217 results about "Flight height" patented technology

The invention provides a low-altitude logistics multi-unmanned aerial vehicle cooperative distribution method based on deep reinforcement learning, and the method comprises the steps: obtaining a distribution task and multi-level dynamic airspace information, and distributing the distribution task to idle unmanned aerial vehicles according to a task distribution mechanism; based on the multi-level dynamic airspace information, an airspace scheduling mechanism is constructed, and the airspace scheduling mechanism comprises an airspace scheduling height level selector and a multi-unmanned aerial vehicle cooperative path-finding solver; the airspace scheduling height level selector determines the flight height level of the unmanned aerial vehicle according to the distribution task and the multi-level dynamic airspace information to obtain a height level decision; the multi-unmanned aerial vehicle cooperative path-finding solver performs cooperative path planning in a horizontal range at each height layer to obtain a path decision; and the unmanned aerial vehicle executes a distribution task according to the height layer decision and the path decision. According to the method provided by the invention, the high-throughput and low-delay delivery target is realized in a complex urban scene, and the operation risk caused by airspace conflicts is reduced.

The invention provides a three-dimensional unmanned aerial vehicle path planning method and device based on an improved A star algorithm. The method comprises the steps that the actual cost of an unmanned aerial vehicle from a starting node to a current node is determined based on the voyage cost, the height cost and the terrain threat cost of the unmanned aerial vehicle from the starting node to the current node, and the voyage cost is used for evaluating the cost consumption brought by the flight path of the unmanned aerial vehicle; the height cost is used for evaluating the cost consumption caused by the flight height change of the unmanned aerial vehicle, and the terrain threat cost is used for evaluating the cost consumption caused by the risk degree of a mountain and a no-fly airspace to unmanned aerial vehicle path planning; and based on the actual cost, the path of the unmanned aerial vehicle from the starting node to the target node is judged and planned by using an A star algorithm and node redundancy. According to the method, unmanned plane path planning is carried out by integrating height cost, terrain threat cost and the like, a three-dimensional unmanned plane path with less flight energy consumption can be obtained, redundant nodes are deleted, and a smoother path with less energy consumption is obtained.

The invention discloses an aircraft wake flow real-time monitoring method based on laser radars, and the method comprises the steps: collecting the real-time wake flow data in a five-side approaching stage of a front aircraft through a plurality of laser radar devices, and determining the space coordinates and three-dimensional contour of the edge of a wake flow region through an edge detection algorithm; according to the coordinates of the left and right edges and the upper and lower edges of the wake flow area, the front and tail flow areas are displayed in real time; the method comprises the following steps: intercepting a two-dimensional cross section of a front aircraft tail flow area at a rear aircraft real-time flight height, calculating a rolling instability index generated by the two-dimensional cross section area to a rear aircraft wing, determining a wake flow minimum safety interval according to a preset rolling instability index threshold value, and displaying the wake flow minimum safety interval on an air traffic control system radar interface in real time, thereby realizing the purpose that in a five-side approach stage of an aircraft, the flight speed of the aircraft is increased. The front aircraft wake flow area and the rear aircraft wake flow area are displayed in real time, and the minimum safety interval of the front aircraft and the rear aircraft considering the real-time flight height is considered, so that an air traffic controller can clearly know the real-time state and the potential risk of the aircraft wake flow, and the wake flow monitoring accuracy and the flight safety are improved.

The invention discloses a reinforcement learning driven hypersonic variant aircraft reentry trajectory optimization method, and belongs to the field of hypersonic aircraft and trajectory optimization. The implementation method comprises the following steps: establishing a waverider-like hypersonic variant aircraft parameterized model comprising a waverider fuselage and two deformed wings based on a category shape conversion (CST) method; the influence of factors such as flight height H, flight Mach number Ma, attack angle alpha, inner wing section sweepback x1, outer wing section sweepback x2 and the like is considered, a Kriging agent model is established, and the aerodynamic performance of the hypersonic speed variant aircraft is obtained. Based on a DDPG reinforcement learning algorithm, according to flight characteristics of a hypersonic speed variant aircraft in a reentry section flight process, and in combination with expert experience and dynamics, start and end states, heat flow, overload and dynamic pressure constraints in the flight process, training of a reentry section trajectory optimization model driven by the DDPG algorithm is realized. Through flight simulation prediction of different hypersonic speed variant aircrafts, variant decisions are given in real time, and reentry section trajectory optimization of the hypersonic speed variant aircrafts is realized.

The invention relates to the technical field of electric power, in particular to an unmanned aerial vehicle route automatic planning method and system for a complex space of a transformer substation. Analyzing at least one inspection task instruction input by a user to obtain a task type corresponding to each inspection task instruction and a data acquisition requirement corresponding to the task type; according to a preset three-dimensional semantic map of the transformer substation and the data acquisition requirement of each inspection task instruction, a flight route of at least one unmanned aerial vehicle is automatically generated by adopting a path planning algorithm, and the flight route comprises a waypoint sequence, a flight height, a shooting angle and a holder action instruction; the three-dimensional semantic map comprises no-fly zone information in the transformer substation, a plurality of devices, the position of each device, the device type, the electrification attribute and the safety rule label. And controlling the corresponding unmanned aerial vehicle to inspect target equipment in the transformer substation according to the flight route of each unmanned aerial vehicle. And large-scale and standardized inspection operation is realized.

The invention discloses an unmanned aerial vehicle low-cost flight path planning method based on a noise protection area. The method comprises the steps that a total cost objective function is established based on the sum of the total energy consumption cost and the total noise cost of the whole flight process of an unmanned aerial vehicle; and based on the total cost objective function and a preset annular noise protection area of a building, on the basis that the unmanned aerial vehicle avoids the noise protection area, planning an optimal operation path of the unmanned aerial vehicle with the minimum total cost at a preset flight height. Based on the combination of the total energy consumption cost, the total noise cost and the Dijkstra algorithm, and based on the arrangement of the noise protection area of the building, an efficient and safe operation path with the minimum noise influence and the lowest cost under the preset flight height can be planned for the unmanned aerial vehicle in the urban low-altitude airspace.

The invention discloses a waypoint file-oriented unmanned aerial vehicle flight route generation method, which comprises the following steps of: acquiring a waypoint file comprising a waypoint to be flied of an unmanned aerial vehicle, analyzing the waypoint file to obtain latitude and longitude information and flight height information of the waypoint to be flied, and generating a flight route of the unmanned aerial vehicle; the latitude and longitude information and the flight height information of the waypoint to be flied are converted into three-dimensional space information under a Cartesian coordinate system, the three-dimensional space information of the waypoint to be flied is obtained, a waypoint sequence is generated by using a self-organizing mapping neural network model or in a Q learning mode, and a flight route file of the unmanned aerial vehicle is generated based on the waypoint sequence. According to the invention, automation of unmanned aerial vehicle flight route generation is realized.

The invention relates to an urban low-altitude three-dimensional logistics network optimization method considering noise constraint, belongs to the technical field of low-altitude airline network design, and solves the problem that environmental influence and distribution efficiency cannot be considered in the prior art. The method comprises the following specific steps: performing grid division based on geographic information of a city planning area to obtain logistics network parameters; constructing an urban low-altitude three-dimensional logistics network model with a multi-layer undirected graph structure in a grid based on take-off and landing nodes, connecting edges and flight height layers by combining logistics network parameters; constructing a target function which aims at minimizing the total cost of the network and minimizing the flight safety risk of the network; the construction constraint conditions comprise a network construction constraint, a flow distribution constraint, a voyage constraint and a noise constraint constructed in combination with a noise tolerance value; the urban low-altitude three-dimensional logistics route network meeting the noise constraint condition is obtained by using the target function and the constraint condition, and the noise influence of a logistics system is reduced while the low-altitude logistics network efficiency is optimized.

To provide a drone system that can eliminate the failure risks that hinder realization of commercial operation of large-sized drones boarded with passengers as no electronic devices cannot achieve the field failure rate of zero, therefore, having possibility of occurrence of the failure, and the failure causes the drone to drop to highly possibly cause a serious accident resulting human death.SOLUTION: There is provided a drone system comprising a large-sized drone to be boarded with passengers, can, when the drone drops, make damage to bodies of the passengers substantially zero by: adopting specifications including flight heights designated to be 1 m above the ground and 3 m above the sea; having a bottom being made from a soft material; and in addition, having somewhat soft seats and / or somewhat soft beds.SELECTED DRAWING: Figure 1

The invention belongs to the field of flight network and channel planning, particularly relates to a ground network point and air channel planning method for urban low-altitude traffic, and aims to solve the problems that in an urban traffic scene, flight of a large number of aircrafts is difficult to manage in a standardized manner, and flight trajectories intersect randomly to easily cause aircraft conflicts in a dense flight scene. The method comprises the following steps: setting a plurality of aircraft flight ground base stations based on a honeycomb structure; determining an initial website and an end website; acquiring a website flight path through a path approximation selection algorithm; an air channel planning path is formed by the starting point-initial network point of the aircraft, the network point flight path and the ending network point-ending point of the aircraft; and carrying out flight height layering according to the flight direction in the branch flight path. The air traffic flight system solves the problems that the flight of a large number of aircrafts is difficult to standardize and the flight tracks are crossed randomly to easily cause aircraft conflicts, and is high in flight efficiency, high in flight safety and convenient to manage.

The invention discloses a flight control method for a tilt transition stage of a tilt-rotor aircraft. The flight control method comprises the following steps: constructing a tilt corridor of the tilt-rotor aircraft; acquiring the vertical control quantity of the aircraft in the tilting transition stage in real time; the tilt angle corresponding to the upper boundary of the tilt corridor serves as a tilt angle feed-forward instruction of the aircraft in the tilt transition stage; a tilt angle beta is obtained; taking the flight speed of the upper boundary of the tilting corridor corresponding to the tilting angle beta as a flight speed instruction of the aircraft in the tilting transition stage; correspondingly obtaining a final accelerator instruction of the tilting rotor; and performing flight control on the aircraft in the tilting transition stage. The tilt angle can be determined based on the tilt corridor so as to correspondingly obtain the final accelerator instruction of the tilt rotor, meanwhile, the flight height is kept unchanged at the moment of the tilt transition stage, and the flight speed is controlled within the preset range, so that the height and speed control precision of the aircraft is improved, and the control precision of the aircraft is improved. And therefore, the controllability and the safety in the tilting flight process are improved.

The invention discloses a heterogeneous multi-agent formation control method based on a composite disturbance observer, and the method comprises the steps: building a nonlinear model of heterogeneous multi-agents based on the composite disturbance observer, and enabling the agents to comprise an unmanned plane and an unmanned vehicle; the composite interference observer is used for estimating unknown composite interference of each agent; a heterogeneous multi-agent intelligent plane formation controller with an internal safe collision avoidance function is constructed based on a nonlinear model, a conversion error function is introduced to restrain an initial collaborative error in a specified interval, and a safe distance between two adjacent agents is ensured; meanwhile, a preset time height formation controller is constructed, so that the consistency of expected flight heights of the unmanned aerial vehicles is realized; and controlling the heterogeneous multi-agent formation according to the constructed controller. The adverse effect caused by unknown interference is effectively eliminated through the composite interference observer, the collision avoidance problem between the intelligent agents is solved by using a specified performance control method, and meanwhile, the system is ensured to be stable within a preset time and the control precision is improved.

The invention relates to the technical field of unmanned aerial vehicle flight control, in particular to a control method of an inspection unmanned aerial vehicle, which comprises the following steps of: in response to a preset inspection instruction, obtaining a current target area wind speed, terrain information and a plurality of humidity values fed back by a plurality of humidity sensors arranged at different heights; flight parameters are constructed based on the wind speed and terrain information of the target area, heat distribution cloud charts at different heights are generated during the period, and the drip irrigation amount in the target area is calculated based on the heat distribution cloud charts. According to the control method of the inspection unmanned aerial vehicle, the flight path can be dynamically optimized through a wind speed self-adaptive flight height adjustment mechanism in combination with single effective width calculation of the thermal imager, and meanwhile, the ground heat distribution data of the same target area are collected at different heights in later data processing, so that the control precision of the inspection unmanned aerial vehicle is improved. And meanwhile, in combination with the environment humidity corresponding to different heights, the interference of the flight height and the environment humidity on the thermal imaging data is reduced, and the environment data acquisition precision is remarkably improved.

The invention discloses an unmanned aerial vehicle control system and method for agricultural seeding, relates to the technical field of unmanned aerial vehicle control, and solves the technical problems of seed falling path deviation and seeding precision reduction caused by the fact that route generation depends on preset topographic data and position points but real-time environment change is not considered in the prior art. The first flight height of the unmanned aerial vehicle at the current wind speed is obtained; acquiring the real-time horizontal offset of the unmanned aerial vehicle based on the first flight height; based on the first flight height and the real-time horizontal offset, determining a target position of the seed falling in the to-be-sown area; the actual depth of the seeds embedded into the soil is calculated through the kinetic energy theorem; judging whether the actual depth is within a preset normal embedding depth range or not; the real-time flight speed of the unmanned aerial vehicle in the flight process is obtained, and the unmanned aerial vehicle is controlled to complete sowing according to the real-time flight speed and the real-time flight speed; according to the invention, the deviation of a seed falling path is corrected, and the seeding precision is improved.

The invention provides a low-altitude airway flight height acquisition method and device, a storage medium and electronic equipment, and the method comprises the steps: determining the current air pressure height of an aircraft relative to a reference plane according to the current air pressure of the aircraft; according to the current geodetic height of the aircraft, the current satellite-based height of the aircraft relative to the datum plane is determined, and the current geodetic height is the height, measured through a satellite-based positioner at the current moment, between the aircraft and the geodetic ellipsoid; and performing fusion processing according to the current air pressure height and the current satellite-based height of the reference surface to determine the current fusion height of the aircraft relative to the reference surface. By uniformly setting the reference surface, acquiring the current air pressure height and the current satellite-based height of the aircraft relative to the reference surface, and performing fusion processing on the current air pressure height and the current satellite-based height, the height measurement error is effectively reduced, and the current fusion height with uniform measurement reference and higher accuracy is obtained.

The invention belongs to the technical field of unmanned system intelligent control and path planning, and discloses a multi-unmanned aerial vehicle path planning method based on a multi-population grey wolf optimization algorithm, and the specific technical scheme comprises the steps: 1, building an environment model and constraint conditions of unmanned aerial vehicle three-dimensional path planning; 2, constructing a multi-target cost function including fuel consumption cost, flight height cost, threat area cost, time cooperation cost and space cooperation cost; 3, optimizing the multi-target cost function by adopting a multi-population grey wolf optimization algorithm to obtain an optimal flight path; according to the method, the global search capability is remarkably enhanced, and the multi-group clustering strategy and the periodic convergence factor act together, so that the algorithm can effectively jump out of local optimum, a path scheme closer to global optimum is found in a complex solution space, the convergence speed and the calculation precision are greatly improved, the collaborative planning effect is excellent, and the method is suitable for large-scale popularization and application. The method shows strong environmental adaptability and robustness, and has a very high practical application value.

The invention discloses a distribution network unmanned aerial vehicle ground simulation flight method and device based on AI vision and a medium, and the method comprises the steps: collecting a first real-time image in a flight direction in real time in a height calibration stage before an unmanned aerial vehicle flies to a next target tower pole; dynamically adjusting the height of the unmanned aerial vehicle according to the image width ratio of the distribution wire in the first real-time image until the target tower safety height is reached; after the unmanned aerial vehicle flies over the target tower pole according to the target safety height, a second real-time image in the flight direction is collected in real time through the visual sensor; and according to the image width ratio of the power distribution wire in each second real-time image and the real-time positioning information of the unmanned aerial vehicle, controlling the unmanned aerial vehicle to perform flight height change with the height fluctuation of the power distribution wire in the flight direction by taking the target tower pole as a starting point, and according to the technical scheme of the embodiment of the invention, autonomous height adjustment of the unmanned aerial vehicle is realized through visual intelligent analysis; the equipment cost and the operation complexity are greatly reduced, and the inspection efficiency and quality of the unmanned aerial vehicle are remarkably improved.

The invention discloses an AI-based unmanned aerial vehicle automatic positioning and navigation system, and the system comprises an unmanned aerial vehicle body which obtains a starting point coordinate through a navigation module by manually inputting a terminal point coordinate and a planned flight time length value t, flies to the terminal point coordinate at a flight speed value V1 and a flight height value H1 from the starting point coordinate, automatically completes a flight task, and automatically judges a return flight; and the monitoring unit is used for acquiring an initial electric quantity value Q of the unmanned aerial vehicle body, transmitting the initial electric quantity value to the coordinate unit, monitoring the output power of the storage battery in real time during flight, and generating a feedback signal and transmitting the feedback signal to the correction unit when abnormal output power is generated during flight. The problems that when an existing unmanned aerial vehicle automatic positioning navigation system is used, the endurance problem of the unmanned aerial vehicle is not considered, and the unmanned aerial vehicle cannot return or even falls in the flight process in the actual flight process are solved.

The invention relates to the technical field of inspection planning, in particular to an unmanned aerial vehicle low-altitude inspection planning system and method based on dangerous case analysis. According to the method, multi-dimensional calculation including flight complexity assessment, flight height danger, building density danger and the like is carried out, dynamic and static risks are integrated, finally, building collision danger and risk assessment results of all areas are integrated to obtain a global route danger assessment result, and a route with the minimum danger is selected according to the global route danger assessment result. According to the whole scheme, multi-dimensional and comprehensive risk assessment is realized, various factors such as environment and building are considered, related standard requirements are met, and the flight risk of the unmanned aerial vehicle can be effectively reduced.

The invention relates to the technical field of micro turbine power generation, in particular to an intelligent micro turbine power generation control system suitable for different flight heights, which comprises a flight environment monitoring module, a vibration compensation and stability enhancement module, an aging and power generation efficiency attenuation compensation module and an electric energy management module, the flight environment monitoring module monitors the flight state of the aircraft in real time; the vibration compensation and stability enhancement module automatically adjusts the working state of the generator through a vibration compensation algorithm; the aging and power generation efficiency attenuation compensation module predicts the aging state of the turbine power generation system and dynamically adjusts power generation power output through an aging compensation algorithm; and the electric energy management module adjusts an electric energy distribution scheme in real time according to the priority of the equipment and task requirements. According to the invention, the influence of vibration and aging effects on system efficiency is effectively reduced, and the service life of power generation equipment is prolonged.

The invention relates to an unmanned aerial vehicle route height adjusting method and device, electronic equipment, a storage medium and a program product, relates to the field of unmanned aerial vehicles, and solves the technical problem of flight danger caused by an unreasonable flight height value in an existing unmanned aerial vehicle route. The method for adjusting the height of the route of the unmanned aerial vehicle comprises the following steps: step S100, acquiring elevation values of one or more interpolation points on a leg according to longitude and latitude values of two end points of the leg in the route of the unmanned aerial vehicle; s200, determining a shielding height value of the interpolation point according to the initial height values of the two end points and the elevation value of the interpolation point; step S300, selecting the maximum shielding height value from the shielding height values of one or more interpolation points; and step S400, updating the initial height values of the two end points according to the maximum shielding height value.

The multi-degree-of-freedom bionic bird flapping-wing aircraft comprises a flight assembly and a fuselage, and a driving assembly and a direction control assembly are arranged on the fuselage; a motor of the driving assembly drives a rotating shaft through a gear transmission mechanism. The flying assembly comprises wing modules which are symmetrically arranged and a linkage driving mechanism, and the linkage driving mechanism converts the rotating motion of the rotating shaft into vertical flapping of the wing modules and different folding angles of the wing modules; the direction control assembly comprises an empennage connected with the fuselage, an empennage steering device, an empennage lifting device and a wing twisting device; the empennage steering device drives the empennage to deflect around the vertical axis of the fuselage to adjust the flight direction of the aircraft; the empennage lifting device drives the empennage to deflect around the transverse axis of the fuselage to adjust the flight height of the aircraft; the wing twisting device drives the wing module to rotate around the longitudinal axis of the wing to adjust the pitch angle of the wing module. According to the invention, flapping folding and twisting can be realized, and a flying function which is extremely similar to that of a real bird is provided.

The invention relates to the technical field of city model construction, and discloses a smart city 3D model construction method and system, and the method comprises the steps: S1, enabling an unmanned aerial vehicle to fly over a city according to a preset flight path, dynamically calculating a proper flight height according to the height of a building and the pitch angle of a camera in the flight process, and automatically adjusting the flight height; the flight height is dynamically adjusted according to the building height and the camera pitch angle in the flight process of the unmanned aerial vehicle, and the coverage range and the resolution of image acquisition are ensured; in combination with the real-time analysis capability of the edge calculation module, data defects can be quickly identified and laser radar supplementary acquisition can be triggered, so that the data acquisition efficiency and integrity are remarkably improved; and finally, through multi-source data fusion and automatic modeling of the cloud processing platform, a high-precision and high-reality smart city 3D model is constructed, and a reliable three-dimensional digital basis is provided for city management and planning.

The invention relates to the technical field of low-altitude flight management, in particular to a low-altitude traffic management system and method based on multi-modal fusion. The method comprises the following steps: collecting flight height information in a low-altitude airspace, and performing layered labeling on the low-altitude airspace based on the flight height information to obtain labeled layers; acquiring flight constraint condition information of the labeled layer, and performing flight constraint on the aircraft in the labeled layer based on the flight constraint condition information; collecting starting point position information and terminal point position information of the flight task, and generating an initial flight path based on the starting point position information and the terminal point position information; the initial flight path is optimized based on the obstacle information, the final flight path is generated, the aircraft executes the flight task based on the final flight path, and the resource utilization rate of the low-altitude airspace and the low-altitude flight management efficiency can be improved.

The invention introduces a tethered unmanned aerial vehicle follow-up and precise landing target system, which comprises a multi-level visual target, and the visual target sequentially comprises a central positioning area, a target positioning area and a target positioning area from the center to the periphery, and the central positioning area is formed by arranging four aruco marks of a first size in a 2 * 2 array mode; the middle transition area is formed by arranging eight aruco marks of the first size in the upper direction, the lower direction, the left direction and the right direction of the center positioning area; four aruco marks of a second size are respectively arranged at the left upper part, the right upper part, the left lower part and the right lower part of the central positioning area; the peripheral auxiliary area is formed by arranging four aruco marks of a third size at four corners of the whole visual target respectively; the maximum peripheral frame is an aruco mark of a fourth size, and the outer frame of the aruco mark of the fourth size forms the outermost layer area of the whole target. According to the invention, continuous and stable visual features can be provided for the mooring unmanned aerial vehicle at different flight heights and attitudes, and the landing reliability is improved.

The invention discloses an aircraft vertical takeoff process control method and system and a storage medium, and realizes stable transition of an aircraft from vertical takeoff to flight in an advancing direction through accurate control of the rotating speed of a tilting rotor and optimization of a tilting process. In the transition process of the aircraft, by reducing the rotating speed of the tilting rotor wings, the influence of aerodynamic disturbance generated by the tilting rotor wings on the wings and the aircraft body is avoided, so that the flight comfort and safety are improved; meanwhile, the flight speed and the flight height of the aircraft in the transition period are maintained by depending on the propelling propellers, the design of a flight control system is simplified, and the complexity and the redundancy of the control system are reduced; the aircraft adopting the takeoff process control method has higher safety, stability and comfort, and is suitable for various application scenes including the fields of urban air traffic and emergency rescue.

The invention discloses an anti-explosion method and system for a vertical fixed-wing unmanned aerial vehicle, the unmanned aerial vehicle and a medium, and the anti-explosion method comprises the steps: monitoring the operation conditions of motors of all rotor assemblies in real time, and determining a fault motor when an abnormality occurs; performing first stability control on other motors without faults, the control surface of the fixed wing and the control surface of the empennage according to the fault motor; acquiring the current flight height of the unmanned aerial vehicle, and performing landing control on the unmanned aerial vehicle according to the current flight height until the unmanned aerial vehicle lands to the ground; and performing fault detection processing on the unmanned aerial vehicle, and generating a fault processing flow record. The fault motor can be accurately identified and positioned in the landing process of the unmanned aerial vehicle, and corresponding emergency measures are taken according to the position relation of the fault motor, so that the unmanned aerial vehicle can maintain stability in the air and stably land on the ground, the phenomenon of explosion of the unmanned aerial vehicle is effectively avoided, and the safety of the unmanned aerial vehicle is improved. And the landing safety and stability of the unmanned aerial vehicle are ensured.

The invention relates to the technical field of aircraft route planning, in particular to an aircraft control method and system for preventing long-distance flight collision, and the method comprises the steps: obtaining the offset distance between an aircraft and a set route at each moment in the flight process, and the measurement distance between the aircraft and a nearest obstacle; obtaining the dynamic stability of the aircraft in each time interval; determining the long-term balance degree of the aircraft in each time interval based on the autocorrelation of the flight heights of the aircraft at all moments in each time interval and the linear growth trend of the distance between the aircraft and the starting point in each time interval in combination with the fluctuation condition of extreme value distribution in the flight speeds of the aircraft at all moments in the time interval; and correcting a Q value updating method in the depth Q network, and performing route planning on the aircraft by using the corrected depth Q network. Therefore, the long-distance flight collision risk of the aircraft is reduced.

The invention provides a low-altitude network signal test method and device and a readable storage medium, and the method comprises the steps: carrying out the grid division of a test region, and setting the flight height and flight path of an unmanned plane according to a grid division result; based on the set flight height and the flight path, network signal testing is carried out through the unmanned aerial vehicle, and a testing result is obtained; performing data analysis according to the test result to obtain an analysis result; and generating a network optimization suggestion according to the analysis result. According to the method, signal testing can be carried out on a low-altitude area more accurately, effective coverage of a signal blind area and a signal weak area is ensured, identification of a signal strong area and a signal weak area is facilitated, the severity of the signal is quantized, and intelligent suggestions are provided for subsequent network optimization according to severity scores.

The invention is suitable for the technical field of flight height prediction, and particularly relates to a flight height prediction method based on a neural network, and the method comprises the steps: collecting sensor data of a passenger plane, and inhibiting outlier data points through a dynamic truncation mechanism based on a distribution quantile threshold; dividing the sensor data by adopting a time sequence slice division strategy to obtain a training set and a verification set, and constructing a prediction feature set based on the training set and the verification set; and constructing a flight height prediction model, training the flight height prediction model through the prediction feature set, and predicting the flight height based on the trained flight height prediction model. According to the invention, a normalization technology is applied to carry out data reduction on a flight time sequence signal, a parallel network structure comprising a multi-scale convolution kernel feature extraction layer and an attention weight distribution module is designed, and gradient iteration is carried out on a Huber loss function through an optimizer for dynamically adjusting a learning rate. And the characterization capability of the model on the nonlinear change mode of the flight state is obviously improved.