676 results about "Spatial mapping" patented technology

The invention relates to the technical field of voice semantics, can be applied to business scenes of financial science and technology, medical health, poster design and the like, and discloses a visual sense generation method and device based on semantic association modeling, equipment and a medium. Generating a demand text containing theme and style parameters; semantic features in the demand text are extracted, semantic association weights are constructed, and element layout coordinates are optimized in combination with spatial distribution constraints; and encoding the layout information into a control matrix, fusing the control matrix with the initial noise, adjusting a noise reduction process through an encoding and decoding network, and generating target visual content highly matched with the semantic meaning of the user instruction. According to the method, the layout optimization function is constructed, the diffusion model is guided to focus the semantic salient region in space, language model output and the visual generation process are closely combined, structured response and space mapping of user semantic requirements are achieved, and the expression consistency and personalized adaptation capacity of visual content generation are improved.

The invention relates to the technical field of defect detection, in particular to a special equipment defect automatic identification method and system, and the method comprises the following steps: marking a continuous path based on an image gray scale gradient and adjacent differences, identifying the pixel connection intensity to obtain a contour image layer, extracting continuous pixels, analyzing gray scale and gradient features, and expanding a texture direction to generate a defect image block. And marking and connecting the small regions to obtain a closed layer, analyzing texture and edge differences, matching classification tags, and clustering and coding to generate a defect identification tag set. According to the method, boundary structure recognition is enhanced through gray gradient and direction continuity analysis, a texture aggregation area is expanded in combination with gray consistency and edge direction stability, brightness abrupt change points are removed, contour extraction precision is improved, micro crack continuity is recovered, and pseudo defects are eliminated; and multi-dimensional image attributes are fused to identify key region feature differences, so that the classification precision and the spatial mapping consistency are improved, and efficient and accurate defect identification and stable classification are realized.

The invention relates to the technical field of document retrieval and information processing in the data processing technology, in particular to a document retrieval method based on multistage indexing and feature clustering, which comprises the following steps: performing high-dimensional space mapping on multi-modal features such as texts and images through a quantum embedding layer to generate cross-modal joint feature representation; a first-level index of a multi-level index architecture is dynamically initialized based on a meta-clustering algorithm, and semantic blocks of a second-level index are divided in combination with a multi-head self-attention mechanism. And an optimal transmission matrix is generated by using a Sinkhorn algorithm to align cross-node feature distribution. The multi-target mixed retrieval strategy is fused with vector retrieval, keyword retrieval and graph retrieval results, and weight distribution is dynamically adjusted. Through collaborative optimization of quantum calculation, federated learning and causal reasoning, a closed-loop technical architecture from feature analysis to dynamic index construction is formed, the problems of insufficient cross-modal fusion, static clustering deviation and semantic association deficiency are solved, and the precision, efficiency and dynamic adaptability of heterogeneous document retrieval are improved.

The invention discloses a low-altitude unmanned aerial vehicle flight statistical system, and relates to the technical field of air traffic management. A flight statistical system of a low-altitude unmanned aerial vehicle comprises a multi-source sensing fusion module, a twin airspace modeling module, a behavior portrait modeling module, a non-cooperative target recognition module, a flight statistical analysis module and a self-adaptive scheduling module. According to the invention, a three-dimensional twin airspace model of a monitoring area is constructed based on high-precision map data, a building three-dimensional model and flight information, and space mapping and collision risk analysis of a flight path are carried out, so that real restoration of the flight path and environment collision prediction are realized; the behavior feature vector is generated and the random forest algorithm is adopted to carry out classification modeling, and the behavior portrait of the unmanned aerial vehicle is constructed, so that accurate identification and classification of the behavior pattern of the unmanned aerial vehicle are realized.

The invention relates to the technical field of three-dimensional models, in particular to an immersive exhibition hall intelligent guide display method and system based on user behaviors, and the method comprises the steps: arranging a multi-modal sensor in a key region of a three-dimensional virtual space, and collecting the visual fixation point coordinates, behavior track coordinates and voice instruction data of visitors; performing spatial mapping processing on the coordinate data through an edge calculation unit to generate three-dimensional user behavior data; constructing a three-dimensional scene analysis model to extract spatial features of the three-dimensional user behavior data set, and generating a user behavior portrait; matching the user behavior portrait, dynamically calling exhibition hall three-dimensional virtual space guide content, and generating a personalized exhibition hall three-dimensional virtual space guide route and a display mode in real time; and performing real-time feedback on a display mode, and performing real-time dynamic adjustment on the exhibition hall guide content in combination with the feedback and the three-dimensional user behavior data analysis.

The invention relates to the technical field of medical intelligent scheduling, in particular to a patient flow data visualization adjusting system and method, and the system comprises a global dynamic sensing unit, a medical intelligent evolution center, a space-time mirror image twinning module, a multi-dimensional decision optimization matrix, and a risk closed-loop management and control unit. The global dynamic sensing unit gathers data to construct a diagnosis and treatment panorama; the medical intelligent evolution center is used for building a medical process evolution system to form a three-dimensional cognitive architecture; the space-time mirror image twinning module is used for constructing physical virtual space mapping, and forming a diagnosis and treatment resource space-time distribution path based on Internet of Things position data and a digital twinning technology; the multi-dimensional decision optimization matrix extracts patient flow data according to the space-time simulation path and a multi-target model, and generates a resource scheduling scheme; and the risk closed-loop management and control unit is used for evaluating the risk, marking congestion early warning and dynamically optimizing a regulation and control strategy and a threshold value. Therefore, the problems of data isolation, poor resource configuration efficiency, insufficient real-time performance and the like in the prior art are solved.

The invention relates to the technical field of warehouse management, in particular to a logistics warehouse management method and system based on digital twinning. The method comprises the following steps: deploying a multi-source sensor array and constructing a cold chain storage standard data set; performing timestamp alignment on the cold chain storage standard data set to generate cold chain storage timestamp alignment data; performing three-dimensional space mapping on the cold chain storage timestamp alignment data, and establishing a feature tensor matrix to generate a cold chain storage fusion feature matrix; therefore, through space-time fusion of multi-source data and dynamic optimization of a digital twinborn technology, the problems of data islands and inaccurate prediction in traditional cold chain storage are solved, the real-time performance of storage environment monitoring and the precision of temperature regulation and control are improved, and safety and efficiency management of cold chain logistics are effectively supported.

The invention discloses a digital production plan scheduling method and system, and belongs to the technical field of optimal scheduling, and the method comprises the steps: constructing a distributed storage architecture based on edge computing nodes; a central coordinator is adopted to realize cross-node data synchronization through an improved Raft consensus algorithm, multi-version concurrency control is realized based on a vector clock, and a global consistent data view is established; a visual scheduling platform is built based on a Vue3 framework, and man-machine interaction is realized by adopting a Canvas and WebGL collaborative rendering framework; establishing a dynamic coordinate conversion model based on bilinear interpolation, designing a space mapping function containing distortion compensation, establishing a multi-thread coordinate service based on WebWorker, and realizing submillimeter-level bidirectional mapping of pixel coordinates and physical coordinates; constructing a three-dimensional space-time analysis model fused with the multi-dimensional features; and all the units are subjected to feature fusion through residual connection, and finally a scheduling scheme with a confidence coefficient weight is output. The method and the device have the effect of meeting various scheduling requirements.

The invention provides a method and system for predicting the uplift bearing capacity of a mud mixed pile. According to the method, modeling is carried out by integrating geomechanics and monitoring data. The method comprises the following steps: firstly, acquiring geometric parameters of a multi-layer heterogeneous soil layer interface, constructing a database through a mechanical test, and improving the reliability by combining gradient recognition and cooperative verification of pile deformation and load data; dividing longitudinal segmentation units, adjusting anti-slip parameters to generate a space mapping table, establishing a debonding dynamic model containing an interlayer retardation effect, simulating a debonding expansion process, and outputting load evolution data. And judging the maximum load of the critical state based on data convergence analysis, and taking the maximum load as a predicted value of the uplift bearing capacity. According to the technical scheme, through dynamic parameter mapping and debonding process simulation, accurate prediction of the uplift bearing capacity of the pile foundation in a complex soil layer is achieved, and the defect that a traditional model is insufficient in characterization of interlayer heterogeneity and dynamic debonding behaviors is overcome.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

Provided is an automatic hail identification method based on radar image spatial mapping, including: obtaining a base reflectivity radar image; grading radar echoes using intensity values represented by different colors, and detecting radar center coordinates; performing spatial mapping, and establishing a dictionary for radar echo intensity in a new radar image space; after expanding the dictionary, transforming V-shaped notch and TBSS characteristics behind an original radar strong echo area into a standard rectangle and a triangle behind a strong echo in the new radar image space; determining whether there is a V-shaped notch and TBSS according to whether there is a standard rectangle or a triangle behind the strong echo; and if so, marking in the original radar image and calculating a position where hail occurs. A rapid and accurate method is provided for automation of hail monitoring and early warning and elimination of continuous high-intensity analysis pressure of forecasters.

The invention relates to the technical field of workpiece airtightness detection, in particular to a workpiece airtightness detection method and system based on multi-source data fusion, and the method comprises the steps: collecting the pressure fluctuation, temperature field distribution and vibration spectrum data of a workpiece through a sensor array, and executing a stepped pressure excitation process; the method comprises the following steps: dynamically configuring an inflation rate according to the heat-conducting property of a material, reversely compensating pressure based on temperature field data in a pressure maintaining stage to eliminate thermal deformation interference, and extracting a pressure attenuation rate and nonlinear characteristics, an area proportion of an abnormal low-temperature region and space aggregation of an energy abrupt change point in a vibration spectrum from compensated data; according to the method, multi-physical field features are fused, the detection precision and reliability are improved, and the method is suitable for the airtightness detection requirement in the high-precision manufacturing field.

The invention relates to the technical field of semiconductor manufacturing, and discloses an edge breakage defect prediction and compensation method in a wafer cutting process, and the method comprises the steps: obtaining a wafer cutting initial scheme and historical cutting data, extracting edge breakage key parameters, defining an edge vulnerability function, and building a high-risk region space mapping model; according to the real-time cutting position, multi-dimensional data are collected through a three-level monitoring mode, the abnormal deviation state of the track is judged, and an edge breakage risk assessment model is constructed and early warning is carried out; a compensation scheme is formulated after early warning is triggered, cutting is implemented, and the effect is verified; according to the method, multi-dimensional data are fused, the risk area is dynamically delimited, self-adaptive adjustment of the cutting parameters is achieved, and the wafer cutting yield is remarkably improved.

The invention relates to the technical field of automatic driving, in particular to an automatic homeward voyage method for multi-sensor data fusion of an unmanned vehicle, which comprises the following steps: acquiring laser radar obstacle points, image edges and attitude data, completing synchronous registration, identifying space mapping, extracting track and boundary trends, screening forward sections, and judging track offset and trafficability. Screening an error band boundary, adjusting a steering instruction, analyzing path overlapping change, and outputting a path keeping label. According to the method, through time synchronization and space registration of various sensor data, high-precision fusion of the data under a unified space-time framework is realized, the reliability of environment perception is improved, accurate mapping of the data in a unified reference coordinate system is ensured, the reliability of path planning and decision making is enhanced, and the optimal advancing direction is determined by combining a homing point; the accuracy of return route planning is improved, the traffic feasibility is dynamically evaluated, the effective error band boundary is screened to improve the safety, and the reliability of the return route is ensured through track stability analysis.

The invention relates to the technical field of geological disaster monitoring, and discloses a point-like landslide and debris flow potential degree adaptive monitoring and early warning method. According to the method, a dot matrix monitoring equipment array is arranged, and displacement data and environmental parameters of multiple monitoring points are collected; analyzing the displacement data timestamp through a timeline analysis module, generating a dynamic timeline, and marking geological event nodes; a geographic information system is combined to carry out spatial mapping on displacement data, and environmental parameters are fused to obtain a preliminary potential degree evaluation result. The pattern recognition engine classifies displacement speed and deformation characteristics in the preliminary result to generate a deformation characteristic spectrum with a space-time label; calculating a dynamic potential degree evaluation map according to the multi-factor weighted evaluation model; and adjusting an early warning threshold according to a user instruction, generating a personalized early warning rule set, and scanning the atlas to output a multi-stage early warning response scheme. On the basis of an early warning feedback iteration updating model and an engine, monitoring data are newly added to trigger and re-analyze in real time, a related scheme is updated in a linkage mode, and different scene requirements are met.

The invention provides an intelligent machine room multi-dimensional dynamic monitoring management method and system based on digital twinning, and relates to the technical field of machine room monitoring, and the method comprises the steps: collecting a three-dimensional point cloud set and an RGB image set of a machine room space, and building a space mapping table; mapping the access control event data to a corresponding position in a digital twin space, generating an access control event space positioning set, identifying access control abnormal events, and constructing an access control abnormal event space positioning set; performing abnormal behavior recognition, and constructing an abnormal event graph structure; reconstructing and visualizing an abnormal track based on the abnormal event graph structure; in combination with the access control abnormal event space positioning set, the abnormal behavior category prediction probability distribution and the track risk level, executing a grading early warning mechanism, and generating an access control abnormal event log; according to the invention, intelligent positioning, track visualization and risk grading early warning of access control abnormal events in the intelligent machine room are realized, and the method is suitable for digital operation and maintenance management systems such as the intelligent machine room.

The invention discloses a laser point cloud shielded vehicle completion method and device based on a thunder-vision fusion deep learning framework in the technical field of automatic driving environment perception. The method comprises the following steps: acquiring original laser point cloud data and an aerial image; constructing a fan-shaped shielding area based on the original laser point cloud data; identifying two-dimensional bounding boxes, orientations and category labels of all vehicle targets in the image; inputting the fan-shaped occlusion area and the two-dimensional bounding boxes, orientation and category labels of all the vehicle targets into a pre-trained double-branch deep learning network, and predicting whether an occluded vehicle exists or not and the position, orientation and category information of the occluded vehicle; and selecting a vehicle point cloud template based on the category information of the shielded vehicle, and then generating a scene point cloud after vehicle point cloud completion as a final result of vehicle completion in the shielded region. According to the invention, an image-point cloud space mapping mechanism based on the Leiyu fusion deep learning framework is introduced, so that the capability of automatically identifying and positioning the shielded vehicle is remarkably improved.

The invention relates to the technical field of computer leakage prevention, in particular to a computer defense system based on the Internet of Things, which comprises an Internet of Things equipment feature acquisition module for acquiring operation data of a computer, a printer and a network camera associated with the computer, and converging instruction time sequence, memory access and peripheral interaction information. According to the method, the dynamic reference model covering the full behavior dimension of the equipment is constructed by collecting multi-source operation data of a computer, a printer and a network camera and extracting quantitative indexes such as instruction time sequence fluctuation, memory access fluctuation and peripheral interaction frequency, and a traditional static protection mode of single encryption or access control is broken through. Spatial mapping and boundary inspection are performed on real-time operation indexes based on a dynamic reference, and automatic closed loop from abnormal signal detection to risk grade evaluation is realized in combination with a data access risk scene matching mechanism, so that the misjudgment rate and delay of manual research and judgment are effectively reduced.

The invention discloses a multi-drive cooperative control method for high-precision electric drive assembly equipment, relates to the technical field of intelligent manufacturing control, and is used for solving the problem of insufficient cooperative control precision of a multi-axis system under parameter mismatch and disturbance. By constructing a multi-source data fusion and dynamic coupling analysis mechanism, the system collects the motion state and control parameter data of multiple driving shafts, generates a multi-shaft collaborative dynamic response sequence, and calculates the dynamic coupling degree between the shafts to identify the uncertainty mode of the system; positioning a performance degradation shaft section by combining the temperature field and the vibration signal characteristics, and generating a system stability evaluation coefficient; and establishing a space mapping relation to construct a dynamic compensation factor matrix, and generating a control parameter adjusting quantity meeting a collaborative matching condition through fuzzy rule reasoning. According to the method, control instability caused by traditional global adjustment is avoided, precise cooperative control of the multi-axis system is achieved, and the control precision, operation stability and reliability of equipment under the dynamic working condition are remarkably improved.

The invention relates to a road disease high-precision positioning method, system and device based on multi-scale dynamic weighted curvature correction and a medium, and the method comprises the steps: constructing a road center line geometric object, obtaining a geographic coordinate, and constructing a multi-scale curvature field characterization parameter according to a spatial mapping relation between the geometric object and the geographic coordinate; establishing a correction model, calculating and generating an initial correction factor, and performing optimization to form a dynamic correction factor; the optimal projection position of the vehicle is determined according to the geographic coordinates and the road center line geometric object, the cumulative distance of the projection position is corrected based on the dynamic correction factor, and road pile number information is generated in combination with pile number reference parameters and constraints; the relative orientation is determined by comparing the distance between the vehicle position and the road end point or verifying the consistency of the driving track vector and the vector space in the tangential direction of the road, and the positioning result is output in combination with the road pile number information. According to the invention, a full-link precision guarantee system from data analysis to result output is formed, and a reliable positioning reference is provided for road maintenance.

The invention relates to the technical field of underground coal gasification, in particular to a coal underground gasification process stability monitoring and evaluating method which comprises the following steps: determining a space mapping relation between a gasifier temperature field and an acoustic emission event according to the temperature gradient and the acoustic emission event of a gasifier; carrying out three-dimensional inversion on a combustion cavity of the gasification furnace, and determining attenuation trends of the coal seam under different temperature gradients; according to the attenuation trend of the coal seam under different temperature gradients, the propelling direction of the boundary of the combustion cavity is determined by utilizing the spatial distribution density and the relative position of each acoustic emission event; according to the propelling direction of the fuel cavity boundary, boundary moving paths are formed, and the propelling area proportion of each position in the propelling direction is calculated according to the distance difference of the boundary moving paths; and according to the propulsion area proportion of each position in the propulsion direction and the expansion rate of the combustion cavity, determining the space stability classification of each part of the combustion cavity. The accuracy and the efficiency of monitoring underground gasification of the coal seam are realized.

The invention discloses an emergency escape path planning system and method based on BIM and Internet of Things dynamic identification, and the method comprises the steps: constructing a three-dimensional building information model, forming a digraph structure for the constructed three-dimensional building information model, and extracting the geometric information of each node; constructing a space mapping table of corresponding space units in the three-dimensional building information model and networking sensors deployed in the building; then multi-dimensional information including environmental parameters, personnel positioning and structural states in the building is collected in real time, so that a real-time dynamic data source in the building is formed; mapping the collected dynamic real-time data to a corresponding space unit in the constructed three-dimensional building information model, and carrying out dynamic identification on a dangerous area and an obstacle; and then on the basis of the real-time space state diagram, an optimal escape path avoiding a high-risk area is automatically generated, and dynamic re-planning can be carried out. According to the scheme, the space expression ability of the BIM and the real-time sensing ability of the Internet of Things are deeply fused, dangerous factors and traffic obstacles in the building can be dynamically recognized, the real-time escape path is generated in combination with the path algorithm, and the problems existing in the prior art can be effectively solved.

The invention discloses an aquaculture capacity prediction method and system based on multi-source data, and relates to the technical field of aquaculture management. An aquaculture capacity prediction system based on multi-source data comprises a data acquisition module, a capacity prediction module, a density adjustment analysis module, a typhoon correction module, an elastic capacity generation module and a space mapping module. According to the method, the typhoon influence correction model is constructed, and the basic capacity is dynamically corrected by using the capacity correction weight, so that automatic adjustment of the prediction capacity in the typhoon season is realized, and the robustness of a prediction result facing extreme meteorological conditions is enhanced; on the basis of recommended capacity, a historical disaster damage data fitting model and risk preference setting, three capacity strategy intervals of conservative, neutral and aggressive are generated, hierarchical selection is provided for breeding decisions of different risk bearing capacities, and decision elasticity and risk control capacity are improved.

The embodiment of the invention provides an unmanned aerial vehicle inspection method and a rail transit line inspection system, and the method comprises the steps: obtaining multi-source inspection data corresponding to an inspection unmanned aerial vehicle, the multi-source inspection data being obtained based on the collection data of a plurality of sensors carried by the inspection unmanned aerial vehicle and based on the time-space consistency fusion; based on the multi-source inspection data, an anomaly detection result is obtained through an anomaly detection model of at least one level, and the anomaly detection result comprises at least one anomaly area; and on the basis of spatial correlation characteristics, mapping an abnormal region in the abnormal detection result to a corresponding actual geographic position to obtain an inspection evaluation result, the spatial correlation characteristics being used for indicating a mapping relationship between the abnormal region and the actual geographic position of the rail transit line. The multi-source fusion improves the detection integrity, the hierarchical model enhances the environmental adaptability, the space mapping improves the positioning accuracy, and the overall inspection effect is optimized.

The invention provides a wheel tread derailment detection method and system for a railway vehicle. According to the method, jacking driving devices with jacking rotating wheels are symmetrically arranged on the two longitudinal sides of a track, a pressure sensing unit is arranged between the two jacking rotating wheels, optical detection units with orthogonal optical paths are installed on the outer sides, and displacement measurement units are integrated on the jacking rotating wheels, so that a three-dimensional detection network is formed. During detection, a top rotating wheel lifts a wheel step by step, a blocking signal, tread pressure and a vertical displacement parameter are synchronously collected, and a space mapping relation between a pressure decreasing path and a displacement abnormal point is established; converting the blocking signal into a blocking release rate, and calculating a displacement accumulated value in combination with a space mapping relation; and when the displacement accumulated value is continuously increased and the blocking release rate exceeds a threshold value, wheel-rail separation is judged, and jacking is controlled to be stopped. Through cooperative detection and analysis of multiple sensors, accurate judgment and self-adaptive jacking control of the wheel-rail separation state are achieved, and the problem of wheel diameter adaptability of a traditional fixed jacking mode is effectively solved.

The invention relates to the technical field of augmented reality, in particular to an AR real-time scene reconstruction and illumination matching system and method based on neural rendering, and the system comprises a real-time scene understanding module, a real-time neural radiation field module and a real-time reflection inference and enhancement module. Comprising a forward network, a feature transformation layer and a backward network, the forward network maps a high-dimensional rendering space to a feature manifold, the feature transformation layer executes mapping from the feature manifold to a rendering manifold, and the backward network performs rendering calculation based on a Riemannian metric driven adaptive ray tracing mechanism; the real-time reflection inference and enhancement module performs material prediction and illumination information calculation based on an illumination dynamic adaptation system on a differential manifold, real-time application of a neural radiation field technology in an AR scene is realized through an innovative differential geometry framework, and a real-time rendering frame rate of 30-60 fps is achieved while a high-quality rendering effect is kept.

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention discloses a positioning calibration method of surface mounting equipment, equipment, a medium and a product, and relates to the technical field of printed circuit boards, a first position of a bonding pad on a circuit board and a second position of an element pin of an element to be mounted are obtained through recognition by using an image recognition model, the confidence coefficient is recorded, and when matching calculation is carried out, the positioning calibration accuracy is improved. And carrying out position transformation calculation on the positions of the bonding pad and the element pin matched in the current iteration, calculating a transformed position error in combination with the confidence coefficient, and outputting a final matching result of the bonding pad and the element pin and a corresponding final space mapping parameter after a first iterative calculation condition is met. According to the method, the influence of a low-confidence-coefficient position detection result on space mapping parameter calculation is effectively reduced, the stability and reliability of the final space mapping parameter are ensured, compared with traditional position error calculation, the method has higher anti-interference capability, and the accuracy of positioning matching of high surface mounting equipment is improved.

The invention discloses a spine tactile sensing system and method based on a multi-modal sensor, and the method comprises the steps: obtaining an original tactile signal, carrying out the preprocessing of the original tactile signal, carrying out the feature extraction based on the preprocessed original tactile signal, and obtaining a spatial distribution feature and a time dynamic feature; on the basis of tail end coordinates collected by forward kinematics of the tail end of the mechanical arm, space mapping of the tactile signals and the spine anatomical structure is constructed, and anatomical position features are obtained on the basis of the preprocessed original tactile signals and the space mapping; constructing task-oriented feature vectors based on the spatial distribution features, the anatomical position features and the time dynamic features, wherein the task-oriented feature vectors comprise a sliding state recognition feature vector and a spine ordinal number positioning feature vector; and constructing a multi-stage adaptive evolution hidden Markov model, and realizing spine ordinal number positioning in combination with the task-oriented feature vector. According to the method, through multi-dimensional feature fusion and dynamic model adjustment, anatomical prior knowledge and real-time tactile signals are combined, and spine segments are accurately recognized.

The invention relates to the technical field of computer vision and three-dimensional modeling, and discloses a 3DGS (three-dimensional ground structure) equipment monomer method based on dynamic segmentation of an SAM2 large model, which comprises the following steps of: calling a visual large model to segment a multi-view two-dimensional image to obtain a component contour mask bearing semantic information; the method comprises the following steps of: establishing a mask of a three-dimensional point cloud, establishing space mapping of the mask and the three-dimensional point cloud, executing structure segmentation on the point cloud under double dimensions of fusing geometry and semantics, and further generating a three-dimensional monomer model for each separated equipment component. The segmentation processing of the three-dimensional point cloud is no longer limited to fuzzy geometric inference, but is carried out under clear semantic affiliation, so that the accuracy and robustness of monomer separation in the component dense adjacent region are improved.