40 results about "Distance mapping" patented technology

The application provides an image target matching method and device based on local homography, and relates to the field of image processing; the method adopts a registration algorithm based on image local features to pre-acquire natural feature point pairs between background image pairs; secondly, the source background image is evenly divided into multiple rectangular grids; a moving direct linear transformation algorithm is used to calculate the local homography matrix of each rectangular grid; and the mapping point of the image target to be matched in the source image in the target image is calculated by using the local homography matrix; and the image target uniquely existing in the mapping area or the image target closest to the mapping point in the Euclidean distance is taken as the matching point. The application uses the local homography matrix to weaken the mapping error introduced by the parallax change, improve the image target matching precision, reduce the mis-matching probability of the cluster target with similar features and dense distribution in a complex environment, and significantly improve the image target matching speed.

The application discloses a mineral prediction method based on spatial intelligence, relates to the technical field of spatial intelligence, and comprises the following steps: identifying the spatial relationship of a spatial entity element list, constructing a topological connected graph, extracting connected structure attributes, executing semantic relationship reasoning, and generating an object-level spatial intelligent scene graph; based on the object-level spatial intelligent scene graph, constructing an anisotropic cost field, calculating a minimum cost channel distance, mapping into a channel weight expression, and generating a channel weight layer; performing spatial alignment and multi-modal spatial correlation feature fusion on a multi-source mineralization evidence data set, the object-level spatial intelligent scene graph and the channel weight layer, executing mineral inference through a mineral prediction machine learning model, and generating a mineral prediction layer. The application realizes dynamic modeling of a mineralization path, and improves prediction accuracy and engineering practicability.

The invention discloses an algorithm, equipment and system for identifying abnormal operation of a coal mine scraper, relates to the technical field of safety monitoring of coal mine equipment, and aims to solve the problems of lagging and low accuracy of traditional manual inspection. According to the algorithm, videos are collected through an intrinsically-safe camera (1920 * 1080 pixels and 25fps) above the middle of the scraper conveyor, identification points are set to construct an ROI so as to establish pixel-actual distance mapping, scraper blades are recognized through a YOLOv8 model, the actual distance (normal 1.0-1.4 m) and the scraper blade inclination slope (the threshold value is larger than or equal to-0.15, and the abnormality lasts for more than 5 seconds) of the adjacent scraper blades are calculated, and if the threshold value is exceeded, it is judged that the scraper blades are abnormal. The device comprises an image acquisition module, an ROI construction module and the like. The system comprises an underground acquisition terminal, an edge server and an AI platform, and realizes local sound-light alarm and remote monitoring. According to the invention, real-time monitoring can be realized, the abnormal response is less than 30 seconds, the accuracy rate is 94.6%, equipment shutdown can be reduced by 35%, and the labor cost is saved.

The invention discloses an optimization decision-making system and method based on energy production scheduling, and belongs to the technical field of energy management and production scheduling, and the method comprises the steps: obtaining the operation parameters of various types of energy equipment, and constructing an initial operation state set; edge response characteristic parameters under the high-frequency variable load condition are extracted; constructing a graph neural network model, calculating a relative response distance between a current state and an edge state of the equipment, and constructing an edge critical judgment function based on the classification model; mapping the relative response distance into a scheduling priority index, and generating an equipment sorting list; according to the real-time load and the predicted fluctuation range, selecting a response equipment combination in combination with the priority, and performing risk correction on the scheduling instruction; the robustness of the scheduling scheme under the disturbance condition is verified through simulation, and if the requirement is not met, feedback optimization is carried out. According to the method, dynamic perception, self-adaptive scheduling and risk avoidance under the multi-source energy system can be realized, and the scheduling stability and intelligence are improved.

The application discloses a digital phase-shifting phase recovery method of ESPI fringe pattern based on distance mapping, which comprises the following steps: 1) extracting the direction of each pixel point of the fringe pattern, and then calculating the normal curve of the fringe according to the fringe direction to obtain the moving direction of the fringe; 2) extracting the center line of the dark fringe, calculating the width of the fringe according to the center line of the fringe, and obtaining the moving distance of the fringe through distance mapping; 3) comprehensively moving the direction and the distance, and performing digital phase-shifting on the initial fringe pattern point by point, and adopting the bilinear gray interpolation to obtain the fringe pattern after digital phase-shifting; 4) calculating the wrapped phase corresponding to the fringe pattern; and 5) performing unwrapping processing on the wrapped phase to recover the full-field phase of the fringe pattern. The application extracts the phase through the digital image processing technology on the single fringe pattern, avoids the problem of inaccurate phase-shifting existing in the traditional physical phase-shifting technology in dynamic measurement, and can effectively improve the accuracy of the phase recovery of the single fringe pattern.

The application discloses a kind of based on energy production scheduling optimization decision system and method, belong to energy management and production scheduling technical field, obtain the operating parameter of various energy equipment, construct initial operating state set;Extract the edge response characteristic parameter under high-frequency variable load condition;Graph neural network model is constructed, the relative response distance of current state and edge state of equipment is calculated, and edge criticality judging function is constructed based on classification model;Relative response distance is mapped as scheduling priority index, and equipment ranking list is generated;According to real-time load and predicted fluctuation range, combined with priority selection response equipment combination, and risk correction is carried out to scheduling instruction;The robustness of scheduling scheme under disturbance condition is verified by simulation, if it does not meet the requirements, then feedback optimization.Edge characteristic parameters are updated after each scheduling cycle, and the application can realize dynamic perception, adaptive scheduling and risk avoidance under multi-source energy system, improve scheduling stability and intelligence.

This application discloses a radar ranging method, apparatus, device, and storage medium. A reflector is installed at one end of the elevator shaft, and a radar is installed in a car moving within the shaft. The method includes: controlling the radar to emit electromagnetic wave signals to the reflector and receiving a first echo signal reflected from the shaft; extracting a first spectral feature of the shaft from the first echo signal; matching the first spectral feature with a second spectral feature pre-learned from the shaft, where each second spectral feature is labeled with a reflector; if the match is successful, measuring the car's position in the shaft based on the distance mapping of the labeled reflectors. By matching the first and second spectral features, the position of the car in the shaft can be measured, reducing interference from highly reflective obstacles such as metal objects, ensuring positional accuracy, and guaranteeing the safe operation of the elevator regardless of where the radar is powered on.

A human interface device includes a touch panel and a processing module that is electrically coupled to the touch panel. The touch panel has a first setting region and defines a second setting region in the first setting region. The processing module has a coordinate detection mechanism and a coordinate conversion mechanism. The coordinate detection mechanism defines a first longitudinal coordinate axis having a distance mapping to a length of the first setting region. The coordinate conversion mechanism defines a second longitudinal coordinate axis having a distance mapping to a length of the second setting region. When the second setting region is operated, a touch point of the second setting region has a coordinate of the first longitudinal coordinate axis that is obtained through the coordinate detection mechanism and that is converted to a second longitudinal coordinate of the second longitudinal coordinate axis through the coordinate conversion mechanism.

The invention discloses a two-point voltage sampling type inductance distance measuring sensor and a control method. According to the method, voltage sampling is carried out at two key time points, the equivalent resistance and the equivalent inductance of an inductance measuring head are calculated based on the sampled voltage, and then matching with a pre-stored high-precision inductance-distance mapping table is carried out through an error minimization algorithm, so that the actual distance of a measured object is obtained. The sensor comprises an inductance distance measuring circuit, a time sequence control module, a signal acquisition module and a data processing module which cooperatively realize high-precision distance measurement. Through time sequence innovation and intelligent calibration, the accuracy and robustness of a distance measurement result are remarkably improved, and the method is particularly suitable for precise positioning and mechanical detection scenes in industrial automation.

The invention provides an old man getting-up safety magnetic attraction alarm system based on pressure induction and delay triggering, and relates to the technical field of monitoring alarm, and the system comprises a serial number processing module which obtains the track of a monitored person; the active state detection and threshold comparison module is used for setting an initial delay trigger period and outputting a judgment result of whether the independent area triggers reset alarm or not; an alarm state resetting and updating module; the spatial distance mapping and extension multiplying power calculation module is used for calculating an alarm extension multiplying power for reflecting the time required by the monitored person to return to the pressure sensing point; and the dynamic delay synthesis triggering module is used for outputting alarm triggering final time. According to the invention, intelligent trajectory tracking and self-adaptive risk response capabilities which cannot be achieved by a conventional system are realized, and the system not only can reset alarm countdown every time an old person enters a new room, but also can automatically prolong a safety time window according to the distance between the room and a getting-up point; and the passing difficulty caused by the ground material and the barrier density is additionally fused.

The invention discloses an age estimation network and method based on an elastic law and a non-uniformly distributed X-ray human skeleton image, and mainly solves the problem of insufficient optimization effect on a sample distribution imbalance task in medical image age estimation in the prior art. The network construction comprises the following steps: establishing a feature extractor for extracting batch features from an input image; introducing a feature queue, and using supervision force, distance elasticity and midpoint elasticity to do work on the samples so as to carry out comparative learning between the samples; defining the basic age estimation loss by using the supervision force, and obtaining the total loss of network iteration training; establishing a monotonicity constraint residual block for mapping the sample pair feature distance into a partial monotone increasing distance feature between samples; and establishing mutually independent age estimation regression heads for outputting an estimation result. According to the method, the performance of the automatic age estimation model on few sample data is improved, and the method has a good aging change characterization effect and can be used for disease risk assessment, individualized health management and medical research.

This invention belongs to the field of quantitative loading technology. It proposes a quantitative loading assistance method and system based on radar scanning and deep learning. The method involves synchronously acquiring the original point cloud data of the vehicle compartment using forward and backward scanning radars arranged along the vehicle's movement direction. After denoising, coarse registration, and ICP fine registration, a high-precision registered point cloud is generated. The point cloud is projected onto the Y-O-Z plane and enhanced, then input into a deep learning model to identify the pixel coordinates of the rear baffle, front baffle, and unloading port. Combined with the actual dimensions of the vehicle compartment, a pixel-to-actual distance mapping coefficient is calculated. The vehicle's movement distance is calculated by weighting the point cloud coordinate displacement and image pixel displacement. Furthermore, based on the vehicle compartment's geometry, dynamic decay of loading progress, and loading deviation, a dynamic correction coefficient for quantitative loading is constructed, and the unloading speed and vehicle movement speed are adjusted in real time accordingly. This achieves multi-vehicle adaptive, high-precision, and fully automated quantitative loading.

This invention relates to the field of image defect detection technology, specifically to a semiconductor wafer defect detection method and system based on machine vision. The method includes acquiring an original wafer image and preprocessing it; generating candidate regions for suspected defects using the Canny edge detection algorithm based on the preprocessed wafer image; and extracting wafer surface feature vectors based on the candidate regions by constructing a gray-level co-occurrence matrix (GLCM). A reflection compensation mechanism is introduced during the GLCM construction process to reduce the interference of light spots on the image through a reflection compensation module. This invention parameterizes the edge blurring characteristics of different process layers and constructs an adaptive gradient kernel and optimal pixel distance mapping mechanism accordingly, enabling Canny edge detection and GLCM texture analysis to dynamically adapt to surface morphology differences caused by various manufacturing processes.

The application provides a microscope fundus surgery image position adjustment method and system, and relates to the technical field of computers, and comprises the following steps: acquiring an original surgery video image sequence collected by a camera in real time; performing surgery scene classification according to the original surgery video image sequence to obtain a judgment signal; performing action region detection processing according to the judgment signal to obtain preliminary action region position information; performing region extraction according to the preliminary action region position information to obtain circumscribed rectangle position information of a surgery region contour; performing adjustment judgment according to the circumscribed rectangle position information to obtain an adjustment direction instruction; and performing position mapping and output according to the adjustment direction instruction, wherein the direction and distance of moving the surgery region to the center in the image plane are mapped into parameters for deflecting an optical reflection element on a two-dimensional plane to obtain adjustment parameters for driving the optical element. The application realizes automatic tracking and centering of the surgery region in the camera imaging picture.

The invention discloses a pure machine vision distance measurement method and system, and relates to the technical field of environmental perception. The method comprises the following steps: a calibration stage: a plurality of concentric arc marks are preset on the flat ground, the marks take the projection of a preset reference point on the ground as the circle center, and the radius is the known distance; a first camera and a second camera are installed on a support with pitch angles capable of being synchronously adjusted at a fixed non-zero relative pitch angle difference, calibration image pairs are synchronously collected at a plurality of preset overall pitch angles, center lines of arc marks in the images are extracted, and a pixel-distance mapping relation pair library in one-to-one correspondence with the overall pitch angles is established. In the distance measuring stage, a first camera and a second camera are fixedly installed on a carrier according to the fixed non-zero relative pitch angle difference, a to-be-measured image pair is collected in real time, the contour of a target object is detected, all preset overall pitch angles are traversed, and a first mapping relation and a second mapping relation under the corresponding overall pitch angles are obtained from a mapping library respectively; and querying the minimum distance of the contour pixels to obtain a first candidate distance and a second candidate distance, selecting the overall pitch angle of the target according to the consistency of the first candidate distance and the second candidate distance, and determining a final distance measurement result based on the two candidate distances corresponding to the overall pitch angle of the target. Through double-view multi-overall pitch angle calibration and consistency matching, the method is adaptive to complex three-dimensional terrains, does not need camera internal reference, does not depend on target priori, does not need any active sensor, and realizes high-precision, low-cost and low-calculation-amount real-time distance measurement.

The application relates to a GPU cluster-based bus synchronous moving mode detection method for large space-time scales, and belongs to the technical field of parallel computing, space-time big data processing and synchronous moving mode mining of moving objects in a heterogeneous environment. First, a space distance mapping function is designed, absolute space distance between a bus and a starting station is mapped into relative distance between 0 and 1 according to the characteristics of bus GPS track round trip cycle, and the GPS track in three-dimensional space is mapped into two-dimensional space (absolute distance, time axis). Then, based on multiple CUDA streams, the two-dimensional space is divided in a GPU cache according to time windows, a CUDA thread group is used to calculate a track inclination angle and an MBR, and a direction-aware MBR index is used to organize track segments. Secondly, a filtering-verification framework based on LCSS is designed, and the mode detection is accelerated in parallel through multiple CUDA streams. Finally, PySpark is used to realize process control and data partition, and GPU is used to execute a computationally intensive task, so that distributed parallel processing is realized.

The invention discloses a front-end picture self-adaptive obstacle avoidance analysis method based on an AGV transport vehicle, relates to the technical field of computer vision, realizes accurate understanding and path planning of a complex driving environment by fusing multi-source sensing data of a camera and a millimeter wave radar, and realizes accurate obstacle avoidance by synchronously acquiring gray images and pixel-level distance information. A reliable ground area is extracted based on gray features and morphological processing, a dynamic drivable area is constructed in combination with an image geometric structure, a strong edge detection and linear clustering technology is utilized, left and right vanishing points are automatically identified, a geometric corridor is constructed, and the robustness of environment modeling is improved; secondly, through distance mapping and clustering analysis, obstacles are identified, an influence circle is generated, and a safety corridor is dynamically constructed; the optimal path is planned in real time in the safe corridor, it is ensured that the AGV runs safely and efficiently in a narrow or dynamic environment, and the method is suitable for an intelligent transportation system in complex scenes such as storage and manufacturing.

This invention discloses a five-axis toolpath generation method applicable to aero-engine blades. First, a surface model of the aero-engine blade is constructed, and the workpiece surface is divided into triangular meshes. All triangular mesh surfaces are traversed to obtain boundary curves, and the boundary curve with the fewest vertices is selected as the source curve. Then, the geodesic distance from all vertices to the source curve is calculated, and the geodesic distance of each vertex is mapped onto the surface model to generate a geodesic distance field. Finally, the toolpath spacing is determined based on the relationship between the uniform residual height and the machining step size. The contour lines of the geodesic distance field are extracted as toolpaths based on the toolpath spacing. Through iterative calculation, the toolpaths are calculated until they cover the entire machining surface, thereby obtaining the tool contact points of each toolpath. The optimal tool axis vector is obtained based on the surface normal vector at the tool contact point. By constructing a geodesic distance field to generate the toolpath, a global toolpath can be generated in one operation, improving machining efficiency and accuracy.

The invention relates to the technical field of farming and pasture breeding, in particular to a cattle neck self-adaptive clamping control method and system based on visual identification, and the method is used for enclosing a neck clamping device. The method comprises the following steps: obtaining a pixel actual mapping relation based on a standard calibration plate image, constructing a world coordinate system, obtaining cattle multi-view image data, and obtaining a cattle neck clamping data set; processing the multi-view image data to obtain an effective contour image which retains the complete contour of the cattle; positioning a neck datum point by using a feature point matching algorithm, and determining world coordinates of the neck datum point according to a pixel actual distance mapping relation; according to the world coordinates of the neck datum point, target control parameters of driving of a driving assembly are determined, wherein the target control parameters comprise the advancing distance of the neck clamping mechanism in the first direction and the advancing distance of the neck clamping mechanism in the second direction. The problems that accurate positioning and self-adaptive clamping of the neck cannot be achieved based on the individual characteristics of the cattle, so that the clamping precision is low, the stability is poor, the cattle is prone to being damaged, and the working efficiency is low are solved.

The invention discloses a mineral product prediction method based on spatial intelligence, and relates to the technical field of spatial intelligence, and the method comprises the steps: recognizing the spatial relationship of a spatial entity element list, constructing a topological connected map, extracting the attribute of a connected structure, executing semantic relationship reasoning, and generating an object-level spatial intelligent scene graph; constructing an anisotropic cost field based on the object-level space intelligent scene graph, calculating a minimum cost channel distance, mapping the minimum cost channel distance into a channel weight expression, and generating a channel weight graph layer; and carrying out space alignment and multi-modal space correlation feature fusion on the multi-source metallogenic evidence data set, the object-level space intelligent scene graph and the channel weight layer, and executing mineral reasoning through a mineral prediction machine learning model to generate a mineral prediction layer. According to the method, the dynamic modeling of the metallogenic path is realized, and the prediction precision and the engineering practicability are improved.

The embodiment of the invention discloses an unmanned aerial vehicle optical flow module calibration method and device, equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle data processing. The method comprises the following steps: acquiring a plurality of sensor heights of a calibration object captured by a sensor of an unmanned aerial vehicle, and correspondingly shot calibration object images; obtaining camera internal parameters of the unmanned aerial vehicle and a plurality of feature point coordinates of the calibration object in a real space; for each calibration object image, determining a pixel point coordinate corresponding to each feature point coordinate according to the calibration object image; for each calibration object image, determining the camera height from the camera of the unmanned aerial vehicle to the calibration object under the calibration object image according to the internal reference of the camera, the coordinate of each feature point and the coordinate of each pixel point, and determining a distance mapping relationship between the distances from the sensor and the camera of the unmanned aerial vehicle to the same object; and calibrating a camera of the unmanned aerial vehicle and a sensor of the unmanned aerial vehicle based on the distance mapping relation. The calibration accuracy of the unmanned aerial vehicle optical flow module can be improved.

The application provides a copper-diamond porous liquid cooling channel optimization method and system, the method comprises the following steps: constructing a three-dimensional parameterized model of a copper matrix, a diamond phase and a liquid cooling channel; taking porosity, pore size gradient, rib thickness and branch angle as design variables; establishing a feasible region evaluation model with the highest temperature of the radiator and the minimum fluid pressure drop as the target and geometric and physical constraints as the boundary; dividing the initial optimization individuals into out-of-bound, boundary and internal individuals; calculating the distance mapping coefficient of the out-of-bound individuals and adjusting the variables; determining the replacement position according to the constraint normal vector in the multi-constraint intersection area; after numerical solution of the flow field and the temperature field, screening the candidate set through non-dominated sorting and crowded distance; generating memory weight by combining the boundary residence times of the individuals; adjusting the parameter iteration optimization of the search step; and outputting the optimal structure parameters of the porous liquid cooling channel until the termination condition is met.

The application provides a kind of UUV submarine pipeline detector positioning and tracking method of acoustic-electric fusion weight, it is related to underwater target following positioning technical field.First, sensor array is arranged at the bottom of UUV, the physical quantity and UUV state data collected in each sampling period are determined;Then, the environment and signal propagation model is established, the acoustic estimated distance and electromagnetic wave estimated distance of detector and UUV are calculated, and then the acoustic-electric weighted fusion distance of detector relative to UUV is calculated;Again, the acoustic-electric weighted fusion distance is mapped to coordinates;Judge the relative distance trend of detector and UUV, and control the rough following of UUV;Based on the unbalance of acoustic wave and electromagnetic wave in four groups of sensor array, fine attitude control is carried out on UUV;Detect the sudden change of detector working condition and carry out emergency control;The method realizes the comprehensive effect of "full distance accurate positioning, complex environment self-adaptation, sudden state stable response and high-precision following in stable stage".

The invention discloses a spectral confocal measurement method and system for a transparent protective coating on the surface of a high-density PCBA, and relates to the technical field of electronic packaging manufacturing detection, and the method comprises the steps: obtaining a panoramic image of the high-density PCBA, recognizing a reference point, and building a coordinate system mapping relation; measuring points are selected, the selected measuring points are converted to a motion coordinate system based on the mapping relation, and a measuring head automatic scanning path is generated; a measuring head is driven to collect spectral signals along a path, and reflection peak wavelengths of the upper and lower surfaces of the coating are extracted through an adaptive algorithm; predicting a lower surface peak search window by using an improved intelligent algorithm, and adaptively extracting a second peak wavelength in combination with the characteristics of the substrate; and finally, calculating the real physical thickness according to the wavelength-distance mapping relation and the refractive index of the coating, and outputting a thickness distribution and qualification judgment result. According to the invention, non-contact, high-precision and full-automatic detection of the thickness of the transparent coating on the surface of the high-density PCBA is realized, and the measurement efficiency and the adaptability to different substrate materials are remarkably improved.

The embodiment of the invention provides a thickness measuring method and device for a tread cleaning device, equipment, a medium and a product. The method comprises the following steps: acquiring an image of the tread cleaning device; performing semantic segmentation on the tread cleaning device image, and determining a mask segmentation region of the tread cleaning device in the tread cleaning device image; determining a minimum enclosing rectangle of the tread cleaning device according to a mask segmentation area of the tread cleaning device in the tread cleaning device image; and determining the thickness of the tread cleaning device according to the minimum enclosing rectangle of the tread cleaning device and the length of the tread cleaning device. According to the method, the image of the tread cleaning device is utilized, based on the image segmentation, the mask enclosing rectangle and the actual distance mapping relation, the thickness measurement of the tread cleaning device is realized, the thickness measurement problem under the condition of lack of reliable point cloud is solved, the thickness measurement accuracy of the tread cleaning device is improved, and the method is stable, reliable and high in feasibility.

The invention relates to the field of artificial intelligence, and particularly discloses an antibody variable region structure prediction method based on structure prior evaluation, which comprises the following steps: S1, screening a high-resolution and high-integrity compound containing a complete antigen and an antibody H / L chain from PDB, and extracting atomic coordinates after processing; s2, calculating geometrical characteristics of a molecular structure and relative spherical polar coordinates of adjacent residues; s3, calculating a structure quality evaluation score according to physical prior and a dihedral angle-atomic spacing mapping formula; s4, screening training data according to scores, and removing low-quality data; s5, constructing an antigen-antibody attribute isomeric graph and initializing a compound graph; s6, embedding related data as enhanced features when the model is initialized; s7, loading an RA-EGN encoder to output a CDR sequence and a 3D coordinate, and splicing a complete antibody structure; and S8, introducing a structure prior loss function fine tuning model, and performing multi-index screening on an optimal result. According to the invention, the problems of low prediction precision and insufficient structure prior utilization of the existing antibody CDR region are solved, and the prediction accuracy of the antibody variable region is improved.

The invention relates to the technical field of antenna proofreading, and particularly discloses a wireless equipment antenna remote automatic proofreading technology based on Beidou communication. The technology comprises the following steps: integrating sensing equipment on an equipment antenna, and collecting antenna attitude data in real time; monitoring a communication link state, and triggering an automatic correction process if the communication link state does not reach the standard; acquiring three-dimensional coordinates of transmitting and receiving ends, and calculating reference attitude parameters in combination with spherical trigonometry and a refraction model; acquiring a current transmission rate, matching the current transmission rate with a standard communication distance mapping table, and calculating a final reference elevation angle according to an actual distance and a correction coefficient; and issuing the final reference elevation angle to drive the antenna to adjust, monitoring the link state according to a preset period, and if not, iteratively adjusting until being qualified. According to the scheme, traditional pain point proofreading is achieved, full-automatic remote operation is achieved, response time is shortened, and emergency communication is guaranteed; the correction precision is improved by depending on the Beidou and a professional model, and a stable link in a severe environment is adapted; the adaptability is enhanced through iterative optimization, and reliable guarantee is provided for smooth communication in the key field.

This application provides an automatic RTM mesh generation method based on implicit distance mapping and normal growth, comprising: acquiring a triangular mesh on the surface of a composite material part; obtaining a local normal field on the part surface by calculating the unit normal vector of each triangular facet; using the implicit distance field as the geometric representation basis, converting the layup thickness into a distance offset value according to the thickness mapping relationship to generate an enhanced initial reference surface; projecting points on the part surface onto the implicit distance field through the thickness mapping relationship, calculating the signed distance value of each point, determining the nested region affiliation, and obtaining supplementary nested space affiliation labels; using the optimized material region as boundary constraint conditions, performing voxel growth operations layer by layer along the local normal field of each layer to generate an initial volume mesh corresponding to each layup; and outputting the final solid mesh for simulation analysis of resin transfer molding process flow and curing.

This application proposes a method for identifying the position of a circuit breaker trolley located in a medium-voltage switchgear. The method includes: acquiring real-time image data of the circuit breaker trolley, the real-time image data including multiple consecutive images acquired within a preset time period; calculating the image distance between each frame of the multiple consecutive images and a reference image to obtain a distance vector; wherein the reference image is an image of the circuit breaker trolley in its working position; comparing the distance vector with a reference distance vector in a pre-constructed image distance mapping table to determine the shortest reference distance vector; and mapping the shortest reference distance vector to the first position of the circuit breaker trolley based on the image distance mapping table.