Communication optical cable path planning method and system based on Internet of Things

Abstract

The invention relates to the field of path planning, in particular to a communication optical cable path planning method and system based on the Internet of Things, and the method comprises the steps: obtaining a grid map, setting a cost mark value for each grid in the grid map, calculating the pheromone volatilization coefficient of each grid in the iteration process of an ant colony algorithm, and calculating the cost mark value of each grid; and obtaining an optimal path for laying the communication optical cable by using an ant colony algorithm. By adaptively adjusting the pheromone volatilization coefficient, the flexibility of the pheromone volatilization coefficient is improved, the accuracy of searching the optimal path is improved, and the accuracy of communication optical cable path planning is further improved.

Description

Technical Field

[0001] The present invention relates to the field of path planning, and in particular to a communication optical cable path planning method and system based on the Internet of Things. Background Art

[0002] When building a national or regional telecommunications backbone network, it is necessary to plan the path of long-distance optical cables. For the communication network within the city, the optical cable path planning must take into account factors such as the city's geographical layout, building distribution, population density, and laying costs. For example, in a busy commercial center area, it is necessary to ensure that the optical cable can cover various office buildings, shopping malls, and public facilities, and at the same time, it is necessary to consider the laying cost and difficulty. In the path planning process, it is necessary to try to choose a path with low laying cost and easy construction to reduce repeated construction and resource waste. The ant colony algorithm is a heuristic algorithm with global search characteristics that can be used to plan communication optical cable paths.

[0003] The Chinese patent application document with publication number CN117824683A discloses an improved ant colony path planning method and system based on a high-precision map, the method comprising: S1, pre-processing the high-precision map based on the starting point A and the end point B to be navigated to obtain a bottom-level map that distinguishes feasible areas from infeasible areas; S2, clustering and partitioning the infeasible areas of the bottom-level map so that each infeasible sub-area contains part of the feasible area; extracting the feasible areas and classification information in all infeasible sub-areas to construct a high-level map; S3, using a leap-layer improved ant colony algorithm to search for the optimal path from the starting point A to the bottom-level map, and when it is determined that the next node of the current node is a leap-layer node, the leap-layer node is used as a new search starting point, and the optimal path is continued to be searched from the high-level map until the end point B is reached; S4, mapping the path searched from the high-level map to the bottom-level map, and integrating to obtain a complete optimal path.

[0004] The above technical solution adopts a fixed pheromone volatility coefficient when using the ant colony algorithm for path planning. The fixed pheromone volatility coefficient causes the pheromones of all paths to decay at the same rate during the entire iteration process, which will cause different impacts at different stages of the algorithm iteration. For example, in the early stage of the iteration, the ant colony needs to conduct global exploration. The fixed pheromone volatility coefficient causes excessive pheromone volatility, resulting in the loss of high-quality path information. In the later stage, the fixed pheromone volatility coefficient may cause the pheromone of the old method path to remain, resulting in an invalid search falling into a local optimum, which ultimately leads to a slow convergence speed and the planned path is not the global optimal path. Summary of the invention

[0005] In order to solve the problem of poor accuracy of planned paths due to fixed pheromone volatility coefficients, the present invention provides a communication optical cable path planning method and system based on the Internet of Things.

[0006] In a first aspect, the present invention provides a communication optical cable path planning method based on the Internet of Things, which adopts the following technical solution: Obtain a grid map, set a cost mark value for each grid in the grid map, calculate the pheromone volatility coefficient of each grid in the iteration process of the ant colony algorithm, and use the ant colony algorithm to obtain the optimal path for laying communication optical cables; the calculation method of the pheromone volatility coefficient of each grid is: The diversity contribution value of each grid in the iteration process is calculated. The diversity contribution value indicates the importance of the grid in the overall path. The sum of the cost mark values ​​of the grids in the path obtained after iteration is taken as the path cost of the corresponding path. The path with the smallest path cost is taken as the preferred path. The pheromone volatility coefficient of each grid is calculated. The expression is:

[0007] In the formula is the pheromone volatility coefficient of the i-th grid in the k-th iteration process, is the preset global pheromone volatility coefficient at the kth iteration, is the cost mark value of the i-th grid, mean( ) is the mean of all grid cost marker values; is the diversity contribution value of the i-th grid during the k-1-th iteration, is the probability that the preferred path obtained before the kth iteration passes through the i-th grid, norm represents the normalization function, and exp represents the exponential function with e as the base.

[0008] According to the importance of a single grid when the ant colony searches for a path during each iteration and its contribution to the diversity of the path, the pheromone volatility coefficient of a single grid is locally and dynamically adjusted in combination with the marking value of the grid, thereby increasing the flexibility of the pheromone volatility coefficient, improving the accuracy of searching for the optimal path, and further improving the accuracy of communication optical cable path planning.

[0009] Preferably, the expression of the global information volatility coefficient is:

[0010] In the formula, is the preset global pheromone volatility coefficient at the kth iteration, represents the path cost of the preferred path obtained after the k-1th iteration, represents the mean of the path costs of all paths obtained after the k-1th iteration, and exp represents an exponential function with e as the base.

[0011] Preferably, the expression of the global information volatility coefficient is:

[0012] In the formula, is the preset global pheromone volatility coefficient at the kth iteration, k represents the kth iteration, K is the maximum number of iterations set by the ant colony algorithm, represents the path cost of the preferred path obtained after the k-1th iteration, represents the mean of the path costs of all paths obtained after the k-1th iteration, and exp represents an exponential function with e as the base.

[0013] The global volatility coefficient is preliminarily adjusted according to the different periods of algorithm iteration and the path cost of the previous iteration, which enhances the global search capability in the early stage of the ant colony algorithm and accelerates the convergence of the ant colony algorithm in the later stage. The problem of improper selection of fixed threshold leading to slow convergence of the ant colony algorithm iteration or convergence to local optimality is solved.

[0014] Preferably, the expression of diversity contribution value is:

[0015] In the formula, is the diversity contribution value of the i-th grid during the k-1-th iteration, Represents the standard deviation of the path costs of all ants passing through the i-th grid in the k-1th iteration.

[0016] Preferably, the expression of diversity contribution value is:

[0017] In the formula, is the diversity contribution value of the i-th grid during the k-1-th iteration, is the number of ants passing through the i-th grid during the k-1th iteration, is the total number of ants in the ant colony, H represents the binary cross entropy loss function, Represents the standard deviation of the path costs of all ants passing through the i-th grid in the k-1th iteration.

[0018] By calculating the diversity contribution value of the grid, we can understand the contribution of the corresponding grid in finding the optimal path, providing a theoretical basis for adjusting the pheromone volatility coefficient.

[0019] Preferably, the method for obtaining the grid map is: obtaining a map image covering the starting point, the end point and the laying area of ​​the optical cable, and converting the map image to obtain the grid map.

[0020] Preferably, the probability of the preferred path passing through the i-th grid is calculated by calculating the number of preferred paths passing through the i-th grid, and taking the ratio of the number of preferred paths passing through the i-th grid to the total number of preferred paths as the probability of passing through the i-th grid.

[0021] The probability of passing through a grid reflects the importance of the corresponding grid in finding the optimal path, which facilitates the adjustment of the pheromone volatility coefficient.

[0022] Preferably, the method of using the ant colony algorithm to obtain the optimal path for laying communication optical cables is: after the iteration, multiple preferred paths are obtained, and the preferred path with the smallest path cost is used as the optimal path.

[0023] The optimal path is obtained through iterative calculation, and the optimal path is the planned path of the communication optical cable, thereby reducing the construction cost of laying the optical cable.

[0024] Preferably, before the ant colony algorithm is iterated, the method further includes a step of initializing the number of ant colonies, initial pheromone concentration, and heuristic factors.

[0025] In a second aspect, the present invention provides a communication optical cable path planning system based on the Internet of Things, which adopts the following technical solutions: A communication optical cable path planning system based on the Internet of Things includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the communication optical cable path planning method based on the Internet of Things is implemented.

[0026] The above-mentioned communication optical cable path planning method based on the Internet of Things is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0027] The present invention has the following technical effects: According to the importance of a single grid and its contribution to the diversity of the path when the ant colony searches for the path in each iteration, the pheromone volatility coefficient of a single grid is locally and dynamically adjusted in combination with the tag value of the grid, thereby realizing adaptive adjustment of the pheromone volatility coefficient, increasing the flexibility of the pheromone volatility coefficient, improving the accuracy of searching for the optimal path, and further improving the accuracy of communication optical cable path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of a communication optical cable path planning method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0029] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0030] The embodiment of the present invention discloses a communication optical cable path planning method based on the Internet of Things, referring to Figure 1 , including the following steps: S1: Get a grid map, and set a cost tag value for each grid in the grid map.

[0031] Obtain a map image covering the starting point, end point and laying area of ​​the optical cable, match the map image with the real geographic coordinate system, eliminate geometric deformation and errors in the map image, make the position on the map correspond to the actual spatial position, and convert the map image to a raster map. It can be understood that the raster map is composed of multiple grids. According to the complexity of the terrain or the difficulty of construction, a cost mark value is set for each grid. The larger the cost mark value, the greater the cost of the optical cable passing through the grid area. Conversely, the lower the cost mark value, the lower the cost of the optical cable passing through the grid area.

[0032] S2: Calculate the pheromone volatilization coefficient of each grid in the iteration process of the ant colony algorithm.

[0033] After each iteration, each ant searches for a path. Each path consists of corresponding grids. The sum of the cost mark values ​​of the grids in the path obtained after iteration is used as the path cost of the corresponding path. The path with the smallest path cost is used as the preferred path.

[0034] S21: Calculate the global information volatility coefficient.

[0035] In one embodiment, the expression of the global information volatility coefficient is:

[0036] In the formula, is the preset global pheromone volatility coefficient at the kth iteration, represents the path cost of the preferred path obtained after the k-1th iteration, represents the mean of the path costs of all paths obtained after the k-1th iteration, and exp represents an exponential function with e as the base.

[0037] The smaller the value of indicates, the wider the search range of the ant colony is during the k-1th iteration, and the difference in the quality of the paths is large. In the early stage of the iteration, during the global search period, the pheromone volatility coefficient should be increased; the larger the ratio, the closer the algorithm is to convergence, and the pheromone volatility coefficient should be reduced.

[0038] In one embodiment, the expression of the global information volatility coefficient is:

[0039] In the formula, is the preset global pheromone volatility coefficient at the kth iteration, k represents the kth iteration, K is the maximum number of iterations set by the ant colony algorithm, represents the path cost of the preferred path obtained after the k-1th iteration, represents the mean of the path costs of all paths obtained after the k-1th iteration, and exp represents an exponential function with e as the base.

[0040] The smaller the value of indicates that the search range of the ant colony is wider during the k-1th iteration, and the difference in the quality of the paths is greater. In the early stage of the iteration, the global search period should increase the pheromone volatility coefficient; and the larger the ratio, the closer the algorithm is to convergence, and the pheromone volatility coefficient should be reduced. At the same time, at the beginning of the iteration, a large-scale global search should be carried out to avoid falling into the local optimal solution. At this time, the pheromone volatility coefficient should be increased. Convergence should be carried out near the end of the iteration, and the pheromone volatility coefficient should be reduced.

[0041] In summary, this embodiment comprehensively considers the path cost and the ordinal number of the iteration in the k-1th iteration process when calculating the global information volatility coefficient, and has high accuracy.

[0042] S22: Calculate the diversity contribution value of each grid in the iteration process. The diversity contribution value indicates the importance of the grid in the overall path.

[0043] In one embodiment, the expression of the diversity contribution value is:

[0044] In the formula, is the diversity contribution value of the i-th grid during the k-1-th iteration, Represents the standard deviation of the path costs of all ants passing through the i-th grid in the k-1th iteration.

[0045] After the k-1th iteration, each ant searches for a path, and each path is composed of corresponding grids. The path cost of the path is further obtained according to the cost mark value of the corresponding grid in the path. The standard deviation of the path cost of the ant corresponding to the path passing through the i-th grid is calculated. The larger the value, the greater the difference in path costs of all paths passing through the i-th grid at the k-1th iteration, and the higher the contribution of the i-th grid to the diversity of the overall path.

[0046] In one embodiment, the expression of the diversity contribution value is:

[0047] In the formula, is the diversity contribution value of the i-th grid during the k-1-th iteration, is the number of ants passing through the i-th grid during the k-1th iteration, is the total number of ants in the ant colony, H represents the binary cross entropy loss function, Represents the standard deviation of the path costs of all ants passing through the i-th grid in the k-1th iteration.

[0048] It indicates the loss value calculated by the binary cross entropy loss function. The larger the value, the more balanced the distribution of the i-th grid in different paths and the higher the diversity contribution. After the k-1th iteration, each ant searches for a path, and each path is composed of corresponding grids. The path cost of the path is further obtained according to the cost mark value of the corresponding grid in the path. The standard deviation of the path cost of the ant corresponding to the path passing through the i-th grid is calculated. The larger the value, the greater the difference in the path cost of all paths passing through the i-th grid at the k-1th iteration, and the higher the contribution of the i-th grid to the diversity of the overall path. The contribution value of diversity is calculated in two dimensions, which further improves the accuracy and stability of the diversity contribution value.

[0049] S23: Calculate the pheromone volatility coefficient of each grid.

[0050] The expression is:

[0051] In the formula is the pheromone volatility coefficient of the i-th grid in the k-th iteration process, is the preset global pheromone volatility coefficient at the kth iteration, is the cost mark value of the i-th grid, mean( ) is the mean of all grid cost marker values; is the diversity contribution value of the i-th grid during the k-1-th iteration, is the probability that the preferred path obtained before the kth iteration passes through the i-th grid. For example, among the 10 preferred paths obtained before the 11th iteration, 6 preferred paths pass through the second grid. The value of is 0.6, norm represents the normalization function, and exp represents the exponential function with e as the base.

[0052] The larger the value of indicates that the proportion of the optical cable laying cost of the i-th grid in all grids is higher. Laying optical cables here may make the cost of the entire path higher. Therefore, the possibility that the i-th grid is a node in the shortest path is low, so its pheromone volatility coefficient should be increased to prevent most ants from passing through the grid.

[0053] is the diversity contribution value of the i-th grid in the k-1th iteration process. The larger its value is, the higher the contribution of the i-th grid to the overall path diversity is, and its exploration value is higher. Its pheromone volatility coefficient should be reduced to promote more ants to pass through the grid; otherwise, The smaller the value of is, the lower the exploration value of the i-th grid is, and its pheromone volatility coefficient should be increased.

[0054] is the probability that the preferred path obtained before the kth iteration passes through the i-th grid, reflecting the importance of the i-th grid in finding the optimal path. The larger the value of , the more likely the i-th grid is to be a node in the optimal path, and its pheromone volatility coefficient should be reduced to promote the algorithm to converge to the optimal path.

[0055] S3: Use the ant colony algorithm to obtain the optimal path for laying communication optical cables.

[0056] The number of ant colonies, initial pheromone concentration, and heuristic factors are initialized, and the pheromone volatility coefficient of each grid is used to update the pheromone during the iteration process. The iteration is terminated when the number of iterations of the ant colony algorithm reaches the preset maximum number of iterations. After the iteration, multiple preferred paths are obtained, and the preferred path with the smallest path cost is taken as the optimal path. The optimal path is the laying path of the communication optical cable, and the communication optical cable path planning is completed.

[0057] An embodiment of the present invention also discloses a communication optical cable path planning system based on the Internet of Things, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a communication optical cable path planning method based on the Internet of Things according to the present invention is implemented.

[0058] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0059] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A communication optical cable path planning method based on the Internet of Things, characterized in that: Includes steps: Obtain a grid map, set a cost mark value for each grid in the grid map, calculate the pheromone volatility coefficient of each grid in the iteration process of the ant colony algorithm, and use the ant colony algorithm to obtain the optimal path for laying communication optical cables; the calculation method of the pheromone volatility coefficient of each grid is: The diversity contribution value of each grid in the iteration process is calculated. The diversity contribution value indicates the importance of the grid in the overall path. The sum of the cost mark values ​​of the grids in the path obtained after iteration is taken as the path cost of the corresponding path. The path with the smallest path cost is taken as the preferred path. The pheromone volatility coefficient of each grid is calculated. The expression is: In the formula is the pheromone volatility coefficient of the i-th grid in the k-th iteration process, is the preset global pheromone volatility coefficient at the kth iteration, is the cost mark value of the i-th grid, mean( ) is the mean of all grid cost marker values; is the diversity contribution value of the i-th grid during the k-1-th iteration, is the probability that the preferred path obtained before the kth iteration passes through the i-th grid, norm represents the normalization function, and exp represents the exponential function with e as the base.

2. A communication optical cable path planning method based on the Internet of Things according to claim 1, characterized in that: The expression of global information volatility coefficient is: In the formula, is the preset global pheromone volatility coefficient at the kth iteration, represents the path cost of the preferred path obtained after the k-1th iteration, represents the mean of the path costs of all paths obtained after the k-1th iteration, and exp represents an exponential function with e as the base.

3. A communication optical cable path planning method based on the Internet of Things according to claim 1, characterized in that: The expression of global information volatility coefficient is: In the formula, is the preset global pheromone volatility coefficient at the kth iteration, k represents the kth iteration, K is the maximum number of iterations set by the ant colony algorithm, represents the path cost of the preferred path obtained after the k-1th iteration, represents the mean of the path costs of all paths obtained after the k-1th iteration, and exp represents an exponential function with e as the base.

4. The method for planning a communication optical cable path based on the Internet of Things according to claim 1, characterized in that: The expression of diversity contribution value is: In the formula, is the diversity contribution value of the i-th grid during the k-1-th iteration, Represents the standard deviation of the path costs of all ants passing through the i-th grid in the k-1th iteration.

5. The method for planning a communication optical cable path based on the Internet of Things according to claim 1, characterized in that: The expression of diversity contribution value is: In the formula, is the diversity contribution value of the i-th grid during the k-1-th iteration, is the number of ants passing through the i-th grid during the k-1th iteration, is the total number of ants in the ant colony, H represents the binary cross entropy loss function, Represents the standard deviation of the path costs of all ants passing through the i-th grid in the k-1th iteration.

6. The method for planning a communication optical cable path based on the Internet of Things according to claim 1, characterized in that: The method for obtaining the raster map is: obtaining a map image covering the starting point, end point and laying area of ​​the optical cable, and converting the map image to obtain the raster map.

7. The method for planning a communication optical cable path based on the Internet of Things according to claim 1, characterized in that: The probability of the preferred path passing through the i-th grid is calculated by calculating the number of preferred paths passing through the i-th grid, and taking the ratio of the number of preferred paths passing through the i-th grid to the total number of preferred paths as the probability of passing through the i-th grid.

8. The method for planning a communication optical cable path based on the Internet of Things according to claim 1, characterized in that: The method of using the ant colony algorithm to obtain the optimal path for laying communication optical cables is as follows: after the iteration, multiple preferred paths are obtained, and the preferred path with the smallest path cost is taken as the optimal path.

9. The method for planning a communication optical cable path based on the Internet of Things according to claim 1, characterized in that: Before the ant colony algorithm is iterated, the steps of initializing the number of ant colonies, initial pheromone concentration, and heuristic factors are also included.

10. A communication optical cable path planning system based on the Internet of Things, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a communication optical cable path planning method based on the Internet of Things according to any one of claims 1-9 is implemented.

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