43 results about "Offline learning" patented technology

A method and system for anomaly detection from time-series input data. A Gaussian Mixing Model (GMM) learns distribution parameters in an offline learning stage using sample data. The data used for offline learning and for a subsequent online anomaly detection stage are time-series data collected for multiple parameters of a machine operation, such as a robot performing a repetitive set of operations. The method includes aligning the data with a known good reference data file and taking a difference from it before providing the data to the GMM. In an online anomaly detection stage, the GMM calculates a probability that each time-series data point fits the distribution, and then a log-sum calculation is performed on each data file to determine the likelihood that the file contains anomaly data.The log likelihood of the file is compared with previous values, and an alert is issued if there are statistical deviations from the historical data.

This invention discloses a method and system for intelligent fault diagnosis in power plants based on adaptive benchmark matching. The diagnostic method includes the construction of an offline benchmark model library and online adaptive benchmark acquisition. The system consists of a data acquisition and preprocessing module, an offline benchmark model library construction module, an online adaptive benchmark acquisition engine, a parallel fault analysis engine, a fusion decision and knowledge base module, and an alarm and report generation module. Both the diagnostic method and system of this invention completely abandon the traditional method of using static fixed thresholds as the diagnostic basis by combining "offline learning of personalized baselines" with "online multi-level dynamic matching." This allows the diagnostic benchmark to intelligently adjust with environmental conditions, greatly reducing false alarms caused by fluctuations in environmental factors and significantly improving diagnostic accuracy.

Disclosed is a device and method for processing tasks through model-based offline learning. The device includes: a dataset input unit configured to receive an offline dataset for offline reinforcement learning; an initialization unit configured to initialize a world model and a model generation dataset for predicting a state transition and a reward without interacting with a real environment; a model rollout unit configured to expand the offline data of the offline dataset based on the world model to generate an imagined trajectory and generate imaginary data of the model generation dataset; and a learning update unit configured to perform critic update and actor update based on the offline data and the imaginary data.

Offline teaching device (1) comprising at least one processor (2, 3, 4, 5) wherein the processor (2, 3, 4, 5): as a result of a motion program comprising several input teaching points, numerous interpolation points are generated on a motion route of a distal tool endpoint of a robot (100), the motion route being formed under the teaching points according to the motion program; Coordinates of at least one joint point of the robot are calculated when the distal tool endpoint is positioned at each of the interpolation points; and detects whether a disturbance occurs between each of the generated interpolation points and the calculated joint point and a peripheral device.

The invention relates to the technical field of education artificial intelligence, and provides a self-adaptive learning path recommendation method for artificial intelligence general recognition education, which comprises the following steps of: 1, calling a large language model to analyze unstructured course resources of AI general recognition education, mining semantic association and implicit dependency of knowledge points, and constructing a learning path; generating a space-time knowledge graph containing confidence scores, automatically capturing the latest literature and tool update of the field every 72 hours, and iteratively optimizing the topological structure of the graph; 2, deploying data acquisition points to acquire multi-source learning data of a user in real time, and calculating a dynamic cognitive state vector with knowledge points as dimensions by mastering an entropy model; the knowledge graph is automatically iterated through LLM, and the problem of update lag is solved; precise personalized recommendation is realized according to multi-dimensional data and a dynamic algorithm, and cognitive differences are adapted; visually presenting decision logic, and cracking a decision black box; the method can be migrated to multiple fields, supports multi-terminal and offline learning, exceeds an expected adaptive scene, and improves the learning efficiency and credibility.

The invention belongs to the technical field of robot intelligent control, and particularly relates to a mechanical arm tracking control method based on transferable depth incremental reinforcement learning, which effectively solves the nonlinear optimal tracking control problem of a mechanical arm in a model-free mode by constructing a transferable incremental reinforcement learning framework. The method comprises the following steps: firstly, constructing a 1-degree-of-freedom depth increment model by utilizing one-step forward data offline learning, and providing universal dynamic representation for a mechanical arm which is difficult to accurately model; furthermore, an asynchronous depth value network is designed for the one-degree-of-freedom mechanical arm, stable and rapid convergence value function approximation is achieved through a separation base layer and an adaptive layer, and a cross-mechanical-arm migration mechanism is established, so that a pre-training model and a network base layer on the one-degree-of-freedom mechanical arm can be directly migrated to a high-degree-of-freedom mechanical arm subsystem; and the system difference is compensated only by updating the adaptive layer online, so that the repeated training overhead is remarkably reduced, and meanwhile, rapid, robust and adaptive tracking control on the mechanical arms with different degrees of freedom is realized.

The invention provides a data supply acceleration system and method for single-machine to small-scale multi-GPU (Graphic Processing Unit) training scenes, and the system adopts an off-line learning and on-line inference architecture. The system comprises an offline training module, an online portrait generation module, an adaptive strategy selection module, an income inference and replacement execution module, a training cooperative scheduling module and a strategy switching control module. In the off-line stage, the system learns'retention revenue 'of data entries based on historical logs and solidifies a revenue prediction model and a strategy selector. In the online stage, the system generates a system state portrait in real time, an optimal strategy is adaptively selected according to the system state portrait, cache replacement is driven based on profit prediction, and GPU idling is reduced through a collaborative scheduling mechanism. According to the method, the model parameters are updated in an off-line manner, the operation overhead is low, the I / O waiting time and the tail time delay in the deep learning training process can be effectively reduced, and the GPU utilization rate and the overall training throughput rate are improved.

A method and system for anomaly detection from time series input data. A Gaussian mixture model (GMM) uses sample data to learn distribution parameters in an offline learning stage. The data for offline learning and for subsequent online anomaly detection phases is time series data of a plurality of parameters collected, such as machine operations of a robot performing a repeated set of operations. The method includes aligning the data with a known good reference data file and taking a difference from the reference data file prior to providing the data to the GMM. In the online anomaly detection stage, the GMM calculates the probability that each time series data point conforms to the distribution, and performs logarithmic summation calculation on each data file to determine the likelihood that the file contains abnormal data. The file logarithm likelihood is compared to previous values and an alert is issued when there is a statistical difference from historical data.

The application provides an agent reinforcement learning method and device based on an iterative policy constraint, comprising: performing policy offline learning on an agent based on a state of the agent in any application scenario; taking an optimized policy obtained through offline learning as an initial policy, and constructing an iterative policy constraint term; introducing the iterative policy constraint term on the basis of online reinforcement learning for maximizing a reward, to generate an optimized target of the agent; and performing policy online reinforcement learning on the agent based on the optimized target. Through iterative updating of the policy constraint, the application can not only avoid a decrease in policy performance in an early online fine-tuning stage of offline-to-online reinforcement learning, but also weaken the policy constraint in a later training stage, to obtain an optimal policy.

This invention discloses a fault mode-driven multi-mode intelligent anomaly diagnosis method and system for power automation systems. The method includes offline learning and online detection phases. In the offline phase, detection knowledge items, including purified historical statistical baselines and ramp-up discrimination models, are periodically generated and stored. In the online phase, the data is preprocessed and adapted, and corresponding dedicated detection operators are activated according to the indicator type, combined with the detection knowledge items for analysis. The dedicated detection operators include ramp-up detection operators for resource-related indicators, pulse detection operators for performance-related indicators, and trend change point detection operators for disk-related indicators. The ramp-up detection operators employ a parallel fusion structure of seasonality analysis and model discriminant analysis. Candidate events output by the detection are converted into alarm records after suppression judgment. This invention achieves accurate and interpretable intelligent fault diagnosis through a closed loop of "offline baseline purification, online mode adaptation, and diagnostic output."

The invention relates to the technical field of laser welding, and discloses a laser welding seam tracking and quality real-time detection system and method based on OCT image guidance. According to the method, through system calibration and offline learning, a coordinate mapping and feature database is established; during welding, the coaxial OCT is used for synchronously scanning a front groove and a rear molten pool area; tracking and correcting a welding path in real time based on the front three-dimensional point cloud; meanwhile, a dynamic feature sequence of the molten pool is extracted and analyzed; inputting the features into a pre-training machine learning model, and judging quality and predicting defects in real time; according to the judgment result, parameters such as laser power and welding speed are adjusted in a self-adaptive mode, and closed-loop control is formed; and full-process data is fed back to optimize the model. According to the invention, synchronous closed-loop control of groove tracking and internal quality detection is realized, and the welding precision and the quality reliability are improved.

The application discloses a kind of water turbine blade topological optimization methods combining double-line learning neural network and conformal mapping.First, the water turbine blade model is mapped to two-dimensional plane by conformal mapping algorithm, generates data sample library and corresponding conformal factor.The fluid-structure interaction analysis is used to obtain the external force field and displacement field of the model nodes under specific flow field conditions.The method of the application introduces a global attention U-NET neural network, and uses the conformal factor, structure topology, external force field and displacement field as the input and output of the network, and trains the neural network.The optimization process reduces the dependence on traditional finite element analysis, improves the optimization efficiency, and realizes efficient topological optimization of the water turbine blade through the combination of offline learning and online learning, thereby improving the performance and reliability of the water turbine.

The application discloses a traction system fault detection method based on neighborhood restriction generalized autoencoder and belongs to the technical field of fault diagnosis. In view of defects such as great information loss, insufficient interpretability of potential variables and poor adaptability to nonlinear dynamic systems in existing high-speed train traction system fault detection methods, the method realizes high-precision fault detection through two stages of offline learning and online detection. In the offline learning stage, the Mahalanobis distance is used to determine a sample neighborhood set and weights, a neighborhood restriction generalized autoencoder loss function is constructed by fusing local linear reconstruction error and mutual information regularization terms, and optimal encoders and decoders are trained. Finally, normal state residuals are calculated, and a fault detection threshold is determined based on statistics. In the online detection stage, data are collected in real time and stacked data are constructed, real-time residuals are calculated by using the trained neighborhood restriction generalized autoencoder, and fault detection is realized by comparing statistics and the threshold.

The present application relates to a kind of process industry control method based on offline learning and online constraint fine tuning.Process industry control method includes the historical operation data of the controlled object, constructs the offline data set including state variable, control variable, state transition information and constraint information;Then based on the offline data set, initial control strategy is learned offline to obtain the initial strategy that meets the distribution of historical feasible control behavior;Then the initial strategy is deployed to actual control object, and a small amount of online fine tuning is carried out in combination with real-time operation data;In online control phase, the safety constraint check and correction of strategy output action are carried out, when candidate action does not meet preset safety constraint, action clipping, feasible region projection or back control are executed.The method can reduce the risk of online exploration while improving the adaptability of control system to working condition change and external disturbance, and can balance control accuracy, stability and safety, and is suitable for closed-loop control of water tank liquid level system and other process industry objects.

Example implementations described herein are directed to systems and methods for generation and deployment of automated and autonomous self-learning machine learning models, which can include generating a predictive model and a prescriptive model through an offline learning process at a first system; controlling operations of a second system through deploying the predictive model and the prescriptive model to the second system; and autonomously updating the predictive model and the prescriptive model from feedback from the second system through an online learning process while the prescriptive model and the predictive model are deployed on the second system.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for offline learning using a reward prediction model. One of the methods includes obtaining robot experience data; training a reward prediction model from a first subset of the robot experience data, the reward prediction model receiving a reward input comprising an input observation and generating a reward prediction as output, the reward prediction being a prediction of a task-specific reward corresponding to a particular task assigned to the input observation; processing experiences in the robot experience data using the trained reward prediction model to generate, for each of the processed experiences, a respective reward prediction; and training a policy neural network from (i) the processed experiences and (ii) the respective reward predictions of the processed experiences.

This invention relates to a DRL-based method for optimizing the data upload path of a wirelessly rechargeable drone, comprising: constructing an IoT communication and wireless charging scenario system; establishing a first mathematical model for the onboard battery consumption of the mission drone; establishing a second mathematical model for the data upload channel; establishing a third mathematical model for energy replenishment during the wireless charging process; establishing a fourth mathematical model for the path optimization objective; and determining the state set S, the action set A, and the reward function r. t The optimal path strategy π is obtained through offline learning using an improved Double DQN algorithm. * This invention provides a more convenient charging service for task units that assist base stations in performing data uploads in IoT communication systems. When data real-time requirements are high, the more convenient charging method significantly improves the endurance of the task units, achieving high data upload efficiency in IoT communication systems assisted by drones.

The present utility model relates to a portable stand-alone learning server with local large language model inference, integrated wireless routing infrastructure, and automatic phone-home synchronization for offline educational deployment. The portable server comprises a portable casing, a micro-computer server module, a local storage module, a learning management system, a web server, a local database server, a learning-material repository, a standalone large language model processing block, a local LLM runtime and local API module, an integrated wireless routing and access-point block, an automatic phone-home synchronization module, a local pending synchronization queue, a report generation and upload module, an update package retrieval and application module, and an update-status and auditupload module. Learner devices connect directly to the portable server through the wireless network without requiring an external router, separate access point, cloud AI platform, or internet connection. Educational content delivery, quiz operation, learner-data storage, and local AI-assisted feedback generation occur within the portable server. When no internet connection is available, generated reports, audit records, update-status records, and synchronization data are preserved in the local pending synchronization queue. When an internet connection becomes available, the automatic phone-home synchronization module initiates an outbound connection to a main server, uploads pending reports and status records, retrieves update packages and documents, and causes the update package retrieval and application module to apply updates locally to the learning management system. The arrangement permits offline learning operation with no public IP requirement while enabling later automatic report upload and synchronization with main-server updates without losing locally generated data, and without requiring inbound remote access or continuous internet connectivity.

The invention relates to the field of geotechnical engineering intelligent calculation, and discloses a PINO-based multilayer sludge vacuum loading consolidation prediction method and system, and the method comprises the steps: defining a parameter space, generating a working condition combination, solving and calculating a high-fidelity numerical model in batches, and obtaining and making a data set; determining input and output data structures of a PINO model, taking the PINO as a reference, combining a high-fidelity numerical solution with the PINO, carrying out offline learning pre-training of the PINO based on a data set, and realizing basic operator learning under physical law constraint; pINO instantiation fine tuning is carried out on the basic operator by using a PINN, so that the general solid operator is rapidly adapted to specific engineering parameters, natural transition from operator-level prediction to engineering-level application is realized, and real-time prediction and engineering optimization are carried out. According to the method, millisecond-level consolidation prediction can be realized in a hundred million-level parameter space.

The application discloses a kind of based on spectrum sensing's ultra-wideband radar signal interference detection method, first, the echo signal of ultra-wideband radar and interference signal are modeled to generate a large number of interference data sets for network learning, then the echo matrix after interference is transformed to frequency domain, along the direction of orientation is divided into pulse vector, and the spectrum sensing model of pulse is constructed, then the interference perception network model including offline learning network and online test network is established, the offline training of offline learning network is carried out, the detector with the length L is developed, and the online test network uses detector to perform sliding detection to the echo signal after processing, finally, the interference part in the echo detected is set to 0, complete ultra-wideband radar signal interference detection.The method of the application proposes a new spectrum sensing network, which can better adapt to the complex electromagnetic environment of ultra-wideband radar system, has the advantages of high interference detection probability, fast network learning speed and easy deployment on hardware platform.

The invention provides an industrial process multi-modal adaptive control method and system, and the method comprises the steps: collecting technological parameters, equipment states and environmental factors in real time through a multi-source heterogeneous sensor, carrying out the feature extraction and fusion through a deep learning model, and generating multi-modal fusion features. The dynamic memory module combines short-term and long-term memory, stores historical experience in a structured manner and supports dynamic retrieval. The cognitive decision module integrates multi-source information by using an attention mechanism, and generates an adaptive control action in combination with reinforcement learning. The adaptive learning mechanism optimizes decision and knowledge management through online and offline learning. The performance evaluation module establishes a multi-dimensional index system and guides the system to be self-optimized. According to the method, the sensing precision, decision-making flexibility and long-term stability of the control system are remarkably improved, and the intelligence and robustness of the industrial process are enhanced.

The invention discloses a reservoir outbound flow prediction method based on multi-class hydrological data feature fusion. The prediction method comprises an offline stage and an online stage. The off-line stage comprises the following steps: acquiring historical hydrological data, and preprocessing the historical hydrological data; performing secondary processing on the preprocessed hydrological database according to statistical characteristics and time data of the hydrological data to obtain multiple types of hydrological data; performing feature extraction on the input fingerprint by using a deep network and an attention mechanism; the extracted features are fused, offline learning is carried out in combination with a regression network, and a reservoir outbound flow prediction model is obtained; the online stage comprises the following steps: carrying out multiple times of preprocessing on acquired hydrological data; obtaining multiple types of hydrological data; and extracting statistical characteristics of the hydrological data, and estimating the output flow in combination with the reservoir output flow prediction model. And the accuracy of reservoir outbound flow prediction is improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for off-line learning using a reward prediction model. One of the methods includes obtaining robot experience data; training, on a first subset of the robot experience data, a reward prediction model that receives a reward input comprising an input observation and generates as output a reward prediction that is a prediction of a task-specific reward for the particular task that should be assigned to the input observation; processing experiences in the robot experience data using the trained reward prediction model to generate a respective reward prediction for each of the processed experiences; and training a policy neural network on (i) the processed experiences and (ii) the respective reward predictions for the processed experiences.

The application discloses an intelligent body running state security deviation real-time detection and self-trap stop method and system, and belongs to the technical field of intelligent body security monitoring. The application carries out sliding window statistical analysis on the intelligent body running time sequence behavior log, parallelly feeds normalized multidimensional security indexes into at least two independent detectors to capture sudden abnormality and persistent slight deviation, determines the security deviation grade through deterministic fusion, and finally executes corresponding control actions through a hierarchical self-trap stop state machine containing an irreversible absorption state. The application determines the security baseline through offline learning period, does not depend on probability distribution hypothesis, random variable or iterative optimization throughout the whole process, always produces the same output for the same input, and provides an intelligent body with a deterministic, auditable and non-bypassable running state security monitoring and self-trap stop scheme.

The invention relates to the technical field of test flight data processing, and discloses a test flight data mining method based on a machine learning algorithm. Fusing the data; analyzing online learning and offline learning; and outputting a result. By acquiring multi-source data, calculating data reliability weight, setting a real-time data verification and retransmission mechanism, optimizing data interpolation filling in combination with a historical trend and eliminating dimensional interference by adopting Z-score standardization, the influence of an external environment on the flight performance of an aircraft is comprehensively considered, the accuracy and credibility of a test flight data mining result are improved, and the test flight data mining efficiency is improved. A scene machine learning basic model is selected according to a test flight task type, model parameters are dynamically updated in combination with an online gradient descent algorithm, a traditional offline processing mode is replaced, rapid capture of real-time test flight state changes is achieved, potential problems and abnormities are found in time, test flight safety is guaranteed, and test flight efficiency is improved.

This invention discloses a multi-stage drawing control system and method for fine copper tube processing, relating to the field of copper tube processing control technology. The mapping module introduces dynamic virtual impedance into a Markov process decision framework, establishing a mapping relationship between multi-objective outputs and virtual parameters in the dynamic virtual impedance. Parameter calibration is performed for different sub-scenarios. The control output module utilizes an improved deep deterministic strategy gradient algorithm and a long short-term memory network to perform offline learning on historical multi-attribute process data, generating a transferable pre-trained parameter set and state prediction capability. The pre-trained parameter set and virtual parameters are injected into an online deep decision optimization unit to perform real-time rolling solution of the Markov process decision framework, outputting the current optimal control action. This control system, through multi-attribute deep coupling modeling combined with a Markov process decision framework, effectively improves the accuracy and stability of the multi-stage drawing process control for fine copper tubes.

This invention relates to a multi-agent cooperative satellite access and anti-jamming method based on deep reinforcement learning, belonging to the field of satellite communication. It utilizes the Actor-Critic offline learning method in deep reinforcement learning to build a partially connected neural network. The target network is used to softly update the neural network parameters, improving decision-making performance during adversarial processes and better adapting to changes in the electromagnetic environment. In environmental modeling and reinforcement learning state modeling, the actions from the previous time step are incorporated into the state, and combined with reward determination, different actions are output within consecutive time slots, making intelligent access more flexible and variable, and improving the anti-jamming capability of access. Using GPU computing networks and offline policy reinforcement learning methods, sample collection and training can be performed and effective intelligent access can be achieved even in the absence of training samples and prior data. This invention is applicable to the field of satellite communication, improving anti-jamming capabilities while ensuring user access accuracy.

The application discloses a task offloading and cache updating method based on federal reinforcement learning in Internet of Vehicles, and steps are as follows: an Internet of Vehicles scene of intelligent vehicle and infrastructure communication is constructed, including a base station and a central cloud server with computing and cache capabilities; an optimization model is established with the sum of vehicle task computing delay and energy consumption benefit minimization as an optimization target; a vehicle obtains an input state by sensing the Internet of Vehicles environment, and takes offloading strategy and resource request as output actions; a base station obtains an input state by vehicle historical request information, and takes cache updating strategy as an output action; task offloading, resource allocation and cache updating strategies are obtained through online output actions of the vehicle and the base station; and a network is trained through offline gradient descent and federal aggregation. The application introduces federal learning into the offline learning link of deep reinforcement learning, realizes distributed task offloading and cache updating, and has better scalability and feasibility in a high-dynamic Internet of Vehicles environment.

A method and system for anomaly detection from time-series input data. A Gaussian mixture model (GMM) learns distribution parameters in an offline learning stage using sample data. The data used for the offline learning, and for a subsequent online anomaly detection stage, is time-series data collected for multiple parameters of a machine operation, such as a robot performing a repetitive set of operations. The method includes aligning the data to and taking a difference from a known good reference data file, before providing the data to the GMM. In the online anomaly detection stage, the GMM computes a probability that each time-series data point fits the distribution, and a log summing computation is performed on each data file to determine the likelihood that the file contains anomaly data. The file log likelihood is compared to previous values and an alarm is issued when statistically variant from the historical data.