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

Abstract

The application provides a post-disaster unmanned aerial vehicle base station deployment method and device, equipment and storage medium, and relates to the technical field of wireless communication. The method comprises the following steps: based on a plurality of candidate hovering positions of the unmanned aerial vehicle base station in a to-be-deployed area, a target function is constructed with the least number of hovering positions of the unmanned aerial vehicle base station as the target, and the constraint conditions comprise the coverage capability constraint and the data transmission capacity constraint of the unmanned aerial vehicle base station; the target function is solved by using a chaotic firefly algorithm under the constraint conditions, and a target hovering position of the unmanned aerial vehicle base station in the to-be-deployed area is obtained, wherein the chaotic firefly algorithm is used to execute a straight line fire catching mode based on a chaotic optimization operator when the number of iterations is less than the number of flames, and execute a spiral fire catching mode when the number of iterations is greater than or equal to the number of flames; and the unmanned aerial vehicle base station is deployed according to the target hovering position, so that the number of deployed unmanned aerial vehicle base stations is less, and the signal coverage range is wider.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, apparatus, equipment and storage medium for deploying a post-disaster drone base station. Background Technology

[0002] Under the impact of natural disasters, the restoration of communication networks plays a crucial supporting role in the restoration of power distribution networks based on distribution automation. Wireless emergency communication plays a vital role in communication network restoration technology. Depending on the post-disaster reconstruction scenario, wireless networks can be deployed at specific locations, providing necessary communication for disaster relief personnel while also effectively ensuring remote unified command and management during power grid repair, thus significantly improving the speed of power distribution network restoration.

[0003] Currently, wireless emergency communication is typically achieved through ground-based emergency communication vehicles. These vehicles have built-in energy storage devices and are not limited by battery power, but they are significantly affected by terrain and environment, and their signal coverage is relatively small. In contrast, drone base stations are less affected by terrain and environment, possess highly reliable line-of-sight links, and offer flexible deployment capabilities. Therefore, there is an urgent need for an effective solution for deploying drone base stations after disasters. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for deploying unmanned aerial vehicle (UAV) base stations after a disaster, thereby providing an effective solution for deploying UAV base stations after a disaster.

[0005] Firstly, this application provides a method for deploying a post-disaster drone base station, including:

[0006] The system acquires multiple candidate hovering locations for drone base stations within the deployment area, which is determined based on the geographical location information of the power distribution network transmission lines.

[0007] Based on multiple candidate hovering positions, an objective function is constructed with the goal of minimizing the number of hovering positions of the UAV base station. The constraints of the objective function include the coverage capability constraint and the data transmission capacity constraint of the UAV base station.

[0008] Under constraints, the chaotic moth-and-flame-catching algorithm is used to solve the objective function and obtain the hovering position of the UAV base station in the area to be deployed. The chaotic moth-and-flame-catching algorithm is used to execute the linear flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and to execute the spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames.

[0009] Deploy drone base stations based on the target's hovering position.

[0010] Optionally, under constraints, a chaotic moth-and-flame trapping algorithm is used to solve the objective function to obtain the target hovering position of the UAV base station within the area to be deployed. This includes: initializing a moth population based on 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, then based on the fitness value, iteratively executing the linear flame trapping mode of the chaotic moth-and-flame trapping algorithm based on the chaotic optimization operator to obtain the updated moth position, with the number of flames decreasing 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 position, iteratively executing the spiral flame trapping mode of the chaotic moth-and-flame trapping algorithm until the total number of iterations is reached to obtain the target hovering position of the UAV base station within the area to be deployed.

[0011] Optionally, the linear flame-catching mode based on the chaotic optimization operator of the chaotic moth-flame-catching algorithm is executed to obtain the updated moth position, 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 position through the following first formula:

[0012]

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

[0014] Optionally, the helical flame capture mode satisfies the following second formula:

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

[0016] 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 This indicates the relationship between the i-th moth and the optimal flame F. i The distance between them.

[0017] Optionally, after executing the linear flame capture mode or the spiral flame capture mode, the fitness value of each moth is obtained, the moths are sorted according to the fitness value, and the sorted moths are used as the data for the next iteration.

[0018] Optionally, the coverage capacity constraint is used to ensure that the automated switching 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 based on the number of automated switching terminals that the UAV base station can accommodate.

[0019] Secondly, this application provides a post-disaster drone base station deployment device, comprising:

[0020] The acquisition module is used to acquire multiple candidate hovering positions of the drone base station in the area to be deployed. The area to be deployed is determined based on the geographical location information of the power distribution network transmission lines.

[0021] The construction module is used to construct an objective function based on multiple candidate hovering positions, with the goal of minimizing the number of hovering positions of the UAV base station. The constraints of the objective function include the coverage capability constraints and data transmission capacity constraints of the UAV base station.

[0022] The processing module is used to solve the objective function under constraints using the chaotic moth-and-flame-catching algorithm to obtain the hovering position of the UAV base station in the area to be deployed. The chaotic moth-and-flame-catching algorithm is used to execute the linear flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and to execute the spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames.

[0023] The deployment module is used to deploy drone base stations based on the target's hovering position.

[0024] Optionally, the processing module is specifically used for: initializing a moth population based on 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, then based on the fitness value, iteratively executing the linear flame-catching mode of the chaotic moth flame-catching algorithm based on the chaotic optimization operator to obtain the updated moth position, with the number of flames decreasing 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 position, iteratively executing the spiral flame-catching mode of the chaotic moth flame-catching algorithm 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.

[0025] Optionally, when the processing module obtains the updated moth position in the linear flame-catching mode based on the chaotic optimization operator for executing the chaotic moth flame-catching algorithm, it specifically employs a multi-elite position combination strategy, randomly selecting a preset number of elite moths to perform chaotic linear combinations with other moths, and obtaining the updated moth position through the following first formula:

[0026]

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

[0028] Optionally, the helical flame capture mode satisfies the following second formula:

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

[0030] 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 This indicates the relationship between the i-th moth and the optimal flame F. i The distance between them.

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

[0032] Optionally, the coverage capacity constraint is used to ensure that the automated switching 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 based on the number of automated switching terminals that the UAV base station can accommodate.

[0033] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0034] The memory stores instructions that the computer executes;

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

[0036] Fourthly, this application provides a computer-readable storage medium storing computer program instructions, which, when executed, implement the post-disaster drone base station deployment method as described in the first aspect of this application.

[0037] Fifthly, this application provides a computer program product, including a computer program that, when executed, implements the post-disaster drone base station deployment method as described in the first aspect of this application.

[0038] This application provides a method, apparatus, equipment, and storage medium for deploying unmanned aerial vehicle (UAV) base stations after a disaster. The method involves acquiring multiple candidate hovering positions of the UAV base station within a deployment area determined by the geographical location information of power distribution network transmission lines. Based on these candidate hovering positions, an objective function is constructed with the goal of minimizing the number of UAV base station hovering positions. The objective function is constrained by the coverage capability and data transmission capacity of the UAV base station. Under these constraints, a chaotic moth-and-flame catching algorithm is used to solve the objective function, obtaining the target hovering positions of the UAV base station within the deployment area. Specifically, the chaotic moth-and-flame catching algorithm is used to execute a linear flame catching mode based on chaotic optimization operators when the number of iterations is less than the number of flames, and a spiral flame catching mode when the number of iterations is greater than or equal to the number of flames. The UAV base station is then deployed based on the target hovering positions. The chaotic moth-and-flame catching algorithm of this application is based on the moth-and-flame catching algorithm and incorporates a new strategy based on chaotic optimization operators. It can give full play to the ergodicity and randomness of chaotic optimization operators and effectively improve the global search capability. Based on the change of the number of flames, it can take into account the advantages of both the linear flame catching mode and the spiral flame catching mode in the optimization process, effectively avoid the discreteness of the UAV base station from getting trapped in local optima, and accelerate the convergence speed of the chaotic moth-and-flame catching algorithm. Solving the objective function to obtain the target hovering position of the UAV base station in the global optimal neighborhood, the obtained target hovering position can reduce the number of UAV base stations deployed and the signal coverage range is wider. Attached Figure Description

[0039] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0040] Figure 1 A flowchart illustrating a method for deploying a post-disaster drone base station according to an embodiment of this application;

[0041] Figure 2 A flowchart illustrating a method for deploying a post-disaster drone base station according to another embodiment of this application;

[0042] Figure 3 A schematic diagram of the structure of a post-disaster drone base station deployment device provided in an embodiment of this application;

[0043] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this 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 used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0047] In recent years, diverse climate changes across regions have led to frequent natural disasters, severely impacting the safe operation of power systems and causing significant economic losses. For example, some areas have experienced torrential rains and localized extreme downpours, resulting in severe damage in multiple localized areas, including landslides and other hazards. Power lines and equipment in low-lying areas have been severely flooded, disrupting the power supply to hundreds of thousands of residents. Under the influence of natural disasters, the restoration of communication networks plays a crucial supporting role in the restoration of distribution networks based on distribution automation. For instance, preliminary surveys, line restoration planning, construction coordination, and information gathering all rely heavily on communication networks. Wireless emergency communication plays a vital role in communication network restoration technology. It can be deployed at specific locations according to the post-disaster reconstruction scenario, providing necessary communication for disaster relief personnel while also effectively ensuring remote unified command and management during power grid repair, thus significantly improving the speed of distribution network restoration.

[0048] Currently, wireless emergency communication is typically achieved through ground-based emergency communication vehicles. These vehicles have built-in energy storage devices, eliminating battery power limitations, but they are significantly affected by terrain and environment, and their signal coverage is relatively limited. In contrast, drone base stations (drones equipped with relay modules to provide mobile communication services) are less affected by terrain and environment, possess highly reliable line-of-sight links, and offer flexible deployment capabilities. Therefore, there is an urgent need for an effective solution for deploying drone base stations after disasters to better utilize them in the emergency repair of power distribution networks.

[0049] Based on the above problems, and considering the high costs involved in constructing optical and electrical cables for disaster relief power grids in mountainous and rural areas, as well as the large number, wide distribution, and complex environment of power distribution terminals, existing medium-voltage feeders in power distribution networks primarily use wireless private networks for data transmission through automated switch terminals, automatic isolation, and self-healing systems to ensure high reliability, economy, and flexibility. This application provides a method, apparatus, equipment, and storage medium for deploying unmanned aerial vehicle (UAV) base stations after a disaster. Based on the Moth-and-Flame (MFO) algorithm, a new strategy based on chaotic optimization operators is combined to obtain the Chaotic Moth-and-Flame (MFO) algorithm. An objective function is constructed with the goal of minimizing the number of hovering positions of UAV base stations. The objective function is solved using the Chaotic Moth-and-Flame (MFO) algorithm to obtain the target hovering positions of UAV base stations within the deployment area. Obtaining these target hovering positions allows for a smaller number of UAV base stations deployed, wider signal coverage, and effectively improved global search performance and convergence speed. Furthermore, UAV base stations can be deployed based on these target hovering positions.

[0050] Deployed drone base stations can be used to coordinate the rapid restoration of emergency communication in the power distribution network after a disaster. The implementation of post-disaster repair work allows the load downstream of the faulty line to be restored. Using drone base stations as an emergency communication medium, the automated switching terminals of the medium-voltage feeders in the power distribution network can be made controllable. By operating the automated switching terminals, self-healing strategies can be quickly executed to restore the load downstream of the power source. At the same time, the restoration of power in non-faulty sections provides power services to communication nodes downstream of the faulty section, enabling automated switching terminals that were unable to supply power due to backup power issues to resume power supply. This better optimizes the flow of power and allows limited post-disaster power generation resources to be supplied to critical loads more effectively.

[0051] It is understood that this application studies the site selection problem of drone base station deployment points (i.e., optimal hovering positions) based on post-disaster recovery plans, considering factors such as the coverage capability of drone base stations and the optimal location of their operating points (hovering positions). The post-disaster drone base station deployment method provided in this application requires certain pre-existing assumptions and limitations on the application scenario. These pre-existing assumptions are as follows:

[0052] Assuming the drone base station has unlimited battery life;

[0053] Assuming that the power consumption required for communication is not considered, only the power consumption under the two operating conditions of hovering and flight patrol is considered, and the power consumption value is the same in the two states;

[0054] Given a fixed data transmission capacity for the drone base station, assume that the data transmission rate of each automated switch terminal on the medium-voltage feeder in the distribution network is the same.

[0055] The main objective of this application is to restore power load. It only requires that the automated switch terminal can be covered by the drone base station and that communication can be restored, without considering other communication issues.

[0056] Assume that the current status of the distribution network system has been obtained through on-site surveys and dispatch system information before the medium-voltage distribution network is restored;

[0057] To reduce interference between drone base stations, we can assume that each drone base station has an antenna with an elevation angle of θ relative to the horizontal plane, where θ satisfies the following formula:

[0058]

[0059] Where h represents the hovering height of the drone base station; r represents the maximum coverage radius of the drone base station's communication; when the drone base station is at a certain hovering height, the maximum coverage radius of the communication is a fixed value, and communication nodes outside this coverage area are not affected by the communication of the drone base station.

[0060] This application is based on the premise that all UAV base stations have the same and fixed hovering elevation, and that all automated switch terminals of medium-voltage feeders within the coverage area of ​​the UAV base stations can communicate with the UAV base stations.

[0061] In addition, the data transmission capacity and data overload issues of the drone base station also need to be considered. The status data throughput of all automated switching terminals within the coverage area of ​​the drone base station shall not exceed the data transmission capacity of the drone base station.

[0062] It should be noted that the post-disaster drone base station deployment method provided in this application embodiment can be applied to a server, which can be an independent server or a service cluster, etc.

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

[0064] Figure 1 A flowchart illustrating a post-disaster drone base station deployment method provided in one embodiment of this application. Figure 1 As shown, the disaster relief drone base station deployment method of this application embodiment includes:

[0065] S101. Obtain multiple candidate hovering positions of the drone base station within the area to be deployed. The area to be deployed is determined based on the geographical location information of the power distribution network transmission lines.

[0066] In this embodiment, the geographical location information of the power distribution network transmission line can be understood as the geographical location information of the line, such as the longitude and latitude of the transmission line. The deployment area of ​​the UAV base station can be determined based on the geographical location information of the power distribution network transmission line, combined with the location information of the disaster area. Evenly distributed work points are set within the deployment area. The drone operator manually operates the UAV (with or without a relay module) within the deployment area, visiting each work point one by one to obtain its geographical location information. Finally, the drone is controlled to return, thus obtaining multiple candidate hovering positions of the UAV base station within the deployment area corresponding to each work point, i.e., obtaining the geographical location information of the candidate hovering positions. Multiple candidate hovering positions constitute a location set. The above method for obtaining multiple candidate hovering positions can be understood as a point-based discretization method, i.e., inserting evenly distributed work points within the deployment area and using these work points as candidate hovering positions for the UAV base station.

[0067] S102. Based on multiple candidate hovering positions, construct an objective function with the goal of minimizing the number of hovering positions of the UAV base station. The constraints of the objective function include the coverage capability constraint and data transmission capacity constraint of the UAV base station.

[0068] It is understood that the purpose of this application's embodiments is to establish an objective function that minimizes the number of hovering positions of a UAV base station while ensuring coverage of all communication points (such as automated switch terminals on medium-voltage feeders in a distribution network). The objective function is then iteratively solved to obtain the optimal target hovering position of the UAV base station. In this step, an objective function can be constructed based on multiple candidate hovering positions, with the goal of minimizing the number of hovering positions of the UAV base station. The constraints of the objective function include coverage capability constraints and data transmission capacity constraints for the UAV base station. Specific objective functions can be found in subsequent embodiments. The coverage capability constraint ensures that the automated switch terminals on medium-voltage feeders in the distribution network are covered by at least one UAV base station. The data transmission capacity constraint ensures that the data throughput of all automated switch terminals within the coverage area of ​​the UAV base station does not exceed the data transmission capacity of the UAV base station. Specific coverage capability constraints and data transmission capacity constraints can be found in subsequent embodiments.

[0069] S103. Under the constraints, the Chaotic Moth Flame-Catching Algorithm is used to solve the objective function to obtain the hovering position of the UAV base station in the area to be deployed. The Chaotic Moth Flame-Catching Algorithm is used to execute the linear flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and to 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 find the UAV location with the fewest configurations and the widest coverage using constraints as boundary conditions. For example, under these constraints, a chaotic moth-and-flame catching algorithm is used to solve the objective function. Specifically, when the number of iterations is less than the number of flames, a linear flame catching mode based on chaotic optimization operators is executed. As the number of iterations increases, the number of flames adaptively decreases. When the number of iterations is greater than or equal to the number of flames, a 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 within the deployment area is obtained, i.e., the optimal solution to the objective function is obtained. The obtained target hovering position allows for a smaller number of UAV base stations to be deployed while providing wider signal coverage. For details on how to use the chaotic moth-and-flame catching algorithm to solve the objective function under constraints and obtain the target hovering position of the UAV base station within the deployment area, please refer to subsequent embodiments; they will not be repeated here.

[0071] Optionally, the optimal path of the drone base station along the line can be obtained based on the target hovering position of the drone base station in the area to be deployed.

[0072] S104. Deploy drone base stations based on the target's hovering position.

[0073] In this step, after obtaining the target hovering position of the drone base station within the deployment area, the drone base station can be deployed based on the target hovering position. For example, the drone base station can be automatically controlled and deployed to the target hovering position based on the target hovering position.

[0074] The post-disaster drone base station deployment method provided in this application involves obtaining multiple candidate hovering positions of the drone base station within a deployment area, which is determined based on the geographical location information of the power grid transmission lines. Based on these candidate hovering positions, an objective function is constructed with the goal of minimizing the number of drone base station hovering positions. The objective function is constrained by both coverage capability and data transmission capacity constraints. Under these constraints, a chaotic moth-and-flame algorithm is used to solve the objective function, obtaining the target hovering positions of the drone base station within the deployment area. Specifically, the chaotic moth-and-flame algorithm executes a linear flame-catching mode based on chaotic optimization operators when the number of iterations is less than the number of flames, and 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 then deployed based on the target hovering positions. The chaotic moth-and-flame catching algorithm of this application is based on the moth-and-flame catching algorithm and incorporates a new strategy based on chaotic optimization operators. It can fully leverage the ergodicity and randomness of chaotic optimization operators to effectively improve global search capabilities. Based on the variation of the number of flames, it can take into account the advantages of both the linear and spiral flame catching modes in the optimization process, effectively avoiding the discreteness of the UAV base station from getting trapped in local optima, and accelerating the convergence speed of the chaotic moth-and-flame catching algorithm. Solving the objective function obtains the target hovering position of the UAV base station in the global optimal neighborhood. The obtained target hovering position allows for the deployment of fewer UAV base stations and a wider signal coverage.

[0075] Figure 2 This is a flowchart illustrating a post-disaster drone base station deployment method according to another embodiment of this application. Based on the above embodiments, this application further describes the post-disaster drone base station deployment method. Figure 2 As shown, the disaster relief drone base station deployment method of this application embodiment may include:

[0076] S201. Obtain multiple candidate hovering positions of the drone base station within the area to be deployed. The area to be deployed is determined based on the geographical location information of the power distribution network transmission lines.

[0077] For a detailed description of this step, please refer to [link / reference]. Figure 1 The relevant description of S101 in the illustrated embodiment will not be repeated here.

[0078] S202. Based on multiple candidate hovering positions, construct an objective function with the goal of minimizing the number of hovering positions of the UAV base station. The constraints of the objective function include the coverage capability constraint and data transmission capacity constraint of the UAV base station.

[0079] For a detailed description of this step, please refer to [link / reference]. Figure 1 The relevant description of S102 in the illustrated embodiment. Optionally, the objective function satisfies the following formula two:

[0080]

[0081] Where K represents the total number of candidate hovering positions; P k Let P be a variable ranging from 0 to 1. If the k-th candidate hovering position is selected as the target hovering position, then... k The value is 1. If the k-th candidate hovering position is not selected as the target hovering position, then P... k The value of P is 0. k The default value is 0; Formula 2 above represents minimizing the number of hovering positions of the drone base station.

[0082] The constraints of the objective function include coverage capability constraints and data transmission capacity constraints for the UAV base station. Optionally, the coverage capability constraint is used to ensure that the automated switching 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 based on the number of automated switching terminals that the UAV base station can accommodate.

[0083] For example, the coverage target of a drone base station is the intersection of multiple automated switching terminals in a power system. It is necessary to find the target hovering position of the drone base station with the minimum configuration and the widest coverage, using the coverage capability and data transmission capacity of the drone base station as boundary conditions. The coverage capability of the drone base station can be quantified using the above formula. At a certain hovering height, the maximum communication coverage radius of the drone base station is a constant, and communication nodes outside this coverage area are not affected by the communication of the drone base station. The coverage capability constraint of the drone base station satisfies the following formula three:

[0084]

[0085] Among them, S ftu S represents the set of automated switch terminals; uav This represents the drone base station as a discrete set of points; x c,k This indicates the coverage of the automated switching terminal (also known as the 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 it is not covered, the value is 0.

[0086] Alternatively, the relationship between the distance between the automated switch terminal and the discrete point of the drone base station and the coverage radius of the drone base station can be obtained using the following formula four:

[0087]

[0088] Among them, (x c y c ) and (x k y k) represent the location coordinates of the automated switch terminal c and the discrete point of the drone base station, respectively; r is the coverage radius of the drone base station; d k,c This represents the relationship between the distance between the discrete points of the automated switch terminal and 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] Where C represents the total number of automated switching terminals that the UAV base station can accommodate; N represents the total number of iterations of the chaotic moth-and-flame algorithm; Formula 5 above indicates that the number of automated switching terminals that the UAV base station can accommodate is limited. When the number of automated switching terminals in the distribution network is large and dense, this constraint needs to be considered.

[0092] It is understandable that Formulas 2, 3, and 5 above constitute an integer linear programming model for the location selection of the UAV base station operating point (hovering position).

[0093] In the embodiments of this application, Figure 1 Step S103 may further include steps S203 to S211 as follows:

[0094] S203. Initialize the moth population based on multiple candidate hovering positions.

[0095] In this step, after obtaining multiple candidate hovering positions of the drone base station within the deployment area, a moth population can be initialized based on these candidate hovering positions. For example, assuming 20 candidate hovering positions of the drone base station are obtained within the deployment area, a moth population x(i,:) can be initialized based on these 20 candidate hovering positions. x(i,:) is a 20-row, 2-column matrix, where each row represents the longitude and latitude of each candidate hovering position. Other parameters can also be set, including a total number of iterations N for the chaotic moth-and-flame trapping algorithm of 20, a population size m for the chaotic moth-and-flame trapping algorithm of 20, and a variable dimension D of 2.

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

[0097] In this step, the fitness value can be obtained based on the location of each moth in the moth population.

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

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

[0100] S206. Based on the moth's fitness value, execute the linear flame-catching mode of the chaotic moth flame-catching algorithm based on the chaotic optimization operator to obtain the updated moth position.

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

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

[0103]

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

[0105] For example, the preset number is 3. After initializing the moth population, the search of individual moths is relatively independent, with little information exchange. Based on a chaotic optimization operator, a multi-elite position combination strategy can be adopted, selecting the top 3 elite moths for chaotic linear combination with other moths, and obtaining the updated moth positions using Formula 6 above. Wherein, the chaotic optimization operator r... n The specific expression 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, and obtain the sorted moths.

[0107] In this step, after executing the linear flame capture mode, the fitness value of each moth is obtained, and the moths are sorted according to the fitness value to obtain the sorted moths. The sorted moths are then used as the data for the next iteration.

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

[0109] It can be understood that at the beginning of the chaotic moth-and-flame algorithm, each moth has its own optimal solution and flame. As the number of iterations increases, the number of flames adaptively adjusts, causing the moths to gradually converge to the global optimum. The adaptive adjustment of the flames follows the rules outlined in Formula Seven below:

[0110]

[0111] Where n is the current iteration number; N represents the number of flames in the initial iteration step, i.e., the initial number of flames; flame represents the number of flames after adaptive adjustment.

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

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

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

[0115] It is understandable that as the number of iterations increases, the flame-catching mode of the chaotic moth flame-catching algorithm changes to the spiral flame-catching mode. During the search process, the moth uses its phototaxis to sense flames that are relatively close to it and captures the flames by spiraling around them.

[0116] Optionally, the helical flame capture 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 8

[0118] 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 M represents the i-th moth. i With the optimal flame F i The distance between them, D i It can be represented as: D i =|M i -F i |

[0119] S212. Deploy drone base stations based on the target's hovering position.

[0120] For a detailed description of this step, please refer to [link / reference]. Figure 1 The relevant description of S104 in the illustrated embodiment will not be repeated here.

[0121] The post-disaster drone base station deployment method provided in this application involves obtaining multiple candidate hovering positions of the drone base station within a deployment area, where the deployment area is determined based on the geographical location information of the power grid transmission lines. Based on these candidate hovering positions, an objective function is constructed with the goal of minimizing the number of drone base station hovering positions. The objective function's constraints include coverage capability constraints and data transmission capacity constraints for the drone base station. A moth population is initialized based on the candidate hovering positions. The fitness value of each moth in the population is obtained. If the number of iterations is less than the number of flames, a chaotic moth-flame-catching algorithm is executed based on the moths' fitness values. The linear flame-catching mode based on the chaotic optimization operator is used to obtain the updated moth position. 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 executed based on the current moth position to obtain the updated moth position. After each flame-catching is completed, the number of flames decreases adaptively with the number of iterations. The fitness value of each moth is obtained, and the moths are sorted according to the fitness value to obtain the sorted moths. 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-and-flame catching algorithm of this application is based on the moth-and-flame catching algorithm and incorporates a new strategy based on chaotic optimization operators. It can fully leverage the ergodicity and randomness of chaotic optimization operators to effectively improve global search capabilities. Through adaptive adjustment of the number of flames, it can take into account the advantages of both linear and spiral flame catching modes in the optimization process, effectively avoid the discreteness of UAV base stations from getting trapped in local optima, and accelerate the convergence speed of the chaotic moth-and-flame catching algorithm. Solving the objective function obtains the target hovering position of the UAV base station in the global optimal neighborhood. The obtained target hovering position allows for the deployment of fewer UAV base stations and a wider signal coverage.

[0122] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0123] Figure 3 This is a schematic diagram of the structure of a post-disaster drone base station deployment device provided in one embodiment of this application. Figure 3 As shown, the disaster relief drone base station deployment device 300 of this application embodiment includes: an acquisition module 301, a construction module 302, a processing module 303, and a deployment module 304. Wherein:

[0124] The acquisition module 301 is used to acquire multiple candidate hovering positions of the UAV base station in the area to be deployed, which is determined based on the geographical location information of the power distribution network transmission lines.

[0125] Module 302 is used to construct an objective function based on multiple candidate hovering positions, with the goal of minimizing the number of hovering positions of the UAV base station. The constraints of the objective function include the coverage capability constraints and data transmission capacity constraints of the UAV base station.

[0126] The processing module 303 is used to solve the objective function under constraints using the chaotic moth-and-flame-catching algorithm to obtain the target hovering position of the UAV base station in the area to be deployed. The chaotic moth-and-flame-catching algorithm is used to execute the linear flame-catching mode based on the chaotic optimization operator when the number of iterations is less than the number of flames, and to execute the spiral flame-catching mode when the number of iterations is greater than or equal to the number of flames.

[0127] Deployment module 304 is used to deploy drone base stations based on the target hovering position.

[0128] In some embodiments, the processing module 303 may be specifically used to: initialize a moth population based on 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 linear flame-catching mode of the chaotic moth flame-catching algorithm based on the chaotic optimization operator to obtain the updated moth position, 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, then based on the updated moth position, iteratively execute the spiral flame-catching mode of the chaotic moth flame-catching 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.

[0129] Optionally, when the processing module 303 obtains the updated moth position in the linear flame-catching mode based on the chaotic optimization operator for executing the chaotic moth flame-catching algorithm, it can specifically be used to: adopt a multi-elite position combination strategy, randomly select a preset number of elite moths to perform chaotic linear combinations with other moths, and obtain the updated moth position through the following first formula:

[0130]

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

[0132] Optionally, the helical flame capture mode satisfies the following second formula:

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

[0134] 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 This indicates the relationship between the i-th moth and the optimal flame F. i The distance between them.

[0135] Optionally, after executing the linear flame capture mode or the spiral flame capture mode, the fitness value of each moth is obtained, the moths are sorted according to the fitness value, and the sorted moths are used as the data for the next iteration.

[0136] Optionally, the coverage capacity constraint is used to ensure that the automated switching 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 based on the number of automated switching terminals that the UAV base station can accommodate.

[0137] The apparatus of this application embodiment can be used to execute the technical solutions of any of the method embodiments shown above. Its implementation principle and technical effect are similar, and will not be repeated here.

[0138] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 may include at least one processor 401 and a memory 402.

[0139] The memory 402 is used to store programs. Specifically, the program may include program code, which includes computer-executable instructions.

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

[0141] The processor 401 executes computer execution instructions stored in the memory 402 to implement the post-disaster drone base station deployment method described in the foregoing method embodiments. The processor 401 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Specifically, when implementing the post-disaster drone base station deployment method described in the foregoing method embodiments, the electronic device may be, for example, a server or other electronic device with processing capabilities.

[0142] Optionally, the electronic device 400 may also include a communication interface 403. In specific implementations, if the communication interface 403, memory 402, and processor 401 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

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

[0144] This application also provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the above-described method for deploying a post-disaster drone base station.

[0145] This application also provides a computer program product, including a computer program that, when executed, implements the above-described method for deploying a post-disaster drone base station.

[0146] The aforementioned computer-readable storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to 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 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 reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in a disaster recovery drone base station deployment device.

[0148] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for post-disaster deployment of an unmanned base station, the method comprising: The method comprises the steps of: obtaining a plurality of candidate hovering positions of a UAV base station in a to-be-deployed area, the to-be-deployed area being determined according to geographical position information of a power transmission line of a power distribution network; constructing a target function with a minimum number of hovering positions of the UAV base station as a target based on the plurality of candidate hovering positions, the constraint conditions of the target function comprising a coverage capability constraint and a data transmission capacity constraint of the UAV base station; solving the target function under the constraint conditions by using a chaotic moth-flame optimization algorithm to obtain a target hovering position of the UAV base station in the to-be-deployed area, wherein the chaotic moth-flame optimization 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; deploying the UAV base station according to the target hovering position.

2. The post-disaster drone base station deployment method of claim 1, wherein, The step of solving the target function under the constraint conditions by using the chaotic moth-flame optimization algorithm to obtain the target hovering position of the UAV base station in the to-be-deployed area comprises the steps of: initializing a moth population according to the plurality of candidate hovering positions; obtaining an adaptive value of each moth in the moth population; if the number of iterations is less than the number of flames, then iteratively executing the straight-line flame catching mode based on the chaotic optimization operator of the chaotic moth-flame optimization algorithm based on the adaptive value 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, then iteratively executing the spiral flame catching mode of the chaotic moth-flame optimization algorithm based on the updated moth position until a total number of iterations is reached to obtain the target hovering position of the UAV base station in the to-be-deployed area.

3. The post-disaster drone base station deployment method of claim 2, wherein, The step of executing the straight-line flame catching mode based on the chaotic optimization operator of the chaotic moth-flame optimization algorithm to obtain the updated moth position comprises the step of: randomly selecting a preset number of elite moths and other moths to perform chaotic linear combination by using a multi-elite position combination strategy, and obtaining the updated moth position by using the following first formula: wherein, 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 a chaos optimization operator.

4. The post-disaster drone base station deployment method of claim 2, wherein, The spiral flame catching mode satisfies the following second formula: S(M i ,F i ) = D i ·e bt cos(2πt + F i second equation where S(M i ,F i ) represents the updated position function; b represents a 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 .

5. The post-disaster drone base station deployment method of claim 2, wherein, After executing the straight-line flame catching mode or the spiral flame catching mode, an adaptive value of each moth is obtained, the moths are sorted according to the adaptive value to obtain sorted moths, and the sorted moths are used as data for the next iteration.

6. The post-disaster drone base station deployment method of any one of claims 1-5, wherein, The coverage capability constraint is used to determine that an automation switch terminal of a medium-voltage feeder of the power distribution network is covered by at least one UAV base station, and the data transmission capacity constraint is determined according to the number of the automation switch terminals that can be accommodated by the UAV base station.

7. A post-disaster drone base station deployment apparatus, characterized by, The method comprises the steps of: obtaining a plurality of candidate hovering positions of a UAV base station in a to-be-deployed area, the to-be-deployed area being determined according to geographical position information of a power transmission line of a power distribution network; constructing a target function with a minimum number of hovering positions of the UAV base station as a target based on the plurality of candidate hovering positions, the constraint conditions of the target function comprising a coverage capability constraint and a data transmission capacity constraint of the UAV base station; The processing module is configured to solve the target function by using a chaotic firefly algorithm under the constraint condition, and obtain a target hovering position of the UAV base station in the to-be-deployed area, wherein the chaotic firefly algorithm is configured to perform a straight line firefly catching mode based on a chaotic optimization operator when the number of iterations is less than the number of fires, and perform a spiral firefly catching mode when the number of iterations is greater than or equal to the number of fires. The deployment module is configured to deploy the UAV base station according to the target hovering position.

8. An electronic device, comprising: The method comprises the following steps: a processor, and a memory connected with the processor in communication; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method for post-disaster deployment of a UAV base station according to 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 the computer program instructions are executed to implement the method for post-disaster deployment of a UAV base station according to any one of claims 1 to 6.

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

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