path set, including: initialization: Let d(v1) = 0, indicating that the shortest path weight from the starting node v1 to itself is 0; let d(v1) = w 1j (j=2,3,…p), indicating the distance from v1 to other nodes v j The shortest path weight is the weight of the link between them; the vertex set S of the determined shortest path is S = {v1}; the node set R of the undetermined shortest path is R = VS = {v2, v3…v p }; Select the next node: Find a node v in the set R k , so that v k Add to the node set S of the determined shortest path; at the same time, update the node set R = VS of the undetermined shortest path; determine whether R is an empty set, if R is an empty set, proceed to the next step; Update the shortest path weight: For each node in the set R, update d(v j ) by comparing the newly added node v k Arrival j The shorter path of the current record is updated to d(v j ); After the update is completed, return to the node selection step to continue the cycle; Get the shortest path: After |V|-1 cycles, all nodes have been processed. At this time, d(v j The final value of the node )(j=1,2,3,…p) is the shortest path length from node v1 to the remaining nodes; output K shortest paths.

6. The method for planning routing of power communication services based on particle swarm algorithm according to claim 1, characterized in that: The particle swarm optimization algorithm is used to solve multi-objective optimization problems, including: Initialize a group of random particles as candidate solutions; Evaluate the objective function value for each particle; Particles update their speed and position according to individual optimal and global optimal; Repeat the above steps until the termination condition is met.

7. The method for planning power communication service routing based on particle swarm algorithm according to claim 6, characterized in that: The termination condition includes the number of iterations reaching a preset value or the objective function value reaching a preset accuracy.

8. A power communication service routing planning system based on particle swarm algorithm, characterized in that: include: A model building module, used to establish a power communication network topology model and a power communication business model, wherein the power communication network topology model is used to describe node and link information, and the power communication business model is used to describe the requirements and importance of different businesses on communication network performance; Indicator building blocks for defining constraints to ensure that routing plans meet business needs while reducing network risks; An initial path generation module is used to generate K shortest paths between a source node and a destination node as an initial path set using an improved Dijkstra algorithm; The optimal path solving module is used to solve multi-objective optimization problems using the particle swarm optimization algorithm and find the optimal path that meets the constraints through iterative search.

9. A power communication service routing planning device based on particle swarm algorithm, characterized in that: It comprises a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the power communication service routing planning method based on particle swarm algorithm as claimed in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the power communication service routing planning method based on particle swarm algorithm as claimed in any one of claims 1 to 7.

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