16 results about "Parallel learning" patented technology

The invention discloses a marine environment real-time monitoring and early warning system based on machine learning, and relates to the technical field of machine learning. Comprising the steps that an ocean multi-source sensing module collects ocean environment data in real time through a sensor and a combined collection scheme; the multi-source feature extraction module performs time domain, change rate and frequency domain feature analysis on the data to construct a unified multi-dimensional feature vector; the multi-model fusion prediction module outputs a marine environment state vector through dynamic weighting and deviation correction based on a parallel learning architecture of a deep neural network, a long-short-term memory network and a one-dimensional convolutional neural network; and the ocean risk identification and early warning module generates graded and classified early warning information through double study and judgment of a sea condition classifier and an abnormal event detector. According to the method, comprehensive acquisition, deep feature mining, high-precision prediction and accurate early warning of marine environment data are realized, the problems of low prediction precision, risk identification lag and the like in the prior art are effectively solved, and reliable guarantee is provided for marine operation safety.

The invention discloses a hydraulic arm safety control method and device based on parallel learning and a high-order CBF, and the method comprises the steps: building a kinetic equation of a hydraulic mechanical arm through a Lagrange method, integrating unmodeled dynamics, structural parameter change and external interference into an uncertain item, and describing the generalized uncertainty of the uncertain item in a linear parameterization form; for the problem of insufficient excitation in a complex environment task, a parallel learning mechanism is introduced, and historical data 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, an obstacle function family is constructed. A dynamic error buffer function is introduced, and a high-order adaptive control barrier function condition is designed. The high-order self-adaptive control obstacle function constraint is embedded into a real-time quadratic programming solving problem, input obtained through optimization solving can keep the track precision, meanwhile, a joint instruction is automatically corrected to prevent constraint failure, and minimum intervention type safety control is achieved.

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.

The invention discloses a dialogue type recommendation method and system based on multi-source collaborative enhancement, and relates to the technical field of artificial intelligence and recommendation systems, and the method comprises the following steps: S1, multi-source feedback collection and matrix construction: extracting hidden feedback data from a dialogue context and extracting dominant feedback data from an external platform, a user and project interaction matrix is constructed based on implicit feedback, a user scoring matrix is constructed based on dominant feedback, and normalization processing is performed on the dominant feedback to improve cross-user comparability; s2, multi-source collaborative weight learning: respectively taking the user and item interaction matrix and the user scoring matrix as collaborative data input, and executing EASE learning in parallel to obtain an implicit weight matrix and an explicit weight matrix to represent an item similarity structure; according to the method, multi-source collaborative enhancement of the dialogue type recommendation candidate set is realized through EASE parallel learning of correlation weights of multi-source items, density statistics-based adaptive weighted fusion and combination of dialogue context dynamic popularity adjustment.

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 invention discloses a gearbox fault early warning system based on a wind power plant cluster operation and maintenance cooperative reasoning network, relates to a wind turbine generator early warning technology, and provides a scheme for solving the problem that the prior art is not suitable for the marine environment. The data preprocessing module is used for converting oil monitoring data of a plurality of offshore wind plant gearboxes into state indexes; the multi-agent cluster learning module captures complex fault modes in the data from different angles through parallel learning to form diversified prediction perspectives; the collaborative reasoning module integrates prediction results generated by the multi-agent cluster through a meta-learning mechanism; and the early-stage fault early-warning decision module converts the collaborative reasoning result into a specific early-warning decision, and provides probability confidence and graded early-warning suggestions. The method has the advantages that complex fault modes in data are captured from different angles through parallel learning, various prediction results generated by a multi-agent cluster are effectively integrated, and the method has important engineering significance for ensuring safe and reliable operation of a wind power plant.

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.

The embodiment of the invention provides a power consumption demand prediction method based on a hybrid model library and dynamic weight optimization, and belongs to the technical field of power load prediction. The power consumption demand prediction method comprises the following steps: acquiring original data about historical power consumption load, temperature and holiday and festival data, and preprocessing the original data; performing nonlinear conversion and quantitative coding on the preprocessed historical electrical load, temperature and holiday and festival data so as to construct a depth feature reflecting the physical driving strength and the social activity modulation effect of the historical electrical load, the temperature and the holiday and festival data; and inputting the depth features into a hybrid model library to drive a plurality of heterogeneous prediction models in the hybrid model library to perform parallel learning and prediction according to the depth features so as to obtain preliminary prediction results of the heterogeneous prediction models. According to the power demand prediction method, the problem that key factors such as temperature, holidays and festivals cannot be depicted sufficiently in existing feature engineering can be solved, and cross-service scene high-precision and high-robustness power demand prediction can be realized.

Embodiments of the present invention provide a divide-and-conquer algorithm which divides expanded data into a cluster of machines. Each portion of data is used to train logistic classification models in parallel, and then combined at the end of the training phase to create a single ordinal model. The training scheme removes the need for synchronization between the parallel learning algorithms during the training period, making training on large datasets technically feasible without the use of supercomputers or computers with specific processing capabilities. Embodiments of the present invention also provide improved estimation and prediction performance of the model learned compared to the existing techniques for training models with large datasets.

This application relates to the field of machine learning technology and discloses a method and apparatus for measuring multiphase flow based on filter enhancement. A differential pressure flowmeter module collects first data, and a capacitance tomography sensor collects second data. A multilayer perceptron fuses the first and second data to obtain fused data, reducing the impact of different sensor data on model performance. Multiple filter enhancement modules adaptively filter the fused data channel by channel, attenuating data noise and mitigating overfitting. A multi-scale convolutional neural network processes the filtered data to obtain multi-scale feature vectors, enabling parallel learning of rich flow information at different scales from random flow points. Finally, multiple fully connected layers process the multi-scale feature vectors, outputting multiple single-phase flow values ​​for the multiphase flow. Multi-task learning allows for simultaneous estimation of the single-phase flow of the multiphase flow.

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.