290 results about "Augmented learning" patented technology

The invention relates to the field of converter station equipment parameter analysis, and provides a converter station equipment parameter analysis method and system based on multi-parameter fusion monitoring, and the method comprises the following steps: collecting multi-parameter characteristic data of converter station equipment; performing streaming processing and abnormal mode identification on the multi-parameter characteristic data to obtain equipment abnormity early warning information of the converter station equipment; constructing a time-varying graph neural network model based on the multi-parameter feature data, obtaining a space-time dependency relationship among parameters through a causal reasoning method, and dynamically adjusting the feature weight of the time-varying graph neural network model in combination with a reinforcement learning algorithm to obtain a running state evaluation model; and inputting the real-time state feature sequence into the operation state evaluation model to obtain an equipment operation state evaluation result, and visually displaying the equipment operation state evaluation result and the abnormal early warning information to obtain a comprehensive operation state evaluation report of the converter station equipment. According to the invention, real-time monitoring and evaluation of the operation state of the converter station equipment are realized.

An example method is provided for training a machine-learned student sequence processing model, the method comprising: obtaining a respective input; obtaining, from the student machine-learned sequence processing model, a respective output corresponding to the respective input; generating a multiscale refinement objective configured to jointly distill knowledge from a teacher machine-learned sequence processing model and reinforce preferred behavior of the student machine-learned sequence processing model, wherein the multiscale refinement objective comprises: a first component based on a divergence metric characterizing, for the respective input, a comparison of a plurality of predictions of the student machine-learned sequence processing model to a plurality of predictions of the teacher machine-learned sequence processing model; and a second component based on a reinforcement learning signal associated with the respective output; and updating the machine-learned student sequence processing model based on the multiscale refinement objective.

The embodiment of the invention provides an intelligent learning path recommendation method and system based on a dynamic state. The method is applied to the technical field of intelligent learning recommendation, and comprises the following steps: calculating a skill adaptation weight, a load weight and an emergency degree weight in real time based on a dynamic state of a target employee, including a current skill improvement condition, a workload and a task emergency degree; combining the skill matching degree, the learning strength and the task association degree of each learning unit in the candidate learning path, and performing comprehensive evaluation by using a multi-weight scoring function; and sorting the paths according to the comprehensive score, generating a personalized recommended learning path set and sending the personalized recommended learning path set to an employee terminal, thereby realizing intelligent learning path recommendation with dynamic adaptation and accurate matching. According to the scheme, personalized learning path pushing aiming at actual post requirements and working states of the employees can be realized, correlation, urgency and acceptability of learning contents are improved, learning efficiency and task adaptability are remarkably enhanced, and the employees are helped to quickly compete with post targets.

Disclosed is a method for enhancement of a low-light image based on reinforcement learning and aesthetic evaluation. The method include: generating images of non-normal luminance under different lighting scenes, and constructing a training dataset for a reinforcement learning system based on the images; initializing the training dataset, a policy network, and a value network; updating, based on a no-reference reward score and an aesthetic assessment reward score, the policy network and the value network; completing model training and outputting an enhanced image result. By expanding the action space range defined in reinforcement learning, the enhancement operations for the input low-light image gain a greater dynamic range, offering higher flexibility for real-world scenarios and better meeting low-light image enhancement needs. Additionally, by incorporating the aesthetic quality assessment scores as part of the loss function, the enhanced image achieves better visual effects and higher subjective user evaluation scores.

The invention discloses a traffic signal and direction-variable lane cooperative control method based on multi-agent learning, and the method comprises the steps: making a decision through the iteration of a traffic signal control module and a lane guiding control module, and carrying out the operation at the same time to control the traffic; the lane guiding control module adopts a dual depth Q network algorithm to enhance a learning model, an intelligent agent selects actions based on traffic flow and traffic signal states to maximize the lane utilization rate, and an award is calculated based on lane queuing length difference and the maximum direction demand traffic flow ratio; the traffic signal control module agent selects an action based on the observed lane density and the current lane state and obtains an award according to the traffic flow satisfaction of the selected phase. According to the method, the lane utilization rate is improved by controlling lane guidance, and the dynamically changing traffic flow is controlled in space and time dimensions by using a reinforcement learning framework; higher stability and efficiency are shown, and the traffic capacity of the intersection is improved.

The invention discloses a frequency switching method and device of an embedded power terminal. The method comprises the steps that frequency switching is triggered by predicting frequency deviation and comparing the predicted frequency deviation with a preset frequency synchronization threshold value, and the transmitting power and the modulation mode in the switching process are optimized through a fuzzy control algorithm so as to suppress transient interference. During switching execution, the node synchronization state is monitored in real time, and an interruption mechanism is triggered when abnormity occurs. And after the switching is completed, calibrating the frequency parameter of each node, and updating the frequency drift compensation model. And dynamically optimizing the compensation model and the switching strategy by utilizing historical data and adopting a reinforcement learning algorithm, and synchronously updating the compensation model and the switching strategy to each node. By means of predictive triggering, intelligent parameter optimization, real-time monitoring and the like, the problems of transient interference, exception handling, model optimization and the like in the power terminal node frequency synchronization process are effectively solved. According to the method, the reliability and stability of power terminal node frequency synchronization are remarkably improved, and a powerful guarantee is provided for safe and stable operation of a power system.

A profit-aware Offloading Framework towards Prediction-assisted MEC Network Slicing is provided. Towards MEC network slicing, formulate the optimization problem of maximizing long-term ESP profits and decouple it into the sub-problems of Edge network Slicing (EnS) and Computation offloading Access (CoA). For the slicing sub-problem, use a gated recurrent neural network (GRNN) to accurately predict user requests in different regions, and then use the optimal partitioning of network slices with the predicted requests and expected demands; For the offloading sub-problem, incorporating results from slice partitioning, use an improved deep reinforcement learning with twin ritic-networks and delay mechanism, solving the Q-value overestimation and high variance for approximating the optimal offloading and resource allocation.

The invention discloses a weak supervision video anomaly detection method based on prompt learning knowledge enhancement, and belongs to the technical field of video intelligent analysis. A video side gives a section of abnormal scene video, video sequence features and audio sequence features are obtained through a feature extraction network, then a trained and complete feature aggregation network is input to carry out multi-modal feature aggregation, an abnormal score is obtained through a score prediction network, and text representation is carried out based on prompt learning. A prompt template is constructed for abnormal video tags through a knowledge graph, semantic expansion is performed on normal tags through a plurality of learnable parameters, cross-modal alignment is performed on the normal tags and a video side, so that features of the video side are close to different normal semantics, knowledge enhancement is performed by introducing external information, positive abnormal boundaries of the video are learned, and the detection performance is improved. And finally, multi-task joint optimization is carried out through different loss functions, and abnormal video clip positioning is carried out.

The present invention relates to a method and system for detecting an anomaly of an electrical submersible pump, ESP. Specifically, the proposed invention provides: physics-based anomaly assessment, automatic data imputation using virtual sensors network (203), automatic continual learning based on automatic model drift detection, automatic failure prevention based on reinforcement learning, remaining useful life (RUL) estimation, and cohort analysis.

The invention relates to the technical field of reinforcement learning intelligent scheduling, in particular to an intelligent scheduling management method for prefabricated part production resources based on reinforcement learning. The specific implementation process comprises the steps of collecting order demands, material distribution and production pedestals in real time, and mapping the order demands, the material distribution and the production pedestals into production mold load tensors; when a scheduling request is triggered, inputting the production modulo tensor into a resource arrangement network, and performing search and reasoning by using a scheduling strategy based on a multi-head attention mechanism to generate a resource scheduling matching instruction; calculating a state difference tensor, and outputting an efficiency reward signal in combination with a delivery constraint and a cost constraint; and packaging the production modulo tensor, the resource scheduling matching instruction and the reward signal into semantic interaction experience, storing the semantic interaction experience into a scheduling experience playback pool for gradient modulation, and iteratively optimizing a scheduling strategy. According to the method, learning can be carried out from a large amount of historical data by utilizing reinforcement learning, iterative optimization of the scheduling strategy is realized, the calculation time consumption of scheduling instruction generation is reduced, and the flexibility of production scheduling is improved.

The invention discloses an image recognition system for artificial intelligence based on reinforcement learning, which relates to the technical field of image recognition and comprises an image input module, a preprocessing module, a feature extraction module, a reinforcement learning module, an adaptability adjustment module, a recognition output module, a self-optimization module and a performance monitoring module. The system has the advantages that image data of various devices can be compatible through the image input module, images with different resolutions are subjected to adaptive processing, the image quality is ensured, the feature extraction module can extract different level features of the images, the granularity and the range of feature extraction are accurately controlled by adjusting the number and the size of convolution kernels, and the image quality is improved. And the enhanced learning module performs learning and decision making by using a Q-learning algorithm, continuously optimizes an identification strategy and interacts with the adaptive adjustment module and the self-optimization module, and the functions act together, so that the image identification accuracy is powerfully improved.

The invention relates to the technical field of anesthesia management, in particular to an anesthesia medicine excess brain function influence assessment system applying artificial intelligence, which comprises a data acquisition module, a data preprocessing module, a model training module, a real-time assessment module, a risk early warning module, a treatment suggestion module, a system interaction module and a remote monitoring module. Through the combination of a convolutional neural network and a long-short-term memory network, spatial-temporal characteristics of brain wave and functional magnetic resonance imaging data are extracted, a brain function thermodynamic diagram is generated in real time, and the influence of anesthetic drugs is analyzed; the system is combined with a reinforcement learning algorithm to dynamically adjust the anesthetic dosage, a risk early warning module dynamically adjusts a threshold value through physiological parameters and tolerance of a patient, an expert team can provide medical assistance in real time through a remote monitoring module, and brain function safety of the patient in the operation or anesthesia process is ensured; according to the invention, the safety, accuracy and individuation level of anesthesia management are effectively improved.

The invention provides a large model parameter optimization and self-adaptive adjustment method based on reinforcement learning, and relates to the technical field of optimization and self-adaptive adjustment of large model parameters based on reinforcement learning in machine learning, and the method comprises an overall architecture fusing a large model main body, a reinforcement learning module, an environment perception module and a reward feedback mechanism. The method is used for realizing dynamic optimization and adaptive adjustment of large model parameters, and can rapidly increase the learning rate according to environment feedback in the initial stage of large model training, so that the model parameters rapidly approach to the direction of an optimal solution, and the early-stage exploration time of training is greatly shortened. In the later stage of training, the learning rate can be accurately reduced, model oscillation is avoided, and it is ensured that the model is stably converged to a globally optimal solution. According to the dynamic adjustment mechanism, the number of iterations of training is effectively reduced, the training efficiency is greatly improved, a large number of computing resources and time cost can be saved, and for example, in large-scale image classification model training, the training time can be shortened by more than 30%.

The invention relates to the technical field of large model optimization, and discloses a large model parameter optimization and adaptive adjustment method and device based on reinforcement learning, and the method comprises the steps: obtaining a current operation state of a target large model, and carrying out the comprehensive evaluation of the operation state of the target large model through combining with data features, and obtaining an operation state vector; constructing an intelligent agent, and inputting the operation state vector into the intelligent agent to obtain a parameter optimization strategy; optimizing parameters of the target large model based on a parameter optimization strategy to obtain an optimized large model; calculating a plurality of preset indexes for optimizing the large model by utilizing a reward function to serve as reward results; and adaptively adjusting parameters of the intelligent agent based on the reward result. In training and practical application of the large model, large model parameters are dynamically adjusted by means of an intelligent agent, it is ensured that rapid convergence can be achieved in the initial stage of training, the global optimal solution can be accurately approached in the later stage of training, the training efficiency and quality are effectively improved, and by means of a reward mechanism, the intelligent agent is made to adjust the parameters in a self-adaptive mode, and the generalization ability of the large model is improved.

The invention discloses a data enhancement and transfer learning method for chip power supply network voltage drop prediction in a small sample scene. The method comprises the following steps: acquiring a disclosed large-scale training data set and a small number of training samples of new process parameters or new chip design types; extracting a label of each sample in the source domain S and the target domain T, calculating a gradient G of each label, respectively calculating average gradients Gmean S and Gmean T of the sample labels in the source domain S and the target domain T, and calculating a scaling coefficient R; scaling the features and labels of the samples in the source domain S according to the scaling coefficient R; and repeating the above steps until a reinforcement learning data set used for subsequent transfer learning is made. The technical problem that the prediction precision is seriously reduced due to scarcity of training samples in the current chip power supply network voltage drop prediction field can be solved.

The invention relates to the technical field of virtual simulation teaching. The e-commerce virtual simulation teaching resource construction method and system based on the ability map are provided, and the method comprises the following steps: on the basis of e-commerce industry technical post requirements, disassembling post capabilities, and generating a structured ability map comprising a core ability unit, a skill point layer and a knowledge point layer; performing classification design processing on the virtual simulation scene resources according to a skill point layer in the structured ability map, and generating a virtual resource library comprising production type scene resources, interactive type scene resources and creative type scene resources; based on the mapping relation between the nodes of the structured ability graph and the virtual resource library, performing label labeling processing on the virtual simulation task to generate a task-knowledge point association network, the technical effects of improving the matching precision of resources and post requirements, optimizing the classification systematicness of virtual simulation resources and enhancing the dynamic relevance of learning paths and knowledge points are achieved.

The data query optimization method, system, database and electronic device based on machine learning provided by the present invention include: obtaining an index automatic recommendation model; wherein the index automatic recommendation model is obtained based on feature training of each historical query in the historical query log; obtaining a query statement, and when the query mode of the query statement is a full table scan, predicting the optimal index corresponding to the query statement through the index automatic recommendation model; creating an optimal index, and optimizing the optimal index through a preset enhanced learning mechanism combined with the query performance after using the optimal index. The present invention dynamically recommends the optimal index for the query statement through the index automatic recommendation model, thereby ensuring the accuracy and pertinence of the index. The present invention can also automatically optimize the index through the enhanced learning mechanism, greatly reducing the complexity and labor cost of index management.

A dual agent reinforcement learning autonomous system (DARLAS) for the autonomous operation of aircraft and / or provide pilot assistance. DARLAS includes an artificial neural network, safe agent, and cost agent. The safe agent is configured to calculate safe reward Q values associated with landing the aircraft at a predetermined destination or calculated emergency destination. The cost agent is configured to calculate cost reward Q values associated with maximum fuel efficiency and aircraft performance. The safe and cost reward Q values are based on state-action vectors associated with an aircraft, which may include state data and action data. The system may include a user output device that provides an indication of an action to a user. The action corresponds to an agent action having the highest safe reward Q value and the highest cost require Q value. DARLAS prioritizes the highest safe reward Q value in the event of conflict.

A system and method for stimulating the activation of neural mirroring mechanisms through personalized avatars to enhance learning and skill development. The system creates hyper-realistic or stylized digital representations of users and animates these avatars to present educational content or demonstrate skills, stimulating mirror neuron activation when users observe themselves demonstrating mastery. Applications include academic learning, language acquisition, professional training, personal development, athletic performance enhancement, and therapeutic interventions including for Autism Spectrum Disorder.

The invention discloses a laboratory detection equipment intelligent management system based on edge calculation and reinforcement learning, and relates to the technical field of equipment intelligent management, the system comprises a multi-dimensional data acquisition module, a model training and feature library construction module, a deviation degree research and judgment module, a model optimization module and an interaction and execution module; according to the invention, through integration of the multi-dimensional data acquisition module and the model training and feature library construction module, all-directional precision monitoring of the operation state of laboratory detection equipment is realized, and various feature data in the operation process of the equipment can be captured in real time through the multi-dimensional sensor array; the model training and feature library construction module uses a deep learning model of a CNN-LSTM mixed structure, combines historical operation data and fault cases of equipment, calculates each feature weight, and dynamically constructs and updates a health state feature vector library of the equipment, thereby improving the accuracy and timeliness of equipment state evaluation, and improving the reliability of equipment state evaluation. Therefore, managers can find potential faults in advance and take preventive maintenance measures.

The invention provides a game real-time intelligent translation method and device based on interactive reinforcement learning. The method comprises the following steps: determining a corresponding screen capture mode according to current game picture information and user operation habit information; dynamically sensing the current game picture according to the screen capture mode; performing text recognition on the image data in the user attention area by using a context-enhanced image recognition model, and obtaining a recognition text according to a context knowledge base exclusive to the game scene and user operation historical information; inputting the recognition text, the corresponding image data and user operation historical information into a multi-modal fusion translation model to generate a target language text; and based on the corresponding relationship between the recognition text and the target language text, rendering the target language text on the original foreign language region in an augmented reality mode. According to the method, real-time, automatic and immersive translation of the game picture content is realized, a large number of continuous texts can be processed, and the translation result better conforms to a game scene.

The invention relates to the technical field of electromagnetic compatibility and signal processing, and discloses a multi-dimensional feature fusion-based electromagnetic leakage signal classification and identification method, which comprises the following steps of: preprocessing an original electromagnetic signal to obtain standardized data, extracting time domain, frequency domain and space domain features to construct a nine-dimensional feature vector, and extracting a three-dimensional feature vector; constructing an initial feature library and completing the initial feature library through a self-supervision verification mechanism, training the initial feature library through a CNN-LSTM fusion model with an attention mechanism to obtain a classification model, classifying signals to be identified, starting a self-supervision reinforcement learning mechanism optimization model according to an F1 score, and dynamically optimizing the feature library each month; the method solves the defects of the traditional technology, improves the recognition accuracy and the system adaptability, and can be used for cable information safety leakage protection.

The invention relates to the field of cross fusion of artificial intelligence and brain science, and particularly discloses a brain-computer coupling-based agent common-situation response method under the guidance of the emotion of a learner, and the method comprises the steps: firstly, collecting the multi-modal emotion data of the learner oriented to the agent common-situation response; secondly, performing emotion analysis on the acquired multi-modal data of the learner under brain-computer coupling by adopting a multi-modal data emotional state judgment method; and designing a multi-modal common-situation response generation framework based on the emotion recognition result and the dialogue content, constructing a common-situation response model based on multi-modal representation, and carrying out joint learning training on the common-situation response model to form an agent common-situation response model based on a large language model multi-agent system under the guidance of the learner emotion. The emotional state of the learner is accurately identified, and the dialogue content of the intelligent agent is dynamically adjusted based on the emotional state, so that the emotional ability cultivation of the learner is enhanced.

A training system and a training method for a domain-specific data model are provided. The training method includes configuring a computing device to perform the following processes: generating, by a training set generation module, a training data set based on a domain knowledge graph; updating the data model based on the training data set; generating, by the training set generation module, training input text corresponding to the domain knowledge graph; inputting the training input text into the data model to obtain training output text; evaluating and generating a score by an evaluation module based on a correlation between the training output text and the domain knowledge graph; and adjusting, by a reinforcement learning module, parameters of the data model according to the score and an optimization goal of the reward model until the score meets a training completion condition, taking the data model as the domain-specific data model.

The invention relates to the field of electronic price tag management, in particular to an electronic price tag position identification method and system. The method comprises the following steps: acquiring real-time image data from a retail environment, and enhancing the image data through a multi-modal sensor to obtain a standardized image sequence; carrying out denoising, enhancement and quality optimization on the image sequence, extracting image features through a deep convolutional neural network, and generating an electronic price tag position candidate region; the real position coordinates of the electronic price tag are calculated in combination with the image space information, and the commodity price is synchronously updated; the updated electronic price tag position and price information are sent to a cloud computing platform for storage, analysis and management; based on real-time feedback and historical data, an image processing and position identification strategy is optimized through reinforcement learning, and adaptive adjustment parameters are generated. According to the invention, the position of the electronic price tag can be efficiently and accurately identified in a complex retail environment, real-time price synchronous updating is realized, and automation and accuracy are high.

The invention relates to the field of quality detection of mechanical parts, in particular to an intelligent recognition system for surface defects of mechanical parts based on deep learning. Comprising a data acquisition module, a differential geometric feature extraction module, a small sample learning module, an automatic labeling module, a data reinforcement learning module, a neural network training module, a defect identification module and a defect quantitative evaluation module. Extracting geometric features including curvature features and differential invariant features; small sample learning is adopted to solve the problem of sample scarcity; the efficiency is improved by utilizing automatic labeling; simultaneously capturing microcosmic details and macroscopic morphology through a multi-scale differential analysis framework; organic fusion of surface image information and depth geometric information is realized; according to the method, the problems of insufficient samples, difficulty in labeling, inaccurate defect evaluation and the like in industrial production are effectively solved.

A computer-implemented method of learning a policy for controlling a computercontrolled system, in particular a robot, by reinforcement learning, comprising the steps of: Observing a current state (s) of an environment of the computercontrolled system. Interacting (S12) with the environment by carrying out the steps of: Determining an action (a) by the policy, which is an actor network, depending on the current state (s), Executing the action (a) by the computercontrolled system, receiving a reward (r) and the next state (s'), and storing the interaction with the environment as an experience in a replay buffer. Sampling (13) a batch of experiences from the replay buffer. Computing (S15) Q values by at least two critic networks for the experiences in said batch. Determining a standard derivation of the Q values for the experiences in said batch for each of the critic networks. Updating (S17) critic parameters depending on the Bellman equation and adding the standard derivations as regularization.

The invention provides a contract generation method and system based on artificial intelligence. Belongs to the technical field of artificial intelligence and comprises the steps that a user inputs basic information and personalized requirements of a contract through an interactive interface, and a management system analyzes the input information, extracts key features and constructs a requirement vector; constructing an intelligent decision model based on a reinforcement learning algorithm; a database containing various contract templates is constructed, and the management system selects the most matched template from the template database as a starting point of contract generation according to the output of the reinforcement learning model. Through an artificial intelligence technology, especially natural language processing and a reinforcement learning algorithm, user demands can be rapidly analyzed and contract texts meeting the requirements can be generated, so that the time of traditional manual contract writing is greatly shortened.

The invention provides an interface test method and device, equipment and a storage medium, a test path is analyzed by using a test analysis model obtained by performing knowledge enhancement based on an assertion knowledge base and a business knowledge base and performing training based on sample data, a test report is generated, and when a test result is obtained by executing a specified UI test task, the test result is displayed on the basis of the test report. Element state test point identification and element state test can be automatically carried out on the interface image corresponding to the test step by utilizing the interface test knowledge learned through knowledge reinforcement, and other abnormal conditions of the program interface involved in the test process are identified; hidden visual effect defects can be actively found for a dynamically changing user interface, an optimization scheme can be provided for defects existing in a program interface, the interface testing efficiency and the intelligent level are greatly improved, testing is more efficient and accurate, and user experience of application programs can be optimized easily.

The invention relates to the technical field of e-commerce, and discloses a cross-border e-commerce abnormal order processing method and system based on reinforcement learning, and the method comprises the steps: collecting historical transaction records and real-time behavior records of a user, and obtaining an original data set; performing format unification according to the original data set to obtain a structured data set, and performing multi-dimensional feature extraction to obtain multi-dimensional behavior features; according to the multi-dimensional behavior characteristics, abnormal sequence analysis and risk level classification are carried out, and risk control adjustment parameters are matched; according to the risk control adjustment parameters and the structured data set, environment grouping, risk order identification and abnormal feature extraction are carried out to obtain abnormal distribution features; according to the abnormal distribution characteristics, comprehensive risk analysis is carried out through a pre-constructed neural network model, and a comprehensive risk score is obtained; and performing abnormal order judgment and risk control processing according to the comprehensive risk score and the historical transaction record, and optimizing a neural network model. The method improves the accuracy of abnormal order detection.