CN116909320A - Electric power collaborative inspection strategy analysis method based on ant colony algorithm - Google Patents
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Abstract
The invention provides an electric power collaborative inspection strategy analysis method based on an ant colony algorithm, which comprises the following steps: s1, initializing the number of unmanned aerial vehicles and the number of power equipment, setting a tentative path of the unmanned aerial vehicle, and obtaining initial pheromone signal concentration according to the total length of the tentative path; s2, randomly selecting power equipment for the unmanned aerial vehicle as a starting point; s3, a corresponding inspection strategy is constructed for each unmanned aerial vehicle, and the unmanned aerial vehicle continuously releases the pheromone signal in the inspection process; s4, updating the pheromone signal concentration of each path every time the unmanned aerial vehicle completes the inspection task; and S5, if the updated pheromone signal concentration meets the ending condition, outputting the optimal inspection strategy and ending the analysis, and turning to the step 2. According to the invention, the electric power collaborative inspection strategy is analyzed by adopting the ant colony algorithm, so that the inspection path of the unmanned aerial vehicle can be continuously and automatically optimized, the shortest inspection path of the unmanned aerial vehicle can be obtained, and the intelligent degree of inspection can be improved.
Description
Technical Field
The invention relates to the field of power, in particular to a power collaborative inspection strategy analysis method based on an ant colony algorithm.
Background
With the continuous expansion of the scale of the power system and the increasing complexity of the power equipment, the difficulty and complexity of the power inspection work are also increasing. The traditional power inspection method mainly relies on manual inspection and periodic maintenance, but has a plurality of problems. Firstly, manual inspection has the problems of manpower resource waste and low efficiency, and regular inspection can bring certain power failure loss to the power system. And secondly, the traditional inspection method cannot fully utilize information and data in the power system, lacks the characteristics of cooperation and intelligence, cannot quickly and accurately identify faults and defects of the power equipment, and cannot timely take measures to repair and maintain. Aiming at the problems of the traditional power inspection method, new technical means such as unmanned aerial vehicle inspection, intelligent inspection robots and the like are developed in recent years, and the technical means can effectively reduce the waste of human resources and improve the inspection efficiency.
The method and the device for formulating the substation inspection strategy disclosed in the prior art CN112817310A comprise the following steps: s1, acquiring a point position, a departure point position and a stop point position of camera shooting inspection; s2, determining the shortest effective path among the stop points under the condition of excluding the point positions of the camera inspection through an improved Dijkstra algorithm, and forming
Shortest path clusters; s3, judging whether the shortest effective path traverses all the stopping points; if yes, executing the step S4, if not, executing the step S2; and S4, analyzing the optimal path cluster by adopting a heuristic simulated annealing algorithm, and determining a global optimal routing inspection path based on the departure point location and the stop point location. Therefore, the cost of the inspection terminal is reduced, and the inspection efficiency of the transformer substation is improved.
Another exemplary method and system for optimizing an overhead line unmanned aerial vehicle inspection strategy as disclosed in the prior art of CN115935610a includes the steps of: s1: collecting inspection data, and transmitting the inspection data to a server; s2: extracting inspection data from a server and establishing a mapping model according to the inspection data; s3: determining model parameters, setting the model parameters, and establishing constraint conditions and objective functions; s4: establishing and solving a patrol strategy model based on the objective function to obtain a patrol optimization path; s5: the mapping model shows the patrol optimization path. The method is used for making and optimizing the inspection path of the unmanned aerial vehicle.
Looking again at a drone for monitoring infrastructure as disclosed in the prior art of US20210173414A1, its monitored object comprises grid components such as high voltage wires. The unmanned aerial vehicle can carry out collaborative inspection, and the inspection behavior of the unmanned aerial vehicle can be controlled through the platform system.
However, these technical means still have some disadvantages, for example, the inspection path of the unmanned aerial vehicle is not the shortest path, omission may occur in the inspection process, and the inspection policy cannot be automatically optimized. In order to solve the problems in the art, the scheme provides an electric power collaborative inspection strategy analysis method based on an ant colony algorithm.
Disclosure of Invention
The invention aims to provide an electric power collaborative inspection strategy analysis method based on an ant colony algorithm aiming at the defects existing at present.
In order to overcome the defects in the prior art, the invention adopts the following technical scheme:
the electric power collaborative inspection strategy analysis method based on the ant colony algorithm is characterized by comprising the following steps of:
s1, initializing the number m of unmanned aerial vehicles and the number N of power equipment by a central processing unit, setting a tentative path of the unmanned aerial vehicle inspection power equipment based on a path selection mechanism, and obtaining the total length of the tentative path according to the following formulaAnd according to tentativeThe total length of the path gives the initial pheromone signal concentration +.>；
=/>+/>+……/>;/>=m//>；
wherein ,representing the distance from power device a to power device B, and so on;
s2, the central processing unit selects random power equipment for the unmanned aerial vehicle as a starting point;
s3, the central processing unit constructs a corresponding inspection strategy for each unmanned aerial vehicle based on the path selection mechanism, the unmanned aerial vehicle performs inspection according to the inspection strategy corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle continuously releases pheromone signals in the inspection process;
s4, updating the pheromone signal concentration of each path by the central processing unit every time the unmanned aerial vehicle completes the inspection task;
and S5, if the updated pheromone signal concentration meets the ending condition, outputting an optimal inspection strategy and ending analysis, and if not, turning the unmanned aerial vehicle which has completed the inspection task to the step 2.
Still further, the path selection mechanism includes the following features: the path covers all the power equipment which needs to be inspected; the starting point and the end point of the path are the same; except for the starting point, each power device only passes once in the inspection process of each unmanned aerial vehicle.
Further, in S1, the method for setting the tentative path of the unmanned aerial vehicle inspection power device includes the steps of:
s11, randomly designating one power device as a starting point of the unmanned aerial vehicle, and screening power devices which can be forwarded by the unmanned aerial vehicle;
s12, judging whether the number of power equipment which the unmanned aerial vehicle can go to is 0 or not according to the current target point of the unmanned aerial vehicle, if so, executing S14, otherwise, executing S13;
s13, selecting the power equipment with the highest visibility from the previous power equipment of the unmanned aerial vehicle as the next target point of the unmanned aerial vehicle, and returning to S12;
s14, setting the end point of the unmanned aerial vehicle as the power equipment where the starting point is located, and obtaining a tentative path of the unmanned aerial vehicle.
Further, in S3, the method for the central processing unit to construct the corresponding inspection policy for the unmanned aerial vehicle includes the following steps:
s31, screening a next target position accessible by the unmanned aerial vehicle according to the characteristics of the unmanned aerial vehicle inspection path;
s32, judging whether the number of the next accessible target positions is 0, if so, determining the next target positions as the starting points selected in S2, and finishing the construction of the patrol strategy; if not, continuing the next step;
s33, calculating the probability that the unmanned aerial vehicle goes to each target position;
specifically, the calculation formula is as follows, P (AB) =；
wherein ,for the pheromone signal concentration of the path between the electrical devices a to B,/>Visibility of the path between power devices a to B, and so on; alpha and beta represent the concentration and visibility of the pheromone signal, respectivelyWeighting; p (AB) represents the probability that the drone selected to fly from the current power device a to power device B;
s34, generating a random number, selecting a target position to which the unmanned aerial vehicle next goes by using a roulette method according to the random number, and returning to the step S31 after the unmanned aerial vehicle reaches the target position.
Further, in S4, the method for updating the pheromone signal concentration includes the steps of:
s41, calculating the path length of each unmanned aerial vehicle to obtain C1 and C2 … … Cm; wherein C1 refers to the total length of the path traversed by the first drone, and so on;
s42, updating the concentration of the pheromone signals on each path according to the following formula;
；
wherein p is the evaporation rate of the pheromone signal, the evaporation rate is used for reducing the growth rate of the concentration of the pheromone signal, and the evaporation rate is more than 0 and less than or equal to 1;the pheromones left by the kth unmanned aerial vehicle on the paths M to N;
in particular, the method comprises the steps of,=；
further, in S5, the ending condition is that the concentration of the pheromone signal on a certain path is greater than or equal to 0.8, and the method for outputting the optimal inspection strategy includes the following steps:
s51, the central processing unit extracts a patrol path of the patrol from the flight records of the unmanned aerial vehicle meeting the end condition;
s52, the central processing unit sets the inspection as an optimal inspection strategy, and marks the inspection path in the map;
and S53, the central processing unit sends the signal of the optimal inspection strategy to each unmanned aerial vehicle.
Further, the central processing unit comprises a signal receiver, a signal transmitter, a calculation module and a storage module; the signal receiver is used for receiving a pheromone signal sent by the unmanned aerial vehicle; the computing module is used for executing various computations; the signal transmitter is used for transmitting the calculation result of the calculation module to the unmanned aerial vehicle; the storage module is used for storing the pheromone signals in the map and marking the concentration of the pheromone signals in the map.
Still further, the central processing unit is arranged at the airport of the unmanned aerial vehicle, and the central processing unit further comprises a communication module and a measurement module, wherein the communication module is used for communicating with satellites to obtain an area map, and the measurement module is used for measuring the distance between all the power equipment in the area map.
According to the invention, the electric power collaborative inspection strategy is analyzed by adopting the ant colony algorithm, so that the inspection path of the unmanned aerial vehicle can be continuously and automatically optimized, the shortest inspection path of the unmanned aerial vehicle can be obtained, omission in the inspection process can be avoided, and the intelligent degree of inspection can be improved.
Drawings
The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. Like reference numerals designate like parts in the different views.
FIG. 1 is a schematic of the overall workflow of the present invention.
Fig. 2 is a flow chart of a method for setting a tentative path of an unmanned aerial vehicle inspection power device according to the present invention.
Fig. 3 is a flow chart of a method for an inspection strategy corresponding to an unmanned aerial vehicle mechanism.
Fig. 4 is a schematic workflow diagram of a second embodiment of the present invention.
Detailed Description
The following embodiments of the present invention are described in terms of specific examples, and those skilled in the art will appreciate the advantages and effects of the present invention from the disclosure herein. The invention is capable of other and different embodiments and its several details are capable of modification and variation in various respects, all without departing from the spirit of the present invention. The drawings of the present invention are merely schematic illustrations, and are not intended to be drawn to actual dimensions. The following embodiments will further illustrate the related art content of the present invention in detail, but the disclosure is not intended to limit the scope of the present invention.
Embodiment one: the embodiment provides an electric power collaborative inspection strategy analysis method based on an ant colony algorithm, which is used for acquiring a shortest inspection path of unmanned aerial vehicle inspection power equipment, as shown in fig. 1, and is characterized by comprising the following steps:
s1, initializing the number m of unmanned aerial vehicles and the number N of power equipment by a central processing unit, setting a tentative path of the unmanned aerial vehicle inspection power equipment based on a path selection mechanism, and obtaining the total length of the tentative path according to the following formulaAnd obtaining the initial pheromone signal concentration +.>；
=/>+/>+……/>;/>=m//>；
wherein ,representing the distance from power device a to power device B, and so on;
specifically, the pheromone signal is released by the unmanned aerial vehicle and received by the central processing unit, the unmanned aerial vehicle can continuously release the pheromone signal in the moving process, and the central processing unit marks the concentration of the pheromone signal of each section of inspection path according to the pheromone signal;
specifically, the number m of unmanned aerial vehicles is smaller than the number N of the power devices to be inspected, the tentative path is that the power devices a to B are inspected to the power device N in sequence according to the alphabetical order, and the tentative path is only used for acquiring the initial pheromone signal concentrationThe tentative path is irrelevant to the actual inspection path of the unmanned aerial vehicle;
s2, the central processing unit selects random power equipment for the unmanned aerial vehicle as a starting point;
s3, the central processing unit constructs a corresponding inspection strategy for each unmanned aerial vehicle based on the path selection mechanism, the unmanned aerial vehicle performs inspection according to the inspection strategy corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle continuously releases pheromone signals in the inspection process;
s4, updating the pheromone signal concentration of each path by the central processing unit every time the unmanned aerial vehicle completes the inspection task;
and S5, if the updated pheromone signal concentration meets the ending condition, outputting an optimal inspection strategy and ending analysis, and if not, turning the unmanned aerial vehicle which has completed the inspection task to the step 2.
Still further, the path selection mechanism includes the following features: the path covers all the power equipment which needs to be inspected; the starting point and the end point of the path are the same; except for the starting point, each power device only passes once in the inspection process of each unmanned aerial vehicle.
The setting is favorable to shortening unmanned aerial vehicle's inspection route, is favorable to improving inspection efficiency and reducing the energy consumption.
Further, in S1, the method for setting the tentative path of the unmanned aerial vehicle inspection power device is as shown in fig. 2, and includes the following steps:
s11, randomly designating one power device as a starting point of the unmanned aerial vehicle, and screening power devices which can be forwarded by the unmanned aerial vehicle;
s12, judging whether the number of power equipment which the unmanned aerial vehicle can go to is 0 or not according to the current target point of the unmanned aerial vehicle, if so, executing S14, otherwise, executing S13;
s13, selecting the power equipment with the highest visibility from the previous power equipment of the unmanned aerial vehicle as the next target point of the unmanned aerial vehicle, and returning to S12;
s14, setting the end point of the unmanned aerial vehicle as the power equipment where the starting point is located, and obtaining a tentative path of the unmanned aerial vehicle.
Further, in S3, the method of the inspection policy corresponding to the unmanned aerial vehicle mechanism by the central processing unit is shown in fig. 3, and includes the following steps:
s31, screening a next target position accessible by the unmanned aerial vehicle according to the characteristics of the unmanned aerial vehicle inspection path;
s32, judging whether the number of the next accessible target positions is 0, if so, determining the next target positions as the starting points selected in S2, and finishing the construction of the patrol strategy; if not, continuing the next step;
s33, calculating the probability that the unmanned aerial vehicle goes to each target position;
specifically, the calculation formula is as follows, P (AB) =；
wherein ,for the pheromone signal concentration of the path between the electrical devices a to B,/>Visibility of the path between power devices a to B, and so on; alpha and beta represent the weights of the pheromone signal concentration and visibility, respectively; p (AB) represents the unmanned aerial vehicle's selection of flight from the current power device A to the power deviceProbability of B being prepared, < >>The larger the probability that the unmanned aerial vehicle selects to fly to the power equipment B from the current power equipment A is, the larger the probability that the unmanned aerial vehicle flies to the power equipment B is;
specifically, the visibility is equal to the reciprocal of the distance between the power devices;
specifically, when the inspection is the first inspection, the pheromone signal concentration on all paths is equal to the initial pheromone signal concentration；
S34, generating a random number, selecting a target position to which the unmanned aerial vehicle goes next by using a roulette method according to the random number, and returning to the step S31 after the unmanned aerial vehicle reaches the target position;
it is worth to be noted that, the roulette method belongs to the existing algorithm, and is not repeated herein, and the value range of the random number is greater than 0 and less than or equal to 1.
Further, in S4, the method for updating the pheromone signal concentration includes the steps of:
s41, calculating the path length of each unmanned aerial vehicle to obtain C1 and C2 … … Cm; wherein C1 refers to the total length of the path traversed by the first drone, and so on;
s42, updating the concentration of the pheromone signals on each path according to the following formula;
；
wherein, p is the evaporation rate of the pheromone signal, which is used for reducing the growth rate of the concentration of the pheromone signal, which is beneficial to avoiding inaccurate analysis results caused by over-fast analysis; the evaporation rate is more than 0 and less than or equal to 1;the pheromones left by the kth unmanned aerial vehicle on the paths M to N;
in particular, the method comprises the steps of,=；
further, in S5, the ending condition is that the concentration of the pheromone signal on a certain path is greater than or equal to 0.8, and the method for outputting the optimal inspection strategy includes the following steps:
s51, the central processing unit extracts a patrol path of the patrol from the flight records of the unmanned aerial vehicle meeting the end condition;
s52, the central processing unit sets the inspection as an optimal inspection strategy, and marks the inspection path in the map;
and S53, the central processing unit sends the signal of the optimal inspection strategy to each unmanned aerial vehicle.
After each unmanned aerial vehicle receives the signal of the optimal inspection strategy, the unmanned aerial vehicle can be inspected according to the inspection path corresponding to the optimal inspection strategy, so that the inspection path of the unmanned aerial vehicle is shortened, the inspection efficiency is improved, and the energy consumption is reduced.
Further, the central processing unit comprises a signal receiver, a signal transmitter, a calculation module and a storage module; the signal receiver is used for receiving a pheromone signal sent by the unmanned aerial vehicle; the computing module is used for executing various computations; the signal transmitter is used for transmitting the calculation result of the calculation module to the unmanned aerial vehicle; the storage module is used for storing the pheromone signals in the map and marking the concentration of the pheromone signals in the map.
Still further, the central processing unit is arranged at the airport of the unmanned aerial vehicle, and the central processing unit further comprises a communication module and a measurement module, wherein the communication module is used for communicating with satellites to obtain an area map, and the measurement module is used for measuring the distance between all the power equipment in the area map.
According to the invention, the electric power collaborative inspection strategy is analyzed by adopting the ant colony algorithm, so that the inspection path of the unmanned aerial vehicle can be continuously and automatically optimized, the shortest inspection path of the unmanned aerial vehicle can be obtained, omission in the inspection process can be avoided, and the intelligent degree of inspection can be improved.
Embodiment two: the embodiment should be understood to include all the features of the first embodiment, and is further improved on the basis of the features, and the embodiment provides an electric power collaborative inspection strategy analysis method based on an ant colony algorithm, and further provides an unmanned aerial vehicle for inspecting electric power equipment.
This an unmanned aerial vehicle for patrolling and examining power equipment includes unmanned aerial vehicle body, signal transmission module, posture adjustment module and camera group, the posture adjustment module the signal transmission module with camera group installs in unmanned aerial vehicle body, camera group is used for taking power equipment's picture in unmanned aerial vehicle's the process of patrolling and examining, the posture adjustment module is used for adjusting unmanned aerial vehicle body with camera group's posture, signal transmission module is used for sending the signal to other equipment.
The unmanned aerial vehicle further comprises a defect recognition module, wherein the defect recognition module is used for recognizing a picture which is firstly shot by the camera group, the camera group can carry out secondary shooting on the position of the defect of the power equipment according to the defect position recognized by the defect recognition module, and the secondary shooting is beneficial to shooting a clear defect image.
Further, the method for performing secondary shooting on the defect position by the camera set, as shown in fig. 4, includes the following steps:
firstly, the defect identification module establishes a rectangular coordinate system by taking a picture for the first time, and marks the defect position in a matrix form; defect locations are respectively expressed as、/>……/>； wherein ,/>Representing the Nth defectA minimum value of the position of (2) on the x-axis; />Representing the maximum value of the position of the nth defect on the x-axis; />Representing the minimum value of the position of the nth defect on the y-axis; />Representing the maximum value of the position of the nth defect on the y-axis;
a second step of establishing a relation between the nth defect position and the shooting angle of the nth shooting according to the following formula to obtain the shooting angle of the camera from the first shooting to the nth shooting，/>）、（/>，/>）……（/>，/>）；/>=/>(/>) ;/>=/>(/>);
Wherein the shooting angle refers to the rotation angle of the camera at the time of the nth shooting relative to the first shooting,for the rotation angle of the camera in the direction of the x-axis, +.>Is the rotation angle of the camera in the y-axis direction; m is half of the length of the photograph which is firstly taken, namely half of the total length of the x axis of the coordinate system; n is half of the width of the photograph taken for the first time, namely half of the total length of the y axis of the coordinate system; />For the maximum rotation range of the camera in horizontal direction, +.>The maximum rotation range of the camera in the vertical direction is set;
thirdly, the defect recognition module sends shooting times N and shooting angles corresponding to each shooting to the gesture adjustment module;
and fourthly, the gesture adjusting module adjusts the camera, the camera takes the images of the defect positions for the second time after being in place, and the steps are repeated for N times until the camera takes the images of the defects of the N positions.
According to the unmanned aerial vehicle for inspecting the power equipment, provided by the embodiment, the defects of the power equipment are identified through the defect identification module, the gesture adjustment module is used for operating the camera to carry out secondary shooting on the defect positions, so that a picture with clearer and more accurate defect positions of the power equipment can be obtained, and the inspection precision is improved.
The foregoing disclosure is only a preferred embodiment of the present invention and is not intended to limit the scope of the invention, so that all equivalent technical changes made by the application of the present invention and the accompanying drawings are included in the scope of the invention, and in addition, the elements in the invention can be updated with the technical development.
Claims (8)
1. The electric power collaborative inspection strategy analysis method based on the ant colony algorithm is characterized by comprising the following steps of:
s1, initializing the number m of unmanned aerial vehicles and the number N of power equipment by a central processing unit, setting a tentative path of the unmanned aerial vehicle inspection power equipment based on a path selection mechanism, and obtaining the total length of the tentative path according to the following formulaAnd obtaining the initial pheromone signal concentration +.>；
=/>+/>+……/>;/>=m//>；
wherein ,representing the power from power device A to powerDistance of device B, and so on;
s2, the central processing unit selects random power equipment for the unmanned aerial vehicle as a starting point;
s3, the central processing unit constructs a corresponding inspection strategy for each unmanned aerial vehicle based on the path selection mechanism, the unmanned aerial vehicle performs inspection according to the inspection strategy corresponding to the unmanned aerial vehicle, and the unmanned aerial vehicle continuously releases pheromone signals in the inspection process;
s4, updating the pheromone signal concentration of each path by the central processing unit every time the unmanned aerial vehicle completes the inspection task;
and S5, if the updated pheromone signal concentration meets the ending condition, outputting an optimal inspection strategy and ending analysis, and if not, turning the unmanned aerial vehicle which has completed the inspection task to the step 2.
2. The method for analyzing the electric power collaborative inspection strategy based on the ant colony algorithm according to claim 1, wherein the path selection mechanism comprises the following characteristics: the path covers all the power equipment which needs to be inspected; the starting point and the end point of the path are the same; except for the starting point, each power device only passes once in the inspection process of each unmanned aerial vehicle.
3. The method for analyzing the electric power collaborative inspection strategy based on the ant colony algorithm according to claim 2, wherein in S1, the method for setting the tentative path of the unmanned aerial vehicle inspection electric power equipment comprises the following steps:
s11, randomly designating one power device as a starting point of the unmanned aerial vehicle, and screening power devices which can be forwarded by the unmanned aerial vehicle;
s12, judging whether the number of power equipment which the unmanned aerial vehicle can go to is 0 or not according to the current target point of the unmanned aerial vehicle, if so, executing S14, otherwise, executing S13;
s13, selecting the power equipment with the highest visibility from the previous power equipment of the unmanned aerial vehicle as the next target point of the unmanned aerial vehicle, and returning to S12;
s14, setting the end point of the unmanned aerial vehicle as the power equipment where the starting point is located, and obtaining a tentative path of the unmanned aerial vehicle.
4. The method for analyzing the electric collaborative inspection strategy based on the ant colony algorithm according to claim 3, wherein in S3, the method for the central processing unit to construct the corresponding inspection strategy for the unmanned aerial vehicle comprises the following steps:
s31, screening a next target position accessible by the unmanned aerial vehicle according to the characteristics of the unmanned aerial vehicle inspection path;
s32, judging whether the number of the next accessible target positions is 0, if so, determining the next target positions as the starting points selected in S2, and finishing the construction of the patrol strategy; if not, continuing the next step;
s33, calculating the probability that the unmanned aerial vehicle goes to each target position;
specifically, the calculation formula is as follows, P (AB) =；
wherein ,for the pheromone signal concentration of the path between the electrical devices a to B,/>Visibility of the path between power devices a to B, and so on; alpha and beta represent the weights of the pheromone signal concentration and visibility, respectively; p (AB) represents the probability that the drone selected to fly from the current power device a to power device B;
s34, generating a random number, selecting a target position to which the unmanned aerial vehicle next goes by using a roulette method according to the random number, and returning to the step S31 after the unmanned aerial vehicle reaches the target position.
5. The method for analyzing the electric collaborative inspection strategy based on the ant colony algorithm according to claim 4, wherein in S4, the method for updating the pheromone signal concentration comprises the following steps:
s41, calculating the path length of each unmanned aerial vehicle to obtain C1 and C2 … … Cm; wherein C1 refers to the total length of the path traversed by the first drone, and so on;
s42, updating the concentration of the pheromone signals on each path according to the following formula;
；
wherein p is the evaporation rate of the pheromone signal, the evaporation rate is used for reducing the growth rate of the concentration of the pheromone signal, and the evaporation rate is more than 0 and less than or equal to 1;the pheromones left by the kth unmanned aerial vehicle on the paths M to N;
in particular, the method comprises the steps of,=。
6. the method for analyzing the electric collaborative inspection strategy based on the ant colony algorithm according to claim 5, wherein in S5, the ending condition is that the concentration of the pheromone signal on a certain path is more than or equal to 0.8, and the method for outputting the optimal inspection strategy comprises the following steps:
s51, the central processing unit extracts a patrol path of the patrol from the flight records of the unmanned aerial vehicle meeting the end condition;
s52, the central processing unit sets the inspection as an optimal inspection strategy, and marks the inspection path in the map;
and S53, the central processing unit sends the signal of the optimal inspection strategy to each unmanned aerial vehicle.
7. The method for analyzing the electric power collaborative inspection strategy based on the ant colony algorithm according to claim 6, wherein the central processing unit comprises a signal receiver, a signal transmitter, a calculation module and a storage module; the signal receiver is used for receiving a pheromone signal sent by the unmanned aerial vehicle; the computing module is used for executing various computations; the signal transmitter is used for transmitting the calculation result of the calculation module to the unmanned aerial vehicle; the storage module is used for storing the pheromone signals in the map and marking the concentration of the pheromone signals in the map.
8. The method for analyzing the electric power collaborative inspection strategy based on the ant colony algorithm according to claim 7, wherein the method comprises the following steps: the central processing unit is arranged at the unmanned aerial vehicle airport, the central processing unit further comprises a communication module and a measurement module, the communication module is used for communicating with the satellite to obtain an area map, and the measurement module is used for measuring the distance between all the power equipment in the area map.
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