134 results about "Statistical filtering" patented technology

The invention discloses a three-dimensional point cloud data filtering method and system based on adaptive clustering segmentation and gradient compensation, and the method comprises the steps: carrying out the data preprocessing of three-dimensional point cloud data through employing a statistical filtering method, obtaining the preprocessed point cloud data, extracting the point cloud features in the preprocessed point cloud data, and obtaining the point cloud feature data; according to the invention, a function of establishing multi-dimensional features including point cloud density, spatial autocorrelation and local curvature features by using an adaptive clustering segmentation algorithm based on density spatial distribution and carrying out dynamic clustering segmentation on a ground feature-ground cluster is realized; and residual mixed point clouds can be further separated through a coarse-fine granularity grid grading processing strategy, so that ground point clouds are separated, and the technical limitations of high parameter dependence, insufficient terrain adaptability and poor real-time performance in the prior art are broken through; and the filtering precision and robustness in a complex scene are remarkably improved through multi-dimensional collaborative optimization, and the method is suitable for being widely popularized and used.

The invention relates to the technical field of mechanical arm obstacle avoidance path planning, in particular to a multi-degree-of-freedom mechanical arm obstacle avoidance path planning method based on three-dimensional reconstruction, which comprises the following steps: step 1, three-dimensional environment perception and dynamic modeling; preferentially offsetting and expanding the near-obstacle nodes towards the concave area or the hole center to generate a candidate node set; and 4, three-dimensional grid collision verification and safe path correction are conducted, specifically, the working space of the mechanical arm is divided into three-dimensional voxel grids, and collision detection is achieved by judging whether path nodes fall into obstacle object elements or not. According to the method, the laser radar and the depth camera are adopted to synchronously collect data through hardware triggering, statistical filtering denoising and three-dimensional grid modeling are combined, geometrical characteristics of static obstacles and motion parameters of dynamic obstacles are restored, and the collision risk caused by environmental perception errors of the mechanical arm is effectively avoided.

The invention discloses an efficient denoising power transmission line point cloud processing method and system, and relates to the technical field of three-dimensional point cloud data processing and intelligent analysis, and the method comprises the steps: carrying out the segmentation processing of original power transmission line point cloud data, dividing the point cloud into power equipment parts, carrying out the local density analysis of the segmented point cloud, and generating a density distribution diagram; based on the density distribution diagram and the local geometric features of the point clouds, multi-stage filtering is carried out, denoising model parameters are optimized, a statistical filtering method is combined to remove noise points, curvature features and normal vector consistency features of the point clouds are extracted, classification feature vectors are generated, and based on the classification feature vectors, fine classification is carried out on the point clouds. Through data preprocessing, multi-stage filtering, self-supervised learning, feature extraction and refined classification, the technical defects of insufficient multi-modal feature fusion efficiency and poor equipment feature adaptability in power transmission line point cloud denoising are effectively solved.

The invention provides a side slope radar point cloud data slice abnormal value complementing method based on space-time fusion, which comprises the following steps: acquiring point cloud data of a plurality of time slices obtained by scanning of a side slope radar, and mapping the point cloud data into a three-dimensional point cloud; identifying a piece of abnormal regions of the three-dimensional point cloud through statistical filtering and a density-based clustering method; constructing a point cloud processing model based on PointNet + +, generating masked data and a mask index by adopting a mask strategy, and masking off pieces of point clouds; spatial features are extracted in different regions, static space weight matrixes generated by longitude and latitude and time difference global features are fused, deformation values of mask regions are predicted through a full-connection network, and model parameters are optimized; and inputting the trained model into latitude and longitude coordinates of an abnormal region of actual missing point cloud data, performing abnormal value completion, outputting a completed deformation value, and calculating a completion precision index. According to the method, the completion effect is good, all abnormal values of multiple time slices can be completed at a time, and the calculation efficiency is improved.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention relates to the technical field of ultrasonic medical robots, and discloses a superficial ultrasonic scanning path planning method and device based on an SVD-improved RRT algorithm. The superficial ultrasonic scanning path planning method is applied to ultrasonic medical robot equipment, and specifically comprises the following steps: S101, receiving a data acquisition request sent by a main control end, controlling a Kinect camera to acquire a superficial depth image, processing 20 groups of calibration board images through a Matlab calibration tool box, and executing a corner detection algorithm to identify checkerboard corners, and optimizing and solving the internal reference matrix of the infrared camera. According to the method, SVD plane fitting and an improved RRT algorithm are fused, the precision and efficiency of an ultrasonic scanning path are remarkably improved, in the point cloud processing stage, statistical filtering and voxel gravity center down-sampling synergistically act, point cloud noise is reduced by 90%, meanwhile, more than 95% of three-dimensional features are reserved, a high-fidelity data basis is provided for path planning, and the method has good application prospects. SVD plane fitting forcibly passes through the center of mass, and the error is smaller than or equal to 0.5 mm

The invention discloses a target vehicle attitude recognition method based on 3D point cloud feature extraction, and the method comprises the steps: carrying out the preprocessing of the point cloud data of a target vehicle obtained through a laser radar, and the preprocessing comprises Gaussian statistical filtering, voxelization sampling, and vehicle tire point cloud extraction based on a region growing algorithm; based on the point cloud data, key points of tire steering features are extracted, and a Harris corner detection method is used for detecting the key points; calculating a fast point feature histogram (FPFH) descriptor based on the extracted key points, and performing coarse registration by adopting an SAC-IA algorithm to obtain a coarse registration matrix; and using a point-to-surface nearest point iteration ICP algorithm to perform fine registration on the point cloud after coarse registration to obtain attitude information of the target vehicle. According to the method, the three-dimensional attitude angle of the target vehicle is accurately estimated by preprocessing the laser radar point cloud, extracting tire key points and carrying out SAC-IA coarse registration and ICP fine registration. Therefore, vehicle attitude recognition with high shielding robustness and high precision is realized, and the safety of automatic driving is enhanced.

The invention relates to a fine-grained target real-time image segmentation method and system based on a dynamic state modeling network, and belongs to the technical field of intelligent image processing. The method comprises the following steps: extracting multi-scale detail features by using a lightweight backbone network; through a dual-scale two-dimensional selective scanning module, the features are divided into a thin branch and a thick branch, and local scanning and global scanning are executed respectively; a dynamic cross-scale feature selection and aggregation module is adopted, redundancy is suppressed through reweighting and statistical filtering, and key target responses are highlighted; at a decoding end, local details and global semantics are fused through jump connection and an edge extractor; and finally, introducing a form-guided pseudo label hierarchical supervision strategy, and improving the structure learning ability of the model by using a coarse-to-fine morphological prior. According to the method, the segmentation precision, the boundary integrity and the tiny target recall rate of the fine-grained target under the scenes of ore separation, industrial defect detection, pavement crack recognition and the like are remarkably improved.

The invention discloses a multi-machine collaborative three-dimensional map updating method based on mobile augmented reality, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the following steps: carrying out the collection of initial collection data of a plurality of target regions through the wearing of AR glasses by a plurality of people; performing frame extraction on the original video image, and calculating to obtain a data grouping result; a sparse pairing list is generated by adopting a hybrid pairing strategy, and sparse point cloud data is obtained by combining incremental SfM calculation; performing distortion removal processing on the frame extraction image, and obtaining pure dense point cloud data through a multi-view solid geometry algorithm, a clustering algorithm and statistical filtering; a triangular mesh model is generated through Poisson surface reconstruction, and a high-precision three-dimensional model is obtained through Markov random field optimization view selection; and constructing a unified space-time reference conversion matrix to obtain a real-time AR visual navigation and map updating result. By adopting the method, the problems that the three-dimensional map is poor in current situation, virtual and real registration is misplaced, the modeling efficiency is low and the precision is insufficient are solved.

The invention belongs to the technical field of three-dimensional modeling, and particularly discloses a pipeline three-dimensional modeling method based on adaptive statistical filtering and ground constraint poses, and the method comprises the steps: controlling a collection device to move along a target pipeline, and collecting the original point cloud data of the target pipeline and the movement distance information of the collection device; on the basis of the moving distance information, continuous pose changes of the collection equipment in the target pipeline are calculated, and a ground constraint pose sequence is generated; taking a matching relationship between the original point cloud data as a constraint between pose nodes of the ground constraint pose sequence, and obtaining an optimized ground constraint pose sequence; on the basis of the optimized ground constraint pose sequence, registering the original point cloud data to obtain a pipeline point cloud model; filtering the pipeline point cloud model; and generating a three-dimensional model of the target pipeline based on the filtered pipeline point cloud model. The method can improve the precision of the generated three-dimensional model.

The application discloses a structure point cloud data multi-scale filtering method considering environmental dynamic influence, which firstly adopts a 'K' nearest field method considering structure dynamic influence to perform dynamic filtering processing on original point cloud data, removes scattered outliers in the original point cloud data, and then divides the point cloud data into flat regions and mutation regions based on an improved principal component analysis algorithm (LMSR-PCA), adopts statistical filtering based on local surface fitting for the flat regions, and adopts spatial adaptive bilateral filtering for the mutation regions. The application is used to solve the problems that the existing point cloud data processing method cannot efficiently process massive point cloud data with complex curve characteristics, and cannot solve the problem of excessive smoothing caused by the loss of edge features in the noise reduction process of point cloud data with complex curve characteristics.

The invention discloses an intelligent shuttle vehicle control system for tray conveying, which belongs to the technical field of intelligent shuttle vehicle control and comprises a multi-mode sensing module S1, a dynamic path planning module S2, a multi-vehicle collaborative scheduling module S3, an energy efficiency optimization module S4 and an edge calculation module S5. The multi-mode sensing module S1 comprises an environment sensing sub-module S11 and a cargo state monitoring sub-module S12, the environment sensing sub-module S11 comprises laser radar data preprocessing and visual sensor calibration and fusion, and the laser radar data preprocessing adopts a filtering algorithm based on statistics to remove outliers generated by environment interference. Through the distributed consensus algorithm and the task allocation strategy, the system throughput is improved, the path conflict rate is reduced, the work waiting time is remarkably shortened, the improved A * algorithm is combined with the reinforcement learning model, the time consumption of single path planning is shortened, the path generation efficiency is improved, and the method adapts to the high-dynamic storage environment.

The invention provides a hybrid filtering denoising method and device based on adaptive parameters, and relates to the technical field of three-dimensional point cloud processing. The method comprises the following steps: acquiring three-dimensional point cloud data of a target object; performing voxel segmentation processing on the three-dimensional point cloud data to obtain a plurality of voxels; according to the segmented three-dimensional point cloud data, combining a plurality of voxels by using a density similarity model to obtain a voxel set with different density distributions; based on the merged voxels, a radius filtering parameter and a statistical filtering parameter of each voxel set are adaptively determined, the radius filtering parameter is used for filtering isolated noise in the three-dimensional point cloud data, and the statistical filtering parameter is used for filtering random noise in the three-dimensional point cloud data; performing random noise removal processing on the three-dimensional point cloud data according to the statistical filtering parameters; and performing isolated noise removal processing on the three-dimensional point cloud data after random noise removal according to the radius filtering parameter.

The invention belongs to the field of submarine pipeline risk early warning, and relates to a submarine oil and gas pipeline burying state identification method based on unsupervised machine learning and 3D Hough transformation. The method comprises three parts of pipeline data acquisition, pipeline point cloud data clustering, and pipeline state detection and identification, wherein the pipeline point cloud data clustering part comprises the following steps: firstly, removing most noise points in point cloud data by using cloth filtering and statistical filtering, and then clustering the point cloud data by using a DBSCAN algorithm of a self-adaptive threshold value; the higher the density is, the higher the class interestingness is. According to the pipeline state detection and identification part, 3D Hough transformation is used for carrying out cylinder and circular truncated cone detection on interest classes, and the exposed state of the pipeline is determined according to different detected results.

The invention discloses a sugarcane live-action three-dimensional phenotype extraction method based on a 3D Gaussian splashing technology. The method comprises the following steps of data acquisition, wherein a 360-degree omnibearing shooting mode is executed around a single sugarcane plant for shooting or video recording to obtain complete image or video data of the sugarcane plant; preprocessing the data; 3D model construction comprises the following sub-steps: SfM sparse reconstruction: instance segmentation; the method comprises the following steps: carrying out 3D Gaussian Splicing guided by a mask; point cloud data processing includes the following sub-steps: point cloud preprocessing including voxel downsampling, statistical filtering and radius filtering; segmenting and identifying stalks / leaves; the segmentation comprises RANSAC plane segmentation, DBSCAN clustering and region growth; the phenotype analysis comprises grid reconstruction and area / volume / perimeter / plant height calculation. According to the invention, the problems of difficult data acquisition, large workload and incomplete information acquisition in the prior art are solved.

The invention discloses a distributed detection device and a 4D millimeter wave point cloud registration method based on three radars. According to the device, a distributed detection module comprises left, middle and right 4D millimeter wave radars; the processor comprises an offline calibration and data synchronization module, an original point cloud preprocessing module, a coordinate system unification module, a point cloud coarse registration module and a point cloud precise registration and fusion output module; the distributed detection module, the processor and the communication module are connected in sequence; the method comprises the steps that off-line external parameter calibration is carried out in a combined mode, and accurate pose external parameters of the left radar and the right radar are obtained based on multi-pose point cloud data; executing statistical filtering processing; uniformly mapping the de-noised point clouds of the left radar and the right radar to a coordinate system of the middle reference radar to form a point cloud set of a three-path common reference system; determining an initial pose matrix through coarse registration; and completing accurate alignment and fusion based on the high-precision pose matrix, and outputting dense 4D millimeter wave radar point clouds. According to the invention, dense and high-quality 4D millimeter wave point cloud data can be obtained.

The invention provides a laser point cloud inter-frame matching method and system, and the method comprises the steps: reading a source point cloud file from a designated path or a data source; performing down-sampling on the point cloud by using a voxel filtering method, and reserving a representative point in each voxel by dividing a point cloud space into voxel grids; removing outliers in the point cloud by using statistical filtering; constructing an ikd-tree for the target point cloud, searching a nearest neighbor point in the target point cloud through the ikd-tree for each point in the transformed source point cloud, and screening out a matching point pair meeting a preset distance threshold; calculating an optimal rigid transformation matrix from the source point cloud to the target point cloud by using an iterative nearest point algorithm in combination with singular value decomposition according to the screened matching point pairs; and transforming the source point cloud by using the optimal rigid transformation matrix to complete point cloud inter-frame matching. The method not only improves the precision and efficiency of point cloud registration, but also has good real-time performance and robustness.

The invention discloses a point cloud deep learning-based prefabricated part stirrup spacing detection method, which comprises the following steps of: firstly, vertically shooting and scanning a reinforcement cage structure of a prefabricated part along a z axis by using a point cloud camera to obtain original point cloud data of the reinforcement cage structure; eliminating discrete noise points in the original point cloud data by adopting an outlier detection algorithm based on statistical filtering to obtain preprocessed point cloud data; distinguishing and extracting a reinforcing steel bar point cloud set and a stirrup point cloud set in the preprocessed point cloud data by using a SimCLR self-supervised learning model through a comparative learning method according to the characteristic difference of reinforcing steel bars and stirrups in the reinforcing steel bar cage structure; and finally, separating the point cloud sets of the upper and lower layers of straight line segments in the stirrup point cloud set through the coordinate value of the axis z of the characteristic value, fitting the straight line segment of each stirrup by using a least square method, and calculating the distance between the adjacent stirrups. According to the invention, the automatic rapid detection of the stirrup spacing is realized, the manual error is greatly reduced, and the detection result reaches the millimeter-level measurement precision.

The invention discloses a small and micro water body quality monitoring method and system based on the Internet of Things, and relates to the field of water body quality monitoring, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module which uses a laser radar and an RGB camera carried on an unmanned aerial vehicle to acquire a whole image of a water body, and uses Canny edge detection to extract a water body boundary; according to the boundary of the water body, the unmanned aerial vehicle flies in a gridding path, laser vertical irradiation is carried out on the water surface, and point cloud data are collected; during vertical irradiation, collecting a water surface image of a vertical area; the point cloud processing module is used for carrying out noise reduction processing on the point cloud data by utilizing statistical filtering; on the basis of IMU and real-time position data, auxiliary positioning is carried out, an NDT algorithm is adopted to carry out registration on the point cloud data, three-dimensional parameter calculation is carried out on the registered point cloud data, and vegetation points and non-vegetation points are distinguished through machine learning; and the image analysis module is used for carrying out RGB color space clustering on the water surface image.

The invention relates to the technical field of optical fiber sensing signal processing, in particular to a DAS signal floor noise elimination method, system and equipment based on statistical analysis, and the method comprises the steps: initializing system parameters, and loading a DAS application field data matrix; performing slicing and frequency domain transformation on the DAS original data; dynamically determining a peak threshold value, and performing adaptive peak searching according to the peak threshold value; sorting the identified and screened local maximum values in a descending order, and selecting frequencies corresponding to the local maximum values as main frequency candidates; performing clustering analysis on the selected dominant frequency candidates; calculating the cumulative proportion of each main frequency in the total statistics, selecting a center frequency, and forming the multi-frequency band elimination of the monitoring unit by using band elimination frequency bands formed by different center frequencies; and adding the multi-frequency band elimination to a statistical filtering frequency band set of all the point locations, storing the statistical filtering frequency band set of all the point locations as a statistical model, and performing background noise elimination on the DAS original signal based on the statistical model.

The invention relates to the technical field of three-dimensional modeling, in particular to a substation point cloud automatic structured three-dimensional modeling method and system. The method comprises the following steps: carrying out multi-angle scanning on a transformer substation through a laser radar and a panorama camera carried by an unmanned aerial vehicle, and synchronously collecting IMU inertial measurement data and GNSS positioning data to generate a transformer substation original point cloud data set with a timestamp and corresponding to space coordinates; performing outlier statistical filtering and down-sampling sparse completion processing on the original point cloud data set of the transformer substation to obtain a high-quality point cloud data set of the transformer substation; performing point cloud semantic segmentation on the substation high-quality point cloud data set and constructing a substation equipment instance structured topology model; and performing Poisson curved surface three-dimensional reconstruction on the substation equipment instance structured topology model based on the substation equipment point cloud instance set to generate a substation point cloud structured three-dimensional model. According to the method, topological relation reconstruction can be designed to realize efficient and accurate three-dimensional modeling of the point cloud data of the transformer substation.

The invention relates to the technical field of industrial internet-of-things monitoring, and discloses an internet-of-things-based remote monitoring method and system for asphalt mixture stirring equipment. The method comprises the following steps: acquiring key component operation parameters and external environment variables, and performing time domain statistical filtering to obtain an initial operation sequence; performing adaptive window length smoothing and frequency domain noise suppression to obtain a clean operation sequence; performing environment equivalent correction on the clean operation sequence by using the external environment variable to obtain a calibration feature vector; performing multi-dimensional feature correlation analysis according to the calibration feature vector, and determining a working condition switching label; determining a dynamic reference range according to the working condition switching label; calculating a preliminary abnormal probability according to the calibration feature vector and the dynamic reference range; and performing historical trend weighting and abnormal integration to obtain a final health assessment result. According to the method, through environment correction and dynamic reference following, environment interference and working condition misjudgment are eliminated, and accurate evaluation of the equipment health state is achieved.

The invention discloses an existing bridge high-precision three-dimensional reconstruction method based on laser point cloud data, and relates to the technical field of bridge engineering digital reconstruction. Multi-source laser point cloud cooperatively collects detail point cloud of key components of an existing bridge, point cloud of a bridge floor and an upper structure and point cloud of a shielding part; multi-source point cloud coarse registration is completed based on a target ball of a preset specification, and then fine registration is carried out by adopting an improved iterative nearest point algorithm of integrated normal vector constraint; and adopting a layered denoising strategy to carry out statistical filtering denoising on the flat area, and carrying out adaptive radius filtering on the vulnerable area to reserve cracks and peel off disease points. Through collaborative design of multi-source acquisition, precise processing, intelligent segmentation, parameterized modeling and hierarchical verification, data integrity improvement, consideration of point cloud processing precision and disease retention, semantic segmentation and instance identification precision optimization, BIM model engineering value improvement and achievement adaptability enhancement are realized.

The invention provides a moving ship detection method based on spatial-temporal characteristics, which comprises the following steps: firstly, eliminating land and thick cloud regions through dual-threshold segmentation preprocessing, and adaptively extracting weak ship candidate targets in a water area by combining top-hat transform with sequential statistical filtering; combining detection results of each wave band, expanding the area of a ship candidate target, realizing signal enhancement and eliminating static false alarms, combining multiple frames of candidate targets, and carrying out filtering processing by utilizing a multi-direction rotation linear kernel by utilizing position offset characteristics of a moving target in five wave bands of a Gaofen-4 satellite due to imaging time difference; according to the method, real ships in smooth track distribution are reserved, isolated and randomly distributed false alarms or non-coherent track false alarms are eliminated, and stable and high-precision ship detection is effectively guaranteed while the low false alarm rate is kept.

The invention provides a marine target detection method based on an unmanned aerial vehicle, and the method comprises the following steps: 1, carrying out the time-space synchronization of point cloud original data collected by a plurality of sensors on the unmanned aerial vehicle, and obtaining the point cloud original data after the time-space synchronization; step 2, preprocessing the point cloud original data after time-space synchronization acquired by each sensor, and obtaining preprocessed single-sensor point cloud data through voxel filtering and statistical filtering; step 3, based on a K-dimensional tree and a DBSCAN clustering method, performing clustering segmentation on the point cloud data of the single sensor to obtain a single sensor target detection result; and 4, performing comprehensive analysis on all single-sensor target detection results by adopting a multi-source point cloud information decision level information fusion method to obtain a final target detection result.

The invention provides a steel rail curvature detection method and system based on machine vision, and the method comprises the steps: synchronously collecting three-dimensional point cloud data and a two-dimensional image of a steel rail; voxel filtering and statistical filtering are carried out on the three-dimensional point cloud data to remove noise, and distortion correction and gray level equalization processing are carried out on a two-dimensional image; processing the two-dimensional image by using a deep learning semantic segmentation algorithm, extracting the edge contour of the steel rail, and generating a three-dimensional contour model of the steel rail in combination with the three-dimensional point cloud data; fitting the three-dimensional contour point cloud of the steel rail by adopting an RANSAC algorithm to obtain a central axis of the steel rail; bending angles of the steel rail in the vertical direction and the tangential direction are calculated, included angles of normal vectors of adjacent sections are calculated, and the torsion degree is determined; bending and torsion angle threshold values are set, when a detection value exceeds the threshold values, sound-light alarm is triggered, real-time communication with a production line PLC system is carried out through an industrial Ethernet, a bending degree value and an abnormal state signal are transmitted to a production control system, and automatic rolling mill parameter adjustment or shutdown maintenance triggering is supported.

A high-speed rail pantograph carbon contact strip abrasion detection method comprises the steps that carbon contact strip point cloud profile data of a high-speed rail pantograph are obtained through a single-line laser sensor; processing the data by adopting a motion blur correction algorithm based on spatial range screening and reflection intensity screening to obtain effective point cloud data; statistical filtering is carried out on the effective point cloud data, then normalization processing is carried out, and normalized effective point cloud data are obtained; obtaining a first demarcation point and a second demarcation point of the pantograph horn and the carbon contact strip by adopting a reflection characteristic identification method and a characteristic point identification method respectively; screening and integrating to obtain a final demarcation point, and intercepting to obtain an actually measured carbon slide plate point cloud data set; and comparing and registering the actually measured point cloud data set of the carbon contact strip and the point cloud data set of the standard carbon contact strip by adopting a point-to-surface ICP (Inductively Coupled Plasma) registration method so as to calculate the abrasion value and the over-limit abrasion area of the pantograph carbon contact strip. According to the invention, high-efficiency and high-precision detection of high-speed railway pantograph carbon contact strip abrasion can be realized.

The invention belongs to the technical field of thermal design of integrated circuits, and particularly relates to a chip wiring three-resistor thermal resistance extraction method based on separated grids. The method solves the problems of local temperature gradient distortion and geometric interface characteristic deficiency near the wiring caused by an existing equivalent thermal resistance model (such as a volume equivalent method and a homogenization method), and avoids the technical problem of overhigh computing resource consumption caused by full-chip fine grids. The method comprises the following steps: analyzing a chip structure to generate a JSON file; the method comprises the following steps: dividing a chip into a plurality of layers by adopting a separated grid technology, embedding wires in a three-dimensional grid to serve as a one-dimensional structure, and marking an interface; the calculated key parameters comprise a total area, a metal coverage area, a dielectric coverage area and an adjacent unit distance; calculating total thermal resistance formed by series connection of in-vivo thermal conduction resistance and interface penetration thermal resistance based on the thermal conductivity; and after statistical filtering, a circuit simulator is imported to output temperature distribution. The method is high in precision, and the calculation cost is remarkably reduced.