An unmanned aerial vehicle-based emergency dispatching method, device, apparatus, and storage medium

Abstract

The application relates to the technical field of emergency response and the technical field of energy scheduling, and discloses an emergency scheduling method and device based on a UAV, equipment and a storage medium, the method comprising the following steps: acquiring each UAV path from a starting point coordinate to a terminal point coordinate; generating the cost of each UAV path according to the risk degree of each grid in each UAV path, the flight distance of each voyage in each UAV path and a preset cost generation model, selecting the UAV path with the minimum cost as an optimal UAV path, and acquiring a UAV scheduling scheme corresponding to the optimal UAV path; generating the total loss cost corresponding to the UAV scheduling scheme according to the user loss cost, the load recovery cost and the deployment cost of the energy storage battery; and selecting the UAV scheduling scheme as an emergency scheduling scheme of a power failure area when the total loss cost is less than a preset loss cost. The application is beneficial to improving the timeliness of emergency response.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of emergency and the technical field of energy scheduling, in particular to an emergency scheduling method and device based on a UAV, equipment and a storage medium. BACKGROUND

[0002] With the large-scale access of new energy to the power grid, due to the inherent randomness and volatility characteristics of new energy power generation, the risk of power grid operation is high, so it is easy to produce power outage areas. The power outage area refers to a geographical area in the power supply system where power supply is interrupted due to a specific reason, resulting in no power available or a serious shortage of power supply.

[0003] However, the existing emergency scheduling scheme of the power outage area has a cumbersome acquisition process, which is not conducive to improving the acquisition efficiency of the emergency scheduling scheme. The reason is that the existing technology mainly adopts a manual acquisition method to acquire the emergency scheduling scheme of the power outage area, and the manual acquisition method consumes a large amount of human resources and time resources, increases the acquisition time of the emergency scheduling scheme of the power outage area, and thus is not conducive to improving the acquisition efficiency of the emergency scheduling scheme. SUMMARY

[0004] The embodiments of the present application provide an emergency scheduling method and device based on a UAV, equipment and a storage medium to solve the technical problem of the above-mentioned cumbersome acquisition process of the existing emergency scheduling scheme of the power outage area, which is not conducive to improving the acquisition efficiency of the emergency scheduling scheme.

[0005] In a first aspect, the embodiments of the present application provide an emergency scheduling method based on a UAV, applied to an electronic device, the emergency scheduling method comprising:

[0006] acquiring the coordinates of the power outage area, selecting the coordinates of the power outage area as the end point coordinates, and acquiring each UAV path from the start point coordinates to the end point coordinates;

[0007] acquiring a grid map, traversing each grid of the grid map to obtain the number of obstacles in the neighborhood range of each grid in each UAV path and the total number of grids, generating the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids and a preset danger degree generation model;

[0008] generating the cost of each UAV path according to the danger degree of each grid in each UAV path, the flight distance of each flight range in each UAV path and a preset cost generation model, selecting the UAV path with the minimum cost as the preferred UAV path, and acquiring the UAV scheduling scheme corresponding to the preferred UAV path;

[0009] acquiring the user loss cost, the load recovery cost and the energy storage battery deployment cost of the UAV scheduling scheme;

[0010] According to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and a preset cost generation model, a total loss cost corresponding to the unmanned aerial vehicle scheduling scheme is generated, and when the total loss cost is less than a preset loss cost, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme of the power failure area.

[0011] In a possible implementation manner of the first aspect, the coordinates of the power failure area are obtained, the coordinates of the power failure area are selected as the terminal point coordinates, and each unmanned aerial vehicle path from the starting point coordinates to the terminal point coordinates is obtained.

[0012] The power grid fault information is obtained, the coordinates of the power failure area are obtained from the power grid fault information, the coordinates of the power failure area are selected as the terminal point coordinates, and each unmanned aerial vehicle path from the starting point coordinates to the terminal point coordinates is obtained.

[0013] In a possible implementation manner of the first aspect, the user loss cost, the load recovery cost, and the energy storage battery deployment cost of the unmanned aerial vehicle scheduling scheme are obtained, and the obtaining includes:

[0014] According to the unit power load loss value of the power failure area, the discharge power of the energy storage battery carried by the unmanned aerial vehicle at each node in the power failure area, the power failure time of the power failure area, the flight time corresponding to the preferred unmanned aerial vehicle path, and a preset user loss cost generation model, the user loss cost of the unmanned aerial vehicle scheduling scheme is generated.

[0015] According to the number of unmanned aerial vehicle deployments at each node in the power failure area, the discharge power of the energy storage battery carried by the unmanned aerial vehicle at each node in the power failure area, the discharge efficiency of the energy storage battery carried by the unmanned aerial vehicle, and a preset load recovery cost generation model, the load recovery cost of the unmanned aerial vehicle scheduling scheme is generated.

[0016] According to the yield rate of the energy storage battery, the operation life of the energy storage battery, the number of unmanned aerial vehicle deployments at each node in the power failure area, the unit power cost of the energy storage battery, the charging power of the energy storage battery carried by the unmanned aerial vehicle, the unit capacity cost, the capacity of the energy storage battery carried by the unmanned aerial vehicle, and a preset deployment cost generation model, the deployment cost of the unmanned aerial vehicle scheduling scheme is generated.

[0017] In a possible implementation manner of the first aspect, after the total loss cost corresponding to the unmanned aerial vehicle scheduling scheme is generated according to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and a preset cost generation model, and when the total loss cost is less than a preset loss cost, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme of the power failure area, the emergency scheduling method includes:

[0018] A display window is created, and the emergency scheduling scheme is displayed through the display window.

[0019] In one possible implementation of the first aspect, the hazard generation model is as follows:

[0020] ;

[0021] in, It is the first The first drone path The danger level of each grid, It is the first The first drone path The number of obstacles within the neighborhood of each grid cell. It is the first The first drone path The total number of grid cells in the neighborhood of a given grid cell.

[0022] In one possible implementation of the first aspect, the cost generation model is as follows:

[0023] ;

[0024] in, Indicates the first The cost of a drone path, As the first weighting coefficient, This is the second weighting coefficient. For the Kth drone path, the th The flight distance of a segment of flight, Let be the distance traveled along the Kth drone path. It is the first The first drone path The danger level of each grid, For the first The number of grid cells for each drone path.

[0025] In one possible implementation of the first aspect, the total loss cost generation model is as follows:

[0026] ;

[0027] in, Total loss cost; Indicates the user's lost costs; Indicates the cost of load restoration; This indicates the deployment cost of energy storage batteries.

[0028] Secondly, embodiments of this application provide an emergency dispatch device based on unmanned aerial vehicles (UAVs), applied to electronic devices, including:

[0029] The first obtaining module is configured to obtain the coordinates of the power failure area, select the coordinates of the power failure area as end point coordinates, and obtain each UAV path from the start point coordinates to the end point coordinates.

[0030] The second obtaining module is configured to obtain a grid map, traverse each grid of the grid map, obtain the number of obstacles in the neighborhood range of each grid in each UAV path and the total number of grids, and generate the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and a preset danger degree generation model.

[0031] The generating module is configured to generate the cost of each UAV path according to the danger degree of each grid in each UAV path, the flight distance of each flight segment in each UAV path, and a preset cost generation model, select the UAV path with the minimum cost as the preferred UAV path, and obtain the UAV scheduling scheme corresponding to the preferred UAV path.

[0032] The third obtaining module is configured to obtain the user loss cost, the load recovery cost, and the energy storage battery deployment cost of the UAV scheduling scheme.

[0033] The scheduling module is configured to generate the total loss cost corresponding to the UAV scheduling scheme according to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and a preset cost generation model, and select the UAV scheduling scheme as the emergency scheduling scheme for the power failure area when the total loss cost is less than a preset loss cost.

[0034] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the emergency scheduling method of any one of the first aspect when executing the computer program.

[0035] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executable on a processor to implement the emergency scheduling method of any one of the first aspect.

[0036] In a fifth aspect, a computer program product is provided, which, when executed on an electronic device, causes the electronic device to perform the emergency scheduling method of any one of the first aspect.

[0037] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0038] The embodiments of the present application have two advantages. On the one hand, according to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and the preset cost generation model, a total loss cost corresponding to the unmanned aerial vehicle scheduling scheme is generated. When the total loss cost is less than the preset loss cost, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme of the power failure area. Since the manual acquisition is not required, the acquisition time of the emergency scheduling scheme of the power failure area is reduced, and the acquisition efficiency of the emergency scheduling scheme of the power failure area is improved. On the other hand, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme of the power failure area. Compared with the traditional ground transportation mode, the emergency scheduling scheme of the present application can avoid road traffic obstacles, reduce the emergency response time, and improve the timeliness of the emergency response. BRIEF DESCRIPTION OF DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0040] Figure 1 The application scenario diagram of the emergency scheduling method provided by the embodiments of the present application is shown in the figure.

[0041] Figure 2 The flowchart of the emergency scheduling method provided by the embodiments of the present application is shown in the figure.

[0042] Figure 3 The flowchart of the cost acquisition provided by the embodiments of the present application is shown in the figure.

[0043] Figure 4 The schematic block diagram of the emergency scheduling device provided by the embodiments of the present application is shown in the figure.

[0044] Figure 5 The structural schematic diagram of the electronic device provided by the embodiments of the present application is shown in the figure.

[0045] Figure 6 The result diagram of the emergency scheduling method provided by the embodiments of the present application is shown in the figure. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] In addition, in the description of the present application and the appended claims, the terms "first", "second", "third" and the like are used only to distinguish descriptions, and cannot be understood as indicating or implying relative importance.

[0048] In addition, the technical solutions among various embodiments can be combined with each other, but it must be based on that a person skilled in the art can realize, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, and is not within the protection scope required by the present application.

[0049] The flowchart shown in the accompanying drawings is only an example description, and does not necessarily include all contents and operations / steps, and is not necessarily executed in the order described. For example, some operations / steps can also be decomposed, combined or partially merged, so the actual execution order may be changed according to the actual situation.

[0050] The emergency dispatch method provided by the embodiments of the present application can be applied to electronic devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, notebook computers, personal computers, personal digital assistants (PDAs) and the like. The specific type of electronic device is not limited by the embodiments of the present application.

[0051] Please refer to Figure 1 , Figure 1 The application scenario diagram of the emergency dispatch method provided by the embodiments of the present application is described in detail as follows:

[0052] Figure 1 It includes unmanned aerial vehicle carrying battery, power failure area and road failure.

[0053] Among them, the unmanned aerial vehicle carrying battery means that the unmanned aerial vehicle carries the energy storage battery.

[0054] The road traffic obstacle includes the traffic obstacle caused by road failure and the traffic obstacle caused by non-road failure.

[0055] The traffic obstacle caused by non-road failure includes but is not limited to illegal occupation of road and accident disposal.

[0056] The road failure includes but is not limited to road collapse, road crack and road closure due to construction.

[0057] Among them, when the natural disaster or sudden accident causes power interruption and ground traffic paralysis, the traditional vehicle may not arrive in time due to the road failure on the ground. While the unmanned aerial vehicle can bypass the road failure on the ground and directly fly to the power failure area to deliver the energy storage battery, providing temporary power support for the power failure area.

[0058] In the embodiment of the present application, since the unmanned aerial vehicle distribution avoids the fuel dependence and long-distance scheduling of ground transportation, the time of power recovery is reduced, and the social and economic losses caused by power failure can be minimized.

[0059] Please refer to Figure 2 , Figure 2 is a flowchart of an emergency scheduling method provided by the embodiment of the present application, and the method can be applied to an electronic device.

[0060] As Figure 2 shown, the emergency scheduling method provided by the embodiment of the present application includes the following steps, which are described in detail as follows.

[0061] S201, obtaining the coordinates of the power failure area, selecting the coordinates of the power failure area as the terminal coordinates, and obtaining each unmanned aerial vehicle path from the starting point coordinates to the terminal coordinates.

[0062] The obtaining of the coordinates of the power failure area, the selection of the coordinates of the power failure area as the terminal coordinates, and the obtaining of each unmanned aerial vehicle path from the starting point coordinates to the terminal coordinates include:

[0063] Obtaining power grid fault information, obtaining the coordinates of the power failure area from the power grid fault information, selecting the coordinates of the power failure area as the terminal coordinates, and obtaining each unmanned aerial vehicle path from the starting point coordinates to the terminal coordinates.

[0064] Exemplarily, the obtaining of the power grid fault information, the obtaining of the coordinates of the power failure area from the power grid fault information, the selection of the coordinates of the power failure area as the terminal coordinates, and the obtaining of each unmanned aerial vehicle path from the starting point coordinates to the terminal coordinates include:

[0065] Obtaining power grid fault information, obtaining the coordinates of the power failure area from the power grid fault information, and selecting the coordinates of the power failure area as the terminal coordinates.

[0066] According to the constraint conditions of path planning, the plurality of original paths from the starting point coordinates to the terminal coordinates are screened to obtain each unmanned aerial vehicle path from the starting point coordinates to the terminal coordinates.

[0067] The constraint conditions include a first condition, a second condition, a third condition, a fourth condition, and a fifth condition, the first condition is that each flight distance of the unmanned aerial vehicle is not less than a minimum path length, the second condition is that the flight range of the unmanned aerial vehicle is less than a maximum range, the third condition is that the current turning angle of the unmanned aerial vehicle in the flight process does not exceed a maximum turning angle, the fourth condition is that the current climbing angle of the unmanned aerial vehicle in the take-off or flight process does not exceed a maximum climbing angle, and the fifth condition is that the current load of the unmanned aerial vehicle in the take-off or flight process does not exceed a maximum load.

[0068] The first condition, the second condition, the third condition, the fourth condition and the fifth condition are used to ensure the rationality of the UAV path and the stability of flight.

[0069] S202, obtain a grid map, traverse each grid of the grid map, obtain the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and generate the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids and a preset danger degree generation model;

[0070] The danger degree generation model is:

[0071]

[0072] The danger degree generation model is: The danger degree of the i-th grid in the j-th UAV path is: The number of obstacles in the neighborhood range of the i-th grid in the j-th UAV path is: The total number of grids in the neighborhood range of the i-th grid in the j-th UAV path is:

[0073] The danger degree generation model is a danger degree generation model.

[0074] The grid map is obtained, and the grid map is used to represent the three-dimensional space information of the urban low-altitude area. In the grid map, the urban low-altitude area is uniformly divided into a plurality of grids, and each grid represents a specific space unit. For example, each grid corresponds to a real area of 10 cubic meters.

[0075] For the sake of illustration, the following examples are given:

[0076] For example, the first UAV path has three grids, which are grid 1, grid 2 and grid 3.

[0077] The neighborhood range is 4-neighborhood, and the neighborhood range of grid 1 is the four adjacent grids above, below, left and right of grid 1. There is only one obstacle in the four adjacent grids above, below, left and right of grid 1, and the number of obstacles in the neighborhood range of grid 1 is 1. Since the total number of grids in the neighborhood range of grid 1 is 4, the number of obstacles in the neighborhood range of grid 1 is divided by the total number of grids in the neighborhood range of grid 1, and therefore the danger degree of grid 1 is 0.25.

[0078] ​​​​​​​The neighborhood range of grid 2 is 4 neighborhoods, and the neighborhood range of grid 2 is the 4 adjacent grids above, below, left and right of grid 2. There is only one obstacle in the 4 adjacent grids above, below, left and right of grid 2, and the number of obstacles in the neighborhood range of grid 2 is 2. Since the total number of grids in the neighborhood range of grid 2 is 4, the number of obstacles in the neighborhood range of grid 2 is divided by the total number of grids in the neighborhood range of grid 2. Therefore, the danger degree of grid 2 is 0.5.

[0079] The neighborhood range of grid 3 is 4 neighborhoods, and the neighborhood range of grid 3 is the 4 adjacent grids above, below, left and right of grid 3. There is only one obstacle in the 4 adjacent grids above, below, left and right of grid 3, and the number of obstacles in the neighborhood range of grid 3 is 0. Since the total number of grids in the neighborhood range of grid 3 is 4, the number of obstacles in the neighborhood range of grid 3 is divided by the total number of grids in the neighborhood range of grid 3. Therefore, the danger degree of grid 3 is 0.

[0080] S203, according to the danger degree of each grid in each unmanned aerial vehicle path, the flight distance of each flight section in each unmanned aerial vehicle path and the preset cost generation model, generating the cost of each unmanned aerial vehicle path, selecting the unmanned aerial vehicle path with the minimum cost as the preferred unmanned aerial vehicle path, and obtaining the unmanned aerial vehicle scheduling scheme corresponding to the preferred unmanned aerial vehicle path;

[0081] The cost generation model is:

[0082] ;

[0083] The cost generation model is: The cost of the Kth unmanned aerial vehicle path is represented by C K. The first weight coefficient is represented by a. The second weight coefficient is represented by b. The flight distance of the Kth flight section in the Kth unmanned aerial vehicle path is represented by L K. The number of flight sections of the Kth unmanned aerial vehicle path is represented by N K. The danger degree of the Kth grid in the Kth unmanned aerial vehicle path is represented by D K. The number of grids of the Kth unmanned aerial vehicle path is represented by M K.

[0084] The cost generation model is a cost generation model.

[0085] Selecting the unmanned aerial vehicle path with the minimum cost as the preferred unmanned aerial vehicle path can reduce the energy loss caused by emergency stop or turning and prolong the endurance of the unmanned aerial vehicle.

[0086] S204, obtaining the user loss cost, load recovery cost and energy storage battery deployment cost of the unmanned aerial vehicle scheduling scheme;

[0087] ​​​​​S205, generating a total loss cost corresponding to the UAV scheduling scheme according to the user loss cost, the load recovery cost, the energy storage battery deployment cost and a preset cost generation model, and selecting the UAV scheduling scheme as the emergency scheduling scheme of the power failure area when the total loss cost is less than a preset loss cost.

[0088] wherein the total loss cost generation model is:

[0089] ;

[0090] wherein, is the total loss cost; represents the user loss cost; represents the load recovery cost; represents the energy storage battery deployment cost.

[0091] wherein the user loss cost represents the loss cost caused by power failure.

[0092] wherein the load recovery cost represents the cost required for power recovery.

[0093] wherein the energy storage battery deployment cost is the deployment cost of the energy storage battery.

[0094] wherein the total loss cost generation model is a generation model of the total loss cost.

[0095] wherein the UAV scheduling scheme is selected as the emergency scheduling scheme of the power failure area, and the UAV can avoid surface obstacles during emergency delivery due to its three-dimensional maneuvering characteristics, thereby reducing the power failure waiting time of the user and the loss cost of the power grid operator, and ultimately forming a win-win situation for both parties.

[0096] wherein, after the total loss cost corresponding to the UAV scheduling scheme is generated according to the user loss cost, the load recovery cost, the energy storage battery deployment cost and a preset cost generation model, and the UAV scheduling scheme is selected as the emergency scheduling scheme of the power failure area when the total loss cost is less than a preset loss cost, the emergency scheduling method comprises:

[0097] creating a display window, and displaying the emergency scheduling scheme through the display window.

[0098] The beneficial effects of this application's embodiments are twofold. Firstly, based on user loss costs, load restoration costs, energy storage battery deployment costs, and a preset cost generation model, the total loss cost corresponding to the drone dispatching scheme is generated. When the total loss cost is less than the preset loss cost, the drone dispatching scheme is selected as the emergency dispatching scheme for the power outage area. Since no manual acquisition is required, the acquisition time for the emergency dispatching scheme in the power outage area is reduced, which is conducive to improving the acquisition efficiency of the emergency dispatching scheme in the power outage area. Secondly, compared with traditional ground transportation methods, selecting the drone dispatching scheme as the emergency dispatching scheme for the power outage area can avoid road traffic obstacles, reduce emergency response time, and improve the timeliness of emergency response.

[0099] Please see Figure 3 , Figure 3 The flowchart illustrating the acquisition cost provided in this application embodiment is detailed below:

[0100] S301. Based on the unit power load loss value of the power loss area, the discharge power of the energy storage battery carried by the UAV at each node in the power loss area, the power loss time of the power loss area, the flight time corresponding to the preferred UAV path, and the preset user loss cost generation model, generate the user loss cost of the UAV scheduling scheme.

[0101] For example, the user loss cost generation model is as follows:

[0102] ;

[0103] in, Indicates the user's lost costs; L The value of unit power load loss in the power outage area. The discharge power of the energy storage battery carried by the UAV at the x-th node in the power-out region. The duration of power outage in the affected area. To optimize the flight time corresponding to the drone's path.

[0104] Among them, the user loss cost generation model is the model for generating user loss costs.

[0105] S302, based on the number of drones deployed at each node in the power outage area, the discharge power of the energy storage battery carried by the drone at each node in the power outage area, the discharge efficiency of the energy storage battery carried by the drone, and the preset load recovery cost generation model, generate the load recovery cost of the drone scheduling scheme.

[0106] For example, the load restoration cost generation model is as follows:

[0107] ;

[0108] wherein, denotes the load recovery cost; denotes the number of UAVs deployed at the xth node in the power failure area, is the number of nodes, is the discharge power of the energy storage battery carried by the UAV at the xth node in the power failure area, η is the discharge efficiency of the energy storage battery carried by the UAV, is the power failure time of the power failure area, is the flight time corresponding to the preferred UAV path.

[0109] wherein, the load recovery cost generation model is a generation model of the load recovery cost.

[0110] Discharge power: refers to the energy released by the energy storage battery in unit time, and the unit is watt or kilowatt. The discharge power reflects the speed of the energy storage battery discharging, and the greater the power, the more energy the energy storage battery releases in the same time, and the faster the discharging speed.

[0111] Discharge efficiency: refers to the ratio of the actual output electric energy to the total electric energy stored by the energy storage battery when discharging. The discharge efficiency is usually expressed in percentage. The discharge efficiency measures the effective degree of energy conversion of the energy storage battery in the discharging process. The higher the efficiency, the smaller the energy loss of the energy storage battery in the discharging process, and the energy storage battery can more efficiently convert the stored chemical energy into electric energy output.

[0112] Exemplarily, the deployment cost generation model is:

[0113] ;

[0114] wherein, denotes the energy storage battery deployment cost; γ is the yield rate of the energy storage battery; T is the operation life of the energy storage battery; denotes the number of UAVs deployed at the xth node in the power failure area; is the number of nodes, is the unit power cost of the energy storage battery, is the rated power of the energy storage battery carried by the UAV, is the unit capacity cost, E is the capacity of the energy storage battery carried by the UAV.

[0115] wherein, the deployment cost generation model is a generation model of the energy storage battery deployment cost.

[0116] In the embodiments of the present application, the user loss cost, the load recovery cost and the energy storage battery deployment cost of the UAV scheduling scheme are obtained, which is beneficial to obtain the total loss cost corresponding to the UAV scheduling scheme.

[0117] Corresponding to the emergency scheduling method described in the above embodiments, please refer to Figure 4 , Figure 4 The schematic block diagram of the emergency scheduling device provided in the embodiments of the present application is shown in Figure 4 The emergency scheduling device 400 shown in the application scenario diagram can be applied to the electronic device in the application scenario diagram as shown in Figure 1 The emergency scheduling device 400 shown in the application scenario diagram can be applied to the electronic device in the application scenario diagram as shown in Figure 4 The emergency scheduling device 400 shown in the application scenario diagram can be applied to the electronic device in the application scenario diagram as shown in

[0118] The first acquisition module 401 is configured to acquire the coordinates of the power failure area, select the coordinates of the power failure area as the end point coordinates, and acquire each UAV path from the start point coordinates to the end point coordinates.

[0119] The second acquisition module 402 is configured to acquire the grid map, traverse each grid of the grid map, obtain the number of obstacles in the neighborhood range of each grid in each UAV path and the total number of grids, and generate the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and a preset danger degree generation model.

[0120] The generation module 403 is configured to generate the cost of each UAV path according to the danger degree of each grid in each UAV path, the flight distance of each flight segment in each UAV path, and a preset cost generation model, select the UAV path with the minimum cost as the preferred UAV path, and acquire the UAV scheduling scheme corresponding to the preferred UAV path.

[0121] The third acquisition module 404 is configured to acquire the user loss cost, the load recovery cost, and the energy storage battery deployment cost of the UAV scheduling scheme.

[0122] The scheduling module 405 is configured to generate the total loss cost corresponding to the UAV scheduling scheme according to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and a preset cost generation model, and select the UAV scheduling scheme as the emergency scheduling scheme of the power failure area when the total loss cost is less than a preset loss cost.

[0123] It should be noted that each embodiment in the present specification adopts a progressive manner for description, and each embodiment focuses on the difference from other embodiments. The same and similar parts of each embodiment can be referred to each other.

[0124] The embodiments of the present application have two advantages. On the one hand, according to the user loss cost, the load recovery cost, the energy storage battery deployment cost and the preset cost generation model, the total loss cost corresponding to the unmanned aerial vehicle scheduling scheme is generated, and when the total loss cost is less than the preset loss cost, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme of the power failure area. Since the manual acquisition is not required, the acquisition time of the emergency scheduling scheme of the power failure area is reduced, and the acquisition efficiency of the emergency scheduling scheme of the power failure area is improved. On the other hand, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme of the power failure area. Compared with the traditional ground transportation mode, the emergency scheduling scheme of the present application can avoid the road traffic obstacles, reduce the emergency response time, and improve the timeliness of the emergency response.

[0125] Please refer to Figure 5 , Figure 5 The structural schematic diagram of the electronic device provided by the embodiments of the present application is shown.

[0126] As Figure 5 shown, Figure 5 The electronic device 2 comprises at least one processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor 20, wherein the processor 20 implements the steps in any of the above method embodiments when executing the computer program 22.

[0127] The electronic device 2 can include, but is not limited to, the processor 20 and the memory 21. Those skilled in the art can understand that Figure 5 The electronic device 2 is only an example and does not constitute a limitation on the electronic device 2, and can include more or fewer components than shown, or combine certain components, or different components, for example, can also include input / output devices, network access devices, etc.

[0128] The processor 20 is configured to run the computer program 22 stored in the memory 21, and implement the following steps when executing the computer program 22:

[0129] Obtain the coordinates of the power failure area, select the coordinates of the power failure area as the end point coordinates, and obtain each unmanned aerial vehicle path from the start point coordinates to the end point coordinates;

[0130] Obtain the grid map, traverse each grid of the grid map, obtain the number of obstacles in the neighborhood range of each grid in each unmanned aerial vehicle path and the total number of grids, generate the danger degree of each grid in each unmanned aerial vehicle path according to the number of obstacles in the neighborhood range of each grid in each unmanned aerial vehicle path, the total number of grids and the preset danger degree generation model;

[0131] According to the risk degree of each grid in each unmanned aerial vehicle path, the flight distance of each section of the unmanned aerial vehicle path, and a preset cost generation model, the cost of each unmanned aerial vehicle path is generated, the unmanned aerial vehicle path with the minimum cost is selected as the preferred unmanned aerial vehicle path, and the unmanned aerial vehicle scheduling scheme corresponding to the preferred unmanned aerial vehicle path is obtained;

[0132] The user loss cost, the load recovery cost, and the energy storage battery deployment cost of the unmanned aerial vehicle scheduling scheme are obtained.

[0133] According to the user loss cost, the load recovery cost, and the energy storage battery deployment cost, and a preset cost generation model, a total loss cost corresponding to the unmanned aerial vehicle scheduling scheme is generated, and when the total loss cost is less than a preset loss cost, the unmanned aerial vehicle scheduling scheme is selected as the emergency scheduling scheme for the power failure area.

[0134] In some embodiments, the processor 20 is configured to:

[0135] The power grid fault information is obtained, the coordinates of the power failure area are obtained from the power grid fault information, the coordinates of the power failure area are selected as the terminal coordinates, and each unmanned aerial vehicle path from the starting point coordinates to the terminal coordinates is obtained.

[0136] In some embodiments, the processor 20 is configured to:

[0137] According to the unit power load loss value of the power failure area, the discharge power of the energy storage battery carried by the unmanned aerial vehicle at each node in the power failure area, the power failure time of the power failure area, the flight time corresponding to the preferred unmanned aerial vehicle path, and a preset user loss cost generation model, the user loss cost of the unmanned aerial vehicle scheduling scheme is generated.

[0138] According to the number of unmanned aerial vehicles deployed at each node in the power failure area, the discharge power of the energy storage battery carried by the unmanned aerial vehicle at each node in the power failure area, the discharge efficiency of the energy storage battery carried by the unmanned aerial vehicle, and a preset load recovery cost generation model, the load recovery cost of the unmanned aerial vehicle scheduling scheme is generated.

[0139] According to the yield of the energy storage battery, the operation life of the energy storage battery, the number of unmanned aerial vehicles deployed at each node in the power failure area, the unit power cost of the energy storage battery, the charging power of the energy storage battery carried by the unmanned aerial vehicle, the unit capacity cost, the capacity of the energy storage battery carried by the unmanned aerial vehicle, and a preset deployment cost generation model, the deployment cost of the unmanned aerial vehicle scheduling scheme is generated.

[0140] In some embodiments, the processor 20 is configured to:

[0141] A display window is created, and the emergency scheduling scheme is displayed through the display window.

[0142] The processor 20 can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0143] The memory 21 can be an internal storage unit of the electronic device 2 in some embodiments, such as a hard disk or a memory of the electronic device 2. The memory 21 can also be an external storage device of the electronic device 2 in other embodiments, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 21 can include both the internal storage unit and the external storage device of the electronic device 2. The memory 21 is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of the computer program, etc. The memory 21 can also be used to temporarily store data that has been output or will be output.

[0144] It should be noted that the information interaction, execution process, etc. between the above devices / units, since based on the same concept as the method embodiments of the present application, the specific functions and the technical effects brought by them can be referred to the method embodiment part, and will not be repeated here.

[0145] Please refer to Figure 6 , Figure 6 The results of implementing the emergency dispatch method provided in the embodiments of the present application are shown.

[0146] As Figure 6 shown, Figure 6 the scheduling quantity of each power failure node and the corresponding load demand are displayed, indicating that the energy storage battery carried by the unmanned aerial vehicle can accurately match the power gap of each node; the actual output of the energy storage battery of each node and the change of the state of charge are displayed, verifying the stability of the efficiency of the energy storage battery in the discharging process; the loss cost before dispatching and the loss cost after dispatching are compared.

[0147] Among them, Figure 6The scheduling quantity, the loss cost before scheduling, the load demand, the energy storage output, the state of charge, and the loss cost after scheduling;

[0148] The nodes in the power failure area are 6, namely node 1, node 2, node 3, node 4, node 5, and node 6; the nodes in the power failure area are referred to as power failure nodes.

[0149] The value range of the scheduling quantity is 0 to 12;

[0150] The scheduling quantity of node 1 is 2, indicating that 2 drones are deployed at node 1;

[0151] The scheduling quantity of node 2 is 2, indicating that 2 drones are deployed at node 2;

[0152] The scheduling quantity of node 3 is 4, indicating that 4 drones are deployed at node 3;

[0153] The scheduling quantity of node 4 is 3, indicating that 3 drones are deployed at node 4;

[0154] The scheduling quantity of node 5 is 3, indicating that 3 drones are deployed at node 5;

[0155] The scheduling quantity of node 6 is 2, indicating that 2 drones are deployed at node 6;

[0156] The value range of the energy storage output and the load demand is 0 KW to 1000 KW;

[0157] The energy storage outputs of node 1, node 2, node 3, node 4, node 5, and node 6 are 412 KW, 500 KW, 920 KW, 650 KW, 750 KW, and 360 KW, respectively.

[0158] The load demands of node 1, node 2, node 3, node 4, node 5, and node 6 are 412 KW, 555 KW, 920 KW, 650 KW, 840 KW, and 360 KW, respectively.

[0159] The value range of the state of charge is 0.2 to 0.5.

[0160] The states of charge of node 1, node 2, node 3, node 4, node 5, and node 6 are 0.38, 0.25, 0.31, 0.35, 0.25, and 0.46, respectively.

[0161] The value range of the loss cost before scheduling and the loss cost after scheduling is 0 to 45 million yuan;

[0162] The scheduling pre-loss costs of node 1, node 2, node 3, node 4, node 5 and node 6 are 185,400 yuan, 249,700 yuan, 414,000 yuan, 292,500 yuan, 378,000 yuan and 162,000 yuan respectively;

[0163] The scheduling post-loss costs of node 1, node 2, node 3, node 4, node 5 and node 6 are 50,000 yuan, 67,400 yuan, 111,800 yuan, 79,000 yuan, 102,100 yuan and 43,700 yuan respectively.

[0164] The scheduling post-loss cost is the total loss cost. After the method is adopted, the total loss costs of node 1 to node 6 are significantly reduced, which reflects the comprehensive advantages of the method in reducing user loss cost, load recovery cost and energy storage deployment cost.

[0165] Figure 6 As can be seen from the table, the deployment quantity and discharge power of the unmanned aerial vehicle are highly adapted to the node demand, which further verifies the effectiveness of the method in path planning and cost collaborative optimization.

[0166] Figure 6 The multi-dimensional data intuitively shows the technical effects of the method in optimizing resource allocation and reducing social and economic loss, which provides empirical support for the beneficial effects of the method.

[0167] The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps in each of the method embodiments.

[0168] The computer readable storage medium stores program code, and the program code can be called and executed by the processor to implement the emergency scheduling method described in the method embodiments.

[0169] The computer readable storage medium has storage space for program code.

[0170] The program code includes the code of any step in the emergency scheduling method described in the method embodiments.

[0171] For example, the program code is called by the processor to execute the following steps:

[0172] Obtain the coordinates of the power loss area, select the coordinates of the power loss area as the end point coordinates, and obtain each unmanned aerial vehicle path from the start point coordinates to the end point coordinates.

[0173] acquire a grid map, traverse each grid of the grid map, obtain the number of obstacles in the neighborhood range of each grid in each UAV path and the total number of grids, generate the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids and a preset danger degree generation model;

[0174] generate the cost of each UAV path according to the danger degree of each grid in each UAV path, the flight distance of each flight section in each UAV path and a preset cost generation model, select the UAV path with the minimum cost as the preferred UAV path, and acquire the UAV scheduling scheme corresponding to the preferred UAV path;

[0175] acquire the user loss cost, the load recovery cost and the energy storage battery deployment cost of the UAV scheduling scheme;

[0176] generate the total loss cost corresponding to the UAV scheduling scheme according to the user loss cost, the load recovery cost, the energy storage battery deployment cost and a preset cost generation model, and select the UAV scheduling scheme as the emergency scheduling scheme for the power failure area when the total loss cost is less than a preset loss cost.

[0177] The specific implementation of each operation can be referred to the foregoing embodiments, which will not be described here.

[0178] The computer readable storage medium can also be an external storage device of the emergency scheduling device or the electronic device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, a non-transitory computer readable storage medium, etc.

[0179] The computer program stored in the computer readable storage medium can execute any of the emergency scheduling methods based on a UAV provided in the embodiments of the present application, so that the computer readable storage medium can achieve the beneficial effects of any of the emergency scheduling methods based on a UAV provided in the embodiments of the present application. Details can be found in the foregoing embodiments, which will not be described here.

[0180] The embodiments of the present application provide a computer program product, which, when running on an electronic device, causes the electronic device to execute the emergency scheduling method described above.

[0181] The computer program product is loaded by the electronic device, and the following steps can be executed:

[0182] Obtain the coordinates of the power failure area, select the coordinates of the power failure area as the end point coordinates, and obtain each UAV path from the start point coordinates to the end point coordinates;

[0183] Obtain the grid map, traverse each grid of the grid map, obtain the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and generate the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and a preset danger degree generation model;

[0184] According to the danger degree of each grid in each UAV path, the flight distance of each flight segment in each UAV path, and a preset cost generation model, generate the cost of each UAV path, select the UAV path with the minimum cost as the preferred UAV path, and obtain the UAV scheduling scheme corresponding to the preferred UAV path;

[0185] Obtain the user loss cost, load recovery cost, and energy storage battery deployment cost of the UAV scheduling scheme;

[0186] According to the user loss cost, load recovery cost, and energy storage battery deployment cost, and a preset cost generation model, generate the total loss cost corresponding to the UAV scheduling scheme, and when the total loss cost is less than a preset loss cost, select the UAV scheduling scheme as the emergency scheduling scheme of the power failure area.

[0187] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium.

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the unit and module in the system can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0189] Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can be implemented by a computer program instructing relevant hardware, the computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium at least includes any entity or device capable of carrying the computer program code to an electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal and a software distribution medium. For example, a U disk, a mobile hard disk, a magnetic disk or an optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium can not be an electrical carrier signal and a telecommunication signal.

[0190] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0191] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for emergency dispatch based on unmanned aerial vehicle, characterized in that, The emergency scheduling method is applied to an electronic device and includes the following steps: Obtaining the coordinates of the power failure area, selecting the coordinates of the power failure area as the end point coordinates, and obtaining each UAV path from the start point coordinates to the end point coordinates; Obtaining a grid map, traversing each grid of the grid map, obtaining the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and generating the danger degree of each grid in each UAV path according to the number of obstacles in the neighborhood range of each grid in each UAV path, the total number of grids, and a preset danger degree generation model; According to the danger degree of each grid in each UAV path, the flight distance of each flight segment in each UAV path, and a preset cost generation model, the cost of each UAV path is generated, the UAV path with the minimum cost is selected as the preferred UAV path, and the UAV scheduling scheme corresponding to the preferred UAV path is obtained; Obtaining the user loss cost, load recovery cost, and energy storage battery deployment cost of the UAV scheduling scheme; According to the user loss cost, load recovery cost, and energy storage battery deployment cost, and a preset cost generation model, the total loss cost corresponding to the UAV scheduling scheme is generated, and when the total loss cost is less than a preset loss cost, the UAV scheduling scheme is selected as the emergency scheduling scheme of the power failure area; The danger degree generation model is: ； wherein, is a danger degree of a th grid in a th path of the UAV, is a number of obstacles in a neighborhood range of a th grid in a th path of the UAV, is a total number of grids in a neighborhood range of a th grid in a th path of the UAV; The cost generation model is: ； wherein, denotes the cost of the th UAV path, is a first weight coefficient, is a second weight coefficient, is the flight distance of the th segment of the th UAV path, is the number of segments of the th UAV path, is the danger degree of the th grid of the th UAV path.

2. The emergency dispatch method of claim 1, wherein, The obtaining of the coordinates of the power failure area, the selection of the coordinates of the power failure area as the end point coordinates, and the obtaining of each UAV path from the start point coordinates to the end point coordinates includes: Obtaining power grid failure information, obtaining the coordinates of the power failure area from the power grid failure information, selecting the coordinates of the power failure area as the end point coordinates, and obtaining each UAV path from the start point coordinates to the end point coordinates.

3. The emergency dispatch method of claim 1, wherein, The obtaining of the user loss cost, load recovery cost, and energy storage battery deployment cost of the UAV scheduling scheme includes: According to the unit power load loss value of the power failure area, the discharge power of the energy storage battery carried by the UAV at each node in the power failure area, the power failure time of the power failure area, the flight time corresponding to the preferred UAV path, and a preset user loss cost generation model, the user loss cost of the UAV scheduling scheme is generated; According to the number of UAV deployments at each node in the power failure area, the discharge power of the energy storage battery carried by the UAV at each node in the power failure area, the discharge efficiency of the energy storage battery carried by the UAV, and a preset load recovery cost generation model, the load recovery cost of the UAV scheduling scheme is generated; According to the yield rate of the energy storage battery, the operation life of the energy storage battery, the number of UAV deployments at each node in the power failure area, the unit power cost of the energy storage battery, the charging power of the energy storage battery carried by the UAV, the unit capacity cost, the capacity of the energy storage battery carried by the UAV, and a preset deployment cost generation model, the deployment cost of the UAV scheduling scheme is generated.

4. The emergency dispatch method of claim 1, wherein, After the total loss cost corresponding to the UAV scheduling scheme is generated according to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and a preset cost generation model, and when the total loss cost is less than a preset loss cost, the UAV scheduling scheme is selected as the emergency scheduling scheme of the power failure area, the emergency scheduling method includes: A display window is created, and the emergency dispatching scheme is displayed through the display window.

5. The emergency dispatch method of any one of claims 1 to 4, wherein, The total loss cost generation model is: ； wherein, is the total loss cost; represents the user loss cost; represents the load restoration cost; represents the energy storage battery deployment cost.

6. An unmanned aerial vehicle-based emergency dispatch device based on the emergency dispatch method according to any one of claims 1 to 5, characterized in that, The application is applied to an electronic device, comprising: The first acquisition module is configured to acquire the coordinates of the power failure area, select the coordinates of the power failure area as the end point coordinates, and acquire each UAV path from the start point coordinates to the end point coordinates. The second acquisition module is configured to acquire the grid map, traverse each grid of the grid map, obtain the obstacle quantity of the neighborhood range of each grid in each UAV path and the total number of grids, and generate the danger degree of each grid in each UAV path according to the obstacle quantity of the neighborhood range of each grid in each UAV path, the total number of grids, and a preset danger degree generation model. The generation module is configured to generate the cost of each UAV path according to the danger degree of each grid in each UAV path, the flight distance of each flight section in each UAV path, and a preset cost generation model, select the UAV path with the minimum cost as the preferred UAV path, and acquire the UAV dispatching scheme corresponding to the preferred UAV path. The third acquisition module is configured to acquire the user loss cost, the load recovery cost, and the energy storage battery deployment cost of the UAV dispatching scheme. The dispatching module is configured to generate the total loss cost corresponding to the UAV dispatching scheme according to the user loss cost, the load recovery cost, the energy storage battery deployment cost, and a preset cost generation model, and select the UAV dispatching scheme as the emergency dispatching scheme of the power failure area when the total loss cost is less than a preset loss cost.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the emergency dispatching method of any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, the computer-readable storage medium comprising: The computer program is executed by the processor to implement the emergency dispatching method of any one of claims 1 to 5.

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