731 results about "Direction vector" patented technology

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention provides an equipment fatigue crack intelligent detection method and system based on deep learning, and the method comprises the steps: firstly obtaining an equipment surface detection image set, generating a first crack detection thermodynamic diagram through a pre-trained deep learning model, and constructing a crack physical expansion constraint model according to equipment design material parameters and real-time operation condition parameters, then performing crack area iterative optimization based on the first crack detection thermodynamic diagram and the crack physical expansion constraint model to obtain a second crack detection thermodynamic diagram, and analyzing the second crack detection thermodynamic diagram to determine equipment surface crack state parameters such as crack area boundary coordinates, crack expansion direction vectors and crack depth gradient values; and finally, generating an equipment fatigue crack intelligent detection report containing a crack space distribution schematic diagram and a crack propagation risk grade identifier, thereby integrating image data and physical constraints, and improving the accuracy and reliability of crack detection.

The invention relates to the technical field of medical image processing, in particular to a neurosurgery image diagnosis method and system based on image processing, and the method comprises the following steps: obtaining gray matter edge nodes of a triaxial section, constructing a symmetric path unit, collecting an edge direction vector, and generating a direction trajectory diagram; and extracting continuous slices, constructing a rotation track sequence, identifying an abnormal region, filling gaps, combining path voxels, and dividing spatial levels to generate an image structure chart. According to the method, the grey matter edge nodes in the three-axis tangent plane are obtained, and the node paths with the symmetrical characteristics are screened out according to the space projection trend, so that the continuous region of the structure can be accurately recognized, the direction vectors in the continuous slices are extracted, the direction mutation region in the track is recognized, and the jump and fracture performance of the structure can be timely captured; and the abnormal region is accurately labeled, so that higher-dimensional expression and finer-grained recognition of the neural structure are realized, and spatial modeling and visual analysis of complex neuropathy are effectively supported.

The invention provides an oral surgery positioning and navigation method and system and a storage medium, and the method comprises the steps: calibrating an implanted mobile phone to obtain a unit vector of a rotating shaft of a drill bit and a three-dimensional coordinate value of the tip of the drill bit under an optical three-dimensional coordinate system, scanning to obtain a CT three-dimensional model of a patient, and segmenting to obtain a tooth CT model and an alveolar bone CT model; generating a tooth three-dimensional point cloud according to the real-time pattern of the speckle structured light on the teeth of the patient, acquiring a matrix transformation relation between a CT three-dimensional coordinate system and an optical three-dimensional coordinate system, and performing real-time navigation on the implanted mobile phone according to the real-time point position deviation and the real-time angle deviation of the drill bit. According to the technical scheme, the matrix transformation relation between the CT three-dimensional coordinate system and the optical three-dimensional coordinate system is calculated through the tooth CT feature points and the tooth point cloud feature points, and auxiliary navigation is carried out by comparing the target position of the implant with the real-time pose of the tip of the drill bit, the direction vector of the planting path and the unit vector of the rotating shaft of the drill bit. Operation is convenient and positioning precision is high.

The invention relates to the technical field of laser pipe cutting machining, and discloses a laser pipe cutting machine machining optimization method for small pipes. The method comprises the following steps: acquiring three-dimensional point cloud data of a to-be-processed pipe, and extracting a pipe axis direction vector and pipe wall thickness distribution data through geometric topology analysis; a dynamic machining coordinate system is established based on the axis direction vector, and the theoretical projection position of the laser focus in the coordinate system is calculated; material absorption characteristic parameters are matched according to the pipe wall thickness distribution data, and a laser power density gradient adjusting instruction is generated; monitoring the radial run-out amount in the rotating process of the pipe in real time, and generating a laser focus track compensation parameter in combination with a theoretical projection position; and finally, the laser power density gradient adjusting instruction and the focus track compensation parameters are fused to generate a cutting path control signal, and a laser cutting head is driven to complete machining. According to the method, the problems of insufficient precision and unstable quality in the machining process of small pipes are effectively solved, and the machining adaptability is improved.

The invention provides an automatic identification method and system for a linear structural plane in a complex rock mass outcrop, and belongs to the field of rock mass engineering, and the method comprises the steps: obtaining a DeepLabV3 + model of a rock mass outcrop image, carrying out the semantic segmentation of an input image, outputting a binary mask image of the trace pixel of the structural plane, and carrying out the topology reconstruction; establishing a mapping relation between the image coordinate system and the three-dimensional point cloud; calculating a space included angle between the direction vector of the trace and the average occurrence of the structural plane groups, classifying the three-dimensional point cloud data according to the structural plane groups according to an angle threshold value, and performing three-dimensional line segment extraction by adopting an improved iterative Hough transform algorithm; and realizing global multi-segment connection and trace length calculation of discrete segments based on a greedy algorithm. The method breaks through the limitation of a topographic region when the rock mass structural plane data is obtained through manual measurement, and improves the precision and efficiency of structural plane extraction in the complex rock mass.

The invention relates to the technical field of target detection, in particular to a search and rescue task-oriented unmanned aerial vehicle thermal imaging visual target detection method, which comprises the following steps of: acquiring multiple frames of thermal imaging images of an unmanned aerial vehicle, extracting regional thermal difference characteristics according to a window, marking a non-background region to generate a candidate set, and fitting and reconstructing a suspected thermal target contour. And analyzing the track and the thermal change rate, screening background interference, identifying jump abnormity, positioning the gravity center, and generating a target repositioning signal. According to the method, the heat value range and variance index sequence in the image area is constructed, the thermal anomaly area is judged in combination with the temperature baseline difference, background disturbance comparison is executed in combination with the direction vector of the coordinate trajectory in the multi-frame image and the thermal change parameter, the false detection probability caused by background noise is reduced, and the detection accuracy is improved. The target jump identification is carried out according to the inter-frame heat value and area change rate in linkage with the thermal isoline closure degree, the target discrimination accuracy in a shielding scene is improved, and the robustness of thermal target extraction in a complex search and rescue environment is integrally improved.

The invention discloses a displacement auxiliary measuring mechanism and a reservoir dam monitoring and early warning system thereof, and belongs to the technical field of dam monitoring. The reservoir dam monitoring and early warning system comprises a data acquisition module, an attitude calibration module, a data fusion judgment module, a risk grading analysis module and a remote early warning trigger module. The distance measurement precision under the complex field condition is remarkably improved, the current mechanical direction and the reference direction are fused, a correction vector pointing to a target reflection point is generated, the direction drift error is effectively eliminated, and the distance measurement accuracy is improved by constructing a double-path model of main path fusion and auxiliary path comparison. According to the method, non-structural anomalies caused by sensor body interference or environmental disturbance are eliminated, data offset is corrected in real time, a stable trend curve is output, when a main path has a drift or error trend, the main path is automatically guided to converge to an auxiliary path, and the abnormal data recognition and restoration capability is enhanced.

The invention relates to the technical field of logistics collaborative optimization, in particular to an urban logistics distribution range collaborative optimization method and system, and the method comprises the following steps: obtaining a path network node and order coordinate time sequence, constructing a path mapping number, extracting a direction vector and an included angle difference value, and recognizing a path sudden change node and a density change node. Dividing boundaries, calculating density ratio differences, screening unbalanced sections, extracting direction and distance abrupt change nodes, reconstructing a path structure and outputting a boundary graph. According to the method, the corresponding relation between the order and the path node is enhanced by constructing the path mapping number sequence, the adaptive capacity of the boundary to the path trend is improved, the density ratio analysis is combined with the spatial distribution difference to identify the non-uniform region, the responsiveness of the boundary to the order aggregation situation is enhanced, and the boundary node is jointly judged through the included angle and the centroid distance. And target judgment and structural stability control of boundary adjustment are realized, connection coherence is kept in path structure reconstruction, and a boundary path shape with dynamic adaptation capability is output.

The invention discloses a method for realizing multi-stage solid-liquid separation by using graded filter pipes, which relates to the technical field of solid-liquid separation, and comprises the following steps: under the condition that the particle size between adjacent filter pipes is at a critical boundary, collecting a continuous motion track of particles in a particle size interval in a filter transition area, extracting a direction vector change sequence and a flow velocity disturbance amplitude, and judging whether particles of which the direction reversal frequency exceeds a preset threshold and the flow velocity disturbance amplitude is lower than a stable fluctuation limit exist or not so as to determine whether a particle repeated movement state occurs or not; a drift factor and a disturbance sensitivity factor are constructed according to a particle repeated movement state and are introduced into an affiliation path identification model as input variables, and affiliation judgment operation is executed through an affiliation probability calculation network. According to the invention, the problem that the path attribution of particles with critical particle sizes in the filtering transition area is unknown is solved, and the dynamic identification and accurate guide control of the particle attribution path are realized.

The invention relates to the technical field of artificial intelligence, in particular to a big data information analysis method and system based on artificial intelligence, and the method comprises the following steps: obtaining a jump segment direction vector, classifying label nodes with consistent directions, splitting a path lacking a mapping relation to form an interruption segment, recognizing a node with continuous directions as a bridge segment entrance, and obtaining a bridge segment direction vector; according to the method, by means of classification according to the node sequence and the channel number in the segment hopping direction, direction aggregation boundary determination and semantic field combination path position interruption recognition, the node sequence structure positioning precision and the paragraph division reasonability are improved, and the focus analysis set is generated by combining the path segments. Bridge segment connection is screened based on direction consistency, path continuation logic, behavior tracks and aggregation numbers are kept in sequence and hierarchy regularization, a behavior aggregation sequence of a semantic chain is constructed, and the continuity of path recognition in a channel aggregation area and the stability of node attribution are enhanced.

The invention provides an intelligent warehouse goods posture recognition and automatic sorting method and system, and the method comprises the steps: S2, extracting the edge contour and surface features of goods from point cloud data for a preliminary value set, calculating the direction vector and shape distribution characteristics of the goods through a principal component analysis method, and obtaining a quantitative result of shape feature extraction; s6, historical sorting data and real-time sensor data are extracted from the warehousing system database according to the classification basis adapted to the complex scene, the dynamic posture change trend of the goods is predicted through a time sequence analysis method, and optimization parameters of real-time processing are obtained; and S7, the movement track and the grabbing angle of the sorting mechanical arm are adjusted through the optimization parameters subjected to real-time processing, a deep reinforcement learning algorithm is adopted to conduct iterative optimization on the sorting action sequence, and an execution scheme of sorting accuracy is determined. According to the method, the accuracy of goods posture recognition and the automatic sorting efficiency in the complex storage environment are remarkably improved.

The invention discloses a distribution network fault positioning method, system and device based on variational mode decomposition and a medium, and relates to the technical field of power system fault positioning, and the method comprises the steps: collecting a traveling wave signal of a power distribution network, and carrying out the preprocessing of the collected traveling wave signal; performing modal decomposition on the preprocessed traveling wave signal, and outputting a plurality of modal components; based on the mode component obtained through decomposition, wave head arrival time is extracted; performing correction compensation based on the wave head arrival time difference, the phase difference and the propagation characteristics, and calculating the relative distance between the fault point and the known node; calculating the position coordinates of the fault point in the distribution network according to the geographic coordinates and the direction vectors of the nodes in combination with the relative distance; and constructing a visual interface, displaying the position of a fault point, and storing the traveling wave signal data and a fault positioning result into a database. According to the method, the adaptive correction function and the time difference compensation model are constructed, so that time difference errors caused by factors such as frequency change and phase nonlinearity can be corrected, and the accuracy of fault positioning is improved.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

The invention relates to the technical field of design service, in particular to a roof waterproof structure intelligent design optimization system which comprises a node contour extraction module, a contact segment recognition module, a close boundary evaluation module, a disturbance area labeling module and a waterproof configuration reconstruction module. According to the invention, by extracting the direction vector change of the boundary point cloud sequence, the continuity and stability of the turning trend in the contour structure can be accurately obtained, the boundary of the roof structure can be identified, and the contact type of the boundary joint can be determined in combination with the direction change rate distribution of the contact surface, so that the boundary sealing state has quantitative index output; in addition, local positioning information of a fitting path and a structure defect can be provided, a node contour reconstruction path with an adjustment space is provided based on a pressing path continuity judgment and simulation edge shift strategy, and the adaptability of a node structure under the action of complex stress and the configuration optimization capability of the overall waterproof performance of a roof are improved.

The invention relates to the technical field of gesture recognition, in particular to an air gesture action intelligent capture and interactive data processing method and system.According to the air gesture action intelligent capture and interactive data processing method and system, significant jumps are marked through timestamps, time sequence deconstruction of action streams is achieved, nonlinear alignment processing of time structures between paragraphs is introduced, and the time sequence deconstruction efficiency is improved. The fragment consistency recognition capability under the conditions of unequal action durations and different rhythms is remarkably improved, spatial topology among three points is modeled by adopting a graph structure, a graph convolutional network is introduced to process dynamic changes of connection relations among different nodes, the continuity and stability of structure judgment can be kept under a shielding or structure deformation scene, and the recognition efficiency is improved. Through a triple constraint mechanism of direction vector included angle amplitude, length ratio and slope fluctuation, the action is subjected to modeling support in three aspects of time dimension, space structure and direction continuity at the same time, and the dynamic stability of gesture recognition and the resolution precision of paragraph judgment are enhanced.

The invention discloses a path planning method and system for corner optimization. The method and system are used for cutter radius compensation and machining instruction smooth transition of a three-dimensional path of a numerical control machine tool. The method comprises the steps that a machining instruction is pre-read, and a path point location is analyzed; calculating a path direction vector and a normal vector, and determining a path plane equation; calculating a plane included angle and an intersection direction vector through a normal vector; generating a compensation curve by translating along the perpendicular direction of the intersecting line by taking the radius of the cutter as a constraint; and generating a smooth control instruction through curve subdivision and inserting the instruction into an instruction queue. The problem that three-dimensional path corner transition is not smooth is solved, the machining precision and efficiency are remarkably improved, and the method is suitable for a numerical control machine tool for complex curved surface machining.

The invention provides a method and a system for transmitting a photographic image of a security camera, relates to the technical field of image transmission, and effectively divides an image frame into a differential image block and a background image block by introducing a partition mechanism based on inter-frame pixel difference, thereby remarkably improving the detection efficiency of a moving target and the pertinence of data processing. Through embedding marks such as a direction vector and a motion timestamp in a differential image block set, priority coding of a dynamic target image area is realized, and further in combination with a periodic compression strategy of a background image frame, redundant data is compressed to the greatest extent, and the overall data transmission load is effectively reduced. Meanwhile, an image fragment priority model is jointly constructed through brightness saliency and texture complexity, and structure entropy and direction gradient analysis is assisted, so that redundant backup generation of key content is realized, and data integrity under a weak network condition is greatly improved.

The invention discloses an aero-engine part fatigue crack propagation identification method based on machine vision. The method comprises the following steps: S1, obtaining an image block sequence; s2, inputting each image block into a pseudo stress estimation network to generate an image block pseudo stress diagram; s3, inputting the image block and the image block pseudo-stress diagram into an image feature decoupling coding module, and outputting a fusion feature vector; s4, inputting the fusion feature vector into an improved fox algorithm, and generating a crack path guide map and a direction vector field; s5, inputting the image block pseudo-stress diagram, the crack path guide diagram, the direction vector field and the fusion feature vector into a crack mask generation module, and outputting an image block crack mask; s6, splicing the image block crack masks according to the positions of the image blocks in the surface image to generate a complete crack structure diagram; and S7, outputting a crack propagation result based on the complete crack structure chart. According to the method, machine vision and an evolutionary path optimization method are fused, and accurate recognition of aero-engine crack propagation is achieved.

The invention provides a multi-dimensional Hash acceleration-based grid ray intersection method, which comprises the following steps of: acquiring original model data, and carrying out standardized preprocessing on the original model data to obtain standardized scene data; self-adaptive space grid division is carried out based on the preprocessed standardized scene data, space coordinates of three-dimensional grid units are mapped to a hash table to construct a one-dimensional index of a space grid, and triangular patches of the scene data are associated with the space grid; performing direction parameter decomposition on rays to be intersected, discretizing direction vectors of the rays, and constructing a ray direction composite hash key; and executing mixed intersection calculation in a mode of combining multi-level cache query construction, direction pruning and invalid intersection filtering and space hash traversal intersection, and determining intersection point information of the ray and the geometry. According to the method, the space grids and the ray direction data are converted into the hash values to be stored, the occupied memory is small, and the ray intersection efficiency is improved.

The invention provides a multi-modal large model mental ability improving method and device based on attention intervention, and relates to the technical field of artificial intelligence. The method comprises the following steps: generating a multi-modal psychological theory data set; inputting the data to a multi-modal large language model to be evaluated, and constructing an input sequence of a Transform network; extracting an activation value of each attention head in each layer of attention network for each input sequence, and defining a belief label of the activation value from the perspective of a leading angle; taking the trained linear classifier as a probe model to obtain a predicted belief label, and constructing a sensitivity score of each attention head; and extracting an intervention target direction vector of each high-sensitivity attention head, and executing directional activation intervention on each high-sensitivity attention head to obtain a multi-modal large language model after the psychological theoretical ability is improved. According to the method, innovation is systematically carried out from three dimensions of data construction, mechanism detection in the model and reasoning intervention enhancement, and the key blank in the prior art is filled.

The invention relates to the technical field of natural language processing, in particular to an AIGC content safety monitoring system and method based on dynamic reasoning and context awareness, and the system comprises a semantic graph construction module which is used for extracting entity nouns and predicate verbs in a text according to a received AIGC interaction text flow, generating a semantic concept node set, and sending the semantic concept node set to a database; and performing directed connection and hierarchical nesting on the concepts in the semantic concept node set according to a logic direction according to a subject-predicate-object dependency relationship rule. According to the method, the curvature value of the semantic track is calculated, and the similarity between the direction vector and the center of the sensitive semantic cluster is combined for double verification, so that sudden turning of an intention in a dialogue process or progressive induction to a sensitive field can be perceived, and abnormal mutation can be recognized through a curvature pulse form; therefore, hostile attack behaviors are accurately captured in real time in dynamic interaction, and the defense capability for context dependent attacks and implicit induction behaviors is improved.

The invention discloses a bicycle center shaft press-fitting machining system based on machine vision positioning. The bicycle center shaft press-fitting machining system comprises a servo press-fitting module, a data acquisition module, a data processing module, a machine vision positioning module, a press-fitting control module, a quality detection module and a data management module. According to the system, image data and assembly quality data are acquired through data, and initial coordinates of mounting holes are extracted through median filtering, Canny edge detection and Hough circle transformation; further adopting an RANSAC algorithm to fit the cylindrical surface of the inner wall of the five-way pipe, accurately calculating a center point space coordinate and an axis direction vector, and converting the center point space coordinate and the axis direction vector to a press-fitting coordinate system through hand-eye calibration; the press-fitting process is dynamically regulated and controlled in three stages of pre-pressing, main pressing and final pressing, the press-fitting speed is dynamically regulated, and meanwhile, online judgment of the assembly quality is achieved through displacement-pressure curve analysis; according to the system, the problem of bearing abrasion caused by alignment deviation of a traditional press-fitting central axis is solved, and the press-fitting precision and the yield are improved.

The invention relates to the technical field of navigation control, in particular to an obstacle light-based unmanned aerial vehicle navigation auxiliary system, which comprises the following steps of: combining an image sequence with inertia time information, segmenting a light source area and extracting a frequency label, so that the stable identification capability on an optical signal is enhanced; the signal state and the time index can be accurately judged especially in a complex shielding environment, the robustness of obstacle light recognition in multiple angles and multiple scales is improved through direction vector extraction and a comparison mechanism of a graph neural network, an edge weight matrix is established in combination with light intensity calculation and shielding state elimination, and the robustness of obstacle light recognition in multiple angles and multiple scales is improved. The method effectively enhances the understanding of the spatial layout of the obstruction lights and the navigability of the graph model, extracts the vector group through the graph model, eliminates the shielding, and compares the path instruction selection logic of the action score, so that the path planning process has stronger dynamic adaptability and error tolerance, and enhances the navigation accuracy and flight safety in the complex airspace.

The invention provides a projection transformation method for hexahedral panoramic data to spherical panoramic data, and the method achieves the seamless splicing of panoramic images of six surfaces of a cube and the mapping of the panoramic images to a spherical coordinate system through the determination of a direction vector, the normalization of the vector, the conversion of spherical coordinates, the projection to an equidistant histogram, and an illumination and color correction algorithm. The problems of splicing gaps, deformation, texture distortion and the like in a traditional method are solved. The method is not only widely applied to the fields of virtual reality, augmented reality, panoramic video production and the like, but also greatly expands the application scenes, including but not limited to panoramic roaming, streetscape roaming experience, integrated use of specific roaming plug-ins, seamless embedding on a map platform and the like, and has wide application prospects. Therefore, the display effect of the panoramic data and the immersive experience of the user are remarkably improved.

The invention discloses an eye movement tracking method and system based on multi-modal fusion, and particularly relates to the technical field of eye movement tracking, and the method comprises the steps: collecting a face image, an eye image and a head movement parameter of a user through an RGB camera, an infrared camera and an IMU which are synchronously arranged; the signals are processed in parallel, sight line direction vectors based on the face and the eyes and reliability measurement of the sight line direction vectors are extracted respectively, and the head space posture is calculated; dynamically selecting high-confidence cooperation, master-slave compensation or conflict arbitration strategies according to reliability measurement, and fusing to generate an initial sight direction; and mapping the direction to a display plane to obtain an initial fixation coordinate, performing optimization calibration in combination with visual saliency analysis of screen content, and outputting a final fixation point coordinate. The system comprises a signal acquisition and processing module, a parallel computing module, a multi-strategy fusion decision-making module and an output optimization module. According to the invention, through multi-modal information fusion and dynamic strategy selection, the precision and robustness of eye movement tracking are significantly improved.

The invention discloses a mountain state real-time monitoring and identification method and system based on deep learning. The method comprises the following steps: performing multi-angle dynamic image acquisition through a PTZ camera; training a dual-target detection network based on a YOLOv7 framework, constructing a rockfall-crack detection model, and synchronously outputting bounding box coordinates of a rockfall target and position information of a crack region; constructing a rockfall trajectory prediction model through a DeepSORT multi-target tracking algorithm, and obtaining rockfall movement trajectory data; pixel-level semantic segmentation is carried out on the crack area, a crack mask graph is generated, and a crack main direction vector is extracted; carrying out registration and difference calculation on the image to obtain a crack width variation and an extension length, identifying a newly-added crack region and marking spatial distribution of the newly-added crack region; and constructing a multistage alarm mechanism which comprises multistage alarm signals of an on-site audible and visual alarm device and a cloud platform. The mountain disaster monitoring and early warning system has the advantages that efficient and real-time monitoring and early warning of mountain disasters are achieved through rockfall detection and tracking and crack analysis and prediction.

The invention relates to the technical field of numerical control machine tool precision machining, in particular to a numerical control machine tool measurement and compensation machining program generation method and system. The method comprises the following steps: obtaining and carrying out theoretical path information analysis on a processing program file to obtain a theoretical feed vector sequence; performing three-axis acceleration analysis on the cutter to obtain a three-axis vibration intensity value; performing dynamic deviation trend sensing according to the triaxial vibration intensity value to obtain an instantaneous deviation trend vector; obtaining cutter structure parameters; performing cutting state identification according to the instantaneous deviation trend vector and the theoretical feed vector sequence to obtain cutting state parameters; determining a surface normal vector of the workpiece according to the cutting state parameters; performing reverse mapping transformation on the instantaneous deviation trend vector according to the cutter structure parameters to obtain a basic compensation direction vector; according to the invention, a closed-loop dynamic error compensation system is established, so that real-time and accurate compensation of dynamic cutting errors is realized.

The invention relates to the field of radar displacement monitoring, in particular to a building facility settlement monitoring method, device and equipment based on single radar and multiple targets. The method comprises the following steps: S1, monitoring building facilities by adopting a single micro-change monitoring radar, acquiring echo signal data and extracting displacement observation data and spatial position information of a plurality of monitoring targets; s2, determining a direction vector of each monitoring target relative to the radar according to the spatial position information; taking the direction vector and the displacement observation data as joint input, establishing a multi-source data association model capable of representing a corresponding relation between the monitoring target and the overall displacement, and performing fusion processing based on the model to obtain an overall three-dimensional displacement result of the building facility; and S3, outputting a vertical component of the overall three-dimensional displacement result as the settlement displacement of the building facility. According to the method, high-precision and automatic three-dimensional displacement monitoring of building facility settlement is realized by adopting a single micro-change monitoring radar, and the interference of transverse and longitudinal displacement on settlement measurement is effectively eliminated.

The invention discloses a wharf ship docking intelligent detection method and system, and the method comprises the following steps: S1, collecting a video image of a wharf operation region, carrying out the synchronous registration through combining a berth direction template, and constructing an image-structure combined input tensor; s2, inputting a visual center guide detection network, and generating a structure perception fusion feature map; s3, on the basis of the structure perception fusion feature map, adopting a berth direction adaptive anchor frame transformation strategy to construct head and tail direction vectors; s4, establishing a direction matching relation between the head-tail direction vector and the berth direction vector, and inputting the direction matching relation to a course deviation dynamic regression module; s5, in a course deviation dynamic regression module, outputting a berthing posture state mark value based on the course deviation angle and the vertical deviation value of the current candidate detection frame; and S6, whether an abnormal condition exists or not is determined according to the berthing posture state mark value, and if yes, abnormal berthing early warning information is generated. According to the invention, the accuracy and response efficiency of ship berthing state identification are effectively improved.