1016results about "Picture taking arrangements" patented technology

The invention discloses an integrated Beidou monitoring machine system integrating panoramic photographing and machine vision deformation monitoring, and relates to the technical field of safety monitoring. The system comprises a hardware structure unit and a software function unit, the hardware structure unit comprises a Beidou positioning module, an inertial navigation compensation module, a panoramic vision acquisition module, a machine vision processing module, a multi-mode communication transmission module and a power management module; the software function unit comprises a multi-source data fusion engine, a panoramic three-dimensional modeling sub-module, an intelligent early warning decision module and a cloud collaborative resolving interface. According to the invention, through real-time fusion of Beidou positioning data and panoramic vision imaging, an improved dense optical flow method is adopted to track local crack propagation, and global displacement monitoring of Beidou is combined, so that cross-scale synchronous sensing from macroscopic displacement to microscopic deformation is realized, and the comprehensive monitoring precision is improved.

The invention provides a geographic information visualization intelligent analysis system based on unmanned aerial vehicle surveying and mapping, and belongs to the field of surveying and mapping, and the system comprises an unmanned aerial vehicle aerial survey module which carries out regional aerial survey based on a multi-source sensor carried by an unmanned aerial vehicle, obtains multi-modal geographic data, and carries out the preprocessing of the multi-modal geographic data; the data processing module is used for forming an initial geographic data set under a standard coordinate system; the three-dimensional modeling module is used for obtaining the fused geographic feature data, constructing a three-dimensional geographic information model, optimizing the three-dimensional geographic information model and embedding time dimension information to form a four-dimensional spatio-temporal data set; the analysis and decision module is used for generating an analysis report comprising a terrain evolution trend and disaster risk assessment; and the visual platform module is used for visualizing the analysis report through a preset visual interaction interface and adjusting the rendering precision of the optimized three-dimensional geographic information model in real time according to the change of the visual angle of the user. The system provides an efficient and intelligent solution for geographic surveying and mapping and disaster early warning.

The invention provides a three-dimensional modeling processing method based on unmanned aerial vehicle oblique photography. The method is applied to the technical field of three-dimensional modeling, and comprises the following steps: obtaining a serialized image set containing geographical coordinate information according to original image data collected by an oblique photography camera carried by a multi-rotor unmanned aerial vehicle; according to time-space synchronization parameters of the serialized image set, determining a multi-view image matching relation matrix with an overlapping degree quantitative index; determining mixed three-dimensional point cloud data fusing sparse point cloud and dense point cloud according to geometric constraint conditions of the multi-view image matching relation matrix; determining an initial three-dimensional grid model with multi-level details according to the topological connection relationship of the mixed three-dimensional point cloud data; and determining an optimized three-dimensional model based on adaptive texture mapping according to the surface curvature distribution characteristics of the initial three-dimensional mesh model. In this way, the efficiency of three-dimensional modeling can be improved.

The invention discloses an unmanned aerial vehicle navigation reasoning method and system based on visual perception and a large language model, and belongs to the field of unmanned aerial vehicle navigation and artificial intelligence, and the method comprises the steps: collecting an image of a target area through a camera carried by an unmanned aerial vehicle; pixel-level perception is carried out on the image through a pre-trained visual perception model, and visual information is converted into structured data; converting the structured data into key statements which can be processed by a large language model by adopting retrieval enhancement generation and combining with a predefined knowledge base; adopting a prompt project and thinking chain technology, and outputting a navigation instruction by the large language model according to the key statement and the current task; when a target of an unknown type is encountered, an active learning process is started, and real-time updating of an edge end knowledge base is realized through human intervention; according to the method, the problems of understanding and decision making of the unmanned aerial vehicle on a dynamic scene in a complex environment are effectively solved, data-driven intelligent support is provided for autonomous flight of the unmanned aerial vehicle, and the method has the advantages of being high in real-time performance, high in environmental adaptability and good in decision making reliability.

The invention discloses a robot dynamic environment adaptive sensing and navigation system based on a three-dimensional laser radar, relates to the technical field of robots, and solves the technical problems that comprehensive environment information is difficult to obtain, and the weight is difficult to adjust by fusing weather types and sensor confidence coefficients. Comprising the following steps: generating original point cloud data by combining a bionic compound eye type laser radar with a silicon photon integrated chip; marking point clouds based on a KITTI data set, performing preprocessing, training a Transform model to output a dynamic obstacle mask, and filtering background point clouds; a weather detection model is constructed, the weight is dynamically adjusted according to the weather type and the sensor confidence coefficient, and position and attitude estimation is fused; laser radar point cloud constructs a geometric map, a camera depth map generates a dense map, and semantic tags are mapped to generate an environmental semantic map; and converting the environmental semantic map into a three-dimensional grid map, and planning an obstacle avoidance path based on the grid map by using an A * algorithm.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention provides an intelligent barn automatic inspection system and method, and relates to the technical field of intelligent inspection, the system comprises an inspection robot, a 5G communication transmission module and a remote computer, the inspection robot comprises an environment sensing module for generating and updating an environment map and identifying obstacle information; the autonomous navigation control module comprises a path planning unit for generating an inspection path and performing local path adjustment; the obstacle avoidance unit is used for generating an obstacle avoidance strategy; the motion control unit controls the movement of the inspection robot; the data acquisition module is used for acquiring information in the granary; the data processing and analyzing module is used for processing and analyzing the collected multi-source data and outputting a monitoring report and early warning information; wherein the 5G communication transmission module is connected with the inspection robot and the remote computer, and is used for transmitting data output by each module in the inspection robot and receiving an instruction sent by the remote computer. According to the invention, real-time sensing, intelligent obstacle avoidance and autonomous inspection of the granary environment are realized.

The embodiment of the invention provides a multi-unmanned aerial vehicle cooperative surveying and mapping method and system for areas along a railway, and belongs to the technical field of railway surveying and mapping. The method comprises the steps that the unmanned aerial vehicle collects monitoring data in the flight process; the relative position and posture between the aircrafts are dynamically adjusted, and an observation configuration with variable baseline parameters is constructed; laser ranging modules carried by the unmanned aerial vehicles are controlled to execute synchronous ranging operation, and multiple sets of laser point cloud data covering the railway structure area are generated; performing coordinate registration and fusion on the collected laser point cloud data, and combining position information and attitude information of each unmanned aerial vehicle; and extracting geometric dimension parameters of the railway structure from the compensated three-dimensional model, and outputting the geometric dimension parameters as measurement results. According to the scheme, high-precision non-contact measurement of geometric dimension parameters such as the track gauge, the height difference and the roll angle of a railway track structure is achieved.

The invention relates to the technical field of geological disaster detection, in particular to a geological disaster hidden danger point detection method and system. Comprising the following steps: S1, synchronously acquiring vegetation canopy penetration data, multispectral soil humidity spatial distribution data and lithology distribution data by adopting an unmanned aerial vehicle carried image data acquisition device and a multi-band laser radar, and generating an orthoimage and a three-dimensional point cloud data set; s2, fusing the orthoimage and the three-dimensional point cloud data through an aerial triangulation encryption algorithm to establish a three-dimensional model, loading the multispectral soil humidity spatial distribution data into the three-dimensional model to generate a dynamic digital elevation model, and generating a contour line topological structure through a TIN triangulation network algorithm; s3, extracting a slope factor, topographic relief, vegetation coverage, fracture distance and a lithologic factor based on the three-dimensional model to construct a geological disaster hidden danger system; according to the system, the geological disaster occurrence time can be estimated, and the rescue and protection treatment period can be effectively planned based on the geological disaster occurrence time.

The invention relates to the technical field of robot navigation and artificial intelligence, in particular to an autonomous navigation robot system based on multi-modal perception, which comprises a visual perception module, a laser radar module, a voice instruction module, a central processing unit and an execution control unit, the visual perception module is used for collecting an environment RGB-D image; the laser radar module is used for acquiring an environment distance point cloud; the voice instruction module is used for receiving and analyzing a user voice instruction; the central processing unit generates a navigation path and an obstacle avoidance strategy through a multi-modal data fusion algorithm and a path planning algorithm; the execution control unit drives the robot chassis to move according to the navigation path and the obstacle avoidance strategy.

The invention relates to the technical field of pavement disease automatic detection, and particularly discloses a pavement disease automatic detection method and system based on deep learning, and the system comprises a municipal pavement data collection module, a pavement image quality analysis module, a pavement image re-collection module, a pavement disease evaluation feedback module and a database. Flight parameters (speed and height) are dynamically adjusted through an environmental quality index (EQI), the image acquisition quality is ensured, the detection precision in a complex environment is improved, a multi-disease coupling risk model is constructed, region weight and traffic volume dynamic weight are integrated, a disease risk level is output, and finally a disease development trend is predicted through a one-time exponential smoothing method. The system achieves the dual functions of current situation evaluation and trend early warning, predicts the disease evolution trend based on historical data, remarkably improves the scientificity and timeliness of municipal road risk management, reduces the maintenance cost, and prevents safety accidents.

The invention discloses a substation unmanned aerial vehicle inspection dynamic route planning method and device. The method comprises the steps of collecting and constructing a substation 3DGS model, optimizing details, dividing areas, recognizing equipment parts, inversely calculating waypoint parameters, planning waypoints of a safety channel and a flight channel, generating a basic model data set, and dynamically generating an inspection route in combination with tasks. The device comprises a model construction module, a region division module and the like. On the basis of a 3DGS model and equipment part identification, through a multi-level waypoint system and a dynamic planning algorithm, full-process automation from model construction to route generation is achieved, it is ensured that unmanned aerial vehicle routing inspection is safe and efficient, equipment updating and task changing are adapted, the problems that traditional two-dimensional planning is insufficient in precision, and static route flexibility is poor are solved, and the unmanned aerial vehicle routing inspection efficiency is improved. And the automation and intelligence level of substation inspection is improved.

The invention belongs to the technical field of hydraulic engineering measurement, and discloses a seawall measurement method and system based on multi-source data fusion, and the method comprises the steps: collecting geological data and hydrological data through a data collection module; the route planning module formulates a route; the overwater data acquisition equipment and the underwater data acquisition equipment are used for monitoring; the auxiliary equipment provides a high-precision positioning reference; the meteorological data module obtains meteorological data; the data processing module processes the data; the three-dimensional modeling module carries out point cloud registration and DEM model construction; the comprehensive analysis module performs comprehensive evaluation and predicts potential problems and risks; when an abnormal condition or a potential risk is monitored, the early warning module gives an alarm. Multi-source data acquisition equipment such as unmanned aerial vehicle aerial survey, a laser radar, a multi-beam depth finder, a side-scan sonar, a shallow profiler and a micro-motion exploration instrument are integrated, seawall overwater and underwater full-dimensional data acquisition, processing, modeling and volume calculation are realized, and high precision and reliability of measurement results are ensured.

The invention provides a prefabricated bridge pier column installation quality automatic evaluation method based on a three-dimensional laser point cloud. The prefabricated bridge pier column installation quality automatic evaluation method is used for measuring the perpendicularity of a high pier and the distance between pier columns. The method comprises the following steps: an unmanned aerial vehicle laser scanning system collects pier point cloud data according to an optimized flight path, and pre-processes the pier point cloud data to improve data quality; quickly segmenting the point cloud by using an improved adaptive octree and a machine learning algorithm, and extracting a pier point cloud; and based on the finely segmented point cloud, calculating the included angle between the center axis of the bridge pier and the plane normal of the capping beam and the coordinate of the center point of the top surface of the bridge pier to realize accurate evaluation of the perpendicularity of the bridge pier and the distance between adjacent bridge piers. The optimization of the flight path of the unmanned aerial vehicle is combined with the point cloud data preprocessing, so that the data quality can be effectively improved, and the data volume is reduced. Meanwhile, point cloud segmentation is performed by fusing an improved adaptive octree and a machine learning algorithm, so that piers, capping beams and backgrounds can be accurately distinguished, and geometric features of pier studs are completely extracted.

The invention discloses an unmanned aerial vehicle real-time surveying and mapping data fusion method and system based on multi-modal edge calculation, and the method comprises the steps: carrying out the modeling based on the physical characteristics of a sensor, and constructing a multi-source sensor dynamic time synchronization model; dynamically calibrating a space coordinate system of the multi-source sensor through a space registration algorithm under kinematics constraint on the basis of edge computing equipment; based on a multi-modal data fusion algorithm of adaptive weight distribution, obtaining a sensor confidence coefficient and an environment characteristic dynamic adjustment fusion weight; real-time three-dimensional reconstruction is carried out based on an edge computing device, and feature level fusion of point cloud and image data is completed at an unmanned aerial vehicle end; and constructing a distributed edge caching mechanism, and performing data life cycle management based on an LRU algorithm. The method has the advantages that the dynamic synchronization model of the sensor is innovatively constructed, the real-time three-dimensional reconstruction architecture is based on edge calculation, and the space-time consistency of surveying and mapping data in a complex environment is remarkably improved.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention relates to the technical field of unmanned aerial vehicle intelligent maintenance and cleaning, in particular to an unmanned aerial vehicle-based main road camera non-closure autonomous cleaning system and method, and the system executes the following steps: an unmanned aerial vehicle hovers at the outer side of a camera, collects millimeter wave-polarization-acoustic signals to generate stain spectrum fingerprints, and obtains cleaning parameters through optimization; and the model prediction controller sequentially executes plasma, acoustic cavitation and ion air curtain dry cleaning under energy constraint, self-adjusting parameter iteration is carried out when residues exceed a threshold value, and return flight and battery replacement are carried out after the standard is reached. And the cleaning progress, the residual index and the energy consumption are packaged and chained, the cloud self-learning model updates online and outputs a next order of instruction, and non-stop, low-consumption and efficient autonomous cleaning is realized.

A method for surveying an environment by a movable surveying instrument with a progressional capturing of 2D-images by at least one camera and applying a visual simultaneous location and mapping algorithm (VSLAM) or a visual inertial simultaneous location and mapping algorithm (VISLAM) with a progressional deriving of a sparse evolving point cloud of at least part of the environment, and a progressional deriving of a trajectory of movement. The method comprises a progressional matching of the sparse evolving point cloud with a previously derived 3D-geometry, with a minimizing of a function configured to model a distance between the sparse point cloud and the previously derived 3D-geometry and deriving a spatial localization and orientation of the surveying instrument. At least one surveying measurement value of the environment by a spatial measurement unit is combined with the sparse point cloud or the previously derived 3D-geometry.

The invention relates to the technical field of vehicle control, and discloses a vehicle positioning and control method and system based on machine vision, and the method comprises the steps: building a vehicle kinematics prediction model, and generating a reference pose sequence according to a driving instruction through the vehicle kinematics prediction model; recording a first reference pose during image acquisition; calculating a deviation vector between a positioning result and the first reference pose when positioning calculation is completed; determining a dynamic damping coefficient according to a direction included angle between the velocity vector and the deviation vector, generating a compensation gain sequence, and converting the compensation gain sequence into a compensation torque of a current loop given value; according to the method, the effective decoupling of the sensing time delay of the sensor and the dynamic response of the carrier is realized, the phase lag is reduced on the premise of ensuring the momentum continuity of the controlled object, the instantaneous control oscillation of positioning correction is eliminated, and the mechanical stress impact of an execution mechanism is inhibited.

The invention provides a high-precision real-time multi-channel cooperative control method and system. The method comprises the following steps: establishing a conversion relation among a geodetic coordinate system, a geocentric coordinate system and a station center coordinate system; each device solves the observation data of the target into the global ECEF coordinate of the target through the conversion relation according to the geographic coordinate of the device; fusion of observation data of multiple cameras (integrated in a turntable cabin) is realized by adopting an iterative weighted least square method, and the optimal estimation of the initial position of a target is obtained; and taking the fused position as an observed quantity, inputting the observed quantity into a Kalman filter constructed based on a uniform-speed motion model, dynamically adjusting an observation noise covariance and a process noise covariance, and predicting and tracking a target motion state, thereby realizing multi-camera cooperative control.

The invention discloses a settlement monitoring system based on computer vision, and relates to the technical field of infrastructure monitoring, the settlement monitoring system comprises a settlement monitoring platform, the settlement monitoring platform is in communication connection with an image acquisition module, a sensor network module, a data fusion analysis module, a settlement abnormity identification module and a settlement early warning module, the modules are in electric signal connection; and the image acquisition module is used for monitoring a foundation area of the infrastructure by using a plurality of cameras deployed in a monitoring area to obtain image data of the infrastructure. Through combination of the image acquisition module and the sensor network module, comprehensive coverage and accurate monitoring of an infrastructure foundation area are realized, a plurality of cameras are utilized to carry out omnibearing and multi-angle image acquisition, dead-corner-free monitoring is ensured, physical parameter data of a foundation are acquired in real time through multiple types of intelligent sensors, and the monitoring precision is improved. Monitoring comprehensiveness and accuracy are obviously improved, and potential safety hazards caused by monitoring blind areas are effectively avoided.

The invention provides a construction dynamic monitoring system based on oblique photography and multi-source data, and the system comprises an unmanned plane collection module which is used for obtaining a construction site oblique image according to a preset rule and generating a three-dimensional real scene model; the sensor network module is deployed on the construction machinery and is used for acquiring the running state and position data of the machinery in real time; the AI analysis module is used for aligning the real scene model and the BIM plan model based on a convolutional neural network, detecting a difference region and calculating an engineering quantity; and the data fusion platform is used for integrating the meteorological data, the historical efficiency library and the construction log and generating a dynamic progress report and optimization suggestions. According to the method, multi-dimensional data are fused, and the accuracy of progress analysis and the decision support capability are improved; the whole process from data collection to report generation is automatic, the labor cost is greatly reduced, and the method has high practical significance.

The invention discloses a filling roof contact online monitoring system and method based on laser ranging, and belongs to the field of filling mining, and the system comprises the following steps: laser ranging, a filling device and a monitoring control module; wherein the laser ranging is used for carrying an airborne laser scanning system and a laser ranging system to scan and obtain the height of a top plate and a bottom plate and the form data of a top plate of a goaf; the monitoring control module is used for dynamically generating a filling rate-roof gap relation curve based on the roof and floor height and roof form data of the goaf; the filling device is used for conducting intelligent filling based on the filling rate-roof gap relation curve. According to the filling roof contact online monitoring system based on laser ranging, the height of the filling body is monitored in real time through the laser ranging device, the filling speed and flow are adjusted at any time, and over-filling or insufficient filling is prevented.

The invention discloses an air-ground collaborative observation unmanned system key target intelligent search method, which comprises the following steps that: an unmanned aerial vehicle carries a positioning module to provide a positioning coordinate, and executes a preset fixed-point cruise task; the unmanned aerial vehicle performs data acquisition on the environment of the cruising area; a height self-adaptive target detection module is adopted to carry out target detection on image data collected by the unmanned aerial vehicle, a rescue target and temporary obstacles generated after a disaster are recognized, coordinate transformation is carried out on the obstacles and target points, and a detection result is mapped to a position under a global coordinate system; transmitting the recognized target point and obstacle coordinates to a ground mechanical dog in real time through a communication module; the ground mechanical dog updates a dynamic cost map according to the received environment information to realize real-time perception of the environment; and the ground mechanical dog performs path planning based on the environment map. According to the method, efficient perception and rapid rescue of the target point can be realized, and the task execution efficiency and the system robustness in the post-disaster complex environment are remarkably improved.

The invention discloses a real-time monitoring and early warning method for deformation of a foundation pit support based on oblique photography, and relates to the technical field of photogrammetry and deformation measurement, high-resolution images of a support structure are acquired in a multi-angle manner through a multi-stage route, and a high-density three-dimensional point cloud reference model is constructed after preprocessing; then periodically acquiring a monitoring point cloud, and realizing high-precision space alignment with the reference model through control point coarse registration and iterative nearest point fine registration; and finally, calculating the three-dimensional coordinate deviation of each point through point cloud matching, extracting the full-surface deformation, and generating a deformation cloud picture and a statistical report. According to the method, the technical spanning from discrete point monitoring to full-field continuous monitoring and from low-dimensional data to true three-dimensional vectorization deformation analysis is realized, and the comprehensiveness and accuracy of deformation monitoring are improved.

The invention relates to the technical field of distribution network unmanned aerial vehicle autonomous tour-inspection navigation, in particular to a distribution network unmanned aerial vehicle tour-inspection autonomous navigation method based on GPS information and visual information, which comprises the following steps: acquiring a GPS and smoothing projection coordinates, constructing a Thiessen region random index, optimizing a route and acquiring a flight image, evaluating a risk and adjusting exposure parameters, and generating a light environment value. According to the method, an environment model is constructed by fusing GPS information, Gaussian projection is adopted to eliminate earth curvature errors, moving average filtering is adopted to suppress signal noise, Thiessen polygon is utilized to quantify routing inspection point and obstacle distribution, a tornado optimization algorithm is combined to generate an optimal route, the shortest path without repetition and obstacle avoidance are realized, and obstacles are identified based on a YOLOv12 model. A risk avoiding strategy is made according to the risk value, a route is dynamically optimized, a light environment fingerprint database is constructed, illumination change is predicted in combination with LSTM, aperture shutter sensitivity and white balance are adjusted in a self-adaptive mode, image brightness and definition are optimized, and inspection efficiency and data reliability are improved.

The invention relates to the technical field of high-rise building crack detection, and particularly discloses a high-rise building crack detector based on an unmanned aerial vehicle and a stair-climbing robot, the bottom of the stair-climbing robot is fixedly connected with an aircraft, and the side surface of the stair-climbing robot is fixedly connected with a contact component. According to the high-rise building crack detector based on the unmanned aerial vehicle and the stair-climbing robot, the sweeping assembly is arranged, when a second connecting arm drives a connecting plate to approach to a to-be-detected area and a sweeping brush makes contact with a wall surface, a first spring immediately generates stretching deformation, a sliding rod is promoted to flexibly slide in a connecting block, and through elastic adjustment of the spring, the sweeping assembly is arranged; the cleaning brush can be adaptively matched with wall surfaces with different flatness, it is ensured that the cleaning brush tightly abuts against an area to be detected all the time, efficient cleaning force can be kept even if the cleaning brush faces the uneven wall surfaces, and therefore a clean and reliable working face is provided for follow-up high-precision crack detection, and the accuracy and reliability of a detection result are improved.

The invention provides a natural resource investigation monitoring method and device and electronic equipment, and relates to the technical field of resource investigation monitoring, based on a natural resource investigation monitoring system, the natural resource investigation monitoring system comprises a master control vehicle and cloud equipment; the natural resource investigation monitoring method comprises the following steps: acquiring a task planning scheme through a decision support system by using cloud equipment; controlling a master control vehicle to collect original multi-modal data through the task planning scheme; performing space-time calibration processing on the original multi-modal data to obtain target multi-modal data; performing target detection on the target multi-modal data to obtain target detection image data; performing data compression on the target detection image data to obtain bit stream data, and sending the bit stream data to a cloud device; and decoding the bit stream data by using the cloud equipment to obtain a natural resource survey monitoring report. According to the invention, the accuracy of natural resource investigation and monitoring is improved.

The invention relates to a hanging box pose sensing method and system based on multi-sensor fusion, and belongs to the technical field of industrial automation and intelligent logistics. According to the method, a staged multi-mode sensing strategy is adopted, and high-precision positioning of the hanging box is achieved: in a non-working area, IMU and UWB tight coupling positioning is utilized, the IMU collects 6D pose data in real time, and coarse positioning is achieved in combination with absolute position information provided by UWB; and after entering a working area, starting a visual fine calibration system, acquiring pixel and depth information of the corner fitting of the hanging box by using a depth camera, extracting feature points based on an improved YOLOv5 algorithm, and obtaining a 6D pose with millimeter-level precision through pose calculation. And finally, dynamically fusing IMU, UWB and visual data by adopting an adaptive Kalman filtering algorithm, adaptively adjusting the weight according to environmental noise, and outputting stable and reliable pose information. The system comprises a multi-sensor module, a pose resolving module and a multi-mechanical-arm cooperation module, the positioning precision and the logistics system efficiency are remarkably improved, and the system is widely suitable for scenes such as mine material transportation.