UAV deployment optimization method for power emergency communication network and related device

Abstract

The invention discloses a UAV deployment optimization method for a power emergency communication network and a related device. The UAV deployment optimization method comprises the following steps: acquiring position information of each power distribution network communication node in the power emergency communication network; the initial position of each unmanned aerial vehicle (UAV) is obtained, and each UAV carries a base station device; solving a UAV position optimization model by taking the position information of each power distribution network communication node and the initial position of each UAV as a starting point to obtain the optimal position of each UAV; deploying the UAVs according to the optimal positions of the UAVs, and completing UAV deployment optimization of the power emergency communication network, the method and the related device can optimize the deployment positions of the UAVs, thereby improving the communication coverage rate of communication nodes of the power distribution network, and enhancing the emergency communication capability and recovery performance of the post-disaster power network.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power wireless communication, and relates to a UAV deployment optimization method and related devices for an electric power emergency communication network. Background Art

[0002] The goal of 5G communication networks is to achieve ultra-high data rates, low latency, and seamless coverage to support applications such as smart cities, the Industrial Internet of Things, and emergency communications for distribution networks. However, the deployment of traditional ground base stations is limited by terrain, building obstructions, and economic costs, making it difficult to effectively cover distribution network communication nodes in remote areas or post-disaster scenarios. In particular, after natural disasters (such as floods, earthquakes, and typhoons), distribution network communication nodes (such as substations and distribution line monitoring points) may fail due to equipment damage, tower collapse, or power outages. This can hinder timely power dispatch, fault location, and repair work, seriously impacting the emergency communications and recovery efficiency of the post-disaster distribution network. To address this issue, low-orbit satellites are being introduced into the 5G non-terrestrial network (NTN), leveraging their wide coverage to provide backbone network support. However, the high signal transmission latency of low-orbit satellites (approximately 250-280 milliseconds) makes it difficult to meet the urgent real-time and reliability requirements of distribution network emergency communications.

[0003] Unmanned aerial vehicles (UAVs) can carry base station equipment and serve as mobile base stations. With the advantages of rapid deployment and flexible relocation, they can work in conjunction with Beidou satellites and low-orbit satellites to enhance the communication coverage capabilities of distribution network communication nodes after disasters, providing temporary communication support for power repair and emergency command. However, in existing technologies, UAV deployment is typically achieved through random placement or manual designation. For example, the random deployment method evenly distributes UAV locations within the target area, but does not fully consider the spatial distribution characteristics and channel conditions (such as path loss and obstruction) of distribution network communication nodes. This results in low coverage efficiency and makes it difficult to meet the quality of service (QoS) requirements of key nodes (such as main substations or important line monitoring points), affecting the operation monitoring and emergency response capabilities of the post-disaster power system. The manual designation method is highly dependent on operator experience and is difficult to adapt to the dynamic changes in the location of distribution network communication nodes after disasters (such as temporary repairs or new nodes). Furthermore, the optimization process is complex when multiple UAVs are deployed in a coordinated manner, making it difficult to quickly respond to emergency needs. In addition, the three-dimensional position optimization of UAVs is a high-dimensional nonlinear problem. Traditional optimization algorithms (such as gradient descent or exhaustive search) have high computational complexity and are difficult to implement in real-time and efficient applications in emergency situations of post-disaster distribution networks.

[0004] Therefore, there is an urgent need for an efficient method that can optimize the deployment location of UAVs in the satellite-assisted 5G network based on the location distribution, channel characteristics, and bandwidth, latency and other requirements of the communication nodes in the post-disaster distribution network, thereby improving the communication coverage of the distribution network communication nodes and enhancing the emergency communication capabilities and recovery performance of the post-disaster power network. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a UAV deployment optimization method and related devices for the power emergency communication network. This method and related devices can optimize the deployment position of UAVs, thereby improving the communication coverage rate of distribution network communication nodes and enhancing the emergency communication capabilities and recovery performance of the post-disaster power network.

[0006] To achieve the above objectives, the present invention discloses a UAV deployment optimization method for a power emergency communication network, comprising:

[0007] Obtain the location information of each distribution network communication node in the power emergency communication network;

[0008] Obtain the initial position of each UAV, where each UAV carries a base station device;

[0009] Taking the location information of each distribution network communication node and the initial position of each UAV as a starting point, solving the UAV position optimization model to obtain the optimal position of each UAV;

[0010] Each UVA is deployed according to the optimal position of each UAV, completing the UAV deployment optimization of the power emergency communication network.

[0011] The UAV deployment optimization method for the electric power emergency communication network of the present invention is further improved in that:

[0012] Furthermore, the process of obtaining the initial position of each UAV is as follows:

[0013] According to the spatial distance model between UAV and distribution network communication nodes, the nearest UAV is assigned to each distribution network communication node to obtain the initial position of each UAV.

[0014] Furthermore, the spatial distance model between the UAV and the communication node of the distribution network is:

[0015]

[0016] Among them, (x i ,y i ) represents the location coordinates of the communication node i in the distribution network, (x k ,y k ,z k ) represents the initial position coordinates of the UAV, di is the spatial distance between the communication node i in the distribution network and the UAV.

[0017] Furthermore, before solving the UAV position optimization model based on the location information of each distribution network communication node and the initial location of each UAV, the method further includes:

[0018] Taking the communication coverage of distribution network communication nodes as the optimization goal, and the communication bandwidth requirements, delay index requirements, repeated coverage of distribution network communication nodes and the ability to cover blind areas as constraints, a UAV position optimization model is constructed.

[0019] Furthermore, the process of solving the UAV position optimization model based on the location information of each distribution network communication node and the initial location of each UAV to obtain the optimal location of each UAV is as follows:

[0020] The adaptive iterative algorithm is used to solve the UAV position optimization model using the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV.

[0021] The present invention discloses a UAV deployment optimization system for an electric power emergency communication network, comprising:

[0022] The first acquisition module is used to obtain the location information of each distribution network communication node in the power emergency communication network;

[0023] The second acquisition module is used to obtain the initial position of each UAV, wherein each UAV carries a base station device;

[0024] An optimization module is used to solve a UAV position optimization model based on the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV;

[0025] The deployment module is used to deploy each UVA according to the optimal position of each UAV to complete the UAV deployment optimization of the power emergency communication network.

[0026] The UAV deployment optimization system for the electric power emergency communication network of the present invention is further improved in that:

[0027] Furthermore, the process of obtaining the initial position of each UAV is as follows:

[0028] According to the spatial distance model between UAV and distribution network communication nodes, the nearest UAV is assigned to each distribution network communication node to obtain the initial position of each UAV.

[0029] Furthermore, the spatial distance model between the UAV and the communication node of the distribution network is:

[0030]

[0031] Among them, (x i ,y i ) represents the location coordinates of the communication node i in the distribution network, (x k ,y k ,z k ) represents the initial position coordinates of the UAV, d i is the spatial distance between the communication node i in the distribution network and the UAV.

[0032] Furthermore, before solving the UAV position optimization model based on the location information of each distribution network communication node and the initial location of each UAV, the method further includes:

[0033] Taking the communication coverage of distribution network communication nodes as the optimization goal, and the communication bandwidth requirements, delay index requirements, repeated coverage of distribution network communication nodes and the ability to cover blind areas as constraints, a UAV position optimization model is constructed.

[0034] Furthermore, the process of solving the UAV position optimization model based on the location information of each distribution network communication node and the initial location of each UAV to obtain the optimal location of each UAV is as follows:

[0035] The adaptive iterative algorithm is used to solve the UAV position optimization model using the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV.

[0036] The present invention discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the UAV deployment optimization method for the electric power emergency communication network are implemented.

[0037] The present invention discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the UAV deployment optimization method of the power emergency communication network are implemented.

[0038] The present invention has the following beneficial effects:

[0039] The power emergency communication network UAV deployment optimization method and related devices described in the present invention, when in operation, use the location information of each distribution network communication node and the initial position of each UAV as a starting point to solve the UAV position optimization model and obtain the optimal position of each UAV. This provides an innovative solution for coverage optimization in post-disaster power system communication restoration, and has significant technological advancement and practical value. The effects achieved by adopting the above technical solution are:

[0040] Improve the coverage efficiency of power emergency communications: By optimizing the deployment location and flight path of drones through adaptive iterative algorithms, it can efficiently cover ground distribution network communication nodes, ensuring that more post-disaster distribution network communication nodes can obtain communication support that meets QoS requirements, effectively improving the coverage capability and communication recovery efficiency of the power emergency communication network in post-disaster environments.

[0041] Improving the adaptive capability of power emergency communication UAVs: The optimization method proposed in this invention adopts an adaptive iterative algorithm, which can adaptively adjust the search strategy according to the dynamic changes in communication needs and real-time fluctuations in environmental parameters, avoid local optimal traps, automatically adjust the UAV deployment plan, reduce the number of UAVs deployed, reduce resource consumption, and improve the overall performance and service quality of the power emergency communication network.

[0042] Improve the efficiency of multi-UAV collaborative operations: This invention optimizes the division of labor and collaboration of each UAV from a global perspective, dynamically adjusts the UAV deployment strategy, rationally allocates communication coverage areas and data transmission tasks, quickly adapts to complex environmental changes, avoids signal interference between multiple UAVs, achieves collaborative optimization of multiple goals, and improves the emergency communication capability and recovery performance of the entire UAV system in power emergency communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0044] Figure 1 This is a structural diagram of the power emergency communication network in the present invention;

[0045] Figure 2 is a flow chart of the method of the present invention;

[0046] Figure 3 The flowchart of UAV position optimization based on adaptive iterative algorithm is shown;

[0047] Figure 4 Schematic diagram of the chromosome structure of the UAV deployment location based on the adaptive iterative algorithm;

[0048] Figure 5 This is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] In the description of the present invention, it is to be understood that the terms “include” and “comprise” indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0051] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0052] It should be further understood that the term "and / or" as used in the present specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0053] It should be understood that although the terms "first," "second," and "third" may be used to describe preset ranges in embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are merely used to distinguish one preset range from another. For example, without departing from the scope of embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0054] The word "if," as used herein, may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] The accompanying drawings illustrate various schematic diagrams of structures according to embodiments disclosed herein. These figures are not drawn to scale; for clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0057] Example 1

[0058] refer to Figure 1 and Figure 2 The UAV deployment optimization method for the electric power emergency communication network of the present invention comprises the following steps:

[0059] 1) Obtain the location of each distribution network communication node in the power emergency communication network and initialize the UAV position;

[0060] 11) In the power emergency communication network, obtain the two-dimensional coordinates (x i ,y i ), create a database, record the location of each distribution network communication node, for example, the coordinates of node 1 are (x1, y1) and node 2 are (x2, y2), and ensure that the coordinate range is within the constraint range.

[0061] 12) In the power emergency disaster scenario, select typical areas such as open areas, valley areas, mountainside or mountaintop areas to deploy UAVs as optimization points, and manually input the initial three-dimensional positions of n UAVs. For example, UAV1 is set to (x k1 ,y k1 ,z k1 ), UAV2 is (x k2 ,y k2 ,z k2 ), UAV3 is (x k3 ,yk3 ,z k3 ), while ensuring that the UAV activity range constraints are met, the coverage capability of the initial position is evaluated based on the spatial distance model between the UAV and the distribution network communication nodes. The spatial distance model between the UAV and the distribution network communication nodes is expressed as:

[0062]

[0063] 13) The position of the communication node in the distribution network (x i ,y i ) and the initial position of the UAV (x k ,y k ,z k ) is used as the basic data for subsequent optimization. The evaluation of the spatial distance model between the UAV and the communication nodes of the distribution network shows that the initial position needs to be adjusted to cover more communication nodes of the distribution network.

[0064] 2) Initial communication coverage F;

[0065] 21) Assign the nearest UAV to each distribution network communication node based on the spatial distance model between the UAV and the distribution network communication node.

[0066] 22) Calculate the data rate Ri of the communication node i in the distribution network as:

[0067] Ri=B·log2(1+SINR i )

[0068] Where Ri is the data rate of the communication node i in the distribution network, B is the bandwidth, and SINR i is the signal to interference plus noise ratio of the communication node in the distribution network.

[0069] When Ri is greater than the threshold, it is considered that the distribution network communication node is effectively covered.

[0070] 23) Calculate the initial communication coverage F as:

[0071]

[0072] Where M is the total number of communication nodes in the distribution network. When the communication node i in the distribution network is covered, then c i =1, otherwise, c i =0.

[0073] 3) If Figure 3 As shown, a UAV position optimization model is constructed;

[0074] 31) Let the optimization objective be: maximize the communication coverage of the communication nodes in the distribution network.

[0075] 32) Set the distribution density of distribution network communication nodes, communication bandwidth and latency requirements, as well as communication node overlap and coverage blind spots as constraints to ensure that the UAV is deployed in the target area and meets the communication index requirements of the connected communication nodes.

[0076] 33) The position of the communication node in the distribution network obtained in step 1) (x i ,y i ) and the initial communication coverage F obtained in step 2) is used as the starting point of the adaptive iterative algorithm.

[0077] 4) solving the UAV position optimization model using an adaptive iterative algorithm to obtain the optimal UAV position;

[0078] 41) Generate candidate position groups and manually input multiple sets of UAV position combinations, each set of UAV position combinations contains the coordinate values ​​of multiple UAVs, while ensuring that the activity range constraints are met.

[0079] 42) Calculate the coverage rate of each group for each group of locations, assign the distribution network communication nodes to the nearest UAV according to the spatial distance model, and use the coverage evaluation model to determine the number of covered distribution network communication nodes.

[0080] 43) Select 3 groups from multiple groups, compare their coverage F, and retain the group with the highest coverage as the parent position.

[0081] 44) Perform a crossover operation on two parents, for example, parent 1(x k1 ,y k1 ,z k1 ) and parent 2(x' k1 ,y' k1 ,z' k1 ), swap some coordinates after a certain coordinate to generate the child position.

[0082] 45) Fine-tune the offspring coordinates and recalculate the coverage F.

[0083] 46) Repeat steps 43) to 45) for n times, for example, n = 100, and retain the position group with the highest coverage F each time, and finally output the optimal UAV position

[0084] 5) deploying each UAV to the optimal UAV position;

[0085] 51) Based on the optimal UAV position Extract the altitude of the UAV According to the coverage model, confirm that the coverage radius meets the requirements, where the coverage radius Rcov is:

[0086] Rcov=k·z k

[0087] Among them, k is the proportional coefficient, z k is the altitude of the UAV.

[0088] 52) According to the optimization results, set the horizontal coordinates of the UAV

[0089] 53) Deploy each UAV to the corresponding position, for example, deploy UAV1 to Location.

[0090] 54) Use signal testing equipment on the ground to measure the number of communication nodes in the distribution network and calculate Confirm that the optimized coverage is higher than the initial coverage.

[0091] 6) If Figure 4 As shown, the position of the UAV is dynamically adjusted.

[0092] 61) Monitor the status changes of the communication nodes in the distribution network and update the status of the communication nodes in the distribution network via satellite every minute. For example, a node from (x i ,y i )Move to (x' i ,y' i ) or add a new repair node, then according to the spatial distance model, the new distance is calculated as:

[0093]

[0094] 62) According to the new position or state of the distribution network communication node, calculate the current Determine whether the current F is decreasing.

[0095] 63) For areas with reduced coverage, adjust the positions of related UAVs, for example, move UAV1 from Move to (x″ k1 ,y″ k1 , z″ k1 ).

[0096] It should be noted that the present invention optimizes the deployment location and flight path of drones through an adaptive iterative algorithm, ensuring that drones can optimally cover the target area when performing their missions. Compared to traditional deployment methods, this method can significantly reduce missed areas and improve coverage efficiency. In addition, the present invention relies solely on the evaluation value of the objective function for search, and can adaptively adjust the search strategy according to environmental changes. Through multi-directional iterative evolution, it avoids local optimal traps. In power emergency scenarios, facing dynamic changes in communication needs and real-time fluctuations in environmental parameters, the adaptive iterative algorithm can automatically adjust the UAV deployment plan based on feedback information. It should also be noted that by optimizing the deployment of UAVs in the power emergency communication network through an adaptive iterative algorithm, the initial deployment location and dynamic adjustment strategy of UAVs can be scientifically planned, the number of drones deployed can be reduced, resource consumption can be reduced, and the overall performance and service quality of the power emergency communication network can be improved. The present invention optimizes the division of labor and collaboration of each UAV from a global perspective, dynamically adjusts the UAV deployment strategy, rationally allocates communication coverage areas and data transmission tasks, quickly adapts to complex environmental changes, avoids signal interference between multiple UAVs, achieves collaborative optimization of multiple goals, and improves the comprehensive performance of the entire drone system in power emergency communication.

[0097] Example 2

[0098] refer to Figure 5 The UAV deployment optimization system for the electric power emergency communication network of the present invention includes:

[0099] The first acquisition module is used to obtain the location information of each distribution network communication node in the power emergency communication network;

[0100] The second acquisition module is used to obtain the initial position of each UAV, wherein each UAV carries a base station device;

[0101] An optimization module is used to solve a UAV position optimization model based on the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV;

[0102] The deployment module is used to deploy each UVA according to the optimal position of each UAV to complete the UAV deployment optimization of the power emergency communication network.

[0103] In this embodiment, the process of obtaining the initial position of each UAV is as follows:

[0104] According to the spatial distance model between UAV and distribution network communication nodes, the nearest UAV is assigned to each distribution network communication node to obtain the initial position of each UAV.

[0105] In this embodiment, the spatial distance model between the UAV and the communication node of the distribution network is:

[0106]

[0107] Among them, (x i ,y i ) represents the location coordinates of the communication node i in the distribution network, (x k ,y k ,z k ) represents the initial position coordinates of the UAV, d i is the spatial distance between the communication node i in the distribution network and the UAV.

[0108] In this embodiment, before solving the UAV position optimization model based on the location information of each distribution network communication node and the initial location of each UAV, the method further includes:

[0109] Taking the communication coverage of distribution network communication nodes as the optimization goal, and the communication bandwidth requirements, delay index requirements, repeated coverage of distribution network communication nodes and the ability to cover blind areas as constraints, a UAV position optimization model is constructed.

[0110] In this embodiment, the process of solving the UAV position optimization model and obtaining the optimal position of each UAV is as follows:

[0111] The adaptive iterative algorithm is used to solve the UAV position optimization model using the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV.

[0112] The division of modules in the embodiments of the present application is illustrative and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the various embodiments of the present application may be integrated into a single processor, or may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules.

[0113] Example 3

[0114] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method implements the steps of the UAV deployment optimization method for the power emergency communication network, including: obtaining the location information of each distribution network communication node in the power emergency communication network; obtaining the initial position of each unmanned aerial vehicle (UAV) (UAV), wherein each UAV carries a base station device; solving a UAV position optimization model based on the location information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV; and deploying each UAV according to the optimal position of each UAV to complete the UAV deployment optimization of the power emergency communication network. The memory may include internal memory, such as high-speed random access memory (RAM), and may also include non-volatile memory, such as at least one disk storage device. The processor, network interface, and memory are interconnected via an internal bus, which may be an industrial standard architecture bus, a peripheral component interconnect standard bus, an extended industrial standard architecture bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operating instructions. The memory may include internal memory and nonvolatile memory and provides instructions and data to the processor.

[0115] Example 4

[0116] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the UAV deployment optimization method of the power emergency communication network are implemented, for example, including: obtaining the location information of each distribution network communication node in the power emergency communication network; obtaining the initial position of each unmanned aerial vehicle (UAV), wherein each UAV carries a base station device; using the location information of each distribution network communication node and the initial position of each UAV as the starting point, solving the UAV position optimization model to obtain the optimal position of each UAV; deploying each UVA according to the optimal position of each UAV to complete the UAV deployment optimization of the power emergency communication network. Specifically, the computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. The volatile memory may include random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include read-only memory (ROM), hard disk, flash memory, optical disk, magnetic disk, etc.

[0117] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0121] Those skilled in the art will readily identify other embodiments of the present invention after considering the specification and disclosure of the invention. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0122] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

[0123] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A UAV deployment optimization method for a power emergency communication network, characterized in that: include: Obtain the location information and satellite channel status information of each distribution network communication node in the power emergency communication network; Obtain the initial position of each UAV, where each UAV carries a base station device; Taking the location information of each distribution network communication node and the initial position of each UAV as a starting point, solving the UAV position optimization model to obtain the optimal position of each UAV; Each UAV is deployed according to the optimal position of each UAV, completing the UAV deployment optimization of the power emergency communication network.

2. The UAV deployment optimization method for the power emergency communication network according to claim 1 is characterized in that: The process of obtaining the initial position of each UAV is as follows: According to the spatial distance model between UAV and distribution network communication nodes, the nearest UAV is assigned to each distribution network communication node to obtain the initial position of each UAV.

3. The method for obtaining satellite channel status information according to claim 1, wherein: The satellite channel state information includes the path loss from the low-orbit satellite to the UAV and the line-of-sight (LoS) probability from the UAV to the communication node of the distribution network.

4. The UAV deployment optimization method for the electric power emergency communication network according to claim 2 is characterized in that: The spatial distance model between the UAV and the communication node of the distribution network is: Among them, (x i ,y i ) represents the location coordinates of the communication node i in the distribution network, (x k ,y k ,z k ) represents the initial position coordinates of the UAV, d i is the spatial distance between the communication node i in the distribution network and the UAV.

5. The UAV deployment optimization method for the electric power emergency communication network according to claim 1 is characterized in that: Before solving the UAV position optimization model based on the position information of each distribution network communication node and the initial position of each UAV, the method further includes: Taking the communication coverage of distribution network communication nodes as the optimization goal, and the communication bandwidth requirements, delay index requirements, repeated coverage of distribution network communication nodes and the ability to cover blind areas as constraints, a UAV position optimization model is constructed.

6. The UAV deployment optimization method for the electric power emergency communication network according to claim 1 is characterized in that: The process of solving the UAV position optimization model and obtaining the optimal position of each UAV by taking the position information of each distribution network communication node and the initial position of each UAV as the starting point is as follows: The adaptive iterative algorithm is used to solve the UAV position optimization model using the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV.

7. The adaptive iterative algorithm for solving the UAV position optimization model according to claim 5 is characterized in that: The specific steps include: Based on the acquired post-disaster distribution network communication node location information and satellite channel status data, an open area is selected in the power emergency disaster scenario and UAVs are deployed there as the optimization starting point. The nearest UAV is assigned to each distribution network communication node according to the spatial distance model, and a set of candidate UAV locations is obtained. Based on a set of UAV positions of the candidate solution, the data rate of each distribution network communication node in the distribution network is calculated and the data rate ≥ R th The ratio of the number of distribution network communication nodes to the total number of distribution network communication nodes is used as the fitness F to evaluate the coverage performance of each candidate solution, R th is the threshold rate of the communication node; With the goal of maximizing the fitness F, the distribution density of communication nodes in the distribution network, communication bandwidth and latency requirements, as well as the repeated coverage and coverage blind spots of communication nodes are set as constraints. These provide inputs for the iterative optimization of the adaptive iterative algorithm. Through the selection, crossover selection and mutation operations in the adaptive iterative algorithm, the positions of m UAVs are iteratively optimized. Output the optimized UAV position configuration to obtain the optimal UAV position.

8. A UAV deployment optimization system for power emergency communication network, characterized in that: include: The first acquisition module is used to obtain the location information of each distribution network communication node in the power emergency communication network; The second acquisition module is used to obtain the initial position of each UAV, wherein each UAV carries a base station device; An optimization module is used to solve a UAV position optimization model based on the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV; The deployment module is used to deploy each UVA according to the optimal position of each UAV to complete the UAV deployment optimization of the power emergency communication network.

9. The UAV deployment optimization system for the electric power emergency communication network according to claim 8, characterized in that: The process of obtaining the initial position of each UAV is as follows: According to the spatial distance model between UAV and distribution network communication nodes, the nearest UAV is assigned to each distribution network communication node to obtain the initial position of each UAV.

10. The UAV deployment optimization system for the electric power emergency communication network according to claim 8, characterized in that: The spatial distance model between the UAV and the communication node of the distribution network is: Among them, (x i ,y i ) represents the location coordinates of the communication node i in the distribution network, (x k ,y k ,z k ) represents the initial position coordinates of the UAV, d i is the spatial distance between the communication node i in the distribution network and the UAV.

11. The UAV deployment optimization system for the electric power emergency communication network according to claim 8, characterized in that: Before solving the UAV position optimization model based on the position information of each distribution network communication node and the initial position of each UAV, the method further includes: Taking the communication coverage of distribution network communication nodes as the optimization goal, and the communication bandwidth requirements, delay index requirements, repeated coverage of distribution network communication nodes and the ability to cover blind areas as constraints, a UAV position optimization model is constructed.

12. The UAV deployment optimization system for the electric power emergency communication network according to claim 8, characterized in that: The process of solving the UAV position optimization model and obtaining the optimal position of each UAV by taking the position information of each distribution network communication node and the initial position of each UAV as the starting point is as follows: The adaptive iterative algorithm is used to solve the UAV position optimization model using the position information of each distribution network communication node and the initial position of each UAV to obtain the optimal position of each UAV.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the UAV deployment optimization method for the electric power emergency communication network are implemented as described in any one of claims 1 to 7.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the UAV deployment optimization method for the electric power emergency communication network are implemented as described in any one of claims 1 to 7.

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