Post-disaster unmanned aerial vehicle base station deployment method, device and equipment and storage medium

Abstract

The invention provides a post-disaster unmanned aerial vehicle base station deployment method and device, equipment and a storage medium, and relates to the technical field of wireless communication. The method comprises the steps that based on a plurality of candidate hovering positions of an unmanned aerial vehicle base station in a to-be-deployed area, a target function is constructed with the minimum number of hovering positions of the unmanned aerial vehicle base station as a target, and constraint conditions comprise the coverage capability constraint and the data transmission capacity constraint of the unmanned aerial vehicle base station; under the constraint condition, a chaos moth flame capturing algorithm is adopted to solve the target function, the target hovering position of the unmanned aerial vehicle base station in the to-be-deployed area is obtained, the chaos moth flame capturing algorithm is used for executing a chaos optimization operator-based linear flame capturing mode under the condition that the number of iterations is smaller than the number of flames, and the target hovering position of the unmanned aerial vehicle base station in the to-be-deployed area is obtained. Under the condition that the number of iterations is larger than or equal to the number of flames, a spiral flame capturing mode is executed; and according to the target hovering position, unmanned aerial vehicle base station deployment is carried out, so that the deployment number of unmanned aerial vehicle base stations is smaller, and the signal coverage range is wider.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and in particular, to a method, device, equipment, and storage medium for deploying an unmanned aerial vehicle (UAV) base station after a disaster. Background Art

[0002] Under the influence of natural disasters, the restoration of the communication network plays an important supporting role in the restoration of the distribution network based on distribution automation. Wireless emergency communication plays an important role in the restoration technology of the communication network. It can lay wireless networks at fixed points according to the post-disaster reconstruction scenario, which can not only provide necessary communication needs for post-disaster rescue personnel, but also effectively ensure the remote unified command and management during the power grid emergency repair, and effectively improve the restoration speed of the distribution network emergency repair.

[0003] Currently, wireless emergency communication is usually achieved through ground emergency communication vehicles. Ground emergency communication vehicles are equipped with on-board energy storage devices and are not limited by battery energy, but are greatly affected by terrain and environment, and have a small signal coverage range. Compared with ground emergency communication vehicles, UAV base stations are less affected by terrain and environment, have a highly reliable line-of-sight link and the ability of flexible deployment. Therefore, there is an urgent need for an effective solution for deploying UAV base stations after a disaster. Summary of the Invention

[0004] This application provides a method, device, equipment, and storage medium for deploying an unmanned aerial vehicle (UAV) base station after a disaster, so as to provide an effective solution for deploying UAV base stations after a disaster.

[0005] In a first aspect, this application provides a method for deploying an unmanned aerial vehicle (UAV) base station after a disaster, including:

[0006] Obtaining a plurality of candidate hovering positions of the UAV base station in the area to be deployed, where the area to be deployed is determined according to the geographical location information of the power transmission lines of the distribution network;

[0007] Based on the plurality of candidate hovering positions, constructing an objective function with the goal of minimizing the number of hovering positions of the UAV base station, where the constraint conditions of the objective function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station;

[0008] Under the constraint conditions, using the chaotic moth-flame algorithm to solve the objective function to obtain the target hovering position of the UAV base station in the area to be deployed, where the chaotic moth-flame algorithm is used to execute a straight-line flame-catching mode based on a chaotic optimization operator when the number of iterations is less than the number of flames, and execute a spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames;

[0009] Deploying the UAV base station according to the target hovering position.

[0010] Optionally, under constraints, the chaotic moth-flame algorithm is used to solve the objective function to obtain the target hovering position of the UAV base station in the area to be deployed, including: initializing the moth population according to multiple candidate hovering positions; obtaining the fitness value of each moth in the moth population; if the number of iterations is less than the number of flames, based on the fitness value, iteratively execute the linear flame-catching mode based on the chaotic optimization operator of the chaotic moth-flame algorithm to obtain the updated moth positions, and the number of flames adaptively decreases with the number of iterations; if the number of iterations is greater than or equal to the number of flames, based on the updated moth positions, iteratively execute the spiral flame-catching mode of the chaotic moth-flame algorithm until the total number of iterations is reached, and obtain the target hovering position of the UAV base station in the area to be deployed.

[0011] Optionally, execute the linear flame-catching mode based on the chaotic optimization operator of the chaotic moth-flame algorithm to obtain the updated moth positions, including: adopting a multi-elite position combination strategy, randomly selecting a preset number of elite moths to perform chaotic linear combination with other moths, and obtaining the updated moth positions through the following first formula:

[0012]

[0013] where ρ represents the preset number; M j represents the current position of the elite moth; M i represents the position of other moths; N represents the total number of iterations; r n represents the chaotic optimization operator.

[0014] Optionally, the spiral flame-catching mode satisfies the following second formula:

[0015] S(M i , F i ) = D i ·e bt cos(2πt) + F i Second formula

[0016] where S(M i , F i ) represents the updated position function; b represents the logarithmic spiral constant; t is a random number within [-1, 1]; D i represents the distance between the i-th moth and the optimal flame F i ;

[0017] Optionally, after executing the linear flame-catching mode or the spiral flame-catching mode, obtain the fitness value of each moth, sort the moths according to the fitness value to obtain the sorted moths, and use the sorted moths as the data for the next iteration.

[0018] Optionally, the coverage capacity constraint is used to determine that at least one automated switch terminal of the medium-voltage feeder in the distribution network is covered by a drone base station; the data transmission capacity constraint is determined according to the number of automated switch terminals that the drone base station can accommodate.

[0019] In a second aspect, the present application provides a device for deploying a drone base station after a disaster, including:

[0020] An acquisition module, configured to acquire a plurality of candidate hovering positions of the drone base station in the area to be deployed, where the area to be deployed is determined according to the geographical location information of the transmission line of the distribution network;

[0021] A construction module, configured to construct an objective function based on the plurality of candidate hovering positions with the goal of minimizing the number of hovering positions of the drone base station, and the constraint conditions of the objective function include the coverage capacity constraint and the data transmission capacity constraint of the drone base station;

[0022] A processing module, configured to solve the objective function by using the chaotic moth-flame algorithm under the constraint conditions to obtain the target hovering position of the drone base station in the area to be deployed, where the chaotic moth-flame algorithm is used to execute a straight-line flame-catching mode based on a chaotic optimization operator when the number of iterations is less than the number of flames, and execute a spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames;

[0023] A deployment module, configured to deploy the drone base station according to the target hovering position.

[0024] Optionally, the processing module is specifically configured to: initialize the moth population according to the plurality of candidate hovering positions; obtain the fitness value of each moth in the moth population; if the number of iterations is less than the number of flames, then based on the fitness value, iteratively execute the straight-line flame-catching mode of the chaotic moth-flame algorithm based on the chaotic optimization operator to obtain the updated position of the moth, and the number of flames decreases adaptively with the number of iterations; if the number of iterations is greater than or equal to the number of flames, then based on the updated position of the moth, iteratively execute the spiral flame-catching mode of the chaotic moth-flame algorithm until the total number of iterations is reached, and obtain the target hovering position of the drone base station in the area to be deployed.

[0025] Optionally, when the processing module is used to execute the straight-line flame-catching mode of the chaotic moth-flame algorithm based on the chaotic optimization operator to obtain the updated position of the moth, it is specifically configured to: adopt a multi-elite position combination strategy, randomly select a preset number of elite moths to perform a chaotic linear combination with other moths, and obtain the updated position of the moth through the following first formula:

[0026]

[0027] where ρ represents the preset number; M j represents the current position of the elite moth; Mi represents the positions of other moths; N represents the total number of iterations; r n represents the chaotic optimization operator.

[0028] Optionally, the spiral flame-catching mode satisfies the following second formula:

[0029] S(M i ,F i ) = D i ·e bt cos(2πt) + F i Second formula

[0030] where S(M i ,F i ) represents the updated position function; b represents the logarithmic spiral constant; t is a random number within [-1, 1]; D i represents the distance between the i-th moth and the optimal flame F i therebetween.

[0031] Optionally, after executing the linear flame-catching mode or the spiral flame-catching mode, the fitness value of each moth is obtained, the moths are sorted according to the fitness value to obtain the sorted moths, and the sorted moths are used as the data for the next iteration.

[0032] Optionally, the coverage ability constraint is used to determine that the automation switch terminals of the medium-voltage feeders in the distribution network are covered by at least one UAV base station; the data transmission capacity constraint is determined according to the number of automation switch terminals that the UAV base station can accommodate.

[0033] In a third aspect, the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0034] The memory stores computer-executable instructions;

[0035] The processor executes the computer-executable instructions stored in the memory to implement the post-disaster UAV base station deployment method as described in the first aspect of the present application.

[0036] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer program instructions are stored, and when the computer program instructions are executed, the post-disaster UAV base station deployment method as described in the first aspect of the present application is implemented.

[0037] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed, the post-disaster UAV base station deployment method as described in the first aspect of the present application is implemented.

[0038] A method, device, equipment and storage medium for deploying an unmanned aerial vehicle (UAV) base station after a disaster provided by this application. By obtaining multiple candidate hovering positions of the UAV base station in the area to be deployed, where the area to be deployed is determined according to the geographical location information of the power distribution network transmission line; based on the multiple candidate hovering positions, a target function is constructed with the goal of minimizing the number of hovering positions of the UAV base station, and the constraint conditions of the target function include the coverage ability constraint and data transmission capacity constraint of the UAV base station; under the constraint conditions, a chaotic moth-flame algorithm is used to solve the target function to obtain the target hovering position of the UAV base station in the area to be deployed, where the chaotic moth-flame algorithm is used to execute a straight flame-catching mode based on a chaotic optimization operator when the number of iterations is less than the number of flames, and execute a spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames; according to the target hovering position, the UAV base station is deployed. The chaotic moth-flame algorithm of this application is based on the moth-flame algorithm and adds a new strategy based on the chaotic optimization operator, which can give full play to the ergodicity and randomness of the chaotic optimization operator and effectively improve the global search ability; based on the change of the number of flames, it can take into account the advantages played by the two flame-catching modes of the straight flame-catching mode and the spiral flame-catching mode during the optimization process, effectively avoid the discreteness of the UAV base station from falling into the local optimum, and can accelerate the convergence speed of the chaotic moth-flame algorithm, solve the target function to obtain the target hovering position of the UAV base station within the global optimal neighborhood, and the obtained target hovering position can make the number of deployed UAV base stations less and the signal coverage range wider. Description of the Drawings

[0039] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0040] Figure 1 It is a flowchart of the method for deploying an unmanned aerial vehicle base station after a disaster provided by an embodiment of this application;

[0041] Figure 2 It is a flowchart of the method for deploying an unmanned aerial vehicle base station after a disaster provided by another embodiment of this application;

[0042] Figure 3 It is a schematic structural diagram of the device for deploying an unmanned aerial vehicle base station after a disaster provided by an embodiment of this application;

[0043] Figure 4 It is a schematic structural diagram of the electronic equipment provided by an embodiment of this application.

[0044] Through the above-mentioned accompanying drawings, the clear embodiments of this application have been shown, and there will be more detailed descriptions later. These drawings and text descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0045] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0046] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties. And the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards, and corresponding operation entrances are provided for users to choose to authorize or refuse.

[0047] In recent years, climate changes have been erratic in various places, leading to frequent natural disasters, seriously affecting the safe operation of the power system and causing huge economic losses. For example, in a certain place, there are heavy rains and local extremely heavy rains. The continuous heavy rainfall has caused serious disasters in multiple local areas, with danger situations such as landslides occurring. The power supply line equipment in low-lying areas is severely waterlogged, affecting the normal power consumption of hundreds of thousands of residential users. Under the influence of natural disasters, the restoration of the communication network plays an important supporting role in the restoration of the distribution network based on distribution automation. For example, preliminary surveys, power restoration arrangements for lines, construction coordination and information aggregation, etc. all rely on the communication network. And wireless emergency communication plays an important role in the communication network restoration technology. It can lay wireless networks at fixed points according to the post-disaster reconstruction scenarios, which can not only provide necessary communication needs for post-disaster rescue personnel, but also effectively ensure the remote unified command and management during the power grid repair process, and effectively improve the restoration speed of the distribution network repair.

[0048] Currently, wireless emergency communication is usually achieved through ground emergency communication vehicles. The ground emergency communication vehicle is equipped with a built-in energy storage device and is not limited by battery energy, but is greatly affected by terrain and environment, and has a small signal coverage area. Compared with the ground emergency communication vehicle, a drone base station (a drone equipped with a relay module for providing mobile communication services) is less affected by terrain and environment and has a highly reliable line-of-sight link and flexible deployment capabilities. Therefore, there is an urgent need for an effective solution for deploying drone base stations after disasters to better apply drone base stations to the repair of the post-disaster distribution network.

[0049] Based on the above problems, considering that the construction of optical cables and cables in mountainous distribution networks and rural areas for post-disaster emergency repair involves high costs, and considering the characteristics of distribution terminals such as large quantity, wide distribution, and complex environment, in the existing medium-voltage feeders of distribution networks, through automated switch terminals control, automatic isolation, and self-healing systems, wireless private networks are basically used for data transmission to ensure high reliability, economy, and flexibility requirements. This application provides a method, device, equipment, and storage medium for deploying post-disaster UAV base stations. Based on the Moth-Flame Optimization (MFO) algorithm, combined with a new strategy based on a chaotic optimization operator, a Chaotic Moth-Flame Optimization algorithm is obtained; a target function is constructed with the goal of minimizing the number of hovering positions of UAV base stations, and the target function is solved through the Chaotic Moth-Flame Optimization algorithm to obtain the target hovering positions of UAV base stations in the area to be deployed. The obtained target hovering positions can make the number of deployed UAV base stations smaller, with a wider signal coverage range, and can effectively improve the global search performance and convergence speed; furthermore, UAV base stations can be deployed according to the target hovering positions.

[0050] The deployed UAV base stations can be utilized to cooperate with the rapid restoration of emergency communication at the post-disaster distribution network level. The implementation of post-disaster emergency repair work gives the loads downstream of the faulty line the opportunity to resume. Using the UAV base station as an emergency communication medium can make the automated switch terminals of the medium-voltage feeders in the distribution network controllable. By operating the automated switch terminals, the self-healing strategy can be quickly executed to restore the loads downstream of the power source; at the same time, the restoration of power in the non-faulty sections provides power services to the communication nodes downstream of the faulty section, enabling the automated switch terminals due to backup power reasons to resume power supply, thereby better optimizing the power flow direction and enabling the limited power generation resources after the disaster to more effectively supply critical loads.

[0051] It can be understood that this application studies the problem of site selection for the deployment points (i.e., optimal hovering positions) of UAV base stations based on the post-disaster recovery plan, from aspects such as the coverage ability of UAV base stations and the optimal site selection of the working points (hovering positions) of UAV base stations. The method for deploying post-disaster UAV base stations provided in this application requires premise assumptions and restrictions on the application scenario. The specific content of the premise assumptions is as follows:

[0052] Assume that the endurance of the UAV base station is not restricted;

[0053] Assume that the power consumption required for communication is not considered, and only the power consumption in the two working conditions of hovering and patrolling is considered, and the power consumption values in the two states are the same;

[0054] Under the condition that the data transmission capacity of the UAV base station is certain, assume that the data transmission rate of each automated switch terminal in the medium-voltage feeder of the distribution network is the same;

[0055] The main objective of this application is power load restoration. It only requires that the automated switch terminals can be covered by the UAV base stations and communication can be restored, without considering communication problems of other factors.

[0056] It is assumed that before the medium-voltage distribution network is restored, the current state of the distribution network system has been obtained through on-site investigation and dispatching system information.

[0057] To reduce interference between UAV base stations, it can be assumed that each UAV base station has an antenna, and the elevation angle between it and the horizontal plane is θ, and θ satisfies the following formula (1):

[0058]

[0059] where h represents the hovering height of the UAV base station; r represents the maximum communication coverage radius of the UAV base station; when the UAV base station is at a certain hovering height, the maximum communication coverage radius is a fixed value, and communication nodes outside this coverage range are not interfered by the communication of this UAV base station.

[0060] This application is based on the fact that the hovering elevations of all UAV base stations are the same and fixed, and all automated switch terminals of medium-voltage feeders within the coverage range of the UAV base stations can communicate with the UAV base stations.

[0061] In addition, the data transmission capacity problem and data overload problem of the UAV base stations need to be considered. The throughput of the status data of all automated switch terminals within the coverage range of the UAV base stations does not exceed the data transmission capacity of the UAV base stations.

[0062] It should be noted that the post-disaster UAV base station deployment method provided in the embodiments of this application can be applied in a server, and this server can be an independent server, or can also be a service cluster, etc.

[0063] The following uses specific embodiments to describe in detail the technical solution of this application and how the technical solution of this application solves the above technical problems. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will be described below with reference to the drawings.

[0064] Figure 1 It is a flowchart of the post-disaster UAV base station deployment method provided in an embodiment of this application. As Figure 1 shown, the post-disaster UAV base station deployment method of the embodiment of this application includes:

[0065] S101. Obtain multiple candidate hovering positions of the UAV base stations in the area to be deployed, and the area to be deployed is determined according to the geographical location information of the distribution network transmission lines.

[0066] In the embodiments of the present application, the geographical location information of the power distribution network transmission line can be understood as the geographical location information of the line navigation. The geographical location information is, for example, the longitude and latitude of the transmission line. The location information of the disaster area can be combined, and the area to be deployed for the UAV base station can be determined according to the geographical location information of the power distribution network transmission line. Working points are evenly distributed in the area to be deployed. The drone pilot manually operates the drone in the area to be deployed (the drone can be equipped with a relay module or not), then visits each working point one by one, obtains the geographical location information of the working point, and finally controls the drone to return, so as to obtain multiple candidate hovering positions corresponding to each working point in the area to be deployed for the UAV base station, that is, obtain the geographical location information of the candidate hovering positions. The multiple candidate hovering positions form a position set. The method for obtaining the multiple candidate hovering positions can be understood as a point-based discretization method, that is, evenly distributed working points are inserted in the area to be deployed, and these working points are used as the candidate hovering positions of the UAV base station.

[0067] S102. Based on the multiple candidate hovering positions, a target function is constructed with the goal of minimizing the number of hovering positions of the UAV base station. The constraint conditions of the target function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station.

[0068] It can be understood that the purpose of the embodiments of the present application is to construct a target function with the minimum number of hovering positions of the UAV base station when all communication points (such as the automation switch terminals of the medium-voltage feeders in the power distribution network) are covered, and iteratively solve the target function to obtain the optimal target hovering position of the UAV base station. In this step, based on the multiple candidate hovering positions, a target function can be constructed with the goal of minimizing the number of hovering positions of the UAV base station. The constraint conditions of the target function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station. The specific target function can be referred to the subsequent embodiments. The coverage ability constraint is used to ensure that at least one UAV base station covers the automation switch terminals of the medium-voltage feeders in the power distribution network; the data transmission capacity constraint is used for the data throughput of all automation switch terminals within the coverage range of the UAV base station not to exceed the data transmission capacity of the UAV base station. The specific coverage ability constraint and data transmission capacity constraint can be referred to the subsequent embodiments.

[0069] S103. Under the constraint conditions, the chaotic moth-flame algorithm is used to solve the target function to obtain the target hovering position of the UAV base station in the area to be deployed, where the chaotic moth-flame algorithm is used to execute the straight-line flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and execute the spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames.

[0070] In this step, the coverage target of the UAV is the intersection of multiple automated switch terminal communication nodes in the power system. It is necessary to use the constraint conditions as boundary conditions to find the UAV positioning with the fewest configurations and the widest coverage. Exemplarily, under the constraint conditions, the chaotic moth-flame algorithm is used to solve the objective function. Among them, when the number of iterations is less than the number of flames, the straight-line flame-catching mode based on the chaotic optimization operator is executed. As the number of iterations increases, the number of flames adaptively decreases with the number of iterations. When the number of iterations is greater than or equal to the number of flames, the spiral flame-catching mode is executed. As the number of iterations increases, when the number of iterations is greater than or equal to the total number of iterations, the target hovering position of the UAV base station in the area to be deployed is obtained, that is, the optimal solution of the objective function is obtained. The obtained target hovering position can make the deployment quantity of the UAV base station less and the signal coverage range wider. For how to specifically use the chaotic moth-flame algorithm to solve the objective function under the constraint conditions and obtain the target hovering position of the UAV base station in the area to be deployed, reference can be made to the subsequent embodiments, which will not be elaborated here.

[0071] Optionally, the optimal path of the UAV base station along the line flight direction can also be obtained according to the target hovering position of the UAV base station in the area to be deployed.

[0072] S104. Deploy the UAV base station according to the target hovering position.

[0073] In this step, after obtaining the target hovering position of the UAV base station in the area to be deployed, the UAV base station can be deployed according to the target hovering position. Exemplarily, the UAV base station can be automatically controlled according to the target hovering position to deploy the UAV base station to the target hovering position.

[0074] The post-disaster UAV base station deployment method provided by the embodiments of the present application obtains multiple candidate hovering positions of the UAV base station in the area to be deployed, and the area to be deployed is determined according to the geographical location information of the power distribution network transmission line; based on the multiple candidate hovering positions, a target function is constructed with the goal of minimizing the number of hovering positions of the UAV base station, and the constraint conditions of the target function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station; under the constraint conditions, the chaotic moth-flame algorithm is used to solve the target function to obtain the target hovering position of the UAV base station in the area to be deployed, where the chaotic moth-flame algorithm is used to execute the straight-line flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and execute the spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames; according to the target hovering position, the UAV base station is deployed. The chaotic moth-flame algorithm of the embodiments of the present application is based on the moth-flame algorithm and adds a new strategy based on the chaotic optimization operator, which can give full play to the ergodicity and randomness of the chaotic optimization operator and effectively improve the global search ability; based on the change of the number of flames, the advantages of both the straight-line flame-catching mode and the spiral flame-catching mode in the optimization process can be taken into account, effectively avoiding the discreteness of the UAV base station from falling into the local optimum, and being able to accelerate the convergence speed of the chaotic moth-flame algorithm, solve the target function to obtain the target hovering position of the UAV base station within the global optimum neighborhood, and the obtained target hovering position can make the number of deployed UAV base stations less and the signal coverage range wider.

[0075] Figure 2 It is a flowchart of the post-disaster UAV base station deployment method provided by another embodiment of the present application. On the basis of the above embodiments, the embodiments of the present application further illustrate the post-disaster UAV base station deployment method. As Figure 2 shown, the post-disaster UAV base station deployment method of the embodiments of the present application may include:

[0076] S201. Obtain multiple candidate hovering positions of the UAV base station in the area to be deployed, where the area to be deployed is determined according to the geographical location information of the power distribution network transmission line.

[0077] For the specific description of this step, reference can be made to Figure 1 the relevant description of S101 in the embodiment shown, which will not be elaborated here.

[0078] S202. Based on the multiple candidate hovering positions, construct a target function with the goal of minimizing the number of hovering positions of the UAV base station, and the constraint conditions of the target function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station.

[0079] For the specific description of this step, reference can be made to Figure 1 the relevant description of S102 in the embodiment shown. Optionally, the target function satisfies the following formula two:

[0080]

[0081] Among them, K represents the total number of candidate hovering positions; P k is a variable from 0 to 1. If the k-th candidate hovering position is selected as the target hovering position, then P k takes the value of 1. If the k-th candidate hovering position is not selected as the target hovering position, then P k takes the value of 0. P k is defaulted to 0; the above formula two represents minimizing the number of hovering positions of the UAV base station.

[0082] The constraint conditions of the objective function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station. Optionally, the coverage ability constraint is used to determine that at least one UAV base station covers the automatic switch terminal of the medium-voltage feeder in the distribution network; the data transmission capacity constraint is determined according to the number of automatic switch terminals that the UAV base station can accommodate.

[0083] Exemplarily, the coverage target of the UAV base station is the intersection of multiple automatic switch terminals in the power system. It is necessary to use the coverage ability and data transmission capacity of the UAV base station for emergency communication as boundary conditions to find the target hovering positions of the UAV base station with the least configuration and the widest coverage. The coverage ability of the UAV base station can be quantified by the above formula one. When the UAV base station is at a certain hovering height, the maximum communication coverage radius is a fixed value, and the communication nodes outside this coverage range are not interfered by the communication of this UAV base station. The coverage ability constraint of the UAV base station satisfies the following formula three:

[0084]

[0085] Among them, S ftu represents the set of automatic switch terminals; S uav represents the set of UAV base stations as discrete points; x c,k represents the coverage situation of the automatic switch terminal (which can also be called a communication node) c of the medium-voltage feeder in the distribution network by the UAV base station at the k-th candidate hovering position. If it is covered, the value is 1; if not, the value is 0.

[0086] In addition, the relationship between the distance between the automatic switch terminal and the discrete point of the UAV base station and the coverage radius of the UAV base station can also be obtained through the following formula four:

[0087]

[0088] Among them, (x c , y c ) and (x k , y k) represent the position coordinates of the automated switch terminal c and the discrete points of the drone base station respectively; r is the coverage radius of the drone base station; d k,c represents the relationship between the distance between the automated switch terminal and the discrete points of the drone base station and the coverage radius of the drone base station.

[0089] The data transmission capacity constraint of the drone base station satisfies the following formula five:

[0090]

[0091] Among them, C represents the total number of automated switch terminals that the drone base station can accommodate; N represents the total number of iterations of the chaotic moth-flame algorithm; the above formula five indicates that the number of automated switch terminals that the drone base station can accommodate is limited. When there are many and relatively dense automated switch terminals in the distribution network, this constraint needs to be considered.

[0092] It can be understood that the above formula two, formula three and formula five constitute an integer linear programming model for the location selection of the working point (hovering position) of the drone base station.

[0093] In the embodiments of the present application, Figure 1 Step S103 in it can further include the following steps S203 to S211:

[0094] S203. Initialize the moth population according to multiple candidate hovering positions.

[0095] In this step, after obtaining multiple candidate hovering positions of the drone base station in the area to be deployed, the moth population can be initialized according to the multiple candidate hovering positions. Exemplarily, assuming that 20 candidate hovering positions of the drone base station in the area to be deployed are obtained, the moth population x(i, :) can be initialized according to the 20 candidate hovering positions. x(i, :) is a matrix with 20 rows and 2 columns, and each row of the matrix corresponds to the longitude and latitude of each candidate hovering position. Other parameters can also be set, including the total number of iterations N of the chaotic moth-flame algorithm being 20, the population number m of the chaotic moth-flame algorithm being 20, and the variable dimension D being 2, etc.

[0096] S204. Obtain the fitness value of each moth in the moth population.

[0097] In this step, the corresponding fitness value can be obtained according to the position of each moth in the moth population.

[0098] S205. Determine whether the iteration number n is less than the number of flames f.

[0099] If the iteration number n is less than the number of flames f, step S206 is executed; if the iteration number n is greater than or equal to the number of flames f, step S211 is executed.

[0100] S206. Based on the fitness value of the moths, execute the straight-line flame-catching mode of the chaotic moth flame-catching algorithm based on the chaotic optimization operator to obtain the updated positions of the moths.

[0101] In this step, when the number of iterations n is less than the number of flames f, the straight-line flame-catching mode can be executed to obtain the updated positions of the moths.

[0102] Optionally, executing the straight-line flame-catching mode of the chaotic moth flame-catching algorithm based on the chaotic optimization operator to obtain the updated positions of the moths may include: adopting a multi-elite position combination strategy, randomly selecting a preset number of elite moths to perform chaotic linear combination with other moths, and obtaining the updated positions of the moths through the following formula six (i.e., the first formula):

[0103]

[0104] where ρ represents the preset number; M j represents the current position of the elite moths; M i represents the positions of other moths; N represents the total number of iterations; r n represents the chaotic optimization operator.

[0105] Exemplarily, the preset number is, for example, 3. After initializing the moth population, the search of the initialized moth individuals is relatively independent and there is not much information exchange. Based on the chaotic optimization operator, a multi-elite position combination strategy can be adopted to select the first 3 elite moths to perform chaotic linear combination with other moths, and the updated positions of the moths can be obtained through the above formula six. Among them, the specific expression of the chaotic optimization operator r n is: r n = 4r 3 n-1 - 3r n-1 .

[0106] S207. Obtain the fitness value of each moth, sort the moths according to the fitness value to obtain the sorted moths.

[0107] In this step, after executing the straight-line flame-catching mode, obtain the fitness value of each moth, sort the moths according to the fitness value to obtain the sorted moths, and use the sorted moths as the data for the next iteration.

[0108] S208. The number of flames decreases adaptively with the number of iterations, and the number of iterations n = n + 1.

[0109] It can be understood that at the beginning of the chaotic moth flame-catching algorithm, each moth corresponds to its own optimal solution and flame. As the number of iterations increases, the number of flames is adaptively adjusted, enabling the moths to gradually converge to the global optimum. Among them, the rule of flame adaptive adjustment is the following formula seven:

[0110]

[0111] Among them, n is the current iteration number; N represents that there are N flames in the initial iteration step, that is, the initial number of flames; flame represents the number of flames after adaptive adjustment.

[0112] S209. Determine whether the iteration number n is greater than or equal to the total number of iterations N.

[0113] If the iteration number n is greater than or equal to the total number of iterations N, then execute S210. Obtain the target hovering position of the UAV base station in the area to be deployed, and continue to execute step S212; if the iteration number n is less than the total number of iterations N, then continue to execute step S205.

[0114] S211. Based on the current position of the moth, execute the spiral flame-catching mode of the chaotic moth flame-catching algorithm to obtain the updated position of the moth, and continue to execute step S207.

[0115] It can be understood that as the number of iterations increases, the flame-catching mode of the chaotic moth flame-catching algorithm turns into a spiral flame-catching mode. During the search process, the moth uses the phototaxis characteristic to sense the relatively optimal flame near itself and captures the flame around the logarithmic spiral.

[0116] Optionally, the spiral flame-catching mode satisfies the following formula eight (i.e., the second formula):

[0117] S(M i ,F i )=D i ·e bt cos(2πt)+F i Formula eight

[0118] Among them, S(M i ,F i ) represents the updated position function; b represents the logarithmic spiral constant; t is a random number within [-1, 1]; D i represents the distance between the i-th moth M i and the optimal flame F i , and D i can be expressed as: D i =|M i -F i |.

[0119] S212. Deploy the UAV base station according to the target hovering position.

[0120] For the specific description of this step, reference can be made to Figure 1 the relevant description of S104 in the illustrated embodiment, which will not be elaborated here.

[0121] The post-disaster UAV base station deployment method provided by the embodiment of the present application obtains multiple candidate hovering positions of the UAV base station in the area to be deployed, and the area to be deployed is determined according to the geographical location information of the power distribution network transmission line; based on the multiple candidate hovering positions, a target function is constructed with the goal of minimizing the number of hovering positions of the UAV base station, and the constraint conditions of the target function include the coverage ability constraint and the data transmission capacity constraint of the UAV base station; according to the multiple candidate hovering positions, the moth population is initialized; the fitness value of each moth in the moth population is obtained; if the number of iterations is less than the number of flames, based on the fitness value of the moth, the straight flame-catching mode based on the chaotic optimization operator of the chaotic moth flame-catching algorithm is executed to obtain the updated position of the moth; if the number of iterations is greater than or equal to the number of flames, based on the current position of the moth, the spiral flame-catching mode of the chaotic moth flame-catching algorithm is executed to obtain the updated position of the moth; each time the flame-catching is completed, the number of flames decreases adaptively with the number of iterations, the fitness value of each moth is obtained, the moths are sorted according to the fitness value to obtain the sorted moths, and the sorted moths are used as the data for the next iteration; when the number of iterations is greater than or equal to the total number of iterations, the target hovering position of the UAV base station in the area to be deployed is obtained, and the UAV base station is deployed according to the target hovering position. The chaotic moth flame-catching algorithm of the embodiment of the present application is based on the moth flame-catching algorithm and adds a new strategy based on the chaotic optimization operator, which can give full play to the ergodicity and randomness of the chaotic optimization operator and effectively improve the global search ability; through the adaptive adjustment measure of the number of flames, the advantages of the straight flame-catching mode and the spiral flame-catching mode in the optimization process can be taken into account, effectively avoiding the discreteness of the UAV base station from falling into the local optimum, and being able to accelerate the convergence speed of the chaotic moth flame-catching algorithm, solving the target function to obtain the target hovering position of the UAV base station in the global optimal neighborhood, and the obtained target hovering position can make the deployment quantity of the UAV base station less and the signal coverage range wider.

[0122] The following is an embodiment of the device of the present application, which can be used to execute the embodiment of the method of the present application. For the details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method of the present application.

[0123] Figure 3 The structure diagram of the post-disaster UAV base station deployment device provided by an embodiment of the present application is as Figure 3 shown. The post-disaster UAV base station deployment device 300 of the embodiment of the present application includes: an acquisition module 301, a construction module 302, a processing module 303, and a deployment module 304. Among them:

[0124] The acquisition module 301 is configured to acquire multiple candidate hovering positions of the UAV base station in the area to be deployed, and the area to be deployed is determined according to the geographical location information of the power distribution network transmission line.

[0125] The building module 302 is configured to construct an objective function based on multiple candidate hovering positions with the goal of minimizing the number of hovering positions of the unmanned aerial vehicle (UAV) base stations. The constraint conditions of the objective function include the coverage ability constraint and the data transmission capacity constraint of the UAV base stations.

[0126] The processing module 303 is configured to solve the objective function by using the chaotic moth-flame algorithm under the constraint conditions to obtain the target hovering positions of the UAV base stations in the area to be deployed. Among them, the chaotic moth-flame algorithm is used to execute the straight-line flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and execute the spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames.

[0127] The deployment module 304 is configured to deploy the UAV base stations according to the target hovering positions.

[0128] In some embodiments, the processing module 303 may be specifically configured to: initialize the moth population according to multiple candidate hovering positions; obtain the fitness value of each moth in the moth population; if the number of iterations is less than the number of flames, then based on the fitness value, iteratively execute the straight-line flame-catching mode of the chaotic moth-flame algorithm based on the chaotic optimization operator to obtain the updated moth positions, and the number of flames decreases adaptively with the number of iterations; if the number of iterations is greater than or equal to the number of flames, then based on the updated moth positions, iteratively execute the spiral flame-catching mode of the chaotic moth-flame algorithm until the total number of iterations is reached, and obtain the target hovering positions of the UAV base stations in the area to be deployed.

[0129] Optionally, when the processing module 303 is used to execute the straight-line flame-catching mode of the chaotic moth-flame algorithm based on the chaotic optimization operator to obtain the updated moth positions, it may be specifically configured to: adopt a multi-elite position combination strategy, randomly select a preset number of elite moths to perform chaotic linear combination with other moths, and obtain the updated moth positions through the following first formula:

[0130]

[0131] where ρ represents the preset number; M j represents the current position of the elite moth; M i represents the positions of other moths; N represents the total number of iterations; r n represents the chaotic optimization operator.

[0132] Optionally, the spiral flame-catching mode satisfies the following second formula:

[0133] S(M i ,F i )=D i ·e bt cos(2πt)+F iSecond formula

[0134] Wherein, S(M i , F i ) represents the updated position function; b represents the logarithmic spiral constant; t is a random number within [-1, 1]; D i represents the distance between the i-th moth and the optimal flame F i .

[0135] Optionally, after executing the straight-line flame-catching mode or the spiral flame-catching mode, obtain the fitness value of each moth, sort the moths according to the fitness value to obtain the sorted moths, and use the sorted moths as the data for the next iteration.

[0136] Optionally, the coverage ability constraint is used to determine that the automation switch terminal of the medium-voltage feeder in the distribution network is covered by at least one UAV base station; the data transmission capacity constraint is determined according to the number of automation switch terminals that the UAV base station can accommodate.

[0137] The device of the embodiment of the present application can be used to execute the technical solutions of any of the above-mentioned method embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.

[0138] Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 4 shown, the electronic device 400 may include: at least one processor 401 and a memory 402.

[0139] The memory 402 is used to store a program. Specifically, the program may include program code, and the program code includes computer execution instructions.

[0140] The memory 402 may include a high-speed random access memory (Random Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0141] The processor 401 is used to execute the computer execution instructions stored in the memory 402 to implement the post-disaster UAV base station deployment method described in the foregoing method embodiments. Among them, the processor 401 may be a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. Specifically, when implementing the post-disaster UAV base station deployment method described in the foregoing method embodiments, the electronic device may be an electronic device with processing functions such as a server.

[0142] Optionally, the electronic device 400 may further include a communication interface 403. In a specific implementation, if the communication interface 403, the memory 402, and the processor 401 are implemented independently, the communication interface 403, the memory 402, and the processor 401 may be interconnected through a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc., but it does not mean that there is only one bus or one type of bus.

[0143] Optionally, in a specific implementation, if the communication interface 403, the memory 402, and the processor 401 are integrated on a chip, the communication interface 403, the memory 402, and the processor 401 may communicate through an internal interface.

[0144] This application also provides a computer-readable storage medium, in which computer program instructions are stored. When the processor executes the computer program instructions, the solution of the above post-disaster UAV base station deployment method is implemented.

[0145] This application also provides a computer program product, including a computer program, which implements the solution of the above post-disaster UAV base station deployment method when executed.

[0146] The above computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read Only Memory (EEPROM), an Erasable Programmable Read Only Memory (EPROM), a Programmable Read Only Memory (PROM), a Read Only Memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disc. The readable storage medium may be any available medium accessible by a general-purpose or special-purpose computer.

[0147] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit. Of course, the processor and the readable storage medium can also exist as discrete components in the post-disaster UAV base station deployment device.

[0148] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: various media such as ROM, RAM, magnetic disk, or optical disc that can store program codes.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A post-disaster UAV base station deployment method, characterized in that: include: Acquire multiple candidate hovering positions of the UAV base station in the area to be deployed, where the area to be deployed is determined based on geographic location information of the power distribution network transmission line; Based on the multiple candidate hovering positions, construct an objective function with the minimum number of hovering positions of the drone base station as the goal, and the constraint conditions of the objective function include the coverage capability constraint and the data transmission capacity constraint of the drone base station; Under the constraint conditions, a chaotic moth-to-flame algorithm is used to solve the objective function to obtain a target hovering position of the UAV base station in the area to be deployed, wherein the chaotic moth-to-flame algorithm is used to execute a linear flame-catching mode based on a chaotic optimization operator when the number of iterations is less than the number of flames, and to execute a spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames; The drone base station is deployed according to the target hovering position.

2. The post-disaster UAV base station deployment method according to claim 1, characterized in that: Under the constraints, the objective function is solved by using a chaotic moth-catching flame algorithm to obtain a target hovering position of the UAV base station in the area to be deployed, including: Initializing a moth population according to the plurality of candidate hovering positions; Obtaining the fitness value of each moth in the moth population; If the number of iterations is less than the number of flames, then based on the fitness value, the chaotic moth flame catching algorithm is iteratively executed based on the linear flame catching mode of the chaotic optimization operator to obtain an updated moth position, and the number of flames is adaptively reduced with the number of iterations; If the number of iterations is greater than or equal to the number of flames, the spiral flame catching mode of the chaotic moth flame catching algorithm is iteratively executed based on the updated moth position until the total number of iterations is reached to obtain the target hovering position of the UAV base station in the area to be deployed.

3. The post-disaster UAV base station deployment method according to claim 2, characterized in that: The method of executing the chaotic moth flame catching algorithm based on the linear flame catching mode of the chaotic optimization operator to obtain the updated moth position includes: A multi-elite position combination strategy is adopted to randomly select a preset number of elite moths and perform chaotic linear combination with other moths, and the updated moth position is obtained by the following first formula: Wherein, ρ represents the preset number; M j Indicates the current position of the elite moth; M i represents the positions of other moths; N represents the total number of iterations; r n Represents the chaos optimization operator.

4. The post-disaster UAV base station deployment method according to claim 2, characterized in that: The spiral flame capture mode satisfies the following second formula: S(M i ,F i )=D i ·e bt cos(2πt)+F i Second formula Among them, S(M i ,F i ) represents the updated position function; b represents the logarithmic spiral constant; t is a random number in [-1,1]; D i represents the relationship between the i-th moth and the optimal flame F i The distance between.

5. The post-disaster UAV base station deployment method according to claim 2, characterized in that: After executing the linear flame catching mode or the spiral flame catching mode, the fitness value of each moth is obtained, the moths are sorted according to the fitness value to obtain sorted moths, and the sorted moths are used as data for the next iteration.

6. The post-disaster UAV base station deployment method according to any one of claims 1 to 5, characterized in that: The coverage capability constraint is used to determine that the automated switch terminal of the medium-voltage feeder of the distribution network is covered by at least one drone base station; the data transmission capacity constraint is determined based on the number of automated switch terminals that the drone base station can accommodate.

7. A post-disaster drone base station deployment device, characterized in that: include: An acquisition module is used to acquire a plurality of candidate hovering positions of the UAV base station in the area to be deployed, where the area to be deployed is determined based on the geographical location information of the power transmission line of the distribution network; A construction module is used to construct an objective function based on the multiple candidate hovering positions with the minimum number of hovering positions of the drone base station as the goal, and the constraint conditions of the objective function include the coverage capability constraint and the data transmission capacity constraint of the drone base station; A processing module, configured to solve the objective function under the constraint conditions by using a chaotic moth-to-flame algorithm to obtain a target hovering position of the UAV base station in the area to be deployed, wherein the chaotic moth-to-flame algorithm is configured to execute a linear flame-catching mode based on a chaotic optimization operator when the number of iterations is less than the number of flames, and to execute a spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames; The deployment module is used to deploy the UAV base station according to the target hovering position.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the post-disaster drone base station deployment method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the post-disaster drone base station deployment method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed, the post-disaster drone base station deployment method according to any one of claims 1 to 6 is implemented.

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