Unmanned aerial vehicle auxiliary emergency communication network optimization method and system considering weather risk

Abstract

The invention belongs to the field of unmanned aerial vehicle auxiliary emergency communication, and provides an unmanned aerial vehicle auxiliary emergency communication network optimization method and system considering weather risks in order to solve the problem that an unmanned aerial vehicle may crash or fail in task due to accidental faults caused by neglecting the weather risks. The unmanned aerial vehicle auxiliary emergency communication network optimization method considering the weather risk comprises the following steps: acquiring related information of an affected area and related information of an unmanned aerial vehicle in an emergency considering the weather risk, and further optimizing the unmanned aerial vehicle auxiliary emergency communication network based on an unmanned aerial vehicle auxiliary emergency communication framework considering time consumption and energy consumption. Constructing an unmanned aerial vehicle auxiliary emergency communication path planning model; based on the unmanned aerial vehicle auxiliary emergency communication path planning model, the optimal path scheme of unmanned aerial vehicle auxiliary emergency communication is calculated, and the purpose of efficiently designing and optimizing the unmanned aerial vehicle auxiliary emergency communication network scheme after an emergency occurs is achieved.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV)-assisted emergency communications, and in particular, relates to a UAV-assisted emergency communications network optimization method and system taking weather risks into consideration. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] After an emergency occurs, traditional ground communication networks are unable to meet the communication needs of the disaster area due to infrastructure damage or environmental impacts. The drone-assisted emergency communication network has become an important means of post-disaster communication recovery due to its flexible deployment and rapid response. By deploying drone nodes in the disaster area, a temporary communication network can be quickly established to support rescue command, information transmission and communication with disaster victims. However, the complex and severe weather after the disaster poses a great challenge to the reliability and stability of the drone-assisted emergency communication network.

[0004] Complex and severe weather after a disaster (including strong winds, heavy rain, ice and snow, and low temperatures, etc.) not only affects the flight stability of drones, but may also cause hardware damage, increased energy consumption, and interruption of flight missions. For example, strong winds may cause drones to deviate from their flight trajectory or be unable to hover; rainfall may cause water to enter the equipment or short-circuit electronic components; ice and snow, and low temperature environments may reduce battery performance, resulting in a significant reduction in battery life. Therefore, in the design and optimization process of drone-assisted emergency communication networks, ignoring weather risks may cause drones to crash or missions to fail due to unexpected failures, thereby increasing rescue costs and delaying critical rescue work. In existing drone-assisted emergency communication network design research, most of the focus is on optimizing communication performance, such as coverage and transmission efficiency, which makes it difficult to cope with the challenges of complex and severe weather conditions in emergencies to drone-assisted emergency communication networks. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and system for optimizing a drone-assisted emergency communication network taking weather risks into consideration. The method and system construct a drone-assisted emergency communication framework taking time consumption and energy consumption into consideration, establish a drone-assisted emergency communication path planning model taking weather risks, operating energy consumption and mission requirements into consideration, and solve the model, thereby achieving the purpose of efficiently designing and optimizing drone-assisted emergency communication network solutions after an emergency occurs.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for optimizing a drone-assisted emergency communication network taking weather risks into consideration.

[0007] In one or more embodiments, a method for optimizing a drone-assisted emergency communication network considering weather risks is provided, comprising: Obtaining relevant information about the disaster-stricken area in the emergency and relevant information about the drone; wherein the relevant information about the disaster-stricken area in the emergency includes the set of user cluster nodes in the disaster area, the distance between each node, the communication demand size of the user cluster and the weather risk; the relevant information about the drone includes the set of drone take-off and landing points, the risk threshold of the drone, battery capacity, flight speed, propulsion and hovering power and communication parameters; Based on the relevant information of the disaster-stricken area and the drone-related information in the emergency, a drone-assisted emergency communication path planning model is constructed based on the drone-assisted emergency communication framework considering time consumption and energy consumption; Based on the UAV-assisted emergency communication path planning model, the optimal path plan for UAV-assisted emergency communication is calculated; Among them, the UAV-assisted emergency communication path planning model considers UAV assignment constraints, UAV path constraints, UAV risk constraints and UAV energy consumption constraints, minimizes the sum of UAV path cost and risk loss cost within the range of decision variables, and optimizes UAV-assisted emergency communication path planning.

[0008] As an implementation method, the objective function of the drone-assisted emergency communication path planning model is to minimize the weighted sum of the drone path cost and the risk loss cost.

[0009] As an implementation method, in the process of determining drone assignment constraints, drone assignment variables are introduced, the relationship between drones and user clusters is analyzed, and the assignment constraints for drone-assisted emergency communication network optimization are constructed.

[0010] As an implementation method, in the process of determining the drone path constraints, drone path variables are introduced, the relationship between path variables and assignment variables is analyzed, and the association constraints of decision variables in the drone-assisted emergency communication network are constructed, which include the relationship constraints between path variables and assignment variables and path feasibility constraints.

[0011] As an implementation method, in the process of determining drone risk constraints, drone risk variables are introduced, the relationship between risk variables and path variables is analyzed, and the association constraints of decision variables in the drone-assisted emergency communication network are constructed, which include the relationship constraints between risk variables and path variables and the maximum risk constraints acceptable to drones.

[0012] As an implementation method, in the process of determining the energy consumption constraints of the UAV, the UAV energy consumption variables are introduced, the UAV communication, propulsion and hovering energy consumption are analyzed, the relationship between the energy consumption variables and the path variables is analyzed, and the association constraints of the decision variables in the UAV-assisted emergency communication network are constructed, which include the relationship constraints between the energy consumption variables and the path variables and the energy consumption level of the UAV when leaving and returning to the take-off and landing points.

[0013] As an implementation method, the Benders decomposition algorithm is used to solve the UAV-assisted emergency communication path planning model; wherein the designed Benders decomposition algorithm consists of a main problem and sub-problems, wherein the main problem involves integer variables and their constraints in the UAV-assisted emergency communication path planning model, which is used to provide a lower bound of the UAV-assisted emergency communication path planning model; the sub-problem involves continuous variables and their constraints in the UAV-assisted emergency communication path planning model, which aims to provide an upper bound of the UAV-assisted emergency communication path planning model; the main problem and sub-problems are solved iteratively until the error between the upper bound and the lower bound is within a preset range.

[0014] A second aspect of the present invention provides a drone-assisted emergency communication network optimization system that takes weather risks into account.

[0015] In one or more embodiments, a drone-assisted emergency communication network optimization system considering weather risks includes: An information acquisition module, which is used to obtain relevant information about the disaster-stricken area in the emergency and relevant information about the drone; wherein the relevant information about the disaster-stricken area in the emergency includes the set of user cluster nodes in the disaster area, the distance between each node, the communication demand size of the user cluster and the weather risk; the relevant information about the drone includes the set of drone take-off and landing points, the risk threshold of the drone, the battery capacity, the flight speed, the propulsion and hovering power and the communication parameters; A model building module is used to build a UAV-assisted emergency communication path planning model based on the relevant information of the disaster-stricken area and the UAV-related information in the emergency, and based on the UAV-assisted emergency communication framework considering time consumption and energy consumption; A model solving module, which is used to calculate the optimal path plan for UAV-assisted emergency communication based on the UAV-assisted emergency communication path planning model; Among them, the UAV-assisted emergency communication path planning model considers UAV assignment constraints, UAV path constraints, UAV risk constraints and UAV energy consumption constraints, minimizes the sum of UAV path cost and risk loss cost within the range of decision variables, and optimizes UAV-assisted emergency communication path planning.

[0016] A third aspect of the present invention provides a computer-readable storage medium.

[0017] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the above-mentioned method for optimizing a drone-assisted emergency communication network taking into account weather risks.

[0018] A fourth aspect of the present invention provides an electronic device.

[0019] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the above-mentioned method for optimizing a drone-assisted emergency communication network taking into account weather risks are implemented.

[0020] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention considers the impact of complex and severe weather on drone-assisted emergency communications in emergencies, constructs a drone-assisted emergency communication path planning model, and then considers drone assignment constraints, drone path constraints, drone risk constraints and drone energy consumption constraints in combination with relevant information of the disaster-stricken area and drone-related information in the emergency, and minimizes the sum of drone path cost and risk loss cost within the range of decision variables, thereby achieving the purpose of efficiently designing and optimizing the drone-assisted emergency communication network solution after the occurrence of an emergency.

[0021] (2) The present invention analyzes the role of drones in assisting emergency communications, considers the time and energy consumption of drone-assisted emergency communications, constructs a drone-assisted emergency communications framework, and combines the relationship between decision variables such as drone assignment, path, risk and energy consumption to construct corresponding relationship constraints. A drone-assisted emergency communications path planning model is established, which effectively improves the performance of the drone-assisted emergency communications network and significantly enhances its stability and reliability under complex and severe weather conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0023] Figure 1 is a schematic diagram of a UAV-assisted emergency communication path planning according to an embodiment of the present invention; Figure 2 is a location distribution map of user cluster nodes and drone take-off and landing points in a disaster area according to an embodiment of the present invention; Figure 3 is a flow chart of a Benders decomposition algorithm according to an embodiment of the present invention; Figure 4 is the cost change of the UAV-assisted emergency communication network under different weather risks in an embodiment of the present invention; Figure 5 is a flow chart of a method for optimizing a UAV-assisted emergency communication network taking into account weather risks according to an embodiment of the present invention; Figure 6 It is a schematic diagram of the structure of a UAV-assisted emergency communication network optimization system considering weather risks according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0026] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0027] Under complex and severe weather conditions, the reliability and stability of the UAV-assisted emergency communication network are greatly challenged. By optimizing the UAV-assisted emergency communication path considering weather risks, it is possible to effectively avoid UAV failures or mission failures caused by weather factors. Therefore, the present invention proposes a UAV-assisted emergency communication network design and optimization method and system considering weather risks, so as to more effectively deal with the impact of complex and severe weather conditions on the UAV-assisted emergency network.

[0028] The present invention takes emergencies as the research background, considers the impact of weather risks on UAV-assisted emergency communications, takes the sum of UAV path cost and risk loss cost as the optimization target, constructs a UAV-assisted emergency communication framework considering time consumption and energy consumption, combines constraints such as UAV assignment, path, risk and energy consumption, proposes a UAV-assisted emergency communication path planning model, and designs a Benders decomposition algorithm, conducts specific analysis in relevant real examples, and provides corresponding measures for the design and optimization of UAV-assisted emergency communication networks, thus achieving the minimum sum of UAV path cost and risk loss cost after an emergency occurs.

[0029] The following is a detailed description of the specific embodiments and drawings of the present invention with reference to the specific embodiments and drawings of the present invention.

[0030] Figure 5FIG. 1 is a flow chart of a method for designing and optimizing a UAV-assisted emergency communication network considering weather risks according to an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides a method for designing and optimizing a UAV-assisted emergency communication network considering weather risks, which specifically includes the following steps: Step S501: Obtain relevant information about the disaster-stricken area in the emergency and relevant information about the drone; wherein the relevant information about the disaster-stricken area in the emergency includes a set of user cluster nodes in the disaster area, the distance between each node, the communication demand size of the user cluster and the weather risk; the relevant information about the drone includes a set of drone take-off and landing points, a risk threshold of the drone, battery capacity, flight speed, propulsion and hovering power and communication parameters; Step S502: Based on the disaster-stricken area related information and drone related information in the emergency, and based on the drone-assisted emergency communication framework that considers time consumption and energy consumption, a drone-assisted emergency communication path planning model is constructed; Step S503: Based on the UAV-assisted emergency communication path planning model, calculate the optimal path plan for UAV-assisted emergency communication; Among them, the UAV-assisted emergency communication path planning model considers UAV assignment constraints, UAV path constraints, UAV risk constraints and UAV energy consumption constraints, minimizes the sum of UAV path cost and risk loss cost within the range of decision variables, and optimizes UAV-assisted emergency communication path planning.

[0031] This embodiment considers the impact of complex and severe weather in emergencies on drone-assisted emergency communications, constructs a drone-assisted emergency communication path planning model, and then considers drone assignment constraints, drone path constraints, drone risk constraints, and drone energy consumption constraints in combination with relevant information about the disaster-stricken area and drone information in the emergency, minimizes the sum of drone path cost and risk loss cost within the range of decision variables, and achieves the purpose of efficiently designing and optimizing drone-assisted emergency communication network solutions after an emergency occurs.

[0032] The drone-assisted emergency communication network of this embodiment is composed of user cluster nodes in the disaster area and drone take-off and landing points, such as Figure 1 As shown, the drone starts from the take-off and landing point and visits each user cluster node in turn to provide emergency communication guarantee services for it. The purpose of this embodiment is to determine the best path plan for the drone to provide emergency communication guarantee services for the user cluster, considering the size of the user cluster demand in the disaster area, weather risks, risk thresholds and battery capacity of the drone, and communication parameters, when the user cluster nodes in the disaster area and the take-off and landing points of the drone are known, and finally to minimize the sum of the drone path cost and risk loss cost after the emergency.

[0033] according to Figure 2The drone-assisted emergency communication network shown is specifically described as follows: like Figure 2 In the UAV emergency communication network shown in the figure, considering the complex and severe weather conditions in the emergency, in step S501, the acquired disaster-affected area related information and UAV related information in the emergency include but are not limited to: the user cluster node set in the disaster area , drone take-off and landing point , drone collection , the distance between each node (Unit: km), the communication demand size of the user cluster ,in, Indicates the computing resources required by the user cluster (unit: MCycles), Indicates the data size generated by the user cluster (unit: MB), weather risk , risk threshold of drones and battery capacity (Unit: kWh), UAV flight speed (Unit: m / s), UAV propulsion power and hovering power (Unit: kW), communication parameters include: the transmission power of the communication equipment carried by the drone (Unit: dBm), communication bandwidth (Unit: MHz), Channel Power Gain , the beam width of a directional antenna , noise power spectral density , the CPU calculation rate of the drone communication equipment (Unit: Hz), a constant related to the hardware architecture of the communication equipment carried by the drone .

[0034] (1) Consider a UAV-assisted emergency communication network with 15 user cluster nodes. The locations and distribution of user cluster nodes and UAV take-off and landing points are known. The size of the UAV cluster is 6, the risk threshold for drones and battery capacity Take three different situations, namely , , UAV flight speed 15, propulsion power The hovering power is 85. is 70.

[0035] (2) Distance between nodes See Table 1, the communication demand size of the user cluster is shown in Table 2, and the weather risk is shown in Table 3; Table 1 The distance between each node;

[0036] Table 2 Communication requirement size of user clusters;

[0037] Table 3 Weather risks;

[0038] (3) Communication-related parameters are shown in Table 4; Table 4 Parameters related to communication;

[0039] In step S502, in the process of constructing the drone-assisted emergency communication path planning model, the objective function of the drone-assisted emergency communication path planning model is to minimize the weighted sum of the drone path cost and the risk loss cost.

[0040] Specifically, drones, as small mobile base stations carrying communication equipment, provide emergency communication guarantee services for user clusters in disaster areas. In the process of processing user cluster communication tasks, the drone emergency communication framework includes three aspects: uplink transmission of tasks, task calculation, and downlink transmission of tasks. Since the time and energy consumed by the downlink transmission of tasks are negligible compared to the first two, only the uplink transmission and calculation parts of the tasks are considered. For user cluster When providing emergency communication guarantee services, the communication rate between the two Calculated as: (1); in, is the communication bandwidth, It's a drone The transmission power of the communication equipment carried, Is a user cluster With drones The power gain of the channels between is the beam width of the directional antenna, is the noise power spectral density, is a positive constant, and Represents user clusters With drones The reference distance and the actual distance between them, the reference distance defines the flight altitude of the drone.

[0041] The time and energy consumption of uplink transmission of the defined task are and , the time and energy consumption of task calculation are and , calculated as: (2); (3); (4); (5); in, Is a user cluster The computing resources required, Is a user cluster The size of the data generated, Drones Carry-on communication equipment to handle user clusters The CPU calculation rate during the task, Are constants related to the hardware architecture of the drone communication equipment.

[0042] Considering constraints such as drone assignment, path, risk and energy consumption, the drone-assisted emergency communication path planning is optimized. Therefore, the established drone-assisted emergency communication path planning model involves decision variables such as drone assignment, path, risk and energy consumption.

[0043] In the drone assignment constraints, the assignment variables include: ,in is a binary assignment variable, when When indicating drone Assigned to user cluster ,otherwise .

[0044] In the process of determining UAV assignment constraints, UAV assignment variables are introduced, the relationship between UAVs and user clusters is analyzed, and the assignment constraints for UAV-assisted emergency communication network optimization are constructed.

[0045] Specifically, the requirements of each user cluster cannot be split, so each drone is assigned to at most one user cluster, which leads to the following constraints: (6); In the UAV path constraint, the path decision variables include: ,in is a binary path variable, when When Slave Node Fly to the node ,otherwise .

[0046] In the process of determining the UAV path constraints, UAV path variables are introduced, the relationship between path variables and assignment variables is analyzed, and the association constraints of decision variables in the UAV-assisted emergency communication network are constructed, including path feasibility constraints and relationship constraints between path variables and assignment variables.

[0047] Specifically, only drones assigned to a user cluster can fly to that location. Drones must depart from and return to the take-off and landing points. After providing emergency communication support services for a user cluster, drones must leave that location. This results in the following constraints: (7); (8); (9); (10); (11); Among them, formula (7) gives the relationship between the assignment variable and the path variable, that is, only the drone assigned to the user cluster can fly to this location, formula (8) ensures that the drone does not fly on the same node, formulas (9) and (10) indicate that the drone must depart from the take-off and landing point and return to the take-off and landing point, and formula (11) indicates that the drone must leave the location after providing emergency communication guarantee services for a user cluster.

[0048] In the drone risk constraint, the risk decision variables include and ,in and are continuous variables, representing drones Arrival and departure nodes The risk level at that time.

[0049] In the process of determining the risk constraints of drones, drone risk variables are introduced, the relationship between risk variables and path variables is analyzed, and the association constraints of decision variables in the drone-assisted emergency communication network are constructed, including the maximum risk constraint acceptable to drones and the relationship constraints between risk variables and path variables.

[0050] Specifically, the risk level of the drone is 0 when it departs from the take-off and landing point. The risk of the drone when returning to the take-off and landing point cannot exceed its acceptable risk threshold. The risk level of the drone when arriving at and leaving the user cluster is gradually accumulated from the risk levels of the nodes it has passed through before, thus obtaining the following constraints: (12); (13); (14); (15); (16); (17); in, is a very large number, It's a drone Slave Node Fly to the node The risk of It's a drone risk threshold.

[0051] Formula (12) indicates that the risk level of the drone is 0 when it departs from the take-off and landing point. Formulas (13) and (14) calculate the risk level of the drone when it arrives at the user cluster. Formulas (15) and (16) calculate the risk level of the drone when it leaves the user cluster. Formula (17) ensures that the risk level of the drone does not exceed its acceptable risk threshold.

[0052] In the UAV energy consumption constraint, the energy consumption decision variables include ,in is a continuous variable, indicating that drone Arrival Node The accumulated energy consumption.

[0053] In the process of determining the energy consumption constraints of UAVs, UAV energy consumption variables are introduced, the communication, propulsion and hovering energy consumption of UAVs are analyzed, the relationship between energy consumption variables and path variables is analyzed, and the association constraints of decision variables in the UAV-assisted emergency communication network are constructed, including the energy consumption levels of UAVs when leaving and returning to the take-off and landing points, as well as the relationship constraints between energy consumption variables and path variables.

[0054] Specifically, the energy consumption level of the drone is 0 when it departs from the take-off and landing point, and the cumulative energy consumption of the drone when it returns to the take-off and landing point cannot exceed its battery capacity. The cumulative energy consumption of the drone between two adjacent user clusters includes the energy consumption of drone communication, propulsion, and hovering, thus obtaining the following constraints: (18); (19); (20);

[0055] (twenty one); (twenty two); in, It's a drone Slave Node Fly to the node The propulsion energy consumption, It's a drone The propulsion power, is the flight speed of the drone, Is a node To Node The distance between It's a drone At the node The hovering energy consumption on It's a drone The hovering power, and UAVs For Node When providing emergency communication support services, the mission uplink transmission link time and mission calculation time, It's a drone of battery capacity.

[0056] Formulas (18) and (19) calculate the energy consumption of the drone propulsion and hovering respectively. Formula (20) requires that the energy consumption of the drone when it departs from the take-off and landing point is 0. Formula (21) defines the cumulative energy consumption relationship of the drone between two adjacent nodes. Formula (22) requires that the cumulative energy consumption of the drone when returning to the take-off and landing point cannot exceed its battery capacity.

[0057] The UAV-assisted emergency communication path planning model is a cost minimization problem, denoted as , involving constraints such as drone assignment, path, risk and energy consumption, and its mathematical model is as follows: (twenty three); Constraint formula (1) - formula (22) (24); (25); (26); (27); (28); (29); In the objective function (23), the first term is the path cost of the drone, and the second term is the risk loss cost of the drone. and are the weight coefficients of path cost and risk loss cost respectively. Formula (24) involves constraints such as drone assignment, path, risk and energy consumption. Formulas (25)-(29) define the value range of decision variables.

[0058] In step S503, Figure 3 As shown in the figure, the Benders decomposition algorithm is used to calculate the path planning model of UAV-assisted emergency communication. To solve; the designed Benders decomposition algorithm consists of the main problem and sub-problems The main problem A model for path planning of UAV-assisted emergency communications The integer variables and their constraints in are used to provide the lower bound of the UAV-assisted emergency communication path planning model; subproblem A model for path planning of UAV-assisted emergency communications The continuous variables and their constraints in the paper are intended to provide an upper bound for the UAV-assisted emergency communication path planning model; the main problem and sub-problems are solved iteratively until the error between the upper bound and the lower bound is within the preset range.

[0059] The following structure provides the model The main problem of the lower bound . Main Question Models involved The integer variables and their constraints in the mathematical model are as follows: (30); (31); (32); (33); (34); (35); (36); (37); (38); To prevent subloops from occurring, the following subloop elimination constraints are added to the main problem middle: (39); (40); in, Indicates drone Access User Cluster order.

[0060] Next, construct the provided model The upper bound subproblem S . Assume that in the main problem The optimal solution is and , given the current optimal solution, subproblem S Models involved Since the objective function (23) contains only integer variables, the subproblem S There is no objective function. In addition, the subproblem S In the index is separable, so the subproblem S can be Decompose into independent sub-problems, denoted by S For each drone , sub-problem S The mathematical model is as follows: (41); (42); (43); (44); (45); (46); (47); (48);

[0061] (49); (50); (51); (52); (53); for Sub-problem S , when all subproblems S When both are feasible, the main problem is feasible, and when there are some subproblems S When it is not feasible, the main problem is not feasible. Therefore, for each main problem There are two situations: (a) one or more subproblems S is not feasible (its dual problem is unbounded), then add the Benders feasible cut to the main problem (b) All sub-problems S are both feasible (the dual problem is bounded), then the main problem The optimal solution of the model is The algorithm terminates when the optimal solution is found.

[0062] Defining constraints - The dual variables are: , , , , Therefore, the Benders feasible cut is described as follows: (54); According to the main problem and sub-problems described above, the model is decomposed using the Benders decomposition algorithm. Solve and determine the optimal path plan for UAV-assisted emergency communication. The basic idea is: in the initialization stage of the algorithm, let the lower bound , upper bound Next, solve the main problem And get the optimal objective function value and path strategy , update the lower bound to For each drone , in the current optimal path strategy Next, solve the subproblem S , if there is an infeasible subproblem S , then add the formula (54) for Benders feasible cut to the main problem Otherwise, the updated upper bound is: .

[0063] When the error between the upper bound and the lower bound is within the preset range, the optimal path strategy for UAV-assisted emergency communication and the optimal objective function value of the model are output, otherwise the above steps are executed in a loop.

[0064] Based on the above given parameters and the model and algorithm of the present invention, a program is written in Python, and the optimization solver Gurobi is called to solve the problem.

[0065] Table 5: Cost changes under different battery capacities and risk thresholds;

[0066] Table 5 analyzes the cost changes under different battery capacities and risk thresholds. The battery capacity in Table 5 is in kWh; the risk threshold of the drone is a dimensionless value, and the path cost, loss cost, and total cost are all dimensionless values. From the solution results in Table 5, it can be found that when the battery capacity is small ( ), no matter how the risk threshold changes, the path cost, loss cost and total cost remain unchanged. This is because the battery capacity is too small, which causes the drone to only run along the most energy-efficient path. Even if the risk threshold increases, the drone cannot choose a more cost-effective path. This situation will improve as the battery capacity increases. From the solution results in Table 5, it can also be seen that as the battery capacity increases, the path cost gradually increases, the loss cost continues to decrease, and the total cost continues to decrease. This is because the increase in battery capacity allows the drone to choose a farther path to avoid the impact of weather risks, but in the case of a particularly large battery capacity ( ), the loss cost is almost reduced to 0, because sufficient battery capacity allows the drone to appropriately increase the path cost, thereby minimizing risk losses. Therefore, in this case, even if the risk threshold increases, the cost remains unchanged. On the contrary, when the battery capacity is large enough, as the risk threshold increases, the path cost gradually decreases, the loss cost continues to increase, and the total cost decreases. This is because the drone can reduce the path cost by increasing a certain risk loss cost in exchange for a reduction in the path cost, thereby reducing the total cost. In some cases, an increase in the risk threshold does not result in a change in cost. For example, when the battery capacity is large enough, the path cost gradually decreases, the loss cost increases, and the total cost decreases. When the risk threshold changes from When it increases to 0.8, the cost remains unchanged. This phenomenon is related to the battery capacity The situation is similar to that of , because the lower battery capacity limits the path selection space of the drone. Even if the risk threshold increases, the room for path selection is very limited. Therefore, the cost has not changed significantly.

[0067] Table 6 Optimal path scheme for UAV-assisted emergency communication under different parameters;

[0068] Table 6 analyzes the optimal path scheme for UAV-assisted emergency communication under different parameters. Table 6 (a) describes the optimal path scheme for UAV-assisted emergency communication under different parameters. In the case of The optimal path plan of the drone changes when the risk threshold increases from 0.2 to 0.8. As can be seen from (a) in Table 6, as the risk threshold increases, the path length of the drone decreases. This means that in order to reduce the path cost, the drone avoids longer path choices by increasing the risk loss cost. In other words, as the risk threshold increases, the drone tends to choose a shorter but potentially riskier path, thereby balancing the path cost and the risk loss cost to a certain extent. Table 6 (b) describes the optimal path plan of the drone when the risk threshold increases. In the case of The change of the optimal path plan of the UAV when the battery capacity increases from 0.2 to 0.8. As can be seen from (b) in Table 6, as the battery capacity increases, the UAV is able to maintain long-distance operation, so it can choose a more flexible path to avoid risks such as weather. This means that a larger battery capacity provides the UAV with more path selection space, and it can choose a longer path without significantly increasing the risk loss, thereby reducing the path cost and improving the safety of the mission. The increase in battery capacity allows the UAV to maintain higher efficiency when operating for a long time, while reducing the additional costs caused by risks.

[0069] Figure 4 The cost variation of the UAV-assisted emergency communication network under different weather risks is analyzed, where the battery capacity and risk threshold parameters are set as: .from Figure 4 It can be seen that as weather risk increases, path cost, loss cost and total cost show an upward trend. However, the path cost increases at a significantly higher rate than the loss cost. This is because when weather risk increases, drones tend to choose relatively safer paths, and by increasing path costs, they avoid the rapid increase in loss costs caused by increased risks.

[0070] Figure 6 is a schematic diagram of the structure of a UAV-assisted emergency communication network optimization system considering weather risks in an embodiment of the present invention. Figure 5 The corresponding method of optimizing UAV-assisted emergency communication network considering weather risks is Figure 6 As shown, the drone-assisted emergency communication network optimization system considering weather risks in this embodiment may include: The information acquisition module 601 is used to obtain information related to the disaster-stricken area and drone related information in the emergency; wherein the information related to the disaster-stricken area in the emergency includes the set of user cluster nodes in the disaster area, the distance between each node, the communication demand size of the user cluster and the weather risk; the drone related information includes the set of drone take-off and landing points, the risk threshold of the drone, the battery capacity, the flight speed, the propulsion and hovering power and the communication parameters; A model building module 602 is used to build a UAV-assisted emergency communication path planning model based on the relevant information of the disaster-stricken area and the UAV-related information in the emergency and based on the UAV-assisted emergency communication framework considering time consumption and energy consumption; A model solving module 603 is used to calculate the optimal path plan for UAV-assisted emergency communication based on the UAV-assisted emergency communication path planning model; Among them, the UAV-assisted emergency communication path planning model considers UAV assignment constraints, UAV path constraints, UAV risk constraints and UAV energy consumption constraints, minimizes the sum of UAV path cost and risk loss cost within the range of decision variables, and optimizes UAV-assisted emergency communication path planning.

[0071] It should be noted here that Figure 6 The various modules in the UAV-assisted emergency communication network optimization system considering weather risks are Figure 5 The various steps in the optimization method for drone-assisted emergency communication network considering weather risks in are corresponding to each other, and the specific implementation process is the same, which will not be repeated here.

[0072] In one or more embodiments, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, Figure 5 The steps in the method for optimizing a drone-assisted emergency communication network considering weather risks are shown.

[0073] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, the computer program including a computer program for executing Figure 5 In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions defined in the device of the present application are executed.

[0074] in, Figure 5 The computer program instructions corresponding to the method shown may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0075] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing a UAV-assisted emergency communication network considering weather risks, characterized in that: include: Obtaining relevant information about the disaster-stricken area in the emergency and relevant information about the drone; wherein the relevant information about the disaster-stricken area in the emergency includes the set of user cluster nodes in the disaster area, the distance between each node, the communication demand size of the user cluster and the weather risk; the relevant information about the drone includes the set of drone take-off and landing points, the risk threshold of the drone, battery capacity, flight speed, propulsion and hovering power and communication parameters; Based on the relevant information of the disaster-stricken area and the drone-related information in the emergency, a drone-assisted emergency communication path planning model is constructed based on the drone-assisted emergency communication framework considering time consumption and energy consumption; Based on the UAV-assisted emergency communication path planning model, the optimal path plan for UAV-assisted emergency communication is calculated; Among them, the UAV-assisted emergency communication path planning model considers UAV assignment constraints, UAV path constraints, UAV risk constraints and UAV energy consumption constraints, minimizes the sum of UAV path cost and risk loss cost within the range of decision variables, and optimizes UAV-assisted emergency communication path planning.

2. The method for optimizing a UAV-assisted emergency communication network considering weather risks according to claim 1, characterized in that: The objective function of the UAV-assisted emergency communication path planning model is to minimize the weighted sum of the UAV path cost and the risk loss cost.

3. The method for optimizing a UAV-assisted emergency communication network considering weather risks as claimed in claim 1, characterized in that: In the process of determining UAV assignment constraints, UAV assignment variables are introduced, the relationship between UAVs and user clusters is analyzed, and the assignment constraints for UAV-assisted emergency communication network optimization are constructed.

4. The method for optimizing a UAV-assisted emergency communication network considering weather risks as claimed in claim 1, characterized in that: In the process of determining the UAV path constraints, UAV path variables are introduced, the relationship between path variables and assignment variables is analyzed, and the association constraints of decision variables in the UAV-assisted emergency communication network are constructed, which include the relationship constraints between path variables and assignment variables and path feasibility constraints.

5. The method for optimizing a UAV-assisted emergency communication network considering weather risks as claimed in claim 1, characterized in that: In the process of determining the risk constraints of drones, drone risk variables are introduced, the relationship between risk variables and path variables is analyzed, and the association constraints of decision variables in the drone-assisted emergency communication network are constructed, which include the relationship constraints between risk variables and path variables and the maximum risk constraints acceptable to drones.

6. The method for optimizing a UAV-assisted emergency communication network considering weather risks as claimed in claim 1, characterized in that: In the process of determining the energy consumption constraints of UAVs, UAV energy consumption variables are introduced, the energy consumption of UAV communication, propulsion and hovering is analyzed, the relationship between energy consumption variables and path variables is analyzed, and the association constraints of decision variables in the UAV-assisted emergency communication network are constructed, which include the relationship constraints between energy consumption variables and path variables and the energy consumption level of the UAV when leaving and returning to the take-off and landing points.

7. The method for optimizing a UAV-assisted emergency communication network considering weather risks as claimed in claim 1, characterized in that: The UAV-assisted emergency communication path planning model is solved using the Benders decomposition algorithm. The designed Benders decomposition algorithm consists of a main problem and sub-problems. The main problem involves integer variables and their constraints in the UAV-assisted emergency communication path planning model, which is used to provide the lower bound of the UAV-assisted emergency communication path planning model. The sub-problem involves continuous variables and their constraints in the UAV-assisted emergency communication path planning model, which aims to provide the upper bound of the UAV-assisted emergency communication path planning model. The main problem and sub-problems are solved iteratively until the error between the upper bound and the lower bound is within the preset range.

8. A UAV-assisted emergency communication network optimization system considering weather risks, characterized in that: include: An information acquisition module, which is used to obtain relevant information about the disaster-stricken area in the emergency and relevant information about the drone; wherein the relevant information about the disaster-stricken area in the emergency includes the set of user cluster nodes in the disaster area, the distance between each node, the communication demand size of the user cluster and the weather risk; the relevant information about the drone includes the set of drone take-off and landing points, the risk threshold of the drone, the battery capacity, the flight speed, the propulsion and hovering power and the communication parameters; A model building module is used to build a UAV-assisted emergency communication path planning model based on the relevant information of the disaster-stricken area and the UAV-related information in the emergency, and based on the UAV-assisted emergency communication framework considering time consumption and energy consumption; A model solving module, which is used to calculate the optimal path plan for UAV-assisted emergency communication based on the UAV-assisted emergency communication path planning model; Among them, the UAV-assisted emergency communication path planning model considers UAV assignment constraints, UAV path constraints, UAV risk constraints and UAV energy consumption constraints, minimizes the sum of UAV path cost and risk loss cost within the range of decision variables, and optimizes UAV-assisted emergency communication path planning.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for optimizing a drone-assisted emergency communication network considering weather risks as described in any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for optimizing a drone-assisted emergency communication network considering weather risks as described in any one of claims 1 to 7 are implemented.

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