A method for local reconstruction of a drone transportation network for special events

Abstract

The application discloses a kind of special event's unmanned aerial vehicle transport network local reconstruction method, belong to unmanned aerial vehicle transport network design technical field, including the steps are as follows: by remote sensing, communication equipment, obtain unmanned aerial vehicle transport network information data, the topological structure of transport network is modeled;Through the analysis of two special events of flight restriction, temporary task, the influence range of special event to unmanned aerial vehicle transport network is identified;Unmanned aerial vehicle transport network local reconstruction strategy is formulated, including original network reconstruction strategy and topological structure adjustment strategy;Transport network structure and operation index set are established, and the performance of unmanned aerial vehicle transport network is evaluated by grey correlation analysis method.The application considers dynamic environment influence, by the local reconstruction of unmanned aerial vehicle transport network, the adaptability of transport network is enhanced, and the unmanned aerial vehicle transport network with flexibility and sustainable development is formed.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) transportation network design technology, and in particular to a method for partial reconstruction of UAV transportation networks for special events. Background Technology

[0002] With the continuous maturation of drone technology and the increasing saturation of ground space resources, drone transportation networks have enormous development potential in logistics, urban commuting, and tourism. They can not only regulate drone operations in low-altitude airspace but also inject strong momentum into socio-economic development. However, drone transportation networks may be affected by special events during actual operation, such as extreme weather, public emergencies, and temporary traffic control, potentially leading to interruptions of some nodes or flight segments, or temporary changes in mission requirements, thus disrupting the normal operation of the transportation network. Therefore, establishing reasonable local restructuring strategies for the transportation network is crucial to ensuring its normal operation. Existing research mainly focuses on drone mission reassignment or the resilience analysis of the transportation network, with less consideration given to adapting the local structure of the transportation network or optimizing flight flow to meet dynamic environmental demands. Summary of the Invention

[0003] The purpose of this invention is to provide a method for partial reconstruction of unmanned aerial vehicle (UAV) transport networks in response to special events. UAV transport networks may be affected by special events during actual operation, such as special weather, sudden public events, temporary traffic control, etc., which may lead to the interruption of some nodes or flight segments or temporary changes in mission requirements, thereby interfering with the normal operation of the transport network. Therefore, establishing a reasonable partial reconstruction strategy for the transport network is crucial to ensuring its normal operation.

[0004] To achieve the above objectives, this invention provides a method for partial reconstruction of a drone transportation network for special events, comprising the following steps:

[0005] Step 1: Obtain UAV transportation network information data through remote sensing and communication equipment, and model the transportation network topology.

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

[0007] Step 3: Develop a partial reconstruction strategy for the UAV transportation network, including the original network reconstruction strategy and the topology adjustment strategy. The original network reconstruction strategy focuses on the redistribution of flight traffic, while the topology adjustment strategy focuses on the dynamic design of new flight segments. The transportation network is partially reconstructed from two aspects: redistribution of flight traffic and dynamic design of new flight segments.

[0008] Step 4: Establish the structural indicators and operational indicators of the UAV transportation network, and evaluate the performance of the UAV transportation network using grey relational analysis.

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

[0010] Preferably, the UAV transportation network is modeled as an undirected graph using graph theory, where take-off and landing points and aerial nodes are vertices of the graph, and flight segments are edges of the graph;

[0011] For the set of nodes in the transportation network, Let be the set of takeoff and landing points, and the number of takeoff and landing points is... , If it is a set of empty nodes, then , Let the adjacency matrix of the transportation network be... Represents a node There are direct flight segments between them. Represents a node There is no direct flight segment between them.

[0012] For the connectivity matrix of the transportation network, Represents a node There are connected paths between them. Represents a node There is no connecting path between them.

[0013] Preferably, in step 2, flight restriction includes a flight restriction area covering a single flight segment, a flight restriction area covering a single node and multiple flight segments, and a flight restriction area covering multiple nodes and multiple flight segments. The impact range of flight restriction on the UAV transportation network is the affected node and its surrounding feasible flight segments.

[0014] Preferably, during temporary missions, temporary flight segments are added to partially reconstruct the transportation network structure. The impact of temporary missions on the UAV transportation network extends to the departure node, arrival node, and potential feasible flight segments in between.

[0015] Preferably, in step 3, the original network reconstruction includes the original network reconstruction when flight is restricted and the original network reconstruction when there is a temporary mission.

[0016] Preferred method for reconstructing the original network when flight is restricted: When flight is restricted and some segments and nodes of the transportation network are ineffective, the set of the nearest UAVs flying into the affected area is set as... Set each flying-in node as Let the set of the nearest outgoing nodes outside the corresponding influence range be denoted as Using the maximum node distance parameter Define the scope of impact of the failed flight segment, from any point to the point of departure. The number of flight segments traversed is less than or equal to ,when When the time is specified, it indicates that the affected area includes nodes and flight segments directly connected to the outgoing node, and is limited to the incoming node. Its flight node The set of nodes affected by the connection is Then there is no

[0017] Set of potential reconfiguration nodes for human-machine interaction The plan is to guide the demand for drone flights through the failed flight segments to the entry node. And set the attracting node of the flying-in node as a potential refactoring node. At this point, the reconstructed OD pair is ;

[0018] If multiple connected paths exist between reconstructed OD pairs, traffic is allocated to the shortest path. The allocation of paths is determined by considering both distance and traffic volume along the connected paths. The set of connected paths is set as follows: ,element Indicates the reconstruction of OD pairs The shortest path length between the nodes is the connection path between them. At this time, the node The drone flight traffic between The data was normalized using deviation standardization, mapping its values ​​to the range of 0 to 1. The normalized data is as follows: and Define the index path impedance This provides a basis for flight traffic allocation decisions, and the path impedance is expressed as:

[0019] ;

[0020] In the formula, As a weighting factor for path length in path impedance, the flight traffic is allocated to the path with the lowest impedance by calculating the impedance values ​​of all connected paths.

[0021] Reconstructing the existing network during ad hoc missions: For new flight plans, there are multiple connected paths between OD pairs, and the path with the least impedance is selected.

[0022] Preferably, topology adjustments include topology adjustments during flight-restricted periods and topology adjustments during temporary missions.

[0023] Preferably, topology adjustment under flight constraints: Graham scan method is used to construct the convex hull of the flight-constrained region to achieve regularization of irregular regions. The flight-constrained region is regarded as an obstacle, and an improved cellular automata algorithm is used between potential reconstructed OD pairs.

[0024] The shortest path is planned, and the shortest paths between all OD pairs constitute a set of candidate paths. The shortest path among them is selected as the reconstructed route.

[0025] Topology adjustment for temporary missions: For new flight plans, an improved cellular automaton algorithm is used to replan the shortest path between the OD pairs as the flight route for the temporary mission.

[0026] Preferably, in step 4, the structural indicators of the UAV transportation network include degree distribution entropy, network efficiency, and standard deviation of segment betweenness coefficients, while the operational indicators include transportation cost, network accessibility, and average satisfaction.

[0027] Degree distribution entropy This is an index used to measure the distribution characteristics of node degree in a transportation network, expressed as:

[0028] ;

[0029] In the formula, For nodes The ratio of the degree of the node to the sum of the degrees of all its nodes;

[0030] Network efficiency This is used to measure the operational efficiency of a transportation network and is expressed as:

[0031] ;

[0032] In the formula, The number of nodes;

[0033] Standard deviation of segment betweenness This is used to assess the differences in transport pressure between flight segments, and is expressed as:

[0034] ;

[0035] In the formula, For nodes Between segments, This represents the average betweenness of the flight segments;

[0036] Transportation costs An important indicator used to evaluate economic benefits, expressed as:

[0037] ;

[0038] In the formula, For nodes Drone flight traffic between;

[0039] Network reachability , representing the average shortest path length between OD pairs for the UAV, is expressed as:

[0040] ;

[0041] Average satisfaction The drone flight plan includes the departure time and the arrival time, with the planned arrival time considered as the user's desired time. A single-sided soft time window is used to characterize the flight plan executed by the UAV. Transportation satisfaction , is represented as:

[0042] ;

[0043] In the formula, The actual arrival time of the drone. For user time sensitivity coefficient, The maximum time that users can tolerate;

[0044] If the set of all drone flight plans within this transportation network is The average user satisfaction after completing all flight plans Represented as:

[0045] .

[0046] Therefore, the present invention provides a method for local reconstruction of UAV transportation networks for special events, employing the aforementioned structure, which has the following beneficial effects: Based on the topology of the UAV transportation network, the present invention analyzes the impact of special events from two aspects: flight restrictions and temporary missions. It formulates original network reconstruction strategies and topology adjustment strategies to locally reconstruct the transportation network, and evaluates the network performance after reconstruction using grey relational analysis based on the transportation network structure and operational indicators. This invention helps improve the connectivity and stability of UAV transportation networks, providing a guarantee for the refined management and operational safety of UAV transportation networks.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] Fig. 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0049] Fig. 2 This is a schematic diagram of a drone transportation network according to an embodiment of the present invention;

[0050] Fig. 3 These are three scenarios where flight constraints affect the network structure, as described in this embodiment of the invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0052] It should be noted that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0053] Similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0054] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," "outer," etc., indicate the orientation or positional relationship 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 when in use. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0055] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0056] Example

[0057] like Figs. 1-3As shown, the present invention provides a method for partial reconstruction of a drone transportation network for special events, comprising the following steps:

[0058] Step 1: Obtain UAV transportation network information data through remote sensing and communication equipment, and model the transportation network topology.

[0059] Based on ground-based and UAV-mounted remote sensing and communication equipment, information on the geographical environment, take-off and landing points, air nodes, and flight segments of the UAV transportation network is acquired. Graph theory is used to model the UAV transportation network as an undirected graph, where take-off and landing points and air nodes are vertices of the graph, and flight segments are edges.

[0060] For the set of nodes in the transportation network, Let be the set of take-off and landing points, the number of which is , If it is a set of empty nodes, then , Let the adjacency matrix of the transportation network be... Represents a node There must be a direct flight segment between them; otherwise... , For the connectivity matrix of the transportation network, Represents a node There is a connected path between them, otherwise .

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

[0062] Flight restrictions are caused by factors such as temporary airspace, special weather conditions, and electromagnetic interference, and may cause some segments of the drone transport network to become unavailable. Flight restrictions include flight restrictions covering a single segment, flight restrictions covering a single node and multiple segments, and flight restrictions covering multiple nodes and multiple segments. The impact of flight restrictions on the drone transport network extends to the affected nodes and their surrounding feasible segments. Drones expected to pass through the affected segments will need to detour and bypass the flight restrictions.

[0063] In response to emergency situations, temporary missions are issued. These missions include changing flight paths and establishing temporary flight paths. If the existing transportation network can meet the needs of the temporary mission, the flight path will be changed; if the existing transportation network cannot meet the needs, a temporary flight path will be established. The impact of temporary missions on the UAV transportation network extends to the departure node, arrival node, and the potentially feasible flight segments in between.

[0064] Step 3: Develop a partial reconstruction strategy for the UAV transportation network, including an existing network reconstruction strategy and a topology adjustment strategy. The existing network reconstruction strategy focuses on the redistribution of flight traffic, while the topology adjustment strategy focuses on the dynamic design of new flight segments.

[0065] The original network reconstruction strategy aims to find reconstructed routes within the original transportation network topology, enabling flight missions to be transported along these reconstructed routes. When the transportation network suffers structural damage due to flight restricted areas, failed segments and nodes are removed, affected flight mission routes are replanned, and flight traffic is locally redistributed. If the transportation network topology is undamaged and temporary flight missions are received, traffic optimization and allocation are performed based on the existing transportation network structure, and the newly generated flight traffic is reasonably allocated to existing segments.

[0066] Restructuring the original network when flight is restricted: When flight is restricted and some segments and nodes of the transportation network are ineffective, the set of the nearest UAVs flying into the affected area is set as the network restructured network. Set each flying-in node as Let the set of the nearest outgoing nodes outside the corresponding influence range be denoted as Using the maximum node distance parameter Define the scope of impact of the failed flight segment, from any point to the point of departure. The number of flight segments traversed must not exceed ,when When the time is specified, it indicates that the affected area only includes nodes and flight segments directly connected to the outgoing node, and does not apply to the incoming node. Its flight node The set of nodes affected by the connection is The set of potential reconfiguration nodes for drones The plan is to guide the demand for drone flights through the failed flight segments to the entry node. And set the attracting node of the flying-in node as a potential refactoring node. At this point, the reconstructed OD pair is There are multiple connected paths between the reconstructed OD pairs. Distributing traffic to the shortest path is an option, but this approach may lead to congestion due to shortest path overload. Therefore, considering both distance and traffic volume of the connected paths, a comprehensive decision is made to allocate paths. The set of connected paths is set as follows: ,element Indicates the reconstruction of OD pairs The shortest path length between the nodes is the connection path between them. At this time, the node The drone flight traffic between The data was normalized using deviation standardization, mapping its values ​​to the range of 0 to 1. The normalized data is as follows: and Define the index path impedance This provides a basis for flight traffic allocation decisions. This indicator reflects the time cost and congestion level of the route, and is expressed as: In the formula, As a weighting factor for path length in path impedance, flight traffic is allocated to the path with the lowest impedance by calculating the impedance values ​​of all connected paths; original network reconstruction during temporary missions: for new flight plans, there are multiple connected paths between OD pairs, and the path with the lowest impedance is selected.

[0067] The topology adjustment strategy aims to introduce new flight segments into the existing transportation network topology, diverting flight missions to these new segments and enhancing the network's connectivity and flexibility. When the transportation network topology is damaged, ineffective segments and nodes are removed, and new flight segments are added to the damaged network to effectively guide flight traffic to newly opened routes. If the transportation network topology is intact and a temporary flight mission is received, a completely new route is planned between the origin and destination of the temporary mission to meet its specific needs.

[0068] Topology adjustments include those for flight-restricted situations and those for temporary missions. For flight-restricted situations: Flight-restricted areas are typically irregular in shape. The Graham scan method is used to construct the convex hull of the restricted area, thus regularizing the irregular region. The flight-restricted area is treated as an obstacle. An improved cellular automaton algorithm is used to plan the shortest path between potential reconstructed OD pairs. The shortest paths between all OD pairs constitute a set of candidate paths, and the shortest path is selected as the reconstructed flight path. For temporary missions: For newly added flight plans, an improved cellular automaton algorithm is used to replan the shortest path between its OD pairs as the flight path for the temporary mission.

[0069] Step 4: Establish the transportation network structure and operational indicators, and evaluate the performance of the UAV transportation network using grey relational analysis.

[0070] The structural indicators of the UAV transportation network include degree distribution entropy, network efficiency, and standard deviation of segment betweenness coefficient. The operational 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 description of each item in the structural and operational indicators:

[0072] (1) Degree distribution entropy The degree distribution characteristic of nodes in a transportation network is denoted by , which reflects the uniformity of node connectivity. The smaller the 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, For nodes The ratio of the degree of a node to the sum of the degrees of all its nodes.

[0075] (2) Network efficiency This metric is used to measure the operational efficiency of a transportation network. It is defined as the average of the reciprocals of the shortest path distances between all nodes. The higher the value, the better the network's transportation efficiency. It is expressed as:

[0076] ;

[0077] In the formula, This represents the number of nodes.

[0078] (3) Standard deviation of the betweenness number of flight segments The betweenness factor of a flight segment reflects its criticality within the transportation network. If the betweenness factor of certain segments is too high, it can easily lead to uneven distribution of network traffic. Therefore, the standard deviation of the betweenness factor is defined as an index to assess the differences in transportation pressure between flight segments, expressed as:

[0079] ;

[0080] In the formula, For nodes Between segments, This represents the average betweenness of the flight segments.

[0081] (4) Transportation costs The cost per unit distance of a transportation network is an important indicator for operators to evaluate economic benefits. Assuming that the unit distance transportation cost of various types of drones is the same, the cost of the transportation network can be quantitatively represented by the total flight distance of the drones, as follows:

[0082] ;

[0083] In the formula, For nodes The flow of drone flights between them.

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

[0085] ;

[0086] (6) Average satisfaction The drone flight plan includes the departure time and the arrival time, with the planned arrival time considered as the user's desired time. A single-sided soft time window is used to characterize the flight plan executed by the UAV. Transportation satisfaction , is represented as:

[0087] ;

[0088] In the formula, The actual arrival time of the drone. For user time sensitivity coefficient, The maximum time that users can tolerate;

[0089] If the set of all drone flight plans within this transportation network is The average user satisfaction after completing all flight plans Represented as:

[0090] ;

[0091] Since the indicators are divided into positive and negative indicators, the min-max normalization method is used to standardize the original indicator data, mapping their values ​​to between 0 and 1. Then, the grey relational analysis method is used to evaluate the performance of multiple locally reconstructed transportation networks. For each evaluation indicator, the average grey relational coefficient of each scheme is defined as the grey relational degree of that indicator. Subsequently, the proportion of the grey relational degree of each indicator to the sum of the grey relational degrees of all indicators is calculated and regarded as the weight of that indicator. For all transportation network local reconstruction schemes, the score of each transportation network is calculated by weighting according to the indicator weights. This score is positively correlated with the network performance.

[0092] Therefore, the present invention provides a method for local reconstruction of UAV transportation networks for special events. It identifies the scope of influence of the transportation network based on the transportation network structure and the type of special event, uses flight traffic redistribution and dynamic design of new flight segments to locally reconstruct the transportation network, and evaluates the network performance after reconstruction through grey relational analysis, thereby providing support for improving the adaptability of UAV transportation networks in dynamic environments.

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

Claims

1. A method for local reconstruction of a drone transportation network for special events, characterized in that: Includes the following steps: Step 1: Obtain UAV transportation network information data through remote sensing and communication equipment, and model the transportation network topology. Step 2: Analyze the impact of special events from two aspects: flight restrictions and temporary missions, and identify the scope of the impact of special events on the UAV transportation network; Step 3: Develop a partial reconstruction strategy for the UAV transportation network, including the original network reconstruction strategy and the topology adjustment strategy. The original network reconstruction strategy focuses on the redistribution of flight traffic, while the topology adjustment strategy focuses on the dynamic design of new flight segments. The transportation network is partially reconstructed from two aspects: redistribution of flight traffic and dynamic design of new flight segments. Step 4: Establish the structural indicators and operational indicators of the UAV transportation network, and evaluate the performance of the UAV transportation network using the grey relational analysis method. We use graph theory to model the UAV transportation network as an undirected graph, where take-off and landing points and aerial nodes are vertices of the graph, and flight segments are edges of the graph. For the set of nodes in the transportation network, Let be the set of takeoff and landing points, and the number of takeoff and landing points is... , If it is a set of empty nodes, then , Let's consider the adjacency matrix of the transportation network. Represents a node There are direct flight segments between them. Represents a node There is no direct flight segment between them. For the connectivity matrix of the transportation network, Represents a node There are connected paths between them. Represents a node There is no connecting path between them; In step 2, flight restrictions include flight restriction areas covering a single flight segment, flight restriction areas covering a single node and multiple flight segments, and flight restriction areas covering multiple nodes and multiple flight segments. The impact of flight restrictions on the UAV transportation network is the affected node and its surrounding feasible flight segments. During temporary missions, temporary flight segments are added to partially restructure the transportation network. The impact of temporary missions on the UAV transportation network extends to the departure node, arrival node, and potential feasible flight segments in between.

2. The method for partial reconstruction of a drone transportation network for special events according to claim 1, characterized in that: In step 1, the geographical environment, take-off and landing points, air nodes and flight segments of the UAV transportation network are obtained based on the remote sensing and communication equipment carried by the ground and the UAV.

3. The method for partial reconstruction of a drone transportation network for special events according to claim 1, characterized in that: In step 3, the original network reconstruction includes the reconstruction of the original network when flight is restricted and the reconstruction of the original network when there is a temporary mission.

4. The method for partial reconstruction of a drone transportation network for special events according to claim 3, characterized in that: Restructuring the original network when flight is restricted: When flight is restricted and some segments and nodes of the transportation network are ineffective, the set of the nearest UAVs flying into the affected area is set as the network restructured network. Set each flying-in node as Let the set of the nearest outgoing nodes outside the corresponding influence range be denoted as Using the maximum node distance parameter Define the scope of impact of the failed flight segment, from any point to the point of departure. The number of flight segments traversed is less than or equal to ,when When the time is specified, it indicates that the affected area includes nodes and flight segments directly connected to the outgoing node, and is limited to the incoming node. Its flight node The set of nodes affected by the connection is The set of potential reconfiguration nodes for drones The plan is to guide the demand for drone flights through the failed flight segments to the entry node. And set the attracting node of the flying-in node as a potential refactoring node. At this point, the reconstructed OD pair is ; If multiple connected paths exist between reconstructed OD pairs, traffic is allocated to the shortest path. The allocation of paths is determined by considering both distance and traffic volume along the connected paths. The set of connected paths is set as follows: ,element Indicates the reconstruction of OD pairs The shortest path length between the nodes is the connection path between them. At this time, the node The drone flight traffic between The data was normalized using deviation standardization, mapping its values ​​to the range of 0 to 1. The normalized data is as follows: and Define the index path impedance This provides a basis for flight traffic allocation decisions, and the path impedance is expressed as: ； In the formula, As a weighting factor for path length in path impedance, the flight traffic is allocated to the path with the lowest impedance by calculating the impedance values ​​of all connected paths. Reconstructing the existing network during ad hoc missions: For new flight plans, there are multiple connected paths between OD pairs, and the path with the least impedance is selected.

5. The method for partial reconstruction of a drone transportation network for special events according to claim 3, characterized in that: Topology adjustments include topology adjustments during flight restrictions and topology adjustments during temporary missions.

6. The method for partial reconstruction of a drone transportation network for special events according to claim 5, characterized in that: Topology adjustment when flight is restricted: Graham scan method is used to construct the convex hull of the flight restricted area to realize the regularization of irregular areas. The flight restricted area is regarded as an obstacle. An improved cellular automaton algorithm is used to plan the shortest path between potential reconstructed OD pairs. The shortest path between all OD pairs constitutes the candidate path set. The shortest path among them is selected as the reconstructed route. Topology adjustment for temporary missions: For new flight plans, an improved cellular automaton algorithm is used to replan the shortest path between the OD pairs as the flight route for the temporary mission.

7. The method for partial reconstruction of a drone transportation network for special events according to claim 4, characterized in that: In step 4, the structural indicators of the UAV transportation network include degree distribution entropy, network efficiency, and standard deviation of segment betweenness coefficients, while the operational indicators include transportation cost, network accessibility, and average satisfaction. Degree distribution entropy This is an index used to measure the distribution characteristics of node degree in a transportation network, expressed as: ； In the formula, For nodes The ratio of the degree of a node to the sum of the degrees of all its nodes; Network efficiency This is used to measure the operational efficiency of a transportation network and is expressed as: ； In the formula, The number of nodes; Standard deviation of segment betweenness This is used to assess the differences in transport pressure between flight segments, and is expressed as: ； In the formula, For nodes Between segments, This represents the average betweenness of the flight segments; Transportation costs An important indicator used to evaluate economic benefits, expressed as: ； In the formula, For nodes Drone flight traffic between; Network reachability , representing the average shortest path length between OD pairs for the UAV, is expressed as: ； Average satisfaction The drone flight plan includes the departure time and the arrival time, with the planned arrival time considered as the user's desired time. A single-sided soft time window is used to characterize the flight plan executed by the UAV. Transportation satisfaction , is represented as: ； In the formula, The actual arrival time of the drone. For user time sensitivity coefficient, The maximum time that users can tolerate; If the set of all drone flight plans within this transportation network is The average user satisfaction after completing all flight plans Represented as: 。

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