4020 results about "Large range" patented technology

The invention discloses a landslide prediction and early warning system and method based on a remote sensing technology, and is applied to the technical field of landslide early warning. Comprising a data acquisition module for acquiring multi-source data in a monitoring area; the cloud computing module is used for large-range landslide risk assessment and deformation monitoring; the edge calculation module is used for landslide risk assessment and deformation monitoring of a specific area; the end-cloud cooperative communication module is used for realizing data interaction between the cloud computing module and the edge computing module; the federal learning module is used for realizing cooperative training under data privacy protection of different areas; and the landslide early warning module is used for carrying out early warning based on landslide risk assessment and deformation monitoring results of the cloud computing module and the edge computing module. According to the invention, by using the deep learning model, the end-cloud collaborative architecture and the federal learning driven data fusion framework, efficient, low-cost and high-precision landslide risk prediction is realized.

The invention belongs to the technical field of digital restoration of computer vision and cultural heritage, and particularly relates to a traditional picture restoration method fusing low-resolution prior and efficient visual selection, which comprises the following steps: constructing a multi-source image data set, taking images in the multi-source image data set as high-resolution images, preprocessing the high-resolution images to obtain low-resolution images, and carrying out high-resolution priori and high-efficiency visual selection on the low-resolution images. The high-resolution image and the low-resolution image are respectively masked to generate simulated damage mask images, and the simulated damage mask images comprise a regular damage mask image and an irregular damage mask image; taking the multi-source image data set and the preprocessed multi-source image data set as training data, and training a multi-source image model; the dual-stage repair network comprises a coarse repair network and a fine repair network; according to the method, the problems of structural semantic loss, high priori information dependency and insufficient global and local coordination when an existing image restoration method is used for processing a complex scene and a large-range missing region are solved.

The invention relates to the technical field of laser material processing, and discloses a laser annealing temperature control method and device.The method comprises the steps that the specific heat capacity, the heat conductivity coefficient, the heat diffusivity, the heat radiation rate, the heat reflectivity and the heat loss coefficient are obtained, and a material thermal characteristic model is established; performing grid division according to the material thermal characteristic model to obtain a laser energy density parameter; extracting a thermal strain coefficient according to the laser energy density parameter, and establishing a temperature-thermal strain mapping relation prediction model; dynamically adjusting a laser power proportion and a pulse width time sequence according to the temperature-thermal strain mapping relation prediction model to obtain temperature field distribution data; according to the temperature field distribution data, carrying out shortest path analysis to obtain a temperature regulation and control decision; and according to the temperature regulation and control decision, inputting a preset control regulation model, and combining reinforcement learning iteration optimization parameters to obtain a temperature control scheme. According to the method, large-range temperature crossing and fine adjustment in an extremely short time can be realized.

The invention discloses an absolute type planar two-dimensional time grating displacement sensor based on an alternating electric field and a measuring method. Excitation signals are applied to X excitation pairs in sequence, when an induction electrode generates an induction signal, the excitation signals are stopped from being applied to the remaining X excitation pairs, and four paths of electric signals generated by an induction group are processed, so that the absolute type planar two-dimensional time grating displacement sensor based on the alternating electric field is obtained. Obtaining the X excitation antipode of the movable ruler base body and the displacement position of the movable ruler base body in the X excitation antipode so as to obtain the two-dimensional time grating absolute displacement value of the movable ruler base body relative to the fixed ruler base body in the X direction; similarly, a two-dimensional time grating absolute displacement value of the movable ruler base body in the Y direction is obtained; and then taking the two-dimensional time grating absolute displacement values in the XY direction as the coordinates of the movable ruler base body in the XY direction to obtain the absolute displacement of the movable ruler base body. The absolute positions in the X direction and the Y direction are obtained in a scanning mode so as to achieve planar two-dimensional absolute displacement measurement, accumulated errors and random errors can be reduced, the measurement precision can be improved, and wide-range high-precision measurement can be achieved.

The invention discloses an unmanned aerial vehicle airport site selection method suitable for large-scale electric power line inspection, and the method comprises the steps: S1, carrying out the primary selection of a geographic space, constructing a geographic constraint evaluation model, carrying out the processing of elements and historical disaster data through discrete coding, generating a safe and suitable region through the multi-factor weighted stacking space analysis, and carrying out the calculation of an evaluation result; and a K-Means clustering algorithm based on Euclidean distance is combined to screen a primary selection region. S2, risk assessment preselection: constructing an entropy weight-TOPSIS risk assessment model, determining a risk factor weight by using an entropy evaluation method, calculating a scheme closeness degree through a TOPSIS algorithm, and screening a low-risk preselection region; and S3, constructing a binary relationship between a demand center set and a pre-selected site set by combining an electric power inspection target and taking an electric power inspection task target as a demand node, measuring weighted traffic cost through Euclidean distance, designing a double-layer nested 0-1 decision variable, and realizing optimization of an airport deployment area by taking minimization of a total weighted distance as a target function.

The invention discloses a forest carbon reserve and carbon sink monitoring and evaluating method based on a multi-source remote sensing technology. The method comprises the following steps: multi-source remote sensing data acquisition: respectively acquiring optical remote sensing data and radar remote sensing data; tree species classification and identification: carrying out tree species classification on the forest in the research area to realize tree species scale space distribution information extraction; for different tree species, biomass model parameters obtained through field investigation are combined, a biomass estimation model based on the tree species is established, model input parameters are biomass model parameters, and vegetation biomass is output; calculating the carbon reserve: calculating the vegetation carbon reserve by utilizing the inverted vegetation biomass and combining the carbon content coefficients of different tree species; estimating the soil carbon reserve, and finally obtaining the total carbon reserve of the forest ecosystem: calculating the carbon reserve variation of the forest ecosystem at different time points based on the multi-source remote sensing data of the long-term sequence. According to the invention, large-range, high-precision and continuous forest carbon reserve and carbon sink monitoring and evaluation considering tree species difference is realized.

The invention discloses an automatic welding device for a rotary alloy furnace tube, and belongs to the technical field of arc welding equipment.The automatic welding device for the rotary alloy furnace tube comprises a base and a U-shaped bin located at the top of the base, and the two ends of the interior of the U-shaped bin are jointly provided with a centering clamping mechanism used for clamping the outer side of the furnace tube; a driving mechanism for providing power for the centering clamping mechanism to clamp the furnace tube is arranged on the outer side of the centering clamping mechanism, and a welding mechanism is arranged on the inner top of the U-shaped bin. A local sealing design is adopted for the inflation mechanism, the annular air bag is attached to the inner wall of the furnace tube after being inflated, an independent cavity communicated with the outside only through a welding seam gap is formed near the welding seam, the protection requirement can be met only by filling the local space with argon, the whole furnace tube or a large-range external space does not need to be filled, and compared with traditional overall argon filling, the argon filling efficiency is greatly improved. The argon consumption is greatly reduced, the industrial gas purchase cost is remarkably reduced, and the production cost investment is reduced.

The invention provides a mining area deformation monitoring and risk assessment method, which comprises the following steps of: carrying out registration, de-leveling, filtering and phase unwrapping on acquired image data through InSAR (Interferometric Synthetic Aperture Radar), generating a coherence graph, a filtering interference graph and an unwrapping phase graph, carrying out phase decomposition by using a singular value algorithm, separating a deformation phase, an atmospheric delay phase and noise, and carrying out risk assessment. The method comprises the steps of expanding discrete deformation points into a continuous deformation field through Kriging interpolation, carrying out deformation grading by taking a deformation rate, an accumulated settlement amount and a deformation gradient as core indexes, identifying a deformation high-risk area, arranging measurement robot monitoring points in the high-risk area, and constructing a risk assessment model in combination with real-time deformation data of a measurement robot and geological parameters. And evaluating the risk grade of each part of the mining area through the dynamic risk index model, and triggering graded early warning. According to the method, the InSAR and measurement robot technology is combined to solve the data fusion problem of large-range deformation monitoring and local high-precision monitoring, and the dynamic risk assessment precision under the complex terrain of the mining area is improved.

The invention provides a visual navigation method and system based on an improved optical flow method, and relates to the technical field of computer vision and inertial navigation. A collaborative optimization framework of an inertial navigation system and visual feature navigation is constructed, carrier motion state parameters are obtained through dead reckoning of an inertial measurement unit, variance parameters of pose variation are calculated, and the layering depth of a pyramid LK optical flow method is dynamically adjusted based on the variance parameters. When the inertial navigation output variance exceeds a preset threshold value, the number of layers of an image pyramid is increased to cope with large-range motion, and otherwise, calculation levels are reduced to improve real-time performance. A dual-period optical flow / feature matching tracking mechanism is designed, improved sub-pixel-level optical flow tracking is adopted in a short period, the calculation complexity is reduced while the positioning precision is guaranteed, feature matching is switched to in a long period, accumulative errors are eliminated, and a period parameter N is determined by inertial navigation precision. According to the method, the calculation complexity can be reduced, and a better effect can be achieved on a low-calculation-performance platform.

The invention provides a sea ice area extraction method and device based on remote sensing, which are applied to the field of seawater detection, and the method comprises the steps: obtaining a remote sensing image of a target area; pixels of the remote sensing image are partitioned based on the normalized vegetation index, the pixels with the normalized vegetation index smaller than a preset vegetation index threshold value are divided into first type regions, and the first type regions comprise at least one of a water body region and a sea ice region; determining a normalized deicing index of the first type region; performing normalized deicing index threshold segmentation on the first type region through a maximum between-class variance method to obtain a sea ice classification threshold; and filtering and post-processing the remote sensing image based on the sea ice classification threshold, and taking the first type region of which the normalized deicing index is greater than the sea ice classification threshold as a sea ice region. The method is suitable for sea ice monitoring in a large area, and sea ice information can be efficiently and accurately extracted.

The invention discloses a wide-range weak feature scene measurement system based on multi-sensor fusion and structured light scanning, and relates to the technical field of wide-range scene scanning, in the system, an extended Kalman filtering algorithm is adopted, fusion is carried out based on positioning data provided by an inertial measurement module and a positioning sensing module, and a wide-range weak feature scene is obtained. And the distance measurement information provided by the laser distance measurement module is combined to correct the fused positioning data, so that the precision and robustness of subsequent point cloud registration are ensured. Then, according to position information and attitude information of the binocular structured light scanning camera during scene scanning, calculating a relative rotation matrix and a relative displacement matrix of each frame of scanning point cloud data relative to the first frame of scanning point cloud data, and performing coarse registration; in the coarse registration process, all frames of point clouds can be converted into a unified coordinate system by directly utilizing global pose information, and the efficiency of coarse registration is greatly improved. And finally, extracting laser marking point clouds projected by the non-contact laser marking device in each frame of scanning point cloud data, and performing iterative precise registration on each frame of scanning point cloud data by using color features and geometric distribution rules of the extracted laser marking point clouds. In conclusion, high-precision, high-efficiency and high-robustness point cloud registration in a large-scale weak feature scene is realized, and the method can be used for realizing high-precision non-contact measurement.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

An assisted driving method and apparatus. The assisted driving method comprises: acquiring multi-modal perception information, wherein the multi-modal perception information is collected by a plurality of sensors provided in a vehicle (100); acquiring a plurality of groups of display information, wherein the display information comprises target areas to be displayed and grid granularities; performing perception prediction on the basis of the multi-modal perception information and the plurality of groups of display information, so as to obtain a plurality of pieces of occupancy grid information, wherein the occupancy grid information is used for describing the distribution of obstacles around the vehicle (100); and displaying a plurality of occupancy grid pictures on a human-computer interaction interface on the basis of the plurality of pieces of occupancy grid information. By means of the present application, not only can the panoramic perception capability of a driver for obstacles within a large range be improved, but the refined perception capability of the driver for obstacles within a short-distance range can also be improved, and the perception capability of the driver for surrounding environments can be improved by means of a combination of the panoramic perception capability and the refined perception capability, thereby avoiding scratching.

The utility model discloses a system for large-range high-efficiency detection of a building facade. The system comprises a wall-climbing robot and a remote controller, the wall-climbing robot comprises a robot body, a climbing module, a propeller propelling module, a detection module and a control module, the control module is in communication connection with the remote controller; and the control module is suitable for controlling the climbing module to drive the wall-climbing robot to climb or descend along the rope, controlling the propeller propelling module to propel the wall-climbing robot to be tightly attached to the building facade and controlling the detection module to carry out large-range damage detection on the building facade in the climbing or descending process of the wall-climbing robot under an instruction of the remote controller. According to the utility model, the wall-climbing robot is controlled to climb up and down stably on the vertical facade through the remote controller, and the large-range detection of the crack damage of the building facade is realized.

The invention discloses a wind turbine generator intelligent control system and method based on rotation laser radar and artificial intelligence fusion. The wind turbine generator intelligent control system comprises a dynamic scanning monitoring module which can carry out three-dimensional point cloud data collection on the azimuth angle range of 0-300 m and + / -90 degrees in front of a generator; the space-time feature processing module comprises a voxel grid denoising unit, a coordinate conversion unit and a space-time convolutional neural network ST-CNN; and a hybrid optimization control module. A variable-speed rotating multi-line laser radar is adopted, a dynamic scanning strategy is combined, and accurate monitoring of a large-range three-dimensional wind field in front of a unit is achieved. A space-time convolutional neural network ST-CNN is constructed, spatial features and time sequence information are creatively fused, and future wind conditions are accurately predicted. A hybrid control algorithm combining model predictive control MPC and deep reinforcement learning DRL is proposed, and control parameters are dynamically generated by taking power generation efficiency and unit load as optimization targets. From hardware deployment to software configuration, a complete and highly integrated intelligent control system is formed.

The invention discloses a multi-dimensional drainage port overflow risk assessment system and assessment method, and belongs to the technical field of drainage port overflow risk assessment systems, and the system integrates ground monitoring and satellite remote sensing multi-source data, and collects related information of a drainage port in an omnibearing manner. The ground monitoring data provides accurate local hydrology, water quality and drainage facility operation state information, the satellite remote sensing data realizes large-range monitoring, the advantages of the ground monitoring data and the satellite remote sensing data are complementary, the data are fused through a weighted average method and a Kalman filtering method, the data accuracy and reliability are improved, and the weighted average method distributes weights according to data characteristics for preliminary fusion, so that the accuracy and reliability of the data are improved. The Kalman filtering method adapts to data dynamic change, such as water level data fusion according to a state equation and an observation equation optimization result, combines the advantages of ground high-precision monitoring and satellite wide-range coverage, accurately reflects water level change, quantifies the overflow risk of a drainage port, and provides effective support for urban water fine supervision.

The invention discloses an intelligent planning method of a geological mineral exploration analysis model, and relates to the field of mineral exploration, and the intelligent planning method comprises the following specific steps: S101, obtaining regional geological mineral data through satellite remote sensing, ground survey and historical data collection, and preprocessing the collected geological mineral data; according to the method, powerful prior knowledge support can be provided for simulation learning, exploration planning is more scientific and reliable, mineral resource distribution can be predicted more comprehensively, model generation efficiency and calculation performance are improved, multi-level reasoning and prediction can be carried out on different geological levels, limitation of traditional exploration can be broken through, and the method is suitable for popularization and application. The overall effect of the intelligent planning method is improved; according to the method, global search can be carried out in a large range, local optimum is avoided, meanwhile, the accuracy and reliability of path planning are improved, the diversity and adaptability of path selection are ensured, and a model can more intelligently select a path with high exploration value when facing an unknown area.

The invention discloses a multi-source remote sensing and unmanned aerial vehicle combined wetland monitoring method, equipment and a medium, relates to the technical field of wetland monitoring, and realizes efficient and accurate wetland monitoring through combination of satellite remote sensing and unmanned aerial vehicle technologies. Firstly, space-time continuity and noise suppression preprocessing is carried out on a satellite remote sensing image, and the image quality is improved; thirdly, the image is optimized through segmentation fusion processing, and the species distribution range and the community boundary are accurately extracted; the unmanned aerial vehicle monitoring path is planned based on the data, and the monitoring efficiency is improved. A high-resolution multispectral image obtained by the unmanned aerial vehicle is subjected to radiation correction and geometric correction and then is fused with a satellite remote sensing image to generate a high-precision wetland species distribution diagram. According to the scheme, the advantages of large-range coverage of satellite remote sensing and high resolution of the unmanned aerial vehicle are fully played, the defects of data quality, information extraction and monitoring efficiency in a traditional method are overcome, and powerful support is provided for wetland protection and management.

The invention discloses a large-range high-frequency all-sky earth surface solar irradiance machine learning estimation method and system suitable for a cloudy area, and relates to the technical field of irradiance estimation, and the method comprises the steps: obtaining earth surface radiation observation data, generating a station position table, and constructing a training data set; constructing a ground solar radiation degree model, and dividing the training data set into a training set and a test set according to a proportion of 80% to 20%; determining hyper-parameters of the Xgboost algorithm through parameter search, training the model on the training set, and checking the model by using the test set; new satellite reflectivity is used for observing data and meteorological elements, data cleaning is carried out, and the model is input to estimate the space-time distribution of the total solar radiation irradiance of the earth surface. According to the method, the space and time precision of radiation inversion is improved, the complex nonlinear characteristics and data interaction relation are accurately captured, efficient inversion of the earth surface solar radiation degree is achieved, and the prediction performance and generalization ability of the model are remarkably improved.

The invention discloses a sky-ground integrated sunlight-induced vegetation chlorophyll fluorescence monitoring system, and belongs to the technical field of remote sensing monitoring. The system provides functions, parameter constraints and cooperation relations of space-based, empty-based and foundation chlorophyll fluorescence information acquisition and data analysis processing and application modules. Spatial scale matching, spectral scale matching, observation angle matching and atmospheric element matching are performed on the acquired sky-ground multi-element data with different scales, so that reflection characteristics, heat dissipation, photochemical indexes, fluorescence emission and atmospheric transmission characteristics during vegetation photosynthesis can be completely acquired; the invention provides a parameter relation and a method for extracting a fluorescence full-spectrum radiation information curve based on oxygen absorption and Fraunhofer dark line. On the basis of sky and landmark calibration and model verification, chlorophyll fluorescence detection of different fineness degrees of global large-range, key areas and ground crown planting scales can be carried out, various stress effects of vegetation can be monitored in real time, and the primary total productivity of the vegetation can be accurately estimated.

The invention provides a mobile base station and unmanned aerial vehicle cooperative monitoring system and method, relates to the technical field of unmanned aerial vehicles, and aims to solve the problems that an existing unmanned aerial vehicle monitoring system is low in single unmanned aerial vehicle operation efficiency and cannot meet the requirement for simultaneous execution of multiple tasks, and a traditional monitoring base station is limited in coverage range. And the method is difficult to adapt to complex construction environments or large-range monitoring scenes. The system comprises a moving and carrying module, a positioning detection module, a data acquisition module, a data processing module and the like, high-precision positioning, rotary multi-position parking apron and multi-task unmanned aerial vehicle cooperative work capability are integrated, the unmanned aerial vehicle has task synchronous execution capability, a base station has autonomous moving capability, and the system is suitable for complex terrains and high-precision unmanned aerial vehicles. And an environment monitoring module and a solar charging system are integrated, so that the long-term operation capability is enhanced. Through hardware integration innovation, the cooperative operation efficiency of the multiple unmanned aerial vehicles is improved, efficient linkage of the base station and the unmanned aerial vehicles is achieved, and the intelligent level of environment monitoring and construction management is improved.

The invention provides a submarine sedimentary environment dynamic change characteristic inversion method based on three-phase potential. The method comprises the steps of in-situ data processing, electrical signal calibration, mathematical model establishment and dynamic change analysis. A seabed electrode array is arranged, signals of natural potential, resistivity, oxidation-reduction potential and the like are collected in real time, and seabed sedimentary environment characteristics such as seabed interface position, suspended particle concentration, sediment density, porosity, water content and the like are deduced by adopting an inversion algorithm. The method is combined with multi-frequency signal analysis, and can monitor seabed sedimentary environment changes in real time, identify sediment movement and the like. Compared with the prior art such as CN118153411B and CN110411923B, more abundant inversion contents are covered, the monitoring precision and efficiency are improved through the complementarity of potential signals, and higher adaptability and reliability are achieved. The method has low equipment and maintenance cost, is suitable for large-range and long-term monitoring, and can provide an innovative solution in the fields of deep sea monitoring, environment evaluation, resource exploration and the like.

A multi-source remote sensing data-based method for monitoring the concentration of suspended matter in an ocean dumping area, belonging to the technical field of remote sensing and monitoring of the concentration of suspended matter in ocean dredging and dumping areas. The method comprises: 1) selecting a certain sea area comprising a dumping area as a research area to acquire actually measured suspended matter concentration data; 2) separately acquiring the actually measured suspended matter data, and preprocessing remote sensing images; 3) matching the acquired data to form a data set, constructing a suspended matter concentration inversion model, and obtaining an image of the spatiotemporal distribution of the concentration of suspended matter in a body of water of the research area; and 4) constructing a multi-source remote sensing spatiotemporal data fusion model to obtain a high-spatiotemporal-resolution diagram of the spatiotemporal distribution of the concentration of suspended matter in the dumping area, and monitoring the conditions of a change in the concentration of suspended matter in the ocean dumping area. The present invention has advantages such as a short monitoring period, high spatiotemporal resolution, and a wide observation range, and can synchronously monitor a sea area having a large range. The present invention can also provide data support for operations such as supervision and management of ocean waste dumping, monitoring and management of an ocean waste dumping area, selection and division of a waste dumping area, supervision of use, and monitoring for illegal dumping.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention discloses an oblique photography method for real-time target identification and route generation of an unmanned aerial vehicle, and relates to the technical field of identification and tracking, and the method comprises the following steps: unmanned aerial vehicle high-altitude cruise, unmanned aerial vehicle target identification and tracking, unmanned aerial vehicle target positioning, route flight range adaptive generation, and low-altitude unmanned aerial vehicle automatic cruise. According to the invention, the unmanned aerial vehicle can have the capabilities of cruising, identifying and tracking the target in a large-range and long-distance scene, on-site investigation of a worker is not needed, the situation that the unmanned aerial vehicle searches the moving target in a large-range cruising manner and moves the moving target for a long time is effectively reduced, time and labor are saved, and the unmanned aerial vehicle is used for identifying and tracking the moving target.

The invention discloses a method for infinitely expanding the visual field of a camera of a chip mounter for error compensation, and relates to the technical field of chip mounting. The method for infinitely expanding the visual field of the camera of the chip mounter for error compensation comprises the following steps: S1, establishing a coordinate system and extracting feature points; s2, homogeneous coordinate matrix calculation and coordinate transformation; s3, expanding the visual field; s4, error analysis and compensation; s5, carrying out surface mounting operation; by infinitely expanding the virtual field of view of the camera, the traditional tedious switching process which is easy to introduce errors is avoided. Firstly, a double-coordinate system is defined, feature points are extracted, a homogeneous coordinate matrix is established to complete coordinate transformation and distortion correction, and then new feature points are continuously selected to achieve infinite extension of the visual field so as to cope with a large-range chip substrate. And then analyzing the mounting position deviation and judging the error property, performing error compensation by adopting a method of recalibrating H matrix parameters or measuring for multiple times and averaging, and finally outputting the compensated coordinates to a chip mounter mobile module to complete mounting.

The invention provides an ICESat-2 turbid water area sounding correction and water depth inversion method based on optical characteristics of a water body. The method comprises the following steps: S1, obtaining photon data and an optical remote sensing image of a near-shore turbid water research area, and carrying out preprocessing operation on the data; s2, performing spectral sensitivity analysis by acquiring an ICESat-2 photon error value and combining optical characteristics of a water body; s3, constructing an ICESat-2 turbid water area sounding compensation model based on water optical characteristics, and selecting the model preferentially according to evaluation indexes; s4, synthesizing a multi-temporal image, and realizing model adaptive adjustment of different space weight distribution based on the spectral sensitivity difference of the turbid water area; s5, constructing an ICESat-2 and remote sensing image joint inversion water depth model based on turbidity partitioning; s6, spatial interpolation is carried out on a water depth inversion result, residual noise and adjacent pixel differences possibly occurring in the image are suppressed, and continuous water depth is obtained; according to the invention, large-range water depth inversion without field actual measurement work can be realized.

The invention discloses a multi-platform real-time field rice ear detection and counting method based on a visual converter, which is suitable for various acquisition platforms from a portable mobile terminal to an unmanned aerial vehicle terminal and the like, and meets the requirements of small-range accurate identification and large-range high-throughput monitoring. Aiming at the characteristics of multiple scales, multiple varieties, phenotype difference, time sequence change, complex field environment and the like in a data set, a rice spike detection counting network comprising a feature enhancement module, an efficient cross-space feature extraction network, a lightweight bidirectional feature fusion module and a number estimation and detection head module is constructed. The average precision mean value (mAP50) of the model reaches 0.8906 and is improved by 11.5% compared with that of an original model, the parameter quantity is reduced by 29%, and the calculated quantity (GFLOPs) is reduced by 48%. The method can effectively process rice ear images of different scales and growth periods, adapts to morphological change, overcomes interference such as illumination, phenotypic difference and shielding, and provides technical support for rice yield evaluation and field rice ear quality analysis.

The invention discloses a single crystal turbine blade crystal orientation defect detection system based on adaptive optics, particularly relates to the field of nondestructive testing, and is used for solving the problem that thermal damage and signal consistency are difficult to consider in acoustic imaging of complex structural parts. Through partition energy threshold setting, dynamic pulse planning, photoacoustic processing and unified coordinate cross-subarea data synthesis, local thermal damage caused by excessive concentration of energy is effectively avoided in the material detection process, and the requirement for collecting high-bandwidth signals can be met in a large range. The signal-to-noise ratio and the detection efficiency are improved by regulating and controlling pulse energy and a scanning path, the amplitude comparability and the image coherence among regions are ensured through signal normalization correction and subregion image set splicing, high-resolution and comprehensive-coverage acoustic mapping is output, the crystal orientation defects of the single crystal turbine blade are positioned and evaluated, the detection precision is improved, the measurement period is shortened, and the method is suitable for large-scale popularization and application. And the method has an applicable value for aero-engine parts requiring rapid and high-precision nondestructive detection.

The invention relates to the technical field of laser ranging, in particular to a double-optical-comb hybrid ranging device which comprises a signal detection optical path system and a signal demodulation optical path system. Wherein the signal detection optical path system comprises a first optical frequency comb, a first optical isolator, a first optical collimator, a first beam splitter prism, a reference mirror, a Faraday rotator, a convergence mirror and a target reflector; the signal demodulation optical path system comprises a second optical frequency comb, a second optical isolator, a second optical collimator, a half-wave plate, a second beam splitter prism, a third beam splitter prism, a first polarization controller, a first photoelectric detector, a reflector, a polarization beam splitter prism, a second polarization controller and a second photoelectric detector. A third polarization controller; and a third photoelectric detector. According to the invention, three ranging technologies of asynchronous optical sampling ranging, multi-heterodyne interference ranging and synthetic wavelength ranging are fused, and the problem that a large ranging range and high ranging precision cannot be simultaneously considered in the ranging process is solved.