552 results about "Location prediction" patented technology

A method and system for analyzing fault data features of an active distribution network. The method comprises: analyzing a voltage variation after a fault in an active distribution network and analyzing a single-phase ground fault (S1); acquiring distribution network fault data by means of analog simulation (S2); and establishing a fault location prediction model to analyze fault data features and realize fault localization (S3). In the method, a voltage variation after a fault in an active distribution network is analyzed, the voltage variation is equivalently represented using an injected current, a single-phase ground fault is analyzed, and the localization of a fault point is realized. By simulating a current variation during an actual fault injection process by means of an injected current function, a model can more closely reflect an actual operating state, thereby enhancing the authenticity of fault simulation and the accuracy of prediction. A complex relationship between a current variation and node impedance is deeply analyzed by means of a complex information filtering function, and more refined fault features are extracted, thereby optimizing the precision of fault localization, and enhancing the adaptability of the model to different fault scenarios.

The invention discloses a video monitoring abnormal behavior identification method and system based on edge AI, and relates to the technical field of intelligent video analysis, and the method comprises the steps: carrying out the frame segmentation processing of a video monitoring data stream, obtaining a video frame sequence, building a target trajectory prediction model, predicting the position region of a current frame, and generating target position prediction data; performing multi-scale feature extraction on the video frame sequence, performing fusion matching on a target feature vector and target position prediction data, and constructing an enhanced feature matrix; carrying out weight distribution on the key behavior characteristics by adopting an attention mechanism, setting a dynamic threshold adjustment mechanism, and dynamically adjusting an abnormal behavior judgment threshold according to the personnel density; and inputting the adjusted feature data into an abnormal behavior classifier for identification and judgment, outputting an abnormal behavior identification result, and generating an abnormal event report. According to the method, the abnormal behavior detection accuracy in a complex monitoring scene is improved, the false report and missing report rate is reduced, and the millisecond-level real-time response capability is realized.

The invention discloses a ship shore-end intelligent management method, system and device and a storage medium, and the method comprises the steps: carrying out the ship position prediction based on an electronic chart, the current navigation state data of a ship, and the current environment data, and obtaining the information of a to-be-docked ship according to the future position information of the ship, constructing a port operation knowledge graph based on the information of the ships to arrive at the port, the equipment data of the ships approaching and leaving the port, the historical tug operation data and the port data, and predicting the tug workload demand based on the port operation knowledge graph; taking tug operation data as a state space, taking a tug scheduling strategy and a cargo stacking layout adjustment strategy as an action space, taking maximization of wharf operation efficiency as a reward target, and adopting a reinforcement learning method and a simulated annealing method to carry out wharf operation scheduling optimization to obtain an optimal wharf operation scheduling strategy. According to the method, the port working efficiency is improved, manual interference is not needed when the wharf operation scheduling strategy is obtained, and autonomous decision-making can be completely achieved.

The invention relates to the technical field of cloth flaw detection and deep learning, in particular to an MAC-YOLO-based cloth flaw detection method, which comprises the following steps of (1) constructing an MAC-YOLO network, and improving the MAC-YOLO network based on a YOLOv8 network; (2) collecting a cloth surface image data set, carrying out defect type labeling on defect images, and randomly dividing the data set into a training set and a test set according to a proportion of 8: 2; (3) the training set is used for training the MAC-YOLO network, an AdamW optimizer is adopted, a learning rate strategy is adjusted in stages, and a loss function comprises binary cross entropy loss of classification branches and distribution focus loss and complete intersection-to-union ratio loss of regression branches; and (4) detecting the input cloth surface image by using the trained MAC-YOLO network, and outputting the category and position prediction map of the flaws.When the extreme length-width ratio image data is processed, various flaws can be accurately detected.

The invention provides a tunnel fire behavior inversion method based on multi-modal fusion and physical constraint, and aims to solve the problem that the position and power of a fire source are difficult to invert accurately in real time in traditional fire behavior monitoring. According to the method, data, temperature distribution images and physical parameters of distributed temperature sensors in a tunnel are collected, and a spatial-temporal feature fusion network is constructed; designing an information leakage prevention training mechanism, and adopting a modal random discarding and sensor shielding technology to avoid overfitting of the model to a specific input mode; a physical constraint loss function is introduced, and the temperature gradient, the maximum temperature rise and the longitudinal attenuation law are combined to ensure the physical rationality of an inversion result; a coarse-fine two-stage position prediction framework is adopted, and meter-scale precision positioning of the position of a fire source is achieved. According to the invention, the power and position of the fire source can be inversed accurately in real time, and reliable technical support is provided for intelligent monitoring and emergency rescue of tunnel fire.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.

The invention provides an unmanned aerial vehicle intelligent decision-making method and system based on deep reinforcement learning, and relates to the technical field of intelligent decision-making, and the method comprises the steps: obtaining a landing region image and depth information through a visual sensor, processing feature representation and three-dimensional environment description through a risk perception type strategy gradient algorithm, and adaptively constructing an information association relationship. According to the method, feature fusion, optimal landing position determination, dynamic obstacle trajectory prediction and track point sequence generation are realized, a control strategy is optimized when the environment is suddenly changed, safe and efficient landing of the unmanned aerial vehicle is realized, and the safety and adaptability of landing of the unmanned aerial vehicle are improved.

The invention relates to the technical field of valve leakage detection, and particularly discloses an ultrahigh pressure gas valve leakage detection method and system. According to the method, a unified leakage feature vector is obtained through multi-source acquisition and processing; capturing a time sequence dependency relationship, and determining a micro leakage mode; carrying out abnormal peak detection to obtain a preliminary leakage probability; combining the initial leakage probability with real-time environmental parameters, and dynamically adjusting a judgment standard; comparing the unified leakage feature vectors through a judgment standard, fusing position coordinate triangulation positioning, judging whether micro crack leakage occurs or not, and obtaining leakage position estimation; the historical data sequence is fused, the micro crack development trend is predicted, and the early warning level is determined; and generating a corresponding response signal according to the early warning level, and outputting micro crack leakage early warning. Multi-source data fusion, dynamic threshold adjustment and position prediction can be carried out, the accuracy and robustness of micro crack leakage detection are remarkably improved, and efficient guarantee is provided for safe operation of a pipeline.

The invention relates to the technical field of unmanned aerial vehicle monitoring and trajectory processing, and discloses a low-altitude unmanned aerial vehicle dynamic trajectory tracking and predicting method based on multi-base-station cooperation. The method comprises the steps that signal parameters are obtained through multi-base-station collaborative observation, and a multi-modal position prediction set is generated; and performing classification and scoring according to the spatial distribution characteristics of the candidate points and the historical track points, and screening out an optimal prediction position point. And inputting the optimal prediction point and the historical trajectory into a generative model, and dynamically adjusting the number of trajectory points by analyzing the point distribution probability to form a preliminary smooth trajectory. And performing physical feasibility verification and fine adjustment on the trajectory according to kinematics constraints to obtain a final smooth continuous trajectory. And matching degree calculation is carried out by fusing alternative trajectories generated by a multi-kinematic model, and the most probable target trajectory is identified. According to the method, the robustness of trajectory prediction and the structural rationality of the generated trajectory in a complex observation environment are improved.

The invention provides a power transmission line icing detection method, device and equipment based on deep learning. The method comprises the following steps: obtaining a power transmission line diagram with a suspected icing area; an icing initial feature extraction model is called to process the power transmission line diagram, multiple first multi-scale icing initial feature maps are obtained, and the multiple first multi-scale icing initial feature maps are composed of intermediate results and final results output by the model; an icing deep feature extraction model is called to process the multiple first multi-scale icing initial feature maps, multiple second multi-scale icing deep feature maps are obtained, and the multiple second multi-scale icing deep feature maps are composed of intermediate results and final results output by the model; and calling an icing position determination model, performing prediction processing on the plurality of second multi-scale icing deep feature maps by using a plurality of prediction heads in the icing position determination model, and determining position prediction information of an icing area in the power transmission line map.

The invention discloses a traffic scene risk identification method and device considering dynamic and static information fusion, and a storage medium. The method comprises the steps of 1, obtaining a traffic scene high-precision map and a time sequence track of each vehicle; track points in the time sequence tracks are converted into a high-precision map coordinate system, lane matching is carried out, and a time sequence track high-precision map of each vehicle is obtained; 2, predicting vehicle positions, and extracting dynamic and static characteristics of each vehicle time sequence track; 3, fusing the dynamic and static features of each vehicle to obtain a fused feature; inputting the fusion features of all vehicles into a risk factor prediction model to predict and obtain dynamic and static risk factor prediction results; on the basis of prediction errors between dynamic and static risk factor prediction results and corresponding real results, combined with vehicle position prediction errors, calculating a comprehensive risk value of the traffic scene; the equipment and the storage medium are used for implementing the method. The method provides a new thought for traffic risk identification.

The invention relates to the technical field of anti-collision systems, in particular to an offshore wind power construction safety pre-warning system based on AIS (automatic identification system) data, which comprises a ship dynamic uncertainty modeling module for acquiring AIS signal updating frequency and positioning precision marks of a target ship and generating a ship future position probability ellipse. According to the invention, the position coordinate, the speed value and the course angle of the ship are obtained in real time based on the AIS data, the reliability and the motion trend of ship position prediction are determined by combining the AIS signal updating frequency and the positioning precision mark, and the future position probability ellipse of the ship is accurately generated; a protection area is dynamically constructed, comprehensive calculation is carried out in combination with the spatial relation between a ship future position probability ellipse and the positions of surrounding fan pile foundations, and a hourly dynamic collision risk index sequence is obtained; and further according to the statistical deviation between the current motion state of the ship and the standard parameter of the function partition, combining with path difference analysis to obtain a navigation abnormity comprehensive index.

The invention discloses an unmanned aerial vehicle navigation method and related equipment based on a visual language model, and the method comprises the steps: carrying out the thinking chain construction of an initial training data set, generating high-quality thinking chain data, and constructing a target training data set according to the initial training data set and the high-quality thinking chain data; based on a supervised fine tuning method and a reinforcement learning method, training the initial visual language model according to the target training data set to obtain a candidate visual language model; deploying the target visual language model passing the model verification to an unmanned aerial vehicle navigation control system; and generating a reasoning result and a target position according to the natural language instruction of the user through the target visual language model, and predicting a target action sequence according to the target position by adopting a foresight mechanism, so that the unmanned aerial vehicle executes the target action sequence. Complex natural language instructions can be accurately understood, spatial reasoning is carried out in combination with environmental semantics, accurate target positioning and path planning are achieved, and the method can be widely applied to the technical field of unmanned aerial vehicle control.

The invention discloses a stacked LSTM trajectory prediction method based on an attention mechanism in a multi-modal environment. The method comprises the following steps: aligning modal data through a sliding window mechanism; designing a three-layer stacked LSTM (Long Short Term Memory) network structure, namely an AS-LSTM network structure; the first layer LSTM inputs inertial measurement unit IMU feature splicing PointNet + + extracted point cloud feature, the second layer LSTM inputs first layer linear processed hidden state splicing CNN extracted image feature, and the third layer LSTM inputs second layer linear processed hidden state splicing GPS feature; and an attention mechanism is added to the hidden state output by the third layer of LSTM to calculate a dynamic weight, and the position prediction at the next moment is output through a Dense layer. And by taking the mean square error and the decision coefficient as evaluation indexes, constructing an objective function for minimizing the prediction error. And an objective function value lower than that of other LSTM prediction methods can be obtained after NCLT data set training.

The invention provides a video coding method and device, electronic equipment and a storage medium, relates to the technical field of image processing, in particular to the field of video coding and the like, and can be applied to application scenes such as video live broadcast and the like. The specific implementation scheme is as follows: acquiring a reference frame which is adjacent to a current frame and has established initial ROI hierarchical division; multiplexing a motion vector generated by the reference frame in a motion compensation time domain filtering process, and determining an ROI position predicted value of the current frame; extracting feature points of the five sense organs of the current frame, and correcting an ROI position predicted value based on a feature point spatial topological relation in the initial ROI; respectively configuring differential quantization parameters for the corrected main face region, the corrected secondary face region and the corrected background region; dynamically allocating a three-layer area code rate according to a real-time network bandwidth; and outputting the coded frame of the current frame and the associated ROI level metadata. According to the scheme, the coding efficiency and quality can be improved.

The invention relates to the technical field of dynamic visual target motion tracking control, in particular to a dynamic visual target motion tracking control method and system based on deep learning, and the method comprises the steps: synchronously collecting continuous multi-frame target scene image data through a visual multi-frame collection module; and performing time sequence association and memory fusion on target features in continuous multi-frame target scene image data through a cross-frame feature memory fusion module, and constructing a target feature model. According to the invention, the current and historical stable features are dynamically fused through the cross-frame feature memory fusion module, time sequence association is realized in combination with the long and short-term memory network, and the problem of slow feature model updating under target deformation and shielding is solved; the deformation-shielding bimodal recognition module accurately recognizes a scene state, provides a basis for the multi-branch Kalman filtering prediction module, enables the multi-branch Kalman filtering prediction module to call a corresponding branch, corrects a prediction equation through a compensation factor, and improves the position prediction accuracy.

The invention provides a catheter tip positioning method and system in central venous catheterization and a medium, and relates to the field of medical image data processing. The system comprises a CT center line extraction module, a puncture position prediction module, a catheterization target calculation module, an ultrasonic center line extraction module, an image fusion module, a catheter position projection module and a catheterization completion judgment module. According to the intelligent navigation positioning technology in the central venous catheterization based on ultrasonic, CT and electromagnetic navigation multi-mode fusion, ultrasonic, CT, electromagnetic positioning and deep learning are creatively fused, accurate positioning and real-time monitoring of the tip of the catheter in the central venous catheterization are achieved, and the learning threshold is lowered.

The invention discloses an energy storage system fault database indexing method, and particularly relates to the technical field of fault prediction. The method comprises the following steps: firstly, constructing an energy disturbance vector sequence matrix in a unified time window, extracting features based on a continuous variation rate and energy residual distribution, and generating an energy disturbance feature spectrogram; establishing an energy propagation path atlas in combination with a system module topological relation, and introducing a time sequence consistency identifier; a fault evolution fingerprint is generated through graph embedding coding, similarity index matching is carried out in combination with a standard fault trajectory, and a fault type and a positioning weight are output; real-time energy indexes are further fused, a micro-fault position prediction map is generated, and dynamic early warning is achieved; according to the method, accurate identification and visual positioning of the micro-fault of the energy storage system in a complex scene can be realized, and the operation safety and the intelligent operation and maintenance capability of the system are improved.

The invention discloses a mechanical arm online calibration method and system. The method comprises the steps that a hydraulic mechanical arm no-load dynamic model based on the Lagrangian method is constructed; the method comprises the following steps: establishing an improved Denavit-Hartenberg kinematic error model; constructing a position prediction model by using a convolutional neural network and training the position prediction model; performing error compensation correction on the predicted position based on the model; online calibration parameters are determined and dynamically adjusted; and iterative optimization is repeated to realize real-time calibration. The system comprises a dynamical model construction unit, a kinematic error model establishment unit, a position prediction model training unit, an error compensation calculation unit, a calibration parameter determination unit and a model iterative optimization unit which work cooperatively. The method and the system can respond to working condition changes in real time, integrate multi-source errors, improve calibration precision, effectively reduce track deviation and guarantee operation quality and stability.

The invention provides a real-time estimation method and system for a small-target radar adaptive signal-to-noise ratio. According to the method, a motion model is established based on a historical trajectory of a maneuvering target, and a dynamic position prediction sequence is generated; acquiring a scanning beam pointing angle sequence and a timestamp echo signal intensity sequence; starting multiple rounds of iterative detection for each target position point in the prediction sequence, and shrinking a detection window bidirectionally until the boundary is smaller than the width of a beam half-power point; a signal intensity peak value and background noise intensity in a final iteration window are extracted, a beam irradiation efficiency factor is generated in combination with the spatial matching degree of the scanning beam directional angle sequence and the position prediction sequence, and a signal-to-noise ratio dynamic correction is cooperatively calculated; and weighting and fusing the correction, the signal peak value and the noise intensity, and outputting a final signal-to-noise ratio value of the maneuvering target in real time. According to the invention, the signal-to-noise ratio real-time estimation precision and stability of a small target radar when a high-speed maneuvering target passes through a beam edge are improved.

The invention discloses a method and a system for rapidly and continuously detecting missing border trees based on dynamic edge calculation. The method comprises the following steps: acquiring an image, a GPS coordinate and a timestamp, and constructing a spatio-temporal trajectory; preprocessing the image, identifying and classifying vegetation, municipal facilities and moving shelter targets, and outputting feature information and confidence score to obtain an image identification result; based on an image recognition result, screening a street tree close to the lane line and predicting a corresponding position, constructing a motion track through continuous multi-frame data association, and performing calibration in a coordinate system to generate a predicted attention area to form a street tree position prediction result; and judging and classifying the plant missing condition in the green belt according to the street tree position prediction result, and generating related event information. By implementing the method provided by the invention, the limitation of the prior art can be overcome, more efficient and accurate technical support can be provided for daily maintenance of urban greening, and the quality and efficiency of urban management can be improved.

The invention relates to a data reading and writing method and device, electronic equipment and a computer storage medium, and belongs to the technical field of data indexing.The data reading and writing method comprises the steps that a key of indexed data is obtained, a preset position prediction function is adopted in a learning index layer to predict the key, and a first storage position corresponding to the key is predicted; when the first storage position does not conflict with the read operation or the write operation of the indexed data, performing the read operation or the write operation on the indexed data at the first storage position; when the first storage position conflicts with the read operation or the write operation of the indexed data, performing adaptive retrieval on the key in an adaptive cardinal number tree layer, and determining a second storage position corresponding to the key; and performing read operation or write operation on the indexed data at the second storage position. The data reading and writing efficiency of the database can be improved.

Systems and methods for tracking a location of an object of interest through a sequence of medical images are provided. First and second input medical images of a patient are received. The first input medical image comprises an annotation of a location of an object of interest. Features are extracted from the first and the second input medical images. A location of the object of interest in the second input medical image is determined using a machine learning based location predictor network based on the annotation of the location of the object of interest in the first input medical image and the extracted features from the first and the second input medical images. The location of the object of interest in the second input medical image is output. The machine learning based location predictor network is trained based on a comparison between 1) locations of a particular object in a sequence of training images determined during a forward tracking of the particular object through the sequence of training images and 2) locations of the particular object determined during a backward tracking of the particular object through the sequence of training images.

The invention discloses a marine radar multi-target tracking method and system. The method comprises the following steps: constructing an RSDT small target detection algorithm and training to obtain a marine radar image ship identification model; identifying the ship position of the current frame of the marine radar video stream through a marine radar image ship identification model to obtain a ship detection frame; constructing a ship trajectory prediction model, and predicting the ship position of the current frame according to the ship position of the previous frame of the marine radar video stream to obtain a ship prediction frame; matching the ship detection frame and the ship prediction frame of the same frame to obtain a matching result; if the matching is successful, inputting the ship detection frame and the ship prediction frame into the ship trajectory prediction model and updating the ship detection frame and the ship prediction frame, and outputting a new ship motion trajectory; and if the matching fails, creating a high-confidence detection frame in the ship detection frame as a new ship motion track, thereby realizing accurate identification of the ship target and stable tracking of the ship track in the marine radar scene.

The invention discloses a humanoid robot falling prediction method, device and equipment and a storage medium, and relates to the technical field of robot control, and the method comprises the steps: obtaining target state information and a target capture point position corresponding to a target humanoid robot in real time in a task execution process of the target humanoid robot; whether the target humanoid robot can reach the target capture point position or not is judged based on the target state information, if yes, the predicted falling time of the target humanoid robot is determined, and the target motion time of the target humanoid robot reaching the target capture point position is determined; and if the predicted fall-down time is greater than or equal to the target motion time, generating a ZMP sequence of the target humanoid robot through a model prediction control algorithm, and predicting whether the target humanoid robot falls down or not based on the ZMP sequence and the target capture point position so as to perform fall-down protection on the target humanoid robot. According to the method, the dynamicity and timeliness of fall prediction of the humanoid robot can be improved, and safety protection is carried out during fall.

A ground collision avoidance method in an ownship vehicle is disclosed. The method includes: retrieving position measurements for the ownship vehicle and for a dynamic obstacle; retrieving mapping data from an airport map database that includes coordinate data for airport travel pathways; adjusting a position measurement for the ownship vehicle and a position measurement for the dynamic obstacle based on coordinate data retrieved from the airport map database and historical aircraft movement data; predicting a series of future positions for the ownship vehicle that are constrained by airport surface operation rules; predicting a series of future positions for the dynamic obstacle that are constrained by airport surface operation rules; calculating whether a potential collision is imminent; and causing a collision alert to be displayed when the processor has determined that a potential collision between the ownship vehicle and the dynamic obstacle is imminent.

The invention provides a hip joint body surface recognition method based on double-model fusion, and the method specifically comprises the following steps: S1, constructing a diversified image data set containing the key features of the body surface of a newborn for a hip joint ultrasonic examination scene; s2, a network structure of a YOLOv8-Tiny target detection model and a network structure of an OpenPose human body posture estimation model are improved; s3, respectively training the improved YOLOv8-Tiny target detection model and the improved OpenPose human body posture estimation model by utilizing the marked training set, and obtaining an optimal training model parameter; and through double-model joint reasoning, feature extraction and position prediction are performed on an input image, output results of the two models are fused in combination with geometrical relationship constraint and a confidence coefficient weighting strategy, and finally, high-precision recognition coordinates of the body surface of the hip joint of the newborn are output. According to the method, a more accurate and reliable identification result can be provided in real time under the condition of relatively low computing resources, and the detection efficiency and precision are remarkably improved.

A system for predicting a resting position of a golf ball may use ball tracking sensor data to identify an impact location of the ball. Modeling of ball movement following impact may be performed that incorporates measured ball impact physics, impact coordinates, and material properties of ground or objects to which the impact coordinates correspond and, together with historical shot data corresponding to the impact coordinates or an encompassing zone, bounce and roll behavior and / or final resting position predictions may be generated.

The invention discloses a beam guidance optimization method based on position prediction in unmanned aerial vehicle communication, and relates to the technical field of unmanned aerial vehicle communication optimization, and the method comprises the steps: obtaining the flight path data of an unmanned aerial vehicle, carrying out the adaptive segmentation according to the flight path data, and constructing a state evolution model for each flight path; performing short-term prediction and medium-term residual error calibration prediction according to the state evolution model, and constructing a prediction correction term to generate a prediction point set; mapping the prediction point set to a direction coordinate system of a ground communication array to construct an angle evolution tensor, and screening to obtain a candidate beam direction set; and screening a standby beam direction with the maximum path coverage redundancy from the candidate beam direction set. According to the method, a beam guiding optimization mechanism with state identification, adaptive prediction, space tolerance, historical correction and multi-path dynamic regulation and control capabilities can be realized, and the beam control precision, the failure recovery capability and the communication link stability in a complex airspace motion state during communication of the unmanned aerial vehicle are remarkably improved.

The invention discloses a partially distinguishable multi-cluster target tracking method and device, and the method comprises the steps: calculating a position prediction value of a contour point n of a cluster target i at a moment k, obtaining a difference value of a jth echo calculation sum at the moment k, and judging whether a limiting condition of a wave gate is satisfied or not according to the difference value; when the limiting condition is met, establishing an association relationship with the nth contour point of the cluster target i; constructing a global incidence matrix at the moment k according to the incidence relation; elements in the global incidence matrix serve as variable nodes in a factor graph, edge posteriori distribution of the elements in the global incidence matrix is solved through the factor graph, and pseudo measurement of the cluster target i is calculated according to the edge posteriori distribution; performing Kalman filtering based on the pseudo measurement of the cluster target i to update the state of the cluster target; according to the invention, the correlation precision between the cluster target and the echo is improved, so that the detection precision is improved.