30 results about "Trajectory database" patented technology

The invention relates to the technical field of industrial robot control, and discloses an industrial robot trajectory optimization control method based on an intelligent algorithm. The method comprises the steps that joint position information, tool center point coordinates and a motion time sequence when a robot executes multiple tasks are collected and stored in a track database; after cleaning and screening data, extracting a feature set containing a path point sequence, speed distribution and an acceleration contour; constructing an intelligent optimization algorithm model of a neural network structure, and training by using the feature set to learn a trajectory optimization strategy; analyzing a target position coordinate and a motion constraint condition of the current task to obtain an initial track parameter; and inputting the initial parameters into the trained model, outputting an optimized track sequence containing a path point list and a speed curve, and generating a control instruction to drive the robot to move. The method can adapt to different tasks, improves track rationality and motion stability, and fits industrial production practice.

The invention discloses a photovoltaic off-grid hydrogen production system operation mode switching instant DC bus voltage stability control method, which comprises the steps of collecting a DC bus voltage signal, and reconstructing a high-dimensional phase space trajectory; calculating real geometric features such as phase space curvature and torsion of the trajectory; searching a historical similar trajectory segment in a historical trajectory database, and generating a final prediction trajectory at a future moment according to a known evolution path of the historical similar trajectory segment; and generating a feedforward control signal according to the deviation between the final prediction track and the reference voltage, and combining the feedforward control signal with traditional feedback control to form a final control instruction. The method does not depend on an accurate system mathematical model, and realizes rapid, smooth and stable control of voltage under mode switching by deeply excavating dynamic geometric information of voltage signals.

The present disclosure provides a trajectory data processing method, device and equipment of a road network map, belonging to the technical field of computer information processing, solving the problem that the trajectory data of the road network map is prone to appear stuttering phenomenon when displayed in a three-dimensional scene, resulting in poor visualization effect. The method comprises: determining a spatial road network model according to a source file in the road network map; generating a trajectory database according to trajectory data in the road network map; correcting trajectory points in the trajectory database according to the spatial road network model to obtain corrected trajectory point data. The technical scheme of the present disclosure can make the trajectory data of the road network map more clear and smooth when displayed in a three-dimensional scene.

The invention discloses a forest region disaster risk assessment method based on machine learning, and relates to the technical field of forest region disaster risk assessment, and the method comprises the steps: S1, building a forest region disaster evolution trajectory database, carrying out the modeling of historical disaster events according to a time sequence, analyzing the similarity between the current forest region environment characteristics and historical disaster trajectories, and obtaining a forest region disaster evolution trajectory database; deducing disaster types and focus points of attention, and clustering disaster features to form a risk category set; according to the forest disaster risk assessment method based on machine learning, the disaster risk condition can be displayed with higher information density and a clearer structure, so that the risk identification speed is improved, the understanding burden caused by information overload is reduced, and the accuracy, interpretability and response efficiency of forest disaster risk assessment are remarkably enhanced on the whole.

The invention relates to the technical field of building thermal environment regulation and control, and discloses a building ventilation thermal load regulation and control method and system based on thermal plume prediction, and the method comprises the steps: constructing a curtain wall surface three-dimensional coordinate system and a thermal state temperature map, and obtaining a current thermal plume trajectory; similar trajectory matching is carried out in combination with a historical thermal plume trajectory database, and a thermal plume development path is predicted; based on a prediction result, establishing a compensation relationship between a floor load grading sequence and an adjacent floor feed-forward temperature, implementing time sequence dislocation dimming through spiral progressive control, and after time sequence dislocation dimming, performing thermal-optical coupling feedback correction; hot air can be effectively prevented from flowing backwards, the high-rise room temperature and the air conditioner load are reduced, indoor natural lighting is guaranteed, the building thermal environment is optimized, and building ventilation thermal load efficient regulation and control are achieved.

The application discloses an indoor and outdoor seamless connection positioning switching method and system based on trajectory prediction and fingerprint verification, and the method comprises the following steps: S1, a virtual simulation environment is established at the building entrance, random trajectories and reward guide trajectories are generated, and a trajectory database is established; a fingerprint database is established; S2, feature extraction is carried out based on the trajectory database, and a support vector machine (SVM) model is used for binary classification training; S3, a sliding window is set, real-time pedestrian trajectory data is input into the SVM model for sliding prediction, and it is judged whether the SVM is continuously triggered in the window; S4, multi-modal fingerprint matching is started, and it is judged whether the fingerprint matching is successful; and S5, switching from a GNSS positioning mode to an indoor positioning mode is executed, and indoor and outdoor seamless connection positioning switching is realized.

The present disclosure relates to a flight trajectory prediction method of an aircraft, and relates to the technical field of hypersonic aircrafts, and the method comprises the following steps: determining a raw trajectory database of a hypersonic aircraft under different initial states and different flight control laws based on a motion equation; training a network model to be trained based on the raw trajectory database to obtain a current trajectory prediction model; determining a trajectory prediction model to be migrated from the current trajectory prediction model according to a target flight trajectory of an aircraft to be predicted and a raw flight trajectory in the raw trajectory database, and migrating model parameters in the trajectory prediction model to be migrated to a trajectory prediction model to be corrected; performing parameter correction on the trajectory prediction model to be corrected based on the target flight trajectory to obtain a target trajectory prediction model, and predicting a flight trajectory of the aircraft to be predicted based on the target trajectory prediction model. The present disclosure improves the accuracy of the predicted flight estimation.

This invention discloses a multi-target flight trajectory recognition device based on dual-channel pre-training, comprising a flight trajectory database, an aircraft radiation source receiver, and a host computer connected to each other. The database stores flight trajectory signals of different types of aircraft. The host computer includes three modules: self-supervised pre-training, supervised training, and flight trajectory recognition. The data in the database is used to perform self-supervised pre-training and supervised training on the flight trajectory recognition model, and the trained model is stored in the flight trajectory recognition module. The receiver receives external flight trajectory signals and transmits them to the flight trajectory recognition module of the host computer for recognition. This invention innovatively introduces a self-supervised pre-training stage to pre-train the flight trajectory recognition model to fully explore its inherent features and enhance its anti-interference capability. Simultaneously, it analyzes the flight trajectory signal from both the time and frequency domains to maximize the extraction of multi-domain and multi-dimensional information from the signal, significantly improving the accuracy of flight trajectory recognition.

This invention discloses a safety monitoring method for operating equipment, applied to an automated warehouse system, which includes a Warehouse Management System (WMS), Automated Guided Vehicles (AGVs), and a guidance and positioning device. The method includes: collecting data on AGV operating status, warehouse environment, WMS tasks, and the mechanical status of the guidance and positioning device; constructing a standard path model based on a historical trajectory database and calculating real-time trajectory deviation; constructing a multi-dimensional risk assessment model to comprehensively calculate the risks of the AGV itself, the environment, the task, and the guidance and positioning device; classifying risk levels according to the comprehensive risk value, implementing differentiated early warning and response measures, and dynamically adjusting AGV task allocation in conjunction with the WMS system. This invention also integrates an automatic monitoring function for near-expiry goods, enabling near-expiry identification, AGV dispatching, and automatic sorting and disposal. This invention achieves comprehensive, three-dimensional monitoring from equipment safety to cargo safety, improving the safety and intelligence level of warehouse operations.

This invention provides a vehicle trajectory extraction method based on the complementarity of LiDAR and video technologies, comprising: constructing an integrated radar device; identifying key targets based on video acquisition data collected by a high-definition video device, and constructing a basic image information database through image optimization and data filtering; converting radar data into readable road and vehicle information based on the LiDAR acquisition device, and constructing an object and behavior database for target identification; fusing the object and behavior database and the basic image information database, and constructing a driving behavior detection dataset to achieve road element calibration, vehicle element calibration, and behavior parameter calibration; identifying and tracking the calibrated objects, thereby outputting a trajectory database. This invention can provide a data foundation for road traffic operation and risk identification, and provide a scientific basis for traffic management departments to evaluate and manage road traffic.

The application discloses a trajectory database creation method and device, a query method, a vehicle, a medium and equipment, and belongs to the field of data processing. The method comprises the following steps: constructing an index based on a spatial position for each road center point in a base map, obtaining first related field information about each road center point, and generating a map database; constructing an index based on a spatial position for each trajectory point, and obtaining second related field information; binding each trajectory point in each trajectory with a road center point in the base map, and filling the second related field information of each trajectory point based on the map database; and generating a trajectory database by using the second related field information of each trajectory point and the first related field information in the map database. According to the application, the content in the trajectory database can be changed according to specified conditions, without the need to read into memory or regenerate the database, so that time is saved, the result can be reused, and the features of roads or trajectories can be quickly extracted according to positions.

The application discloses a trajectory similarity analysis method based on trajectory compression and clustering. Firstly, N reference trajectories are compressed, and some trajectory data is removed within a certain error range, so as to facilitate subsequent processing and analysis. Secondly, the range of trajectory segments participating in calculation is reduced by intercepting common trajectories and angle pre-judgment, and the reference trajectory data is clustered offline. When calculating the similarity matrix, the similarity of each two trajectories only needs to be calculated once within the error range to reduce the complexity. Finally, the representative trajectory of each cluster is found, the similarity of the to-be-matched trajectory and the representative trajectory of each cluster is calculated, and whether the to-be-matched trajectory exists in the reference trajectory database is judged. If the to-be-matched trajectory exists in the reference trajectory database, the trajectory similarity of the to-be-matched trajectory and each reference trajectory of the corresponding cluster is calculated, so that it is determined that the actual trajectory is similar to which reference trajectory, and a similarity result is obtained.

Disclosed is a trajectory design method for online height adjustment of a cruise section of a supersonic aircraft, comprising the following steps: dividing the cruise height into a climbing cruise height section and. a descending cruise height section according to the cruise height range of the supersonic aircraft, and obtaining a trajectory database; performing normalization processing on the height instruction of any flight profile to obtain a new trajectory database; when the supersonic aircraft receives a target cruise height instruction, calling the new trajectory database, and obtaining a ballistic trajectory voyage, a ballistic trajectory dip angle instruction and an attack angle instruction under the target cruise height instruction; obtaining all ballistic trajectory instructions of the flight. profile corresponding“ to the target cruise height. The present invention solves the problem that in the prior art, the ballistic trajectory instruction for adjusting the cruise height cannot be rapidly and accurately generated.

This invention discloses a ball mill filling rate calibration method based on EDEM (Digital Emission Modeling). The method includes the following steps: S1: Determine simulation parameters based on actual operating conditions and construct a simulation environment in EDEM software; S2: Adjust the grinding conditions of the ball mill and conduct simulation experiments under different grinding conditions; statistically analyze the changes in the grinding media trajectory at the overflow weir baffle at the ball mill discharge port under different grinding conditions, and construct a grinding media trajectory database; S3: Based on real-time images of the grinding media trajectory at the overflow weir baffle at the ball mill discharge port collected on-site, and combined with the current operating conditions, compare the grinding media trajectory images under different operating conditions in the grinding media trajectory database, and calibrate to obtain the current internal filling rate of the ball mill. This invention enables high-precision, real-time monitoring of the internal filling rate of a ball mill operating with mixed grinding media, which is beneficial for maintaining the continuous stability of the production process, thereby promoting the improvement of overall efficiency and product quality.

The application relates to the technical field of logistics tracking, in particular to a shipping logistics tracking method.The method comprises the following steps: detecting a signal interruption time point and a signal interruption duration to obtain a signal interruption interval; according to the signal interruption interval, extracting a last recorded sailing speed, direction and position of a ship before interruption from a historical sailing track database to generate an initial state vector; predicting a future transportation path of the ship through a corrected position sequence to generate a predicted path set; according to the predicted path set, constructing a ship motion track in a three-dimensional virtual scene to generate a visual track model; combining a confidence degree of the calculated position sequence to obtain a decision support view; and through the decision support view, updating a port scheduling plan in real time to generate an optimized loading and unloading time sequence.The application solves the problem of low calculation accuracy of the position of the ship in a complex scene with signal interruption, and improves the position accuracy of the ship and the prediction accuracy of the future transportation path.

The present application relates to the technical field of building thermal environment regulation, and discloses a building ventilation thermal load regulation method and system based on thermal plume prediction, which comprises the following steps: constructing a curtain wall surface three-dimensional coordinate system and a thermal state temperature atlas, and obtaining a current thermal plume trajectory line; combining a historical thermal plume trajectory database to perform similar trajectory matching and predict a thermal plume development path; based on the prediction result, establishing a floor load grading sequence and a neighboring layer feedforward temperature compensation relationship, and implementing time sequence misplacement dimming through spiral progressive control; and after time sequence misplacement dimming, performing thermal-light coupling feedback correction; the present application can effectively inhibit thermal air backflow, reduce high-rise room temperature and air conditioning load, protect indoor natural lighting, optimize building thermal environment, and realize efficient regulation of building ventilation thermal load.

The invention discloses a three-axis turntable non-iteration synchronous trajectory planning method and system based on a global time reference, and belongs to the technical field of turntable control, and the method comprises the following steps: S1, constructing a standard motion trajectory database; s2, performing first trajectory planning based on position-driven scaling on the three-axis turntable according to the target position; s3, determining a global time reference; s4, trajectory reconstruction based on time driving is carried out on the non-reference axis; s5, non-reference axis parameter calculation; s6, the relation between the track position of the three-axis turntable and time is obtained; s7, performing trajectory smoothing processing on a position curve obtained according to the relationship between the trajectory position of the three-axis turntable and the time; through the method, the technical problem that the three-axis turntable strictly and synchronously arrives at the target position under the constraint of independent dynamics is solved, calculation is simple, iterative operation is not needed, and the track is smooth and executable.

The invention discloses a building penetration trajectory prediction method based on a graph neural network, and the method comprises the steps: building a three-dimensional refined finite element model based on key factors, carrying out the numerical simulation through LS-DYNA software, obtaining building penetration trajectory data, processing the data, and obtaining a building target penetration trajectory database. Dividing the database into a training set, a verification set and a test set in proportion; building a building penetration trajectory prediction model based on the graph neural network architecture, training the building penetration trajectory prediction model by using the training set, performing performance evaluation on the trained model by using the verification set, and stopping training when a prediction error converges to obtain a trained building penetration trajectory prediction model; and inputting the building penetration trajectory data into the trained building penetration trajectory prediction model to obtain a final predicted building penetration trajectory. The method has the advantages of high prediction precision, wide application range and high calculation efficiency.

The application discloses a power distribution network source and load prediction method, system and device based on multi-modal data and a storage medium, relates to the technical field of power dispatching, and comprises the following steps: acquiring multi-source data of a power distribution network, constructing a graph window sequence, merging state extraction and node state coding to obtain unified node state coding; a historical event trajectory database is introduced, similarity matching is performed to generate a candidate set, a path selection weight is calculated in combination with a weight matching mechanism, behavior prior vectors are generated in combination with behavior response information, and the node state coding is spliced and input into a feedforward neural network to output a prediction result; by comparing the behavior prior vectors with the prediction result, an abnormal node is identified, and a preset strategy is executed; an actual observation and a prediction value error are calculated and fed back to the database, and graph node attributes are synchronously updated; the application can realize multi-scale high-precision prediction and situation deduction of power distribution network source and load, enhance non-stationary scene modeling, fuse multi-modal data, and improve complex scene understanding and power distribution network management effect.

The invention provides a lane departure detection method and system based on AI vision, and the method comprises the steps: collecting the absolute position coordinates of a vehicle running on a fixed line and the front view image of a forward camera, and constructing a golden track database through track fusion processing; matching the elements with elements in the golden trajectory database to calculate a space offset; identifying a lane line and calculating an original offset of the vehicle, and performing adaptive correction on the original offset in combination with a confidence coefficient output by the AI visual model and a scene perception parameter to generate a visual offset; predicting the deviation trend of the vehicle in a short time in the future, wherein the deviation trend is a future deviation trend prediction value; and carrying out weighted scoring by combining the space offset, the visual offset and the offset trend, and generating a risk score. According to the method, the comprehensive requirements of a tourism transportation scene on reliability, advance and engineering deployability are met.

The application provides a berthing planning auxiliary decision-making method based on Bi-kmeans, adopts binocular vision to collect ship trajectory information so as to construct a berthing trajectory database; when performing berthing decision, a trajectory set with a terminal point being a specified berth is selected from the trajectory database, and Bi-kmeans is adopted for clustering; and a trajectory with a starting point closest to the current berthing starting point of a yacht is selected from k trajectories formed by k cluster centers to generate a recommended trajectory.

The invention provides a credibility identification and correction method and system for data reported by mining positioning system equipment, and relates to the field of mine navigation and positioning. The method comprises the following steps: acquiring a plurality of historical position data sequentially reported by equipment for a plurality of monitoring objects according to a time sequence, constructing a historical track database, and establishing and training a target position direction prediction model based on machine learning; acquiring real-time trajectory data reported by the equipment to the monitoring target, and judging whether the position direction in the data is opposite to the predicted trajectory direction or not; when the directions are opposite, judging whether the data is credible or not according to the nearest effective position of the monitored target, and further adding a credible or uncredible identifier; when the directions are the same, judging as credible data; determining the credibility level of the equipment; and determining a data cleaning rule corresponding to the data for cleaning according to the credibility level and the identifier of the data based on the personnel limit scene, and correcting the track of the monitoring target. According to the invention, by improving the accuracy of the reported data, an error overcrowding event is avoided.

The embodiment of the invention provides an agent implementation method and device and a medium, and relates to the technical field of large models.The method comprises the steps that a problem request is received, retrieval is conducted in a maintained track database based on the problem request, and a historical task track is obtained. The problem request is combined based on the historical task trajectory, the target cue word is constructed, the large language model is called, the action plan for the problem request is generated based on the target cue word, and the action trajectory is obtained, so that reliable processing of the problem request is realized based on the intelligent agent, the trajectory database and the large language model, and the implementation performance of the intelligent agent is ensured.

This invention relates to the field of industrial robot control technology and discloses a control method for industrial robot trajectory optimization based on intelligent algorithms. The method includes collecting joint position information, tool center point coordinates, and motion time sequences of the robot when performing various tasks, and storing them in a trajectory database; after cleaning and filtering the data, extracting a feature set containing path point sequences, velocity distributions, and acceleration profiles; constructing an intelligent optimization algorithm model with a neural network structure, and training it with the feature set to learn trajectory optimization strategies; parsing the target position coordinates and motion constraints of the current task to obtain initial trajectory parameters; inputting the initial parameters into the trained model, outputting an optimized trajectory sequence containing a list of path points and velocity curves, and then generating control commands to drive the robot's motion. This method can be adapted to different tasks, improve trajectory rationality and motion stability, and is well-suited to actual industrial production.

The invention provides a literature-driven drug-loading corneal contact lens candidate molecule proxy type automatic research and development method and system, and the method comprises the steps: S1, executing semantic retrieval and analysis through a literature semantic analysis engine, and outputting a structured molecule feature vector set; s2, inputting the feature vector set into a molecule generation model through a generation strategy controller, and generating a candidate molecule set; s3, performing parallel property prediction on the candidate molecules through a multi-dimensional property evaluator to generate a multi-dimensional property score vector; and S4, screening candidate molecules based on the score vector, feeding back the candidate molecules to a generation strategy controller, and constructing a research and development trajectory database. By constructing a closed-loop system architecture of four functional modules including a literature semantic analysis engine, a generation strategy controller, a multi-dimensional property evaluator and a dynamic feedback optimizer, full-process automatic research and development from scientific literatures to candidate molecules are realized.

The application discloses a storage system fault repairing method and device, electronic equipment, medium and product, and relates to the technical field of storage systems. When an external request initiated by an external initiator of a storage system is initiated to the storage system and needs to be processed by the storage system, the external request is processed through a complex call chain. The storage system is monitored, and an alarm event of the storage system is captured. A request identifier in the alarm event needs to be obtained. From a pre-constructed request track database, track data corresponding to the request identifier is obtained. When the alarm event is captured, the track data corresponding to the request identifier is aggregated, and a fault feature sequence is constructed according to the track data. From a pre-constructed fault repairing database, a repairing strategy corresponding to the fault feature sequence is obtained, and the repairing of a known fault can be realized. This process does not need manual analysis of a call link, speculation of a fault reason, and manual intervention for completing the repairing, and the efficiency and accuracy of fault repairing are improved.

The invention discloses a power distribution network source load prediction method, system and device based on multi-modal data and a storage medium, and relates to the technical field of power dispatching, and the method comprises the steps: obtaining power distribution network multi-source data, constructing a map window sequence, and obtaining a unified node state code through state extraction and node state code combination; a historical event trajectory database is introduced, similarity matching is performed to generate a candidate set, a weight matching mechanism is combined to calculate a path selection weight, behavior response information is combined to generate a behavior prior vector, and the behavior prior vector is spliced with a node state code and then input into a feedforward neural network to output a prediction result; comparing the behavior prior vector with a prediction result, and identifying an abnormal node to execute a preset strategy; actual observation and prediction value errors are calculated and fed back to a database, and map node attributes are synchronously updated; according to the method, power distribution network source load multi-scale high-precision prediction and situation deduction can be realized, non-stationary scene modeling is enhanced, multi-modal data is fused, and complex scene understanding and power distribution network management effects are improved.

The invention provides a pedestrian commission method and system based on historical trajectory prediction and multi-level insurance assessment, and relates to the field of automatic driving, and the method comprises the steps: collecting the original trajectory data of a pedestrian, and carrying out the smoothing of the original trajectory data through a dual-stage index, and obtaining the processing data; carrying out multi-frame window time sequence prediction and motion analysis calculation on the processed data to obtain a future motion trend of the pedestrian; active proximity is calculated for the future movement trend of the pedestrian, and risk level evaluation is carried out according to the active proximity; and generating a comity decision according to the risk level evaluation result. According to the method, the historical trajectory database is established, the future movement trend of the pedestrian is predicted through time sequence analysis, detected danger reprocessing is converted into potential danger prediction, whether the pedestrian is approaching the vehicle path or not is accurately recognized through trajectory slope analysis, and response lag is avoided.

The invention discloses a storage system fault repairing method and device, electronic equipment, a medium and a product, and relates to the technical field of storage systems, and the method comprises the steps that when an initiator from the outside of a storage system initiates an external request which needs to be processed by the storage system to the storage system, the external request is cooperatively processed through a complex call chain. A storage system is monitored, an alarm event of the storage system is captured, and a request identifier in the alarm event needs to be acquired. Obtaining track data corresponding to the request identifier from a pre-constructed request track database; when the alarm event is captured, track data identified by the request are aggregated, and a fault feature sequence is constructed according to the track data. And obtaining the repair strategy corresponding to the fault feature sequence from the pre-constructed fault repair database, so that the known fault can be repaired. The process does not need to manually analyze and call the link, speculate the fault reason and complete repair through manual intervention, so that the fault repair efficiency and accuracy are improved.

This invention discloses an intelligent debugging method and system for LC filters, relating to the field of automation control technology. It generates an initialization parameter set suitable for the LC filter debugging task based on a meta-learning framework; constructs a quantum particle swarm optimization debugging model; and builds a parameter change trajectory database by combining the concept of psychological fields and knowledge distillation techniques. The database is then used to perform time-series modeling of the parameter change trajectory database through an LSTM network to predict the optimal initial guess. The initial debugging of the LC filter does not require extensive reliance on historical initial data, and the complex procedures of manual debugging are eliminated, significantly reducing the time for initializing the LC filter. It solves the dilemma of local solutions during intelligent debugging, with fast convergence speed and shorter debugging time. During debugging, it quickly and efficiently obtains result data based on a small amount of historical data, and can continuously optimize the automated intelligent debugging process of the LC filter.