Unmanned aerial vehicle dynamic 3D trajectory design method suitable for disaster scenarios

Abstract

The application relates to a dynamic 3D trajectory design method for unmanned aerial vehicles suitable for disaster scenes and belongs to the field of unmanned aerial vehicle wireless communication. According to the priority division of cluster groups constructed by ground nodes according to the situation of a disaster area, a cluster group information age growth coefficient is calculated, and a cluster group information age model is constructed. According to the transmission power of a cluster head and a channel model of the cluster head and an unmanned aerial vehicle, the maximum transmission radius of each cluster head is calculated. Within the maximum transmission radius, the best collection point problem of the unmanned aerial vehicle is modeled as a Markov decision model, a weighted sum of the propulsion energy consumption of the unmanned aerial vehicle and the average information age of the network is taken as the target, an optimization problem is established and solved, and the best collection point of the unmanned aerial vehicle in each cluster group is obtained. The best collection point of the cluster group with the minimum target function value is selected as the next hovering collection point of the unmanned aerial vehicle. The application distinguishes the disaster degrees of different areas, preferentially collects data of seriously-disaster areas within limited energy consumption, and improves the data collection efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) wireless communications and relates to a method for designing a dynamic 3D trajectory of an UAV suitable for disaster scenarios. Background Art

[0002] As a flexible, efficient, and rapidly deployable aerial platform, drones are becoming an ideal choice for data collection in disaster environments such as forest fires and nuclear leaks. Using drones as aerial relays for data collection effectively ensures the stable operation of wireless networks in disaster areas, extending their service life. This can also be extremely helpful in post-disaster rescue, disaster assessment, and resource allocation.

[0003] Existing drone data collection methods in disaster scenarios typically employ a uniform approach, failing to differentiate the severity of damage across different areas. This results in drones wasting valuable energy collecting data from less severely affected areas, while failing to obtain critical information from severely affected areas, thus delaying rescue operations. Drone data collection path planning is often poorly optimized, resulting in significant energy consumption during flight, shortening flight time and preventing them from performing missions in disaster areas for extended periods. This lack of targeted data collection prevents base station centers from obtaining timely critical information from severely affected areas, making it difficult to implement timely and effective response measures. This leads to worsening disaster conditions and expanding the affected area.

[0004] In view of the shortcomings of the existing technology, the present invention proposes a dynamic 3D trajectory design method for unmanned aerial vehicles (UAVs) suitable for disaster scenarios. Summary of the Invention

[0005] In light of this, the present invention aims to provide a method for designing dynamic 3D trajectories for drones in disaster scenarios. While minimizing drone energy exhaustion, this method collects data from disaster-affected areas of varying priority, minimizing the average information age of the entire wireless network. This allows base station centers to promptly understand the real-time conditions in severely affected areas and take necessary countermeasures. This allows disasters such as forest fires and nuclear leaks to be promptly contained, preventing their spread and strengthening disaster control efforts.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for designing a dynamic 3D trajectory of a UAV suitable for disaster scenarios includes the following steps:

[0008] The steps for designing the flight trajectory of a drone entering a disaster area from the current time slot to the next target collection are as follows:

[0009] S1. In the current time slot, the drone first collects information about the surrounding environment, defines the information age growth coefficient of each cluster based on the disaster situation transmitted by the cluster and the average data generation cycle, and establishes an information age model;

[0010] S2. Establish the UAV energy consumption model and channel model, and calculate the maximum transmission radius of each cluster head based on the channel model;

[0011] S3. Determine the hovering collection points of the drone. Within the transmission radius of each cluster head, establish an optimization problem with the goal of minimizing the weighted sum of the drone's energy consumption and the average information age of the sensor nodes. Solve the problem and obtain the hovering collection points of each cluster head. Record the hovering collection points of each cluster and their corresponding objective function values.

[0012] S4. Compare the objective function values ​​of each cluster and select the best collection point of the cluster when the objective function value is the smallest as the next collection position of the UAV, that is, the hovering collection point.

[0013] After the drone completes all acquisition tasks, the trajectory formed by all hovering points is the three-dimensional flight trajectory of the drone.

[0014] Furthermore, in S1, the priority is divided according to the disaster situation of the clusters and the cluster information age growth coefficient is calculated to establish an information age model. The specific content is:

[0015] First, the location coordinates of each cluster member are obtained to cluster the sensor nodes. Next, the K-means clustering algorithm is used to determine the centroid of each cluster, and the sensor node with the shortest Euclidean distance to the centroid is designated as the cluster head. The drone's flight trajectory is discretized into small time slots of length T. Assuming the current time slot is n, the drone collects basic information about the nearby disaster area from the cluster head, including the number of M clusters detected within its transmission range and the severity of the disaster in each cluster's area.

[0016] In order to reduce the loss of life and property in the disaster-stricken area, the disaster situation of each detected cluster is evaluated and the collection priority is determined. To characterize the degree of disaster, the priority of the i-th (i=1,2,...,M) cluster in time slot n is defined as Priority of cluster i The comprehensive disaster condition parameter F of the response cluster received by the UAV i (n) Decisions, including casualties Property damage temperature Smoke density represents the ratio of the number of casualties in the i-th cluster at time slot n to the total number of casualties in the M clusters detected, represents the ratio of the property loss of the i-th cluster in time slot n to the total property loss of the M clusters detected, represents the ratio of the average temperature detected by each node in the i-th cluster at time slot n to the maximum reference temperature, It represents the ratio of the average smoke concentration detected by each node in the i-th cluster at time slot n to the reference smoke concentration. The comprehensive disaster parameter F of cluster i i (n) is determined by the above four parameters and is expressed as:

[0017]

[0018] in and Represent the weights of casualties, property losses, temperature and smoke density parameters, and satisfy

[0019] According to the disaster situation, the priority of clusters is divided into 5 levels. The priority of cluster i in time slot n is Expressed as:

[0020]

[0021] In the above formula, priority The larger it is, the more severe the disaster is in the area where the cluster is located, and the data of this cluster will have a greater chance of being collected by the drone first.

[0022] In order to reduce the average information age of wireless networks, in addition to the priority of clusters, the establishment of the information age model should also take the average data generation period of clusters into consideration. Assume that the i-th cluster contains K i cluster members, then the cluster member j (j=1,2,...,K i ) The data generation cycle in n time slots is expressed as Then the average data generation period of cluster i in time slot n is It can be calculated by the following formula:

[0023]

[0024] Further, define is the maximum average data generation period in M ​​clusters at time slot n, expressed as:

[0025]

[0026] Since the information age growth coefficient w of cluster i i(n) is related to the average data generation period of the cluster. In order to avoid the large difference in the growth coefficient between clusters, which may cause it to become the only decisive indicator for determining the trajectory of the UAV, w i (n) is specifically expressed as:

[0027]

[0028] Based on the cluster priority and information age growth coefficient, an information age model is established. In time slot n, the change in information age of the data of member j of cluster i is expressed as follows:

[0029]

[0030] During time slots when a cluster's data is not being collected by a drone, the cluster's information age increases linearly. However, during time slots when a drone completes data collection from a sensor node or performs a periodic update, the sensor node's information age jumps to a function of its cluster's information age growth coefficient and priority at the end of that time slot. It's worth noting that if node j is not scheduled for an information age update within a time slot, the information age at the end of that time slot and the beginning of the next time slot are continuous; otherwise, it experiences a jump.

[0031] Furthermore, in S2, it is necessary to establish a UAV energy consumption model and a channel model, and calculate the maximum transmission radius of each cluster head based on the channel model. The specific content is:

[0032] Propulsion power of UAV in horizontal flight for:

[0033]

[0034] Among them, v H (n) is the horizontal flight speed of the UAV at time slot n, N u is the number of rotors of the UAV, c l is the rotor length of the UAV, ω u is the angular velocity, R u is the rotor disk radius, A u is the reference area of ​​the UAV front end, W u is the weight of the drone, ρ o and are air density and drag coefficient, λ u is a constant related to the physical quantities of the drone and the air density.

[0035] Assume that at time slot n, the drone is in a hovering state. At this time, the horizontal speed of the drone is v H (n)=0, then the drone hovering power P H for:

[0036]

[0037] Affected by air resistance and gravity, the propulsion power required for the drone to ascend and descend in the vertical direction is different. If the drone's vertical flight speed at time slot n is v V (n), then the rising propulsion power and descent propulsion power The expressions are:

[0038]

[0039]

[0040] Since the transmission energy consumption of the UAV is too small compared to the propulsion energy consumption, it can be ignored. Therefore, at time slot n, the energy consumption model of the UAV is:

[0041]

[0042] Considering the impact of signal propagation environment and UAV flight posture on air-ground channel in disaster environment, a probabilistic path loss model is adopted for line-of-sight link (LoS). In time slot n, the LoS probability between UAV and cluster head i is Expressed as:

[0043]

[0044] where a and b are constants that depend only on the environment, Indicates the relationship between the coordinates of the drone (x(n), y(n), z(n)) and the coordinates of cluster head i (x i ,y i ,z i ) is the Euclidean distance in the two-dimensional plane; h i (n)=|z(n)-z i | represents the vertical height difference between the UAV and cluster head i. Thus, the probability of non-line-of-sight link NLoS can be calculated as:

[0045] At time slot n, the LoS and NLoS path losses between the UAV and cluster head i are:

[0046]

[0047]

[0048] Among them, f c is the carrier frequency, c is the propagation speed of light in vacuum, r i (n) is the three-dimensional Euclidean distance between the UAV and cluster head i, ηLoS and η NLoS denote the LoS and NLoS propagation loss, respectively. Thus, the path loss L i (n) between the UAV and the cluster head i at time slot n is represented as:

[0049]

[0050] At time slot n, the cluster head i transmits data to the UAV with power The received power at the UAV is represented as To ensure that the UAV can reliably receive data, the received power should not be less than a threshold P min , i.e. is represented as:

[0051]

[0052] By substituting the link loss and corresponding probability under LoS and NLoS into the above equation, we have The transmission radius R i (n) of the cluster head i can be obtained as:

[0053]

[0054] In the above equation, when the received power P r (n) = P min , the maximum transmission radius R

[0055] Further, in S3, to make the UAV complete the collection task as quickly as possible with the minimum energy consumption, the optimization objective is to minimize the weighted sum of the network information age and the UAV energy consumption. The specific content is:

[0056] According to the established information age model, the calculation expression of the average information age of the entire wireless network within n time slots is:

[0057]

[0058] where N is the total number of sensor nodes in the network.

[0059] Define δ1 and δ2 to represent the weights of the average information age and the UAV energy consumption of the wireless network, respectively. The objective function is represented as the weighted sum of the UAV energy consumption and the network information age, i.e.: Assume that the cluster head collection strategy is G = {g1(n), g2(n),..., g M (n)} and g i (n) ∈ {0, 1} represents the indicator function of whether the UAV collects the data of cluster head i at time slot n. When gi When (n) = 0, it means that the UAV does not collect cluster head i. i When (n) = 1, the UAV collects cluster head i. Considering the UAV 3D trajectory S and cluster head collection strategy G, for any positive integer N, the following optimization problem is established:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] Among them, constraints C1, C2, and C3 respectively represent restrictions on the UAV's flight space; constraint C4 divides the priority of clusters; constraint C5 limits the elevation angle range between the UAV and the cluster head; constraints C6 and C7 respectively limit whether the UAV should be scheduled and the scheduling rules; constraint C8 limits the maximum transmission radius of the cluster head.

[0070] Furthermore, S3 is specifically:

[0071] S31, the UAV determines whether it is within the maximum transmission radius based on the relative position of the current coordinates and the cluster head; if it is not within the maximum transmission radius, the UAV coordinates are updated; if it is within the maximum transmission radius, the steps below this step are continued;

[0072] S32. Determine whether the current position is the cluster head hovering collection point based on the optimization objective function value. If not, continue to update the drone coordinates. If it is a hovering collection point, continue with the following steps.

[0073] S33, the UAV sends a collection instruction, the cluster head sends data information to the UAV, and updates the cluster information age;

[0074] Optionally, in S32, within the maximum transmission radius of each cluster head, the dual deep Q network algorithm (DDQN) is used to calculate the corresponding cumulative objective function values ​​of all possible actions, and the collection strategy and the optimal hovering point when the minimum value of the target cumulative function value is selected from all cluster heads according to the ε-greedy strategy. The optimal hovering point is the hovering collection point of the drone in the next time slot.

[0075] The beneficial effects of the present invention are:

[0076] (1) By prioritizing the disaster-stricken areas and combining them with the information age model, drones can prioritize data collection in severely affected areas, obtain key information in a timely manner, provide strong support for rescue operations, and effectively improve data collection efficiency.

[0077] (2) By establishing a UAV energy consumption model and a channel model, and combining it with optimization problem solving and the DDQN algorithm, the UAV can optimize its flight path, reduce energy consumption, and extend its flight time, enabling it to perform tasks in disaster areas for a long time and ensure the continuity of data collection.

[0078] (3) Through optimization problem solving and DDQN algorithm, the UAV can complete the collection task with minimum energy consumption, while minimizing the average information age of the entire wireless network, improving the timeliness of information, helping the base station center to understand the disaster situation in a timely manner and take necessary response measures, thereby curbing the spread of the disaster and strengthening the control of the disaster.

[0079] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0081] Figure 1 A schematic diagram of a model for drone-assisted data collection in disaster environments;

[0082] Figure 2 Schematic diagram of the information age of sensor node j in cluster i. DETAILED DESCRIPTION

[0083] Following, the advantages and effects of the present application can be easily understood by those skilled in the art from the disclosure of the specification. The present application can also be implemented or applied by other different specific embodiments, and various modifications or changes can be made to the details in the specification based on different views and applications without departing from the spirit of the present application. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0084] The drawings are only used for exemplary illustration, and the representation is only a schematic diagram, not a physical diagram, and cannot be understood as a limitation of the present application; in order to better illustrate the embodiments of the present application, some components in the drawings are omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings can be omitted.

[0085] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it should be understood that if the terms "upper", "lower", "left", "right", "front", "back" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation of the present application, for those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0086] Please refer to Figures 1 and 2 , the unmanned aerial vehicle dynamic 3D trajectory design method suitable for disaster scenes, specifically:

[0087] Step 1: as shown in Figure 1 , the unmanned aerial vehicle enters the disaster area, first acquires the position coordinates of each cluster member, and performs clustering of the sensing nodes; according to the disaster situation and emergency degree transmitted by the cluster, the cluster is divided into different priorities, and the information age growth coefficient of each cluster is defined according to the data average generation period, and the information age model is jointly established; in the present application, there are M clusters in the three-dimensional flight space of the unmanned aerial vehicle within the detection range, cluster i contains K inodes; in the system's first time slot, the node's information age starts at zero and increases. A K-means clustering algorithm is used to determine the centroid of each cluster, and the sensor node with the shortest Euclidean distance to the centroid is designated as the cluster head. The drone's flight trajectory is discretized into small time slots of length T. Assuming the current time slot is n, the drone collects basic information about the nearby disaster-stricken area from the cluster head, including the number of M clusters detected within its transmission range and the severity of the disaster in each cluster's area.

[0088] In order to reduce the loss of life and property in the disaster-stricken area, the disaster situation of each detected cluster is evaluated and the collection priority is determined. To characterize the degree of disaster, the priority of the i-th (i=1,2,...,M) cluster in time slot n is defined as Priority of cluster i The comprehensive disaster condition parameter F of the response cluster received by the UAV i (n) Decisions, including casualties Property damage temperature Smoke density represents the ratio of the number of casualties in the i-th cluster at time slot n to the total number of casualties in the M clusters detected, represents the ratio of the property loss of the i-th cluster in time slot n to the total property loss of the M clusters detected, represents the ratio of the average temperature detected by each node in the i-th cluster at time slot n to the maximum reference temperature, It represents the ratio of the average smoke concentration detected by each node in the i-th cluster at time slot n to the reference smoke concentration. The comprehensive disaster parameter F of cluster i i (n) is determined by the above four parameters and is expressed as:

[0089]

[0090] in and Represent the weights of casualties, property losses, temperature and smoke density parameters, and satisfy

[0091] According to the disaster situation, the priority of clusters is divided into 5 levels. The priority of cluster i in time slot n is Expressed as:

[0092]

[0093] In the above formula, priority The larger it is, the more severe the disaster is in the area where the cluster is located, and the data of this cluster will have a greater chance of being collected by the drone first.

[0094] In order to reduce the average information age of wireless networks, in addition to the priority of clusters, the establishment of the information age model should also take the average data generation period of clusters into consideration. Assume that the i-th cluster contains K i cluster members, then the cluster member j (j=1,2,...,K i ) The data generation cycle in n time slots is expressed as Then the average data generation period of cluster i in time slot n is It can be calculated by the following formula:

[0095]

[0096] Further definition is the maximum average data generation period in M ​​clusters at time slot n, expressed as:

[0097]

[0098] Since the information age growth coefficient w of cluster i i (n) is related to the average data generation period of the cluster. In order to avoid the large difference in the growth coefficient between clusters, which may cause it to become the only decisive indicator for determining the trajectory of the UAV, w i (n) is specifically expressed as:

[0099]

[0100] According to the priority of clusters and information age growth coefficient, an information age model is established, such as Figure 2 As shown. In time slot n, the information age of the data of member j of cluster i is expressed as follows:

[0101]

[0102] During time slots when a cluster's data is not being collected by a drone, the cluster's information age increases linearly. However, during time slots when a drone completes data collection from a sensor node or performs a periodic update, the sensor node's information age jumps to a function of its cluster's information age growth coefficient and priority at the end of that time slot. It's worth noting that if node j is not scheduled for an information age update within a time slot, the information age at the end of that time slot and the beginning of the next time slot are continuous; otherwise, it experiences a jump.

[0103] Step 2: Establish the UAV energy consumption model and channel model, and calculate the maximum transmission radius of each cluster head based on the channel model. for:

[0104]

[0105] Among them, v H (n) is the horizontal flight speed of the UAV at time slot n, N u is the number of rotors of the UAV, c l is the rotor length of the UAV, ω u is the angular velocity, R u is the rotor disk radius, A u is the reference area of ​​the UAV front end, W u is the weight of the drone, ρ o and are air density and drag coefficient, λ u is a constant related to the physical quantities of the drone and the air density.

[0106] Assume that at time slot n, the drone is in a hovering state. At this time, the horizontal speed of the drone is v H (n)=0, then the drone hovering power P H for:

[0107]

[0108] Affected by air resistance and gravity, the propulsion power required for the drone to ascend and descend in the vertical direction is different. If the drone's vertical flight speed at time slot n is v V (n), then the rising propulsion power and descent propulsion power The expressions are:

[0109]

[0110]

[0111] Since the transmission energy consumption of the UAV is too small compared to the propulsion energy consumption, it can be ignored. Therefore, at time slot n, the energy consumption model of the UAV is:

[0112]

[0113] Considering the impact of signal propagation environment and UAV flight posture on air-ground channel in disaster environment, a probabilistic path loss model is adopted for line-of-sight link (LoS). In time slot n, the LoS probability between UAV and cluster head i is Expressed as:

[0114]

[0115] where a and b are constants that depend only on the environment, Indicates the relationship between the coordinates of the drone (x(n), y(n), z(n)) and the coordinates of cluster head i (x i ,y i ,z i ) is the Euclidean distance in the two-dimensional plane; h i (n)=|z(n)-z i | represents the vertical height difference between the UAV and cluster head i. Thus, the probability of non-line-of-sight link NLoS can be calculated as:

[0116] At time slot n, the LoS and NLoS path losses between the UAV and cluster head i are:

[0117]

[0118] Among them, f c is the carrier frequency, c is the propagation speed of light in vacuum, r i (n) is the three-dimensional Euclidean distance between the UAV and cluster head i, η LoS and η NLoS denote the LoS and NLoS propagation losses, respectively. Therefore, at time slot n, the path loss L between the UAV and the ground cluster head i is i (n) is expressed as:

[0119]

[0120] In time slot n, cluster head i uses power When uploading data to the drone, the drone receiving power is expressed as In order to ensure that the drone can receive data reliably, the receiving power Should not be less than the threshold P min ,Right now Expressed as:

[0121]

[0122] Substitute the link loss and corresponding probability under LoS and NLoS into the above formula for calculation, let The transmission radius R of cluster head i can be obtained i (n) is:

[0123]

[0124] In the above formula, when the UAV's receiving power P r (n) = P min When , the maximum transmission radius of cluster head i can be obtained

[0125] Step 3: To enable the UAV to complete the collection task as quickly as possible with minimal energy consumption, the optimization goal is to minimize the weighted sum of the network information age and the UAV energy consumption. According to the established information age model, the calculation expression for the average information age of the entire wireless network within n time slots is:

[0126]

[0127] in, is the total number of sensor nodes in the network.

[0128] Define δ1 and δ2 as the weights of the average information age of the wireless network and the energy consumption of the drone, respectively. Then the objective function is expressed as the weighted sum of the energy consumption of the drone and the network information age, that is: Assume that the cluster head acquisition strategy is G = {g1(n),g2(n),...,g M (n)}, g i (n)∈{0,1} represents the indicator function of whether the UAV collects cluster head i data in time slot n. When g i When (n) = 0, it means that the UAV does not collect cluster head i. i When (n) = 1, the UAV collects cluster head i. Considering the UAV 3D trajectory S and cluster head collection strategy G, for any positive integer N, the following optimization problem is established:

[0129]

[0130] Among them, constraints C1, C2, and C3 respectively represent restrictions on the UAV's flight space; constraint C4 divides the priority of clusters; constraint C5 limits the elevation angle range between the UAV and the cluster head; constraints C6 and C7 respectively limit whether the UAV should be scheduled and the scheduling rules; constraint C8 limits the maximum transmission radius of the cluster head.

[0131] Step 3: The specific content of the drone obtaining the hovering collection point according to the optimization target is as follows:

[0132] At each time slot, the drone determines the target cluster by calculating the optimized objective function value, and then determines the action command based on the target location, executing the corresponding flight action to complete the coordinate update. The three-dimensional coordinates of the drone at time slot n are represented as dir(n) = (x(n), y(n), z(n)). After the drone makes a flight action strategy, the drone updates its coordinates according to the flight action command act(n) = (Δx(n), Δy(n), Δz(n)), which is specifically expressed as:

[0133] x(n+1)=x(n)+Δx(n)

[0134] y(n+1)=y(n)+Δy(n)

[0135] z(n+1) = z(n) + Δz(n)

[0136] dir(n+1) = (x(n+1), y(n+1), z(n+1))

[0137] wherein Δx(n), Δy(n), Δz(n) respectively represent the difference values of the UAV to be transformed in the horizontal plane and height under the flight action instruction act(n). After the coordinate update is completed, the coordinate transformation record is stored. Step 3 can be divided into the following specific steps:

[0138] Step 3.1: The UAV judges whether the UAV is within the maximum transmission radius according to the relative position of the current coordinates and the cluster head; if not within the maximum transmission radius, the UAV coordinates are updated; if within the maximum transmission radius, the following steps of this step are continued.

[0139] Step 3.2: According to the optimization objective function value, it is judged whether the current position is the hovering collection point of the cluster head, if not, the UAV coordinates are continuously updated; if it is the hovering collection point, the following steps of this step are continued.

[0140] In particular, in step 3.2, within the maximum transmission radius of each cluster head, the double deep Q network algorithm (DDQN) is used to calculate the corresponding cumulative objective function values of all possible actions, and according to the ε-greedy strategy, the collection strategy and the best hovering point when the minimum value of the optimization objective cumulative function value are selected from all cluster heads, the best hovering point is the hovering collection point of the UAV in the next time slot.

[0141] Step 3.3: The UAV sends a collection instruction, and the cluster head sends data information to the UAV, and updates the cluster group information age.

[0142] Step 4: Compare the objective function values of each cluster group, and select the best collection point of the cluster group with the minimum objective function value as the next collection position of the UAV, that is, the hovering collection point.

[0143] Step 5: After the UAV completes all collection tasks, the trajectory composed of all hovering points is the three-dimensional flight trajectory of the UAV.

[0144] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should be covered in the scope of the claims of the present application.

Claims

1. A method for designing dynamic 3D trajectories of drones suitable for disaster scenarios, characterized by: The method comprises the following steps: S1: The drone enters the disaster-stricken area and designs a flight trajectory from the current time slot to the next hovering collection point. In the current time slot, the drone first collects information about the surrounding environment. Based on the disaster situation transmitted by the cluster and the average data generation cycle, the information age growth coefficient of each cluster is defined to establish an information age model. S2: Establish the UAV energy consumption model and channel model, and calculate the maximum transmission radius of each cluster head based on the channel model; S3: Within the transmission radius of each cluster head, with the goal of minimizing the weighted sum of the drone's energy consumption and the average information age of the sensor nodes, an optimization problem is established and solved to obtain the best collection point for each cluster. The best collection point for each cluster and its corresponding objective function value are recorded. S4: Compare the objective function values ​​of each cluster and select the best collection point of the cluster when the objective function value is the smallest as the next collection position of the UAV, that is, the hovering collection point; After the drone completes all collection tasks, the trajectory formed by all hovering collection points is the three-dimensional flight trajectory of the drone; In S1, the priority is divided according to the disaster situation of the clusters, the cluster information age growth coefficient is calculated, and the information age model is established. The specific content is: Each ground node broadcasts its location information and is clustered. A K-means clustering algorithm is used to determine the centroid of each cluster, and the sensor node with the shortest Euclidean distance to the centroid is selected as the cluster head. The drone's flight trajectory is discretized into small time slots of length T. Assuming the current time slot is n, where n = 1, 2, 3, ..., the drone collects basic information about the nearby disaster-stricken area from the cluster head, including the number of M clusters detected within the transmission range and the extent of the disaster in the area where each cluster is located. In order to reduce the loss of life and property in the disaster-stricken area, the disaster situation of each detected cluster is evaluated and the collection priority is determined; in order to characterize the degree of disaster, the priority of the i-th cluster in time slot n is defined as i=1,2,...,M; priority of cluster i The comprehensive disaster condition parameter F of the response cluster received by the UAV i (n) Decisions, including casualties Property damage temperature and smoke concentration represents the ratio of the number of casualties in the i-th cluster at time slot n to the total number of casualties in the M clusters detected, represents the ratio of the property loss of the i-th cluster in time slot n to the total property loss of the M clusters detected, represents the ratio of the average temperature detected by each node in the i-th cluster at time slot n to the maximum reference temperature, It represents the ratio of the average smoke concentration detected by each node in the i-th cluster at time slot n to the reference smoke concentration. The comprehensive disaster parameter F of cluster i i (n) is determined by the above four parameters and is expressed as: in and Represent the weights of casualties, property losses, temperature and smoke density parameters respectively, and meet According to the disaster situation, the priority of clusters is divided into 5 levels. The priority of cluster i in time slot n is Expressed as: In the above formula, priority The larger the value is, the more serious the disaster is in the cluster area, and the data of this cluster will have a greater chance of being collected by drones first. In order to reduce the average information age of wireless networks, in addition to the priority of clusters, the establishment of the information age model should also take into account the average data generation period of clusters; assuming that the i-th cluster contains K i The data generation period of cluster member j in the i-th cluster group in n time slots is expressed as j=1,2,...,K i , then the average data generation period of cluster i in time slot n is Calculated by the following formula: definition is the maximum average data generation period in M ​​clusters at time slot n, expressed as: Since the information age growth coefficient w of cluster i i (n) is related to the average data generation period of the cluster. In order to avoid the large difference in the growth coefficient between clusters, which may cause it to become the only decisive indicator for determining the trajectory of the UAV, it is expressed in exponential form. i (n) is specifically expressed as: According to the priority of clusters and the information age growth coefficient, an information age model is established; at time slot n, the information age of member j in cluster i is Expressed as: In the time slots when cluster data is not collected by the drone, the information age of the cluster increases linearly. In the time slots when the drone completes data collection of the sensor node or the sensor node data is periodically updated, the information age of the sensor node will jump to the function value of the information age growth coefficient and priority of the cluster to which it belongs at the end of the time slot. If node j is not scheduled for information age update in a time slot, the information age at the end of the time slot and the beginning of the next time slot will be continuous, otherwise it will experience a jump.

2. The method for designing a dynamic 3D trajectory of a UAV suitable for disaster scenarios according to claim 1, characterized in that: In S2, it is necessary to calculate the maximum transmission radius of each cluster head by establishing a system channel model. The specific content is: Considering the impact of the signal propagation environment and the flight posture of the UAV on the air-ground channel in a disaster environment, a probabilistic path loss model is adopted for the LoS of the line-of-sight link; in time slot n, the LoS probability between the UAV and cluster head i is Expressed as: where a and b are constants that depend only on the environment, Indicates the relationship between the coordinates of the drone (x(n), y(n), z(n)) and the coordinates of cluster head i (x i ,y i ,z i ) is the Euclidean distance in the two-dimensional plane; h i (n)=|z(n)-z i | represents the vertical height difference between the UAV and cluster head i; the probability of non-line-of-sight link NLoS is calculated as: At time slot n, the LoS and NLoS path losses between the UAV and cluster head i are: Among them, f c is the carrier frequency, c is the propagation speed of light in vacuum, r i (n) is the three-dimensional Euclidean distance between the UAV and cluster head i, η LoS and η NLoS denote the LoS and NLoS propagation losses, respectively; therefore, at time slot n, the path loss L between the UAV and the ground cluster head i is i (n) is expressed as: In time slot n, cluster head i uses power P t i (n) uploads data to the drone, the drone receiving power is expressed as In order to ensure that the drone can receive data reliably, the receiving power Not less than the threshold P min ,Right now Expressed as: P t i (n)-L i (n)≥P min Substitute the path loss and corresponding probability under LoS and NLoS into the above formula for calculation, let Get the transmission radius R of cluster head i i (n) is: In the above formula, when the receiving power of the UAV is When the maximum transmission radius of cluster head i is obtained 3. The method for designing a dynamic 3D trajectory of a UAV suitable for disaster scenarios according to claim 2, characterized in that: In S3, within the transmission radius of each cluster head, with the goal of minimizing the weighted sum of the drone energy consumption and the average information age of the sensor nodes, an optimization problem is established and solved to obtain the optimal collection point for each cluster. The specific content is: In order to enable the UAV to complete the collection task as quickly as possible with the minimum energy consumption, the optimization goal is to minimize the weighted sum of network information age and UAV energy consumption; the average information age of the entire wireless network in n time slots is expressed as: in, is the total number of sensor nodes in the network; Establish the energy consumption model of the UAV; the propulsion power of the UAV in horizontal flight for: Among them, v H (n) is the horizontal flight speed of the UAV at time slot n, N u is the number of rotors of the UAV, c l is the rotor length of the UAV, ω u is the angular velocity, R u is the rotor disk radius, A u is the reference area of ​​the UAV front end, W u is the weight of the drone, ρ o and are air density and drag coefficient, λ u is a constant related to the physical quantity of the drone and the air density; Assume that at time slot n, the drone is in a hovering state and the horizontal speed of the drone is v H (n)=0, then the drone hovering power P H for: Affected by air resistance and gravity, the propulsion power required for the drone to ascend and descend in the vertical direction is different; at time slot n, the vertical flight speed of the drone is v V (n), then the rising propulsion power and descent propulsion power They are: The transmission energy consumption of the UAV is negligible; in time slot n, the energy consumption model of the UAV is: Define δ1 and δ2 as the weights of the average information age of the wireless network and the energy consumption of the drone, respectively. Then the objective function is expressed as the weighted sum of the energy consumption of the drone and the network information age, that is: Assume that the cluster head acquisition strategy is G = {g1(n),g2(n),...,g M (n)}, g i (n)∈{0,1} represents the indicator function of whether the UAV collects cluster head i data in time slot n. When g i When (n) = 0, it means that the UAV does not collect cluster head i. i When (n) = 1, the UAV collects cluster head i; considering the UAV 3D trajectory S and cluster head collection strategy G, for any positive integer N, the following optimization problem is established: Among them, constraints C1, C2, and C3 respectively represent restrictions on the UAV's flight space; constraint C4 divides the priority of clusters; constraint C5 limits the elevation angle range between the UAV and the cluster head; constraints C6 and C7 respectively limit whether the UAV should be scheduled and the scheduling rules; constraint C8 limits the maximum transmission radius of the cluster head.

4. The method for designing a dynamic 3D trajectory of a UAV suitable for disaster scenarios according to claim 3, characterized in that: The S4 is specifically: S41: The UAV determines whether it is within the maximum transmission radius based on the relative position of the current coordinates and the cluster head; if it is not within the maximum transmission radius, the UAV coordinates are updated; if it is within the maximum transmission radius, the following steps are continued; S42: Determine whether the current position is the cluster head hovering collection point according to the optimization objective function value. If not, continue to update the drone coordinates; if it is a hovering collection point, continue with the following steps; S43: The UAV sends a collection instruction, the cluster head sends data information to the UAV, and updates the cluster information age.

5. The method for designing a dynamic 3D trajectory of a UAV suitable for disaster scenarios according to claim 4, characterized in that: In S42, within the maximum transmission radius of each cluster head, the dual deep Q network algorithm DDQN is used to calculate the corresponding cumulative objective function values ​​of all possible actions, and the collection strategy and the optimal hovering point when the minimum value of the target cumulative function value is selected from all cluster heads according to the ε-greedy strategy. The optimal hovering point is the hovering collection point of the drone in the next time slot.

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