CN115755954B - Routing inspection path planning method, system, computer equipment and storage medium - Google Patents
Routing inspection path planning method, system, computer equipment and storage medium Download PDFInfo
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Abstract
The scheme relates to a routing inspection path planning method, a routing inspection path planning system, computer equipment and a storage medium. The method comprises the following steps: obtaining inspection data, and calculating the clustering quantity according to inter-class partition indexes BWP of the inspection data based on distance; dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area; calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area; and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm. The method has the advantages that the large-scale power transmission line inspection areas are divided based on BWP and K-Means algorithms, the inspection areas are adjusted, and the optimal paths in the areas are solved by combining ant colony algorithm and genetic algorithm, so that the solving efficiency of the optimal path algorithm can be improved, and the workload of each unmanned aerial vehicle is balanced.
Description
Technical Field
The invention relates to the technical field of transmission line inspection, in particular to an inspection path planning method, an inspection path planning system, computer equipment and a storage medium.
Background
The transmission line is an important component of the power system, and the safe operation of the transmission line is an important guarantee for the overall stability of the system. In order to ensure stable and safe operation of the power transmission line, the power transmission line needs to be periodically inspected. The daily inspection of the power transmission line is an important work on the basis of guaranteeing the reliable power supply. At present, the transmission line inspection mode is established in modes of manual inspection, unmanned aerial vehicle inspection, vehicle inspection and the like, and in recent years, due to the rapid development of Unmanned Aerial Vehicles (UAVs), the UAVs have better flexibility and controllability, and the UAVs are adopted in the transmission line to finish daily inspection tasks. In the process of the inspection of the power transmission line, the inspection time required by the inspection groups of different unmanned aerial vehicles is different due to the fact that the inspection area range of the power transmission line is generally larger.
Therefore, when the traditional unmanned aerial vehicle is used for carrying out power transmission line inspection, in the process of solving the optimal path, the working time required by inspection paths of inspection groups of different unmanned aerial vehicles can be greatly different, and the problem of unbalanced workload of the inspection groups of the unmanned aerial vehicles exists.
Disclosure of Invention
Based on the above, in order to solve the above technical problems, a method, a system, a computer device and a storage medium for routing inspection path planning are provided, which can adjust the routing inspection area and balance the workload of each unmanned aerial vehicle.
A method of patrol path planning, the method comprising:
obtaining inspection data, and calculating the clustering quantity according to inter-class partition indexes BWP of the inspection data based on distance;
dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area;
calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area;
and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
In one embodiment, the dividing the inspection area by the K-Means algorithm according to the inspection data and the number of clusters includes:
calculating each BWP index value of each single data object based on the single data object in the inspection data;
calculating an average BWP value according to each BWP index value;
Determining optimal cluster data from the number of clusters according to the average BWP value;
and dividing a patrol area by a K-Means algorithm according to the patrol data and the optimal clustering quantity.
In one embodiment, the dividing the inspection areas by the K-Means algorithm to obtain each initial inspection area includes:
according to the optimal clustering quantity, determining each initial clustering center from each data point of the inspection data through a K-Means algorithm;
calculating the distance between each remaining data point and each initial clustering center, and dividing each remaining data point into the class to which each initial clustering center belongs according to the distance;
judging whether each initial clustering center converges or not, if so, outputting a classification result, and dividing the inspection areas according to the classification result to obtain each initial inspection area.
In one embodiment, the calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload includes:
calculating and sequencing the workload of each initial inspection area, calculating the difference value between the maximum workload and the minimum workload, if the difference value is larger than a workload threshold, removing the farthest data point from the initial clustering center in the initial inspection area corresponding to the maximum workload, and adding the farthest data point into the class with the second smallest distance from the initial clustering center;
When an initial inspection area with the maximum workload being larger than a maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to the initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance;
and calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving the data points according to the ratio.
In one embodiment, before the path planning by the ant colony algorithm and the genetic algorithm, the method further comprises:
generating an initial population by adopting an ant colony algorithm, and initializing parameters of the ant colony algorithm;
constructing a solution space of the ant colony algorithm, and initializing the positions of ants;
randomly distributing ants, updating pheromones, and calculating paths traversed by the ants;
the path iterated once is used as the initial population of the genetic algorithm.
In one embodiment, before the path planning by the ant colony algorithm and the genetic algorithm, the method further comprises:
setting parameters of the genetic algorithm, and initializing the genetic algorithm;
The initialization parameters comprise population scale, crossover probability, variation probability and iteration times.
In one embodiment, path planning by ant colony algorithm and genetic algorithm includes:
crossing and mutating chromosomes in the initial population by the genetic algorithm;
the chromosome performs decoding operation to obtain planning and dividing results of the distribution path;
acquiring the fitness value of each individual in the initial population, and selecting excellent individuals to enter the next generation by adopting a roulette mode;
adopting a variable-scale population scale genetic algorithm to adjust the population scale;
and when the genetic algorithm reaches the maximum iteration times, ending the algorithm and outputting the shortest delivery path.
A patrol path planning system, the system comprising:
the clustering number calculation module is used for acquiring the inspection data and calculating the clustering number according to inter-class partition indexes BWP of the inspection data based on the distance;
the initial inspection area dividing module is used for dividing the inspection areas through a K-Means algorithm according to the inspection data and the clustering quantity to obtain all initial inspection areas;
the inspection area adjusting module is used for calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area;
And the path planning module is used for carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
obtaining inspection data, and calculating the clustering quantity according to inter-class partition indexes BWP of the inspection data based on distance;
dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area;
calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area;
and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
obtaining inspection data, and calculating the clustering quantity according to inter-class partition indexes BWP of the inspection data based on distance;
dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area;
Calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area;
and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
According to the routing inspection path planning method, the routing inspection path planning system, the computer equipment and the storage medium, the clustering quantity is calculated by acquiring the routing inspection data and according to the inter-class division index BWP of the routing inspection data based on the distance; dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area; calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area; and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm. The method has the advantages that the large-scale power transmission line inspection areas are divided based on BWP and K-Means algorithms, the inspection areas are adjusted, and the optimal paths in the areas are solved by combining ant colony algorithm and genetic algorithm, so that the solving efficiency of the optimal path algorithm can be improved, and the workload of each unmanned aerial vehicle is balanced.
Drawings
FIG. 1 is an application environment diagram of a patrol path planning method in one embodiment;
FIG. 2 is a flow chart of a method for routing inspection path planning in one embodiment;
FIG. 3 is a flow chart illustrating the division of inspection areas according to one embodiment;
FIG. 4 is a schematic flow chart of a partitioning preliminary patrol area based on the K-Means algorithm in one embodiment;
fig. 5 is a schematic diagram of a routing inspection path planning procedure based on an ant colony algorithm and a genetic algorithm in one embodiment;
FIG. 6 is a block diagram of a patrol path planning system in one embodiment;
fig. 7 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The inspection path planning method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. As shown in fig. 1, the application environment includes a computer device 110, wherein the computer device 110 may be a robotic, unmanned aerial vehicle, or the like. The computer device 110 may acquire the inspection data, and calculate the number of clusters according to the inter-class partition index BWP of the inspection data based on the distance; the computer equipment 110 can divide the inspection areas through a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area; the computer device 110 may calculate the workload of each initial inspection area, and divide each initial inspection area twice according to each workload to obtain each target inspection area; the computer device 110 may perform path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
In one embodiment, as shown in fig. 2, there is provided a routing inspection path planning method, including the steps of:
step 202, obtaining inspection data, and calculating the clustering quantity according to inter-class division indexes BWP of the inspection data based on the distance.
The computer equipment can acquire inspection data, wherein the inspection data can comprise data such as a map, inspection points, the number of unmanned aerial vehicles to be inspected, and the like, which are required to be inspected on the transmission line. In the process of dividing the inspection area, the computer equipment can calculate the clustering quantity according to the inter-class division index BWP of the inspection data based on the distance. The size of the clustering quantity can directly influence the clustering result.
And 204, dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area.
The K-Means algorithm is affected by various factors when dividing the inspection area, and specific influencing factors can include the number of clusters, the relation among the classes, the selection of an initial cluster center, the shortest distance among target points, the maximum workload of the inspection area, the minimum load rate, the workload balance and the like.
Wherein, the relationship between classes: the data sets belonging to different classes do not have intersections, and each data point only belongs to one class; when the K-Means algorithm is used, no intersection exists between classes, and each data point is classified into a unique class; the Euclidean distance between two data points is used as the shortest distance between the target points; too large working load difference among the inspection areas can cause that some inspection groups finish tasks too early or too late, so that the inspection efficiency is low, therefore, the inspection areas are required to meet the requirements of maximum working load and minimum load rate, and the overall balance of the working load of the areas is ensured.
And 206, calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area.
The computer equipment can respectively calculate the workload of each initial inspection area, and optimize and adjust the area division result according to the constraint conditions of the maximum workload, the minimum load rate and the workload balance of the inspection area, so as to obtain each target inspection area.
And step 208, carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
In the embodiment, the computer equipment calculates the clustering number by acquiring the inspection data and according to the inter-class partition index BWP of the inspection data based on the distance; dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area; calculating the workload of each initial inspection area, and carrying out secondary division on each initial inspection area according to each workload to obtain each target inspection area; and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm. The method has the advantages that the large-scale power transmission line inspection areas are divided based on BWP and K-Means algorithms, the inspection areas are adjusted, and the optimal paths in the areas are solved by combining ant colony algorithm and genetic algorithm, so that the solving efficiency of the optimal path algorithm can be improved, and the workload of each unmanned aerial vehicle is balanced.
In one embodiment, the provided routing inspection path planning method may further include a process of dividing an initial routing inspection area, and the specific process includes: based on the single data object in the inspection data, calculating each BWP index value of each single data object; calculating an average BWP value according to each BWP index value; determining optimal cluster data from the number of clusters according to the average BWP value; and dividing the inspection area by a K-Means algorithm according to the inspection data and the optimal clustering quantity.
The K-Means algorithm calculates different inspection areas by taking the distance as a measure, so that the clustering result presents the effect of tight in-class and separation among classes for determining the optimal clustering quantity, and the BWP algorithm is adopted for clustering evaluation to obtain the optimal clustering quantity.
Specifically, BWP starts from a distance measure and defines the best clustering result as the result that minimizes the distance inside the clusters and between clusters. Firstly, aiming at single data objects in a data set to be divided, obtaining BWP index values of the single data objects based on defined inter-class distances and intra-class distances; and calculating to obtain an average BWP value of the whole data set, and judging the clustering effectiveness of the whole data object set.
The relevant parameters of BWP index are now defined as follows: the clustering space of the data objects is set to k= { X, R }, where x= { X 1 ,x 2 ,...,x n }. Assuming that n data are divided into c classes, the inter-class distance d (i, j) is the average distance from the j-th data of the i-th class to the data object of the other class, and the calculation formula is:the calculation formula of the minimum inter-class distance min d (i, j) is as follows: /> Wherein k represents the number of clusters, i represents the i-th class,>a j-th data object representing a i-th class,/>The (r) th data representing the kth class, n k Represents the amount of data in class k 2 Representing the square of the euclidean distance; defining the intra-class distance w (i, j) as the average distance from the j-th data of the i-th class to other data objects in the class, and calculating the average distance by the following formula: /> Wherein (1)>The s-th data representing the i-th class; defining the cluster distance baw (i, j) as the sum of the minimum inter-class distance and the intra-class distance, and the cluster deviation distance bsw (i, j) as the difference between the minimum inter-class distance and the intra-class distance, the BWP value is the ratio of baw (i, j) to bsw (i, j), and the calculation formula is: />
Wherein, the larger the BWP index value is, the better the clustering effect of the data is represented; and designing an average BWP value based on the definition of the BWP index, and utilizing the average BWP index value of all data in the data set to realize the clustering effect evaluation of the whole data set. When the average BWP index value of the whole data set is maximum, the corresponding cluster number is the optimal cluster number of the data set, and the calculation formula is as follows: k opt ＝argmax{BWP avg (k)}。
When dividing an initial inspection area, the computer equipment adopts a K-Means algorithm to preliminarily divide the area based on BWP evaluation criteria, firstly inputs a data set to be classified containing n data objects and a value range of the clustering number K, respectively calculates clustering results in the value range of K, calculates corresponding BWP values according to the clustering results obtained by different K values, and outputs the clustering number with the maximum BWP value and the clustering result as final results.
In one embodiment, the provided routing inspection path planning method may further include a process of obtaining each initial routing inspection area, and the specific process includes: according to the optimal clustering quantity, determining each initial clustering center from each data point of the inspection data through a K-Means algorithm; calculating the distance between each remaining data point and each initial clustering center, and dividing each remaining data point into the class to which each initial clustering center belongs according to the distance; judging whether each initial clustering center converges, if so, outputting a classification result, and dividing the inspection areas according to the classification result to obtain each initial inspection area.
In this embodiment, when the initial clustering center is selected, in order to avoid the clustering result from falling into the local optimal solution, a point with the largest density is selected from all the data points to be used as a first initial clustering center point, a point with the largest distance from the first initial clustering center is selected to be used as a second initial clustering center point, a point with the largest shortest distance from the first two points is selected to be used as a center point of a third initial clustering, and so on until k initial clustering center points are selected.
Specifically, when calculating a clustering result, first, selecting k data points (c 1, c2,.,) as an initial clustering center according to an initial clustering center selection strategy; secondly, calculating the distance between the rest data points and each clustering center, and dividing each data point into the class of the closest clustering center based on the minimum distance constraint; then calculating new cluster center according to the formulaJudging whether the clustering center is converged, if so, stopping calculation and outputting a classification result, and if not, returning to perform calculation again.
In one embodiment, the provided routing inspection path planning method may further include a process of performing secondary division on the initial routing inspection area, that is, optimizing and adjusting the area division result in consideration of constraint conditions of maximum workload, minimum load rate and workload balance of the initial routing inspection area; the specific process comprises the following steps: and calculating the workload of each initial inspection area, sorting, calculating the difference value between the maximum workload and the minimum workload, if the difference value is larger than a workload threshold, removing the farthest data point from the initial clustering center in the initial inspection area corresponding to the maximum workload, and adding the farthest data point into the class with the second smallest distance from the initial clustering center.
Firstly, adjusting and optimizing based on workload balance conditions: and calculating and sequencing the workload of each region in the region division result, selecting the maximum workload and the minimum workload for comparison, finishing adjustment if the difference value of the maximum workload and the minimum workload is not larger than a set workload threshold value, removing the point which is farthest from the clustering center in the region with the maximum workload if the difference value is larger than the set workload threshold value, adding the point into the class which is second smallest from the clustering center, and recalculating the workload and the clustering center until the constraint condition is met.
When an initial inspection area with the maximum workload being larger than the maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to an initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance.
And then, adjusting and optimizing based on the maximum workload condition: on the basis of the last step of adjustment result, combining the longest working time of the unmanned aerial vehicle, setting a maximum workload threshold value in each area, calculating whether each area has the condition of exceeding the maximum workload, if not, finishing adjustment, if so, calculating the distance Di between each node i in the area and the clustering center point of the area, namely a first distance, and simultaneously calculating the distance Di between each node and other clustering centers, namely a second distance, moving the node with the maximum Di to the area with smaller Di and meeting the workload limit, and recalculating the workload until the constraint condition is met.
And calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving the data points according to the ratio.
Finally, adjusting and optimizing based on the minimum load rate: defining the load rate as the ratio of the workload of each region to the maximum workload in all regions, setting a region minimum load rate threshold value on the basis of the adjustment result of the last step, calculating the load rate values of each region, sorting, finishing adjustment if no region lower than the threshold value exists, starting adjustment from the region with the minimum load rate, calculating the distance Di from each node i of other regions to the clustering center point of the region, moving the node with the minimum Di value to the current region, and recalculating the workload and the clustering center until the constraint condition is met. And obtaining a final inspection area dividing result after finishing adjustment.
In one embodiment, the process of dividing the patrol area is as shown in fig. 3: after inputting data, firstly calculating BWP value; selecting the optimal clustering quantity according to the calculated BWP value; solving based on a K-Means algorithm to obtain a preliminary region division result; and then, carrying out regional division optimization, and obtaining a final regional division result, namely each target inspection region, based on the constraint conditions of the maximum workload, the minimum load rate and the workload balance of the inspection region.
In one embodiment, the partitioning of the preliminary patrol area based on the K-Means algorithm is shown in FIG. 4: after the data and the cluster number range are input, the cluster result and the BWP value can be calculated, and then the cluster number and the result with the maximum BWP value are output. The process for calculating the clustering result comprises the following steps: setting an initial clustering center, calculating the distance between a data point and each clustering center, dividing the data point according to a minimum distance principle, calculating a new clustering center, judging whether the clustering center is converged, and acquiring a clustering result if the clustering center is converged; if not, resetting the initial cluster center.
In one embodiment, the method for planning a patrol path may further include a process of initializing an ant colony algorithm, and the specific process includes: generating an initial population by adopting an ant colony algorithm, and initializing parameters of the ant colony algorithm; constructing a solution space of an ant colony algorithm, and initializing the positions of ants; randomly distributing ants, updating pheromones, and calculating paths traversed by the ants; the path iterated once is used as the initial population of the genetic algorithm.
In one embodiment, the provided inspection path planning method may further include a process of initializing a genetic algorithm, and the specific process includes: setting parameters of a genetic algorithm, and initializing the genetic algorithm; the initialization parameters comprise population scale, crossover probability, variation probability and iteration times.
In one embodiment, the method for planning a patrol path may further include a path planning process, and the specific process includes: crossing and mutating chromosomes in the initial population by a genetic algorithm; the chromosome carries out decoding operation to obtain planning and dividing results of the distribution path; acquiring the fitness value of each individual in the initial population, and selecting excellent individuals to enter the next generation by adopting a roulette mode; adopting a variable-scale population scale genetic algorithm to adjust the population scale; and when the genetic algorithm reaches the maximum iteration times, ending the algorithm and outputting the shortest delivery path.
Wherein, the crossing part adopts two-point crossing, and the variation part adopts two-point exchange variation. When the chromosome is decoded, the chromosome can be decoded based on the constraints of maximum working time, elimination of sub-loops and the like, so that planning and dividing results of a distribution path are obtained.
In one embodiment, the path planning process is as shown in FIG. 5:
generating an initial population by adopting an ant colony algorithm, initializing parameters of the algorithm, constructing a solution space of the ant colony algorithm, and initializing the positions of ants; then carrying out random distribution on ants, updating pheromones, and calculating paths traversed by the ants; taking the path iterated once as an initial population of a genetic algorithm;
Setting parameters of a genetic algorithm, and initializing the algorithm, wherein the parameters comprise population scale, crossover probability, variation probability and iteration times;
and (3) crossing and mutating chromosomes in the population. Wherein the crossing part adopts two-point crossing, and the variation part adopts two-point exchange variation;
decoding the chromosome based on the constraints of maximum working time, elimination of sub-loops and the like, and obtaining planning and dividing results of a distribution path;
obtaining the fitness value of each individual in the population according to the objective function, and selecting excellent individuals to enter the next generation by adopting a roulette mode;
adopting a variable-scale population scale genetic algorithm to adjust the population scale;
judging whether the ending condition of the genetic algorithm is satisfied. If the maximum iteration number is reached, ending the algorithm and outputting the shortest delivery path; otherwise, the next iteration is continued until the end condition is reached.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the sub-steps or stages of other steps or other steps.
In one embodiment, as shown in fig. 6, there is provided a patrol path planning system, comprising: a cluster number calculation module 610, an initial patrol area division module 620, a patrol area adjustment module 630, and a path planning module 640, wherein:
the cluster number calculation module 610 is configured to obtain inspection data, and calculate a cluster number according to an inter-class partition index BWP of the inspection data based on a distance;
the initial inspection area dividing module 620 is configured to divide the inspection areas according to the inspection data and the number of clusters by using a K-Means algorithm to obtain each initial inspection area;
the inspection area adjustment module 630 is configured to calculate workload of each initial inspection area, and divide each initial inspection area twice according to each workload to obtain each target inspection area;
and the path planning module 640 is used for planning paths of all target inspection areas through an ant colony algorithm and a genetic algorithm.
In one embodiment, the initial patrol area-dividing module 620 is further configured to calculate, based on the individual data objects in the patrol data, respective BWP index values of the individual data objects; calculating an average BWP value according to each BWP index value; determining optimal cluster data from the number of clusters according to the average BWP value; and dividing the inspection area by a K-Means algorithm according to the inspection data and the optimal clustering quantity.
In one embodiment, the initial inspection area division module 620 is further configured to determine, according to the optimal number of clusters, each initial cluster center from each data point of the inspection data by using a K-Means algorithm; calculating the distance between each remaining data point and each initial clustering center, and dividing each remaining data point into the class to which each initial clustering center belongs according to the distance; judging whether each initial clustering center converges, if so, outputting a classification result, and dividing the inspection areas according to the classification result to obtain each initial inspection area.
In one embodiment, the inspection area adjustment module 630 is further configured to calculate and sort the workload of each initial inspection area, calculate a difference between the maximum workload and the minimum workload, if the difference is greater than a workload threshold, remove a farthest data point from the initial cluster center in the initial inspection area corresponding to the maximum workload, and add the farthest data point to a class having a second smallest distance from the initial cluster center; when an initial inspection area with the maximum workload being larger than the maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to an initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance; and calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving the data points according to the ratio.
In one embodiment, the path planning module 640 is further configured to generate an initial population using an ant colony algorithm, and initialize parameters of the ant colony algorithm; constructing a solution space of an ant colony algorithm, and initializing the positions of ants; randomly distributing ants, updating pheromones, and calculating paths traversed by the ants; the path iterated once is used as the initial population of the genetic algorithm.
In one embodiment, the path planning module 640 is further configured to set parameters of the genetic algorithm and initialize the genetic algorithm; the initialization parameters comprise population scale, crossover probability, variation probability and iteration times.
In one embodiment, the path planning module 640 is further configured to perform crossover and mutation operations on chromosomes in the initial population by using a genetic algorithm; the chromosome carries out decoding operation to obtain planning and dividing results of the distribution path; acquiring the fitness value of each individual in the initial population, and selecting excellent individuals to enter the next generation by adopting a roulette mode; adopting a variable-scale population scale genetic algorithm to adjust the population scale; and when the genetic algorithm reaches the maximum iteration times, ending the algorithm and outputting the shortest delivery path.
In one embodiment, a computer device is provided, which may be a drone, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor implements a routing method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in fig. 7 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
obtaining inspection data, and calculating the clustering quantity according to inter-class division indexes BWP of the inspection data based on the distance;
dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area;
calculating the workload of each initial inspection area, and carrying out secondary division on each initial inspection area according to each workload to obtain each target inspection area;
and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
In one embodiment, the processor when executing the computer program further performs the steps of: based on the single data object in the inspection data, calculating each BWP index value of each single data object; calculating an average BWP value according to each BWP index value; determining optimal cluster data from the number of clusters according to the average BWP value; and dividing the inspection area by a K-Means algorithm according to the inspection data and the optimal clustering quantity.
In one embodiment, the processor when executing the computer program further performs the steps of: according to the optimal clustering quantity, determining each initial clustering center from each data point of the inspection data through a K-Means algorithm; calculating the distance between each remaining data point and each initial clustering center, and dividing each remaining data point into the class to which each initial clustering center belongs according to the distance; judging whether each initial clustering center converges, if so, outputting a classification result, and dividing the inspection areas according to the classification result to obtain each initial inspection area.
In one embodiment, the processor when executing the computer program further performs the steps of: calculating and sequencing the workload of each initial inspection area, calculating the difference value between the maximum workload and the minimum workload, if the difference value is larger than a workload threshold value, removing the farthest data point from the initial clustering center in the initial inspection area corresponding to the maximum workload, and adding the farthest data point into a class with the second smallest distance from the initial clustering center; when an initial inspection area with the maximum workload being larger than the maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to an initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance; and calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving the data points according to the ratio.
In one embodiment, the processor when executing the computer program further performs the steps of: generating an initial population by adopting an ant colony algorithm, and initializing parameters of the ant colony algorithm; constructing a solution space of an ant colony algorithm, and initializing the positions of ants; randomly distributing ants, updating pheromones, and calculating paths traversed by the ants; the path iterated once is used as the initial population of the genetic algorithm.
In one embodiment, the processor when executing the computer program further performs the steps of: setting parameters of a genetic algorithm, and initializing the genetic algorithm; the initialization parameters comprise population scale, crossover probability, variation probability and iteration times.
In one embodiment, the processor when executing the computer program further performs the steps of: crossing and mutating chromosomes in the initial population by a genetic algorithm; the chromosome carries out decoding operation to obtain planning and dividing results of the distribution path; acquiring the fitness value of each individual in the initial population, and selecting excellent individuals to enter the next generation by adopting a roulette mode; adopting a variable-scale population scale genetic algorithm to adjust the population scale; and when the genetic algorithm reaches the maximum iteration times, ending the algorithm and outputting the shortest delivery path.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
obtaining inspection data, and calculating the clustering quantity according to inter-class division indexes BWP of the inspection data based on the distance;
dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area;
calculating the workload of each initial inspection area, and carrying out secondary division on each initial inspection area according to each workload to obtain each target inspection area;
and carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of: based on the single data object in the inspection data, calculating each BWP index value of each single data object; calculating an average BWP value according to each BWP index value; determining optimal cluster data from the number of clusters according to the average BWP value; and dividing the inspection area by a K-Means algorithm according to the inspection data and the optimal clustering quantity.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the optimal clustering quantity, determining each initial clustering center from each data point of the inspection data through a K-Means algorithm; calculating the distance between each remaining data point and each initial clustering center, and dividing each remaining data point into the class to which each initial clustering center belongs according to the distance; judging whether each initial clustering center converges, if so, outputting a classification result, and dividing the inspection areas according to the classification result to obtain each initial inspection area.
In one embodiment, the computer program when executed by the processor further performs the steps of: calculating and sequencing the workload of each initial inspection area, calculating the difference value between the maximum workload and the minimum workload, if the difference value is larger than a workload threshold value, removing the farthest data point from the initial clustering center in the initial inspection area corresponding to the maximum workload, and adding the farthest data point into a class with the second smallest distance from the initial clustering center; when an initial inspection area with the maximum workload being larger than the maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to an initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance; and calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving the data points according to the ratio.
In one embodiment, the computer program when executed by the processor further performs the steps of: generating an initial population by adopting an ant colony algorithm, and initializing parameters of the ant colony algorithm; constructing a solution space of an ant colony algorithm, and initializing the positions of ants; randomly distributing ants, updating pheromones, and calculating paths traversed by the ants; the path iterated once is used as the initial population of the genetic algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of: setting parameters of a genetic algorithm, and initializing the genetic algorithm; the initialization parameters comprise population scale, crossover probability, variation probability and iteration times.
In one embodiment, the computer program when executed by the processor further performs the steps of: crossing and mutating chromosomes in the initial population by a genetic algorithm; the chromosome carries out decoding operation to obtain planning and dividing results of the distribution path; acquiring the fitness value of each individual in the initial population, and selecting excellent individuals to enter the next generation by adopting a roulette mode; adopting a variable-scale population scale genetic algorithm to adjust the population scale; and when the genetic algorithm reaches the maximum iteration times, ending the algorithm and outputting the shortest delivery path.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (9)
1. A method of routing inspection path planning, the method comprising:
obtaining inspection data, and calculating the clustering quantity according to inter-class partition indexes BWP of the inspection data based on distance;
dividing the inspection areas by a K-Means algorithm according to the inspection data and the clustering quantity to obtain each initial inspection area;
calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area;
Carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm;
the calculating the workload of each initial inspection area, and the performing secondary division on each initial inspection area according to each workload comprises the following steps: calculating and sequencing the workload of each initial inspection area, calculating the difference value between the maximum workload and the minimum workload, if the difference value is larger than a workload threshold, removing the farthest data point from the initial clustering center in the initial inspection area corresponding to the maximum workload, and adding the farthest data point into a class with the second smallest distance from the initial clustering center; when an initial inspection area with the maximum workload being larger than a maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to the initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance; and calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving the data points according to the ratio.
2. The method for planning a routing inspection path according to claim 1, wherein the dividing the routing inspection area by a K-Means algorithm according to the routing inspection data and the clustering number comprises:
Calculating each BWP index value of each single data object based on the single data object in the inspection data;
calculating an average BWP value according to each BWP index value;
determining optimal cluster data from the number of clusters according to the average BWP value;
and dividing a patrol area by a K-Means algorithm according to the patrol data and the optimal clustering quantity.
3. The method for planning a routing inspection path according to claim 2, wherein the dividing the routing inspection areas by the K-Means algorithm to obtain each initial routing inspection area includes:
according to the optimal clustering quantity, determining each initial clustering center from each data point of the inspection data through a K-Means algorithm;
calculating the distance between each remaining data point and each initial clustering center, and dividing each remaining data point into the class to which each initial clustering center belongs according to the distance;
judging whether each initial clustering center converges or not, if so, outputting a classification result, and dividing the inspection areas according to the classification result to obtain each initial inspection area.
4. The inspection path planning method according to claim 1, characterized in that before path planning by an ant colony algorithm and a genetic algorithm, the method further comprises:
Generating an initial population by adopting an ant colony algorithm, and initializing parameters of the ant colony algorithm;
constructing a solution space of the ant colony algorithm, and initializing the positions of ants;
randomly distributing ants, updating pheromones, and calculating paths traversed by the ants;
the path iterated once is used as the initial population of the genetic algorithm.
5. The inspection path planning method of claim 4, characterized in that before path planning by the ant colony algorithm and the genetic algorithm, the method further comprises:
setting parameters of the genetic algorithm, and initializing the genetic algorithm;
the initialization parameters comprise population scale, crossover probability, variation probability and iteration times.
6. The inspection path planning method according to claim 5, wherein path planning by an ant colony algorithm and a genetic algorithm comprises:
crossing and mutating chromosomes in the initial population by the genetic algorithm;
the chromosome performs decoding operation to obtain planning and dividing results of the distribution path;
acquiring the fitness value of each individual in the initial population, and selecting excellent individuals to enter the next generation by adopting a roulette mode;
Adopting a variable-scale population scale genetic algorithm to adjust the population scale;
and when the genetic algorithm reaches the maximum iteration times, ending the algorithm and outputting the shortest delivery path.
7. A patrol path planning system, the system comprising:
the clustering number calculation module is used for acquiring the inspection data and calculating the clustering number according to inter-class partition indexes BWP of the inspection data based on the distance;
the initial inspection area dividing module is used for dividing the inspection areas through a K-Means algorithm according to the inspection data and the clustering quantity to obtain all initial inspection areas;
the inspection area adjusting module is used for calculating the workload of each initial inspection area, and performing secondary division on each initial inspection area according to each workload to obtain each target inspection area; comprising the following steps: calculating and sequencing the workload of each initial inspection area, calculating the difference value between the maximum workload and the minimum workload, if the difference value is larger than a workload threshold, removing the farthest data point from the initial clustering center in the initial inspection area corresponding to the maximum workload, and adding the farthest data point into a class with the second smallest distance from the initial clustering center; when an initial inspection area with the maximum workload being larger than a maximum workload threshold exists, calculating a first distance from each data point in the initial inspection area to the initial clustering center, calculating a second distance from each data point to other initial clustering centers, and moving the data points according to the first distance and the second distance; calculating the ratio of the workload of each initial inspection area to the maximum workload in all areas, and moving data points according to the ratio;
And the path planning module is used for carrying out path planning on each target inspection area through an ant colony algorithm and a genetic algorithm.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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