264 results about "Space transformation" patented technology

The invention relates to the technical field of highway engineering informatization, in particular to a geometric registration method and system based on a BIM model and an AR real scene. The method comprises the following steps: obtaining multi-source data of a construction site, and generating an AR real scene of the construction site according to the multi-source data; fusing multi-source data to obtain accurate parameters of the construction equipment; calculating a spatial transformation matrix of a construction site through accurate parameters of construction equipment, and obtaining an initial registration result of the BIM model and the AR real scene by combining engineering characteristics and the spatial transformation matrix; a dynamic error compensation mechanism is introduced, the initial registration result is corrected through the dynamic error compensation mechanism, and a local registration result is obtained; and constructing a global and local coordinate system mapping optimization model, and mapping the local registration result to a global coordinate system by using the global and local coordinate system mapping optimization model to obtain a global registration result. According to the method, a reliable spatial data basis is provided for highway engineering quality inspection, construction guidance and defect identification.

The invention discloses a power transmission line inspection unmanned aerial vehicle obstacle intelligent detection and obstacle avoidance method. The method comprises the steps that multi-source sensing data is acquired, and alignment is completed through calibration and timestamp matching; heterogeneous data preprocessing and feature enhancement; constructing a high-precision environment fusing a geometric structure and a semantic tag, mapping a two-dimensional target detection result output by the recognition network to a three-dimensional coordinate system through spatial transformation, and fusing the two-dimensional target detection result with a point cloud structure to construct a semantic occupation grid map; performing preliminary route planning according to a preset power grid topological structure and task coverage requirements, and generating a barrier-free flight path covering the whole inspection area; reinforcing learning of a dynamic obstacle avoidance strategy; track dynamic reconstruction and energy consumption optimization scheduling are carried out; the technical problems that an existing technical system has defects in the aspects of obstacle recognition accuracy, complex environment adaptability, data fusion capacity and obstacle avoidance strategy intelligence, and the requirements for high-reliability, low-energy-consumption and high-efficiency unmanned aerial vehicle power transmission line inspection are difficult to meet are solved.

The invention provides a building equipment operation data intelligent analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: extracting historical positioning data in a space calibration interval, and calculating a position offset vector set between a predicted trajectory point and an actual trajectory point according to the trajectory point on a predicted motion trajectory; taking the weighted sum of squares of minimizing the position offset vector as an optimization target to obtain a three-dimensional space transformation parameter optimization model; according to the optimization model, a quasi-Newton optimization algorithm is adopted to solve the optimization model, and a final calibration parameter set is generated; and based on the final calibration parameter set, correcting the predicted motion trajectory, performing spatial relationship analysis on the corrected trajectory and a spatial risk field, calculating a collision risk probability and an accident severity level, generating a comprehensive risk score, and triggering a response action according to a preset threshold value. The construction safety level and the management efficiency are improved.

The invention relates to the technical field of laser engraving, and discloses a laser engraving method and system capable of automatically correcting coordinates of a galvanometer and a camera, and the method comprises the following steps: calculating a target conversion coefficient based on a calibration size and a pixel size of an image collected by the camera, and controlling the galvanometer to perform laser etching on a cross mark on the surface of a PCB (Printed Circuit Board), recording origin data of a galvanometer coordinate system; adjusting the position of the camera to enable the cross center of the view of the camera to coincide with the cross mark, and calculating the coordinate offset; coordinate space transformation is carried out on all the points to be machined, and galvanometer marking coordinate data are obtained; after laser engraving is carried out on the galvanometer marking coordinate data, an engraving area image is collected, the position residual error and the size proportion deviation are calculated, laser engraving is carried out again, and a laser engraving result is obtained. The technical problem that the visual positioning coordinate and the galvanometer marking coordinate in the laser engraving system are not consistent is effectively solved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention provides a method and a device for predicting influence of sea wave fluctuation on signal intensity based on ship data. The method provided by the invention comprises the following steps: acquiring multi-attitude angle data of a ship based on a nine-axis inertial measurement unit; the multi-attitude angle data comprises a rolling angle, a pitch angle and a yawing angle; establishing a Cartesian coordinate system taking the gravity center of the ship as an original point; based on the multi-attitude angle data, calculating spatial transformation of the antenna position in the Cartesian coordinate system through an automatic spatial transformation algorithm to obtain the transformed antenna position; determining the direction of the transformed antenna through vector operation by combining the initial orientation of the antenna and the position of the transformed antenna; and determining a corresponding angle through an angle resolving algorithm based on the initial antenna direction and the transformed antenna direction, matching a corresponding antenna gain in an antenna gain database based on the angle, and determining a signal strength change prediction value based on the antenna gain.

The present invention relates to the field of robotic arm teleoperation technology, and specifically to a construction method and system for real-time teleoperation of a dual arm and hand robot based on vision guidance, which includes setting up a first experimental platform, acquiring pose information of the dual arm and hand robot and a homogeneous transformation matrix of an end joint and transforming a first coordinate system; setting up a second experimental platform; performing calibration by a plurality of cameras and transforming a second coordinate system; obtaining spatial transformation pose information of the end joint of the robotic arm under a tracking state, reading finger pose data, and performing real-time following. The real-time teleoperation system of the present disclosure includes a control system host, an operation glove, an optical locator, a positioning coordinate plate, a marker ball tool, and a dual arm and hand robot. The present disclosure introduces a method of identifying the marker ball tool by the optical locator into the teleoperation control of the dual arm and hand robot, which is more accurate and faster. Meanwhile, the method of remotely controlling the robotic arm by an operator wearing the operation gloves can provide better human-machine interaction function.

Embodiments described herein provide techniques to enable spatial sensor data from multiple devices to be fused into a single coordinate space. This fused sensor data can then be processed into a point cloud. Point cloud data can then be transformed to enable the classification of objects detected within the sensor data. Features within multiple feature spaces can be extracted from the point cloud data for use in classification. Classification of objects can also be used to correlate objects detected by multiple sensor equipped devices to determine a coordinate space transformation between those devices.

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

Innovations in encoding or decoding when switching color spaces are presented. For example, some of the innovations relate to signaling of control information for adaptive color space transformation (“ACT”). Other innovations relate to ACT operations. These innovations can improve coding efficiency when switching between color spaces during encoding and decoding.

The invention discloses a target detection method and system for enhancing multi-scale target detection based on YOLOv8, and the method comprises the steps: designing a cross-scale feature recombination module guided by channel attention, and constructing a feature enhancement scheme adaptive to a target scale through the collaborative design of dynamic space transformation and channel re-calibration, richer feature interaction and fusion are realized; the neck network adopts switchable cavity convolution, receptive fields of targets with different scales are dynamically matched under the condition that the number of parameters or the calculated amount is not increased, and the flexibility of the network to the size and the scale of features is improved; space depth conversion convolution is introduced, so that the representation capability of the model is improved; deformable convolution is integrated in a C2f module of a backbone network, a sampling position is dynamically adjusted through grouping offset learning, and geometric adaptability to irregular targets is improved. A large number of experiments on PASCAL VOC and MS COCO data sets prove the effectiveness of the model provided by the invention.

The invention relates to the technical field of artificial intelligence, and provides a network intrusion detection method based on multidimensional feature collaborative analysis, and the method comprises the steps: calculating the correlation degree eta ij of any two feature columns in an original traffic feature matrix, and when eta ij is greater than eta thr, generating a new feature through Qij = Xi * Xj + (1-eta ij) * (Xi + Xj), outputting a dimension reduction feature matrix to perform nonlinear spatial transformation on the dimension reduction feature matrix X ', dynamically retrieving associated knowledge through a learnable memory library, fusing current input features and memory enhancement features, and outputting refined features; decomposing the refined features to a biological neural oscillation frequency band, applying carrier modulation, and performing feature reconstruction to generate a space-time correlation tensor; the refined features and the space-time correlation tensor are fused, and the intrusion behavior classification probability is output through multi-stage abstract compression and regularization processing; screening difficult samples based on classification confidence to construct a training library, and obtaining a conventional detection model and a special and precise detection model; and preferentially calling the special and precise model during real-time detection, and switching to the conventional model when the confidence coefficient is smaller than a confidence coefficient threshold value.

There are provided methods, systems, and non-transitory storage mediums for performing a meta-prediction of time series by using a set of forecasting models each associated with a forecasting theme. Time series data is received, and a set of forecast signals is generated. At least one signal and feature processing model generates a set of features. A meta-learner having been trained on historical time series data generates, based on the time series data and the set of features, a set of weights for the set of forecasting models. A meta-prediction is generated by using the set of features and forecast signals. Implementations may use combinations of endogenous and exogenous data, latent space transformations and generate interpretations and explanations for the meta-prediction.

The invention discloses a lithology analysis and identification method and system based on imaging logging, and the method comprises the steps: collecting imaging logging image data of different reservoir types, and constructing a lithology-imaging feature original data set; performing feature space transformation on the lithology-imaging feature original data set to generate enhanced feature representation in a manifold space, and taking the enhanced feature representation as a lithology-imaging feature enhanced data set; training an adaptive noise injection network by using the lithology-imaging feature enhanced data set to obtain a preliminary lithology classification model; judging a confidence index according to lithological characters output by the initial lithological character classification model to obtain a final lithological character classification model; and acquiring imaging logging image data of a to-be-identified well section, generating corresponding enhanced feature representation, inputting the enhanced feature representation into the final lithology classification model, and outputting a corresponding lithology identification result. The lithology identification accuracy and stability can be improved.

According to the method, a universe control equation set comprising a control equation, a free surface, upper and lower layer fluid interface continuity, structure-fluid interface continuity and fluid interface and seabed constraint conditions is constructed, and a vertical-horizontal decoupling modal analysis technology is combined, so that a multi-body radiation coupling model of the bottom-supported cylindrical array with relative motion between columns is provided; an intrinsic mode expansion technology is adopted to synchronously represent a joint radiation mechanism of a surface gravity wave and an internal wave, potential function space transformation and mode matching under a multi-column coordinate system are realized through a Graf addition theorem, and a radiation matrix coupling model considering a bottom-supported boundary constraint is constructed. According to the method, the wave-column coupling effect of the density layered interface can be accurately analyzed, a multi-column interference energy transfer path is quantified, and the modal truncation error is remarkably reduced.

The invention discloses a continuous financial fraud detection method based on dynamic feature space transformation, and belongs to the technical field of financial fraud detection. Dynamically adjusting the target dimension of the random projection according to the data volume, and carrying out dynamic feature space transformation; constructing a self-adaptive spherical decision boundary, and adapting to new data through a centroid calculation and radius updating mechanism; and performing financial fraud detection according to the decision boundary. Through dynamic feature space transformation, the feature space can be automatically expanded to capture more complex modes when data is sufficient; when the data is scarce, overfitting of the detection model can be avoided by compressing the feature space, the generalization ability of the model is improved, and good detection performance can be kept on new data. Meanwhile, the mass center of the normal transaction sample is updated according to the newly added transaction sample, the radius of the spherical decision boundary is dynamically adjusted, a financial dynamic detection scene can be continuously adapted, and interference of data noise and abnormal values is effectively reduced.

The invention discloses a high-enthalpy rarefied gas hypersonic wind tunnel flow field prediction method based on machine learning. In order to solve the problems of too high wind tunnel experiment cost, slow simulation response and the like in the aircraft design process, the invention provides a fast response solution for wind tunnel prediction by using a neural network. Simulation software is adopted to simulate the flow field and the radiation field of the hypersonic aircraft, and flow field and radiation field prediction is performed on the constructed model in combination with a farthest point sampling method, a symmetric function space transformation network technology and a separated attention technology, so that experiment waste caused by improper design of the aircraft model is avoided, and the experiment frequency is reduced; in the wind tunnel test process, the flow field and the radiation field of the wind tunnel are predicted synchronously, the wind tunnel experiment parameters are adjusted according to the prediction result, and disturbance and the like of the experiment are avoided. The method provided by the invention accelerates the scheme iteration process at the initial stage of aircraft design, and can quickly respond to flow field and radiation field prediction.

The invention relates to foresight sonar image enhancement, in particular to a foresight sonar image enhancement method based on feature space conversion and multi-frame fusion, and the method comprises the steps: mapping an original foresight sonar image from a low-quality pixel space to a feature space which is more stable to noise and is more consistent with a heterogeneous image through a feature space conversion module; the forward-looking sonar data self-supervising method comprises the following steps: constructing a self-supervising model to reduce a field gap between a sonar image and a heterogeneous image, so that the self-supervising model more effectively adapts to forward-looking sonar data, the modeling capability of the self-supervising model for unknown degradation factors is enhanced, and consistent input is provided for a multi-frame image fusion enhancement network; complementary information among continuous multi-frame images output by the feature space conversion module is mined and integrated through a multi-frame image fusion enhancement network, fine reconstruction of image details and improvement of image brightness are achieved, and noise in the images is eliminated; according to the technical scheme provided by the invention, the defects that the image brightness is difficult to improve while the noise is suppressed and the high-frequency details of the target cannot be well reserved can be overcome.

The invention discloses a three-dimensional point cloud registration method based on statistical spatial distance distribution, and the method comprises the following steps: extracting edge points and angular points in a source point cloud and a target point cloud through employing an HARRIS angular point detection algorithm, and generating a source point cloud key point set and a target point cloud key point set; counting the relative distribution frequency of the spatial distance between each point in the two key point sets and other key points in the field, and generating a high-dimensional feature vector; constructing spatial distance distribution feature descriptors by the spatial distance feature elements; according to the cosine similarity of the feature descriptors, points with the similarity degree of the feature descriptors larger than a set threshold value in the key points of the target point cloud are searched, and an initial corresponding point pair set is obtained; an initial spatial transformation matrix between corresponding point sets is solved by using an SVD algorithm through RANSAC, and coarse registration of the source point cloud and the target point cloud is realized; and after a relatively good initial corresponding pose is obtained, performing fine calibration on a registration result through an iterative closest point ICP algorithm. The method has the advantages of low operation cost, high speed, high precision and the like.

The invention provides an AI-based intelligent regulation and control method and system for a building automatic control system, and the method comprises the steps: carrying out the feature space conversion of state variable monitoring data of the building automatic control system through an adaptive learning network model, and generating a first state feature space matched with a feature mode extracted by a building state recognition network; according to the method, the building automatic control system regulation and control network can be guided to perform adaptive regulation and control decision on a plurality of different fault disturbance events according to the first state feature space, and a plurality of adaptive regulation and control decision results associated with building operation categories are generated, so that actual adaptive regulation and control can be performed on the building automatic control system, and the service life of the building automatic control system can be prolonged. The method is further used for optimizing the building state recognition network, so that a closed-loop intelligent regulation and control and optimization system is formed. According to the method, the coping capacity and the regulation and control precision of the building automatic control system for the fault disturbance event are improved, the self-adaptability and the intelligent level of the system are enhanced, and the operation efficiency and the energy utilization efficiency of the building are effectively improved.

The invention relates to the technical field of machine vision, and discloses an intraoperative hand-eye calibration method for a surgical robot, which comprises the following steps of: rigidly connecting a tracking camera with an endoscope, acquiring a fixed pose relationship of the tracking camera and the endoscope through spatial calibration, converting a pose of the tracking camera into a pose of the endoscope, and recording a paired sample of an image and the pose of the endoscope; based on the image and endoscope pose pairing sample, training an image pose regression model, and outputting the rotation amount and the translation amount of the endoscope at the image acquisition moment; inputting the trained regression model, estimating the posture of the endoscope, and recording the posture information of an end effector of the robot; image relative movement and mechanical arm relative movement are constructed, and the spatial transformation relation between the endoscope and the tail end of the surgical robot is calculated. According to the method, preoperative calibration is adopted, the space fixed pose relation between the tracking camera and the endoscope is combined, the absolute pose estimation model of the endoscope is established through the accurate calibration process, and it is ensured that the pose of the endoscope can be inferred only through images in the operation.

The invention provides a dental implant robot path generation method. The method specifically comprises the following steps: constructing a tooth space pose vector; constructing a regression model for predicting spatial features of target missing teeth by combining spatial pose features of opposite-side homonymous teeth and upper and lower occlusal teeth, and predicting spatial positions and attitude angles of the target missing teeth; tCP calibration is carried out on the dental implant robot; carrying out TCF calibration on the dental implant robot; constructing a spatial transformation relation between an end effector of a mechanical arm of the robot and an external visual positioning system; constructing a spatial transformation matrix between the digital model and the entity printing model by adopting a point cloud registration technology; an initial obstacle avoidance path of the mechanical arm is constructed based on a fast expansion random tree sampling algorithm, and meanwhile, smoothing and speed regulation are performed on a joint track through polynomial interpolation and B spline curve interpolation in combination with an improved algorithm RRT *. According to the technical scheme, the problems that in the prior art, planting errors are large, and the fitting degree is not high are solved.

The invention provides a PTv3-based intelligent automatic tooth arrangement method and device, and the method comprises the steps: collecting three-dimensional tooth point cloud data after orthodontic treatment, carrying out the noise filtering, outlier removal and triangular meshing processing of the point cloud data, and separating a single tooth model through a deep learning segmentation network; carrying out principal component analysis and principal axis alignment on the segmented tooth model, calculating curvature characteristics in combination with normal covariance, and dynamically allocating voxel retention density by adopting a curvature-driven gradient voxel downsampling strategy; generating data pairs before and after simulation orthodontics, and constructing an orthodontics database in combination with displacement direction probability constraint and an adjacent tooth collision punishment mechanism; a PTv3-based double-branch network architecture is constructed as an intelligent automatic tooth arrangement network model, a multi-objective loss function fusing geometric errors, collision constraints and clinical prior is set, and the model is trained through weighted optimization; and inputting the preprocessed tooth point cloud into the trained model, outputting spatial transformation parameters and generating a final arrangement result.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving-oriented 4D annotation data processing method and system. A 4D annotation data processing method oriented to automatic driving comprises the steps that a WebAssessment module is loaded at a browser end, multiple frames of point cloud data are fused through the browser end, and the method comprises the steps that the multiple frames of point cloud data to be fused are obtained; taking a first frame of point cloud data in multiple frames of point cloud data to be fused as a reference frame, and converting pose information of subsequent frames of point cloud data into a spatial transformation matrix according to a time sequence to obtain a target spatial transformation matrix; according to the target space transformation matrix, calculating frame point cloud coordinates except the first frame point cloud data to obtain a multi-frame point cloud data coordinate set; and according to the multi-frame point cloud data coordinate set, creating a rendering space data structure, and obtaining automatic driving oriented 4D annotation data. The real-time performance of point cloud data fusion can be remarkably improved through real-time processing of a browser end.

The invention discloses a gait recognition method based on contour and skeleton mixed attention feature fusion. The gait recognition method comprises the following steps: constructing a double-branch gait recognition framework GitFusion; in the skeleton branch, using a graph convolution network GitGCN with a channel attention mechanism to extract skeleton features; in a contour branch, a convolutional neural network GaitContour of an integrated space converter is adopted to extract contour features, and the integrated space converter captures a long-distance dependency relationship; and designing an adaptive feature fusion module to realize intelligent fusion of the two features. The method can effectively cope with gait recognition challenges in complex scenes such as article carrying and wearing change, has the characteristics of high recognition accuracy and strong robustness, gives consideration to model lightweight and calculation efficiency, is suitable for being deployed on resource-constrained equipment, and has wide application prospects in the fields of intelligent security and protection, identity authentication and the like.

The invention relates to the field of active phased array radar array signal processing, in particular to an azimuth super-resolution technology based on beam space transformation. The method comprises the following steps: step 1, establishing an array response matrix; step 2, wave beam space conversion: converting the received signal matrix into a small-dimension wave beam space; and step 3, carrying out azimuth super-resolution estimation on the signal after the beam space transformation by using a deterministic maximum likelihood algorithm, and the like. According to the method, the bandwidth of data transmission is greatly reduced, implementation of a system communication system is facilitated, and meanwhile the calculation complexity is reduced.

The invention discloses a reduced-order modeling and integrated rapid forecasting method for dynamic motion and structural response of a floating fan. The method comprises the following steps: performing finite element modal analysis on a floating fan structure, extracting a rigid body modal and a main elastic modal, constructing a low-dimensional modal space, orthogonalizing mass and stiffness matrixes, and reducing the degree of freedom of a system; hydrostatic force recovery, mooring, gravity rigidity and external exciting force are calculated according to the initial position of the structure, a hydrostatic balance control equation is established through modal space transformation, and generalized displacement is solved through iteration; recalculating the water pressure at the Gaussian point of the structural grid based on the radiation diffraction theory, calculating the pneumatic load in combination with the blade element momentum theory, and obtaining a generalized external load through modal transformation after superposition; a motion equation is established in a modal space, generalized displacement is solved through numerical integration, a global displacement field is reconstructed, a stress time history is directly calculated, and integrated rapid forecasting is achieved. According to the method, the calculation efficiency is remarkably improved while the key dynamic characteristics and the response precision of the structure are kept, and reliable support is provided for near-real-time evaluation of operation and maintenance of the floating type draught fan.

The invention discloses a simple method for robot hand-eye calibration and a structured calibration device, and aims to solve the problems of complex operation, large calculation amount, low efficiency and poor repeated precision of a calibration method in the prior art. The structured calibration device comprises a calibration plate, a connecting bracket, a disc base and a positioning device for accurate installation, the core of the calibration device is that the spatial transformation relation between the calibration plate and the disc base is a predetermined fixed constant. The method comprises the steps that the device is installed at the tail end of the robot, an image of the calibration plate is collected through the camera, the pose of the camera and the calibration plate is calculated through a pose estimation algorithm, and finally the final hand-eye transformation relation is obtained through calculation in combination with the fixed transformation relation predetermined by the device. Through structure solidification and algorithm simplification, complex motion of the robot is not needed, and the method has the advantages of simple operation, light calculation, high repetition precision, high calibration efficiency and the like.

The present invention discloses a multi-condition distributed output process compensation migration modeling method. By transforming in the label space, the characteristics of the label space of a small amount of known target domain data are migrated to the label space of the source domain data, so as to better reduce the difference in the label space distribution of the distributed output process data between different conditions. The deviation compensation migration method based on the Gaussian process regression model proposed by the present invention effectively handles the multi-condition and large-noise characteristics in the styrene radical polymerization reaction, and enhances the robustness and accuracy of the prediction.