UAV-assisted emergency communication network optimization method and system considering weather risks

Abstract

The present invention relates to the field of drone-assisted emergency communications. To address the problem of ignoring weather risks, which may lead to drone crashes or mission failures due to unexpected malfunctions, a method and system for optimizing drone-assisted emergency communications networks that consider weather risks are provided. The method includes obtaining information related to the affected area and drones in an emergency event that considers weather risks, and then constructing a drone-assisted emergency communications path planning model based on a drone-assisted emergency communications framework that considers time and energy consumption. Based on the drone-assisted emergency communications path planning model, an optimal drone-assisted emergency communications path plan is calculated, thereby achieving the goal of efficiently designing and optimizing drone-assisted emergency communications network plans after an emergency event occurs.

Description

Technical Field

[0001] The present invention belongs to the field of drone-assisted emergency communications, and in particular relates to a drone-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, traditional terrestrial communication networks struggle to meet the communication needs of disaster-stricken areas due to infrastructure damage or environmental impacts. Drone-assisted emergency communication networks, with their flexible deployment and rapid response, have become a crucial tool for post-disaster communication restoration. By deploying drone nodes in disaster-stricken areas, temporary communication networks can be quickly established to support rescue command, information transmission, and communication with affected personnel. However, complex and severe post-disaster weather poses significant challenges to the reliability and stability of drone-assisted emergency communication networks.

[0004] Complex and severe weather conditions after a disaster (including strong winds, heavy rain, ice and snow, and low temperatures) not only affect 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 the drone's flight trajectory to deviate or become 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 flight time. 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 malfunctions, 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 posed by 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 drone-assisted emergency communication network optimization method and system taking weather risks into account. It constructs a drone-assisted emergency communication framework that takes time consumption and energy consumption into account, establishes a drone-assisted emergency communication path planning model that takes weather risks, operating energy consumption and mission requirements into account, and solves it, 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 solutions:

[0007] A first aspect of the present invention provides a method for optimizing a drone-assisted emergency communication network taking weather risks into consideration.

[0008] In one or more embodiments, a method for optimizing a UAV-assisted emergency communication network considering weather risks is provided, including:

[0009] Obtaining 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 weather risks; the information related to the drone includes the set of drone take-off and landing points, drone risk threshold, battery capacity, flight speed, propulsion and hovering power, and communication parameters;

[0010] Based on the 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;

[0011] Based on the UAV-assisted emergency communication path planning model, the optimal path plan for UAV-assisted emergency communication is calculated;

[0012] Among them, the drone-assisted emergency communication path planning model takes into account drone assignment constraints, drone path constraints, drone risk constraints and drone energy consumption constraints, minimizes the sum of drone path cost and risk loss cost within the range of decision variables, and optimizes drone-assisted emergency communication path planning.

[0013] 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.

[0014] 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 assignment constraints for drone-assisted emergency communication network optimization are constructed.

[0015] 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.

[0016] 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.

[0017] As an implementation method, in the process of determining the energy consumption constraints of drones, drone energy consumption variables are introduced, the drone communication, propulsion and hovering energy consumption are analyzed, the relationship between energy consumption 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 energy consumption variables and path variables and the energy consumption levels of drones when leaving and returning to the take-off and landing points.

[0018] 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.

[0019] A second aspect of the present invention provides a drone-assisted emergency communication network optimization system that takes weather risks into account.

[0020] In one or more embodiments, a drone-assisted emergency communication network optimization system considering weather risks includes:

[0021] An information acquisition module is used to obtain information related to the disaster-stricken area and drone-related information in the emergency; 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 weather risks; the information related to the drone includes the set of drone take-off and landing points, drone risk threshold, battery capacity, flight speed, propulsion and hovering power, and communication parameters;

[0022] A model building module is used to build a UAV-assisted emergency communication path planning model based on information related to the disaster-stricken area and UAV information in the emergency, and based on a UAV-assisted emergency communication framework that considers time consumption and energy consumption;

[0023] A model solving module is used to calculate the optimal path plan for UAV-assisted emergency communication based on the UAV-assisted emergency communication path planning model;

[0024] Among them, the drone-assisted emergency communication path planning model takes into account drone assignment constraints, drone path constraints, drone risk constraints and drone energy consumption constraints, minimizes the sum of drone path cost and risk loss cost within the range of decision variables, and optimizes drone-assisted emergency communication path planning.

[0025] A third aspect of the present invention provides a computer-readable storage medium.

[0026] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for optimizing a drone-assisted emergency communication network considering weather risks.

[0027] A fourth aspect of the present invention provides an electronic device.

[0028] 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 of the above-described method for optimizing a drone-assisted emergency communication network taking into account weather risks are implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (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 about the disaster-stricken area and drone information in the emergency. The sum of drone path cost and risk loss cost is minimized within the range of decision variables, achieving the purpose of efficiently designing and optimizing drone-assisted emergency communication network solutions after an emergency occurs.

[0031] (2) The present invention analyzes the role of drones in assisting emergency communications, considers the time consumption 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

[0032] The accompanying drawings, 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.

[0033] Figure 1 This is a schematic diagram of a UAV-assisted emergency communication path planning according to an embodiment of the present invention;

[0034] 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;

[0035] Figure 3is a flow chart of the Benders decomposition algorithm according to an embodiment of the present invention;

[0036] Figure 4 is the cost change of the UAV-assisted emergency communication network under different weather risks in an embodiment of the present invention;

[0037] Figure 5 1 is a flow chart of a method for optimizing a UAV-assisted emergency communication network considering weather risks according to an embodiment of the present invention;

[0038] Figure 6 2 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

[0039] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0040] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0042] In complex and severe weather conditions, the reliability and stability of drone-assisted emergency communication networks are greatly challenged. By optimizing drone-assisted emergency communication paths based on weather risks, we can effectively prevent drone failures or mission failures caused by weather factors. Therefore, this paper proposes a method and system for designing and optimizing a drone-assisted emergency communication network that considers weather risks, thereby more effectively addressing the impact of complex and severe weather conditions on drone-assisted emergency networks.

[0043] This paper takes emergencies as the research background, considers the impact of weather risks on drone-assisted emergency communications, takes the sum of drone path cost and risk loss cost as the optimization target, constructs a drone-assisted emergency communication framework considering time consumption and energy consumption, combines constraints such as drone assignment, path, risk and energy consumption, proposes a drone-assisted emergency communication path planning model, and designs a Benders decomposition algorithm. Specific analysis is carried out in relevant real-world examples, providing corresponding measures for the design and optimization of drone-assisted emergency communication networks, and achieving the minimum sum of drone path cost and risk loss cost after an emergency occurs.

[0044] The following is a detailed description of the design and optimization of a UAV-assisted emergency communication network and system considering weather risks of the present invention, with reference to specific embodiments and accompanying drawings.

[0045] Figure 5 FIG 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:

[0046] Step S501: Obtaining 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 weather risks; the information related to the drone includes the set of drone take-off and landing points, drone risk threshold, battery capacity, flight speed, propulsion and hovering power, and communication parameters;

[0047] Step S502: Based on the information related to the disaster-stricken area and the 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;

[0048] Step S503: Calculate the optimal path plan for UAV-assisted emergency communication based on the UAV-assisted emergency communication path planning model;

[0049] Among them, the drone-assisted emergency communication path planning model takes into account drone assignment constraints, drone path constraints, drone risk constraints and drone energy consumption constraints, minimizes the sum of drone path cost and risk loss cost within the range of decision variables, and optimizes drone-assisted emergency communication path planning.

[0050] 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. The sum of the drone path cost and the risk loss cost is minimized within the range of decision variables, achieving the purpose of efficiently designing and optimizing the drone-assisted emergency communication network solution after the emergency occurs.

[0051] 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 departs from its takeoff and landing point and sequentially visits each user cluster node to provide emergency communication support services. The purpose of this embodiment is to determine the optimal path plan for the drone to provide emergency communication support services to the user cluster, given the known locations of the user cluster nodes and drone takeoff and landing points in the disaster area. This plan considers the size of the user cluster in the disaster area, weather risks, drone risk thresholds, battery capacity, and communication parameters. Ultimately, this plan minimizes the sum of the drone's path cost and risk loss cost after the emergency.

[0052] according to Figure 2 The drone-assisted emergency communication network shown is specifically described as follows:

[0053] like Figure 2 In the UAV emergency communication network shown, considering the complex and severe weather conditions in the emergency, in step S501, the acquired disaster-affected area information and UAV related information 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 , drone risk threshold and battery capacity (Unit: kWh), drone 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 the directional antenna , noise power spectral density , the CPU computing rate of the drone communication equipment (Unit: Hz), a constant related to the hardware architecture of the communication equipment carried by the drone .

[0054] (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, drone risk threshold and battery capacity Take three different situations, namely , , UAV flight speed 15, propulsion power Hover power is 85 is 70.

[0055] (2) The distance between each node 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;

[0056] Table 1. Distances between nodes.

[0057]

[0058] Table 2 Communication requirement size of user clusters;

[0059]

[0060] Table 3 Weather risks;

[0061]

[0062] (3) Communication-related parameters are shown in Table 4;

[0063] Table 4 Parameters related to communication;

[0064]

[0065] 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.

[0066] 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. User cluster When providing emergency communication guarantee services, the communication rate between the two Calculated as:

[0067] (1);

[0068] 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 beamwidth 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 flying altitude of the drone.

[0069] 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:

[0070] (2);

[0071] (3);

[0072] (4);

[0073] (5);

[0074] in, Is a user cluster The required computing resources, Is a user cluster The size of the data generated, drones Carried communication equipment handles user clusters The CPU calculation rate during the task, It is a constant related to the hardware architecture of the drone communication equipment.

[0075] 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.

[0076] In the UAV assignment constraints, the assignment variables include: ,in is a binary assignment variable, when When indicating drone Assigned to user cluster ,otherwise .

[0077] In the process of determining UAV assignment constraints, UAV assignment variables are introduced, the relationship between UAVs and user clusters is analyzed, and assignment constraints for UAV-assisted emergency communication network optimization are constructed.

[0078] 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:

[0079] (6);

[0080] In the UAV path constraint, the path decision variables include: ,in is a binary path variable, when When the drone Slave nodes Fly to the node ,otherwise .

[0081] 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.

[0082] 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 to a user cluster, drones must leave that location. This results in the following constraints:

[0083] (7);

[0084] (8);

[0085] (9);

[0086] (10);

[0087] (11);

[0088] 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 that 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.

[0089] 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, including the maximum risk constraint acceptable to drones and the relationship constraints between risk variables and path variables.

[0090] In the drone risk constraints, drone risk variables include and ,in and are continuous variables, representing drones Arrival and departure nodes The risk level at that time.

[0091] Specifically, the risk level of a drone is 0 when it departs from the take-off and landing point. The risk it bears when returning to the take-off and landing point cannot exceed its acceptable risk threshold. The risk level of a drone when arriving at and leaving a user cluster is gradually accumulated from the risk levels of the nodes it has passed through before. This results in the following constraints:

[0092] (12);

[0093] (13);

[0094] (14);

[0095] (15);

[0096] (16);

[0097] (17);

[0098] in, is a very large number, It's a drone Slave nodes Fly to the node The risk of It's a drone risk threshold.

[0099] 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.

[0100] 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.

[0101] In the process of determining the energy consumption constraints of drones, drone energy consumption variables are introduced, the energy consumption of drone 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 drone-assisted emergency communication network are constructed, including the energy consumption levels of drones when leaving and returning to the take-off and landing points, as well as the relationship constraints between energy consumption variables and path variables.

[0102] Specifically, the energy consumption level of the drone is 0 when it departs from the take-off and landing point. The cumulative energy consumed by the drone when returning to the take-off and landing point cannot exceed its battery capacity. The cumulative energy consumed by the drone between two adjacent user clusters includes the energy consumption of drone communication, propulsion, and hovering. Therefore, the following constraints are obtained:

[0103] (18);

[0104] (19);

[0105] (20);

[0106]

[0107] (twenty one);

[0108] (twenty two);

[0109] in, It's a drone Slave nodes Fly to the node 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 Hover energy consumption on It's a drone The hovering power, and UAVs For nodes When providing emergency communication support services, the mission uplink transmission link time and mission calculation time, It's a drone battery capacity.

[0110] Formulas (18) and (19) calculate the energy consumption of the drone's propulsion and hovering, respectively. Formula (20) requires that the drone's energy consumption when departing from the take-off and landing point is 0. Formula (21) defines the relationship between the drone's cumulative energy consumption between two adjacent nodes. Formula (22) requires that the drone's cumulative energy consumption when returning to the take-off and landing point cannot exceed its battery capacity.

[0111] 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. Its mathematical model is as follows:

[0112] (twenty three);

[0113] Constraint formula (1)-formula (22) (24);

[0114] (25);

[0115] (26);

[0116] (27);

[0117] (28);

[0118] (29);

[0119] 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.

[0120] In step S503, Figure 3 As shown in the figure, the Benders decomposition algorithm is used to calculate the UAV-assisted emergency communication path planning model. To solve; Among them, the designed Benders decomposition algorithm is composed of the main problem and subproblems Composition, in which the main problem A path planning model involving drone-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 path planning model involving drone-assisted emergency communications The continuous variables and their constraints in the model 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.

[0121] 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:

[0122] (30);

[0123] (31);

[0124] (32);

[0125] (33);

[0126] (34);

[0127] (35);

[0128] (36);

[0129] (37);

[0130] (38);

[0131] To prevent subloops from occurring, the following subloop elimination constraints are added to the main problem: middle:

[0132] (39);

[0133] (40);

[0134] in, Indicates drone Access User Cluster order.

[0135] Next, construct the provided model Upper bound subproblem S . Assume that in the main problem The optimal solution obtained 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 subproblems, denoted as S For each drone , sub-problem S The mathematical model is as follows:

[0136] (41);

[0137] (42);

[0138] (43);

[0139] (44);

[0140] (45);

[0141] (46);

[0142] (47);

[0143] (48);

[0144]

[0145] (49);

[0146] (50);

[0147] (51);

[0148] (52);

[0149] (53);

[0150] for Sub-problem S , when all subproblems S When both are feasible, the main problem feasible, and when there are some subproblems S When it is infeasible, the main problem Therefore, for each main problem There are two situations: (a) one or more subproblems S If it is not feasible (its dual problem is unbounded), then add the Benders feasible cut to the main problem (b) All subproblems S are both feasible (their dual problems are bounded), then the main problem The optimal solution of the model is The algorithm terminates when the optimal solution is found.

[0151] Defining constraints - The dual variables are: , , , , Therefore, the Benders feasible cut is described as follows:

[0152] (54);

[0153] 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 obtain 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 Benders feasible cut formula (54) to the main problem Otherwise, the updated upper bound is:

[0154] .

[0155] 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 repeatedly.

[0156] 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.

[0157] Table 5 Cost changes under different battery capacities and risk thresholds;

[0158]

[0159] Table 5 analyzes the cost changes under different battery capacities and risk thresholds. The battery capacity in Table 5 is in kWh; the drone risk threshold is a dimensionless value; 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 ( ), regardless of how the risk threshold changes, the path cost, loss cost, and total cost remain unchanged. This is because the battery capacity is too small, causing the drone to only operate 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 longer 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 becomes larger, 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 amount of risk loss 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 as the risk threshold increases, the loss cost continues to increase, 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 does not change significantly.

[0160] Table 6 Optimal path schemes for UAV-assisted emergency communications under different parameters;

[0161]

[0162] 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 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 optimal path plan for the drone changes when the battery capacity increases from 0.2 to 0.8. As shown in Table 6 (b), as the battery capacity increases, the drone is able to maintain long-range operations, allowing it to choose more flexible paths to avoid risks such as weather. This means that a larger battery capacity provides the drone with more path options, allowing it to choose longer paths without significantly increasing risk losses, thereby reducing path costs and improving mission safety. Increasing battery capacity allows the drone to maintain higher efficiency during extended operations while reducing the additional costs associated with risk.

[0163] 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 As can be seen, as weather risk increases, path cost, loss cost, and total cost all show an upward trend. However, the rate of increase in path cost is significantly higher than the rate of increase in loss cost. This is because when weather risk increases, drones tend to choose relatively safer paths, increasing path cost to avoid the rapid increase in loss cost caused by increased risk.

[0164] Figure 6 This 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 UAV-assisted emergency communication network optimization considering weather risks is as follows: Figure 6 As shown, the UAV-assisted emergency communication network optimization system considering weather risks in this embodiment may include:

[0165] Information acquisition module 601 is used to obtain information related to the disaster-stricken area and drone-related information in the emergency; 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 weather risks; the information related to the drone includes the set of drone take-off and landing points, drone risk threshold, battery capacity, flight speed, propulsion and hovering power, and communication parameters;

[0166] A model building module 602 is used to build a UAV-assisted emergency communication path planning model based on information related to the disaster-stricken area and UAV-related information in the emergency, and based on a UAV-assisted emergency communication framework that considers time consumption and energy consumption;

[0167] 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;

[0168] Among them, the drone-assisted emergency communication path planning model takes into account drone assignment constraints, drone path constraints, drone risk constraints and drone energy consumption constraints, minimizes the sum of drone path cost and risk loss cost within the range of decision variables, and optimizes drone-assisted emergency communication path planning.

[0169] 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 UAV-assisted emergency communication network optimization method considering weather risks correspond one to another, and the specific implementation process is the same, which will not be repeated here.

[0170] In one or more embodiments, an electronic device is further provided, comprising a memory, a processor, and a computer program stored in the memory and operable 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.

[0171] 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 5In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion and / or installed from a removable medium. When the computer program is executed by the central processing unit, the various functions defined in the apparatus of the present application are performed.

[0172] in, Figure 5 The computer program instructions corresponding to the method shown can also be stored in a computer readable memory that can guide 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 including 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.

[0173] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0174] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for optimizing UAV-assisted emergency communication networks considering weather risks, characterized in that: include: Obtain information related to the disaster-stricken area and drone-related information in the emergency; 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 weather risk; the drone-related information includes the set of drone take-off and landing points, drone risk threshold, battery capacity, flight speed, propulsion and hovering power, and communication parameters; weather risk is the risk of a drone flying from one node to another; drone risk is characterized by a drone risk variable, which represents the risk level of a drone when arriving at and leaving a certain node. The drone risk variable is used to quantify the risk level of the drone along its flight path, that is, the accumulated risk value of the drone passing through each node on its flight path; Based on the 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; The UAV-assisted emergency communication path planning model takes into account 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. In the process of determining drone risk constraints, drone risk variables are introduced, the relationship between drone risk variables and path variables is analyzed, and the association constraints of drone risk variables in the drone-assisted emergency communication network are constructed, which include the relationship constraints between drone risk variables and path variables and the maximum risk constraints acceptable to drones.

2. The method for optimizing a UAV-assisted emergency communication network considering weather risks according to claim 1, wherein: 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.

3. The method for optimizing a UAV-assisted emergency communication network considering weather risks according to claim 1, wherein: In the process of determining UAV assignment constraints, UAV assignment variables are introduced, the relationship between UAVs and user clusters is analyzed, and 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 according to claim 1, wherein: 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 according to claim 1, wherein: In the process of determining the energy consumption constraints of drones, drone energy consumption variables are introduced, the energy consumption of drone 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 drone-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 drone when leaving and returning to the take-off and landing points.

6. The method for optimizing a UAV-assisted emergency communication network considering weather risks according to claim 1, wherein: The Benders decomposition algorithm is used to solve the UAV-assisted emergency communication path planning model. The designed Benders decomposition algorithm consists of a main problem and sub-problems. The main problem involves the 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 the 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.

7. A UAV-assisted emergency communication network optimization system considering weather risks, characterized in that: include: An information acquisition module is used to obtain information related to the disaster-stricken area in the emergency and information related to drones. 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 weather risk. The information related to drones includes the set of drone take-off and landing points, drone risk threshold, battery capacity, flight speed, propulsion and hovering power, and communication parameters. Weather risk is the risk of a drone flying from one node to another. Drone risk is characterized by a drone risk variable, which represents the risk level of a drone when arriving at and leaving a node. The drone risk variable is used to quantify the risk level of the drone along its flight path, that is, the accumulated risk value of the drone passing through each node on its flight path. A model building module is used to build a UAV-assisted emergency communication path planning model based on information related to the disaster-stricken area and UAV information in the emergency, and based on a UAV-assisted emergency communication framework that considers time consumption and energy consumption; A model solving module is used to calculate the optimal path plan for UAV-assisted emergency communication based on the UAV-assisted emergency communication path planning model; The UAV-assisted emergency communication path planning model takes into account 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. In the process of determining drone risk constraints, drone risk variables are introduced, the relationship between drone risk variables and path variables is analyzed, and the association constraints of drone risk variables in the drone-assisted emergency communication network are constructed, which include the relationship constraints between drone risk variables and path variables and the maximum risk constraints acceptable to drones.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for optimizing a UAV-assisted emergency communication network considering weather risks are implemented as described in any one of claims 1 to 6.

9. 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 program, the steps of the method for optimizing a drone-assisted emergency communication network considering weather risks are implemented as described in any one of claims 1 to 6.

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