134 results about "Flight zone" patented technology

The invention relates to the field of unmanned aerial vehicle environment perception, in particular to a multi-mode environment perception method and system for a low-altitude medical unmanned aerial vehicle. The method comprises the following steps: collecting a multi-modal data stream, carrying out adaptive data optimization processing, and constructing a multi-modal fusion data set; performing environment multi-level obstacle identification and evaluation on the multi-modal fusion data set to generate a threat mapping environment map; multi-dimensional environment parameters are collected based on the unmanned aerial vehicle, wind field time-varying prediction and safe flight area calculation are performed based on the threat mapping environment map, and a flight area map is constructed; performing multi-position collision risk assessment based on the multi-modal fusion data set to generate collision risk coefficients of different positions; and carrying out safe flight constraint analysis on the flight area map according to the collision risk coefficient, and extracting an optimal flight path. According to the invention, in combination with real-time environment data, comprehensive flight path planning is provided, and the flight safety and task completion efficiency of the unmanned aerial vehicle are improved.

The invention discloses a reinforcement learning unmanned aerial vehicle route planning method and system based on a safe flight corridor, and relates to the technical field of route planning. A safe flight corridor is generated through a grid map of a target scene and starting point information; calculating a plurality of initial B spline control points through observation information of the unmanned aerial vehicle; iteratively generating a new B-spline control point based on the plurality of initial B-spline control points, the key features extracted based on the safe flight corridor and the time variable through a reinforcement learning algorithm; and generating a B spline path of the unmanned aerial vehicle according to all B spline control points. According to the invention, a feasible flight area in a complex environment is abstracted as a safe flight corridor. Environment characteristics are hidden in the safe flight corridor, and common application in different complex environments can be achieved through training in one environment. A B-spline control point is iteratively generated by using a reinforcement learning algorithm, and environment change can be iteratively adapted in real time, so that the generated B-spline path better meets the actual flight demand of the unmanned aerial vehicle.

The invention provides an unmanned aerial vehicle path planning method and system and a storage medium, and the method comprises the steps: constructing a three-dimensional path planning model of an unmanned aerial vehicle in a target flight region; solving the three-dimensional path planning model by using an improved artificial bee colony algorithm, obtaining the optimal flight path of the unmanned aerial vehicle under each target function, and summarizing the optimal flight path into a Pareto optimal solution set; constructing a plurality of decision intelligent agents in one-to-one correspondence with the plurality of objective functions, performing multi-dimensional scoring under different objective functions on each flight path in the Pareto optimal solution set by using the plurality of decision intelligent agents, obtaining a comprehensive score of each flight path, and determining a global optimal unmanned aerial vehicle flight path based on the comprehensive score; according to the method, through refined multi-dimensional constraint modeling, an improved multi-target artificial bee colony algorithm and multi-agent collaborative decision based on a near-end strategy optimization algorithm, full-process optimization from path generation to intelligent decision is realized.

The invention relates to the technical field of unmanned aerial vehicle trajectory planning, in particular to a bidirectional tree random search method based on a complex environment node cost function, which comprises the steps of surveying obstacle data in a flight area and modeling, then introducing a target deviation strategy and a bidirectional search tree mechanism, and combining with an improved minimum cost method to obtain a random search result. Alternately expanding the two trees to generate an initial path, and integrating the path length, the obstacle distance and the flight height energy consumption by a cost function; and finally, pruning the initial path, removing redundant nodes, smoothing the trajectory by adopting a cubic spline interpolation method, ensuring curvature continuity, and generating an optimal path meeting the flight performance of the unmanned aerial vehicle. According to the invention, through an improved bidirectional minimum cost fast expansion random tree algorithm, dynamic association of obstacle constraint, target deviation and path cost in unmanned aerial vehicle path planning in a complex environment is represented, and a more efficient search convergence speed and better path quality are obtained. And rapid convergence and global optimization of the track of the unmanned aerial vehicle in a high-dynamic environment are realized.

The invention provides a point cloud downsampling method applied to unmanned aerial vehicle obstacle avoidance, and aims to solve the problems of low mapping efficiency and geometric detail loss caused by large point cloud data volume and non-uniform density in environmental perception. The method comprises the following steps: preprocessing an original point cloud through a regular grid to balance density, and extracting an effective flight area of the unmanned aerial vehicle by adopting bidirectional cloth simulation filtering and combining with a flight height; multi-level geometric features including elevation gradient, curvature and roughness are calculated, and point cloud priority scores are fused and generated based on principal component analysis; voxel grids are divided according to the size of the unmanned aerial vehicle, the sampling rate of each voxel is adaptively set in combination with the priority, farthest point sampling is executed in the feature space to select representative points, and finally a simplified point cloud model is output. The method can be integrated to a mapping module of a multi-aircraft cooperative obstacle avoidance and planning system, is suitable for unmanned aerial vehicle path planning tasks in a complex three-dimensional environment, and has the characteristics of light weight, accuracy, high efficiency and the like.

The invention relates to an urban low-altitude unmanned aerial vehicle flight path optimization method and system, and relates to the technical field of unmanned aerial vehicle path optimization, and the method comprises the steps: obtaining a three-dimensional building map of a target flight region, marking the positions of a flight starting point and a flight ending point, and determining an initial flight path; constructing a fast airflow simulator, and combining with the macroscopic airflow data to obtain microscopic airflow data; calculating the path cost for executing the initial flight path based on the microscopic airflow data at the current moment in combination with the performance parameters of the target model unmanned aerial vehicle; and performing iterative optimization on the initial flight path by taking the three-dimensional building map and the target model unmanned aerial vehicle as constraints and taking the lowest path cost as a target to obtain an optimal flight path. The problems that in traditional unmanned aerial vehicle path planning, the wind direction and the wind speed on the air route are difficult to pre-judge due to the urban low-altitude complex building environment, the path is planned only from the single distance dimension, the urban low-altitude airflow difference cannot be adapted, and the flight safety of the unmanned aerial vehicle is affected are solved.

The invention relates to the technical field of aircraft route design, in particular to a Beidou grid-based low-altitude aircraft three-dimensional route design method, which comprises the following steps of: performing airspace type subdivision processing on a target flight area in a flight space model; judging the airspace available state of the airspace type; dividing an initial route coverage space of the target low-altitude aircraft; carrying out conflict investigation on the airspace use condition in the initial route range; when the conflict checking result is a conflict-free state, performing arc fitting on the air route space trajectory of the target low-altitude aircraft; and verifying the fitted route trajectory, and if the current airspace state is changed, adjusting the fitted route trajectory to obtain a three-dimensional route design scheme. According to the method, the airline can be planned by combining airspace type division and a dynamic available state, the airline can be changed and adjusted according to the real-time airspace, and the flexibility and safety of airline planning are improved.

The invention discloses a dynamic MOEA / D unmanned aerial vehicle flight path optimization method based on a hybrid prediction model. The method comprises the steps that an unmanned aerial vehicle is used for collecting surrounding road environment data through a carried laser radar sensor and preprocessing the surrounding road environment data; constructing a dynamic multi-target path optimization problem, carrying out modeling by using a vector objective function, constructing a feasible path diagram in a flight area, and carrying out unmanned aerial vehicle route planning modeling; an MOEA / D algorithm population is initialized; in the flight process of the unmanned aerial vehicle, continuously detecting whether environmental parameters are changed or not, and if the environment is changed, calling an unmanned aerial vehicle path optimization method based on an improved dynamic MOEA / D algorithm; and if the environment is not changed, path optimization is carried out by using a conventional MOEA / D algorithm. The method has good real-time adaptability, multi-target balance capability and robustness, and is suitable for an intelligent flight path optimization task in a complex environment.

The invention discloses an unmanned aerial vehicle building inspection route adjustment method and system based on wind field constraint and a storage medium, and the method comprises the steps: carrying out the simulation calculation of a three-dimensional wind field distribution result of an outer contour model according to the outer contour model of a to-be-detected building and the meteorological data of the geographic position of the to-be-detected building; determining external wind field wind speed data of the to-be-detected building so as to obtain coordinate points of which the wind speed is greater than the maximum wind resistance speed of the unmanned aerial vehicle in the external wind speed data; determining a limited flight area with the wind speed greater than the maximum wind-resistant speed of the unmanned aerial vehicle in the three-dimensional wind field distribution result according to the coordinate points; obtaining a target waypoint falling in the limited flight area in an initial inspection route generated by the unmanned aerial vehicle for the outer contour model; and adjusting and updating the target waypoint according to the limited flight area to obtain a target inspection route. A limited flight area is generated through wind field simulation and wind resistance threshold constraint, and the waypoint position of the limited flight area is adjusted, so that the cruising stability and safety are improved.

The invention provides an unmanned aerial vehicle task guidance method based on augmented reality, a storage medium and a system, and the method comprises the steps: determining a flight task of an unmanned aerial vehicle, and obtaining a corresponding flight region; determining a target route of the unmanned aerial vehicle; determining target environment information corresponding to the target route from the initial environment information, and performing target identification on the target environment information to obtain a target object passed by the unmanned aerial vehicle in the target route and a target type corresponding to the target object; determining a target operation needing to be executed on the target object by the unmanned aerial vehicle for executing the flight task and an operation type corresponding to the target operation according to the target type, and determining a task identifier of the target object according to the operation type; and performing virtual display according to the target route and the task identifier in combination with the initial environment information to obtain a target virtual display result of the unmanned aerial vehicle. According to the invention, the task identifier and the flight route are displayed virtually, so that the error rate of a pilot during task execution is reduced, and the task execution accuracy of the unmanned aerial vehicle is improved.

The invention discloses a rectangular area unmanned aerial vehicle hunting method based on differential game and reinforcement learning. The our unmanned aerial vehicle cluster obtains environment state information; spatial constraint loss is calculated through a rectangular flight area limitation loss function module, time sequence consistency loss is calculated through a batch time sequence behavior constraint module, and the spatial constraint loss and the time sequence consistency loss are combined to generate a state behavior joint hidden variable fusing space and time sequence constraints. And fusing the hidden variable with the current environment state, inputting the fused hidden variable and the current environment state into a differential game value evaluation module, obtaining a future expected value based on Nash equilibrium by solving a differential game between our part and the black flight unmanned aerial vehicle, and updating decision network parameters based on the future expected value. The cluster performs the optimized hunting action and stores empirical data for continuous training. According to the method, the decision stability, the cooperation efficiency and the task success rate of unmanned aerial vehicle cluster hunting in the rectangular limited flight area are effectively improved.

The embodiment of the invention provides a planning method and device for an unmanned aerial vehicle route and a storage medium. The planning method comprises the steps of determining a view angle range of the unmanned aerial vehicle for each sub-region in a flight region; determining an actual shooting range of the unmanned aerial vehicle for each sub-region according to the view angle range; determining a surrounding rectangle where the flight area is located; determining a first center point according to the surrounding rectangle and the standby position of the unmanned aerial vehicle; determining a plurality of circular trajectories of the unmanned aerial vehicle for the flight area according to the actual shooting range, the visual angle range and the first center point; and determining a flight route of the unmanned aerial vehicle for the flight area according to the plurality of circular trajectories. The method can reasonably plan the flight route according to the terrain, effectively shortens the route distance under the condition of ensuring the coverage rate, and effectively reduces the energy consumption of the unmanned aerial vehicle.

The invention provides a data acquisition system, method and device based on a low-altitude unmanned aerial vehicle in water affair management, and relates to the technical field of intelligent water affair. The system comprises a water affair management and control platform used for acquiring position information of the wading equipment, dividing at least one flight area according to the position information of the wading equipment, generating flight routes corresponding to the flight areas, and configuring communication frequency bands uniquely corresponding to the flight areas; the unmanned aerial vehicles are in one-to-one correspondence with the flight areas, the unmanned aerial vehicles are used for flying according to the corresponding flight routes, and under the condition that the distance between the unmanned aerial vehicles and the wading equipment meets the distance requirement, a communication link is established with the wading equipment in a preset communication mode, and a data acquisition process is executed; and sending the collected data to the water affair management and control platform through the uniquely corresponding communication frequency band under the condition that the communication condition with the water affair management and control platform meets the communication requirement. The labor cost of data acquisition can be reduced, and the acquisition efficiency can be improved.

The invention discloses a multi-target vision enhancement tracking method and system for an airport flying area, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: performing target detection on a current frame to obtain detection features; accessing a historical track feature library, fusing the appearance of the historical target and the historical motion sequence, and generating enhanced track context features; using a decoder based on an attention mechanism to perform cross-frame association decoding on the current detection features as queries and the enhanced track context features to generate an identity association scoring matrix; and carrying out optimal matching and decision making based on the scoring matrix, completing track updating, new registration and loss elimination, and obtaining a tracking result. According to the method, through end-to-end depth correlation and time sequence feature modeling, the problems of tracking interruption and identity switching caused by target shielding, similar appearance and complex motion in a complex scene of an airport are effectively solved, and the tracking robustness and identity consistency are remarkably improved.

The invention discloses a flight trajectory coordinate and orientation correction method and device based on an airport flight area sliding structure chart, equipment and a medium, and the method comprises the steps: constructing a flight area sliding route chart structure chart of a target airport, and obtaining an airport sliding line path segment data set; performing path matching and coordinate point matching on a current track point and a historical track point in the track time sequence of the target aircraft to obtain a matched path and a matched coordinate point; according to the matched coordinate point, determining an end point of a path section of the target aircraft in the advancing direction of the current matched path; according to the matched coordinate point corresponding to the current track point and the end point of the path section, determining a traveling point; and determining a corrected direction value of the target aircraft and a matched coordinate point corresponding to the advancing point based on the matched coordinate point corresponding to the current track point and the advancing point. The invention belongs to the field of aircraft advancing track correction. According to the invention, accurate navigation of the aircraft on the ground can be realized.

The invention provides an airport flying area crossing traffic command system based on an A-SMGCS system, and belongs to the technical field of intelligent traffic, and the system comprises a sensor layer which is used for collecting aircraft dynamic data, to-be-passed authorized object data, environment meteorological data, and dynamic obstacle data; the data processing and decision-making layer is used for carrying out standardization processing and space-time mapping processing on the collected data, judging existing potential conflicts in combination with a dynamic threshold adjustment mechanism, and generating an issuing instruction based on a multi-target optimization decision; and the execution layer is used for controlling crossing traffic management and control equipment according to the issued instruction and feeding back an execution result to the data processing and decision-making layer in real time to form closed-loop control. The traffic conflict risk of the airport flight area crossing is effectively reduced, the crossing passing efficiency is improved, the vehicle waiting time and the aircraft taxiing delay are reduced, the fine scheduling requirement for scene movement is met, and reliable support is provided for safe and efficient traffic operation of the airport flight area.

The invention discloses a method and system for identifying dangerous goods at the bottom of a vehicle in an airport flight area, and the method comprises the steps: obtaining the vehicle type of a to-be-detected vehicle, transmitting a detection wave to the bottom of the to-be-detected vehicle, and synchronously collecting a reflection sequence of the detection wave at the bottom of the to-be-detected vehicle in real time; performing differential processing on the reflection sequence to generate a maximum response deviation value, calculating an abnormal fusion response intensity value according to the maximum response deviation value, and identifying a plurality of areas at the bottom of the to-be-detected vehicle as abnormal areas according to the abnormal fusion response intensity value; and constructing a physical property response vector of each abnormal region, and searching a dangerous goods physical property response vector corresponding to the physical property response vector of the abnormal region in a dangerous goods database, thereby determining the dangerous goods category of the abnormal region.

The embodiment of the invention provides an unmanned aircraft lightning protection method and system and a medium, and the method comprises the steps: obtaining the route information of an aircraft, and collecting the historical weather information in a flight region corresponding to a route according to the route information; establishing a weather prediction model according to the historical weather information in the flight area corresponding to the route, and generating weather prediction information; cloud layer distribution in the flight area is analyzed according to the meteorological prediction information, and cloud layer parameter information is obtained; calculating space charge distribution in the flight area according to the cloud layer parameter information; generating a lightning protection strategy according to the space charge distribution; adjusting the flight route of the aircraft according to the lightning protection strategy; the meteorological prediction model is established to predict the weather on the course of the aircraft, cloud layer parameters are obtained in advance, the space charges in the flight area are detected in real time, intelligent lightning protection is performed according to the space charge quantity, and the flight safety of the aircraft is improved.

A system for imaging an individual performing a parachuting activity, the system comprising a plurality of imaging devices configured to capture images of a flight area, the camera being controllable to capture images of different portions of the flight area, one or more processing devices in communication with the plurality of imaging devices, the one or more processing devices are configured to receive image data from the imaging device, the image data indicating one or more images captured by the imaging device, and analyze the one or more images to track the individual within the flight area.

The invention discloses a sky path planning design method and system based on an artificial intelligence algorithm, and relates to the technical field of unmanned aerial vehicles, and the technical scheme is characterized by comprising the following steps: obtaining environment data of a flight area of an unmanned aerial vehicle, and obtaining environment condition data of the flight area according to the environment data; setting a sub-planning area of the unmanned aerial vehicle according to the environmental condition data, and setting a planning parameter and a planning weight corresponding to the sub-planning area; real-time monitoring parameters of the sub-planning areas are collected, and sub-area planning coefficients of the sub-planning areas are obtained according to the real-time monitoring parameters, the planning parameters and the planning weights; performing comparative analysis on the sub-region planning coefficient and a preset planning coefficient threshold to obtain a first high-risk sub-planning region; obtaining a first high-risk area factor corresponding to the first high-risk sub-planning area; and obtaining the influence area factor of the first high-risk area factor according to the area correlation model, so that the unmanned aerial vehicle can actively avoid the path during planning, and is prevented from falling into a high-risk environment.

The application discloses a flight trajectory coordinate and direction correction method and device based on an airport flight area sliding structure diagram, equipment and a medium, and comprises the following steps: constructing a flight area sliding route structure diagram of a target airport, and obtaining an airport sliding line path segment data set; performing path matching and coordinate point matching on a current trajectory point and a historical trajectory point in a trajectory time sequence of a target aircraft, and obtaining a matching path and a matching coordinate point; determining an endpoint of a path segment in the advancing direction of the target aircraft on the current matching path according to the matching coordinate point; determining a travel point according to the matching coordinate point corresponding to the current trajectory point and the endpoint of the path segment; and determining a correction direction value of the target aircraft and a matching coordinate point corresponding to the travel point based on the matching coordinate point corresponding to the current trajectory point and the travel point. The application belongs to the field of aircraft travel trajectory correction. The application can realize accurate navigation of the aircraft on the ground.

The invention provides a low-altitude airspace flight path planning method and device, and the method comprises the steps: carrying out the screening of a to-be-planned airspace based on geographic information data, unmanned plane performance parameters and a comprehensive constraint model, and obtaining a candidate flight region; and based on the potential risk data and the risk assessment function, performing search sampling on the candidate flight area to obtain a current flight path planning result. According to the method provided by the invention, based on geographic information data, unmanned aerial vehicle performance parameters and a comprehensive constraint model, candidate flight areas are screened from a to-be-planned airspace; based on the potential risk data and the risk assessment function, the candidate flight area is searched and sampled to obtain the current flight path planning result, and information of multiple aspects such as the geographical environment, the unmanned aerial vehicle performance and the dynamic risk is effectively integrated, so that the planned flight path can meet the basic performance requirements of the aircraft, and the flight path planning efficiency is improved. Physical obstacles and potential risks can be accurately avoided, and the efficiency, the safety and the robustness of low-altitude flight path planning are remarkably improved.

The application discloses an environment-friendly bird repelling bomb device with an electromagnetic conductive electric ignition head structure, which is mainly used for bird repelling in an airport flight area. The device comprises a launching shell body (1), an electromagnetic conductive electric ignition head combination unit (2) arranged on the bottom of the inner cavity of the launching shell body, an electric ignition head inner sleeve unit (3), a bird repelling bomb body unit (6), at least one flame jet hole (4) arranged on the center of the top end surface of the electric ignition head inner sleeve unit, and a propellant layer (5) attached to the top end surface of the electric ignition head inner sleeve unit. The launching port end of the launching shell body is sealed by a sealing end cover (7). The bird repelling bomb device with the integrated structure which is combined by the above components is matched with an aviation special bird repelling launcher to realize electromagnetic induction conductive ignition launching. The bird repelling bomb device with the above structure has the advantages of vertical fixed connection between the electric ignition head and the two electrode ends in the center of the electromagnetic induction magnetic plate surface, realization of rapid ignition of the explosive body, no continuous ignition, no residue, environmental protection safety and the like.

The invention provides a virtual co-pilot assisted flight simulation method and system based on MR technology, and relates to the technical field of flight simulation training, and the method comprises the steps: firstly obtaining a basic environment data set of a flight simulation scene, including flight area topographic features, meteorological conditions and airspace traffic situation data, and flight operation intention data of a pilot; the features include direction, force and operation target features. And constructing an MR virtual copilot dynamic interaction scene based on the basic environment data set, converting the basic environment data into guide parameters through a scene dynamic adaptation module, and generating an associated guide signal. Collecting real-time operation feedback data of a pilot aiming at a guide signal, generating a scene state adjustment instruction through collaborative coupling processing, updating the virtual copilot image model interaction action and the virtual operation interface element display state, outputting a flight simulation collaborative data set containing an operation collaborative process and a scene adjustment result, and performing scene state adjustment on the flight simulation collaborative data set. And a more real and effective support is provided for flight simulation training.

The invention provides a flight area track project progress evaluation method, which can be applied to the technical field of artificial intelligence and the technical field of Internet of Things. The method comprises the following steps: acquiring unmanned aerial vehicle shooting data and satellite remote sensing data of a flight area channel; performing image fusion processing on the unmanned aerial vehicle shooting data and the satellite remote sensing data to obtain fused image data; analyzing the fused image data by using a pre-trained target detection model to obtain a detection result; based on the detection result, extracting image feature data reflecting the actual completion project amount of the flight area track project, and obtaining actual completion project amount data; and comparing the actual completion project amount data with a preset construction plan progress, and performing project progress evaluation based on a comparison result.

The invention discloses an auxiliary control method and system for an autorotorcraft in the flight process, and relates to the technical field of the auxiliary control method for the autorotorcraft, and the remaining flight paths of the autorotorcraft are determined according to the flight path and the current position of the autorotorcraft. And determining a current flight state of the autorotorcraft according to each current flight parameter and flight mode of the autorotorcraft, and determining a first auxiliary control mode of the autorotorcraft in the remaining flight areas according to the current flight state, the plurality of flight routes of the remaining flight areas and the corresponding real-time weather events. Determining a flight influence factor from the identification of the flight event; and determining a second auxiliary control mode of the autorotorcraft in the next remaining flight area according to the flight influence factor, the plurality of flight routes of the next remaining flight area and the abnormal signal of the autorotorcraft, thereby improving the accuracy of the second auxiliary control mode of the autorotorcraft in the next remaining flight area.

The application relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle path planning method based on an ellipse tangent graph method, which comprises the following steps: a flight environment modeling step, which is used for constructing an elliptical obstacle model by taking buildings as obstacles, and constructing a three-dimensional flight environment model based on the elevation information of a flight area and the distribution of the obstacle model; a three-dimensional path acquisition step, which is used for path searching and outputting a flight path; and a three-dimensional path smoothing step, which is used for smoothing the path points of the flight path output by the three-dimensional path acquisition step to obtain a flight trajectory for unmanned aerial vehicle control. The application effectively solves the comprehensive decision problem among path length, motion smoothness and energy consumption of a traditional method by taking energy consumption as a core optimization target of path planning, thereby remarkably prolonging the task range of the unmanned aerial vehicle.

The utility model discloses a temperature and humidity monitoring system for a box-type substation in a flying area, which comprises an upper computer system and a lower computer system, the upper computer system and the lower computer system are connected through a communication management unit, and the lower computer system comprises an inductive probe, a temperature and humidity sensing unit and a data conversion unit which are sequentially connected. When the device is applied, the inductive probes and the temperature and humidity sensors are installed in the UPS battery cabinet and the box transformer box of the flying area box-type substation to acquire temperature and humidity information in the UPS battery cabinet and the box transformer box and transmit the temperature and humidity information to the upper computer system, and the temperature and humidity in the UPS battery cabinet and the box transformer box are remotely monitored through the upper computer system. Therefore, the service life of the box-type substation is guaranteed, the service life of UPS equipment is prolonged, potential safety hazards are reduced, the equipment guarantee level is improved, and cost including but not limited to labor cost, equipment fault cost and fuel cost is saved.

The application provides a method and system for planning reconstruction and expansion of an airport underpass, and relates to the technical field of airport engineering planning. The method comprises the following steps: generating at least one feasible reconstruction and expansion planning scheme based on the geological conditions of the target underpass and the non-stop construction constraint conditions of the flight area; establishing a three-dimensional finite element model considering the action of the airplane moving load, using the three-dimensional finite element model to verify the structural safety of the scheme, adjusting the relevant parameters and re-verifying the structural safety of the scheme when the scheme fails to pass the verification, and verifying the structural safety of the scheme until the scheme passes the verification; when the number of schemes is at least two, constructing a simulation model, simulating the schemes by using the simulation model, calculating the comprehensive influence degree of the schemes on the airport operation efficiency according to the simulation results, and selecting the scheme with the minimum comprehensive influence degree as the first target feasible reconstruction and expansion planning scheme. The application can improve the comprehensiveness of the planning results, reduce the whole life cycle cost and safety hidden danger of the project, and improve the airport operation efficiency.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle black box control method and system based on deep learning, and the method comprises the steps: obtaining a real-time flight region image of an unmanned aerial vehicle, and extracting a region image before the unmanned aerial vehicle falls according to the real-time flight region image; and based on a deep learning model, carrying out feature extraction and environment type determination on the regional image. Meanwhile, interaction data of the search and rescue end and the black box and positioning data of the search and rescue end are collected in real time. Afterwards, the environment types, the interaction data and the positioning data are input into a reinforcement learning model, state benefits of different prompt actions are calculated and selected through a strategy network, a black box control strategy including light, sound and smoke control is generated, and self-adaptive scheduling and energy consumption optimization of the prompt actions are achieved. The reachability and effectiveness of black box prompt signals are improved in a complex environment, and the search and rescue efficiency in a falling scene is improved.