648 results about "Urban environment" patented technology

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.

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.

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 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 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 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 discloses a multi-target anti-interference detection system based on a holographic staring radar, relates to the technical field of holographic staring radars, and constructs a technical architecture consisting of an array calibration module, a beam forming and optimizing module, a self-adaptive filtering module and a self-adaptive control module. Interference between beams is effectively suppressed by adopting a minimum sidelobe beam forming algorithm, dynamic suppression of near-angle strong interference is realized by combining spatial domain-time domain combined filtering, and beam parameters are adaptively optimized by utilizing fusion analysis and a feedback mechanism, so that the multi-target detection precision and the anti-interference capability of the system in a complex urban environment are remarkably improved, and the system has a wide application prospect. The stability and the practicability are relatively high.

The invention discloses an urban environment monitoring regulation and control system and method based on global greening data. The system comprises an image acquisition module, a monitoring module, an image analysis module, a sampling screening module, a global speculation module and a strategy generation module. The image acquisition module periodically acquires greening image data; the monitoring module collects vegetation monitoring data through a sensor group; the image analysis module extracts vegetation coverage areas and classifies the areas according to texture feature parameters (leaf area index, canopy height and texture uniformity); the sampling and screening module calculates a standard value of each unit area and screens and optimizes sampling areas; the global speculation module is combined with the sampling data to calculate monitoring data of an uncovered area; and the strategy generation module generates a targeted microclimate regulation and control strategy according to the monitoring data and the classification result. According to the invention, image analysis and sensor data are combined, precise monitoring and intelligent regulation and control of vegetation distribution are realized, and the ecological function and microclimate regulation and control effect of an urban greening area are improved.

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 relates to the technical field of urban environmental pollution, and particularly discloses a method for establishing a refined attribution model of urban atmospheric environmental pollution. The method comprises the following steps: S1, collecting and preprocessing multi-source data, analyzing a spatial and temporal change rule of air quality, and defining change characteristics of human activity intensity; s2, researching an influence factor parameterization method of the urban air quality, performing optimal variable screening, and selecting an optimal variable according to a contribution rate and incorporating the optimal variable into a model; s3, performing repeated sampling simulation on the original observation data to solve an average regression coefficient and a root-mean-square error of a simulation result so as to evaluate the uncertainty of model fitting, and comparing the difference between a predicted value and the concentration of the urban air quality observation station to perform precision verification; and S4, quantitatively analyzing the cause of urban air quality pollution according to the obtained quantitative attribution model. According to the technical scheme, the difference of urban environmental pollution levels can be quickly clarified, and a supporting effect on early warning and intervention of heavy pollution events is achieved.

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.