Pre-disaster unmanned aerial vehicle pre-distribution method and pre-disaster unmanned aerial vehicle pre-distribution system for electric power emergency communication

Abstract

The invention discloses a pre-disaster unmanned aerial vehicle pre-distribution method and system for electric power emergency communication, and belongs to the technical field of electric power system optimization control. The method comprises the steps that the fault probability of a communication component is generated based on a vulnerability model; according to historical meteorological data, communication network topology and risk factors of communication equipment components, a plurality of possible damage scenes are generated through analogue simulation iteration, variables in different damage scenes are obtained, and the damage conditions of the communication components in the damage scenes are obtained by comparing the relationships between the variables in different damage scenes and fault probabilities; predicting an electric power emergency communication demand when the disaster occurs according to the regional electric power business and the importance degree of the electric power business under the disaster condition; the method comprises the following steps: constructing a pre-disaster unmanned aerial vehicle pre-distribution model based on a power emergency communication demand and a communication component damage condition in a loss scene, determining constraints of a recovery target and an unmanned aerial vehicle pre-distribution position, and solving the unmanned aerial vehicle pre-distribution model based on the loss scene; and obtaining a pre-allocation position and a communication resource reservation strategy of the unmanned aerial vehicle under the condition of meeting the minimum load loss. According to the method, the rapid recovery capability of power emergency communication is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system optimization control and relates to a pre-disaster UAV pre-allocation method for power emergency communication. Background Art

[0002] Modern power systems rely heavily on information and communication technologies to perform critical functions such as load control. Information networks significantly improve the reliability of distribution systems through real-time control. However, the deep coupling between information and physical networks makes power systems more vulnerable to extreme events such as natural disasters. These disasters can disrupt communication lines, especially in areas with complex terrain, such as flooding and valleys. The complex geographical environment makes communication signals susceptible to obstruction and interference, posing severe challenges to power emergency communications and seriously hindering power system recovery.

[0003] To address these challenges, unmanned aerial vehicles (UAVs), as a flexible means of emergency communications, can be rapidly deployed after a disaster, providing temporary coverage and restoring communication capabilities at critical nodes, thereby supporting rapid power system recovery. However, traditional drone deployment, typically through temporary dispatch after a disaster, has significant shortcomings. Post-disaster dispatching takes time, preventing immediate response, which severely impacts power system restoration. Furthermore, temporary drone dispatch may not meet demand due to limited resources or blocked routes, significantly reducing the efficiency of post-disaster power system recovery.

[0004] The pre-allocation method of drones currently used in the military field mainly focuses on maximizing combat effectiveness, involving complex battlefield environments, dynamically changing enemy threats and multi-task coordination. However, the core goal of drone applications in the field of power system emergency communications is to use drones as communication relay nodes to achieve optimal coverage of communication nodes, thereby improving the emergency communication capabilities of the power system. It is mainly aimed at relatively fixed scenarios such as natural disasters. The above method has significant differences in application scenarios and goals from power system emergency communications, and is difficult to be directly applied to the field of power system emergency communications.

[0005] Pre-allocating drone resources before a disaster occurs and optimizing their deployment locations and coverage have become key issues in improving the power emergency communication capabilities. They need to be optimized and adapted specifically to the specific needs of the power system to meet the unique requirements of power emergency communications. Summary of the Invention

[0006] The present invention provides a pre-disaster drone pre-allocation method and system for power emergency communications, which solves the above-mentioned problems by proposing a drone pre-allocation method based on loss scenarios and improves the rapid recovery capability of power emergency communications.

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: In a first aspect, the present invention provides a pre-disaster drone pre-allocation method for power emergency communications, comprising: Generate failure probabilities of communication components based on vulnerability models; Based on historical meteorological data, communication network topology, and risk factors for communication equipment components, multiple possible damage scenarios are generated through iterative simulations. The variables under different damage scenarios are obtained. By comparing the relationship between the variables and failure probabilities under different damage scenarios, the damage to the communication components in each loss scenario is determined. Predict power emergency communication needs during disasters based on regional power services and the importance of power services; Based on the power emergency communication needs and the damage of communication components in the loss scenario, a pre-disaster drone pre-allocation model is constructed, its recovery goals and the constraints of drone pre-allocation positions are clarified, and the drone pre-allocation model based on the loss scenario is solved to obtain the drone pre-allocation position and communication resource reservation strategy while meeting the minimum load loss.

[0008] As a further improvement of the present invention, generating the failure probability of the communication component based on the vulnerability model includes: Collect and standardize historical disaster data and failure records of communication components; Based on the disaster type and intensity, a vulnerability model of communication components is constructed, and the failure probability is quantified using probability distribution function.

[0009] As a further improvement of the present invention, the vulnerability model of the communication component is constructed based on the disaster type and disaster intensity, including: Different vulnerability models are established for different disaster types. Considering the damage to substations and transmission line components, a vulnerability model is constructed. The vulnerability model is specifically:

[0010] Where, Indicates natural disaster factors, represents the historical damage probability of the communication component, m represents the performance index of the communication component, and n represents the operating status of the communication component. Indicates the destructive power of natural disasters on transmission lines or distribution line communication components, represents the resistance of communication components to natural disasters, G(m,n) represents the reliability of equipment operation, is the adjustment factor.

[0011] As a further improvement of the present invention, the variables under different damage scenarios are obtained ,include:

[0012] Where, Represents the calculated variables The importance of weather conditions, equipment reliability, equipment failure conditions and communication component redundancy during the process, and ; Indicates the damage coefficient of a certain weather condition to the component, Indicates the intensity of the weather condition. represents the failure probability of a component per unit time, and Represents the total component operation time and failure time, It represents the redundancy of the communication equipment, which is a binary variable.

[0013] As a further improvement of the present invention, obtaining the damage status of the communication components in the loss scenario by comparing the relationship between the variables and the failure probability in different damage scenarios includes: ① If > , it is assumed that the component fails in an extreme disaster scenario; ②If ≤ , it is assumed that the components remain normal under extreme disaster scenarios; Where, The variables determined for the different damage scenarios generated by the simulation, is the failure probability of the communication component calculated by the fragility model.

[0014] As a further improvement of the present invention, the method of predicting the power emergency communication demand when a disaster occurs based on regional power services and the importance of power services under disaster conditions includes: The business density of backbone nodes such as substations with high voltage levels is higher than that of other low voltage levels. The importance of the business carried refers to the importance of the equipment in carrying different types of power industries. According to the safety zoning principle of power business, the business importance of the first safety zone is the highest, with a weight value of 100-75; the business importance of the second safety zone is relatively high, with a weight value of 75-50; the business importance of the third safety zone is average, with a weight value of 50-25; and the business importance of the fourth safety zone is the lowest, with a weight value of 25-0.

[0015] As a further improvement of the present invention, the pre-disaster drone pre-allocation model is constructed based on the power emergency communication needs and the damage of the communication components in the loss scenario, including: Based on the communication needs of the power business in the disaster area and the damage to the communication components, the communication bandwidth, end-to-end service transmission distance, and communication resources required to reserve service delay communication indicators that drones need to provide as communication relay nodes are determined, and a pre-disaster drone pre-allocation model is constructed.

[0016] As a further improvement of the present invention, the pre-disaster drone pre-allocation model takes into account the load loss in the power grid and aims to minimize it. The pre-disaster drone pre-allocation model is expressed as follows:

[0017] In the formula Represents the scene in the simulation The probability of occurrence, Representative Node The node weight at is a node The active load demand at is a node The active load at.

[0018] As a further improvement of the present invention, the constraints of the pre-disaster drone pre-allocation model include: The pre-assigned position constraints of the drone are as follows:

[0019]

[0020]

[0021] The number of drones and service users are constrained as follows:

[0022]

[0023] The number of drone services for a certain communication node is constrained as follows:

[0024] In the above constraints, 、 、 、 They represent the horizontal limits of the drone’s movement, 、 Indicates the altitude limit of the drone. The drone fleet representing the allocation process, Indicates that the candidate location is assigned to the drone, otherwise, Indicates drone Serving nodes ,At the same time, the service status is affected by the drone coverage.

[0025] As a further improvement of the present invention, solving the drone pre-allocation model based on the loss scenario includes: Obtain the pre-assigned position of the UAV. Considering the UAV channel gain constraint, the channel gain of the communication node is a monotonically decreasing function of the flight altitude. The optimal flight altitude and maximum coverage radius of the UAV are solved using the following optimization model:

[0026] st

[0027]

[0028] According to the optimization model, the flight height of the UAV is solved, and the three-dimensional position optimization of the UAV is decoupled into two-dimensional position optimization. After the flight height is determined, it is no longer used as an optimization variable; by constructing a two-dimensional position optimization model for the UAV and linearizing the three-dimensional constraints, the two-dimensional position is optimized with the help of the existing solver. Solve it.

[0029] As a further improvement of the present invention, it also includes: On the basis of pre-allocation, a dynamic adjustment mechanism is established to adjust the allocation of drones according to real-time disaster information and task execution status; the dynamic adjustment mechanism includes task reallocation, drone redeployment and temporary allocation of resources.

[0030] In a second aspect, the present invention provides a pre-disaster drone pre-allocation system for power emergency communications, comprising: A failure probability generation module, used to generate the failure probability of the communication component based on the vulnerability model; The communication component damage acquisition module is used to generate multiple possible damage scenarios through simulation iterations based on historical meteorological data, communication network topology, and risk factors of communication equipment components. This module obtains variables under different damage scenarios and compares the relationship between variables and failure probabilities under different damage scenarios to determine the damage status of communication components in the loss scenario. The power emergency communication demand prediction module is used to predict the power emergency communication demand when a disaster occurs based on the regional power business and the importance of the power business; The location pre-allocation module is used to build a pre-disaster drone pre-allocation model based on the power emergency communication needs and the damage of communication components in the loss scenario, clarify its recovery goals and the constraints of drone pre-allocation locations, solve the drone pre-allocation model based on the loss scenario, and obtain the drone pre-allocation location and communication resource reservation strategy while meeting the minimum load loss.

[0031] Optionally, the failure probability generating module is specifically configured to: Collect and standardize historical disaster data and failure records of communication components; Based on the disaster type and intensity, a vulnerability model of communication components is constructed, and the failure probability is quantified using probability distribution function.

[0032] Optionally, constructing a vulnerability model of a communication component based on the disaster type and disaster intensity includes: Different vulnerability models are established for different disaster types. Considering the damage to substations and transmission line components, a vulnerability model is constructed. The vulnerability model is specifically:

[0033] Where, Indicates natural disaster factors, represents the historical damage probability of the communication component, m represents the performance index of the communication component, and n represents the operating status of the communication component. Indicates the destructive power of natural disasters on transmission lines or distribution line communication components, represents the resistance of communication components to natural disasters, G(m,n) represents the reliability of equipment operation, is the adjustment factor.

[0034] Optionally, the variables under different damage scenarios are obtained ,include:

[0035] Where, Represents the calculated variables The importance of weather conditions, equipment reliability, equipment failure conditions and communication component redundancy during the process, and ; Indicates the damage coefficient of a certain weather condition to the component, Indicates the intensity of the weather condition. represents the failure probability of a component per unit time, and Represents the total component operation time and failure time, It represents the redundancy of the communication equipment, which is a binary variable.

[0036] Optionally, obtaining the damage status of the communication component in the loss scenario by comparing the relationship between the variable and the failure probability in different damage scenarios includes: ① If > , it is assumed that the component fails in an extreme disaster scenario; ②If ≤ , it is assumed that the components remain normal under extreme disaster scenarios; Where, The variables determined for the different damage scenarios generated by the simulation, is the failure probability of the communication component calculated by the fragility model.

[0037] Optionally, the power emergency communication demand prediction module is specifically used to: The business density of backbone nodes such as substations with high voltage levels is higher than that of other low voltage levels. The importance of the business carried refers to the importance of the equipment in carrying different types of power industries. According to the safety zoning principle of power business, the business importance of the first safety zone is the highest, with a weight value of 100-75; the business importance of the second safety zone is relatively high, with a weight value of 75-50; the business importance of the third safety zone is average, with a weight value of 50-25; and the business importance of the fourth safety zone is the lowest, with a weight value of 25-0.

[0038] Optionally, the position pre-allocation module is specifically configured to: Based on the communication needs of the power business in the disaster area and the damage to the communication components, the communication bandwidth, end-to-end service transmission distance, and communication resources required to reserve service delay communication indicators that drones need to provide as communication relay nodes are determined, and a pre-disaster drone pre-allocation model is constructed.

[0039] Optionally, the pre-disaster drone pre-allocation model is expressed as follows by considering the load loss in the power grid and aiming at minimizing it:

[0040] In the formula Represents the scene in the simulation The probability of occurrence, Representative Node The node weight at is a node The active load demand at is a node The active load at.

[0041] Optionally, the constraints of the pre-disaster drone pre-allocation model include: The pre-assigned position constraints of the drone are as follows:

[0042]

[0043]

[0044] The number of drones and service users are constrained as follows:

[0045]

[0046] The number of drone services for a certain communication node is constrained as follows:

[0047] In the above constraints, 、 、 、 They represent the horizontal limits of the drone’s movement, 、 Indicates the altitude limit of the drone. The drone fleet representing the allocation process, Indicates that the candidate location is assigned to the drone, otherwise, Indicates drone Serving nodes ,At the same time, the service status is affected by the drone coverage.

[0048] Optionally, solving a drone pre-allocation model based on a loss scenario includes: Obtain the pre-assigned position of the UAV. Considering the UAV channel gain constraint, the channel gain of the communication node is a monotonically decreasing function of the flight altitude. The optimal flight altitude and maximum coverage radius of the UAV are solved using the following optimization model:

[0049] st

[0050]

[0051] According to the optimization model, the flight height of the UAV is solved, and the three-dimensional position optimization of the UAV is decoupled into two-dimensional position optimization. After the flight height is determined, it is no longer used as an optimization variable; by constructing a two-dimensional position optimization model for the UAV and linearizing the three-dimensional constraints, the two-dimensional position is optimized with the help of the existing solver. Solve it.

[0052] Optionally, it also includes: On the basis of pre-allocation, a dynamic adjustment mechanism is established to adjust the allocation of drones according to real-time disaster information and task execution status; the dynamic adjustment mechanism includes task reallocation, drone redeployment and temporary allocation of resources.

[0053] In a third aspect, the present invention provides an electronic 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 pre-disaster drone pre-allocation method for power emergency communications is implemented.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the pre-disaster drone pre-allocation method for power emergency communications.

[0055] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which instruct a computer to execute the pre-disaster drone pre-allocation method for power emergency communications.

[0056] The beneficial effects of the present invention compared to the prior art are: The present invention proposes a pre-disaster drone pre-allocation model, which effectively evaluates the damage of the power system physical network and communication network before the disaster, determines the drone pre-deployment position through a two-stage random optimization model, and can quickly establish an emergency communication link after a fault occurs, greatly shortening the emergency communication recovery time. While solving the problem that existing emergency communication means are difficult to deploy in complex terrain areas, the present invention saves manpower, material resources and other resources for post-disaster power system recovery. By establishing a dynamic adjustment mechanism, the present invention can adjust the allocation of drones according to real-time disaster information and task execution status, thereby improving the adaptability and reliability of the emergency communication system, thereby improving the efficiency of post-disaster power system recovery. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 is a flow chart of the present invention; Figure 2 This is a schematic diagram of drone emergency communications; Figure 3 Schematic diagram of drone coverage; Figure 4 The present invention provides a pre-disaster UAV pre-allocation system for power emergency communications; Figure 5 This is a schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0059] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0060] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0061] The first object of the present invention is to provide a pre-disaster UAV pre-allocation method for power emergency communication, such as Figure 1 As shown, the method includes: Step a: Analyze the relationship between extreme disaster climate and damage to power system communication components. Based on the analysis and prediction of historical data on power grid communication component failures, generate the failure probability of communication components through vulnerability models. ; Furthermore, the constructing of a vulnerability model based on the historical fault data of the power grid communication component includes: ① Collect historical disaster data and communication component failure records and standardize them to ensure they are suitable for model construction; ② Based on the disaster type and intensity, a vulnerability model of the communication components is constructed, and the probability of failure is quantified using a probability distribution function; Based on information such as the failure probability of the component, multiple possible damage scenarios are generated through simulation, and the damage status of the communication component is determined through comparison.

[0062] As a specific solution, this embodiment constructs a vulnerability model based on the extreme disaster type and the damage to the communication components in the power system to obtain its failure probability, and considers the damage caused by the disaster to components such as substations and transmission lines.

[0063] In this application example, the vulnerability model of the grid communication component is calculated as follows:

[0064] Where, Indicates natural disaster factors, represents the historical damage probability of the communication component, m represents the performance index of the communication component, and n represents the operating status of the communication component. Indicates the destructive power of natural disasters on transmission lines or distribution line communication components, represents the resistance of communication components to natural disasters, G(m,n) represents the reliability of equipment operation, is the adjustment factor.

[0065] Step b: Input historical meteorological data, communication network topology, risk factors of communication equipment components and other information, and generate multiple possible damage scenarios through iteration, and compare the variables under different damage scenarios. and failure probability The relationship between them is used to obtain the damage of communication components in the loss scenario.

[0066] As a specific solution, in this application example, the calculation variable The process is as follows:

[0067] Where, Represents the calculated variables The importance of weather conditions, equipment reliability, equipment failure conditions and communication component redundancy during the process, and ; Indicates the damage coefficient of a certain weather condition to the component, Indicates the intensity of the weather condition. represents the failure probability of a component per unit time, and Represents the total component operation time and failure time, It represents the redundancy of the communication equipment, which is a binary variable.

[0068] In this application example, the specific process of determining whether a communication component is damaged is as follows: ① If > , it is assumed that the component fails in an extreme disaster scenario; ②If ≤ , it is assumed that the component remains normal under extreme disaster scenarios; Where, The variables determined for the different damage scenarios generated by the simulation, That is, the probability of communication component failure calculated by the vulnerability model.

[0069] Step c: Figure 2As shown in the figure, the power emergency communication demand during a disaster is predicted based on the regional power business and the importance of the power business under disaster conditions. Combined with the damage to the communication components in the predicted loss scenario, the deployment location of the UAV communication relay node and the corresponding communication resource reservation strategy are determined.

[0070] Furthermore, based on the regional distribution of power services and the importance of power services, it is characterized in that the services of backbone nodes such as substations with high voltage levels are more densely populated than those of other low voltage levels. The importance of the carried services refers to the importance of the equipment in carrying different types of power services. According to the safety zoning principle of power services, the first safety zone has the highest business importance, with a weight value of 100-75; the second safety zone has a relatively high business importance, with a weight value of 75-50; the third safety zone has an average business importance, with a weight value of 50-25; and the fourth safety zone has the lowest business importance, with a weight value of 25-0.

[0071] Step d: Based on the above-mentioned component failure conditions and power emergency communication requirements, and based on the damage to the communication components in the loss scenario, a pre-disaster drone pre-allocation model is constructed to clarify its recovery goals and the constraints of the drone pre-allocation position. By solving the drone pre-allocation model based on the loss scenario, the drone pre-allocation position and communication resource reservation strategy are obtained while meeting the minimum load loss.

[0072] Furthermore, based on the communication needs of the power business in the disaster area and the damage to the communication components, the communication bandwidth, end-to-end service transmission distance, service delay and other communication indicators that the drone needs to provide as a communication relay node are determined, and the communication resources required to be reserved are constructed to build a pre-disaster drone pre-allocation model.

[0073] Furthermore, the construction of the pre-disaster drone pre-allocation model includes: Set the objective function of the pre-disaster pre-allocation model; Set the constraints of drones in the pre-disaster pre-allocation model, including the movement space of drones and the number of drones and the number of drone service recipients wait; Setting up communication nodes Communication routing constraints; Based on the solved position variables, the pre-assigned position of the UAV is obtained.

[0074] This embodiment builds a pre-disaster drone pre-allocation model based on the damage of components in the loss scenario. In this application example, by comparing the variables in the randomly generated scenario and communication component failure probability Determine the damage of components and pre-allocate drones with the goal of minimizing load loss.

[0075] Step c-1: Set the objective function of the pre-disaster pre-allocation model, that is, to achieve the pre-allocation of drones with the goal of minimizing the expected load loss under all disaster scenarios.

[0076] In this application example, the objective function of drone pre-allocation is as follows:

[0077] In the formula Indicates the situation The probability of occurrence, Representation node The load loss weight at Representation node The active load demand at Representation node The active load at.

[0078] Step c-2: Set the constraints for UAV pre-allocation, for example, the number of UAVs is constrained to , Limitation on the number of drone service users A series of constraints; In this application example, the pre-assigned position constraints of the drone are as follows:

[0079]

[0080]

[0081] In this application example, the number of drones and service user constraints are as follows:

[0082]

[0083] In this application example, the number of drone services for a certain communication node is constrained as follows:

[0084] In the above constraints, 、 、 、 They represent the horizontal limits of the drone’s movement, 、 Indicates the altitude limit of the drone. The drone fleet representing the allocation process, Indicates that the candidate location is assigned to the drone, otherwise, Indicates drone Serving nodes ,At the same time, the service status is affected by the drone coverage.

[0085] Step c-3: Set up communication nodes Related communication state constraints, the communication state of the node is affected by the UAV's influence on the service state.

[0086] In this application example, the node The communication state constraints are as follows:

[0087] in, Faulty node The communication status of

[0088] In this application example, the drone and communication nodes The channel gain between satisfies the following requirement:

[0089] in, represents the minimum channel gain required for communication nodes, UAVs and communication nodes The channel gain model is as follows:

[0090] Where, For drones With network nodes The distance between is the probability of line-of-sight transmission, is the probability of non-line-of-sight transmission, is the path loss per unit distance under line-of-sight transmission conditions, is the attenuation factor caused by non-line-of-sight transmission, are modeling parameters related to the path transmission model.

[0091] Furthermore, the method of solving the pre-assigned position of the drone includes: The relationship between the UAV’s flight altitude and expected coverage radius is obtained. While satisfying the UAV’s channel gain constraint, the UAV’s flight altitude and horizontal position are decoupled, thereby converting the three-dimensional allocation model into a two-dimensional problem.

[0092] Linearize the constraints; The optimization problem with inequality constraints is solved by constructing a Lagrangian function and introducing the KKT condition. That is, the coverage radius of the UAV is solved by the gradient condition and complementary relaxation condition in KKT, and the coverage radius and the flight height of the UAV are solved.

[0093] More specifically, this embodiment solves the drone pre-allocation model to obtain the drone pre-allocation position. Since the deployment of drones involves flight altitude and horizontal position, directly solving the three-dimensional problem is more complex. In this application example, the decoupling of flight altitude and horizontal position is achieved through the following steps: Step d-1: Since the channel gain is a monotonically decreasing function of the flight altitude, and there is a corresponding relationship between the flight altitude and the coverage radius (such as Figure 3 As shown in the figure, this patent solves the optimal flight altitude and maximum coverage radius of the drone through the following optimization model:

[0094] st

[0095]

[0096] In the example of this application, the optimization problem with inequality constraints is solved by constructing a Lagrangian function and introducing the KKT condition. That is, the coverage radius of the UAV is solved by the gradient condition and complementary relaxation condition in KKT, and then the coverage radius and flight altitude of the UAV are obtained.

[0097] Step d-2: Decouple the flight altitude from the three-dimensional problem. Once the flight altitude is determined, it is no longer used as an optimization variable. This transforms the UAV deployment problem into a two-dimensional problem. The constraints are linearized to obtain the horizontal two-dimensional position. The UAV communication constraints are then two-dimensionalized in the allocation model, yielding the following equation:

[0098] And linearize it to get the following formula:

[0099] In the example of this application, in order to solve the problem of time-consuming calculation when the number of damage scenarios is very large, the progressive hedging (PH) algorithm is used to solve it, that is, the large-scale random optimization problem is converted into multiple small-scale sub-problems, which significantly reduces the computational complexity.

[0100] Step e: Based on the pre-allocation, a dynamic adjustment mechanism is established to adjust the allocation of drones based on real-time disaster information and mission execution status. The dynamic adjustment mechanism includes task reallocation, drone redeployment, and temporary resource allocation.

[0101] In response to the problem of unknown damage to communication components in power systems after extreme disasters, the present invention evaluates the failure probability of communication components under extreme weather events by constructing its vulnerability model. The model effectively obtains the failure probability of each communication component under different disaster scenarios by considering the intensity of disasters, the physical characteristics of communication components, and environmental factors. At the same time, combined with the specific meteorological disasters that may occur and the specific application scenarios of electricity, a simulation method is used to generate a large number of random disaster scenarios to simulate the damage of communication components under different disaster intensities. According to the regional power business and the importance of power business under disaster conditions, the power emergency communication needs when a disaster occurs are predicted. Combined with the damage to the communication components in the predicted loss scenario, the deployment location of the UAV communication relay node and the corresponding communication resource reservation strategy are determined, and a dynamic adjustment mechanism including task reallocation, UAV redeployment and resources is established. The allocation of UAVs can be adjusted according to real-time disaster information and task execution status. If overlap or blind spots are found in the coverage area during execution, task redistribution and dynamic adjustment mechanisms will be activated to ensure coverage without overlap or blind spots. Assuming that each drone is responsible for a coverage area of 0.8km² and the drone is lifted to an altitude of 200 meters, the communication coverage radius can be expanded to 15km. The drone uses the Mesh networking mode to build a temporary communication chain in the power flood disaster area. The effect after optimization is shown in the following table:

[0102] like Figure 4 As shown, the second purpose of the present invention is to provide a pre-disaster drone pre-allocation system for power emergency communications, including: a fault probability generation module 100, a communication component damage status acquisition module 200, a power emergency communication demand prediction module 300, and a position pre-allocation module 400. The pre-disaster drone pre-allocation system for power emergency communications of the present invention is based on the pre-disaster drone pre-allocation method for power emergency communications.

[0103] A failure probability generating module 100 is configured to generate a failure probability of a communication component based on a vulnerability model; The communication component damage condition acquisition module 200 is used to generate multiple possible damage scenarios through simulation iterations based on historical meteorological data, communication network topology, and risk factors of communication equipment components, obtain variables under different damage scenarios, and compare the relationship between variables and failure probabilities under different damage scenarios to determine the damage condition of the communication components in the loss scenario. The power emergency communication demand prediction module 300 is used to predict the power emergency communication demand when a disaster occurs based on the regional power services and the importance of the power services; The location pre-allocation module 400 is used to build a pre-disaster drone pre-allocation model based on the power emergency communication needs and the damage to the communication components in the loss scenario, clarify its recovery goals and the constraints of the drone pre-allocation location, solve the drone pre-allocation model based on the loss scenario, and obtain the drone's pre-allocation location and communication resource reservation strategy while meeting the minimum load loss.

[0104] like Figure 5 As shown, a third object of an embodiment of the present invention is to provide an electronic device, comprising a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned pre-disaster drone pre-allocation method for power emergency communication. The electronic device also includes a communication interface 703 and a bus 704.

[0105] A fourth object of an embodiment of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned pre-disaster drone pre-allocation method for power emergency communications.

[0106] A fifth objective of an embodiment of the present invention is to provide a computer program product, comprising computer instructions that instruct a computer to execute the above-mentioned pre-disaster drone pre-allocation method for power emergency communications.

[0107] 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.

[0108] 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 A step that specifies a function in one or more boxes.

[0109] The present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to magnetic disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0110] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, 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 processes in the flowcharts and / or block diagrams. 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.

[0111] Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A pre-disaster UAV pre-allocation method for power emergency communications, characterized in that: include: Generate failure probabilities of communication components based on vulnerability models; Based on historical meteorological data, communication network topology, and risk factors for communication equipment components, multiple possible damage scenarios are generated through iterative simulations. The variables under different damage scenarios are obtained. By comparing the relationship between the variables and failure probabilities under different damage scenarios, the damage to the communication components in each loss scenario is determined. Predict power emergency communication needs during disasters based on regional power services and the importance of power services; Based on the power emergency communication needs and the damage of communication components in the loss scenario, a pre-disaster drone pre-allocation model is constructed, its recovery goals and the constraints of drone pre-allocation positions are clarified, and the drone pre-allocation model based on the loss scenario is solved to obtain the drone pre-allocation position and communication resource reservation strategy while meeting the minimum load loss.

2. The pre-disaster UAV pre-allocation method for power emergency communication according to claim 1 is characterized in that: Generating the failure probability of the communication component based on the vulnerability model includes: Collect and standardize historical disaster data and failure records of communication components; Based on the disaster type and intensity, a vulnerability model of communication components is constructed, and the failure probability is quantified using probability distribution function.

3. The method for pre-disaster UAV allocation for power emergency communication according to claim 2, characterized in that: The vulnerability model of the communication components is constructed based on the disaster type and disaster intensity, including: Different vulnerability models are established for different disaster types. Considering the damage to substations and transmission line components, a vulnerability model is constructed. The vulnerability model is specifically: Where, Indicates natural disaster factors, represents the historical damage probability of the communication component, m represents the performance index of the communication component, and n represents the operating status of the communication component. Indicates the destructive power of natural disasters on transmission lines or distribution line communication components, represents the resistance of communication components to natural disasters, G(m,n) represents the reliability of equipment operation, is the adjustment factor.

4. The pre-disaster UAV pre-allocation method for power emergency communication according to claim 1, characterized in that: Obtaining variables under different damage scenarios ,include: Where, Represents the calculated variables The importance of weather conditions, equipment reliability, equipment failure conditions and communication component redundancy during the process, and ; Indicates the damage coefficient of a certain weather condition to the component, Indicates the intensity of the weather condition. represents the failure probability of a component per unit time, and Represents the total component operation time and failure time, It represents the redundancy of the communication equipment, which is a binary variable.

5. The method for pre-disaster UAV allocation for power emergency communication according to claim 1, characterized in that: The damage status of the communication components in the loss scenario is obtained by comparing the relationship between variables and failure probabilities in different damage scenarios, including: ① If > , it is assumed that the component fails in an extreme disaster scenario; ②If ≤ , it is assumed that the components remain normal under extreme disaster scenarios; Where, The variables determined for the different damage scenarios generated by the simulation, is the failure probability of the communication component calculated by the fragility model.

6. The method for pre-disaster UAV allocation for power emergency communication according to claim 1, characterized in that: The prediction of power emergency communication needs when a disaster occurs based on regional power services and the importance of power services under disaster conditions includes: The business density of backbone nodes such as substations with high voltage levels is higher than that of other low voltage levels. The importance of the business carried refers to the importance of the equipment in carrying different types of power industries. According to the safety zoning principle of power business, the business importance of the first safety zone is the highest, with a weight value of 100-75; the business importance of the second safety zone is relatively high, with a weight value of 75-50; the business importance of the third safety zone is average, with a weight value of 50-25; and the business importance of the fourth safety zone is the lowest, with a weight value of 25-0.

7. The pre-disaster UAV pre-allocation method for power emergency communication according to claim 1, characterized in that: The pre-disaster drone pre-allocation model is constructed based on the power emergency communication needs and the damage of communication components in the loss scenario, including: Based on the communication needs of the power business in the disaster area and the damage to the communication components, the communication bandwidth, end-to-end service transmission distance, and communication resources required to reserve service delay communication indicators that drones need to provide as communication relay nodes are determined, and a pre-disaster drone pre-allocation model is constructed.

8. The method for pre-disaster UAV allocation for power emergency communication according to claim 1, characterized in that: The pre-disaster UAV pre-allocation model considers the load loss in the power grid and aims to minimize it. The pre-disaster UAV pre-allocation model is expressed as: In the formula Represents the scene in the simulation The probability of occurrence, Representative Node The node weight at is a node The active load demand at is a node The active load at.

9. The method for pre-disaster UAV allocation for power emergency communication according to claim 8, characterized in that: The constraints of the pre-disaster drone pre-allocation model include: The pre-assigned position constraints of the drone are as follows: The number of drones and service users are constrained as follows: The number of drone services for a certain communication node is constrained as follows: In the above constraints, 、 、 、 They represent the horizontal limits of the drone’s movement, 、 Indicates the altitude limit of the drone. The drone fleet representing the allocation process, Indicates that the candidate location is assigned to the drone, otherwise, Indicates drone Serving nodes ,At the same time, the service status is affected by the drone coverage.

10. The method for pre-disaster UAV allocation for power emergency communication according to claim 1, characterized in that: The method of solving the loss scenario-based drone pre-allocation model includes: Obtain the pre-assigned position of the UAV. Considering the UAV channel gain constraint, the channel gain of the communication node is a monotonically decreasing function of the flight altitude. The optimal flight altitude and maximum coverage radius of the UAV are solved using the following optimization model: s.t. According to the optimization model, the flight height of the UAV is solved, and the three-dimensional position optimization of the UAV is decoupled into two-dimensional position optimization. After the flight height is determined, it is no longer used as an optimization variable; by constructing a two-dimensional position optimization model for the UAV and linearizing the three-dimensional constraints, the two-dimensional position is optimized with the help of the existing solver. Solve it.

11. The method for pre-disaster UAV allocation for power emergency communication according to claim 1, characterized in that: Also includes: On the basis of pre-allocation, a dynamic adjustment mechanism is established to adjust the allocation of drones according to real-time disaster information and task execution status; the dynamic adjustment mechanism includes task reallocation, drone redeployment and temporary allocation of resources.

12. A pre-disaster UAV pre-allocation system for power emergency communications, characterized in that: include: A failure probability generation module, used to generate the failure probability of the communication component based on the vulnerability model; The communication component damage acquisition module is used to generate multiple possible damage scenarios through simulation iterations based on historical meteorological data, communication network topology, and risk factors of communication equipment components. This module obtains variables under different damage scenarios and compares the relationship between variables and failure probabilities under different damage scenarios to determine the damage status of communication components in the loss scenario. The power emergency communication demand prediction module is used to predict the power emergency communication demand when a disaster occurs based on the regional power business and the importance of the power business; The location pre-allocation module is used to build a pre-disaster drone pre-allocation model based on the power emergency communication needs and the damage of communication components in the loss scenario, clarify its recovery goals and the constraints of drone pre-allocation locations, solve the drone pre-allocation model based on the loss scenario, and obtain the drone pre-allocation location and communication resource reservation strategy while meeting the minimum load loss.

13. An electronic device, characterized in that: The invention comprises 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 method for pre-disaster UAV pre-allocation for power emergency communication according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the pre-disaster drone pre-allocation method for power emergency communication according to any one of claims 1 to 11.

15. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computer to execute the pre-disaster drone pre-allocation method for power emergency communications as described in any one of claims 1-11.

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