800 results about "Urban environment" patented technology

The invention provides a multi-level low-altitude air route network construction method in a complex urban environment, and relates to the technical field of low-altitude air route construction, and the method comprises the steps: building a low-altitude flight rule base; performing three-dimensional discretization on the urban space, and dividing the airspace into three-dimensional grids; designing a risk assessment function R; dividing a plurality of height layers, and distributing the height layers for different types of aircrafts; constructing a multi-stage air route network; constructing an airspace three-dimensional airway model; based on the airspace three-dimensional air route model, adopting a hybrid iterative algorithm to optimize the air route planning of each height layer; performing path smoothing on the discrete path points generated by the hybrid iterative algorithm by adopting a B spline curve, and performing flight performance verification on the smoothed path to obtain a continuous flight path; and generating a low-altitude air route network according to the continuous flight path. According to the method, the defects in the aspects of complex urban environment processing, multi-source data integration, multi-level airspace division, path optimization and the like in the prior art can be overcome, so that safe, efficient and flexible low-altitude flight management is realized.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The present disclosure relates to a system to proactively detect in real time one or more threats in crowded areas. The present disclosure presents a proactive system for real-time threat detection in crowded areas. Utilizing a network of imaging sensors and advanced machine learning algorithms, the system identifies suspicious individuals, objects, and behavioral patterns within a predefined area. The system detects potential threats such as individuals on watch lists, suspicious objects like unattended bags, and abnormal behaviors indicative of security risks, by continuously monitoring and analyzing images and video feeds. Upon detection, the system promptly notifies authorities, providing detailed information on threat location, suspected individuals, and behavioral analysis. Privacy-preserving measures, including encryption of facial recognition data, ensure compliance with privacy regulations. The present disclosure offers a scalable, efficient, and automated solution to enhance security measures, reduce response times, and safeguard public safety in dynamic urban environments.

The invention discloses a lineation robot control system based on a satellite positioning and orientation technology, which relates to the technical field of robot motion control and comprises a satellite positioning and orientation module, an inertial navigation module, a fusion positioning and orientation software module, a motor control software module, a motion control software module and a pattern planning software module. The satellite positioning and orientation module is used for acquiring latitude and longitude coordinates of the current position of the robot by receiving multi-frequency satellite signals; the inertial navigation module is used for collecting posture and motion data of the robot in real time through a gyroscope and an accelerometer. According to the lineation robot control system provided by the invention, through collaborative design of the multi-frequency-point GNSS receiver and the anti-multipath interference antenna array, centimeter-level continuous positioning capability in a complex urban environment is realized, and satellite signal reflection and shielding interference in scenes such as viaducts and avenues are effectively overcome; and satellite positioning and inertial navigation data are deeply fused through a tight coupling Kalman filtering algorithm.

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 relates to the field of environmental noise monitoring, and discloses a noise monitoring point position automatic determination method fused with a noise map. The method comprises the following steps: acquiring a base map and sound source data of a target area, layering the base map according to a preset interval in a vertical direction, and carrying out gridding processing to obtain a three-dimensional grid; performing spatial analysis on the sound source data to obtain a sound source intensity matrix, and mapping the sound source intensity matrix to a three-dimensional grid to obtain a three-dimensional noise map; the base map comprises building structure data; performing three-dimensional path exploration on the three-dimensional noise map based on an ant colony algorithm to obtain a noise hotspot path and a pheromone concentration distribution diagram; and carrying out site selection analysis based on a genetic algorithm on the noise hotspot path and the pheromone concentration distribution diagram to obtain a Pareto optimal monitoring site set, the Pareto optimal monitoring site set being used for indicating site selection of the noise automatic monitoring site. By adopting the method, accurate and efficient automatic noise monitoring site selection in a complex urban environment can be realized, and the urban noise monitoring and treatment level is improved.

The invention belongs to the technical field of communication, and discloses a detection and identification method and system based on a radio frequency signal of an unmanned aerial vehicle, and the method considers how to utilize zero-intermediate-frequency radio frequency signal collection, short-time Fourier transform and an improved deep learning model in a complex urban electromagnetic interference environment, reduces the detection delay as much as possible, and improves the detection accuracy. And high-accuracy identification of the low-slow small unmanned aerial vehicle is maintained, and the method has a rapid expansion capability for a new target type. Through the detection and identification method provided by the invention, high-precision detection and classification of common civil unmanned aerial vehicles and potential novel unmanned aerial vehicles can still be realized under the conditions of urban environment multi-source interference, complex noise and variable flight heights of low-altitude small targets.

The invention belongs to the technical field of computer vision and natural language processing, and discloses a small sample city scene image analysis method based on BERT and self-supervised learning. Potential information of unlabeled urban environment image data is fully mined in combination with self-supervised learning, and multi-modal features of urban environment images and description texts are integrated by using a cross-modal semantic enhancement mechanism, so that accurate diagnosis of small sample urban environment images is realized. According to the method, the generalization ability of the model in a small sample scene is improved, the diagnosis efficiency and accuracy of an existing method in a complex analysis scene are remarkably improved, and the defects that in the prior art, data dependence is high, the requirement for hardware resources is high, and cross-modal information utilization is insufficient are overcome.

The invention discloses a multi-model coupling water quality optimization method, system and equipment based on digital twinning, and belongs to the technical field of urban water pollution simulation. An existing water quality optimization scheme is mainly applied to a single system and lacks comprehensive consideration of a multi-system synergistic effect in a complex urban environment, and pollutant diffusion prediction and layout scheme optimization cannot be carried out on an urban underground pipe network. According to the multi-model coupling water quality optimization method based on digital twinning, sudden pollution diffusion information is obtained by simulating a hydrological process and carrying out sudden pollution diffusion simulation calculation; meanwhile, based on a digital twinning mechanism and sudden pollution diffusion information, a hydraulic structure operation optimization scheme is obtained, so that full-process simulation and optimization scheduling can be performed on urban sudden pollution diffusion, timely control over the sudden pollution diffusion is realized, manual intervention and hysteresis of decision can be greatly reduced, and the working efficiency is improved. The scheduling response time is obviously shortened, and the emergency processing efficiency is improved.

The embodiment of the invention relates to the field of natural disaster risk prediction, and discloses a green land optimization method based on an urban inland inundation risk assessment model, parameters in a maximum entropy model are optimized, and the maximum entropy model after parameter optimization is used as the urban inland inundation risk assessment model, so that model parameters suitable for a city can be optimized; compared with a default parameter method adopted in the prior art, the urban inland inundation risk assessment method has the advantage that the assessment precision can be effectively improved. Meanwhile, according to the method, the obtained urban environment variable test data is pre-evaluated to obtain the urban environment variable evaluation data, so that the operation precision and evaluation accuracy of the urban waterlogging risk model can be improved. In addition, according to the urban inland inundation risk probability distribution diagram and the urban supplementary greenbelt rate output by the urban inland inundation risk assessment model, the greenbelt optimization scheme is determined, and the urban inland inundation risk probability can be effectively reduced.

A network of Intermediate Device Structure (IDS) members communicatively coupled, mounted to elevated structure / s within urban settings, each configured to operate by a processor / controller with resident code, and in real time receive sensed environmental inputs from each IDS' vicinity, remote data / instruction / s processing the inputs with resident code parameters generating outputs that are configured to mitigate / avert intermittent environmental events harmful / hazardous to humans. The IDS's include a launch and charging pad that hosts a UAV, which patrols an area within communication range of at least one of the IDSs so the UAV, perhaps with AI-engine assistance, can detect events in the area patrolled by the UAV.

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 relates to the technical field of smart city management, and particularly discloses a smart city classification and identification management platform and method based on Internet of Things perception. And an advanced image processing and deep learning technology is utilized to analyze the garbage surface state image so as to automatically identify and classify the garbage. Finally, by comparing the garbage type label with the garbage can type label, the system can intelligently judge whether garbage classification errors occur or not and send out a prompt signal in time, and therefore urban managers and citizens are helped to jointly maintain a good urban environment. Thus, the intelligent level of the garbage classification and recovery process can be improved, garbage classification and recovery management work is promoted, and the management requirement of a smart city and the city construction requirement are met.

The invention discloses an urban low-altitude wind field monitoring method and system, and relates to the field of urban environment forecasting, and the method comprises the steps: building a four-dimensional variation assimilation system based on a four-dimensional variation target extensive function formula by taking a wind field state variable of a target urban region as a state variable in the four-dimensional variation target extensive function formula; inputting the wind field observation data of each observation point in the target city area into a four-dimensional variational assimilation system for initial field assimilation to obtain assimilated wind field data; taking the assimilated wind field data as initial meteorological field data, performing wind field simulation calculation by using a preset WRF-LES mode, and obtaining wind field forecast data in the target urban area; real-time monitoring data of each observation point in a target city area is assimilated into an initial and boundary meteorological condition input field of a preset WRF-LES mode through a four-dimensional variational assimilation system, so that wind field information in the target city area is reflected, and city low-altitude wind field forecast data can be acquired in near real time with high resolution.

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 discloses an urban garbage cleaning path dynamic adjustment method and device, equipment and a medium, and relates to the technical field of artificial intelligence, historical garbage collection data, urban activity data, seasonal change data and weather condition data in a city are acquired and preprocessed; establishing a dynamic garbage generation quantity prediction model based on a long short-term memory network, and training and testing the dynamic garbage generation quantity prediction model by using the processed data; inputting the to-be-predicted urban activity data and the meteorological data into the target dynamic garbage output prediction model to output a garbage output prediction value; an urban garbage cleaning path is planned based on a garbage output predicted value by using an ant algorithm and a local search strategy, and the urban garbage cleaning path is dynamically adjusted and optimized based on real-time garbage output and traffic condition data, so that the cleaning path of the garbage cleaning vehicle is dynamically adjusted. The resource allocation flexibility and adaptability and the garbage treatment efficiency are improved, and the urban environmental sanitation level is improved.

The invention discloses a factor graph RTK positioning method based on ambiguity detection, recognition and repair, which comprises the following steps: detecting an unreliable solution by using an ambiguity domain and a state domain, recognizing possible cycle slip through forward and backward repair of window information, and finally adding a cycle slip repair factor to adjust a factor graph so as to ensure reliable and correct fixation of ambiguity. And therefore, the RTK positioning robustness in a complex environment is improved. In a complex urban environment, the method can basically realize continuous position estimation with centimeter-level precision. Besides, when the available satellites are few, the ambiguity fixing and positioning effects of the method are far better than those of the existing method, which shows that the method has better robust robust capability, and the RTK positioning capability in a complex environment is effectively improved.

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 discloses a low-slow-small unmanned aerial vehicle detection method based on acoustic fusion features, relates to an unmanned aerial vehicle detection method, and belongs to the field of signal processing technologies and deep learning. The objective of the invention is to solve the problem that the traditional unmanned aerial vehicle detection method mainly depends on radar, vision, infrared and other technologies, but the detection accuracy of a low-speed small unmanned aerial vehicle is low in a complex urban environment. The method comprises the following steps: establishing an unmanned aerial vehicle audio signal data set in an urban environment; constructing an unmanned aerial vehicle detection network model; taking the unmanned aerial vehicle audio signal in the urban environment as the input of the unmanned aerial vehicle detection network model, and taking the detection result as the output of the unmanned aerial vehicle detection network model; the detection result is whether the unmanned aerial vehicle exists; obtaining a trained unmanned aerial vehicle detection network model; and inputting an audio signal in a to-be-detected urban environment into the trained unmanned aerial vehicle detection network model, and outputting a detection result by the trained unmanned aerial vehicle detection network model.

The invention discloses a Beidou inertial navigation fusion positioning method and system for adaptive intelligent parameter adjustment, and the method comprises the steps: constructing an adaptive denoising model based on a state estimator and an actor-criticer network; training the adaptive denoising model, and inputting actual data into the trained adaptive denoising model to obtain a noise parameter correction value; performing adaptive adjustment on a noise covariance matrix of a Kalman filtering fusion positioning model according to the noise parameter correction value; and obtaining accurate position information according to the fusion positioning model after adaptive denoising. The system comprises a denoising model building module, a parameter learning module, a parameter adjusting module and a fusion positioning module. By using the method, dynamic noise which cannot be modeled in fusion positioning and self-adaptive adjustment parameters can be effectively removed, and the adaptability of a positioning system to a complex and dynamically changed urban environment is improved. The method can be widely applied to the field of satellite navigation.

The invention discloses a noise exposure evaluation method and system considering an unmanned aerial vehicle, and the method comprises the steps: obtaining urban environment data and unmanned aerial vehicle related data, carrying out the sound source modeling of the noise of the unmanned aerial vehicle, simulating the propagation of the noise of the unmanned aerial vehicle in an urban environment, and generating an unmanned aerial vehicle noise map; combining and calculating the unmanned aerial vehicle noise map and the current city noise map in combination with the current city noise map to obtain a city noise map; identifying crowd distribution conditions in different time periods and mapping the crowd distribution conditions to geographic space to form crowd distribution maps in different time periods, and performing feature identification and analysis on crowds in combination with multi-source data to obtain a crowd distribution map with crowd feature information; and according to the urban noise map and the crowd distribution map, calculating the total amount of regional noise pollution and the per capita noise pollution index, and calculating the noise exposure levels of different crowds. According to the method, the noise exposure evaluation method is perfected, and the space-time accuracy of unmanned aerial vehicle noise exposure evaluation is improved.

The invention discloses a high-definition map generation method and system based on point cloud and multi-objective optimization, and the method comprises the steps: obtaining sensor data, carrying out the data preprocessing of the sensor data, so as to generate a 3D point cloud image, and carrying out the semantic tag recognition, semantic segmentation and probabilistic reasoning, so as to obtain a semantic tag; the method comprises the following steps: optimizing a 3D point cloud image by using a probabilistic algorithm, carrying out semantic mapping to obtain topological information corresponding to the 3D point cloud image, fusing a semantic tag and the topological information to generate a high-definition map, and planning a safe and efficient path for a vehicle according to the generated high-definition map to complete the generation of the high-definition map. A mobile L i DAR system and probability-based multi-objective optimization are combined, a high-definition map is generated in a complex urban environment, a highly accurate and dense map is realized, key factors such as autonomous ability, rapid data acquisition, minimum error and dense point cloud are considered, and the accuracy of the map is improved. The integration of the multi-objective random model improves the reliability of map generation by incorporating various risk metrics.