1017results about "Photogrammetry/videogrammetry" patented technology

The invention provides a terrain surveying method and device based on unmanned aerial vehicle cooperation and electronic equipment, and relates to the technical field of engineering surveying and complex terrain surveying and mapping. The method comprises the following steps: determining a first surveying flight route of an unmanned aerial vehicle in combination with ground elevation data of a to-be-surveyed area, controlling the unmanned aerial vehicle to collect initial area topographic data, identifying and classifying the initial area topographic data, and determining a weak texture false topographic area; re-planning a second survey flight route of the unmanned aerial vehicle according to the weak texture false terrain area, starting the millimeter wave radar, and scanning and collecting supplementary terrain survey data at different angles; fusing the supplementary topographic survey data at different angles to obtain local topographic survey data; and splicing and fusing the initial regional topographic data and the local topographic survey data to obtain real topographic survey data. According to the technical scheme, the measuring precision and the surveying efficiency of the topographic surveying data of the complex topographic region can be improved, and the reliability and the stability of the real topographic surveying data are improved.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention provides an unmanned aerial vehicle route layout method and device for complex terrains, equipment and a medium, and relates to the technical field of unmanned aerial vehicle aerial photogrammetry. The method comprises the following steps: carrying out preliminary region division on a terrain region to be surveyed, and determining a plurality of region units; clustering the area units according to topographic observation data to obtain survey sub-areas; determining a survey priority corresponding to each survey sub-region, and matching a basic flight route corresponding to the unmanned aerial vehicle according to the survey priority; and splicing the different types of basic flight routes corresponding to the survey sub-regions, and performing optimization adjustment on the spliced basic flight routes to obtain a flight route of the target unmanned aerial vehicle so as to perform topographic survey on the topographic region to be surveyed. According to the technical scheme, the route planning of the unmanned aerial vehicle can adapt to various types of landform features, and the safety and aerial survey efficiency of the unmanned aerial vehicle are ensured while the integrity and accuracy of aerial survey data are improved.

The invention provides a GNSS-RTK-based topographic mapping method and system, and the method comprises the steps: obtaining multi-source positioning signal data in a target region, the multi-source positioning signal data comprising a satellite original observation value, a receiver antenna phase center deviation parameter, and real-time dynamic differential correction information; performing multi-path interference suppression processing on the multi-source positioning signal data to generate a phase observation sequence after interference suppression; inputting the phase observation sequence after interference suppression into a topographic feature calculation model, and extracting topographic elevation change feature parameters of the target area; performing spatial fusion processing based on the terrain elevation change characteristic parameters and reference elevation parameters in a preset terrain database to generate three-dimensional terrain curved surface data of the target area; and dynamically adjusting the topographic map updating frequency according to the difference degree between the three-dimensional topographic surface data and the historical topographic surveying and mapping result, and outputting a real-time topographic surveying and mapping result. According to the invention, precision, real-time performance and resource efficiency can be considered in complex terrain environment application.

The invention provides a refuse landfill methane emission monitoring method and system for air-ground combined monitoring, ground sampling points are selected by combining topographic features, possible emission hot spot areas and historical operation information of a refuse landfill, and areas which are difficult to reach by ground sampling can be realized through aerial survey air sampling; by integrating ground sampling and aerial survey, high-resolution methane concentration data can be obtained, and meanwhile interference of flowing wind on hot spot area recognition is reduced; by combining ground surface sampling data and aerial survey data, a Getis-Ord Gi * statistical method and a nuclear density estimation method are adopted to carry out bimodal hot spot area identification, a methane emission source area can be positioned more accurately, the precision and efficiency of methane emission monitoring of the refuse landfill are remarkably improved, and the method is suitable for popularization and application. The methane emission source of the refuse landfill is accurately and efficiently found, the emission amount of the methane emission source is evaluated, targeted suggestions are provided for operation of the landfill, and the method has important environmental protection and economic values.

The invention discloses a building health state detection system, and relates to the technical field of photogrammetry and image processing, the system firstly obtains four-dimensional space-time coordinate data of a plurality of monitoring points on a target building based on satellite image analysis, constructs a four-dimensional space-time network model containing spatial topology and time sequence evolution information according to the four-dimensional space-time coordinate data, and then obtains a four-dimensional space-time network model containing space topology and time sequence evolution information; the system maps network model nodes to a predefined standard reference network associated with a local gravity direction, then, the system analyzes dynamic relevance in the four-dimensional network model by adopting a graph self-attention network, and meanwhile, calculates relative relation changes of monitoring points relative to the predefined standard reference network through geometric analysis; the deformation interpretation module distinguishes and determines internal relative deformation, overall displacement and absolute posture change (including settlement and inclination) of the building according to the two types of analysis results and by referring to surrounding environment information, and the state evaluation module integrates the information to generate a health state report containing risk levels.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a road slope modeling method based on an unmanned aerial vehicle inspection route, and relates to the technical field of unmanned aerial vehicle inspection. The method comprises the following steps: measuring the road slope length, the landform and the geological complexity, and selecting an adaptive unmanned aerial vehicle type and sensor combination according to the road slope length, the landform and the geological complexity; planning unmanned aerial vehicle surface inspection and shallow geological exploration routes, ensuring space matching, and synchronously obtaining high-resolution images and ground penetrating radar data; and processing image data through a photogrammetry technology, generating a digital elevation model and an orthophoto map, and combining to construct a three-dimensional model of the earth surface. Geophysical inversion is carried out on the ground penetrating radar data, reflection features are extracted and projected to the ground surface three-dimensional model, and space correlation between the ground surface and a shallow layer structure is established; the reflection features are digitalized and geometrically reconstructed into three-dimensional surface elements, the three-dimensional surface elements are embedded into an earth surface model to form a three-dimensional geologic body model, time dimensions are introduced, and a dynamic evolution model is constructed. According to the dynamic evolution model, the dynamic analysis of the slope stability is realized.

The invention provides a wall decoration detection method and system, and relates to the technical field of building detection, and the method comprises the steps: obtaining wall decoration data information based on a laser level meter and a camera device, the data information comprising height, level and image information; the data information is sent to a wall flatness model for flatness calculation, and flatness error data is obtained. Edge calculation is carried out based on the flatness error data and the decoration data information, crack data are obtained, and the crack data comprise position and form information. And analyzing the data information through the data analysis model to obtain smoothness and color data. And finally, the flatness error data, the crack data, the smoothness and the color data are sent to a qualification analysis model for analysis, and a detection result of wall decoration is obtained. Through analysis and detection of various data, comprehensive evaluation of the wall decoration quality is realized, whether the wall is qualified or not can be accurately judged, and the method has the characteristics of high efficiency, accuracy and high automation degree.

The invention discloses a mobile tunnel detection method and system, and relates to the field of tunnel detection, and the method comprises the steps: dividing a tunnel into a shield tunnel and a mine tunnel; for a shield tunnel, obtaining a three-dimensional point cloud of the shield tunnel, and extracting bolt holes by adopting a CSF algorithm and a DBSCAN algorithm; adopting a mean-shift algorithm to obtain a central point of the bolt hole, and performing ring-dividing and block-dividing on the three-dimensional point cloud of the shield tunnel according to the central point of the bolt hole to obtain a shield tunnel point cloud after ring-dividing and block-dividing; the method comprises the following steps of: for a mine tunnel, arranging one target every preset distance along the axial direction of the tunnel by using mobile tunnel laser equipment for target arrangement to obtain a mine tunnel point cloud with target position information; generating a mine tunnel lining diagram according to the mine tunnel point cloud with the target position information; establishing an index relationship between the mine tunnel point cloud and the mine tunnel lining graph; and positioning the mine tunnel point cloud according to the index relationship to obtain the positioned mine tunnel point cloud and the like, so that the tunnel defect detection efficiency is improved.

The invention discloses an unmanned aerial vehicle cooperative inspection monitoring system based on BIM construction. The system comprises a data acquisition module which acquires an original visible light image, infrared thermal imaging and point cloud data of a construction area through unmanned aerial vehicle carrying equipment; the data processing module carries out denoising, enhancement, noise reduction, temperature calibration or downsampling processing on the three types of data; the BIM module performs spatial pairing on the processed three types of data, constructs a basic three-dimensional model of a construction area based on point cloud data, maps image and thermal imaging information, and adds a construction progress and a project amount plan to form a 4D BIM model; the construction inspection module obtains time and engineering quantity deviation from the 4D BIM model, generates a construction inspection report, and calculates a monitoring priority value of each building area according to the construction inspection report; and the user interaction module generates an inspection target according to the monitoring priority value, and a user drives the unmanned aerial vehicle to obtain new data according to the inspection target. The system realizes accurate monitoring of the construction progress and intelligent allocation of resources, improves the construction management efficiency and quality, and is suitable for building construction scenes.

The invention relates to a task-driven unmanned aerial vehicle autonomous search method. The method comprises the following steps: acquiring an unmanned aerial vehicle speedometer by using a binocular RGB image and IMU data acquired by an unmanned aerial vehicle; obtaining three-dimensional point cloud data under a world coordinate system by using a depth map obtained by the unmanned aerial vehicle and an unmanned aerial vehicle speedometer, and establishing a probability voxel map through voxelization processing; performing target detection on the binocular RGB image by using a target detection network, and obtaining a target pose; establishing and updating a task scene map by using the target with the detection confidence exceeding the confidence upper limit and the probability voxel map; establishing a space boundary and a potential target boundary; establishing a search strategy of the unmanned aerial vehicle by using the space boundary and the potential target boundary, and determining a search viewpoint; and performing autonomous safety navigation of the unmanned aerial vehicle by using an unmanned aerial vehicle speedometer and a search viewpoint. According to the method, the problems of completely exploring a task space and simultaneously detecting a task target at high precision in a communication denial and unfamiliar and unknown scene are solved.

The invention relates to the technical field of ruin three-dimensional construction, in particular to a ruin three-dimensional model construction method fusing SLAM point cloud and image features, which comprises the following steps: acquiring a depth image and RGB data, aligning and eliminating inconsistent data according to a timestamp, calculating a frame rate to node ratio, establishing a binding relationship between an image and a point cloud, and extracting three-dimensional coordinate points. Recognizing and classifying geometric structures, evaluating texture and edge response, adjusting resolution, analyzing static and dynamic structures, eliminating dynamic interference, calculating geometric curvature and fitting error, optimizing three-dimensional model display, and obtaining detail reduction degree scores. According to the method, through accurate synchronization of the point cloud data and the RGB image, strict verification of timestamp matching and data consistency is allowed, data errors and noise are significantly reduced, the accuracy and reliability of the model are ensured, structural elements are identified, dense areas of geometric structures are refined and analyzed, richer data level support is provided, and the method is suitable for large-scale popularization and application. And higher-quality data is provided for archaeological research.

The invention relates to the technical field of hydraulic ring surveying and mapping, in particular to a hydraulic ring surveying and mapping data acquisition method and system based on geographic space information, and the method comprises the following steps: S1, data acquisition and preliminary data set generation: generating an initial geographic space data set; s2, space-time alignment and coupling feature extraction: constructing a preliminary geographic space model; s3, dynamic verification and cross verification: generating a dynamic verification parameter set; s4, three-dimensional geologic modeling and parameter fusion: generating a comprehensive three-dimensional geologic model; s5, environment interference correction and data optimization: analyzing the offset of an environment interference factor to sensor data through an adaptive learning algorithm, and outputting corrected hydraulic ring surveying and mapping data; and S6, safety threshold comparison and early warning map generation: generating a visual early warning map. According to the method, deep support is provided for underground structure analysis, and the comprehensive capability of hydraulic ring surveying and mapping is improved.

The invention belongs to the technical field of bridge damage identification, and relates to a bridge structure intelligent damage identification method based on a physical information neural network, which comprises the following steps: 1, acquiring spatial relation data of a bridge structure, 2, acquiring real-time data, 3, preprocessing the data after the data is acquired, 4, performing hyper-parameter adjustment, 5, constructing a model, 6, carrying out model training and evaluation; 7, carrying out early warning and repairing scheme matching; the health state of the bridge can be monitored in real time, data support can be provided for future maintenance and repair, the safety of the bridge is improved, and the service life of the bridge is prolonged; therefore, a novel efficient tool is provided for bridge health monitoring and damage assessment through a unique data and physical driving method, an efficient and intelligent bridge monitoring and assessment system is constructed, and the safety and maintenance efficiency of the bridge are improved.

The invention relates to the technical field of track foreign matter real-time detection, and particularly discloses a track foreign matter real-time detection method based on YOLOv5 improvement. Comprising the steps of video vibration synchronous acquisition, space offset compensation parameter generation, dynamic feature enhancement processing, feature weight thermodynamic diagram generation, hierarchical perception feature aggregation, cross-scale target verification, foreign matter positioning instruction packaging and multistage early warning execution control. Image distortion caused by mechanical vibration and illumination fluctuation is effectively overcome by synchronously acquiring track video and vibration time sequence signals, constructing a spatial offset compensation parameter set and combining with a dynamic feature enhancement template to correct video frames, a feature weight thermodynamic diagram is generated through visual saliency detection, features are aggregated through a layered perception model, and the visual saliency detection accuracy is improved. Early warning levels are dynamically matched according to the sizes and the positions of the foreign matters, and a sound-light alarm and braking system is linked; according to the invention, the reliability of data transmission is ensured, the operation and maintenance cost is greatly reduced, and the safety requirement of millisecond response of modern rail transit is met.

The invention relates to the technical field of flight path planning, in particular to an autonomous flight path planning method and system for a road slope inspection unmanned aerial vehicle, and the method comprises the following steps: controlling the unmanned aerial vehicle to fly in a road slope region according to a preset flight path, collecting the aerial image data of the unmanned aerial vehicle, analyzing the aerial inspection adaptive feature value of the unmanned aerial vehicle, and determining the flight path of the unmanned aerial vehicle. Judging whether the feature value is lower than a preset inspection adaptation feature threshold value or not; analyzing a field-of-view missing feature set of the unmanned aerial vehicle based on the aerial image data of the unmanned aerial vehicle if the field-of-view missing feature set is lower than the preset routing inspection adaptive feature value threshold; performing flight path planning processing on the unmanned aerial vehicle based on the field-of-view missing feature set; according to the method, the unmanned aerial vehicle is subjected to flight path planning processing on the basis of the field-of-view missing feature set, so that when an aerial picture is deviated, fuzzy or blind areas, judgment and automatic adjustment are performed in real time, self-adaptive optimization of the flight path is realized, and the road slope inspection efficiency is remarkably improved.

To provide a wave monitoring system for a construction area based on image information recognition, which can optimize the route and adjust the work strategy based on accurate risk judgment results, reduce the probability of the ship blindly entering the risky sea area, ensure the smooth implementation of the navigation plan, and complete the construction work on time. [Solution] The system includes a monitoring area setting module, a wave image collection module, a wave parameter analysis module, a break condition monitoring module, a swell condition monitoring module, and a risk situation recognition module, and is configured to further determine whether a risk exists for the target dredging construction vessel based on the determination result of whether a wave risk situation will occur in each monitoring area.

The invention discloses an AI-driven unattended water environment quality monitoring system, and relates to the technical field of water environment monitoring, and the system comprises a data collection module, a cloud processing module, an edge calculation module, an intelligent analysis and early warning module, and a system management module. Through comprehensive and real-time intelligent monitoring of a water body, monitoring of water quality physical indexes is concerned, aquatic organism behavior monitoring is introduced, so that the health condition of the water body can be reflected more comprehensively, and meanwhile, through the data preprocessing and local model fine tuning functions of the edge calculation module, the water quality monitoring accuracy is improved. The intelligent analysis and early warning module can perform correlation analysis, set an early warning threshold value and generate early warning information based on the AI model after cloud side end collaborative optimization, and provides powerful decision support for water quality management.

The invention provides a lodging area determination method and system based on a lodging monitoring spectral index image, and relates to the field of crop form prediction.The method comprises the steps that firstly, a target farmland remote sensing image and a digital surface model are obtained, radiation and geometric correction are completed in combination with GNSS positioning data and meteorological data, and a standardized orthoimage is generated; calculating a vegetation index on the image and carrying out difference to obtain change information; executing opening and closing operation by adopting a structural element adaptive to the image resolution and the crop row spacing, filtering noise according to the area of a connected domain or a pixel number threshold value, and extracting a spectral index abnormal region and a boundary thereof; and then fusing the boundary and the digital surface model under a unified coordinate reference, accumulating the area according to the pixel resolution, and outputting the area of the abnormal region. The method has parameter self-adaption and multi-source data fusion capabilities, can stably obtain a consistent abnormal region area in a multi-resolution and complex field environment, and provides reliable technical support for agricultural condition monitoring and disaster assessment.

The invention relates to a large-scale offshore engineering geologic model modeling system and method, and relates to the technical field of offshore engineering geologic investigation and modeling. According to the method, through the steps of multi-source data acquisition, preprocessing, fusion, three-dimensional geological modeling, model optimization, dynamic updating, visual analysis and the like, large-scale geological survey data can be efficiently processed, a high-precision three-dimensional geological model is generated, and dynamic updating and visual analysis are supported. The method has the advantages of high efficiency, high precision, real-time performance, engineering applicability and the like, and is suitable for geological modeling and geological condition analysis of engineering such as offshore wind power, submarine pipelines, ocean platforms and the like.

The invention relates to the field of unmanned aerial vehicle surveying and mapping and building three-dimensional modeling, and discloses a high-rise building three-dimensional modeling surveying and mapping method based on unmanned aerial vehicle laser scanning, and the method comprises the following steps: deploying a laser radar, a millimeter wave radar, a polarized light camera and an intelligent blind area supplement unmanned aerial vehicle for cooperative scanning, and generating an optimal path through topological manifold learning; multi-source point cloud high-precision registration is achieved through geometric algebra and Lie group optimization, multi-sensor heterogeneous data are fused in combination with tensor decomposition, blind area dynamic adjustment paths are detected in real time, and finally a complete three-dimensional building model is generated. According to the method, the modeling precision and data integrity of a complex building structure (such as a glass curtain wall) can be remarkably improved, the problems of scanning blind areas and equipment single-point failure are effectively avoided, and an efficient and robust solution is provided for high-rise building three-dimensional modeling.

The invention provides a building surveying and mapping method based on an unmanned aerial vehicle, and the method comprises the steps: integrating data, constructing an intelligent algorithm server, planning a flight path, and distributing tasks; receiving and confirming flight tasks and path information, and flying to respective initial surveying and mapping points; flying along an intelligent algorithm dynamic optimization path, and synchronously collecting building data by a multi-source heterogeneous sensor according to preset parameters; encrypting and transmitting the processed surveying and mapping data to the block chain network, and recording a data hash value, a timestamp and an unmanned aerial vehicle identifier; generating a high-precision building three-dimensional model and a material distribution report; and completing the task, landing according to the plan, returning task details, and comprehensively checking and maintaining the unmanned aerial vehicle and equipment. An intelligent algorithm server is constructed by integrating various types of information, a flight path and a distribution task can be scientifically planned, multiple unmanned aerial vehicles cooperatively work and are linked with a data processing center and a block chain network, and a multi-source heterogeneous sensor collects data and performs real-time fusion processing and monitoring, so that the accuracy and efficiency of the data are guaranteed; and data security and traceability are ensured by using block chain storage, and the precision, efficiency, security and reliability of building surveying and mapping are greatly improved.

The invention belongs to the technical field of navigation, and relates to an unmanned aerial vehicle multi-target image acquisition path planning method, equipment and a storage medium. The method comprises the following steps: acquiring a city live-action three-dimensional model; rasterizing the building model to create a three-dimensional grid map; acquiring distribution information of obstacles through the live-action three-dimensional model, judging whether the corresponding grid units are occupied by the obstacles or not, and generating an obstacle map according to a judgment result; dividing and storing space nodes in the grid map based on the obstacle map; performing initial path search by calculating a cost function of an initial node; and taking the obtained initial path as the basis of initial pheromone distribution, and obtaining an optimized path by adopting an ant colony optimization algorithm. According to the method, the influence of building shielding on image quality is avoided, time and energy waste of the unmanned aerial vehicle on an unnecessary path is reduced, the target image is obtained in the shortest time and the optimal path, and a flight task is completed.

The invention discloses an unmanned aerial vehicle ground-to-air conversion-based arch bridge cantilever attitude and line shape real-time monitoring method, which comprises the following steps that: after segments are assembled on the ground, a plurality of prisms which are not positioned on the same straight line and a plurality of targets which are not positioned on the same straight line are arranged on the upper surfaces of the segments; three-dimensional point clouds of the segments are constructed, and coordinates of the three-dimensional point clouds are corrected through the targets; constructing a key point set, constructing a prism coordinate system (XYZ) L0, and converting coordinates of the key point set to the prism coordinate system (XYZ) L0; when the segments are spliced in the air, whether the prisms are subjected to construction disturbance deviation or not is checked, and the spatial positions and postures of chord members and flange holes of the segments are obtained according to the prism disturbance deviation condition. According to the invention, the world coordinates of the chord member pipe orifice center and the flange hole center of the main arch segment can be accurately obtained, and the spatial position and posture of the segment can be effectively reflected.

The invention provides a non-contact releasing system and method for a self-positioning image control point device in a steep mountain, and relates to the technical field of large reservoir construction protection and geological disaster monitoring. The device comprises a multi-rotor unmanned aerial vehicle flying platform, a cable take-up and pay-off device, a magnetic attraction device, a self-positioning image control point device, a ground control module, an aerial photography sensor and other modules. According to the application method, the self-positioning image control point device is hung on a flight platform of the multi-rotor unmanned aerial vehicle, the self-positioning image control point device is put at an operation position through the cable take-up and pay-off device, and after operation is completed, the self-positioning device is recycled through the magnetic attraction device, and repeated use is achieved. According to the invention, three-dimensional reconstruction in a steep mountain and non-contact throwing and laying of ground surface deformation monitoring image control points can be realized, automation of image control point measurement is realized, measurable precision, measurement efficiency and safety of a periodic three-dimensional model are improved, and safety risks possibly faced by traditional manual laying and measurement in a steep mountain environment are effectively avoided.

The invention relates to an underwater bionic observation and collection integrated robot which is divided into two parts, namely a child cabin and a mother cabin, the child fish part comprises a fish body assembly, a fish fin assembly, a crab foot assembly and a child fish auxiliary assembly, and the fish body assembly is a main body of the robot and plays a supporting role; the fin assembly is divided into a pectoral fin mechanism and a tail fin mechanism, pectoral fins swing up and down to control the robot to sink and float, meanwhile, the pectoral fins are unfolded to become crab claws, and tail fins swing left and right to control the robot to advance; the crab foot assembly is unfolded and is matched with the crab pincers to realize a function of sampling an underwater sample; the child fish auxiliary assembly comprises a camera, a searchlight and a sonar device which are located at the front end of a fish body, and an infrared sensor at the rear end of the fish body; the mother cabin part comprises a cabin body assembly, a cabin cover assembly and a mother cabin auxiliary assembly, the upper layer of the cabin body assembly is a son fish recovery cabin, and the lower layer is a sample recovery cabin; the cabin cover assembly is arranged above the cabin body, and the cabin cover assembly and the cabin body are connected in an up-down openable mode through the mother cabin auxiliary assembly. According to the invention, underwater charging and underwater observation and acquisition integrated operation of the robot can be realized.

The present invention relates to the technical field of environment monitoring, and specifically to an automatic inspection and identification system and method for sewage outfalls along a shoreline. The system comprises a carrier, a four-view camera, a side-scan sonar and an intelligent inspection integrated machine. An on-bridge water drain opening identification model, an overwater sewage outfall detection model, an underwater sewage outfall detection model, and a personal posture detection model are carried in the intelligent inspection integrated machine. The on-bridge water drain opening identification model analyzes a riverway environment collected by a front camera to identify an on-bridge water drain opening; the overwater sewage outfall detection mode identifies an overwater sewage outfall according to image information collected by two side cameras, i.e. a left side camera and a right side camera; the underwater sewage outfall detection model identifies an underwater sewage outfall according to underwater scene information collected by the side-scan sonar; the personal posture detection model determines whether an operator is in a safe operation state according to image information collected by a rear camera. The present invention can improve accuracy and efficiency in sewage outfall inspection processes.

The invention relates to the technical field of unmanned aerial vehicle cooperative surveying and mapping, and discloses an unmanned aerial vehicle cluster cooperative space-space-ground integrated surveying and mapping system which comprises a space-time reference construction module, a multi-source data acquisition module, a distributed fusion optimization module, a system adaptive reconstruction module and a four-dimensional model generation module. An internal high-precision space-time reference independent of the GNSS is constructed; uniformly modeling the whole surveying and mapping task into a space-time factor graph through a distributed fusion optimization module, and solving the space-time factor graph; and closed-loop adaptive adjustment is performed on the geometric configuration of the beacon network and the measurement data quality through a system adaptive reconstruction module. The technical problem that a traditional surveying and mapping system is poor in precision and reliability in a GNSS disturbed environment is solved, the robustness, autonomy and environment adaptability of the system are remarkably improved by constructing a self-consistent internal reference and introducing intelligent feedback control, and a globally consistent high-precision four-dimensional space-time model can be efficiently generated.

The invention discloses a method for correcting a high-precision surveying and mapping error based on the flight state of an unmanned aerial vehicle, and relates to the technical field of data error correction, and the method comprises the steps: obtaining task flight path surveying and mapping attitude data of a to-be-surveyed area of the unmanned aerial vehicle, and carrying out the dynamic weight distribution according to federated filtering; generating a task flight path space-time synchronization matrix of the surveying and mapping area of the unmanned aerial vehicle; establishing an exposure control-distortion compensation function according to the task flight path space-time synchronization matrix of the surveying and mapping area of the unmanned aerial vehicle, and correcting task initialization image parameters of the surveying and mapping area of the unmanned aerial vehicle to obtain task correction image parameters of the surveying and mapping area of the unmanned aerial vehicle; and updating the optimal estimation of the surveying and mapping attitude data of the task flight path of the surveying and mapping area of the unmanned aerial vehicle, and performing iterative matching with the map point cloud of the surveying and mapping area to generate a high-precision image of the surveying and mapping area of the unmanned aerial vehicle. The method has the advantages that the surveying and mapping precision of the unmanned aerial vehicle is improved, error accumulation is reduced, and image quality is optimized.