345 results about "Distance based" patented technology

The invention belongs to the technical field of intelligent traffic, and discloses a road vehicle speed and distance detection method, system and device based on unmanned aerial vehicle vision. The method comprises the steps of tracking a vehicle in a to-be-processed video aerially photographed by an unmanned aerial vehicle in real time through an improved YOLOv11 network and a BotSort algorithm to obtain a vehicle trajectory, and converting a vehicle pixel coordinate detected by each frame into a world coordinate in combination with a conversion matrix H of the pixel coordinate and the world coordinate to form a trajectory sequence of the vehicle in the world coordinate system; and for a certain vehicle i, according to the pixel coordinates at the t-th moment and the (t + t)-th moment in several continuous video frames, corresponding world coordinates are obtained through the conversion matrix H, the displacement between two points is calculated according to an Euclidean distance formula, and the average speed of the vehicle in the time period is calculated. According to the invention, efficient, stable and reliable vehicle speed measurement and vehicle distance calculation can be realized.

The invention relates to a multi-modal perception fusion target following method and system. The method comprises the following steps: adopting an improved KCF algorithm to realize a closed-loop process of target tracking, multi-scale space construction, feature fusion, response calculation, optimal scale decision and model updating; designing a four-level shielding processing mechanism fusing motion prediction and depth verification, and realizing tracking recovery in a shielding scene through shielding judgment, motion prediction, fine search and template protection; constructing a target distance mapping function fusing geometric distortion correction and attitude compensation, and realizing high-precision distance estimation based on monocular vision; laser radar point cloud information is integrated, an obstacle threat degree model is constructed, and cooperative path planning of following and obstacle avoidance is realized in combination with an improved TEB algorithm; and designing a linear velocity control law and an angular velocity control law based on the distance deviation and the azimuth angle deviation, and driving the robot to complete target following motion. According to the invention, high-precision and robust following of the robot to the target can be realized.

The invention discloses an efficient frequency domain enhanced traffic sign detection method suitable for a snowy city scene, and belongs to the technical field of computer vision and intelligent traffic. According to the efficient frequency domain enhanced traffic sign detection method suitable for the snowy city scene, by designing a frequency domain compensation enhancement module, capture and fusion of multi-scale frequency domain information are achieved, and therefore the feature representation capacity and the model generalization performance are improved. According to the method, the YOLOv5 is used as a basic model for construction, the OfficientViT is used as a backbone network for feature extraction, the problem that the feature representation ability and generalization of a traditional model in snowy days are reduced is solved through a different-frequency feature complementation method, and meanwhile a loss function is optimized based on the Wasserstein distance and the thought of Gaussian reweighting. According to the method, different frequency features can be effectively complemented and enhanced, the feature representation capability in a snowy day scene is improved, and the provided FENet has good effectiveness and robustness in snowy day traffic sign detection.

The invention discloses a mine car obstacle recognition and distance measurement method and device and a storage medium. The method comprises the steps of constructing a segmentation template, marking image feature points, extracting an obstacle contour line, performing coarse and fine segmentation on a target obstacle based on the intersection point proportion of a background segmentation surface and the obstacle contour line, and determining a contour segmentation area of the target obstacle; the method comprises the following steps: marking point cloud feature points, constructing an obstacle template, carrying out matching mapping on a target obstacle and the obstacle template by adopting a non-rigid iterative nearest point algorithm, correcting feature point offset in a matching process in combination with a KD-Tree method, and calculating to obtain point cloud parameters of the target obstacle. And identifying the type of the target obstacle and measuring the distance according to the contour segmentation region and the point cloud parameters. According to the method, the target obstacle image and the point cloud information are combined, the target obstacle is segmented according to the intersection point proportion, the fuzzy area is smoothly segmented, the point cloud parameters are measured while the target obstacle is segmented, and the purpose of identifying the type and the distance of the target obstacle is achieved.

The invention discloses a multi-modal large model reasoning acceleration method based on token variation discarding, and relates to the field of computer vision and artificial intelligence. The method comprises the following steps of: firstly, identifying an inert token with relatively small change amplitude by analyzing representation change of a visual token among layers of a large language model; secondly, designing a token variation measurement module based on an L2 distance, and quantifying the importance degree of each visual token; besides, a progressive discarding strategy is adopted, the visual tokens with low variation are gradually removed from a plurality of preset levels, and the problem of position deviation is avoided; and finally, by reserving a key visual token with high variation, the calculation complexity is remarkably reduced while the model performance is maintained, and the reasoning speed is increased. The method is completely compatible with a high-efficiency attention mechanism, does not need extra attention weight calculation, is mainly applied to the fields of intelligent question answering, video understanding, multi-modal content generation and the like, and has a wide application prospect.

The invention discloses a multi-grid interpolation method and device for an unstructured grid in an aircraft simulation process, equipment and a storage medium, and relates to the technical field of fluid calculation research, and the method comprises the steps: determining a coarse grid interpolation unit having a common point with a to-be-interpolated fine grid unit based on the topological relation of the unstructured grid of an aircraft simulation model; determining a first central point and a first central point coordinate of the fine grid unit to be interpolated, and a second central point, a second central point coordinate and a corresponding first physical quantity value of the coarse grid interpolation unit, and determining a central point distance based on the first central point coordinate and the second central point coordinate; determining a first interpolation coefficient by using a target pseudo Laplace operator and based on the distance between the first physical quantity value and the central point; and normalizing the first interpolation coefficient to obtain a second interpolation coefficient, and interpolating the fine grid unit to be interpolated by using a second physical quantity value determined based on the second interpolation coefficient and the first physical quantity value. In this way, the robustness of the interpolation of the unstructured grid can be improved.

The invention discloses a business question answering method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining source domain and target domain data sets corresponding to new and old businesses of an electric power business hall, and extracting subsets to form a differential training set; determining a semantic association distance based on the difference between the vocabulary feature vector and the word vector, and obtaining a clustering cluster semantic association difference analysis value in combination with the similarity between the inter-cluster vocabulary semantic association distance and the text topic vector after the word is clustered; the vocabulary topic distribution conditions of different data set clustering clusters and the analysis values are fused, and the overall semantic feature values of the vth topic of the source domain and the kth topic of the target domain are calculated; determining attention weight and establishing a matrix, and generating a migrated intelligent language model by combining the two types of data sets; and answering can be obtained by inputting the questioning service text. The method can improve the accuracy of the output reply of the intelligent language model.

The invention provides a vibration acceleration monitoring point clustering optimization method and system for a satellite mechanical test, and the method comprises the steps: obtaining initial data of a frequency response function of each vibration acceleration monitoring point of a satellite through a dynamic test, carrying out the standardization and merging, and obtaining the final data of the frequency response function; dividing the vibration acceleration monitoring points with similar dynamic characteristics into a cluster by taking data correlation as a clustering distance based on a k-means clustering algorithm; classifying the clusters according to the number of the measuring points in the clusters, and determining reserved measuring points of each cluster and cutting measuring points of each cluster according to the correlation of each vibration acceleration monitoring point in each cluster; the number of the reserved measuring points is determined by calculating the redundant correlation coefficient between the cutting measuring points of each cluster and the reserved measuring points in the same cluster and the independent correlation coefficient between the reserved measuring points of each cluster, so that the information independence of the reserved measuring points and the information redundancy of the cutting measuring points are ensured, the cutting method is quantified, the measuring point cutting result is more reliable, and the measurement accuracy is improved. And the efficiency of a ground environment test link is improved.

The invention provides a cable-type temperature-sensing fire detection method and system with an intelligent positioning function, and relates to the technical field of artificial intelligence, and the method comprises the steps: laying an optical fiber temperature-sensing cable along a preset path, obtaining the real-time three-dimensional coordinates of a temperature measurement point through employing a reflective mark point and a multi-view three-dimensional reconstruction technology, enabling the temperature data to be correlated with the coordinates to form a temperature field data set, and obtaining a temperature field data set; and generating three-dimensional temperature field grid data by adopting a weight optimization method based on distance attenuation, and giving temperature early warning and positioning information according to the three-dimensional temperature field grid data. According to the invention, accurate positioning and real-time early warning of temperature abnormity at the early stage of the fire disaster are realized, and the accuracy and response speed of fire disaster monitoring are improved.

The invention discloses an incomplete multi-mode smooth contrast clustering method based on information interpolation, which comprises the following steps of: firstly, modeling a missing view into multivariate Gaussian distribution based on distance weighted nearest neighbor by utilizing a view interpolation mechanism based on statistical information, and carrying out interpolation on the missing view to obtain a complete multi-view data set; secondly, using an encoder to extract potential features, capturing global structure information of multiple views through an FFN-Attention-FFN structure, and extracting local structure information of the multiple views through a graph convolutional network; thirdly, reconstructing a view by using a graph decoder; and finally, generating a clustering distribution result by using the MLP. According to the method, a view interpolation mechanism based on statistical information is introduced, the influence of a weak correlation view on a completion result is reduced by introducing distance weighting, and the data recovery quality is improved; the FFN-Attention-FFN structure is combined with the graph convolutional network, so that the global and local structure information of the data can be effectively captured; and negative sample pairs are expanded by adding Gaussian noise, so that sample distribution is smoother.

Systems and methods are disclosed for identifying relationships between complex datasets and / or high-dimensional datasets for predictive modeling. The method includes clustering data associated with one or more entities in a first dataset based on distance data; clustering the data associated with the one or more entities in a second dataset based on longitudinal data; consolidating the first dataset and the second dataset into a third dataset based on weights assigned to one or more edges between one or more nodes in the first dataset and the second dataset; generating a diagnosis space indicating a condition of the one or more entities based on the third dataset; and determining, via input of the diagnosis space into a machine learning model, an optimization of a weighting scheme for assigning weights to one or more features within the longitudinal data.

The invention provides a cable force detection method, device and system for a cable bearing bridge, and relates to the technical field of bridge monitoring. The method comprises the following steps: acquiring an echo signal of an inhaul cable in a to-be-detected bridge; constructing a corresponding radar cube matrix according to the echo signal; peak value searching is carried out on the signal amplitude of the signal data under the distance dimension in the radar cube matrix, the distance index of each peak value point on the distance dimension is determined, and the distance index is used as the distance index corresponding to each inhaul cable; based on the distance index and the radar cube matrix, incident angle information corresponding to each inhaul cable is determined; and based on the distance index and the incident angle information corresponding to each inhaul cable, determining an enhanced signal corresponding to each inhaul cable by using a beam forming technology. According to the invention, the signal data of each inhaul cable can be effectively separated, the signal data precision of each inhaul cable is improved, and the cable force detection precision is further improved.

The invention discloses an incremental sample screening method for power grid transient stability discrimination model training. The method comprises the steps of obtaining a basic sample set based on a plurality of initial operation modes of a power grid; obtaining a to-be-selected sample based on the generated power grid operation mode; calculating the distance between the to-be-selected sample and each basic sample in the basic sample set; constructing a neighbor sample subset of the to-be-selected sample based on the distance; calculating a neighbor distance between the to-be-selected sample and the neighbor sample subset; judging whether the to-be-selected sample is an incremental sample based on the neighbor distance; and training the power grid transient stability discrimination model by adopting the incremental sample. The screening of the incremental samples only depends on time domain simulation and distance calculation, the identification difficulty of the incremental samples can be greatly reduced, and the data requirement of the artificial intelligence model training for the transient stability discrimination of the power grid can be effectively reduced by identifying the key samples with important value for stability discrimination.

A smart mirror system is provided, comprising a mirror, a camera, and a processor in communication with the camera and the mirror. In use, an image of a face is received for performing facial recognition. Based on the facial recognition, at least one of a plurality of users of the mirror is identified, for tailoring operation of the mirror for the at least one user.

The invention provides a virtual model generation method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of determining description information for generating a virtual model in response to a received generation instruction of the virtual model, and generating a grid model based on the description information; determining a distance field set corresponding to the grid model, and performing structure sensing alignment processing on the grid model based on the distance field set to obtain a first model, the first model comprising a plurality of first geometric primitives; determining a first distance field subset corresponding to each first geometric primitive from the distance field set, and trimming the first model based on each first distance field subset to obtain a second model; and determining a plurality of target components based on the second model, constructing a target model based on the plurality of target components, and outputting the target model. According to the method and the device, efficient generation and accurate optimization of the virtual model can be realized, so that the quality and the efficiency of model generation are improved.

The invention discloses an occupation grid map construction method based on distance weighting, and belongs to the field of intelligent vehicle digital maps and high-precision positioning. The method comprises the following steps: acquiring pose data of an intelligent vehicle and sensing data of a surrounding environment; constructing a distance weight factor; generating an occupation grid map according to the obtained pose data of the intelligent vehicle and the perception data of the surrounding environment, dividing the occupation grid map based on the Bayesian theorem and a distance weight factor, and endowing each grid with state information; and dynamically updating the occupied grid map by using a double-end linked list structure. According to the method, the distance weight factor is constructed, and the grid map is divided through the Bayesian theorem and the distance weight factor, so that the obstacle state can be accurately expressed in a dynamic non-structural environment, and the problem of track noise is avoided.

The invention discloses a self-adaptive intelligent obstacle avoidance method for a water surface unmanned vehicle, which comprises the following steps: establishing a kinematic model of the water surface unmanned vehicle, delimiting an environment boundary, and presetting a plurality of obstacle avoidance algorithms in a system; sensing an obstacle set in a range through a distance-based intelligent local sensing mechanism, modeling a static obstacle, and performing trajectory prediction on a dynamic obstacle; grading the obstacle environment; calculating an overall threat level; based on an obstacle environment grade division result and an overall threat grade, scoring the calculation applicability of each algorithm, and selecting an obstacle avoidance algorithm according to a scoring result and a historical success rate of the algorithm; and generating or updating an obstacle avoidance path according to the selected algorithm. According to the method, the autonomous obstacle avoidance capability with high safety, good real-time performance and relatively low calculation overhead can be provided for the USV under typical complex sea area conditions such as static-dynamic mixing, dense narrow channels and rapid environment change.

The invention discloses an intelligent blind person walking stick based on artificial intelligence, and relates to the field of intelligent walking sticks, and the intelligent blind person walking stick comprises a sensing module used for collecting environmental physical information in the advancing direction of a user in real time, and a processing module used for receiving multi-source environmental information collected by the sensing module, establishing a dynamic three-dimensional space model of the current local environment by taking a landing point at the bottom end of the walking stick as a current origin of coordinates; when the user touches the walking stick to the ground once, a'drop point event 'is triggered once, the three-dimensional space model is updated based on new drop point data, and the space offset between the current drop point and the previous drop point is calculated, including the moving distance and the direction change; compared with a scheme only based on distance perception or fixed path navigation, the method has higher environment understanding capability, navigation adaptive capability and real-time correction capability.

Techniques for matching security recommendation tasks with a regulatory compliance standard are disclosed. A regulatory compliance standard is received as input at a first Machine Learning (ML) model. Security recommendation tasks are received as input at the first ML model. A distance matrix defining a threshold of alignment that specifies a distance between the security recommendation tasks and the regulatory compliance standard is determined by the first ML model. Based on the distance matrix, identifying a predetermined number N of the security recommendation tasks that are within the threshold of alignment. A prompt including the predetermined number N of the security recommendation tasks and the regulatory compliance standard is generated. The prompt is inputted to a second ML model. Based on the prompt, the second ML model identifies a subset of the predetermined number N of the security recommendation tasks that match the regulatory compliance standard.

The invention relates to a power line detection data processing method and system, a storage medium and an inspection terminal, and relates to the technical field of power line detection, and the method comprises the steps: recognizing a power transmission line image inspected by an unmanned plane, and obtaining a predicted arbor-line distance of a plurality of identified arbor; according to predicted meteorological information and historical inspection data of a preset time zone, carrying out volatility compensation on the predicted distance, and outputting a compensated arbor-line distance; evaluating the danger coefficient of each identified arbor according to a preset evaluation index based on the historical inspection data; and determining data importance according to the compensation distance and the danger coefficient, and performing differentiation processing on the line inspection data of the identification arbor coverage area according to the importance. According to the method, the problems of low processing precision and efficiency caused by lack of pertinence, much redundant information and large invalid data interference of data processing which affects line safety due to arbors in traditional power line detection are solved, and the precision and efficiency of power line detection data processing are improved.

This disclosure provides a method and apparatus for estimating the latitude and longitude of a base station. The method includes acquiring measurement and statistical MR data of multiple cells covered by the base station to be estimated. The MR data includes at least one of the following: the identifier of the base station to be estimated, the received reference signal strength (RSRP), and the timing advance (TA). The MR data is preprocessed to obtain a set of MR sample points for the base station to be estimated. Based on RSRP or TA, a distance calculation model and an objective function are established. The distance calculation model is used to calculate the distance between the multiple MR sample points and the base station to be estimated. Based on the distance calculation model and the objective function, a gradient descent algorithm is used to determine the longitude and latitude of the base station to be estimated. This method reduces the amount of data required for solving the problem by preprocessing the MR data to obtain the set of MR sample points. The gradient descent algorithm is used to iteratively solve the objective function to obtain the longitude and latitude of the base station to be estimated, improving the speed of calculating the latitude and longitude of the base station and the accuracy of the measurement results.

The application belongs to the technical field of load identification of structural health monitoring, and provides a concentrated load positioning method based on principal stress constraint and strain gradient trajectory identification, which comprises the following steps: arranging strain sensors in a monitoring area of a planar structure plate and collecting strain data of measuring points, constructing a strain field inversion function based on a radial basis function and establishing an error optimization function containing a fitting error term and a smooth constraint, introducing a principal stress direction constraint and a gradient projection smooth constraint to form a comprehensive error function and solving to obtain an optimal strain field distribution, then calculating strain gradient vectors of each grid node and tracking a gradient trajectory through a gradient descent method, and finally performing an iterative clustering analysis based on distance statistics on a trajectory end point to output a cluster center as a concentrated load application position identification result. The application can improve the concentrated load positioning accuracy, enhance the anti-noise capability, and realize fast, stable and large-sample-free load position identification.

The invention relates to a power transmission network security management system, and the system comprises a final-stage conversion mechanism which is used for successively executing Wiener filtering processing and median filtering processing on a secondary conversion picture; and the model application device is used for intelligently identifying whether the field spacing distance between the grounding piece and the operation electrician is smaller than or equal to the safe spacing distance or not by adopting an AI identification model. The power transmission network security management system is intelligent in design and simple to operate and control. The image content capturing action can be executed on the replacement scene for replacing the strain insulator string so as to obtain and output the corresponding replacement scene picture, and the AI identification model is adopted to intelligently identify whether the field spacing distance between the grounding piece and the operation electrician is smaller than or equal to the safe spacing distance based on various visual information in the replacement scene picture; therefore, when a strain insulator string is replaced in a power transmission network, a reliable and safe distance between an operation electrician and a grounding body is maintained.

In the motion state detection method and self-moving cleaning device disclosed in this invention, when the self-moving cleaning device determines that it has moved a certain distance based on internal parameters, if the distance measurement value detects a small change in the relative distance between the self-moving cleaning device and objects in the environment, and the data state at this time matches the data state when slipping occurs, it can be directly determined that slipping has occurred. By combining the original hardware configuration of the self-moving cleaning device with relevant data statistics, it is possible to determine whether slipping has occurred. The hardware and software costs for slipping detection are extremely low, and the detection results are accurate. Even self-moving cleaning devices with lower hardware configurations can achieve slipping detection through software upgrades; without increasing purchase costs, users of self-moving cleaning devices with lower hardware configurations can also enjoy a good user experience.

The application discloses a kind of attention mechanism trajectory generation and simulation methods for sparse data completion, belong to intelligent transportation and trajectory prediction field.The application fills in trajectory missing value using high-precision sensor and map information, combines graph attention network (GAT) and multi-modal fusion technology;Adopt the attention module based on distance (D-GAT) and based on view (V-GAT), capture the interaction between vehicles, improve the understanding of complex traffic scene;Through prediction supervision generator and multi-modal trajectory generator, combine LSTM and Gaussian mixture model (GMM) to generate multiple possible trajectories, and use Kalman filter for online adjustment, ensure the accuracy and real-time of trajectory.The application realizes the intelligent completion of sparse traffic data and the accurate generation of trajectory, provides reliable data support and decision basis for intelligent transportation system, helps the efficient operation and sustainable development of urban traffic planning and management.

Embodiments of the present application provide a field of view angle range correction method, computer equipment and a storage medium. The method comprises: obtaining a target point cloud; determining a target inter-plate distance based on the target point cloud; obtaining a second point cloud set, determining a candidate inter-plate distance based on each frame of point cloud in the second point cloud set; and determining a target driving parameter based on the candidate inter-plate distance and the target inter-plate distance. The target point cloud obtained by the embodiments of the present application suppresses the point cloud distortion caused by the wide-angle lens, which is conducive to obtaining a reliable and accurate target inter-plate distance. The target inter-plate distance is used as a quantitative target for adjusting the driving parameter, and the driving parameter is constantly adjusted to obtain a target driving parameter that can make the candidate inter-plate distance consistent with the target inter-plate distance. The laser radar can drive the galvanometer to scan through the wide-angle lens at the target field of view angle range based on the target driving parameter. Therefore, the embodiments of the present application can help the laser radar to accurately and reliably output the laser line corresponding to the target field of view angle range.

This invention provides a cable-based temperature-sensing fire detection method and system with intelligent positioning function, relating to the field of artificial intelligence technology. It includes deploying fiber optic temperature-sensing cables along a preset path, acquiring real-time three-dimensional coordinates of temperature measurement points using reflective markers and multi-view 3D reconstruction technology, correlating temperature data with coordinates to form a temperature field dataset, generating a three-dimensional temperature field grid data using a distance-attenuation-based weight optimization method, and providing temperature warnings and location information accordingly. This invention achieves accurate location and real-time warning of early-stage temperature anomalies in fires, improving the accuracy and response speed of fire monitoring.

The application relates to the technical field of artificial intelligence and Internet of Things, and provides an Internet of Things model training method, device and equipment and medium based on zero sample learning, which can establish a feature vector space system comprising multi-dimensional features such as device attribute features, core data features and auxiliary features, and further improve the features in combination with artificial labeling and machine learning automatic labeling, utilize a preset convolutional neural network to establish a unified Internet of Things scene recognition model to assist in scene recognition of to-be-processed data, utilize distance based on a semantic space data feature vector to verify relevance, further check a scene recognition result, and when the verification is passed, train an Internet of Things model according to the recognized scene data, thereby solving the modeling problem of an Internet of Things scene lacking samples.

The invention relates to an intelligent detection method and system for the right-turning behavior of a large truck, and the method comprises the steps: extracting the positioning data of three key track points before, during and after turning distributed according to a time sequence in the right-turning process of the truck, and combining the fence information of a geographic information system; multi-dimensional feature vectors including speed, time, distance, direction and space area are constructed; spatial discrimination characteristics based on a distance threshold value and a region position are innovatively proposed, and four-level classification discrimination is performed according to the distance and the region attribution by calculating the distance and the relative position relation between a vehicle track point and a geo-fence. Through feature optimization processing and classification model training, three-classification prediction of vehicle parking behaviors is realized: direct passing, slow driving and parking. According to the invention, the right-turning parking behavior of the large truck can be accurately identified in real time, and an effective technical means is provided for traffic safety supervision.

This invention relates to the field of mining area traffic planning and discloses a method for planning vehicle speed in mining areas that integrates AI algorithms and multimodal data. The method includes the following steps: S1, determining that the current vehicle is in a following state and collecting the current vehicle's driving parameters, the position of the leading vehicle, and the distance between the two vehicles; S2, based on the positions of the current vehicle and the leading vehicle, determining whether the leading vehicle is traveling on a slope or about to leave the slope; S3, if the leading vehicle is on a slope or about to leave the slope, determining a safe following distance based on the distance between the two vehicles and the leading vehicle's slope travel distance; S4, planning the current vehicle's driving speed. This invention identifies vehicle following patterns and collects multimodal data in real time and comprehensively. It utilizes AI algorithms to perform high-precision perception and forward-looking prediction of the leading vehicle's slope travel status, enabling the solution to fully understand the complex terrain environment currently and in the future, thus allowing for pre-emptive risk assessment.