508 results about "Urban environment" patented technology

The invention relates to the technical field of intelligent driving, in particular to a method for generating an obstacle avoidance and planning cooperative path in an urban complex environment, which comprises the following steps: acquiring environment real-time sensing data through a vehicle-mounted multi-sensor, and constructing a dynamic semantic traffic matrix in combination with high-precision map static semantic information; based on the matrix, a multi-objective optimization algorithm is adopted to calculate the security cost, the efficiency cost and the rule conformity cost of the path, and a global optimization path is generated; inputting the global optimization path and the dynamic obstacle motion vector into an intention prediction model, generating dynamic obstacle future trajectory probability distribution and interactive intention classification, and further generating an avoidance strategy and adjusting a local path in real time; and inputting the adjusted local path into a kinematics model to carry out kinematics feasibility verification, and outputting an executable track or path re-planning. According to the method, the integration of dynamic environment understanding, path planning and obstacle avoidance strategies is realized, and the method is suitable for the path generation task of an automatic driving system in an urban complex traffic scene.

The invention discloses an unmanned aerial vehicle path planning method and system for a complex urban environment, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a plurality of algorithms are deeply fused, and a combination mode and an information transmission and optimization result inheritance mechanism between stages are beneficial to path planning of the unmanned aerial vehicle; a self-adaptive learning factor with a time-varying period is designed, the global exploration and local development balance capability of PSO is remarkably improved, and the population diversity and the global search capability are enhanced; through rapid global guidance of PSO, population dynamic enhancement of SSA, solution space depth optimization of GA and final precision improvement of LS, the path of the unmanned aerial vehicle can be rapidly and efficiently planned.

The invention discloses a three-dimensional noise mapping method and system based on point cloud, and belongs to the technical field of urban environmental noise prediction.The method comprises the steps that point cloud data driven three-dimensional modeling and parameter extraction are carried out, an urban three-dimensional model with a geometric topological structure is constructed through laser scanning point cloud data, and related parameters are extracted; based on sound field calculation of ISO 9613 standards, a multi-path propagation model including an atmospheric effect, a ground effect and obstacle diffraction is established, a green infrastructure noise reduction effect is especially considered, and propagation attenuation of noise in a three-dimensional space is simulated; and performing visual expression of three-dimensional noise, and constructing fusion expression of a sound pressure level contour surface and a three-dimensional city model. In view of difficulty in simulation of three-dimensional reconstruction and sound waves in a spatial propagation process in the prior art, the method improves the precision of quantitative prediction, and fuses a green infrastructure noise reduction effect to realize rapid and efficient regional three-dimensional reconstruction and noise mapping.

The invention discloses an unmanned aerial vehicle take-off and landing site selection method for urban end logistics, and relates to the technical field of facility site selection, and the method comprises the following steps: S1, obtaining a building contour of a region to be subjected to site selection, and constructing a customer buffer region according to the building contour; s2, based on the customer buffer area, performing noise analysis and safety analysis on the unmanned aerial vehicle, and constructing an overall restricted area; and S3, based on the overall restricted area, constructing a target function taking the lowest cost as an optimization target, a target function taking the highest satisfaction as an optimization target and a plurality of constraint conditions, and obtaining a site selection layout result. According to the method, the applicability of the grid map method in the complex urban environment is verified, and a new thought is provided for the layout of the urban logistics unmanned aerial vehicle take-off and landing points.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention relates to the technical field of safe flight of unmanned aerial vehicles, in particular to an air-ground multi-mode bird monitoring and early warning method and system for guaranteeing low-altitude safety, and the method comprises the steps: obtaining radar, visual, infrared and other multi-sensor data, and determining a first bird recognition probability, a second bird recognition probability and a third bird recognition probability through the data; and determining a final bird recognition probability based on the basic weight matrix, when the final bird recognition probability is greater than a preset threshold value, obtaining a relative distance and an included angle between the unmanned aerial vehicle and the bird, and establishing a multi-stage early warning mechanism according to the relative distance and the included angle to perform bird early warning. According to the invention, the contradiction between load limitation of small and medium-sized unmanned aerial vehicles and wide-area monitoring requirements is solved, all-weather, all-weather, all-direction, non-blind-area, all-route and real-time bird monitoring, recognition and tracking under a complex urban environment are realized, the real-time monitoring accuracy of birds is improved, early warning is performed in combination with distance and included angle information, and the real-time monitoring accuracy of the birds is improved. The collision between the unmanned aerial vehicle and birds can be more accurately and effectively avoided, and the low-altitude flight safety is guaranteed.

The invention relates to the technical field of aircrafts, and discloses a noise-considered urban low-altitude unmanned aerial vehicle path optimization method, which comprises the steps of urban environment rasterization and environment parameter setting; constructing an unmanned aerial vehicle noise attenuation model; calculating a noise interference critical distance and building sensitivity weight assignment; solving a noise interference grid matrix; a low-altitude unmanned aerial vehicle path planning model; and improving A * algorithm design. According to the method, the urban environment is rasterized, environmental parameters are set, an outdoor noise attenuation model is established by integrating geometric divergence, atmospheric absorption, ground effect and other factors, the propagation law of unmanned aerial vehicle noise in a complex environment is accurately described, the overall noise exposure and the sensitive area noise exposure are remarkably reduced while the flight safety is guaranteed, and the unmanned aerial vehicle noise attenuation performance is improved. The calculation efficiency is improved through the critical distance, path redundancy is avoided, collaborative optimization of noise control and operation efficiency is achieved, and technical support is provided for standardized operation of urban ultra-low-altitude unmanned aerial vehicles.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to a multi-aerial vehicle cooperative path planning method, system and device in a city and a storage medium. Comprising an information acquisition module, a path generation module, a clustering updating module and an optimal judgment module. According to the method, the initial position, the target position and the obstacle information of each unmanned aerial vehicle are acquired, an urban low-altitude airspace task scene is defined, the MP-GWO algorithm is utilized to generate a multi-aerial-vehicle cooperative initial path, differential penalty coefficients are set for different types of no-fly zones, a K-means clustering mechanism is embedded into a GWO main cycle, and the multi-aerial-vehicle cooperative initial path is obtained. According to the method, parallel sub-populations are divided according to a multi-target fitness vector, each sub-population independently executes an alpha, beta and delta guide updating strategy, a triple termination judgment mechanism integrating fixed iteration times, a convergence threshold value and elite stagnation is adopted, a current optimal planning path is output, efficient optimization of the path in a complex urban environment is achieved, and the path optimization efficiency is improved. The method is suitable for urban low-altitude airspace multi-unmanned aerial vehicle cooperative tasks.

The invention discloses a VR-HIL multi-sensor closed-loop test platform for remote driving, and particularly relates to the technical field of remote driving analogue simulation control, and the VR-HIL multi-sensor closed-loop test platform comprises a time calibration module, a scene disturbance module, a fusion judgment module, a response management module and a closed-loop driving module. Through multi-source time alignment, three-dimensional environment disturbance simulation, multi-channel fusion judgment and control instruction dynamic scheduling, high-precision closed-loop interaction among perception, control and disturbance is realized, the reproduction and response capability of a system to positioning abnormity and perception uncertainty in a complex urban environment is enhanced, and the overall authenticity and stability of a test platform are improved; according to the method, high-precision time alignment of multi-source sensing data is realized, the reproduction capability to a city complex interference environment is enhanced, adaptive optimization of fusion precision and stable control response rhythm are realized through a fusion-feedback-strategy linkage mechanism, and the closed-loop reliability and sensing control collaboration of a test platform are improved.

The invention relates to a method for predicting soil displacement caused by double-line shield construction based on number-object fusion. Comprising the steps of establishing a finite element numerical model based on shield parameters and geological data; numerical model parameters are corrected through actual monitoring data, the double-line shield distance, the advancing speed difference and the soil displacement field evolution law are analyzed, and dynamic visual prediction of the displacement field at different construction stages is achieved. According to the method, a two-way feedback mechanism of numerical simulation and engineering actual monitoring data is constructed, a three-dimensional dynamic coupling analysis method is innovatively provided, and the problem of insufficient prediction precision caused by dynamic change of soil parameters and a double-line construction coupling effect in traditional construction is solved. Through iterative optimization of a digital model and measured data, the maximum displacement error of a soil body is further reduced, the stratum standard-exceeding deformation risk is predicted in advance, the quantitative evaluation problem of the stratum disturbance superposition effect in double-line shield construction is effectively solved, and intelligent decision support is provided for tunnel construction safety control in a complex urban environment.

The invention discloses an unmanned aerial vehicle target detection method and system based on a visual detection algorithm. The unmanned aerial vehicle target detection method comprises the steps of generating unmanned aerial vehicle cruise route data containing a GPS coordinate sequence through a path planning algorithm; collecting multi-frame street lamp RGB image data through a visible light camera; meanwhile, single-channel infrared thermal radiation image data of the corresponding space-time position are collected through a carried infrared thermal imaging module; establishing a mapping relation between the RGB image data and the infrared thermal radiation image data to form a multi-modal original data set D; preprocessing the multi-modal original data set D to obtain a standardized multi-modal data set; according to the standardized multi-modal data set, constructing an improved UAV-YOLO model for identifying a street lamp heat source; aiming at the problems that a street lamp heat source presents small-size hot spots (10-30 pixels) in an image and is mixed with other heat sources (an automobile, an air conditioner outdoor unit and the like) in an urban environment and the distinguishing accuracy of a traditional algorithm is low under the overlook angle of an unmanned aerial vehicle, the method improves the detection precision.

The invention provides a modular and multifunctional karst collapse physical simulation experiment platform. Comprising a rigid frame, a bearing device, a moving device, a loading device, a lifting device, a pile foundation construction dynamic load simulation device, a ground traveling dynamic load simulation device, a rainfall simulation device, a computer vision monitoring device, an underground water pumping and draining simulation device and an underground space excavation disturbance simulation device. The moving device is arranged on the bearing device, the lifting device, the loading device and the pile foundation construction dynamic load simulation device are arranged on the moving device, the travelling crane dynamic load simulation device is connected with a traction device of the moving trolley, and the camera shooting monitoring device and the rainfall simulation device are arranged on the rigid frame through a support. According to the method, key disaster-causing conditions in the urban environment are fully considered, the working condition scene coverage is wide, and the applicability is high; modularized design is adopted as a whole, all the modules can be flexibly loaded according to requirements, the real environment can be simulated more truly, and the catastrophe evolution process of surface collapse can be reproduced.

The invention provides an unmanned aerial vehicle flight path dynamic planning method and system, and relates to the technical field of flight path planning, and the method comprises the steps: obtaining the starting and ending points of the flight of an unmanned aerial vehicle, and planning an initial unmanned aerial vehicle flight path based on the starting and ending points of the flight of the unmanned aerial vehicle and a preset constraint; constructing an unmanned aerial vehicle flight environment model based on a preset flight area map, the elevation information and the obstacle information; route optimization is carried out based on the initial unmanned aerial vehicle flight route and a flight environment model, and an unmanned aerial vehicle initial obstacle avoidance route is generated; the unmanned aerial vehicle initial obstacle avoidance route is optimized based on a preset iterative optimization model, and an optimized unmanned aerial vehicle obstacle avoidance route is obtained; and updating the optimized unmanned aerial vehicle obstacle avoidance route to the initial unmanned aerial vehicle flight route to generate a final unmanned aerial vehicle flight route. According to the invention, the flight time and cost are saved, the adaptability to the environment, especially the urban complex environment, is enhanced, and the safety is guaranteed.

The invention belongs to the technical field of laser radar-camera panoramic segmentation, and particularly relates to a multi-mode panoramic segmentation method for multi-view space-time alignment and implicit feature interaction, and the method is executed by a multi-mode panoramic segmentation network, and comprises the steps: S1, obtaining laser radar point cloud data and multi-view camera image data in the same scene; s2, performing double-branch feature coding on the laser radar point cloud data; performing multi-scale image feature extraction on the camera image data; s3, generating false point cloud features with geometric perception capability; s4, generating semantic pixel features; s5, performing implicit fusion on the pseudo point cloud features generated in the S3 and the semantic pixel features generated in the S4 to obtain cross-modal fusion features; and S6, based on the cross-modal fusion features obtained in the S5, generating a unified panoramic segmentation result containing semantic tags and instance IDs. The method can effectively improve the robustness and precision of multi-mode panoramic segmentation in a complex urban environment.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an intelligent logistics unmanned aerial vehicle and system, and the system comprises a space-time synchronization module which is used for obtaining an original data flow of a multi-source sensor on an unmanned aerial vehicle body, and carrying out the space-time synchronization processing of the data flow of the multi-source sensor; the quality evaluation module is used for carrying out quality evaluation on the sensor data subjected to time-space synchronization; the fusion positioning module adopts a multi-sensor fusion positioning method to obtain the high-precision three-dimensional position, attitude and speed state of the unmanned aerial vehicle; the semantic map module is used for constructing a three-dimensional semantic map comprising a static obstacle, a dynamic target and a landing area; the path planning module is used for performing intelligent path planning under a multi-constraint condition; the control execution module performs path execution and real-time adjustment; according to the invention, the technical problems of insufficient unmanned aerial vehicle positioning precision, limited environment perception capability and low path planning intelligence level in a complex urban environment are solved.

The invention relates to the technical field of data processing, in particular to a surface feature rapid classification method based on a remote sensing image, and the method comprises the steps: collecting data in real time; determining a temporary grid; determining grid boundaries and non-classified grids; determining a region of interest; determining an ideal building area; classifying building areas; and adjusting the classification threshold value and the grid side length. According to the method, various data sources such as infrared spectral reflectivity, average brightness and terrain height are fully utilized in the remote sensing image processing process, a processing chain of organic association between data can be constructed, and brightness fluctuation analysis and terrain height data are combined; the dynamic adjustment of the classification threshold value and the grid side length is based on the area change of the classification region and the ideal building region, the self-adaptability of the classification precision in different data environments is ensured, and the problems of low classification precision and slow response speed in the dynamic complex urban environment due to excessive dependence on the construction of the training set and the test set are effectively solved.

The invention provides an intelligent logistics management system based on a large language model, and relates to the field of intelligent logistics management. According to the method, a low-rank adaptation technology general large language model is adopted in advance for fine tuning, and an external LLM service obtained through fine tuning is innovatively and deeply fused into a task decision process, so that deep understanding of user intentions and accurate analysis of complex environment situations can be realized, and an optimal distribution scheme is autonomously generated based on the deep understanding of the user intentions and the accurate analysis of the complex environment situations. Besides, the urban environment management module, the logistics management scheduling module, the simulation engine module and the large language model interface module included in the system do not work independently, but closely cooperate through an event-driven and message-passing mechanism, so that a complete unmanned aerial vehicle logistics management closed loop from environment perception to decision making and execution to event feedback is formed. The architecture can reflect and control the distribution task of the unmanned aerial vehicle in real time, and carries out self-correction on a set scheme, thereby finally remarkably improving the intelligent level and operation efficiency of logistics management.

The invention discloses a multi-unmanned aerial vehicle cooperative task allocation and path planning method based on a genetic algorithm, and relates to an unmanned aerial vehicle path planning method. And operating a Dijkstra algorithm to accurately solve the shortest feasible path between all airports and task points, and finally generating a global path cost matrix for quick query. A complete and safe flight path that each unmanned aerial vehicle starts from an airport, sequentially accesses task points and returns is graphically displayed, a convergence curve of an algorithm, population diversity changes and performance comparison data under different task scales, unmanned aerial vehicle numbers and obstacle densities can be output, and a parameter sensitivity analysis function is supplemented. And the effectiveness, the stability and the practical value of the proposed method in a static urban environment are comprehensively verified.

The invention discloses an unmanned aerial vehicle inspection optimal waypoint discovery method based on three-dimensional space shielding detection, and the method comprises the steps: 1) obtaining the latitude and longitude coordinates and height information of a to-be-inspected point, and loading a triangular patch model of a surrounding building or terrain; 2) generating a plurality of candidate observation waypoints with different heights and orientations above the to-be-inspected point; 3) calculating whether the sight line from the candidate waypoint to the ground inspection point is shielded by a triangular patch or not by using an R tree spatial index and a ray tracing algorithm; and 4) comprehensively considering the sight shielding condition, the observation distance and the observation angle, and selecting a waypoint which meets the non-shielding condition and has the optimal observation effect as an unmanned aerial vehicle inspection position. Compared with the prior art, the method has the advantages of being high in efficiency, low in cost and good in quality, the problem of viewpoint shielding in unmanned aerial vehicle routing inspection path planning under the urban complex environment is effectively solved, and the routing inspection efficiency and the data acquisition quality are improved.

The invention relates to the technical field of environment monitoring, in particular to an urban carbon emission intelligent prediction method based on multi-modal data and machine learning integration, which comprises the following steps: feature acquisition and preprocessing: acquiring multi-source heterogeneous data and processing the multi-source heterogeneous data into a standardized data set; constructing an LSTM (Long Short Term Memory) and XGBoost hybrid prediction model; predicting future data on the basis of existing data, and predicting input features of future time steps of the weight integration prediction model on the basis of historical data in a standardized data set; and predicting carbon emission in the future. Industrial structure indexes, environment variables and social and economic substitution indexes are integrated through time characteristic engineering, and emission driving factors in different urban environments can be comprehensively captured; a recursive prediction mechanism is established, development trajectories of different cities are considered, reliable emission prediction can be carried out, meanwhile, time consistency is kept, and prediction uncertainty is quantified; interference influences under different policies are quantified, and a quantitative basis is provided for carbon management decisions.

The invention relates to an unmanned aerial vehicle body cognition alignment method based on man-machine cooperation. The method comprises the following steps: collecting an entity observation picture, and constructing an entity alignment task by taking the entity observation picture as a navigation graph discrete node; an unmanned aerial vehicle body cognitive alignment model constructed for the task comprises four modules. The problem modeling module converts tasks into POMDP containing elements such as states, actions and observation. The observation module questions a target fact with a self-reflection mechanism based on POMDP, and generates a suspected entity description. And the prediction module adopts zero sample learning, inputs the suspected entity description and the target fact into VLM, and outputs a prediction result. The action module depends on a navigation-questioning mechanism: during navigation, selecting an unexplored entity and getting close to obtain a picture, and controlling the distance through a bounding box proportion; during question asking, explored entity pictures are input into the VLM to generate distinguishing questions, and answers related to target facts are obtained through conversation with human beings. By adopting the method, accurate entity alignment in a complex urban environment can be realized.

The invention discloses a tourist route planning method and system based on artificial intelligence. The method comprises the following steps: step 1, collecting multi-modal input data and constructing a multi-modal data set; step 2, inputting the multi-modal data set into a Perciver IO model for encoding; 3, constructing an original urban environment map; 4, generating a personalized urban environment map based on the intention-environment bidirectional remapping network; 5, constructing a tourism path set; step 6, optimizing the travel path set based on an improved Rhododendron search algorithm, and introducing a heterogeneous reconstruction egg exchange mechanism to realize jumping type evolution updating; and step 7, ending evolution updating when a preset evolution termination condition is met, generating an optimized travel path set, and selecting the travel path with the highest fitness as a current recommended path. According to the method, multi-modal modeling and the improved Rhododendron search algorithm are fused, and efficient planning of the personalized travel route is realized.

The invention relates to the field of city analysis, and discloses a method for analyzing the nonlinear influence of a city environment on city vitality, and the method comprises the steps: carrying out the preprocessing of multi-source geographic big data and multi-source remote sensing data; according to a Deeplab V3 + deep learning semantic segmentation algorithm, predicting the pixel ratio of each category in each streetscape image, and constructing streetscape features based on the pixel ratio; constructing urban vitality evaluation indexes, and aggregating the urban vitality evaluation indexes by using a TOPSIS algorithm to obtain an evaluation result of the urban vitality; constructing an urban environment index according to the preprocessed multi-source remote sensing data; analyzing the spatial distribution difference of the non-linear influence of the urban environment on the urban vitality by using a geographically weighted random forest algorithm; and analyzing the threshold effect of the non-linear influence of the urban environment on the urban vitality by using a Gaussian fitting line algorithm. According to the method, street view features are extracted through a semantic segmentation technology, and comprehensive, scientific and refined quantitative evaluation of urban vitality is realized.

The invention discloses a multi-machine collaborative mobile measurement robot path control method, which belongs to the technical field of path planning, and specifically comprises the following steps: collecting surface height information of a target area, and binding the surface height information with geographic coordinates to generate a simplified three-dimensional model; based on the sun moving trajectory data in the current season, calculating an accumulated time length distribution diagram of receiving direct sunlight by each coordinate point on the ground in the current time period; dividing a high sunlight intensity region and a continuous shadow region according to the cumulative duration distribution diagram, and carrying out topological association on the coordinates of the target point to be measured and the continuous shadow region; establishing a dynamic path distribution protocol in a multi-robot communication network, enabling each robot to select a non-overlapping measurement path in a path network formed by continuous shadow areas according to a real-time position and a task queue, and updating a path occupation state through periodic broadcast; according to the invention, the path planning of the mobile measurement robot in the urban high-temperature environment is optimized.

The invention discloses a non-line-of-sight moving target three-dimensional reconstruction method based on an MIMO millimeter wave radar, and is applied to the technical field of radar target detection in a complex urban environment. Aiming at the problem of difficulty in realizing three-dimensional reconstruction of a non-line-of-sight moving target in the existing radar target detection technology, the method comprises the following steps: firstly, establishing a non-line-of-sight multipath propagation model, and exporting a multipath signal model of an MIMO millimeter wave radar; the invention further provides a multipath signal departure angle (DoD) / arrival angle (DoA) unambiguous estimation algorithm, and solves the problem that a main lobe and a grating lobe are difficult to distinguish in the DoD / DoA estimation process of an existing commercial MIMO radar. And finally, proposing a path-oriented non-line-of-sight target three-dimensional positioning algorithm, respectively positioning the non-line-of-sight target by using different paths, and fusing different path positioning results to form a three-dimensional point cloud. According to the method, three-dimensional reconstruction of a plurality of non-line-of-sight moving targets can be realized, and the correlation problem between paths does not need to be considered.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses a low-altitude unmanned aerial vehicle logistics intelligent path planning and scheduling method, which comprises the following steps: acquiring urban environment space data, then establishing an unmanned aerial vehicle dynamics model, satisfying a given physical constraint condition, constructing a path cost function, and establishing a path cost function based on an optimal control theory. And performing time-space discrete processing on the trajectory optimization problem, and solving by adopting a numerical method to obtain an optimal state trajectory and control input, thereby obtaining a discrete trajectory, generating a continuous control instruction sequence for the unmanned aerial vehicle flight control system, obtaining a control trajectory, and taking the control trajectory as a task scheduling basis. And distributing logistics tasks among the plurality of unmanned aerial vehicles according to a preset priority strategy. A dual-stage task scheduling structure is introduced, an adaptive unmanned aerial vehicle set is screened firstly, and then path cost minimum matching is executed, so that effective compression of a scheduling solution space and remarkable improvement of matching efficiency are realized, and a scheduling effect still having high real-time performance and high feasibility in a multi-task concurrent scene is obtained.

The invention provides an unmanned aerial vehicle communication intensity calculation method and system based on data driving, and belongs to the technical field of low-altitude traffic network communication, and the method comprises the steps: processing obtained communication parameter data through employing a pre-trained graph neural network model, and achieving the prediction of the signal intensity of each grid; wherein the graph neural network model is composed of a plurality of graph attention network modules, a full connection layer and an output layer. According to the method, characteristic parameters related to urban environment and signal attenuation serve as input, signal intensity values serve as output, a graph neural network model is trained, and a high-precision and high-efficiency low-altitude communication signal attenuation estimation model is established. The model can effectively estimate the low-altitude communication signal intensity in different urban scenes, and the estimation result can provide a scientific basis for communication quality partition and system optimization of an urban environment.

The invention discloses an unmanned aerial vehicle route automatic planning system based on AI identification, and relates to the technical field of unmanned aerial vehicle route planning and obstacle avoidance. The unmanned aerial vehicle route automatic planning system based on neural network identification processes camera and laser radar data in real time through a lightweight convolutional neural network of an environment sensing module; accurate identification and classification of dynamic obstacles are realized, the perception ability in a dense city environment is effectively improved, and the risk of obstacle avoidance failure caused by sensor data updating delay is reduced. The fusion and tracking module adopts a space-time alignment and multi-source data fusion technology to generate uniform occupation representation and motion trail, so that the system can adapt to sudden obstacle change, the dependence on a preloaded map is reduced, and the navigation reliability in an unknown or dynamic scene is enhanced; the path planning module integrates a reinforcement learning algorithm, takes a dynamic obstacle state as input, and optimizes path generation through a multi-target reward function.