45 results about "Learning problem" patented technology

The invention belongs to the technical field of intelligent education, and discloses a multi-modal artificial intelligence learning assistant based on an embedded platform, and the method comprises the steps: a data collection and processing module receives learning problems and student learning data, and carries out the standardization processing, and obtains a problem standardized data set; the cognitive model construction module constructs a learner cognitive model based on the problem standardized data set and student learning data, and marks knowledge gap nodes; the teaching scheme generation module determines a self-adaptive teaching scheme including knowledge depth parameters, a learning material combination strategy and an explanation expression strategy according to the structural features of the cognitive model; the learning sequence construction module constructs a customized learning sequence according to the knowledge gap nodes and the knowledge depth parameters; and the learning content generation module generates personalized learning content according to the learning sequence and the teaching scheme. Through the self-adaptive processing flow driven by the cognitive model, personalized teaching for different students is realized, and the learning efficiency and the learning effect are remarkably improved.

The invention discloses a large-model sensitive large-content filtering method in a learning scene, and the method comprises the steps: extracting an image-side visual coding feature and a text-side semantic coding feature of a learning problem from the learning problem inputted by a user through employing a neural network model based on deep learning; and multi-modal feature fusion is further carried out on the characters and the pictures to form more comprehensive and accurate question joint representation, and the joint representation can fully mine the deep relation between the characters and the pictures, so that sensitive or improper contents in complex input are effectively identified, and automatic and intelligent filtering of the compound learning questions is realized. The problems that a traditional single-mode filtering scheme is narrow in coverage, low in detection accuracy, low in manual auditing efficiency and the like are solved, the recognition capacity of a platform for sensitive content is improved, and then a firm and reliable safety guarantee is provided for an intelligent education platform.

The invention relates to the technical field of big data, in particular to an intelligent teaching evaluation method, system and device based on data analysis and a medium. According to the method, the initial evaluation index is obtained by responding to the evaluation trigger instruction, and the standardization of the evaluation process is ensured; secondly, historical learning data of students are collected, knowledge point mastering coefficients are calculated, and quantitative analysis of individual learning conditions is achieved; calculating the overall grasp degree of the class, setting an early warning threshold value, and timely discovering a universal problem in learning; further identifying weak knowledge points and analyzing pre-association and subsequent influence of the weak knowledge points, and deeply understanding the root of learning disorder and possibly caused chain reaction; and finally, dynamically updating an evaluation index according to an analysis result, generating a teaching suggestion report, and providing a targeted teaching optimization scheme for teachers. The learning problem can be found and intervened as soon as possible, more effective teaching suggestions can be provided according to the relevance between the knowledge points, and the scientificity and practicability of teaching evaluation are remarkably improved.

The invention discloses an engineering vehicle driver behavior analysis system based on federal learning. The system comprises a multi-source sensing layer which forms vehicle-road-person collaborative all-weather scenarized data input; the edge calculation layer is used for capturing a complex space relationship of a driving scene, embedding a lightweight cross-modal attention module, calculating an attention weight, and processing long-sequence driving operation data by adopting a Mamba state space model to capture a long-term dependency relationship of a driver state; the federated learning layer adopts a pFedMe algorithm to realize a driver customized model, during local training, the model learns local data distribution characteristics through a Moreau Envelopes regularization item, the cloud aggregates distribution of adaptive local data of each driver, models a federated learning process as a meta-learning problem, and performs training updating; a privacy and security layer; and differential privacy dynamic adjustment, Paillier improved homomorphic encryption and a Hyperledger Fabric block chain are adopted to prevent a log from being tampered.

The invention discloses a data stream processing method and system based on deep reinforcement learning, and relates to the technical field of industrial Internet of Things. Comprising the following steps: S1, constructing a topological graph neural network, determining a feature similarity weight of equipment topological features through a multi-head graph attention mechanism, and dynamically aligning the equipment topological features of a source domain and a target domain; s2, setting a main channel through a deep reinforcement learning model, setting an auxiliary channel through a time sequence contrast element learning model, and obtaining health degree evaluation and fault mode characteristics of the equipment; and S3, monitoring a data flow according to the health degree evaluation and the fault mode characteristics, and recombining a network structure through a neural architecture search model. According to the method, the transfer learning problem caused by equipment isomerism in the industrial Internet of Things is solved, the physical distance, the process coupling and the feature similarity can be dynamically balanced, the initial accuracy of the source domain model in the target domain is improved, and the annotation data volume required by the target domain is reduced.

An analogy of Post-Selections is: “Somebody claims that his scheme provided a lottery ticket number that has won $1M, but he conceals that the scheme has spent 2 millions of lottery tickets of $1 each. The reported ticket is only the luckiest. The luckiest ticket was Post-Selected after the actual lottery test. The luckiest lottery ticket will not have the same luck next time.” Many machine learning methods suffer from Post-Selections, from neural networks, to reservoir computation, to swam intelligence to evolutionary computation. The numbers $1M, 2M and $1 and the chance to win in the analogy differ across different machine learning problems, but the nature of the flaw in the reports is basically the same. This invention presents a method that does not need any Post-Selections since it trains only one network that is computed in a closed form that corresponds to the most-probable network from training experience.

The invention discloses an emergency protection place multi-target site selection optimization method based on integrated reinforcement learning, and belongs to the field of place site selection optimization, and the method comprises the steps: S1, obtaining candidate point and demand point data in a region, and constructing a multi-target reinforcement learning problem; s2, Markov modeling is carried out, and a deep reinforcement learning problem is decided based on a single agent; s3, constructing an integrated reinforcement learning framework; s4, training the constructed integrated reinforcement learning network; s5, obtaining a site selection result by using an integrated reinforcement learning framework, and performing visual presentation; according to the method, an integrated learning strategy and a self-adaptive optimization technology are introduced, and a spatial information technology with remote sensing and geographic information system cores is combined, so that multi-target site selection optimization of the urban emergency protection site is realized, and the robustness and practicability of the model are enhanced through aggregation of the Actor-Critic network.

The invention provides a data analysis method for personalized diagnosis and intervention of student learning questions, and the method comprises the following steps: S1, collecting an answer event sequence of a target student in a target question, the answer event sequence comprising an operation type and a corresponding timestamp; and S2, performing process segmentation on the answer event sequence based on a time interval of adjacent answer events, a submission event, a jump event and a rollback event. According to the method, process segmentation, step alignment and abnormal evidence extraction are carried out on the answering event sequence in the answering process of the student, and the learning abnormity of the student is specifically positioned to the corresponding standard question solving step and the trigger evidence thereof, so that a structured learning question positioning result is formed; therefore, a learning analysis result is converted from a single result index into traceable and explainable process diagnosis information, coarse-grained judgment only depending on an answer result or an abnormal score is avoided, and usability and pertinence of learning problem analysis are improved.

The invention discloses a continuous learning algorithm based on multi-task learning, which is used for solving the problems of real-time target detection and continuous learning of an unmanned system in a complex dynamic environment. The method comprises the steps that S1, an intelligent unmanned vehicle carries a high-precision sensor to collect multi-dimensional environment data, and importance samples are screened through a maximum gradient retrieval algorithm; s2, performing preliminary training on the pre-training model by using the screening data to enable the pre-training model to have basic target detection and recognition capability; s3, building a sea area image data enhancement continuous learning framework, and enhancing the image feature learning ability of the model under different weather conditions through an image compression reconstruction model, a cross attention module and an alternate training mode; s4, developing a stability and plasticity balancing strategy based on multi-task learning, relieving disastrous forgetting by solving a dual-objective optimization problem, and introducing a novel objective selection strategy to enhance the core set selection efficiency; s5, adding a regularization technology based on an influence function, and optimizing the performance of the model in the current environment; s6, in combination with a fine tuning technology, a general data pre-training model is firstly used, then the model is fine-tuned by using environment specific data, and the detection precision in a specific environment is improved; and S7, carrying out online learning and model evaluation optimization, continuously collecting new data to update the model, establishing a real-time evaluation and feedback mechanism, and continuously optimizing a target detection algorithm. According to the invention, powerful real-time target detection and continuous learning capabilities are provided for the unmanned system in a complex and changeable actual scene.

Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.

This invention discloses a face recognition method based on semi-supervised dual-source face feature fusion, addressing the semi-supervised, multi-view, and cost-sensitive learning problems existing in real-world face recognition applications. The method includes: acquiring multiple face image data under preset dual views; inputting the face image data into a trained semi-supervised cost-sensitive canonical correlation analysis model; performing dual-source feature fusion using the cost-sensitive feature extraction matrix in the semi-supervised cost-sensitive canonical correlation analysis model to obtain the feature representation of the face image data; and classifying the face image data using a classification model based on the feature representation to obtain the face recognition result. This invention can obtain accurate and reliable face recognition results using only a small number of supervised dual-source face images, effectively improving the classification performance of the face recognition model.

The application provides a password-based authentication key agreement method based on a lattice, which allows a user to use a password to agree with a server on a session key and authenticate the user identity. The method is constructed based on an ideal lattice, and its security is established on a ring-based learning problem with errors, so that the method can effectively resist quantum attacks. The user's credentials are saved in the form of password-encrypted in the server end, and only the user with the correct password can decrypt the legal credentials and establish the subsequent session key. When the session key is established, the user and the server perform an authenticated key exchange with a key hiding attribute to prevent the user's credential information from being leaked. Even if an enemy obtains the user's password-encrypted credential ciphertext and exhaustively searches the password space to decrypt the ciphertext to obtain a possible user credential set, the correct password corresponding to the credential cannot be identified from the set, so the application can resist offline dictionary attacks. In addition, two-way key confirmation can ensure the consistency of the session key and verify the user identity.

This invention discloses a data stream processing method and system based on deep reinforcement learning, relating to the field of industrial Internet of Things (IIoT) technology. It includes: S1: Constructing a topological graph neural network and determining the feature similarity weights of device topological features through a multi-head graph attention mechanism, dynamically aligning the device topological features of the source and target domains; S2: Setting up a main channel through a deep reinforcement learning model and an auxiliary channel through a temporal contrastive meta-learning model to obtain device health assessment and fault mode features; S3: Monitoring the data stream based on the health assessment and fault mode features, and reorganizing the network structure through a neural architecture search model. This invention solves the transfer learning problem caused by device heterogeneity in the IIoT, dynamically balancing physical distance, process coupling, and feature similarity, improving the initial accuracy of the source domain model in the target domain, and reducing the amount of labeled data required in the target domain.

The invention discloses an open intelligent algorithm adaptive combination method and system based on meta learning and a dynamic pipeline. According to the method, by introducing an algorithm directed acyclic graph construction mechanism of a type system and an adapter, dynamic integration of heterogeneous components in an open algorithm library is realized, and the limitation that a traditional AutoML system algorithm combination space is closed and depends on a preset process is overcome. Environment perception and a performance monitoring closed loop during operation are integrated into a decision process, and a dynamic adjustment mechanism based on deviation triggering is designed, so that the system has the capability of coping with concept drift and resource fluctuation after deployment. By constructing and continuously updating the knowledge base recording the task-environment-performance mapping relation, the system has the ability of making decisions based on historical experience. According to the meta-learning mechanism, when the system processes similar tasks, repeated search overhead can be reduced, continuous evolution of performance is achieved, and a feasible technical path is provided for solving the continuous learning problem of a universal intelligent system.

We provide the system. [Solution] A means for acquiring the user's learning history information and selecting learning problems optimized for the user, A means for dynamically generating dialogue content based on the selected learning questions, A means for receiving user voice input, converting it into text data, and analyzing the text data, A means of providing feedback to users based on the analysis results, A means for detecting a user's mental stress and providing dialogue content for mental care as needed, A means of recording the user's learning results and updating the learning profile for the next session, A system that includes this.

The invention relates to the technical field of e-government affairs, in particular to an intelligent grassroots party building system which comprises a table body, and a convenient assembly is arranged at the top of the table body. According to the intelligent grassroots party building system, by installing a convenient assembly, when students submit own life or learning problems online, a stool at the bottom of a table body can be pulled out, then an adjusting block is pulled, so that a limiting column can be moved out of an adjusting hole, then the stool can be stretched, and the length of a telescopic rod can be adjusted; after the stool is adjusted to a proper height, the limiting columns can be inserted into the specified adjusting holes, and the height of the stool can be adjusted to be the height suitable for students, so that the sitting posture is more comfortable through the more proper height when an opinion is submitted or communication is carried out, the students can concentrate on more attention, and meanwhile, after the communication is finished, the students can conveniently and quickly sit on the stool. And the stool can be placed on the fixed sliding rod in the storage groove, so that the stool can be stored, and the occupied space is reduced.

The invention provides an intelligent tutoring system driven by an LLM-based collaborative intelligent agent. The intelligent agent innovatively utilizes a teaching strategy to perceive a discussion stage, discover learning problems, determine an intervention opportunity and generate instructive feedback. The invention provides a novel prompting strategy based on exploration of a community theory, so as to cultivate an intelligent agent to understand the overall discussion situation and timely discover problems encountered by each student. In order to discover and learn unknown influence of feedback of an AI tutor on subsequent collaborative learning activities of students, the invention provides a multi-agent framework, and collaborative learning behaviors between a tutor agent and a plurality of student agents are simulated by automatically executing collaborative learning tasks. Meanwhile, synthetic learning data is generated to further finely tune the LLM, and the influence of an AI tutor on the cooperation behavior of the students is learned, so that instructive feedback is generated in time under the condition that the cooperation of the students is not interfered. Finally, instructive feedback can be generated at a proper time to support collaborative learning of students.

The application discloses a stamping process intelligent decision-making method of man-machine cooperation, constructs a stamping forming prediction model based on a physical information neural network, embeds Hill48 yield criterion, Swift hardening model and friction law into a loss function in the form of a regular term, trains process sensitivity gradient by using small sample data and outputs the process sensitivity gradient, calculates information gain by using an expectation improvement or a confidence upper bound acquisition function and recommends a die trial point based on model cognitive uncertainty, triggers manual confirmation when uncertainty is higher than a threshold value, executes process parameters, collects a stamping force-displacement curve in an initial die trial stage, inverses sheet metal parameters by using an extended Kalman filter to correct model input, records results and feeds back and updates the model, solves a small sample learning problem by embedding physical constraints, reduces die trial times by active learning, realizes material self-adaptation by online inversion, and forms a closed-loop optimization of prediction-die trial-inversion-updating.

The application discloses an emergency protection site multi-target location optimization method based on integrated reinforcement learning, belongs to the field of site location optimization, and comprises the following steps: S1, acquiring candidate points and demand point data in a region, and constructing a multi-target reinforcement learning problem; S2, performing Markov modeling based on a single-agent decision deep reinforcement learning problem; S3, constructing an integrated reinforcement learning framework; S4, training the constructed integrated reinforcement learning network; and S5, obtaining a location result by using the integrated reinforcement learning framework and performing visual presentation; the application introduces an integrated learning strategy and an adaptive optimization technology, combines spatial information technology of a remote sensing and a geographic information system core, realizes multi-target location optimization of urban emergency protection sites, and enhances robustness and practicability of an aggregated reinforcement model of an Actor-Critic network.

The invention provides an intelligent information processing method for family-school cooperative cultivation. The method is applied to the technical field of data processing. The method comprises the steps of preprocessing multi-modal data; obtaining a student learning semantic vector according to the extracted single-mode characteristics of the preprocessed multi-mode data; a knowledge graph is constructed based on the student learning semantic vector, and a personalized ability vector is generated in combination with student historical learning data; calculating the matching similarity between the personalized capability vector and the educational resource vector, screening a plurality of educational resources with the highest matching degree, and pushing the educational resources to a corresponding terminal; predicting a learning problem of the student in a future preset period through an LSTM time sequence model, and generating an intervention strategy in combination with the knowledge graph; receiving demand information of home and school users, determining demand intentions and key entities, and generating targeted response information based on the personalized capability vector; and performing access control on the processed multi-modal data and the student learning semantic vector. In this way, the information processing efficiency can be improved.

According to the correction scheme recommendation method based on small sample learning, the meta-learning technology is introduced to solve the small sample learning problem of correction measure recommendation of prisoners serving prisoners, the incidence relation between prisoners and criminals can be more fully modeled, and correction measure recommendation strategies of small sample groups are more effectively mined; a meta-learning method is used, hierarchical information is used, and the capacity of obtaining parameters matched with tasks through internal circulation gradient descent of initial parameters is enhanced; by considering priori knowledge of prisons and criminals, modeling is carried out on the incidence relation on the initial parameter level, and the capacity and rationality of processing task space heterogeneity are enhanced; the globally shared internal loop learning rate is scaled, and a more reasonable internal loop learning rate is obtained according to the relative semantic information, so that the internal loop gradient descent can better obtain parameters adapted to the task; and more appropriate global parameters can be obtained according to the learning ability of each task support set when the global parameters are updated in the outer circulation.

This application relates to the field of hybrid solid-state drive (SSD) technology, and discloses a hybrid SSD space management method and apparatus based on multi-agent reinforcement learning. This method models the coupled garbage collection and flash mode switching operations in a hybrid SSD as a reinforcement learning problem decided by independent agents. A multi-agent reinforcement learning scheduler is added. By monitoring the shared system status in real time, such as the SLC space ratio and write frequency, two agents independently select the optimal action from an action space containing cross-region garbage collection and mode degradation options, and combine them into a joint command for execution. Finally, the scheduling strategy is dynamically adjusted based on a differentiated reward function that balances performance metrics and internal write overhead. This invention solves the problem that the two background operations, garbage collection and mode switching, are executed independently and compete for shared resources, easily interfering with each other, leading to decreased front-end I / O performance and a sharp increase in write latency.

The application discloses a cementing material production energy consumption evaluation method based on Internet of Things collection, comprising a meta-learning and transfer learning fusion framework, and is characterized in that the method further comprises the following steps: S1: the meta-learning and transfer learning fusion framework first divides source domain cementing material production data into batches and classification tasks as basic units of training cementing material production data, and the cementing material production data divided in the task unit mode is used as a basic input unit of meta-learning. Through the multi-task learning paradigm of meta-learning, the division of the support set and the query set is utilized to realize rapid adaptive learning of the model under the condition of a small amount of cementing material production data samples, solve the small sample learning problem, in addition, the knowledge transfer from the source domain cementing material production data to the target domain data is realized through the transfer learning, the cross-domain learning problem is solved, and the adaptability of the model to different production environments is enhanced.

We provide the system. [Solution] A means for users to take pictures of and upload learning problems they answered incorrectly, An image analysis means that analyzes uploaded images and extracts text data, An analytical method for identifying a user's weak areas from extracted text data, A problem generation method that generates and provides similar problems based on the user's areas of weakness, A means of providing explanations that prepare and present to users explanations for similar problems that have been generated, A feedback system for recording learning progress and creating new learning plans, A system that includes this.

The utility model discloses an intelligent learning monitoring device, which comprises a host and a camera, the camera can support horizontal rotation and vertical rotation, the host is provided with a coding compression module, a network transmission module, a coding playing module, a storage module and a display screen, a base is arranged below the host, the base is connected with the host through a connecting mechanism, and the host is connected with the network transmission module. A charging panel is arranged on the base, a receiving module is arranged in the host, the camera can be checked by a monitoring person through the arrangement of a network transmission module, so that the learning condition of a learner is observed, and the learner is helped to solve the learning problem through video connection with the learner through a display screen; through the arrangement of the base, the contact area between the device and a placement plane can be increased, the stability is improved, meanwhile, through the arrangement of the charging panel, the device can be charged in real time during use, and therefore the situation that the device is charged by changing the position of the device is avoided.

This invention discloses a federated learning method and system based on unified representation and classifier correction, designed to address the federated learning problem under long-tailed data distributions. First, this invention obtains a globally unified prototype by aggregating local prototypes (i.e., the average features of categories extracted by the global model). These prototypes are then used to adjust the feature space, bringing features within the same category closer to the corresponding globally unified prototype while pushing away features from other categories. Furthermore, this invention reduces classifier bias through prototype mixing using the global prototype. It generates a balanced virtual feature set by fusing the globally unified prototype and local features. The classifier is then retrained on this feature set to correct the decision boundary and mitigate bias. This invention can effectively improve the classification performance of models under long-tailed data while reducing the bias in the feature space and the classifier. The superior performance of the method has been verified on multiple datasets.

The application discloses the technical field of coding strategy and relates to a multi-instance multi-label learning method based on a combined error correction coding strategy. The multi-instance multi-label learning method based on the combined error correction coding strategy comprises the following steps: a feature embedding representation method of multi-instance bag level data structure information is designed based on a Fisher kernel, and an original multi-instance multi-label learning problem is converted into a multi-label learning problem; under the guidance of an error correction output coding idea, a coding strategy of randomly connecting multi-label sub-problems is proposed; a support vector machine model is constructed and trained, and hard labels representing the specific category performance of a predicted sample under each label are obtained based on a T criterion. The multi-instance multi-label learning method based on the combined error correction coding strategy effectively alleviates the class imbalance problem. In addition, the model divides different training data blocks based on coding random division to learn base classifiers, so that the similarity between the base classifiers is weakened, and the classification coding error correction capability is stronger.

This invention relates to a method and apparatus for training a neural network model on noisy multi-label data. The method includes the following steps: selecting a clean set of samples for each category as a meta-dataset using a sample selection algorithm, and estimating the category-dependent label noise transition matrix; initializing some parameters in the instance feature-dependent label noise transition matrix network using the category-dependent label noise transition matrix; transforming the learning problem into a two-layer optimization problem based on statistically consistent label noise learning loss, and simultaneously learning the instance feature-dependent label noise transition matrix network parameters, data imbalance parameters, and multi-label classification neural network parameters using a meta-learning algorithm. This invention innovatively utilizes a meta-learning algorithm in a data-driven manner to unify the learning of instance feature-dependent label noise transition matrix network parameters, data imbalance parameters, and multi-label classification neural network parameters within a single framework.

The invention relates to the field of aerospace metrology testing, and particularly discloses a high dynamic response characteristic simulation method based on a causal constraint interpretable model, which can summarize a basic'causal relationship ': an initial condition is a place where a variable begins to evolve, and a boundary condition is activation of variable evolution in a trigger solution domain. Therefore, the time causal principle of the learning process is that the PINNs should start learning from the activation point, and the space causal principle is that the PINNs should start learning from the activation point, namely t = 0, # imgabs0 #. The adaptive sampling learning method starts from causal constraints, designs adaptive weight criteria for sampling points, realizes adaptive sampling learning on different scales, relieves the difficult learning problem caused by rigidity, and further, improves the learning efficiency. High-dynamic and strong-interruption phenomena exist in high-temperature and high-pressure gas release of a driving section, and a conventional neural network structure is difficult to accurately capture. The method mainly adopts a region segmentation and hard constraint implantation method to relieve the problem of difficulty in learning of high dynamic and large solution domains.

The application provides a multi-UGV path planning method and device based on multi-agent reinforcement learning. The method comprises steps 1 to 8. The application operates on the basis of existing resources, improves mutual cooperation and coordination between each agent in the multi-agent system, uses a distributed search structure, automatically decomposes a complex learning problem into a local sub-problem that is easier to learn, improves the intelligent degree, expands the application field, learns a decentralized strategy in a centralized setting, greatly reduces the calculation amount, and has high practicability.