Communication service route planning method and system based on ant colony algorithm

Abstract

The invention relates to a communication service route planning method and system based on an ant colony algorithm, and the specific steps are as follows: collecting communication data in a power communication network, the communication data including service data, optical path data, channel data, station data and network element data, the network element data comprises the site equipment and the fault rate, the heavy load rate, the voltage class and the optical cable length of the site equipment; data cleaning and normalization processing are carried out on the collected data; the method comprises the following steps: constructing an optical path topological graph by taking each station device in a power communication network as a node and taking an optical fiber for connecting each station device as a link; determining a starting site and a terminating site according to the target service, and searching all candidate routes of the target service according to the optical path topological graph; and according to the fault rate, the heavy load rate and the voltage grade of each station device, calculating the voltage grade, the fault rate, the heavy load rate and the optical cable length on all candidate routes.

Description

Technical Field

[0001] The present invention relates to the field of electric power communication technology, and in particular to a communication service routing planning method and system based on ant colony algorithm. Background Art

[0002] The power communication network is a vital physical network in the power system. As the physical carrier platform for the production and operation of the power grid, it not only serves the safe production and enterprise management of the power grid, but also constitutes the core component and technical support means of the power system. With the rapid development of the power industry, the role and function of the power communication network are increasingly enhanced, especially in the context of promoting the construction of smart grids, it has undertaken more arduous and important support tasks. In recent years, with the improvement of the level of intelligence of the power grid, the tasks of the power communication network have become more complex. The data information generated in various links of the power system increasingly depends on the efficient transmission of the power communication network, which not only involves real-time monitoring and data collection, but also includes remote control and automated management of power equipment. The reliability and efficiency of the power communication system directly affect the stability and security of the power grid. Therefore, the reliability, efficiency and advancement of power communication have become important factors to ensure the normal operation of the power grid. In this context, the optical transmission network, as the main component of the power backbone communication network, undertakes the information exchange, tandem and transmission functions of the entire network. The optical transmission network provides a high-bandwidth, low-latency communication channel to ensure the fast and stable transmission of various business data of the power grid.

[0003] In particular, the synchronous digital hierarchy (SDH) optical transmission network plays a vital role in carrying core grid services such as relay protection, safety control, and dispatching automation. Through SDH technology, the power communication network can achieve efficient data multiplexing and transmission, improving the utilization rate and anti-interference ability of the network. In summary, the supporting role of the power communication network in various fields of the power system has become increasingly significant, and it is the basic guarantee for achieving safe, stable, and efficient grid operation.

[0004] In the prior art CN110309943A, an optimal service path planning method for a power transmission network is disclosed, which sets service path weight factors according to key attributes of the entire transmission network network resources, wherein the service path weight factors include: optical cable weight factors, optical path weight factors, and communication equipment weight factors; the service path weights are calculated according to the above-mentioned weight factors and the weights corresponding to the weight factors; and the routing priorities are sorted according to the service path weights to form a service path selection plan.

[0005] In the prior art, all weight values ​​are pre-set, and there is no more scientific determination method. They tend to be subjective, which leads to errors in the calculation results. At the same time, if the influencing factors change, the weights cannot be adjusted in real time, which has a greater impact on the calculation results. Summary of the invention

[0006] In order to solve the above problems existing in the prior art, the present invention proposes a communication service routing planning method and system based on ant colony algorithm.

[0007] The technical solution of the present invention is as follows:

[0008] On the one hand, the present invention proposes a communication service routing planning method based on ant colony algorithm, and the specific steps include:

[0009] Collecting communication data in the power communication network, the communication data includes service data, optical path data, channel data, site data and network element data, wherein the network element data includes site equipment and failure rate, overload rate, voltage level and optical cable length of site equipment; and performing data cleaning and normalization processing on the collected data;

[0010] The equipment at each site in the power communication network is used as a node, and the optical fiber between the equipment at each site is used as a link to construct an optical path topology diagram;

[0011] Determine the starting site and the ending site according to the target service, and find all candidate routes for the target service according to the optical path topology diagram;

[0012] The voltage level, failure rate, overload rate and cable length of all candidate routes are calculated based on the failure rate, overload rate and voltage level of the equipment at each site, and multiple weight combinations are set for each influencing parameter. The scores of the candidate routes under each weight combination are calculated, and the candidate route with the highest score is selected as the optimal independent route.

[0013] Compare the optimal independent routes determined by multiple groups of target services with the actual routes of the target services, and select a group of weight combinations with the highest accuracy as the optimal weight combination;

[0014] Construct a service routing planning model based on the ant colony algorithm, set basic parameters based on the optimal weight combination, and calculate the optimal primary and backup routes for the target service.

[0015] As a preferred implementation, the step of normalizing the collected data in data cleaning and normalization is specifically as follows:

[0016]

[0017] In the formula, x represents the type of communication data, x max and x min represent the maximum and minimum values ​​in x respectively.

[0018] As a preferred implementation, the steps of constructing a service routing planning model based on an ant colony algorithm are specifically as follows:

[0019] Initialize basic parameters, including the maximum number of iterations N, the number of ants m, the pheromone importance factor α, the heuristic information importance factor β, the pheromone volatility rate ρ, and the pheromone constant Q;

[0020] Construct the solution space and calculate the probability of the ant choosing the next device node when it is at the initial device node. The specific calculation formula is as follows:

[0021]

[0022] In the formula, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on path i to j; η ij is the heuristic information from node i to node j, represents the neighbor set of node i;

[0023] After each ant completes a move, the pheromone increment is calculated and the pheromone concentration is updated:

[0024] Set the maximum number of iterations, and the algorithm will terminate after all iterations are completed;

[0025] According to the path lengths that the ants have crawled in all solutions, two smallest paths are selected as the optimal service primary and backup routes for output.

[0026] As a preferred implementation, in the step of setting the basic parameters in combination with the optimal weight combination, the basic parameters set are the heuristic information η from node i to node j. ij , the specific calculation formula is as follows:

[0027] η ij =f(A j , B j , C j , E ij )=ω 1 A j +ω 2 B j +ω 3 C j +ω 4 E ij

[0028] In the formula, A j ,B j ,C j ,E ij are the voltage level, failure rate, overload rate, and path length from node i to node j, ω 1 ,ω 2 ,ω 3 ,ω 4are the weights of each parameter indicator.

[0029] On the other hand, the present invention proposes a communication service routing planning system based on ant colony algorithm, comprising:

[0030] The data acquisition and processing module collects communication data in the power communication network, wherein the communication data includes service data, optical path data, channel data, site data and network element data, wherein the network element data includes site equipment and failure rate, overload rate, voltage level and optical cable length of site equipment; and performs data cleaning and normalization processing on the collected data;

[0031] The optical path topology construction module uses the equipment at each site in the power communication network as a node and the optical fiber between the equipment at each site as a link to construct an optical path topology;

[0032] The candidate route determination module determines the starting site and the ending site according to the target service, and searches for all candidate routes for the target service according to the optical path topology diagram;

[0033] The optimal route calculation module calculates the voltage level, failure rate, overload rate and optical cable length of all candidate routes according to the failure rate, overload rate and voltage level of the equipment at each site, sets multiple weight combinations for each influencing parameter, calculates the score of the candidate route under each weight combination, and selects the candidate route with the highest score as the optimal independent route;

[0034] The optimal weight combination determination module compares the optimal independent routes determined by multiple groups of target services with the actual routes of the target services, and selects a group of weight combinations with the highest accuracy as the optimal weight combination;

[0035] The optimal route calculation module for the target business builds a business route planning model based on the ant colony algorithm, sets basic parameters in combination with the optimal weight combination, and calculates the optimal primary and backup routes for the target business.

[0036] As a preferred implementation, the step of normalizing the collected data in data cleaning and normalization is specifically as follows:

[0037]

[0038] In the formula, x represents the type of communication data, x max and x min represent the maximum and minimum values ​​in x respectively.

[0039] As a preferred implementation, the steps of constructing a service routing planning model based on an ant colony algorithm are specifically as follows:

[0040] Initialize basic parameters, including the maximum number of iterations N, the number of ants m, the pheromone importance factor α, the heuristic information importance factor β, the pheromone volatility rate ρ, and the pheromone constant Q;

[0041] Construct the solution space and calculate the probability of the ant choosing the next device node when it is at the initial device node. The specific calculation formula is as follows:

[0042]

[0043] In the formula, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on path i to j; η ij is the heuristic information from node i to node j, represents the neighbor set of node i;

[0044] After each ant completes a move, the pheromone increment is calculated and the pheromone concentration is updated:

[0045] Set the maximum number of iterations, and the algorithm will terminate after all iterations are completed;

[0046] According to the path lengths that the ants have crawled in all solutions, two smallest paths are selected as the optimal service primary and backup routes for output.

[0047] As a preferred implementation, in the step of setting the basic parameters in combination with the optimal weight combination, the basic parameters set are the heuristic information η from node i to node j. ij , the specific calculation formula is as follows:

[0048] η ij =f(A j , B j , C j , E ij )=ω 1 A j +ω 2 B j +ω 3 C j +ω 4 E ij

[0049] In the formula, A j ,B j ,C j ,E ij are the voltage level, failure rate, overload rate, and path length from node i to node j, ω 1 ,ω 2 ,ω 3 ,ω 4are the weights of each parameter indicator.

[0050] On the other hand, the present invention proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a communication service routing planning method based on an ant colony algorithm as described in any embodiment of the present invention is implemented.

[0051] On the other hand, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a communication service routing planning method based on an ant colony algorithm as described in any embodiment of the present invention.

[0052] The present invention has the following beneficial effects:

[0053] 1. The present invention combines known services with the optimal route to analyze and calculate the possible impact of each communication data on route selection, determine the weight of each influencing factor, and then determine the optimal weight combination of influencing factors to ensure the accuracy of the ant colony algorithm.

[0054] 2. The present invention uses a unified normalization formula to normalize all influencing factor data to reduce errors as much as possible and ensure the accuracy of the prediction results.

[0055] 3. The present invention combines the optimal weight combination of influencing factors with the basic parameters of the ant colony algorithm, selects the optimal primary and backup routes while fully considering the device properties and optical fiber properties, and ensures the rationality and accuracy of route selection. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a flow chart of the present invention;

[0057] Figure 2 This is a schematic diagram of the service routing planning process based on the ant colony algorithm;

[0058] Figure 3 This is the optical path topology diagram of a province's power communication network;

[0059] Figure 4 It is the optimal primary and backup route map for the target business. DETAILED DESCRIPTION

[0060] 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.

[0061] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0062] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0063] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0064] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0065] Embodiment 1:

[0066] See also Figure 1 , a communication service routing planning method based on ant colony algorithm, the specific steps include:

[0067] Collecting communication data in the power communication network, the communication data includes service data, optical path data, channel data, site data and network element data, wherein the network element data includes site equipment and failure rate, overload rate, voltage level and optical cable length of site equipment; and performing data cleaning and normalization processing on the collected data;

[0068] In this embodiment, the data mainly includes business data, optical path data, channel data, site data, network element data, etc. These data are collected by the power communication management system and contain information such as communication network elements, sites, services, optical paths, etc. These data constitute the basis for the routing planning of power communication services. In order to achieve effective routing planning, the ant colony algorithm is used to systematically analyze the routing. In the planning process, the pros and cons of each path are first evaluated by comprehensively considering indicators such as the number of routing hops, voltage level, equipment failure rate, equipment overload rate, and optical cable length. This multi-dimensional evaluation ensures that the selected path can safely and reliably meet business needs. The use of the ant colony algorithm not only improves the search efficiency, but also can quickly find K optimal paths in a complex network environment. By continuously optimizing the pheromone concentration, the algorithm can gradually evolve a routing solution that better meets actual needs.

[0069] The equipment at each site in the power communication network is used as a node, and the optical fiber between the equipment at each site is used as a link to construct an optical path topology diagram;

[0070] In this embodiment, the network topology is essentially a graph data model. When studying the routing problem, based on the theory of complex networks, the devices in the power communication network are abstracted as nodes, and the optical fibers connecting the device nodes are abstracted as links. Therefore, the network can be represented as an undirected weighted graph G = (V, E), where V = {v 1 ,v 2 ,…v n} represents the set of device nodes in the network, E = {e 12 ,e 13 ,…e (n-1)n} represents the set of links in the network topology; |V| represents the number of nodes in the network topology, |E| represents the number of links in the network topology, and e ij ∈E represents node v i and v j There is a link between nodes and links e ij Contains additional attribute values, including equipment failure rate, overload rate, link length, voltage level, optical cable length, etc. For any node combination p(i,j)={v i ,v k ,…v j}, whose starting nodes are v i and v j , if there are no duplicate nodes in the set p(i,j) and there is no cycle in the subgraph, then p(i,j) is v i and v j A route in .

[0071] Determine the starting site and the ending site according to the target service, and find all candidate routes for the target service according to the optical path topology diagram;

[0072] The voltage level, failure rate, overload rate and cable length of all candidate routes are calculated based on the failure rate, overload rate and voltage level of the equipment at each site, and multiple weight combinations are set for each influencing parameter. The scores of the candidate routes under each weight combination are calculated, and the candidate route with the highest score is selected as the optimal independent route.

[0073] Compare the optimal independent routes determined by multiple groups of target services with the actual routes of the target services, and select a group of weight combinations with the highest accuracy as the optimal weight combination;

[0074] In this embodiment, all candidate routes for the service are first found, and the voltage level, failure rate, overload rate, and cable length on each route are calculated, and the actual route of the service is used as a training sample. A reasonable range and step size are defined for each weight, such as [0,1], and all possible weight combinations are generated at intervals of 0.1. The weight combinations are traversed, and the scores of each route are calculated using the current weights. Then, the differences between the routes screened out according to the scores and the actual routes are compared, and the set of weight combinations with the highest accuracy is selected as the optimal weight combination.

[0075] Construct a service routing planning model based on the ant colony algorithm, set basic parameters based on the optimal weight combination, and calculate the optimal primary and backup routes for the target service.

[0076] In this embodiment, the ant colony optimization algorithm is a random search algorithm that simulates the foraging process of real ants in nature. During the foraging process, ants leave pheromones whose concentration is proportional to the quality of the food source on the path they pass through, and can sense the presence and concentration of pheromones to guide their movement direction, tending to move in the direction of high pheromone concentration.

[0077] The power communication service has high reliability and real-time requirements, and needs to ensure stable and fast data transmission. When planning routes for power communication services, the selection of routes should ensure multiple redundancy to cope with equipment failures or network interruptions, avoid the impact of single point failures on the system, and ensure that a backup route can be quickly switched to when a route fails. The present invention can effectively improve the overall performance and reliability of power communication services by using the ant colony algorithm to quickly find the primary and backup routes for the service. The service route planning model process based on the ant colony algorithm is as follows Figure 2 shown.

[0078] As a preferred implementation of this embodiment, the step of normalization processing in the data cleaning and normalization processing of the collected data is specifically:

[0079]

[0080] In the formula, x represents the type of communication data, x max and x min represent the maximum and minimum values ​​in x respectively.

[0081] In this embodiment, due to the different ways of calculating the metric index, the calculation results vary greatly, so the above index values ​​need to be normalized respectively. When normalizing, considering that the optical cable length has a long-tail distribution and there may be some extreme values, the formula is used to process the optical cable length. The voltage level is normalized using the maximum-minimum formula.

[0082] As a preferred implementation of this embodiment, the steps of constructing a service routing planning model based on an ant colony algorithm are specifically as follows:

[0083] Initialize basic parameters, including the maximum number of iterations N, the number of ants m, the pheromone importance factor α, the heuristic information importance factor β, the pheromone volatility rate ρ, and the pheromone constant Q;

[0084] Construct the solution space and calculate the probability of the ant choosing the next device node when it is at the initial device node. The specific calculation formula is as follows:

[0085]

[0086] In the formula, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on path i to j; η ij is the heuristic information from node i to node j, represents the neighbor set of node i;

[0087] After each ant completes a move, the pheromone increment is calculated and the pheromone concentration is updated:

[0088] Set the maximum number of iterations, and the algorithm will terminate after all iterations are completed;

[0089] According to the path lengths that the ants have crawled in all solutions, two smallest paths are selected as the optimal service primary and backup routes for output.

[0090] In this embodiment, the specific process of each step is as follows:

[0091] Initialize basic parameters:

[0092] Maximum number of iterations N, number of ants m, pheromone importance factor α, heuristic information importance factor β, pheromone volatility rate ρ, pheromone constant Q.

[0093] Initialize the pheromone matrix τ ij =τ 0 , represents the pheromone on the path from node i to node j. The pheromone concentration of all paths is initialized to a small constant, that is, all paths are considered equally possible.

[0094] Construct the solution space:

[0095] For each ant k: place the ant at the starting device node. When the ant does not reach the ending device node:

[0096] According to p ij (t) Select the next node j. The probability of the ant choosing the path is calculated as follows:

[0097]

[0098] Among them, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on the path from i to j. If a path is chosen by many ants, the pheromone concentration on this path will increase, suggesting to other ants that this may be a good choice. η ij is the heuristic information from node i to node j, represents the set of neighbors of node i. The denominator is the sum of the values ​​on all paths that the ant can choose, which is used to normalize the probability.

[0099] Pheromone Update:

[0100] After all ants have completed a move:

[0101] Calculate the pheromone increment:

[0102]

[0103] Among them, Q is the pheromone increase factor, which is used to control the increase of pheromone, indicating the contribution of each ant to the pheromone increment of the path after finding the effective path; L k is the length of the path ant k takes when finding the path, calculated using the edge length and the number of routing hops.

[0104] Update pheromone concentration:

[0105]

[0106] Where ρ is the pheromone volatilization rate, is the information intensity added by ant k on the path from node i to node j at time t.

[0107] Set the termination judgment condition:

[0108] If the maximum number of iterations N is reached, the algorithm terminates.

[0109] Select the optimal primary and backup routes:

[0110] For L k The values ​​are sorted from small to large, and L is selected according to the principle of route independence. k The two smaller routes are output as the service routing path in the following format: [device number-optical path number-device number] or [device name-optical path-device name].

[0111] As the best implementation of this embodiment, in the step of setting the basic parameters in combination with the optimal weight combination, the basic parameters set are the heuristic information η from node i to node j. ij , the specific calculation formula is as follows:

[0112] η ij =f(A j , B j , C j , E ij )=ω 1 A j +ω 2 B j +ω 3 C j +ω 4 E ij

[0113] In the formula, A j ,B j ,C j ,E ij are the voltage level, failure rate, overload rate, and path length from node i to node j, ω 1 ,ω 2 ,ω 3 ,ω 4 are the weights of each parameter indicator.

[0114] Embodiment 2:

[0115] Based on the above method, in order to verify the reliability and effectiveness of the algorithm in the routing planning of power communication services, the present invention is verified on the power communication subnet topology of a certain province, involving a total of 20 device nodes and 35 optical links. The topological structure is as follows Figure 3 As shown:

[0116] In this topology, two independent routes need to be planned for a single-port 2M service [6,16] for protection. The source node, sink node and service type of the service can be queried through the service data, and the voltage level, overload rate, failure rate and other attribute information of the nodes in the topology can be determined, and the link length and other information can be determined.

[0117] In the process of training the sample data, the accuracy rate reached 97.8%. In the test sample, the ant colony algorithm was used to plan the route of the service. The ant colony algorithm can also achieve good results in this invention, but the iteration time is long. Therefore, this method can plan two independent routes for the service. As shown in the figure below, the algorithm plans two independent routes that meet the requirements for a certain single-port 2M service [6,16], route 1: [6-7-16] and route 2: [6-5-16]. The selected routes are as follows: Figure 4 shown.

[0118] Embodiment three:

[0119] A communication service routing planning system based on ant colony algorithm, comprising:

[0120] The data acquisition and processing module collects communication data in the power communication network, wherein the communication data includes service data, optical path data, channel data, site data and network element data, wherein the network element data includes site equipment and failure rate, overload rate, voltage level and optical cable length of site equipment; and performs data cleaning and normalization processing on the collected data;

[0121] The optical path topology construction module uses the equipment at each site in the power communication network as a node and the optical fiber between the equipment at each site as a link to construct an optical path topology;

[0122] The candidate route determination module determines the starting site and the ending site according to the target service, and searches for all candidate routes for the target service according to the optical path topology diagram;

[0123] The optimal route calculation module calculates the voltage level, failure rate, overload rate and optical cable length of all candidate routes according to the failure rate, overload rate and voltage level of the equipment at each site, sets multiple weight combinations for each influencing parameter, calculates the score of the candidate route under each weight combination, and selects the candidate route with the highest score as the optimal independent route;

[0124] The optimal weight combination determination module compares the optimal independent routes determined by multiple groups of target services with the actual routes of the target services, and selects a group of weight combinations with the highest accuracy as the optimal weight combination;

[0125] The optimal route calculation module for the target business builds a business route planning model based on the ant colony algorithm, sets basic parameters in combination with the optimal weight combination, and calculates the optimal primary and backup routes for the target business.

[0126] As a preferred implementation of this embodiment, the step of normalization processing in the data cleaning and normalization processing of the collected data is specifically:

[0127]

[0128] In the formula, x represents the type of communication data, x max and x min represent the maximum and minimum values ​​in x respectively.

[0129] As a preferred implementation of this embodiment, the steps of constructing a service routing planning model based on an ant colony algorithm are specifically as follows:

[0130] Initialize basic parameters, including the maximum number of iterations N, the number of ants m, the pheromone importance factor α, the heuristic information importance factor β, the pheromone volatility rate ρ, and the pheromone constant Q;

[0131] Construct the solution space and calculate the probability of the ant choosing the next device node when it is at the initial device node. The specific calculation formula is as follows:

[0132]

[0133] In the formula, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on path i to j; η ij is the heuristic information from node i to node j, represents the neighbor set of node i;

[0134] After each ant completes a move, the pheromone increment is calculated and the pheromone concentration is updated:

[0135] Set the maximum number of iterations, and the algorithm will terminate after all iterations are completed;

[0136] According to the path lengths that the ants have crawled in all solutions, two smallest paths are selected as the optimal service primary and backup routes for output.

[0137] As a preferred implementation of this embodiment, in the step of setting the basic parameters in combination with the optimal weight combination, the basic parameters set are the heuristic information η from node i to node j. ij , the specific calculation formula is as follows:

[0138] η ij =f(A j , B j , Cj, E ij )=ω 1 A j +ω 2 B j +ω 3 C j +ω 4 E ij

[0139] In the formula, A j ,B j ,C j ,E ij are the voltage level, failure rate, overload rate, and path length from node i to node j, ω 1 ,ω 2 ,ω 3 ,ω 4 are the weights of each parameter indicator.

[0140] Embodiment 4:

[0141] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, a communication service routing planning method based on an ant colony algorithm as described in any embodiment of the present invention is implemented.

[0142] Embodiment five:

[0143] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements a communication service routing planning method based on an ant colony algorithm as described in any embodiment of the present invention.

[0144] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A communication service routing planning method based on ant colony algorithm, characterized in that: The specific steps include: Collecting communication data in the power communication network, the communication data includes service data, optical path data, channel data, site data and network element data, wherein the network element data includes site equipment and failure rate, overload rate, voltage level and optical cable length of site equipment; and performing data cleaning and normalization processing on the collected data; The equipment at each site in the power communication network is used as a node, and the optical fiber between the equipment at each site is used as a link to construct an optical path topology diagram; Determine the starting site and the ending site according to the target service, and find all candidate routes for the target service according to the optical path topology diagram; The voltage level, failure rate, overload rate and cable length of all candidate routes are calculated based on the failure rate, overload rate and voltage level of the equipment at each site, and multiple weight combinations are set for each influencing parameter. The scores of the candidate routes under each weight combination are calculated, and the candidate route with the highest score is selected as the optimal independent route. Compare the optimal independent routes determined by multiple groups of target services with the actual routes of the target services, and select a group of weight combinations with the highest accuracy as the optimal weight combination; Construct a service routing planning model based on the ant colony algorithm, set basic parameters based on the optimal weight combination, and calculate the optimal primary and backup routes for the target service.

2. A communication service routing planning method based on ant colony algorithm according to claim 1, characterized in that: The steps of normalization processing in the data cleaning and normalization processing of the collected data are specifically as follows: In the formula, x represents the type of communication data, x max and x min represent the maximum and minimum values ​​in x respectively.

3. A communication service routing planning method based on ant colony algorithm according to claim 1, characterized in that: The steps of constructing a service routing planning model based on the ant colony algorithm are specifically as follows: Initialize basic parameters, including the maximum number of iterations N, the number of ants m, the pheromone importance factor α, the heuristic information importance factor β, the pheromone volatility rate ρ, and the pheromone constant Q; Construct the solution space and calculate the probability of the ant choosing the next device node when it is at the initial device node. The specific calculation formula is as follows: In the formula, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on path i to j; η ij is the heuristic information from node i to node j, represents the neighbor set of node i; After each ant completes a move, the pheromone increment is calculated and the pheromone concentration is updated: Set the maximum number of iterations, and the algorithm will terminate after all iterations are completed; According to the path lengths that the ants have crawled in all solutions, two smallest paths are selected as the optimal service primary and backup routes for output.

4. A communication service routing planning method based on ant colony algorithm according to claim 3, characterized in that: In the step of setting the basic parameters by combining the optimal weight combination, the basic parameters set are the heuristic information η from node i to node j. ij , the specific calculation formula is as follows: or ij =f(A j ,B j ,C j ,E ij )=ω1A j +ω2B j +ω3C j +ω4E ij In the formula, A j ,B j ,C j ,E ij are the voltage level, failure rate, overload rate, and path length from node i to node j of node j, respectively. ω1, ω2, ω3, and ω4 are the weights of each parameter indicator.

5. A communication service routing planning system based on ant colony algorithm, characterized in that: include: The data acquisition and processing module collects communication data in the power communication network, wherein the communication data includes service data, optical path data, channel data, site data and network element data, wherein the network element data includes site equipment and failure rate, overload rate, voltage level and optical cable length of site equipment; and performs data cleaning and normalization processing on the collected data; The optical path topology construction module uses the equipment at each site in the power communication network as a node and the optical fiber between the equipment at each site as a link to construct an optical path topology; The candidate route determination module determines the starting site and the ending site according to the target service, and searches for all candidate routes for the target service according to the optical path topology diagram; The optimal route calculation module calculates the voltage level, failure rate, overload rate and optical cable length of all candidate routes according to the failure rate, overload rate and voltage level of the equipment at each site, sets multiple weight combinations for each influencing parameter, calculates the score of the candidate route under each weight combination, and selects the candidate route with the highest score as the optimal independent route; The optimal weight combination determination module compares the optimal independent routes determined by multiple groups of target services with the actual routes of the target services, and selects a group of weight combinations with the highest accuracy as the optimal weight combination; The optimal route calculation module for the target business builds a business route planning model based on the ant colony algorithm, sets basic parameters based on the optimal weight combination, and calculates the optimal primary and backup routes for the target business.

6. A communication service routing planning system based on ant colony algorithm according to claim 5, characterized in that: The steps of normalization processing in the data cleaning and normalization processing of the collected data are specifically as follows: In the formula, x represents the type of communication data, x max and x min represent the maximum and minimum values ​​in x respectively.

7. A communication service routing planning system based on ant colony algorithm according to claim 5, characterized in that: The steps of constructing a service routing planning model based on the ant colony algorithm are specifically as follows: Initialize basic parameters, including the maximum number of iterations N, the number of ants m, the pheromone importance factor α, the heuristic information importance factor β, the pheromone volatility rate ρ, and the pheromone constant Q; Construct the solution space and calculate the probability of the ant choosing the next device node when it is at the initial device node. The specific calculation formula is as follows: In the formula, p ij (t) is the probability of the ant choosing from node i to node j at time t, τ ij (t) is the pheromone concentration on path i to j; η ij is the heuristic information from node i to node j, represents the neighbor set of node i; After each ant completes a move, the pheromone increment is calculated and the pheromone concentration is updated: Set the maximum number of iterations, and the algorithm will terminate after all iterations are completed; According to the path lengths that the ants have crawled in all solutions, two smallest paths are selected as the optimal service primary and backup routes for output.

8. A communication service routing planning system based on ant colony algorithm according to claim 7, characterized in that: In the step of setting the basic parameters by combining the optimal weight combination, the basic parameters set are the heuristic information η from node i to node j. ij , the specific calculation formula is as follows: or ij =f(A j ,B j ,C j ,E ij )=ω1A j +ω2B j +ω3C j +ω4E ij In the formula, A j ,B j ,C j ,E ij are the voltage level, failure rate, overload rate, and path length from node i to node j of node j, respectively. ω1, ω2, ω3, and ω4 are the weights of each parameter indicator.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the communication service routing planning method based on ant colony algorithm as described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a communication service routing planning method based on an ant colony algorithm as described in any one of claims 1 to 4 is implemented.

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