9 results about "Parallel learning" patented technology

The application discloses a kind of FFC self-learning assembly methods based on parallel reinforcement learning, establishes including real physical assembly system and simulation system parallel learning system, and physical assembly system and simulation system are run in parallel, experience data in assembly process is gathered to experience pool of parallel learning system, simulation system is trained by experience data provided by physical assembly system in experience pool, and feedback guides real physical system to execute assembly task;Wherein, the physical information of physical assembly system is stored into parameter server for simulation system to train after being handled by Softmax classifier of parallel learning system.The self-learning assembly method of the application not only has the advantages of high assembly efficiency and learning efficiency, but also has the advantage of high assembly success rate.

This invention discloses a deep learning-based method for ultra-short-term photovoltaic (PV) power prediction. First, historical data is preprocessed to eliminate irrelevant variables and accelerate model training. Then, the advantages of three clustering algorithms are combined to obtain a more reasonable dataset partitioning. Next, particle swarm optimization is used to optimize the parameters of variational mode decomposition. Finally, parallel learning of CNN and GRU networks is employed to identify local and temporal features of the data, enabling the network structure to fully leverage the input data. Finally, a deeper learning process is achieved through CNN neural network concatenation and fusion, resulting in high-precision prediction. The PV power prediction method of this invention demonstrates excellent performance, significantly outperforming other traditional models in predicting PV power under different weather conditions.

This invention relates to the field of asphalt pavement inspection technology and discloses a method for optimizing the configuration of mechanical equipment for asphalt pavement construction. The method includes: step S2: collecting various data during the asphalt pavement construction process and preprocessing the collected data to generate dynamic sensing data; step S3: constructing a digital twin model based on the dynamic sensing data; step S4: deploying a multi-agent decision engine on edge computing nodes, and based on the prediction results of the digital twin model, solving the dynamic reconfiguration scheme of the machine group in real time through a multi-agent collaborative algorithm to generate control commands including roller operation adjustment and paver speed adjustment; step S5: issuing the control commands to the construction machine group for execution; and step S6: performing a parallel learning process on a cloud server, performing counterfactual inference on historical construction data, constructing a construction knowledge graph, and continuously optimizing the digital twin model and the multi-agent decision process based on actual construction feedback. This application improves collaborative capabilities.

The application discloses a kind of based on digital twinning turnout switch machine monitoring and fault diagnosis system and method, belong to rail transit field, including based on edge orientation and edge edge federal union's cloud edge collaborative mode to the data of turnout switch machine is collected and is governed;Establish turnout switch machine twinborn model, including three-dimensional model, behavior model and diagnostic model, and will switch machine work twinborn data integration and fusion in three-dimensional model, realize three-dimensional visualization;Based on one-dimensional power signal twinborn data and two-dimensional GASF image parallel learning fault diagnosis method diagnoses whether turnout switch machine fails.This application uses the above-mentioned based on digital twinning turnout switch machine monitoring and fault diagnosis system and method, effectively improve the switch machine operation and maintenance efficiency, reduce the operation and maintenance cost of switch machine, can be more intuitive, efficient, intelligently realize switch machine health management.

This invention proposes a multi-branch parallel learning method and system for identifying individual frequency-hopping radio stations, achieving effective identification of individual frequency-hopping radio stations. By constructing a deep residual convolutional network with multi-branch parallel learning, data processing is performed based on statistical features and decision thresholds to obtain the final identification result of the individual frequency-hopping radio station. The identification method of this invention yields accurate results and high efficiency, providing a solid foundation for communication security.

This invention discloses a gearbox fault early warning system based on a collaborative reasoning network for wind farm cluster operation and maintenance, relating to wind turbine early warning technology. It addresses the problem that existing technologies are unsuitable for offshore environments by proposing this solution. A data preprocessing module converts oil monitoring data from gearboxes in several offshore wind farms into status indicators; a multi-agent cluster learning module captures complex fault patterns from the data from different perspectives through parallel learning, forming diverse prediction perspectives; a collaborative reasoning module integrates the prediction results generated by the multi-agent cluster through a meta-learning mechanism; and an early fault warning decision module transforms the collaborative reasoning results into specific early warning decisions, providing probability confidence levels and tiered early warning suggestions. The advantage lies in its ability to capture complex fault patterns from different perspectives through parallel learning, effectively integrating various prediction results generated by the multi-agent cluster, which has significant engineering implications for ensuring the safe and reliable operation of wind farms.

The application discloses a hydraulic arm safety control method and device based on parallel learning and high-order CBF, adopts the Lagrange method to establish a hydraulic mechanical arm dynamics equation, integrates unmodeled dynamics, structural parameter changes and external disturbances into an uncertain term, and adopts a linear parameterization form to describe the generalized uncertainty; in view of the problem of insufficient excitation in a complex environment task, a parallel learning mechanism is introduced, historical and real-time data are combined to realize parameter identification; for high relative order safety constraints (such as obstacle distance constraints and joint limiting constraints) in a task space, a family of obstacle functions is constructed; a dynamic error buffer function is introduced, and a high-order adaptive control obstacle function condition is designed; the above high-order adaptive control obstacle function constraint is embedded into a real-time quadratic programming solving problem, and the obtained input can automatically correct joint instructions to prevent constraint failure while maintaining trajectory accuracy, so that the "minimum intervention" safety control is realized.

This invention discloses a deep learning-based dynamic prediction system for the output power of a PVT photovoltaic-thermal array, comprising: a state processing module for collecting operational state data of the PVT photovoltaic-thermal array; a coupling construction module for generating array coupling state feature tensors; a spatial mapping module for generating array spatial correlation feature tensors; a dual-graph prediction module for inputting data into an improved MTGNN model, introducing a dual-graph parallel learning mechanism to generate electrical correlation graphs and thermal correlation graphs; performing multivariate time-series modeling to generate an output power prediction sequence; and a constraint output module for introducing array operational state reachability constraints to generate dynamic prediction results for output power. This invention achieves high-precision, strong consistency, and engineering reachability dynamic prediction of the output power of a PVT photovoltaic-thermal array under complex operating conditions.