Unmanned aerial vehicle transportation network local reconstruction method for special events

Abstract

The invention discloses an unmanned aerial vehicle transportation network local reconstruction method for a special event, and belongs to the technical field of unmanned aerial vehicle transportation network design, and the method comprises the steps: obtaining unmanned aerial vehicle transportation network information data through remote sensing and communication equipment, and carrying out the modeling of a transportation network topological structure; identifying the influence range of the special events on the unmanned aerial vehicle transportation network by analyzing the two special events of flight limitation and temporary tasks; making a local reconstruction strategy of the unmanned aerial vehicle transportation network, wherein the local reconstruction strategy comprises an original network reconstruction strategy and a topological structure adjustment strategy; and establishing a transportation network structure and an operation index set, and performing performance evaluation on the unmanned aerial vehicle transportation network through a grey correlation analysis method. According to the method, the dynamic environment influence is considered, the adaptability of the transportation network is enhanced through local reconstruction of the unmanned aerial vehicle transportation network, and the unmanned aerial vehicle transportation network with flexibility and sustainable development is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) transportation network design, and particularly to a method for locally reconstructing a UAV transportation network for special events. Background Art

[0002] With the continuous maturity of UAV technology and the saturation of ground space resources, UAV transportation networks have great development potential in fields such as logistics distribution, urban commuting, and tourism sightseeing. It can not only standardize the operation order of UAVs in the low-altitude airspace but also inject strong impetus into social and economic development. However, during the actual operation of UAV transportation networks, they may be affected by special events, such as special weather, sudden public events, and temporary control, which may cause some nodes and flight segments to be interrupted or task requirements to change temporarily, thus interfering with the normal operation of the transportation network. Therefore, establishing a reasonable local reconstruction strategy for the transportation network is crucial for ensuring its normal operation state. Existing research mainly focuses on task reallocation of UAVs or vulnerability analysis of transportation networks, and rarely considers adapting to dynamic environmental requirements by adjusting the local structure of the transportation network or optimizing flight traffic. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for locally reconstructing a UAV transportation network for special events. During the actual operation of UAV transportation networks, they may be affected by special events, such as special weather, sudden public events, and temporary control, which may cause some nodes and flight segments to be interrupted or task requirements to change temporarily, thus interfering with the normal operation of the transportation network. Therefore, establishing a reasonable local reconstruction strategy for the transportation network is crucial for ensuring its normal operation state.

[0004] To achieve the above purpose, the present invention provides a method for locally reconstructing a UAV transportation network for special events, including the following steps:

[0005] Step 1: Obtain UAV transportation network information data through remote sensing and communication devices, and model the topological structure of the transportation network;

[0006] Step 2: Analyze the impact of special events from two aspects of flight restrictions and temporary tasks, and identify the impact range of special events on the UAV transportation network;

[0007] Step 3: Develop a local reconstruction strategy for the UAV transportation network, including an original network reconstruction strategy and a topological structure adjustment strategy. The original network reconstruction strategy focuses on reallocating flight traffic, and the topological structure adjustment strategy focuses on dynamically designing new flight routes. Locally reconstruct the transportation network from two aspects of flight traffic reallocation and new flight segment dynamic design;

[0008] Step 4: Establish the index of the UAV transportation network structure and the index of the UAV transportation network operation, and evaluate the performance of the UAV transportation network through the grey relational analysis method.

[0009] Preferably, in Step 1, based on the remote sensing and communication equipment carried by the ground and the UAV, obtain the geographical environment, take-off and landing points, air nodes and flight segments information of the UAV transportation network.

[0010] Preferably, use graph theory to model the UAV transportation network as an undirected graph, where the take-off and landing points and air nodes are the vertices of the graph, and the flight segments are the edges of the graph;

[0011] V is the set of transportation network nodes, V o is the set of take-off and landing points, and the number of take-off and landing points is m, V n is the set of air nodes, then V = V o ∪V n , C = (c ij ) m×m is the adjacency matrix of the transportation network, c ij = 1 indicates that there is a direct flight segment between nodes i and j, c ij = 0 indicates that there is no direct flight segment between nodes i and j,

[0012] A = (a ij ) m×m is the connectivity matrix of the transportation network, a ij = 1 indicates that there is a connected path between nodes i and j, a ij = 0 indicates that there is no connected path between nodes i and j.

[0013] Preferably, in Step 2, flight restrictions include that the flight restricted area covers a single flight segment, the flight restricted area covers a single node and multiple flight segments, and the flight restricted area covers multiple nodes and multiple flight segments. The influence range of flight restrictions on the UAV transportation network is the affected nodes and the feasible flight segments around them.

[0014] Preferably, during temporary tasks, add temporary flight segments to locally reconstruct the transportation network structure. The influence range of temporary tasks on the UAV transportation network is the departure node, arrival node of the temporary task and the potential feasible flight segments between them.

[0015] Preferably, in Step 3, the original network reconstruction includes the original network reconstruction during flight restrictions and the original network reconstruction during temporary tasks.

[0016] Preferably, the original network reconstruction during flight restrictions: When there are flight restrictions and some flight segments and nodes of the transportation network fail, set the set of the nearest UAVs flying into the nodes outside the influence range as P i , and set each flying-in node as p i (p i ∈Pi ), set the set of the nearest fly-out nodes outside the corresponding influence range as P io , use the maximum node distance parameter λ to define the influence range of the failed flight segment. For any node to the fly-out node p io (p io ∈P io ), the number of flight segments passed is less than or equal to λ. When λ = 1, it means the influence range includes the nodes and flight segments directly connected to the fly-out node. For the fly-in node p i , the set of influence nodes connected by its fly-out node p io is P λ , then there is no

[0017] the potential reconstruction node set R of the unmanned aerial vehicle j = P λ ∪ {p io}, guide the flight requirements of the unmanned aerial vehicle planned to pass through the failed flight segment to the fly-in node p i , and set the attracting node of the fly-in node as the potential reconstruction node r j (r j ∈R j ), at this time the reconstructed OD pair is (p i , r j );

[0018] If there are multiple connected paths between the reconstructed OD pairs, allocate the traffic to the shortest path. Combining the distance and traffic of the connected paths, comprehensively decide the allocation path. The set of connected paths is set as T, and the element t ij (t ij ∈T) represents the connected path between the reconstructed OD pair (p i , r j ), the shortest path length between nodes is d ij , and the traffic on the path is q ij at this time. Use the deviation normalization method to normalize the data and map its value to between 0 and 1. The normalized data is d i ' j and q i ' j . Define the index path impedance I to provide a decision basis for flight traffic allocation. The path impedance is expressed as:

[0019] I = ωd i ' j + (1 - ω)q i ' j ;

[0020] In the formula, ω∈(0,1) is the weight factor of the path length in the path impedance. By calculating the impedance values of all connected paths, allocate the flight traffic to the path with the minimum impedance;

[0021] Original network reconstruction during temporary tasks: For newly added flight plans, there are multiple connected paths between OD pairs, and the path with the minimum impedance is selected.

[0022] Preferably, the topological structure adjustment includes the topological structure adjustment during flight restrictions and the topological structure adjustment during temporary tasks.

[0023] Preferably, the topological structure adjustment during flight restrictions: Use the Graham scanning method to construct the convex hull of the flight restricted area, realize the regularization of the irregular area, regard the flight restricted area as an obstacle, and use the improved cellular automata algorithm between potential reconstructed OD pairs

[0024] Plan the shortest path. The shortest paths between all OD pairs form a set of alternative paths, and the shortest path among them is selected as the reconstructed flight route;

[0025] Topological structure adjustment during temporary tasks: For newly added flight plans, use the improved cellular automata algorithm to re-plan a shortest path between its OD pairs as the flight route for this temporary task.

[0026] Preferably, in step 4, the UAV transportation network structure indicators include degree distribution entropy, network efficiency, and standard deviation of segment betweenness, and the operation indicators include transportation cost, network reachability, and average satisfaction;

[0027] The degree distribution entropy E, an indicator used to measure the node degree distribution characteristics in the transportation network, is expressed as:

[0028]

[0029] In the formula, r i is the ratio of the degree of node i to the sum of the degrees of all nodes;

[0030] The network efficiency P, an indicator used to measure the operation efficiency of the transportation network, is expressed as:

[0031]

[0032] In the formula, n is the number of nodes;

[0033] The standard deviation of segment betweenness D, used to evaluate the difference in transportation pressure between segments, is expressed as:

[0034]

[0035] In the formula, b ij is the betweenness of the segment between nodes i and j, and b is the average value of the betweenness of segments;

[0036] The transportation cost O, an important indicator used to evaluate economic benefits, is expressed as:

[0037]

[0038] Wherein, q ij is the UAV flight flow between nodes i and j;

[0039] The network reachability R, which represents the average value of the shortest path lengths of UAVs between OD pairs, is expressed as:

[0040]

[0041] The average satisfaction degree S. The UAV flight plan includes the departure time and the arrival time. The planned arrival time is regarded as the expected time t of the user w , and a unilateral soft time window is adopted to characterize the transportation satisfaction degree S of the UAV executing the flight plan u u , which is expressed as:

[0042]

[0043] Wherein, t r is the actual arrival time of the UAV, θ (θ > 1) is the user time sensitivity coefficient, and t max is the maximum tolerable time of the user;

[0044] If the set of all UAV flight plans in the transportation network is U, then the average satisfaction degree of users after executing all flight plans is expressed as:

[0045]

[0046] Therefore, the local reconstruction method of the UAV transportation network for special events adopting the above structure in the present invention has the following beneficial effects: Based on the topological structure of the UAV transportation network, the present invention analyzes the impacts of special events from two aspects of flight restriction and temporary tasks, formulates the original network reconstruction strategy and the topological structure adjustment strategy to locally reconstruct the transportation network, and evaluates the performance of the reconstructed network through grey relational analysis according to the transportation network structure and operation indexes. The present invention helps to improve the connectivity and stability of the UAV transportation network, and provides a guarantee for realizing the refined management and operation safety of the UAV transportation network.

[0047] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0048] Figure 1 is the schematic flow chart of the method of the embodiment of the present invention;

[0049] Figure 2 is the schematic diagram of the UAV transportation network of the embodiment of the present invention;

[0050] Figure 3 These are three cases of the flight-limited impact network structure according to the embodiments of the present invention. Detailed implementation manners

[0051] In order to make the objectives, technical solutions and advantages of the embodiments disclosed in the present invention clearer and more understandable, the following further elaborates on the embodiments of the present invention in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not used to limit the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts fall within the scope of protection of this application. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout.

[0052] It should be noted that the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0053] Similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0054] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of this invention is usually placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0055] In the description of the present invention, it should also be noted that unless otherwise clearly specified and limited, the terms "set", "install", "connect" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Embodiment

[0057] Such as Figures 1-3As shown in the figure, a method for local reconstruction of an unmanned aerial vehicle (UAV) transportation network for special events according to the present invention includes the following steps:

[0058] Step 1: Obtain UAV transportation network information data through remote sensing and communication devices, and model the topological structure of the transportation network.

[0059] Based on the remote sensing and communication devices carried on the ground and UAVs, obtain the geographical environment, takeoff and landing points, aerial nodes, and flight segment information of the UAV transportation network. Use graph theory to model the UAV transportation network as an undirected graph, where the takeoff and landing points and aerial nodes are the vertices of the graph, and the flight segments are the edges of the graph.

[0060] V is the set of transportation network nodes, V o is the set of takeoff and landing points, and the number thereof is m, V n is the set of aerial nodes, then V = V o ∪V n , C = (c ij ) m×m is the adjacency matrix of the transportation network, c ij = 1 indicates that there is a direct flight segment between nodes i and j, otherwise c ij = 0, A = (a ij ) m×m is the connectivity matrix of the transportation network, a ij = 1 indicates that there is a connected path between nodes i and j, otherwise a ij = 0.

[0061] Step 2: Analyze the impact of special events from two aspects of flight restrictions and temporary tasks, and identify the impact range of special events on the UAV transportation network.

[0062] The flight restricted area is generated by factors such as temporary airspace, special weather, and electromagnetic interference, which may cause some flight segments in the UAV transportation network to fail. Flight restrictions include that the flight restricted area covers a single flight segment, the flight restricted area covers a single node and multiple flight segments, and the flight restricted area covers multiple nodes and multiple flight segments. The impact range of flight restrictions on the UAV transportation network is the affected nodes and the feasible flight segments around them. UAVs expected to pass through the affected flight segments need to change their flight routes to bypass the flight restricted area.

[0063] In the face of an emergency, a temporary task is issued. The temporary task includes changing the flight route and adding a temporary flight route. If the existing network transportation network can meet the requirements of the temporary task, the flight route is changed; if the existing transportation network cannot meet the requirements of the temporary task, a temporary flight route is added. The impact range of the temporary task on the UAV transportation network is the departure node, arrival node of the temporary task, and the potential feasible flight segments between them.

[0064] Step 3, formulate the local reconstruction strategy of the UAV transportation network, including the original network reconstruction strategy and the topological structure adjustment strategy. The original network reconstruction strategy focuses on the redistribution of flight traffic, and the topological structure adjustment strategy focuses on the dynamic design of new routes.

[0065] The original network reconstruction strategy aims to find the reconstructed routes in the topological structure of the original transportation network, so that the flight tasks are transported on the reconstructed routes. When the transportation network is structurally damaged due to the influence of flight restricted areas, remove the failed flight segments and nodes, re-plan the routes of the affected flight tasks, and conduct local redistribution of flight traffic. If the topological structure of the transportation network is not damaged and a temporary flight task is received, based on the existing transportation network structure, optimize the traffic distribution and reasonably allocate the newly generated flight traffic to the existing flight segments.

[0066] Original network reconstruction under flight restrictions: When flight is restricted and some flight segments and nodes of the transportation network fail, set the set of UAVs flying into the nearest node outside the affected area as P i , and set each flying-in node as p i (p i ∈P i ). The set of the nearest flying-out nodes outside the corresponding affected area is set as P io . Use the maximum node distance parameter λ to define the influence range of the failed flight segment. The number of flight segments passed by any node to the flying-out node p io (p io ∈P io ) shall not exceed λ. When λ = 1, it means that the influence range only includes the nodes and flight segments directly connected to the flying-out node. For the flying-in node p i , the set of its affected nodes connected to the flying-out node p io is P λ . Then the set of potential reconstruction nodes R j = P λ ∪ {p io}. Guide the UAV flight demand planned to pass through the failed flight segment to the flying-in node p i , and set the attracting node of the flying-in node as the potential reconstruction node r j (r j ∈R j ). At this time, the reconstructed OD pair is (p i , r j ); there are multiple connected paths between the reconstructed OD pairs. Allocate the traffic to the shortest path, but this method may cause traffic overload and congestion on the shortest path. Therefore, considering the dual factors of the distance and traffic of the connected paths, make a comprehensive decision on the allocation path. The set of connected paths is set as T, and the element t ij (t ij ∈T) represents the reconstructed OD pair (p i , r jThe connected path between them, and the shortest path length between nodes is d ij , and the traffic on the path is q at this time ij , using the deviation normalization method to normalize the data, mapping its value to between 0 and 1, and the normalized data is d i ' j and q i ' j , define the index path impedance I to provide a decision basis for flight traffic allocation. This index reflects the time cost and congestion degree of the path, and is expressed as: I = ωd i ' j +(1 - ω)q i ' j , where ω ∈ (0, 1) is the weight factor of the path length in the path impedance. By calculating the impedance values of all connected paths, the flight traffic is allocated to the path with the minimum impedance; the reconstruction of the original network during temporary tasks: For the newly added flight plan, there are multiple connected paths between the OD pairs, and the path with the minimum impedance is selected.

[0067] The topology adjustment strategy aims to introduce new flight segments into the original transportation network topology, so that flight tasks are diverted to the new flight segments for transportation, enhancing the connectivity and flexibility of the transportation network. When the transportation network topology is damaged, the failed flight segments and nodes are removed, and by adding new flight segments in the damaged network, the flight traffic is effectively guided to the newly opened flight routes; if the topology of the transportation network is not damaged and a temporary flight task is received, a brand-new flight route for temporary flight is planned between the starting and ending points of the temporary task to meet the special needs of the temporary task.

[0068] The topology adjustment includes the topology adjustment during flight restrictions and the topology adjustment during temporary tasks. The topology adjustment during flight restrictions: The flight restricted area usually presents an irregular shape. The Graham scan method is used to construct the convex hull of the restricted area to realize the regularization of the irregular area. The flight restricted area is regarded as an obstacle, and the improved cellular automata algorithm is used to plan the shortest path between potential reconstructed OD pairs. The shortest paths between all OD pairs form the alternative path set, and the shortest path among them is selected as the reconstructed flight route. The topology adjustment during temporary tasks: For the newly added flight plan, an improved cellular automata algorithm is used to re-plan a shortest path between its OD pairs as the flight route for this temporary task.

[0069] Step 4, establish the transportation network structure and operation indicators, and evaluate the performance of the UAV transportation network through the grey relational analysis method.

[0070] The structural indicators of the UAV transportation network include degree distribution entropy, network efficiency, and standard deviation of segment betweenness. The operation indicators include transportation cost, network accessibility, and average satisfaction. The grey relational analysis method is used to evaluate the performance of the transportation network after local reconstruction.

[0071] The following is a detailed introduction to each of the structural and operation indicators:

[0072] (1) Degree distribution entropy E is an indicator to measure the degree distribution characteristics of nodes in the transportation network, reflecting the uniformity of node connection degree. The smaller its value, the more uniform the degree distribution of nodes in the transportation network and the more stable the network structure. It is expressed as:

[0073]

[0074] In the formula, r i is the ratio of the degree of node i to the sum of the degrees of all nodes.

[0075] (2) Network efficiency P is used to measure the operation efficiency of the transportation network and is defined as the average of the reciprocals of the shortest path distances between all nodes. The higher its value, the better the network transportation efficiency. It is expressed as:

[0076]

[0077] In the formula, n is the number of nodes.

[0078] (3) Standard deviation of segment betweenness D. Segment betweenness reflects the key degree of a segment in the transportation network. If the betweenness of some segments is too large, it is easy to cause unbalanced network traffic distribution. Therefore, the standard deviation of segment betweenness index is defined to evaluate the difference in transportation pressure between segments. It is expressed as:

[0079]

[0080] In the formula, b ij is the segment betweenness between nodes i and j, is the average value of segment betweenness.

[0081] (4) Transportation cost O is an important indicator for operators to evaluate economic benefits. Assuming that the unit mileage transportation cost of various UAVs is the same, the cost of the transportation network can be quantitatively characterized by the total flight mileage of UAVs. It is expressed as:

[0082]

[0083] In the formula, q ij is the UAV flight flow between nodes i and j.

[0084] (5) Network reachability R, which is the average value of the shortest path lengths of drones between OD pairs, is positively correlated with the reachability of the transportation network and is expressed as:

[0085]

[0086] (6) Average satisfaction S. The drone flight plan includes the departure time and the arrival time. The planned arrival time is regarded as the expected time t of the user w , and a unilateral soft time window is used to characterize the transportation satisfaction S of the drone executing the flight plan u u , which is expressed as:

[0087]

[0088] In the formula, t r is the actual arrival time of the drone, θ (θ > 1) is the user time sensitivity coefficient, and t max is the maximum tolerable time of the user;

[0089] If the set of all drone flight plans in this transportation network is U, then the average satisfaction of users after executing all flight plans is expressed as:

[0090]

[0091] Since the indicators are divided into positive and reverse indicators, the minimum-maximum normalization method is used to standardize the original indicator data, map its value to between 0 and 1, and then the grey relational analysis method is used to evaluate the performance of multiple locally reconstructed transportation networks. For each evaluation indicator, the average value of the grey relational coefficients of each scheme is defined as the grey relational degree of this indicator. Subsequently, calculate the proportion of the grey relational degree of each indicator in the total sum of the grey relational degrees of all indicators, and regard it as the weight of this indicator. For all local reconstruction schemes of the transportation network, calculate the scores of each transportation network according to the indicator weights, and this score is positively correlated with the network performance.

[0092] Therefore, for a method for local reconstruction of a drone transportation network for special events described in the present invention, the influence range of the transportation network is identified according to the transportation network structure and the type of special event, and local reconstruction of the transportation network is carried out by means of flight flow reallocation and dynamic design of new flight segments, and the performance of the reconstructed network is evaluated by the grey relational analysis method, providing support for improving the adaptability of the drone transportation network in a dynamic environment.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A local reconstruction method for an unmanned aerial vehicle transportation network for special events, characterized in that: It includes the following steps: Step 1: Obtain the information data of the UAV transportation network through remote sensing and communication devices, and model the topological structure of the transportation network; Step 2: Analyze the impact of special events from two aspects of flight restrictions and temporary tasks, and identify the impact scope of special events on the UAV transportation network; Step 3: Develop local reconstruction strategies for the UAV transportation network, including the original network reconstruction strategy and the topological structure adjustment strategy. The original network reconstruction strategy focuses on the redistribution of flight traffic, and the topological structure adjustment strategy focuses on the dynamic design of new routes. Locally reconstruct the transportation network from two aspects of flight traffic redistribution and dynamic design of new flight segments; Step 4: Establish the structure indicators and operation indicators of the UAV transportation network, and evaluate the performance of the UAV transportation network through the grey relational analysis method.

2. The local reconstruction method of the UAV transportation network for special events according to claim 1, characterized in that: In Step 1, based on the remote sensing and communication devices on the ground and carried by the UAV, obtain the geographical environment, take-off and landing points, air nodes, and flight segment information of the UAV transportation network.

3. A method for local reconstruction of an unmanned aerial vehicle transportation network for special events according to claim 2, characterized in that: Use graph theory to model the UAV transportation network as an undirected graph, where the take-off and landing points and air nodes are the vertices of the graph, and the flight segments are the edges of the graph; V is the set of nodes in the transportation network, V o is the set of takeoff and landing points, and the number of takeoff and landing points is m, V n is the set of aerial nodes, then V = V o ∪V n , C = (c ij ) m×m is the adjacency matrix of the transportation network, c ij = 1 indicates that there is a direct flight segment between nodes i and j, c ij = 0 indicates that there is no direct flight segment between nodes i and j, A = (a ij ) m×m is the connectivity matrix of the transportation network, a ij = 1 indicates that there is a connected path between nodes i and j, a ij = 0 indicates that there is no connected path between nodes i and j.

4. A method for local reconstruction of a drone transportation network for special events according to claim 1, characterized in that: In Step 2, flight restrictions include that the flight restricted area covers a single flight segment, the flight restricted area covers a single node and multiple flight segments, and the flight restricted area covers multiple nodes and multiple flight segments. The impact scope of flight restrictions on the UAV transportation network is the affected nodes and the feasible flight segments around them.

5. A method for local reconstruction of an unmanned aerial vehicle transportation network for special events according to claim 4, characterized in that: During temporary tasks, add temporary flight segments to locally reconstruct the transportation network structure. The impact scope of temporary tasks on the UAV transportation network is the departure node, arrival node of the temporary task, and the potential feasible flight segments between them.

6. A method for local reconstruction of an unmanned aerial vehicle transportation network for special events according to claim 1, characterized in that: In Step 3, the original network reconstruction includes the original network reconstruction during flight restrictions and the original network reconstruction during temporary tasks.

7. A method for local reconstruction of an unmanned aerial vehicle transportation network for special events according to claim 6, characterized in that: Original network reconstruction under flight restrictions: When flight is restricted and some segments and nodes of the transportation network fail, the set of the nearest drones outside the affected area flying into the node set is set as P i and each flying-in node is set as p i (p i ∈P i ). The set of the nearest flying-out nodes outside the corresponding affected area is set as P io . The influence range of the failed segment is defined by using the maximum node distance parameter λ. The number of segments passed by any node to the flying-out node p io (p io ∈P io ) is less than or equal to λ. When λ = 1, it means that the influence range includes the nodes and segments directly connected to the flying-out node. For the flying-in node p i , the set of the affected nodes connected to its flying-out node p io is P λ . Then the potential reconstruction node set R j = P λ ∪ {p io}}. Guide the flight demand of the drones planned to pass through the failed segment to the flying-in node p i , and set the attracting nodes of the flying-in node as the potential reconstruction node r j (r j ∈R j ). At this time, the reconstructed OD pair is (p i , r j ); If there are multiple connected paths between the reconstructed OD pairs, the traffic is allocated to the shortest path. Considering both the distance and traffic factors of the connected paths, the allocation path is comprehensively determined. The set of connected paths is set as T, and the element t ij (t ij ∈T) represents the connected path between the reconstructed OD pairs (p i , r j ). The shortest path length between nodes is d ij , and the traffic on the path at this time is q ij . The deviation normalization method is used to normalize the data, mapping its value to between 0 and 1. The normalized data is d i ' j and q i ' j . Define the index path impedance I to provide a decision basis for flight traffic allocation. The path impedance is expressed as: I = ωd i ' j +(1 - ω)q i ' j ; In the formula, ω∈(0,1) is the weight factor of the path length in the path impedance. By calculating the impedance values of all connected paths, distribute the flight traffic to the path with the minimum impedance; Original network reconstruction during temporary tasks: For the newly added flight plan, there are multiple connected paths between the OD pairs, and select the path with the minimum impedance.

8. A method for local reconstruction of an unmanned aerial vehicle transportation network for special events according to claim 6, characterized in that: Topological structure adjustment includes topological structure adjustment during flight restrictions and topological structure adjustment during temporary tasks.

9. A method for local reconstruction of a drone transportation network for special events according to claim 8, characterized in that: Topological structure adjustment during flight restrictions: Use the Graham scan method to construct the convex hull of the flight restricted area to realize the regularization of the irregular area. Regard the flight restricted area as an obstacle, and use the improved cellular automata algorithm to plan the shortest path between potential reconstructed OD pairs. The shortest paths between all OD pairs form the alternative path set, and select the shortest path among them as the reconstructed flight route; Topological structure adjustment during temporary tasks: For the newly added flight plan, use the improved cellular automata algorithm to re-plan a shortest path between its OD pairs as the flight route for this temporary task.

10. A method for local reconstruction of a UAV transportation network for special events according to claim 1, characterized in that: In Step 4, the structure indicators of the UAV transportation network include degree distribution entropy, network efficiency, and standard deviation of segment betweenness. The operation indicators include transportation cost, network reachability, and average satisfaction; Degree distribution entropy E, an indicator used to measure the node degree distribution characteristics of the transportation network, is expressed as: where r i is the ratio of the degree of node i to the sum of the degrees of all nodes; Network efficiency P, an indicator used to measure the operation efficiency of the transportation network, is expressed as: Where n is the number of nodes; The standard deviation D of the betweenness of the segments is used to evaluate the difference in transportation pressure between segments, and is expressed as: where b ij is the betweenness of the flight segment between nodes i and j, and is the average value of the betweenness of flight segments; The transportation cost O, which is an important indicator for evaluating economic benefits, is expressed as: where q ij is the UAV flight flow between nodes i and j; The network reachability R represents the average value of the shortest path length between OD pairs by the UAV, and is expressed as: Average satisfaction The UAV flight plan includes the departure time and the arrival time, and the planned arrival time is regarded as the expected time t of the user w , and a unilateral soft time window is used to describe the transportation satisfaction S of the UAV executing the flight plan u u , which is expressed as: where \(t\) r is the actual arrival time of the UAV, \(\theta(\theta\gt1)\) is the user time sensitivity coefficient, and \(t\) max is the maximum tolerable time for the user; If the set of all UAV flight plans in the transportation network is U, the average user satisfaction after executing all flight plans is expressed as:

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