CN114355918A - Deicing vehicle path planning method and device and storage medium - Google Patents
Deicing vehicle path planning method and device and storage medium Download PDFInfo
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Abstract
The application provides a method, a device and a storage medium for planning a route of an ice removing vehicle, wherein the method comprises the following steps: constructing a deicing machine position distance matrix; combining the deicing paths respectively corresponding to the plurality of deicing vehicles to determine a plurality of path individuals, and forming an initial path population by the plurality of path individuals; calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix; selecting target path individuals from the initial path population to carry out cross variation treatment based on the fitness of each path individual to obtain a plurality of descendant path individuals; and selecting the path individual with the maximum fitness as an optimal path individual from the initial path population and the plurality of descendant path individuals. By adopting the deicing vehicle path planning method, the deicing device and the storage medium, the problems of low deicing efficiency and high deicing cost in the existing airport deicing operation process are solved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a device for planning a route of an ice removal vehicle and a storage medium.
Background
The airplane as a modern high-efficiency transport tool has the characteristics of high speed, high safety and the like, and brings great convenience to people's traveling and cargo transportation. However, severe weather can greatly affect normal flight of the aircraft, especially when the aircraft is frozen, the aerodynamic structure of the aircraft can be seriously damaged to cause lift loss, so that the flight safety of the aircraft is affected, and the control difficulty of the aircraft by a pilot is increased, therefore, the timely deicing and anti-icing operation of the aircraft is very important. When the deicing and ice releasing operation is carried out, the deicing vehicle sequentially runs to each appointed stand according to the deicing paths after the deicing liquid is added at a deicing preparation station (deicing vehicle aggregation point), the deicing liquid is sprayed to the airplanes on the stand until the frost on the surfaces of the airplanes on all the deicing paths is cleaned, and then the deicing vehicle aggregation point is returned.
At present, in the process of executing deicing operation, the deicing path of each deicing vehicle is artificially established by workers, the deicing path of the deicing vehicle is long, and deicing liquid is not fully utilized and returns to the ice removing vehicle aggregation point, so that the problems of low deicing efficiency and high deicing cost are caused.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method, an apparatus, and a storage medium for planning a route of an ice removal vehicle, which can plan the ice removal routes of all ice removal vehicles through a genetic algorithm, and solve the problems of low ice removal efficiency and high ice removal cost in the existing airport ice removal operation process.
In a first aspect, an embodiment of the present application provides a method for planning a route of an ice-removing vehicle, including:
constructing a deicing machine position distance matrix;
combining the deicing paths respectively corresponding to the plurality of deicing vehicles to determine a plurality of path individuals, wherein the plurality of path individuals form an initial path population, and each path individual comprises a plurality of deicing machine position numbers;
calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix;
selecting target path individuals from the initial path population to carry out cross variation treatment based on the fitness of each path individual to obtain a plurality of descendant path individuals;
and selecting the path individual with the maximum fitness as an optimal path individual from the initial path population and the plurality of descendant path individuals.
Optionally, constructing the de-icer location distance matrix comprises: setting a deicing machine position number for each deicing machine position; determining the distance between a plurality of deicing machine positions and the distance between each deicing machine position and an ice removing vehicle aggregation point; and constructing a deicing machine position distance matrix based on the deicing machine position numbers, the distances among the plurality of deicing machine positions and the distance between each deicing machine position and the deicing vehicle aggregation point, wherein the origin of the deicing machine position distance matrix is the deicing vehicle aggregation point, and the abscissa and the ordinate of the deicing machine position distance matrix are the deicing machine position numbers.
Optionally, combining the deicing paths respectively corresponding to the multiple deicing vehicles to determine multiple path individuals, including: determining a plurality of deicing paths corresponding to each deicing vehicle, wherein each deicing path starts from a deicing vehicle aggregation point and returns to the deicing vehicle aggregation point after passing through a plurality of deicing machine positions; selecting a deicing path from all deicing paths corresponding to the deicing vehicle aiming at each deicing vehicle; combining the selected multiple deicing paths end to end in sequence to obtain a single path individual; and acquiring a plurality of path individuals by referring to the determination process of the single path individual.
Optionally, based on the deicing machine location distance matrix, calculating the fitness of each path individual in the initial path population through a preset fitness function includes: acquiring distances among a plurality of deicing machine positions corresponding to the plurality of deicing machine position numbers from a deicing machine position distance matrix based on the plurality of deicing machine position numbers corresponding to the path individuals; calculating the sum of the reciprocals of the distances between the plurality of deicing machine positions by using a preset fitness function; and taking the accumulated sum as the fitness of the path individual.
Optionally, based on the fitness of each path individual, selecting a target path individual from the initial path population to perform cross variation processing, and obtaining a plurality of offspring path individuals includes: selecting target path individuals from the initial path population by a roulette selection method based on the fitness of each path individual; and performing cross variation processing on the selected target path individuals to determine a plurality of descendant path individuals.
Optionally, performing cross mutation processing on the selected target path individuals, and determining a plurality of offspring path individuals includes: selecting two of the target path individuals as a parent path individual and a parent path individual; generating a first random value, and determining whether the first random value is smaller than a preset cross probability; if the probability is smaller than the intersection probability, performing two-point intersection processing on the parent path individual and the parent path individual, performing mutation processing on the new path individual generated after the two-point intersection processing, and determining a descendant path individual; and if the determination result is larger than or equal to the cross probability, taking the two path individuals as the descendant path individuals.
Optionally, performing mutation processing on the new path individuals generated after the two-point intersection processing to determine the offspring path individuals, including: generating a second random value, and determining whether the second random value is greater than or equal to a preset variation probability; if the probability of variation is determined to be more than or equal to the variation probability, carrying out gene interchange variation treatment on the new path individuals generated after the two-point intersection treatment, and taking the path individuals meeting the path individual requirements after the gene interchange variation treatment as offspring path individuals; and if the probability of variation is determined to be less than the mutation probability, directly taking the path individuals meeting the path individual requirements as the descendant path individuals.
Optionally, the method further comprises: determining whether the individual number of the offspring routes reaches a second preset number; if the second preset number is determined not to be reached, returning to the step of selecting two path individuals in the target path individuals as the parent path individuals and the parent path individuals; and if the second preset number is determined to be reached, ending the process of determining the individual descendant paths.
In a second aspect, an embodiment of the present application further provides an ice-removing vehicle path planning apparatus, where the apparatus includes:
the matrix construction module is used for constructing a deicing machine position distance matrix;
the population acquisition module is used for combining the deicing paths respectively corresponding to the deicing vehicles to determine a plurality of path individuals, the plurality of path individuals form an initial path population, and each path individual comprises a plurality of deicing machine position numbers;
the fitness calculation module is used for calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix;
the cross variation processing module is used for selecting target path individuals from the initial path population to carry out cross variation processing on the target path individuals based on the fitness of each path individual to obtain a plurality of descendant path individuals;
and the path selection module is used for selecting the path individual with the maximum fitness as the optimal path individual from the initial path population and the plurality of descendant path individuals.
In a third aspect, embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the deicing vehicle path planning method as described above.
The embodiment of the application brings the following beneficial effects:
compared with the deicing vehicle path planning method in the prior art, the deicing vehicle path planning method and device and the storage medium solve the problems of low deicing efficiency and high deicing cost in the deicing operation process of the existing airport.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 shows a flowchart of an ice-removing vehicle path planning method provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of a two-point intersection processing front-and-back path individual provided by an embodiment of the present application;
FIG. 3 is a schematic diagram showing the individual pathways before and after gene exchange treatment provided in the examples of the present application;
fig. 4 shows a schematic structural diagram of the deicing vehicle path planning device provided by the embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. Every other embodiment that can be obtained by a person skilled in the art without making creative efforts based on the embodiments of the present application falls within the protection scope of the present application.
It is worth noting that before the application is provided, the airplane serving as a modern high-efficiency transportation tool has the characteristics of high speed, high safety and the like, and great convenience is brought to people's traveling and goods transportation. However, severe weather can greatly affect normal flight of the aircraft, especially when the aircraft is frozen, the aerodynamic structure of the aircraft can be seriously damaged to cause lift loss, so that the flight safety of the aircraft is affected, and the control difficulty of the aircraft by a pilot is increased, therefore, the timely deicing and anti-icing operation of the aircraft is very important. When the deicing and ice releasing operation is carried out, the deicing vehicle sequentially runs to each designated stand according to the deicing paths after the deicing liquid is added at the deicing preparation station, the deicing liquid is sprayed to the airplanes on the stand until the frost on the surfaces of the airplanes on all the deicing paths is cleaned, and then the deicing vehicle returns to the ice removing vehicle aggregation point. At present, in the process of executing deicing operation, the deicing path of each deicing vehicle is manually set by workers, the distribution condition of airport parking spaces cannot be comprehensively considered, the deicing path of the deicing vehicle is often long, and the situation that deicing liquid returns to an ice removing vehicle aggregation point when not fully used is caused, so that the problems of low deicing efficiency, long deicing period and high deicing cost are caused.
Based on this, the embodiment of the application provides a route planning method for an ice removing vehicle, so as to improve the ice removing efficiency and reduce the ice removing cost.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for planning a route of an ice-removing vehicle according to an embodiment of the present disclosure. As shown in fig. 1, the method for planning a route of an ice-removing vehicle provided in the embodiment of the present application includes:
and S101, constructing a deicing machine position distance matrix.
In this step, the deicing-station distance matrix may refer to a matrix representing the mutual distances between a plurality of deicing stations in the airport and the distance between each deicing station and the deicing-vehicle aggregation point, and the deicing-station distance matrix is used to determine the fitness of the deicing path.
In an alternative embodiment, performing step S101 comprises: setting a deicing machine position number for each deicing machine position; determining the distance between a plurality of deicing machine positions and the distance between each deicing machine position and an ice removing vehicle aggregation point; and constructing a deicing machine position distance matrix based on the deicing machine position numbers, the distances among the plurality of deicing machine positions and the distance between each deicing machine position and the deicing vehicle aggregation point, wherein the origin of the deicing machine position distance matrix is the deicing vehicle aggregation point, and the abscissa and the ordinate of the deicing machine position distance matrix are the deicing machine position numbers.
Here, the deicing machine positions may refer to positions where airplanes to be deiced stop in an airport, and if there are multiple airplanes to be deiced in the airport, multiple deicing machine positions exist correspondingly, and a certain distance is left between the multiple deicing machine positions, and different deicing machine positions can be distinguished by numbering the deicing machine positions.
The ice detachment machine station number may refer to a digital identification of the ice detachment machine station, which is used to characterize a particular ice detachment machine station, and may be, for example, an arabic number, such as: the number of the deicing machine positions is 1, 2, 3, 4, 5 and 6 … … in sequence.
The ice removing vehicle aggregation point can be referred to as an ice removing vehicle preparation station, the ice removing vehicle is filled with ice removing liquid in the ice removing vehicle preparation station, and the ice removing vehicle drives to a deicing machine position from the ice removing vehicle preparation station after the ice removing liquid is filled.
In the embodiment of the application, the ice removing vehicle aggregation point is set as the origin of the ice removing distance matrix, and the numbers of the ice removing machine positions are arranged from small to large and sequentially used as the abscissa and the ordinate of the ice removing machine position distance matrix.
The de-icer location distance matrix is described below with reference to table 1.
As shown in table 1, the first row and the first column of table 1 are an abscissa and an ordinate of the deicing machine location distance matrix, the abscissa 0 and the ordinate 0 both represent deicing vehicle aggregate points, and the abscissas 1, 2, 3, 4, 5 and the ordinates 1, 2, 3, 4, 5 both represent deicing machine location numbers. In the deicing machine position distance matrix, the value corresponding to the coordinate [0, 1] is 100, the distance from the deicing machine position corresponding to the deicing machine position number 1 to the ice removing vehicle aggregation point is 100 meters, the value corresponding to the coordinate [3,2] is 60, and the distance between the deicing machine position number 3 and the deicing machine position number 2 is 60 meters. Therefore, the number of the deicing machine position numbers determines the size of a deicing machine position distance matrix, meanwhile, the abscissa and the ordinate of the deicing machine position distance matrix correspond to the deicing machine position numbers, the corresponding values of the coordinate points with the equal abscissa and the equal ordinate are 0, the connecting lines of the values of all the coordinate points with the equal abscissa and the equal ordinate are used as axes, and the left lower part and the right upper part of the deicing machine position distance matrix are symmetrical to each other.
And S102, combining the deicing paths respectively corresponding to the multiple deicing vehicles, determining multiple path individuals, and forming an initial path population by the multiple path individuals. Each individual path includes a plurality of deicing machine position numbers.
In this step, the deicing path may refer to an array including a number of a plurality of deicing stations, and the deicing path is used to represent a deicing sequence when one deicing vehicle performs deicing operation on the plurality of deicing stations.
As an example, the starting point and the end point of the deicing path are both deicing vehicle accumulation points, and a deicing machine position number is between the starting point and the end point, for example: the deicing path is [0, 2, 5, 0], where 0 represents the deicing vehicle staging point and 2 and 5 represent the deicing machine station number. It can be understood that the deicing vehicle starts from the position No. 0, namely, starts from the ice-removing vehicle accumulation point, sequentially carries out deicing operation on the position No. 2 and the position No. 5, and then returns to the position No. 0, namely, returns to the ice-removing vehicle accumulation point.
The path individual can refer to the combination of a plurality of deicing paths, and the path individual is used for representing a set of driving path schemes when all deicing vehicles perform deicing operation.
Wherein, many deicing routes correspond a plurality of different deicing cars, for example: and 3 deicing vehicles are used in total, the deicing path of the first deicing vehicle is [0, 2, 5, 0], the deicing path of the second deicing vehicle is [0, 3, 6, 1, 0], the deicing path of the third deicing vehicle is [0, 4, 7, 0], and then the three deicing paths are combined to obtain a path individual [0, 2, 5, 0, 3, 6, 1, 0, 4, 7, 0 ]. It can be seen that when a plurality of deicing paths are combined to obtain a path individual, the end point numbers and the start point numbers of two adjacent deicing paths are merged together, that is, the end point number 0 of the previous deicing path and the start point number 0 of the next deicing path are merged into one 0, and the path individual also starts from the number 0 and ends with the number 0.
It can be seen that the path individuals have the following characteristics: first, the path individuals start with 0 and end with 0, and second, there are no repeated non-zero numbers in the path individuals, such as: two 2 occurs, and third, there is no case where two adjacent numbers are both 0 in the path individual.
The initial path population may refer to a set of a plurality of path individuals, and is used for representing a plurality of sets of driving path schemes when all deicing vehicles perform deicing operation.
Here, taking the above example as an example, the number of the ice vehicles is 3, there are 3 path individuals in total, and the path individuals are [0, 2, 5, 0, 3, 6, 1, 0, 4, 7, 0], [0, 1, 5, 0, 3, 4, 2, 0, 6, 7, 0], [0, 2, 6, 0, 1, 5, 3, 0, 4, 7, 0], respectively, then the initial path population can be determined to be { [0, 2, 5, 0, 3, 6, 1, 0, 4, 7, 0], [0, 1, 5, 0, 3, 4, 2, 0, 6, 7, 0], [0, 2, 6, 0, 1, 5, 3, 0, 4, 7, 0] }. Wherein [0, 2, 5, 0], [0, 1, 5, 0] and [0, 2, 6, 0] are possible deicing paths for a first deicing vehicle, [0, 3, 6, 1, 0], [0, 3, 4, 2, 0] and [0, 1, 5, 3, 0] are possible deicing paths for a second deicing vehicle, and [0, 4, 7, 0] and [0, 6, 7, 0] are possible deicing paths for a third deicing vehicle.
In an alternative embodiment, performing step S102 includes: determining a plurality of deicing paths corresponding to each deicing vehicle, wherein each deicing path starts from a deicing vehicle aggregation point and returns to the deicing vehicle aggregation point after passing through a plurality of deicing machine positions; selecting a deicing path from all deicing paths corresponding to the deicing vehicle aiming at each deicing vehicle; combining the selected multiple deicing paths end to end in sequence to obtain a single path individual; and acquiring a plurality of path individuals by referring to the determination process of the single path individual.
Here, before determining the plurality of deicing paths corresponding to each deicing vehicle, the number of deicing vehicles may be determined. The minimum number of the deicing vehicles meeting the requirement of deicing operation can be determined according to the number of the deicing machine positions, the amount of deicing liquid required by the deicing machine positions and the capacity of the deicing liquid contained in the deicing vehicles, for example: total 18 deicing machine positions, all deicing machine positions need 1.2 tons of deicing fluid altogether, and the deicing fluid capacity of every deicing car is 0.2 tons, then satisfies the deicing car quantity of deicing operation demand and is 6 for 1.2/0.2, promptly, needs 6 deicing cars to fill with once deicing fluid after, can accomplish the deicing operation demand to all deicing machine positions. It should be noted that the amount of deicing fluid required by different deicing machine positions is different, so that a plurality of different deicing paths can be determined by the amount of deicing fluid required by each deicing machine position, and a plurality of different deicing paths are allocated to each deicing vehicle.
In this application embodiment, set up the serial number for every deicing vehicle to distinguish different deicing vehicles, and distribute a plurality of deicing machine positions for every deicing vehicle, in order to obtain a plurality of deicing routes that every deicing vehicle corresponds, for example: the number of the deicing machine positions corresponding to the first deicing vehicle is 1, 3 and 6, the number of the deicing machine positions corresponding to the second deicing vehicle is 2 and 5, the number of the deicing machine positions corresponding to the third deicing vehicle is 6, 7 and 9, and so on, the number of the deicing machine positions corresponding to each deicing vehicle is obtained, and then a plurality of deicing paths of each deicing vehicle are determined according to the amount of deicing liquid required by each deicing machine position, for example: the deicing path corresponding to the first deicing vehicle may be [0, 3, 1, 6, 0], or [0, 1, 3, 6, 0 ].
Supposing that each deicing vehicle in 6 deicing vehicles corresponds to 5 deicing paths, selecting one deicing path from the 5 deicing paths corresponding to each deicing vehicle respectively to obtain 6 different deicing paths, sequentially connecting and combining the 6 deicing paths from beginning to end according to the sequence of deicing vehicle numbers from small to large to obtain a path individual, repeating the process to obtain a plurality of path individuals, and selecting a preset number of path individuals from the obtained plurality of path individuals to form an initial path population. The preset amount is a value determined by a person skilled in the art according to actual conditions, and the application is not limited herein.
And S103, calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix.
In this step, the fitness function may refer to a function for calculating the fitness of the path individual.
The larger the distance between two deicing machine positions is, the longer the distance traveled by the deicing vehicle is, and the longer the deicing time is, the more the oil amount of the deicing vehicle is consumed, the lower the deicing efficiency is, and the higher the deicing cost is, so that the larger the reciprocal of the distance is, the higher the deicing efficiency is, and the lower the deicing cost is. It can be seen that the cumulative sum of the reciprocals of the distances between all two adjacent deicing stations in the path individual can be used to characterize the degree of goodness, i.e., fitness, of the path individual, and therefore, a formula for calculating the sum of the reciprocals of the distances is determined as a fitness function.
The fitness can refer to a numerical value obtained through calculation of a fitness function, and the fitness is used for representing the degree of goodness of the path individual, namely, the higher the fitness is, the higher the deicing efficiency is, and the lower the deicing cost is.
In an alternative embodiment, performing step S103 comprises: acquiring distances among a plurality of deicing machine positions corresponding to the plurality of deicing machine position numbers from a deicing machine position distance matrix based on the plurality of deicing machine position numbers corresponding to the path individuals; calculating the sum of the reciprocals of the distances between the plurality of deicing machine positions by using a preset fitness function; and taking the accumulated sum as the fitness of the path individual.
Here, since one path individual includes a plurality of deicing machine location numbers, the distance between the deicing machine locations corresponding to two adjacent deicing machine location numbers in the path individual can be obtained from the deicing machine location distance matrix, and then the obtained plurality of distances are substituted into the fitness function, and the cumulative sum of the reciprocals of the distances is calculated, and the cumulative sum is the fitness of the path individual.
In the embodiment of the present application, taking a path individual [0, 2, 5, 0, 3, 4, 1, 0] as an example, the path individual corresponds to two deicing vehicles, the first deicing vehicle starts from the deicing vehicle aggregation point, sequentially deicing the deicing machine positions No. 2 and No. 5 and then returns to the deicing vehicle aggregation point, and the second deicing vehicle starts from the deicing vehicle aggregation point, sequentially deicing the deicing machine positions No. 3, No. 4 and No. 1 and then returns to the deicing vehicle aggregation point. Here, according to the sequence of the number of the deicing machine positions corresponding to the path individuals, the distance between two deicing machine positions corresponding to two adjacent deicing machine position numbers is sequentially obtained, as can be seen by referring to table 1, the value corresponding to the abscissa 0 and the ordinate 2 is 50, which indicates that the distance from the first deicing vehicle to the No. 2 deicing machine position from the deicing vehicle accumulation point is 50, the value corresponding to the abscissa 2 and the ordinate 5 is 50, which indicates that the distance from the first deicing vehicle to the No. 5 deicing machine position from the No. 2 deicing machine position is 50, the value corresponding to the abscissa 5 and the ordinate 0 is 80, which indicates that the first deicing vehicle completes deicing operation, the running distance from the No. 5 deicing machine position back to the deicing vehicle accumulation point is 80, and so on, the running distances of the second deicing vehicle through a plurality of deicing machine positions are sequentially obtained: 60. 30, 40 and 100. Then, the fitness of the individual of the route is calculated to be 1/50+1/50+1/80+1/60+1/30+1/40+1/100 which is 0.1375 according to the fitness function.
And S104, selecting target path individuals from the initial path population to carry out cross variation treatment based on the fitness of each path individual to obtain a plurality of descendant path individuals.
In this step, the offspring route individuals may refer to route individuals obtained through cross mutation processing or route individuals obtained through cross probability and mutation probability screening.
Here, it is necessary to find more possible combinations of the numbers of the deicing stations, and determine new path individuals according to the combinations of the numbers of the deicing stations, so as to find out the optimal path individuals from the path individuals and the initial path population. Here, the path individuals may be subjected to cross mutation processing by using a genetic algorithm to obtain more path individuals.
In an alternative embodiment, performing step S104 includes: selecting target path individuals from the initial path population by a roulette selection method based on the fitness of each path individual; and performing cross variation processing on the selected target path individuals to determine a plurality of descendant path individuals.
Firstly, traversing the fitness of each path individual in the initial path population, calculating the sum of the fitness of all path individuals in the initial path population, determining the sum of the fitness as the initial population fitness, generating a random value, multiplying the random value by the initial population fitness to obtain a population split value, starting from the first path individual, calculating the sum of the fitness of the first 1 path individuals, determining whether the sum of the fitness of the first 1 path individuals is greater than or equal to the population split value, if the sum of the fitness of the first 2 path individuals is less than the population split value, determining whether the sum of the fitness of the first 2 path individuals is greater than or equal to the population split value, if the sum of the fitness of the first 3 path individuals is less than the population split value, calculating the sum of the fitness of the first 3 path individuals, determining whether the sum of the fitness of the first 3 path individuals is greater than or equal to the population split value, and so on, and selecting the Nth path individual as a target path individual until the sum of the fitness of the first N path individuals is greater than or equal to the population segmentation value. And repeating the process until a second preset number of target path individuals are determined. It should be noted that, each time one target path individual is determined, the random value needs to be regenerated, and a new population segmentation value needs to be determined again. The second predetermined amount is a value determined by those skilled in the art according to practical situations, and the present application is not limited thereto.
After a second preset number of target path individuals are determined, the target path individuals are arranged according to the sequence from large fitness to small fitness to obtain an initial path sub-population, and the path individuals in the initial path sub-population are subjected to cross variation.
Compared with other selection methods, the roulette selection method for selecting the target path individuals can take fitness as the probability of selecting the path individuals, not the probability of selecting each part in the initial path population, so that the situation that some path individuals are selected due to the fact that the selection probability is very small in clearness but the accumulated probability is very high due to the fact that the path individuals are located backwards is prevented, and the problem of local optimization is avoided.
In an optional embodiment, performing cross mutation processing on the selected target path individuals, and determining a plurality of offspring path individuals includes: selecting two of the target path individuals as a parent path individual and a parent path individual; generating a first random value, and determining whether the first random value is smaller than a preset cross probability; if the probability is smaller than the intersection probability, performing two-point intersection processing on the parent path individual and the parent path individual, performing mutation processing on the new path individual generated after the two-point intersection processing, and determining a descendant path individual; and if the determination result is larger than or equal to the cross probability, taking the two path individuals as the descendant path individuals.
Here, the first random value and the second random value are independent random values, and a new first random value is generated each time the determination is compared with the preset crossover probability. As an example, the first random value is a numerical value having a value range of [0, 1 ].
The crossover probability can refer to a preset numerical value, is a probability commonly used in a genetic algorithm, and is used for determining whether to carry out crossover processing on the selected path individuals.
In the embodiment of the present application, first, cross processing is performed on the first two path individuals in the initial path sub-population, and the two path individuals are also the two path individuals with the maximum fitness in the initial path sub-population. Generating a random value which is a first random value, judging whether the random value is smaller than a preset cross probability, if so, performing two-point cross processing on the two selected path individuals, and if not, directly taking the two selected path individuals as descendant path individuals.
The process of the two-point intersection process will be described with reference to fig. 2.
Fig. 2 is a schematic diagram illustrating an individual path before and after a two-point intersection process provided by an embodiment of the present application.
As shown in fig. 2, before the two-point intersection processing, the route unit 1 is [0, 1, 3, 7, 0, 2, 4, 0, 5, 6, 8, 0], and the route unit 2 is [0, 2, 4, 0, 3, 6, 0, 7, 8, 1, 5, 0 ]. When two path individuals are subjected to cross treatment, firstly, index subscripts corresponding to gene positions are randomly selected, for example: 9 and 11, the genes between the two index subscripts in route individual 1 and route individual 2 remain unchanged, that is, genes 5, 6 and 8 of route individual 1 remain unchanged, genes 8, 1 and 5 of route individual 2 remain unchanged, and the genes at other positions in route individual 1 and the genes at other positions in route individual 2 are exchanged to obtain that route individual 1 subjected to two-point crossing processing is [0, 2, 4, 0, 3, 6, 0, 7, 5, 6, 8, 0], and route individual 2 subjected to two-point crossing processing is [0, 1, 3, 7, 0, 2, 4, 0, 8, 1, 5, 0 ].
Therefore, the gene crossing operation is carried out on the two path individuals in a two-point crossing mode, the method has the characteristics of low calculated amount and good gene crossing effect, and the gene diversity of the path individuals can be effectively improved.
In an optional embodiment, performing mutation processing on the new path individuals generated after the two-point intersection processing to determine the offspring path individuals includes: generating a second random value, and determining whether the second random value is greater than or equal to a preset variation probability; if the probability of variation is determined to be more than or equal to the variation probability, carrying out gene interchange variation treatment on the new path individuals generated after the two-point intersection treatment, and taking the path individuals meeting the path individual requirements after the gene interchange variation treatment as offspring path individuals; and if the probability of variation is determined to be less than the mutation probability, directly taking the path individuals meeting the path individual requirements as the descendant path individuals.
Here, the second random value and the first random value are independent random values, and a new second random value is generated each time the second random value is compared with the predetermined variation probability. As an example, the second random value is a numerical value having a value range of [0, 1 ].
The mutation probability may refer to a preset value, and is used to determine whether to perform mutation processing on the selected path individual.
In the embodiment of the application, a second random value is generated, whether the second random value is greater than or equal to a preset variation probability is judged, if the second random value is determined to be greater than or equal to the preset variation probability, gene exchange variation processing is performed on a new path individual generated through two-point intersection processing, the path individual subjected to gene exchange variation is detected, whether the path individual subjected to gene exchange variation processing meets the requirement of the path individual is judged, if the path individual requirement is met, the path individual is used as a descendant path individual, and if the path individual requirement is not met, the path individual is discarded. If the path individual is determined to be smaller than the preset intersection probability, judging whether the path individual meets the requirement of the path individual, if so, taking the path individual as a descendant path individual, and if not, discarding the path individual.
In the above, the three features of the route individual are the route individual requests, that is, the route individual requests are: firstly, the path individuals start with 0 and end with 0, secondly, no repeated non-zero number exists in the path individuals, and thirdly, the condition that two adjacent numbers are 0 does not exist in the path individuals.
The process of the gene-swapping mutation process of the pathway entity 3 will be described with reference to fig. 3, and the process of the gene-swapping mutation process of the pathway entity 4 is the same as that of the pathway entity 3, and will not be described again.
FIG. 3 is a schematic diagram showing the individual pathways before and after gene exchange treatment provided in the examples of the present application.
As shown in fig. 3, before the gene exchange mutation process, the path individuals 3 are [0, 2, 4, 0, 3, 6, 0, 7, 5, 6, 8, 0], and when the gene exchange process is performed on the path individuals 3, index subscripts at two different positions in the path individuals 3 are randomly selected, for example: the selected index subscripts are 5 and 10, and the genes corresponding to the two selected index subscripts are exchanged, that is, the gene 3 of the index subscript 5 is exchanged with the gene 6 of the index subscript 10, so that the path individual 5 subjected to the gene exchange processing is [0, 2, 4, 0, 6, 6, 0, 7, 5, 3, 8, 0 ]. The path individual 5 after the gene exchange processing is detected to judge whether the path individual 5 meets the path individual requirement, and the genes in the path individual 5 can know that two repeated non-zero numbers exist in the path individual 5, so that the path individual 5 does not meet the path individual requirement and needs to be abandoned.
In an optional embodiment, the method further comprises: determining whether the individual number of the offspring routes reaches a second preset number; if the second preset number is determined not to be reached, returning to the step of selecting two path individuals in the target path individuals as the parent path individuals and the parent path individuals; and if the second preset number is determined to be reached, ending the process of determining the individual descendant paths.
Here, a certain number of offspring path individuals need to be selected to ensure that the path individuals have sufficient diversity. Therefore, it is required to determine whether the number of the offspring path individuals reaches a second preset number, and if the number of the offspring path individuals does not reach the second preset number, the process of acquiring the offspring path individuals is repeatedly executed until the number of the acquired offspring path individuals reaches the second preset number. The specific value of the second preset number can be determined by those skilled in the art according to actual situations, and the application is not limited herein.
And S105, selecting the path individual with the maximum fitness as an optimal path individual from the initial path population and the plurality of descendant path individuals.
In the step, the initial path population comprises a plurality of path individuals, the offspring path individuals are also a plurality of path individuals, and the optimal path individuals are required to be selected from the path individuals, so that each deicing vehicle carries out deicing operation according to the deicing path corresponding to the optimal path individuals.
In this embodiment of the application, after step S103 is executed, the first N path individuals with the largest fitness are selected from the initial path population as the first excellent individuals, where a person skilled in the art may determine a specific value of N according to an actual situation, and the application is not limited herein. Then, after a plurality of offspring route individuals are obtained, the fitness of each offspring route individual is calculated, and the route individual with the largest fitness is selected as a second excellent individual. And finally, comparing the first excellent individual with the second excellent individual, and selecting the path individual with the maximum fitness as the optimal path individual.
Compared with the deicing vehicle path planning method in the prior art, the method has the advantages that the selection and genetic mechanism of the nature can be simulated through the genetic algorithm to find the optimal path individual, the adopted selection, intersection and variation operation processes can overcome the problem that the path optimization algorithm is easy to fall into local optimization, the dead circulation phenomenon is avoided, meanwhile, the method has the characteristics of strong convergence, short calculation time period and high robustness, and the problems of low deicing efficiency and high deicing cost in the existing airport deicing operation process are solved through the overall planning of the deicing paths of all deicing vehicles.
Based on the same inventive concept, the embodiment of the present application further provides an ice-removing vehicle path planning device corresponding to the ice-removing vehicle path planning method, and as the principle of solving the problem of the device in the embodiment of the present application is similar to that of the ice-removing vehicle path planning method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and repeated parts are not described again.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an ice-removing vehicle path planning device according to an embodiment of the present disclosure. As shown in fig. 4, the deicing vehicle path planning apparatus 200 includes:
a matrix construction module 201, configured to construct a deicing machine location distance matrix;
the population obtaining module 202 is configured to combine deicing paths corresponding to multiple deicing vehicles, respectively, determine multiple path individuals, form an initial path population from the multiple path individuals, and each path individual includes a deicing machine position number combination corresponding to multiple deicing paths;
the fitness calculation module 203 is used for calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix;
a cross variation processing module 204, configured to select a target path individual from the initial path population to perform cross variation processing based on fitness of each path individual, so as to obtain a plurality of descendant path individuals;
and the path selecting module 205 is configured to select, from the initial path population and the plurality of descendant path individuals, a path individual with the highest fitness as an optimal path individual.
Optionally, the matrix construction module 201 is further configured to: setting a deicing machine position number for each deicing machine position; determining the distance between a plurality of deicing machine positions and the distance between each deicing machine position and an ice removing vehicle aggregation point; and constructing a deicing machine position distance matrix based on the deicing machine position numbers, the distances among the plurality of deicing machine positions and the distance between each deicing machine position and the deicing vehicle aggregation point, wherein the origin of the deicing machine position distance matrix is the deicing vehicle aggregation point, and the abscissa and the ordinate of the deicing machine position distance matrix are the deicing machine position numbers.
Optionally, the population obtaining module 202 is further configured to: determining a plurality of deicing paths corresponding to each deicing vehicle, wherein each deicing path starts from a deicing vehicle aggregation point and returns to the deicing vehicle aggregation point after passing through a plurality of deicing machine positions; selecting a deicing path from all deicing paths corresponding to the deicing vehicle aiming at each deicing vehicle; combining the selected multiple deicing paths end to end in sequence to obtain a single path individual; and acquiring a plurality of path individuals by referring to the determination process of the single path individual.
Optionally, the fitness calculating module 203 is further configured to: acquiring distances among a plurality of deicing machine positions corresponding to the plurality of deicing machine position numbers from a deicing machine position distance matrix based on the plurality of deicing machine position numbers corresponding to the path individuals; calculating the sum of the reciprocals of the distances between the plurality of deicing machine positions by using a preset fitness function; and taking the accumulated sum as the fitness of the path individual.
Optionally, the cross mutation processing module 204 is further configured to: selecting target path individuals from the initial path population by a roulette selection method based on the fitness of each path individual; and performing cross variation processing on the selected target path individuals to determine a plurality of descendant path individuals.
Optionally, the cross mutation processing module 204 is further configured to: selecting two of the target path individuals as a parent path individual and a parent path individual; generating a first random value, and determining whether the first random value is smaller than a preset cross probability; if the probability is smaller than the intersection probability, performing two-point intersection processing on the parent path individual and the parent path individual, performing mutation processing on the new path individual generated after the two-point intersection processing, and determining a descendant path individual; and if the determination result is larger than or equal to the cross probability, taking the two path individuals as the descendant path individuals.
Optionally, the cross mutation processing module 204 is further configured to: generating a second random value, and determining whether the second random value is greater than or equal to a preset variation probability; if the probability of variation is determined to be more than or equal to the variation probability, carrying out gene interchange variation treatment on the new path individuals generated after the two-point intersection treatment, and taking the path individuals meeting the path individual requirements after the gene interchange variation treatment as offspring path individuals; and if the probability of variation is determined to be less than the mutation probability, directly taking the path individuals meeting the path individual requirements as the descendant path individuals.
Optionally, the cross mutation processing module 204 is further configured to: determining whether the individual number of the offspring routes reaches a second preset number; if the second preset number is determined not to be reached, returning to the step of selecting two path individuals in the target path individuals as the parent path individuals and the parent path individuals; and if the second preset number is determined to be reached, ending the process of determining the individual descendant paths.
An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for planning a route of an ice-removing vehicle in the embodiment of the method shown in fig. 1 may be executed.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present application, and are used for illustrating the technical solutions of the present application, but not limiting the same, and the scope of the present application is not limited thereto, and although the present application is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope disclosed in the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the exemplary embodiments of the present application, and are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for planning the path of an ice removing vehicle is characterized by comprising the following steps:
constructing a deicing machine position distance matrix;
combining the deicing paths respectively corresponding to the plurality of deicing vehicles to determine a plurality of path individuals, wherein the plurality of path individuals form an initial path population, and each path individual comprises a plurality of deicing machine position numbers;
calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix;
selecting target path individuals from the initial path population to perform cross variation processing based on the fitness of each path individual to obtain a plurality of descendant path individuals;
and selecting the path individual with the maximum fitness as an optimal path individual from the initial path population and the plurality of descendant path individuals.
2. The method of claim 1, wherein the constructing a de-icing stand distance matrix comprises:
setting a deicing machine position number for each deicing machine position;
determining the distance between a plurality of deicing machine positions and the distance between each deicing machine position and an ice removing vehicle aggregation point;
and constructing a deicing machine position distance matrix based on the deicing machine position numbers, the distances among the plurality of deicing machine positions and the distance between each deicing machine position and the deicing vehicle aggregation point, wherein the origin of the deicing machine position distance matrix is the deicing vehicle aggregation point, and the abscissa and the ordinate of the deicing machine position distance matrix are the deicing machine position numbers.
3. The method according to claim 1, wherein the combining the deicing paths respectively corresponding to the plurality of deicing vehicles to determine a plurality of path individuals comprises:
determining a plurality of deicing paths corresponding to each deicing vehicle, wherein each deicing path is a path starting from a deicing vehicle aggregation point and returning to the deicing vehicle aggregation point after passing through a plurality of deicing machine positions;
selecting a deicing path from all deicing paths corresponding to each deicing vehicle;
combining the selected multiple deicing paths end to end in sequence to obtain a single path individual;
and acquiring a plurality of path individuals by referring to the determination process of the single path individual.
4. The method according to claim 1, wherein the calculating the fitness of each individual path in the initial path population through a preset fitness function based on the deicing machine location distance matrix comprises:
acquiring distances among a plurality of deicing machine positions corresponding to the plurality of deicing machine position numbers from the deicing machine position distance matrix based on the plurality of deicing machine position numbers corresponding to the path individuals;
calculating the accumulated sum of the reciprocals of the distances between the plurality of deicing stations by using a preset fitness function;
and taking the accumulated sum as the fitness of the path individual.
5. The method of claim 1, wherein the selecting target path individuals from the initial path population for cross mutation processing based on the fitness of each path individual to obtain a plurality of offspring path individuals comprises:
selecting target path individuals from the initial path population by a roulette selection method based on the fitness of each path individual;
and performing cross variation processing on the selected target path individuals to determine a plurality of descendant path individuals.
6. The method of claim 5, wherein the performing cross mutation processing on the selected target path individuals to determine a plurality of offspring path individuals comprises:
randomly selecting two of the target path individuals as a parent path individual and a parent path individual;
generating a first random value, and determining whether the first random value is smaller than a preset cross probability;
if the intersection probability is determined to be smaller than the intersection probability, performing two-point intersection processing on the parent path individual and the parent path individual, performing mutation processing on a new path individual generated after the two-point intersection processing, and determining a descendant path individual;
and if the two path individuals are determined to be larger than or equal to the cross probability, the two path individuals are taken as the descendant path individuals.
7. The method as claimed in claim 6, wherein the performing mutation processing on the new path individuals generated after the two-point crossing processing to determine the offspring path individuals comprises:
generating a second random value, and determining whether the second random value is greater than or equal to a preset variation probability;
if the variation probability is determined to be more than or equal to the variation probability, performing gene interchange variation processing on the new path individual generated after the two-point intersection processing, and taking the path individual meeting the path individual requirement after the gene interchange variation processing as a descendant path individual;
and if the probability is determined to be less than the mutation probability, directly taking the path individuals meeting the path individual requirements as the descendant path individuals.
8. The method of claim 6, further comprising:
determining whether the individual number of the offspring routes reaches a second preset number;
if the second preset number is determined not to be reached, returning to execute the step of randomly selecting two path individuals in the target path individuals as a parent path individual and a parent path individual;
and if the second preset number is determined to be reached, ending the process of determining the individual descendant paths.
9. An ice removal vehicle path planning device, comprising:
the matrix construction module is used for constructing a deicing machine position distance matrix;
the population acquisition module is used for combining the deicing paths respectively corresponding to the plurality of deicing vehicles to determine a plurality of path individuals, the plurality of path individuals form an initial path population, and each path individual comprises a deicing machine position number combination corresponding to the plurality of deicing paths;
the fitness calculation module is used for calculating the fitness of each path individual in the initial path population through a preset fitness function based on the deicing machine position distance matrix;
the cross variation processing module is used for selecting a target path individual from the initial path population to carry out cross variation processing based on the fitness of each path individual to obtain a plurality of descendant path individuals;
and the path selection module is used for selecting the path individual with the maximum fitness as the optimal path individual from the initial path population and the plurality of descendant path individuals.
10. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method of routing an ice removal vehicle as claimed in any one of claims 1 to 8.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114491317A (en) * | 2022-04-18 | 2022-05-13 | 中国民航大学 | Centralized deicing operation method and system for airplane, storage medium and computer equipment |
| CN115265543A (en) * | 2022-07-19 | 2022-11-01 | 中环洁环境有限公司 | Method, system, medium and equipment for planning path of environmental sanitation washing and sweeping vehicle |
| CN115451974A (en) * | 2022-11-09 | 2022-12-09 | 广东电网有限责任公司湛江供电局 | Power equipment routing inspection path planning method, system, equipment and medium |
| CN116206463A (en) * | 2023-03-06 | 2023-06-02 | 吉林大学 | Public road operation vehicle dispatch system |
| CN117592633A (en) * | 2024-01-18 | 2024-02-23 | 深圳金智凌轩视讯技术有限公司 | Intelligent inspection optimization method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799820A (en) * | 2019-01-22 | 2019-05-24 | 智慧航海（青岛）科技有限公司 | Unmanned ship local paths planning method based on the random road sign figure method of comparison expression |
| CN111238521A (en) * | 2020-02-11 | 2020-06-05 | 北京理工大学 | Path planning method and system for unmanned vehicle |
| CN211498699U (en) * | 2019-07-29 | 2020-09-15 | 哈尔滨工业大学 | Deicing vehicle capable of navigating and avoiding obstacles and used in deicing mode |
| CN111949020A (en) * | 2020-07-21 | 2020-11-17 | 合肥工业大学 | AR path guidance-based path planning method and system for picking multiple persons in warehouse |
| CN112288166A (en) * | 2020-10-29 | 2021-01-29 | 重庆理工大学 | Optimization method for logistics distribution based on genetic-simulated annealing combined algorithm |
| CN113033895A (en) * | 2021-03-25 | 2021-06-25 | 浙江中烟工业有限责任公司 | Multi-source multi-point path planning method, equipment and storage medium |
-
2021
- 2021-12-27 CN CN202111616426.6A patent/CN114355918A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799820A (en) * | 2019-01-22 | 2019-05-24 | 智慧航海（青岛）科技有限公司 | Unmanned ship local paths planning method based on the random road sign figure method of comparison expression |
| CN211498699U (en) * | 2019-07-29 | 2020-09-15 | 哈尔滨工业大学 | Deicing vehicle capable of navigating and avoiding obstacles and used in deicing mode |
| CN111238521A (en) * | 2020-02-11 | 2020-06-05 | 北京理工大学 | Path planning method and system for unmanned vehicle |
| CN111949020A (en) * | 2020-07-21 | 2020-11-17 | 合肥工业大学 | AR path guidance-based path planning method and system for picking multiple persons in warehouse |
| CN112288166A (en) * | 2020-10-29 | 2021-01-29 | 重庆理工大学 | Optimization method for logistics distribution based on genetic-simulated annealing combined algorithm |
| CN113033895A (en) * | 2021-03-25 | 2021-06-25 | 浙江中烟工业有限责任公司 | Multi-source multi-point path planning method, equipment and storage medium |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114491317A (en) * | 2022-04-18 | 2022-05-13 | 中国民航大学 | Centralized deicing operation method and system for airplane, storage medium and computer equipment |
| CN114491317B (en) * | 2022-04-18 | 2022-06-21 | 中国民航大学 | Centralized deicing operation method and system for airplane, storage medium and computer equipment |
| CN115265543A (en) * | 2022-07-19 | 2022-11-01 | 中环洁环境有限公司 | Method, system, medium and equipment for planning path of environmental sanitation washing and sweeping vehicle |
| CN115451974A (en) * | 2022-11-09 | 2022-12-09 | 广东电网有限责任公司湛江供电局 | Power equipment routing inspection path planning method, system, equipment and medium |
| CN116206463A (en) * | 2023-03-06 | 2023-06-02 | 吉林大学 | Public road operation vehicle dispatch system |
| CN117592633A (en) * | 2024-01-18 | 2024-02-23 | 深圳金智凌轩视讯技术有限公司 | Intelligent inspection optimization method |
| CN117592633B (en) * | 2024-01-18 | 2024-03-26 | 深圳金智凌轩视讯技术有限公司 | Intelligent inspection optimization method |
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