145 results about "Interactive Learning" patented technology

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.

The invention discloses a scenarized hierarchical management and control method for an education application in a data space, and the method comprises the steps: obtaining a multi-modal input content in a corresponding preset period after the starting of the current time in response to a starting instruction of the education application in a target data space; calling a corresponding analysis strategy according to the content type of the multi-modal input content; extracting target features from the multi-modal input content based on an analysis strategy; according to an education scene corresponding to the target data space, obtaining a matching result of the target feature and a target database; determining a corresponding data space risk level according to a matching result; and calling and executing an application management and control strategy corresponding to the data space risk level. According to the method, education application hierarchical management and control adaptive to different scenes can be provided, the management and control precision is high, meanwhile, automatic analysis and management and control processes are achieved, and the method is suitable for scenes such as classroom teaching management, after-class and extracurricular training, remote examination and family interactive learning.

The invention relates to the field of industrial automation control, in particular to an intelligent monitoring system oriented to complex working conditions and a reinforcement learning optimization method thereof. Comprising a data acquisition module used for acquiring operation data of industrial equipment under complex working conditions; the data preprocessing module is used for preprocessing the operation data acquired by the data acquisition module and then acquiring standard data; the feature extraction module is used for acquiring key feature vectors capable of reflecting the operation state of the industrial equipment; the intelligent monitoring model building module is used for building an intelligent monitoring model, and the intelligent monitoring model takes the key feature vector as input and outputs a prediction result of the operation state of the industrial equipment after interactive learning; and the alarm module is used for judging whether the industrial equipment is in an abnormal state according to the prediction result, and sending an alarm signal if the industrial equipment is in the abnormal state. According to the method, the problems of low disk monitoring efficiency, poor accuracy, insufficient model adaptability, incomplete alarm mechanism and the like under complex working conditions in the prior art are solved.

The invention discloses an algae abundance dynamic feature selection method based on multi-agent reinforcement learning, which belongs to the technical field of artificial intelligence and data mining, and comprises the steps of multi-source heterogeneous data acquisition and convergence, time sequence data cleaning and standardization processing, driving factor identification and weight calculation key driving factor set establishment, and dynamic feature selection. Convergence and determination of an optimal feature subset selected by multi-agent reinforcement learning and dynamic features: an MARLN system performs multi-round iteration and interactive learning, and each agent continuously optimizes a decision strategy thereof according to a global reward signal fused with a specification item; according to the method, the problems that a traditional static feature selection method cannot adapt to data changes and neglects interaction among features are solved, the most critical driving factors, namely the features, for algae abundance prediction are automatically recognized from multi-source heterogeneous data through a dynamic feature selection method, and the accuracy of algae abundance prediction is improved. And an optimal feature subset is constructed to improve the precision and robustness of the prediction model.

A protein interface prediction method based on three-track coding comprises the following steps: combining a fine-tuned protein language model SiteT5 with evolutionary, geometric and statistical features extracted from a sequence, sending the combined features into a three-track coding network, and integrating a cyclic gating module, a multi-resolution aggregation module and a long sequence deformation module to obtain a protein interface prediction model SiteT5; the method comprises the following steps: respectively capturing a time sequence relation, a local mode and long-range dependence among residues, respectively mapping the three codes into different weights, carrying out point multiplication on the three codes, and carrying out aggregation through a multi-view cross attention module; then the protein residues are sent to a three-layer hierarchical interactive learning module, local structure and global dependency are cooperatively mined through an eight-head gating self-attention module and a position-by-position feedforward module, and finally the probability that each protein residue is an interface is obtained through a classifier. According to the invention, a protein-DNA interface, a protein-RNA interface, a protein-protein interface and an antibody-antigen interface can be effectively captured. And the robustness is ensured, and meanwhile, relatively high prediction precision is also shown.

The invention relates to the field of intelligent temperature control, and provides a refrigeration house PID control parameter optimization method and device, equipment and a storage medium. The method comprises the steps of collecting temperature data and energy consumption data of the refrigeration house at a constant-temperature stage; based on the temperature data and the energy consumption data, a mathematical model representing the mapping relation between the temperature and the energy consumption of the refrigeration house is established; constructing a deep reinforcement learning model based on a preset reward function and a mathematical model giving consideration to the temperature stability and energy consumption optimization of the refrigeration house; and through interactive learning of the intelligent agent of the deep reinforcement learning model and the refrigeration house environment, determining optimization parameters used for adjusting refrigeration house PID control. According to the refrigeration house PID control parameter optimization method, device and equipment and the storage medium provided by the invention, the mathematical model of the temperature and energy consumption mapping relation of the refrigeration house can be effectively established, the deep reinforcement learning model is constructed through the reward function and the mathematical model, and the optimization parameters of refrigeration house PID control are adjusted through deep reinforcement learning.

The invention belongs to the field of multi-room negative pressure gradient control, and discloses a multi-room negative pressure gradient automatic control method and system based on reinforcement learning. An environment model containing multi-dimensional dynamic parameters such as an environment pressure state, an access control state, a fan air volume and a room coupling relation is constructed, so that the method can directly obtain an optimal control strategy through interactive learning on the premise of not needing an accurate physical model. Through the self-adaptive decision-making capability of reinforcement learning, linkage adjustment of the exhaust air volume and the fresh air volume of each room and the power of a total exhaust fan and a fresh air fan of the whole system is achieved, so that the stability of gradients of a corridor-5Pa, a ward-10Pa, a semi-polluted area-15Pa, a polluted area-20Pa and the like is kept, and it is ensured that the pressure difference of all areas is always within the safety range of-7Pa to-2Pa.

The invention provides a marine image data identification method and system. The method comprises the following steps: step 1, inputting an original marine image; 2, a visual feature extraction module extracts hierarchical features through a multi-scale convolutional neural network; 3, pre-training an LLM semantic reasoning hierarchical attention mechanism semantic mapping multi-task cooperative training framework by the language model agent module; and 4, a dynamic strategy optimization module reinforces learning optimization strategies to adjust identification parameters in real time, and generates a preliminary identification result. And 5, when the user needs to optimize, combining user feedback, a data enhancement interactive learning mechanism and a semantic consistency data enhancement optimization model to realize model updating and parameter adjustment, and returning to the step 2 again. And when the user does not need optimization, outputting a final identification result of the ocean image. According to the invention, a visual coding technology is combined with the semantic understanding capability of LLM, and a recognition solution capable of adaptively processing a multi-modal ocean image is constructed.

The invention discloses an RAG intelligent knowledge base management method based on a large language model. The method comprises the following steps: analyzing a query context of an enterprise-level user through a situation analysis module, constructing a user portrait, and dynamically adjusting a GraphRAG retrieval and generation strategy; data input by a user is analyzed based on a multi-modal LLM technology, cross-modal retrieval and generation are executed, and knowledge documents or instructions related to multi-modal query are output; through an interactive learning and feedback loop mechanism, revision evaluation of a user on generated content is collected, and revision data is fused to optimize subsequent answer generation; multi-level privacy protection is realized by adopting data desensitization, federal learning and encryption technologies, and answer complexity and professional depth are dynamically adjusted through semantic understanding; the technical problems that multi-modal data processing is difficult, dynamic updating of the knowledge base is insufficient, and retrieval efficiency is low are solved, and meanwhile the functions of business-driven model fine adjustment, knowledge base updating, self-adaptive difficulty grading and privacy protection are achieved.

The invention discloses a music teaching touch device for blind people, which comprises a supporting seat and a teaching touch seat, and a plurality of dynamic dot matrix seats are arranged on the teaching touch seat in an array. Three rows and two columns of flexible pads are arranged at the top of each dynamic dot matrix base to form a Braille display matrix, and an electromagnetic plunger assembly is arranged below each dynamic dot matrix base to independently drive each dot position to protrude and reset, so that dynamic display of music Braille and graphs is achieved. The bottom of the dynamic dot matrix seat is connected through a silica gel bowl, and a conducting strip is arranged in the bowl and can be in contact with a corresponding conducting material on a circuit board in the teaching touch seat. When a user presses the dynamic dot matrix seat displaying specific notes, pitch sounding of the notes is triggered. According to the invention, the dynamic tactile display function and the pressing input function are integrated, so that refreshing and interactive learning of the braille music score are realized, and a multi-mode feedback closed loop is realized through tactile sense, auditory sense and operation.

The invention relates to the technical field of automatic control, and discloses a large-scale organ uniform controllable cooling method and system based on deep reinforcement learning and fuzzy control. The method comprises the following steps: constructing a high-precision temperature sensor array, and collecting multi-point temperature data on the surface and in the organ in real time; based on a biological heat transfer theory inverse problem model, reconstructing an organ three-dimensional temperature field in real time; a depth deterministic strategy gradient algorithm is adopted to optimize a fuzzy proportional-integral-differential controller. The depth deterministic strategy gradient algorithm takes minimization of organ internal temperature gradient as a primary optimization target, and parameters of a fuzzy PID controller are adjusted on line through interactive learning of an intelligent agent and a cooling environment, so that the power and the rate of a cooling module are accurately controlled. By means of the method, intelligent prediction and dynamic optimization of the cooling process can be achieved, the temperature gradient in the organ is smaller than 10 K / cm while it is guaranteed that the cooling rate reaches 15 K / min, and the success rate of low-temperature preservation of the large-scale organ and the activity of the large-scale organ after resuscitation are improved.

The invention discloses a video quality evaluation method and system based on space-frequency combination and time sequence interaction, and relates to the technical field of computer vision, and the method comprises the steps: carrying out the standardization and data enhancement of an immersive video key frame; then, spatial domain and frequency domain double-path features are constructed through learnable Gabor convolution, two-dimensional Fourier transform and a logarithmic magnitude spectrum; space-frequency dynamic fusion is realized through bidirectional Cross-Attention, and a joint representation is generated; then, inputting Transform architecture modeling inter-frame time sequence dynamic and global dependence, and obtaining time sequence correlation characteristics; and finally, outputting frame-by-frame quality scores through learning weighted MLP regression, and aggregating the frame-by-frame quality scores into an overall quality score. According to the method, high-fidelity no-reference quality evaluation of immersive video distortion is realized through space-frequency joint modeling and time sequence interactive learning.

The invention belongs to the technical field of virtual reality organization ideological education, and discloses an organization ideological education application system combined with a virtual reality technology, and an interactive learning development module and a learning assistance and recommendation module can solve the problems. The interaction module creates three types of core scenes: role playing is fused with motion capture and organization related term semantic recognition, and the motion specification degree and the verbal skill accuracy are evaluated in real time; the difficulty of ideological education scene simulation is adjusted according to user roles, new organization members simplify the change process, and basic organization affair personnel add exception handling scenes and can deduce decision influence; multi-person collaboration supports 8-30-person hierarchical collaboration, a branch principal can allocate tasks, basic organization affair personnel can watch data, and points can be synchronized when the tasks are completed; the intelligent recommendation module refines four types of user roles and four types of content tags, and combines an algorithm to deduce a daily list, an NLP knowledge base and a difficulty disassembling tool to assist in real time, so that organization members can participate actively from passive learning, and the learning difficulty is accurately solved.

The invention relates to the technical field of video processing, and discloses a micro-lesson video processing method and device and a storage medium, in the method, in the process that a recording user records a micro-lesson video through a recording device, after a video clip generating knowledge points is recorded, media supplementary enhancement information of the knowledge points is inserted. In the process that the playing user plays the micro-lesson video through the media player, after the video clips of the knowledge points are played, the interaction content can be displayed to the playing user based on the media supplementation enhancement information of the knowledge points, so that the playing user performs interactive learning based on the interaction content to deeply understand and master the knowledge points in the micro-lesson video. Therefore, when the user performs online learning through the micro-course video, the learning efficiency is improved.

The application discloses a multi-sensory interactive ASD user interaction learning system and an implementation method thereof. The multi-sensory interactive ASD user interaction learning system comprises a table board, a plurality of control modules and a plurality of telescopic pipes with different marks. The plurality of control modules are connected with the plurality of workstations on the table board one by one, and each control module is connected with a corresponding telescopic pipe. The plurality of telescopic pipes are used to be touched by ASD users to be connected with the plurality of control modules. The table board is used to carry the plurality of control modules on the plurality of workstations. The control module is used to carry out audio-visual feedback to complete the interactive learning of the ASD user. The application can control the audio-visual feedback after the control module identifies the corresponding mark of the telescopic pipe, and cooperate with the tactile feedback of the ASD user to the telescopic pipe and the control module, so as to realize the purpose of attracting the attention of the ASD user through multi-sensory interaction stimulation, and further adapt to the diversified learning needs of the ASD user.

The application discloses a remote sensing multi-task interpretation method and device based on cross-knowledge interactive learning and self-supervision auxiliary learning, relates to the technical field of remote sensing visual interpretation, and comprises the following steps: acquiring an image to be processed, setting task requirements of the image to be processed, the task requirements comprising one or more of a single-mode data classification task, a multi-mode data classification task, a single-mode data segmentation task, a multi-mode data segmentation task, a single-mode data change detection task and a multi-mode data change detection task; inputting the image to be processed into a trained multi-task interpretation network, and obtaining a result of the image to be processed after executing the task requirements according to the task requirements of the image to be processed; wherein the trained multi-task interpretation network is trained in a self-supervision learning mode by taking data of preset categories as a training data set, and is obtained by training an initial multi-task interpretation network. The application can realize the interpretation of remote sensing visual single-mode classification, segmentation, change detection and multi-mode classification, segmentation and change detection, and obtain high-precision interpretation results.

The invention discloses a multi-modal tampering information detection and positioning method based on mitigation of modal misleading. The method comprises the steps of S1, image tampering region identification, S2, non-tampering region and text interactive learning, S3, LLM knowledge supplementation, and S4, tampering identification and positioning. After a tampered area and a non-tampered area of an image are recognized, an LLM generates explanation of text modal background knowledge, and the consistency of the text modal and the background knowledge and the consistency of the image modal and the background knowledge are compared to serve as the basis of multi-modal tampering information detection and positioning, so that the modal misleading of multi-modal tampering information detection and positioning is relieved.

The invention provides a mechanical design chart hybrid intelligent analysis method and system based on interactive learning, and provides an efficient and systematized solution for intelligent processing of mechanical design chart knowledge by adopting a multi-modal data fusion technology and combining deep learning, an OCR (Optical Character Recognition) technology and natural language processing. Through an accurate chart and text recognition method, the analysis precision and the practical efficiency of design knowledge are remarkably improved, and the precision requirement in standardized mechanical design is met; the system adopts a convolutional neural network and an attention mechanism, solves the problems of insufficient precision and inaccurate structured data output when a complex chart is processed in a traditional technology, and optimizes data integration and processing flows by seamlessly connecting an analysis result with an agent knowledge base and a database.

The invention discloses an operation management system suitable for a 9H gas turbine, which belongs to the technical field of direct application of power plant operation and comprises a network learning platform module, an intelligent defect registration module, a data updating and maintenance module, an online examination evaluation module, a live code management module, a defect flow monitoring module and a user authority control module. According to the invention, through visual monitoring, a three-level early warning mechanism and cross-professional cooperative processing, the defect processing timeliness and quality are ensured; a network learning platform is combined with a live code scanning technology to realize parameter query, interactive learning and historical defect tracing of subsystems such as a # 12 gas turbine fuel gas system, the skill acquisition efficiency of operators is improved, data security is guaranteed by authority hierarchical control and multi-factor authentication, real-time synchronization of technical documents is guaranteed by data version management and an intelligent verification function, and the technical efficiency is improved. And the misoperation risk is reduced.

The invention discloses a protein ligand binding affinity prediction method based on two-channel hierarchical interactive learning, and particularly relates to an affinity prediction method based on two-channel hierarchical interactive learning. The method comprises the following steps: S1, based on a protein-ligand compound space structure containing a three-dimensional structure and chemical data, constructing six chemical entity interaction diagram networks from an atomic scale to a substructure scale; s2, extracting covalent and non-covalent interaction information of protein-ligands from the internal and external channels at the same time by taking a dual-channel coding framework as a backbone; and S3, adopting a hierarchical interactive learning strategy. According to the affinity prediction method based on the two-channel hierarchical interactive learning, provided by the invention, understanding of protein-ligand complex interaction is enhanced through a technology of modeling richness and complexity of protein-ligand interaction information, so that the protein-ligand binding affinity is comprehensively and accurately predicted.

The present application relates to a kind of single-station hourly temperature prediction methods based on data fusion and mixed convolution, belong to meteorological prediction field.The present application selects important single-station meteorological observation element, obtains single-station historical observation sequence;Extract multiple-element prediction data, obtain numerical prediction space-time sequence;Data normalization;A three-dimensional convolution module is constructed, input after numerical prediction space-time sequence of normalization, finally produce time series containing spatial features;Build interactive learning model ICM, to single-station historical observation sequence and the time series extracted by three-dimensional convolution processing, generate the time characteristic information of two kinds of data;Build MCNN network model, fuse the two branch time characteristic information obtained in S3, after again interactive learning and fully connected, form prediction result;Temperature prediction is carried out using the model.The time series prediction model built by the present application improves stability and prediction accuracy, and the method is suitable for single-station 72-120 hours of hourly temperature prediction.

The invention discloses a language interactive learning system and method. The system comprises a microphone unit, a loudspeaker unit, a display unit, a storage unit and a control unit. The storage unit stores a language database, a plurality of learning screens defining a question region, and a plurality of predetermined answers. The control unit is provided with a voice recognition module, an auxiliary module and a voice synthesis module, the voice synthesis module outputs a signal corresponding to the questioning area to the loudspeaking unit, and the voice recognition module converts input of the microphone unit into answer text information; and the auxiliary module judges whether the answer text information accords with the preset answer corresponding to the questioning area, if not, the language database is read according to the answer text information, and after a guide conversation is generated, the interactive voice is sent out through the loudspeaker unit, so that the situation that a user possibly has a high-low score due to excessive attention can be avoided, and the user experience is improved. And the learning quality is influenced, so that the user is prevented from backstepping.

The invention discloses a lightweight visual decision interactive learning method and system based on knowledge migration, and aims to realize efficient migration and reuse of pre-trained visual knowledge by constructing association constraints of a feature distance and a decision value, integrating domain and task knowledge and designing a lightweight interactive mechanism. The problems that in the prior art, pre-training features are directly reused, and a traditional feature distillation method is disjointed in feature and decision-making requirements, weak in cross-task generalization, low in dynamic environment efficiency and the like are solved. A pre-trained visual representation model is used as a knowledge source, a downstream feature coding module is used for learning feature representation of an adaptive decision task, a distance regularization constraint mechanism is designed to bind a feature distance with a decision value, and finally, a stable and high-generalization decision model is output through a training strategy fusing constraint loss and distillation loss. According to the method, an interactive learning mechanism is constructed through a low-dimensional interactive interface, so that the decision model can realize a lightweight decision training target based on an external dynamic instruction optimization strategy.

The invention relates to the technical field of robots, and provides a mobile robot active obstacle crossing method and system based on environment interactive learning, and the method comprises the steps: carrying out environment interactive perception, and obtaining multi-modal parameters; defining an interaction action space comprising a plurality of different types of interaction actions; acquiring obstacle information gain, action time consumption and maximum contact force brought by the interaction action, and constructing an active interaction strategy model according to the obstacle information gain, the action time consumption and the maximum contact force; based on multi-modal parameters and an active interaction strategy model, the optimal interaction mode is dynamically selected with information gain maximization as a target, and active obstacle crossing of the mobile robot is achieved. The active interaction strategy model is constructed according to the obstacle information gain, the action time consumption and the maximum contact force, and finally the optimal interaction mode is dynamically selected by taking information gain maximization as a target, so that active obstacle crossing of the mobile robot is realized, and the working efficiency is improved.

The invention discloses an interactive learning method for an educational robot, which aims at overcoming the defects of an existing educational robot in the aspect of constructing comprehensive and interactive learning experience, adopts a multi-modal interaction technology, combines technologies such as voice recognition, text analysis and computer vision, supports various input modes such as voice, text, handwriting and gestures, and improves the learning experience of the educational robot. And the evaluation result is optimized through expression and concentration detection, so that the accuracy and comprehensiveness of evaluation are improved. According to the method, each dimension of answering the question by the user can be evaluated in real time, the question content of the next round is dynamically adjusted according to the answering condition of the user, the learning difficulty is ensured to be matched with the cognitive level of the user, the interactive learning effect is improved, and compared with a traditional education robot adopting a fixed question and scoring standard mode, the learning efficiency is improved. According to the invention, self-adaptive adjustment can be realized, so that the learning process is more targeted and interactive.

The application discloses a face forgery detection method and system based on a dual mode, equipment and a medium. The method comprises the following steps: inputting a face image into a first shallow convolutional neural network to extract RGB information and obtain an RGB feature; inputting a high-frequency image into a second shallow convolutional neural network to extract high-frequency information and obtain a high-frequency feature; wherein a first feature output by a first convolutional block in a current layer of the first shallow convolutional neural network and a second feature output by a second convolutional block in a corresponding layer of the second shallow convolutional neural network are interactively learned through a preset information interaction module to obtain a third feature, and the third feature is added to the first feature to serve as an input of a first convolutional block in a next layer of the first shallow convolutional neural network; and the RGB feature and the high-frequency feature are combined and input into a classifier to obtain the authenticity of an RGB face image. Through the two modes and the interactive learning of the two modes, the accuracy and the generalization performance of face forgery detection are improved.

We provide the system. [Solution] An artificial intelligence means for collecting user information and generating an individualized learning plan based on said information, A user interface means that provides the user with an interactive learning experience based on the aforementioned learning plan, A business plan development support tool that analyzes users' business ideas and provides feedback on improvement suggestions, A means of forming a network for collaborating with other users on projects, Resource management tools that provide financial support and mentoring for projects, A system that includes this.

The application relates to a rotor airfoil optimization method and system based on deep reinforcement learning, and belongs to the field of airfoil design. Through a large number of interactive learning between an intelligent agent and an environment, an optimization strategy is obtained, a new airfoil with better performance can be obtained by dynamically optimizing a target airfoil, and the optimization process corresponding to the optimization strategy has physical interpretability; an agent model for predicting a rotor airfoil aerodynamic force hysteresis loop is established by using a deep neural network, and the agent model has better nonlinear prediction capability.

The present disclosure belongs to the technical field of live-line work robots of power distribution networks, and provides a multi-parameter collaborative optimization method for an electromechanical coupling system based on a GRPO reinforcement learning algorithm, which comprises the following steps: acquiring multi-parameter operation data of the electromechanical coupling system during operation; pre-processing the acquired operation data; constructing a state vector based on the pre-processed operation data; constructing a GRPO reinforcement learning collaborative optimization model based on the GRPO reinforcement learning algorithm; inputting the state vector into the GRPO reinforcement learning collaborative optimization model; and the GRPO reinforcement learning collaborative optimization model obtains an optimization strategy through interactive learning with the electromechanical coupling system. The framework based on the reinforcement learning of the present disclosure enables the optimization strategy to adapt to changes in system operation conditions, environmental disturbances and parameter drift caused by component aging in real time, and maintains the robustness of the optimization effect.

1. Name of the product in this design: Learning Machine (Pipi). 2. Purpose of this design: To accompany children in interactive learning. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.