64results about How to "Implement tracking" patented technology

The invention discloses a multi-unmanned aerial vehicle target exploration and tracking method and system based on multi-agent ant colony reinforcement learning. The method comprises the following steps: dividing an actual environment for target tracking of unmanned aerial vehicles into grid units; performing target state initialization based on the grid units to obtain an initial observation position of the unmanned aerial vehicle; based on an ant colony algorithm and the initial observation position of the unmanned aerial vehicle, the transition probability of the unmanned aerial vehicle among the grid units is calculated; establishing a reward function based on the transition probability and the grid unit; based on the reward function and the existing exploration information and tracking information, performing centralized training on the multiple unmanned aerial vehicles by using a multi-agent near-end strategy optimization algorithm to obtain an optimal joint decision; and based on the optimal joint decision, enabling the unmanned aerial vehicle to complete exploration and tracking of multiple targets to be explored. According to the technical scheme, the possibility that the unmanned aerial vehicle finds the target in the unknown area is increased, and the target can be effectively followed.

The invention provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy is improved.

The invention provides a hovercraft path tracking method and device based on reinforcement learning, equipment and a storage medium, and the method comprises the steps: obtaining the initial positions and motion information of a hovercraft and a dock landing ship, and determining the expected path of the hovercraft based on the initial positions and motion information; calculating a target heading angle of the hovercraft in the expected path based on a first guidance algorithm; taking the target heading angle as input of a reinforcement learning network and an anti-interference controller, generating a first control parameter, training the first control parameter based on a first neural network, and generating a target control parameter; and controlling the hovercraft to move according to the expected path based on the target control parameter.

The invention discloses a building construction site arrangement optimization method and system and a storage medium. According to the main scheme, the method comprises the steps that topographic data, building layout data and equipment layout data of a building construction site are acquired; drawing a construction site rough arrangement diagram according to the collected building layout data and equipment layout data by using CAD software, and importing the rough arrangement diagram into BIM modeling software to generate a construction site basic model; analyzing the construction site basic model of the BIM technology, and marking a problem area of the basic model; an optimization model is formed by changing the layout of a problem area of the basic model; and analyzing the optimization model to judge whether the optimization model meets the standard or not. According to the technical scheme, intelligent optimization and real-time monitoring of construction site arrangement can be realized, the construction efficiency is improved, the cost is reduced, and the construction safety is enhanced.

A periodic attitude tracking task-oriented unmanned aerial vehicle predetermined time anti-interference learning control method comprises the following steps: establishing a kinetic model of a four-rotor aircraft system, dividing the uncertainty of the system into a periodic uncertainty part and a non-periodic uncertainty part, and initializing the state and control parameters of the system; designing a predetermined time disturbance observer, and quickly estimating and compensating a non-periodic uncertain part; designing a predetermined time sliding mode variable; designing a full-saturation repetitive learning law, and accurately estimating and compensating the uncertain part of the period; and designing a repetitive learning controller based on the predetermined time disturbance observer, the predetermined time sliding mode variable and the full-saturation repetitive learning law. According to the method, the influence of the system uncertainty on the control precision of the unmanned aerial vehicle can be effectively suppressed, and it is ensured that the upper bound of the minimum convergence time of the observation error can be preset by adjusting an adjustable parameter, so that the tracking error is converged to the neighborhood near the original point within the preset time.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent crop growth state monitoring method and device based on deep learning, and the method comprises the steps: regularly obtaining pictures of crops in seedling trays in a greenhouse; wherein the seedling tray is divided into a plurality of grids, and is used for performing crop seeding on each grid; on the basis of the trained grid detection model, identifying grids in the crop photo, and counting the number of all the grids; on the basis of the trained crop detection model, identifying leaves of crops in the grids in the crop picture, and marking the contour of each leaf; the crop detection model comprises a seedling detection model and an adult seedling detection model, and calculating various growth state indexes of the crops according to the counted number of all the grids and the identification result of the leaves of the crops in the grids. Therefore, automatic identification and tracking of the growth state of the crop plant are realized, personal errors are reduced, and powerful support is provided for precision agriculture.

The invention provides a flexible trailer detection method and system based on monocular vision. The method comprises the steps that monocular vision information and 3D point cloud information of a trailer are obtained respectively; information fusion is carried out on the monocular vision information and the 3D point cloud information to obtain image information, and the image information at least comprises posture, position and motion state information of the flexible pull part; performing feature extraction and target detection on the image information to obtain target information of the flexible pull part; monocular direction estimation of a front pose is carried out on the target information, and pose information of the flexible pull part is obtained; and performing path planning and decision adjustment based on the pose information in combination with the dynamic change of the front vehicle, and generating a control instruction to control vehicle tracking. According to the invention, the high-efficiency detection of the flexible towed object is realized by using the monocular vision technology, and the safety and the operation efficiency of the towed vehicle are improved.

The invention discloses a traffic target monitoring and tracking system and method based on monocular vision daytime scene reconstruction. The system comprises a monocular camera, an inertial navigation module, a lightweight processor, a radio frequency communication module, a solar energy storage module and a Beidou positioning module. The monocular camera is used for continuously capturing multiple frames of images; the inertial navigation module utilizes a built-in high-precision gyroscope to measure the three-dimensional attitude of the monocular camera during photographing, and calculates an external parameter matrix of the camera; the lightweight processor is responsible for processing multiple frames of images acquired by the camera, performing image preprocessing, vehicle detection and trajectory calculation, and executing a core algorithm; the radio frequency communication module transmits the detected traffic flow information back to the traffic monitoring center; the Beidou positioning module provides geographical location information, so that traffic flow information can be synchronized with the geographical location. On the premise of not depending on high-power-consumption equipment, the vehicle information is transmitted back to the monitoring center through the radio frequency communication module, and the system is suitable for daily traffic monitoring and signal control optimization.

The invention discloses a crane working control method and system based on Beidou positioning and application of the crane working control method and system. According to the scheme, feature parts of a tower crane are positioned in real time through a Beidou positioning module, so that the positions of the feature parts can be updated in time when the crane works, and the working efficiency of the crane is improved. The motion state data of the characteristic parts of the crane can be further obtained by combining with the inertial measurement unit deployed on the crane, and the working state of the crane can be further tracked after the motion state data and the positioning data are fused, so that the working state of the crane can be tracked when a plurality of cranes in a working site serve at the same time; the working interference between the tower cranes can be reduced, and early warning can be provided for background management personnel; in addition, according to the scheme, the possible working range is obtained by simulating the operation instruction, whether the problem of working interference exists or not is judged according to the intersection of action track functions between different cranes, and a mechanism guarantee is provided for safe working of the cranes in a working site.

The invention discloses an artwork repair management method based on LBS (Location Based Service), and relates to the field of artwork repair management. The equipment with the LBS records events in the process of repairing the artwork by the repairing personnel, forms artwork repairing data with LBS information, uploads the artwork repairing data to the server, and updates the artwork repairing data to a data set generated by each repairing scene of the artwork; before and / or after restoration, special acquisition equipment or equipment with LBS (Location Based Service) acquires the artwork to form artwork feature data, uploads the artwork feature data to a server, and updates the artwork feature data to a data set generated by each restoration scene of the artwork. According to the method, unified process records are formed by using a digital tool, association of space-time and field environment data is established with the process records, an artwork management data basis based on LBS is constructed, multi-dimensional internet-of-things data is archived and comprehensively presented, the authenticity of the space-time and field environment is objectively reflected, and once a problem is found, the problem is solved. And traceability and relief can be carried out through process records.

The invention discloses an electro-hydrostatic actuator simulation method and device based on RBF neural network control, a medium and a product, and relates to electro-hydrostatic actuator emulation.The method comprises the steps that an electro-hydrostatic actuator mathematical model is established, important parameters of a plunger pump and a hydraulic cylinder are obtained, and the parameters of the plunger pump and the hydraulic cylinder are calculated; establishing an electro-hydrostatic actuator simulation model, a signal and interference module and an RBF (Radial Basis Function) neural network control model in the Matlab / Simulink; inputting the position input curves and the interference of the electro-hydrostatic actuator under different working conditions into an RBF neural network control model to realize the rotating speed control of the permanent magnet synchronous motor, thereby obtaining the tracking curves of the electro-hydrostatic actuator under different working conditions; and inputting the tracking curve into an electro-hydrostatic actuator simulation model to realize simulation of the electro-hydrostatic actuator. According to the invention, tracking of the position signal under different working conditions can be realized, influences of disturbance and the like are fully considered, anti-interference performance and tracking precision are improved, control parameters are optimized, and an optimal control effect is achieved.

The invention discloses a pasture sheep flock detection and tracking method based on an unmanned aerial vehicle and a safety early warning system, and belongs to the technical field of animal husbandry automatic management, and the method comprises the following steps: S1, data collection and preprocessing; s2, constructing and training a detection model; and S3, sheep flock tracking. Through the aerial work capacity of the unmanned aerial vehicle platform, the field of view of monitoring is greatly expanded, the labor cost is reduced, the timeliness and accuracy of monitoring are improved, and animal husbandry managers are helped to master the dynamic state of a sheep flock in time and make scientific decisions; through a proposed target detection model and a multi-target tracking algorithm, high recognition rate and tracking stability can still be kept in a complex environment, the problems of target shielding and the like are effectively solved, and accurate detection and tracking of sheep flock targets are realized; through the safety early warning system, missing livestock conditions can be found and reported in time, and powerful emergency response support is provided for animal husbandry managers.

The invention belongs to the technical field of robot teleoperation and haptic interaction, and provides a multi-mode force feedback control method and system for nuclear facility robot operation, and the method comprises the steps: collecting the state information of a master arm and a slave arm in real time, and dynamically scheduling a haptic calculation strategy according to a task state; a fast estimation strategy is enabled in the fast transit mode. Further, real environment interaction force is obtained based on dynamic model compensation and net interaction force extraction; combining the dynamically selected multi-mode force feedback parameters, and synthesizing and outputting a touch instruction to the main arm. Meanwhile, admittance correction and feed-forward compensation are carried out on a slave arm motion instruction through net interaction force and dynamic internal force, synchronous output is carried out after safety optimization, and the slave arm is controlled to move. The problems of poor task adaptability, force sense distortion and insufficient safety in nuclear facility teleoperation are solved, and intelligent, high-fidelity and safe robot teleoperation is achieved.

The invention relates to the technical field of wheel monitoring, in particular to a wheel polygon monitoring method based on an improved self-adaptive wave trap, which can accurately and efficiently represent and quantify wheel polygon characteristics in real time and realize wheel state monitoring. The method is applied to the state monitoring and detection application of the polygon of the high-speed train wheel with harmonic characteristics for the first time. By introducing an adjustable factor into a traditional NG algorithm, compared with a traditional ANF, the improved MANF has a higher convergence speed and still keeps good robustness in a large-noise environment, wheel polygon features can be accurately and efficiently represented and quantified in real time, and wheel state monitoring is achieved.

A vehicle-mounted movable ground station for satellite-to-ground laser communication relates to the field of laser communication technology, and solves the problems in the prior art that laser communication ground stations have high requirements for infrastructure conditions and cannot be moved and used once deployed. The ground station includes an optoelectronic payload system and an integrated signal processing system; the optoelectronic payload system is rotatable; the optoelectronic payload system is used to collect satellite-to-ground downlink signal lasers, and is also used to perform beam reduction and fine tracking control on the signal lasers, and is also used to perform spot correction on the signal lasers, and is also used to couple space light into optical fibers and send them to the integrated signal processing system; the integrated signal processing system is used to receive laser signals in optical fibers, process them, and then send the processed data through the optoelectronic payload system; the integrated signal processing system is also used to obtain target satellite orbit information, and perform orbit recursion, and control the optoelectronic payload system through the recursion results to achieve coarse and fine signal locking respectively. The present invention is suitable for space laser communication.

The invention discloses an inverter special waveform control system which comprises a repetitive control module, and the repetitive control module comprises a repetitive control internal model, an advanced link zm, a gain parameter Kr and a forward low-pass filter S (z) which are connected in sequence. The repetitive control internal model comprises a backward low-pass filter Q (z), a backward delay link z-N and a forward delay link z-N. A high-frequency special waveform is controlled by applying a repetitive control method, and a control system can have very high gains on fundamental waves and infinite-dimensional harmonic waves by introducing an internal model of a periodic signal, so that tracking of the high-frequency special waveform is realized.

The application provides a kind of indoor global vision-based unmanned vehicle positioning method and device, the method comprises: step 1, visual sensor is fixed on indoor ceiling, and the ground environment image of the periphery of mobile robot in running is collected;Two two-dimensional code labels are arranged on the top of the mobile robot, and the mobile robot is identified by two two-dimensional codes;Step 2, the ground environment image obtained in step 1 is transformed by image processor Abnormal image is obtained by distortion correction;Step 3, the normal image is extracted to obtain feature points;More than two normal images with feature points captured by different vision sensors are matched;Step 4, the image is fused to obtain the wide-area panoramic image after splicing;Step 5, the pose information of the mobile robot is solved.The application can realize the control and tracking of mobile robot in wide-area complex indoor environment, and realize the accurate navigation of mobile robot.

The embodiment of the invention discloses a three-dimensional space configuration tracking method and device for formation group targets and a medium, relates to the technical field of radar target tracking, and is used for solving the problem that the current method is difficult to adapt to the dynamic change of a formation target structure and the structure estimation accuracy is low. The method comprises the following steps: modeling a three-dimensional space structure of a current formation group target according to a preset number of substructures with three-dimensional orientation angles to obtain a three-dimensional structure model; predicting the posterior distribution of each variable at the previous moment according to the dynamic model to obtain predicted distribution; determining an observation equation corresponding to the current formation group target, and performing multi-layer linearization processing on the observation equation in a linearization processing mode based on multi-layer Taylor expansion to obtain a parameter updating formula of the three-dimensional structure model; and updating and correcting the parameters of the current formation group target in an iteration mode based on a parameter updating formula and prediction distribution, and realizing tracking and three-dimensional configuration estimation of the current formation group target.

The invention belongs to the technical field of inter-satellite laser link establishment pointing, and provides an inter-satellite laser link establishment pointing method and system suitable for a low-orbit mobile communication constellation in order to solve the problem that an existing azimuth angle and pitch angle pointing technology based on an east-north-sky coordinate system has defects. According to an inter-satellite laser communication ephemeris mutual transmission mechanism, a mathematical model of inter-satellite laser link establishment angle pointing requirements is established, and azimuth angle and pitch angle calculation formulas of laser link establishment of two adjacent satellites in a constellation orbital plane, satellites in the orbital plane and satellites in two adjacent orbital planes are deduced. On one hand, by introducing an orbital coordinate system, an inter-satellite azimuth angle and a pitch angle are defined; on the other hand, a mathematical model of an inter-satellite laser link establishment pointing angle is obtained based on a positioning orbit determination ephemeris data source of an on-satellite GPS. And the inter-satellite laser link establishment pointing technology can be realized through a two-dimensional rotating mechanism of the laser. The method can be widely applied to an inter-satellite laser link establishment pointing scene of a low-orbit mobile communication constellation.

The invention provides a liquefied petroleum gas cylinder tracking integrated system and method, relates to the technical field of gas cylinder management, and aims to solve the problems that in the existing gas cylinder tracking technology, only a single link of gas cylinder tracking is considered, the gas cylinder tracking process is incomplete, a bar code, a two-dimensional code and the like are easy to pollute and damage at the same time, so that information reading fails, and the gas cylinder tracking efficiency is low. And gas cylinder tracking cannot be realized. The system comprises an NFC label used for writing liquefied petroleum gas cylinder specification archive data, a cloud server used for storing the gas cylinder archive data and gas cylinder operation log data, and an NFC reading terminal used for identifying the gas cylinder data in the NFC label, binding operation information of an operator in the tracking process with the gas cylinder data in the NFC label and uploading the information to the cloud server. Through cooperation of the NFC label, the NFC reading terminal, the cloud server and the traceability platform, tracking of the whole process of activation, filling, distribution and the like of the gas cylinder is achieved, and the problem that information reading fails due to pollution and damage of bar codes and the like is solved.

This invention provides a control method for dual-motor driven tracked vehicles based on a neural network prediction model. By introducing a Long Short-Term Memory (LSTM) neural network framework, it trains a neural network prediction model for the tracked unmanned platform, fully leveraging the LSTM's ability to process sequential data. This allows for the extraction of vehicle dynamics features from past driving data to predict the state at the next moment. This method can comprehensively control lateral displacement deviation, heading angle deviation, longitudinal velocity, and yaw rate, as well as track the desired trajectory position, while also ensuring steering controllability. Furthermore, this invention provides a trajectory tracking control method based on Neural Network Model Predictive Control (NN-MPC), simultaneously considering the vehicle's kinematics and dynamics, effectively overcoming the problem of poor trajectory tracking performance caused by the strong coupling of lateral and longitudinal dynamics in the physical model MPC method.

The present application belongs to the field of structural damage detection, and specifically discloses a method and device for risk assessment of composite material structures under multiple impacts, the method comprising: obtaining a probability imaging map of the impact location; thresholding the probability imaging map to obtain a thresholded probability imaging of the impact location; obtaining the total signal energy and energy feature vector; inputting the total signal energy, energy feature vector and position feature vector into a trained BP neural network to obtain the predicted value of the impact energy corresponding to each position point; thresholding the predicted value of the impact energy at each position point to obtain a thresholded impact energy distribution; repeatedly obtaining the risk matrix of multiple impacts, accumulating the risk matrix of each impact, and obtaining the risk assessment result of the composite material structure after multiple impacts. The present application can improve the positioning accuracy and reliability, thereby more accurately assessing the risk situation of the structure.

The embodiment of the invention provides a real-time cross-lens multi-target tracking method for multiple fixed lenses, a storage medium and electronic equipment, and relates to the technical field of target tracking, and the method comprises the steps: projecting a plurality of target center positions between cameras into a same position coordinate system through employing a pre-calculated projection transformation matrix between every two cameras; and then multi-target pairwise matching with the minimum cost is carried out by using a bipartite graph matching algorithm, the same ID is allocated to the two matched targets, and the unique ID is reallocated to the unmatched targets, so that multi-target tracking is realized. According to the method and the device, target features do not need to be calculated, the requirement on calculation resources is relatively low, multi-target tracking can be realized only by utilizing the projection transformation relationship between the cameras, and the method and the device can be migrated to any scene.

The application discloses a wind turbine gearbox cooling system fault prediction system and method in the technical field of wind power generation fault diagnosis, which comprises the following steps: collecting multiple source operation parameters of a wind turbine gearbox lubricating cooling system in real time; calculating key performance indicators reflecting the performance of the lubricating cooling system based on the multiple source operation parameters, and extracting features, wherein the key performance indicators at least include a cooling performance ratio; performing performance degradation identification and fault prediction based on the time sequence change trend of the key performance indicators; and outputting the performance degradation identification and fault prediction results. By introducing the comprehensive performance indicator of the cooling performance ratio, the application can sensitively identify the progressive degradation of the cooling system, overcome the defects of the traditional single parameter threshold alarm lag and fault positioning ambiguity, and provide an effective means for realizing predictive maintenance of the wind turbine.

The invention discloses a multi-extended target joint tracking and classification method based on star convex RHM and LMB filters, and belongs to the field of radar target tracking. According to the method, an LMB parameter set is initialized by using target prior information, and then a sensor measurement set is divided through a mean shift algorithm; then, state prediction is achieved by combining survival target parameter updating and new target parameter sampling, and then measurement updating is completed through LMB-to-GLMB, GLMB updating and GLMB-to-LMB; and then trimming and fusing the LMB parameter set, estimating the number of targets, extracting state information, and circularly executing until observation is finished. According to the method, a star convex RHM modeling expansion state is adopted to reduce dimensions, the low detection probability / high clutter scene performance is improved based on an LMB framework, the tracking classification effect and the real-time performance are both considered, and the engineering application value is high.

The application belongs to the technical field of image recognition segmentation, and discloses a medical image cell segmentation and tracking method, which comprises the following steps: step 1: data processing; feature extraction: the backbone part in the model is used to extract features from the preprocessed image, and the CSPDarknet structure is adopted in YOLOv8; step 3: FPN-PAN multi-scale feature fusion; step 4: Head prediction according to multi-scale features. The application realizes real-time tracking of the motion trajectory of cells by combining with a tracking algorithm such as deepsort. The method is mainly based on the YOLOv8 framework, and the Simam attention mechanism and the multi-scale proto method are adopted to optimize the model, so that the detection effect of YOLOv8 is further improved. The application can automatically complete the analysis and detection of medical images, is high in convenience and easy to use.

The invention provides a data source version management method and device.The method comprises the steps that a snapshot version library is maintained, the snapshot version library is used for recording related information of table snapshot files of at least two data sources, and the table snapshot files are historical full-data snapshot files of corresponding tables; and under the condition that historical table data of a specified table at specified historical time needs to be acquired, performing version backtracking according to related information of the table snapshot file of the specified table in the snapshot version library. According to the method, the snapshot version library of the table snapshot file is maintained in advance, so that tracking and backtracking of the specified version and the specified data source can be efficiently, accurately and quickly realized based on the snapshot version library. In addition, the size of the table and the updating frequency of the table are analyzed by monitoring a data source log, and the snapshot collection period is intelligently adjusted.

The invention provides an intelligent maintenance, inspection, evaluation and management system for a wind power plant, and relates to the technical field of environmental protection energy power plant management, and the system comprises a central control subsystem which is used for providing terminal control and facilitating the observation and control of each index of the wind power plant by a worker, and the central control subsystem comprises a control terminal. According to the system, environment and equipment data of a wind power plant are comprehensively monitored by integrating various sensors, analysis is performed by using real-time monitoring, power prediction and fault alarm programs, accurate overall condition judgment is provided, the operation and maintenance efficiency is improved, and a scientific basis is provided for power dispatching and maintenance plans. Meanwhile, automatic inspection equipment and an intelligent management module are adopted, the inspection frequency and the coverage range are remarkably improved, and through defect, work order and safety management functions, equipment defects are recorded and tracked in time, signals to be overhauled are automatically sent, the fault response time is shortened, the shutdown risk is reduced, and the overall operation efficiency and safety are improved.

The application provides a signal timing and networked automatic driving vehicle speed hierarchical collaborative control method based on reinforcement learning, and relates to the technical field of intelligent traffic control and vehicle-road collaborative control, and comprises the following steps: obtaining the stop-and-drive vehicle state information on the import lane served by each phase at each signal cycle boundary, and generating a reference timing scheme of the next cycle by a cycle timing intelligent agent based on the stop-and-drive vehicle state information; obtaining a refined discharge window; and outputting a continuous longitudinal acceleration control instruction to a target networked automatic driving vehicle by a networked automatic driving vehicle speed intelligent agent according to the refined discharge window, so that the arrival time of the target networked automatic driving vehicle matches the discharge window, and the collaborative control of traffic signal timing and vehicle speed is realized.

The application discloses a multi-target group tracking method based on an ellipse model with angular parameters and variational inference, and comprises the following steps: determining a state parameter prediction value of a current moment according to a joint probability density of state parameters of a target group of a previous moment, wherein extended shape parameters in the state parameters of the target group are described according to an ellipse model with angular parameters; filtering clutter according to the prediction value of the state parameters of the current moment to determine effective measurements; and determining a joint probability density of the state parameters of the current moment based on variational inference according to the prediction value of the state parameters of the current moment and a related measurement of the target group determined according to the effective measurements. According to the method provided in the application, the extended shape of the target group is described by using the ellipse model with angular parameters, the background clutter is filtered, and the state parameters of the target group of a next moment are approximately estimated based on the method of variational inference, so that the direction angle of the target group can be tracked, the estimation collapse problem caused by excessively large extended shape variance can be avoided, and the tracking accuracy is improved.