834 results about "Learning based" patented technology

A system answers natural language questions using retrieval-augmented generation. The system stores a set of domain specific documents in a vector database. The system receives a natural language question. The system retrieves a subset of documents relevant to the natural language question from the vector database. The system determines prior knowledge information required in addition to the subset of documents retrieved from the vector database for answering the natural language question. The system generates a prompt for a machine learning based language model including instructions to the machine learning based language model to refrain from using prior knowledge obtained by the machine learning based language model during training of the machine learning based language model. The receives a response generated by executing the machine learning based language model based on the prompt. The system performs an action based on the response.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention discloses a medical data structured extraction method based on machine learning, and the method comprises the following steps: carrying out the standardization processing of multi-source heterogeneous data in different medical scenes, constructing a time and condition two-dimensional filtering rule, and extracting preliminary data; and a modular index structure is formed according to medical process and technical attribute division. And generating analysis limiting conditions by fusing the medical knowledge graph and the knowledge base, guiding an analysis engine to perform semantic routing and reasoning, and outputting a structured result. Finally, disease identification and quality judgment are achieved, and structured information meeting or not meeting the standard is output. The method aims at efficiently extracting the structured information from various types of medical documents.

Some embodiments of a method may include: decoding a motion feature by accessing a motion bitstream; predicting a predicted feature based on the motion feature and one or more reference point cloud frames; decoding a first feature representing an occupancy status of a child level voxel; predicting a second feature based on the first feature and the predicted feature; and decoding a tree voxel occupancy status of the child level voxel via the second feature.

The invention relates to an Internet of Vehicles edge computing multi-target unloading method and system fusing dynamic environment modeling and improved SARSA, and belongs to the field of intelligent traffic and edge computing fusion. The method and the system comprise MEC environment perception and multi-dimensional state construction, dynamic reward feedback oriented to multi-dimensional performance indexes, intelligent decision model construction and learning based on improved SARSA, and antagonism training oriented to real disturbance. A high-fidelity environment model is constructed through a space-time attention mechanism, an SARSA algorithm is improved to realize hierarchical qualification trace attenuation and collaborative Q table updating, a multi-target hierarchical reward engine is combined to implement differential optimization on an emergency task and a conventional task, and an adversarial training mechanism is introduced to improve robustness. The core problems of high mobility, task diversity, resource limitation and the like in the Internet of Vehicles are effectively solved, the comprehensive performance is optimal in multiple dimensions of delay, energy consumption, resource utilization rate and the like, and the actual landing of the edge computing technology of the Internet of Vehicles is promoted.

A system receives a natural language request for configuring a computing infrastructure using a cloud platform. The system executes a machine learning based language model to generate infrastructure-as-code (IaC) to configure a cloud platform to obtain the desired computing infrastructure. The system may display the IaC generated by the machine learning based language model via a user interface as an example for use by the user. The system may send instructions to the cloud platform to provision computing infrastructure in accordance with the IaC obtained from the machine learning based language model. The system may repeatedly determine whether the desired computing infrastructure is deployed on the cloud platform and if the computing infrastructure currently provisioned on the cloud platform fails to match the desired computing infrastructure according to the natural language request, the system reconfigures the computing infrastructure deployed on the cloud platform.

The invention provides a personalized learning path planning system and method, and belongs to the technical field of emerging software and emerging technical services, and the system comprises a data acquisition module which is used for collecting learning behavior data of a terminal user, obtaining a learner portrait package based on the learning behavior data, and sending the learner portrait package to a server; the path planning module is used for carrying out node matching on the learning ability feature vector and a pre-constructed knowledge and skill map, outputting a to-be-learned content node set, dividing learning advanced levels and generating learning path description containing to-be-learned nodes and the advanced levels, and the path generation module is used for generating a primary path planning scheme, the scheme feasibility verification module is used for carrying out scheme feasibility verification in combination with the learning advanced level and the path adaptation parameters, and then outputting a final executable path scheme, and the scheme execution module is used for executing the final executable path scheme and controlling learning content pushing and progress adjustment. The problems that in the prior art, personalized learning path planning is not high in precision, not high in adaptability, lack of dynamic optimization and the like are solved.

Techniques for applying a generative artificial intelligence (AI) model to identify and correct anomalies in remediation records are disclosed. A system trains and applies a generative AI model to displayed datasets to predict remediation record anomalies. If the system detects the generation of a remediation record in a dataset to reconcile the displayed datasets, the system generates a generative AI prompt that includes the remediation record. The generative AI model generates an output that identifies anomalies in the remediation record and the datasets being reconciled. The generative AI model further generates recommendations for remediating errors in the remediation record.

The invention discloses an enterprise and policy service automatic matching method based on machine learning, and the method comprises the steps: carrying out the processing of enterprise multi-source data, constructing an enterprise portrait graph, and generating an enterprise graph embedding and structuring feature set; performing policy text analysis and condition recognition, constructing a policy condition graph and generating condition graph embedding representation; constructing a condition constraint field based on policy conditions, and generating a cross-graph alignment relationship and fusion features; integrating a multi-source feature input improved model to carry out joint modeling, and outputting a basic matching score; constructing a condition boundary manifold, and generating an anti-fact feature sample and a matching elasticity score; and based on the basic and elastic scores, generating a comprehensive score and outputting matching result information. According to the invention, by introducing graph structure perception alignment, conditional constraint guide interaction and a multi-channel scoring aggregation mechanism, high-precision, high-interpretability and intelligent reachability automatic matching between enterprises and policy services is realized.

A multimodal content management system having a block-based data structure can include an artificial intelligence (AI)-based embeddings generator and indexer. After receiving an item update instruction that includes an object (e.g., a block content, a block property, or a block schema) identifier and an update payload, the system can transform the update payload—for example, by generating a chunk to capture at least a portion of the update payload. The chunk can correspond to a particular content modality included in the update payload. The system can generate and retrievably store a vector comprising a set of embeddings corresponding to the chunk, where the embeddings represent a vectorized portion of block content, block property, or block schema.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The invention relates to the technical field of numerically-controlled machine tool diagnosis, and discloses a numerically-controlled machine tool fault diagnosis system based on machine learning. The system comprises a multi-source sensing data acquisition module for acquiring multi-dimensional sensing data such as vibration spectrum, spindle current waveform, temperature distribution, servo motor encoder feedback and the like; the operation feature coding module receives the multi-dimensional sensing data, extracts time domain statistical features and frequency domain energy distribution features, and generates a multi-source feature coding result; the incremental learning analysis module dynamically updates the feature weight through an incremental learning algorithm, and constructs an incremental training data set; the genetic optimization module optimizes the network structure and hyper-parameter configuration of the fault diagnosis model according to the incremental training data set, and generates optimized network structure parameters; and the integrated diagnosis decision module receives the current operation state data and the optimized network structure parameters, fuses diagnosis results of a plurality of base classifiers through an integrated learning algorithm, and outputs fault type classification signals.

The invention discloses a learning-based composite layered anti-interference control system and method suitable for a large-windward-area unmanned aerial vehicle, is used for realizing robust wind resistance control of the large-windward-area unmanned aerial vehicle, and aims at the flight characteristics of large windward area and sensitivity to external wind field change in a vertical take-off and landing stage. A wind speed estimation method without an additional sensor is provided, modeling of external disturbing force is realized in combination with a Gaussian process regression algorithm, and compensation of the disturbing force is realized by using quaternion-based model prediction control, so that the influence of an external change wind field on the dynamics of the large-windward-area unmanned aerial vehicle is reduced, and the trajectory tracking precision is remarkably improved. A backstepping controller based on SO (3) is designed in an attitude control loop, a nonlinear disturbance observer is integrated, and active compensation of external disturbance torque and robust tracking control of attitude are realized. Based on the technical characteristics, a complete dynamic control link capable of estimating and feeding back compensation disturbance in real time is further constructed.

The invention discloses a mining area ecological restoration scheme intelligent decision-making system based on machine learning, and relates to the technical field of mining area ecological restoration, and the system comprises a multi-source data collection module, a preprocessing module, an ecological damage diagnosis module, a scheme generation and optimization module, and a model iteration optimization module. Multi-dimensional data information of a mining area is obtained through the multi-source data acquisition module, multi-source data are fused through a space-time alignment algorithm, a structured data set is formed, a basis is provided for subsequent analysis, specific ecological problem types of the mining area are recognized through a GBDT model in the ecological damage diagnosis module, ecological damage indexes of all areas are calculated, and the mining area ecological damage diagnosis method is applied to the mining area. According to the method, damage grades are divided, the accuracy of ecological damage diagnosis is improved, and based on an ecological damage assessment report, a high-matching-degree scheme is preliminarily screened in combination with historical case data, and an optimal scheme is finally screened out, so that the pertinence and effectiveness of the scheme are ensured.

The invention discloses a digital twinning multi-agent reinforcement learning intelligent decision-making system and method with a secure memory playback mechanism, and the system comprises a digital twinning module which is used for constructing a virtual model and synchronizing the virtual model with a physical entity in real time; the multi-agent reinforcement learning module is used for carrying out strategy learning based on a constrained Markov decision process and balancing performance and safety through a Lagrange multiplier; the safe memory playback module is used for weighting and playing back the experience samples according to the risk and the timeliness so as to improve the learning safety; the reversible grey influence network module is used for causal modeling and reasoning and enhancing decision interpretability; the double-loop self-constraint control module ensures that a control action is always in a physical safety boundary through a barrier function and safety projection; and the convergence and stability criterion module is used for verifying strategy security convergence and system asymptotic stability. According to the method, the problems of strategy border crossing, virtual-real mismatching and the like in the high-risk manufacturing process are solved, and multi-target optimal control under the safety constraint is realized.

An object of a system according to an embodiment is to provide optimal learning support according to individual learning needs of learners.SOLUTION: A system according to an embodiment includes a learning history analysis unit, a customized content generation unit, a comprehension degree monitoring unit, and an advice providing unit. A learning history analysis part analyzes the learning history and interest of the learner. The customized content generation unit generates learning content on the basis of the result analyzed by the learning history analysis unit. The comprehension degree monitoring unit provides the learning content generated by the customized content generation unit and monitors the comprehension degree and progress of the learner. The advice providing unit provides advice based on a result of monitoring by the comprehension degree monitoring unit.SELECTED DRAWING: Figure 1

The invention relates to the technical field of data processing, in particular to a relational database intelligent query method based on machine learning, which comprises the following steps of: processing multi-modal flow data through time sequence alignment, generating a unified semantic representation vector, constructing a dynamic psychological state map, and modeling a psychological state evolution track by utilizing a neural common differential equation mechanism. After user query is received, historical dialogue nodes are retrieved from the graph, enhanced query intention representation is generated, the enhanced query intention representation is converted into an execution plan through a neural symbol inference engine, and a graph neural network is adopted to predict execution cost. And finally, a personalized analysis report is generated by combining a causal discovery algorithm, and system adaptive optimization is realized through feedback signals. According to the method, the problems of inconsistent time sequence semantics and strong context dependency of the multi-modal psychological data are effectively solved, and the query accuracy and the personalized level in a psychological dialogue scene are improved.

The invention relates to an instruction-level man-machine collaborative driving method and device, and the method comprises the steps: designing an uncertainty quantitative model for the problem of hybrid uncertainty in a man-machine fusion process, and proposing a human instruction optimal approximation strategy based on safety constraints, and guaranteeing that when man-machine decisions are inconsistent, the human instruction optimal approximation strategy is not consistent with the human instruction optimal approximation strategy. Safety and human intention are considered; a human intention prediction model is constructed based on a learning and interaction model, so that the efficiency and effectiveness of man-machine fusion intelligent decision making are improved. Therefore, the problems that control right switching of a traditional cooperation mode lacks smooth transition, and the movement freedom degree of a driver is limited are solved.

The invention discloses a teaching evaluation method and system based on artificial intelligence, and relates to the technical field of teaching evaluation, and the method comprises the steps: constructing a student learning analysis time series data set based on an LMS learning management system integration platform; constructing a dynamic student learning behavior evaluation model by using an unsupervised learning algorithm, and generating a student personalized learning portrait; based on the personalized learning portrait of the student, combining the historical score data of the student and the learning progress of the student, utilizing a deep neural network optimization model to realize dynamic real-time prediction and self-adaptive adjustment of the learning state of the student, and generating a continuously updated student learning state evaluation map; and based on the continuously updated student learning state evaluation map, analyzing the relationship between the student learning progress and the teacher teaching effect, dynamically adjusting the student learning strategy and the teaching plan, and generating an artificial intelligence teaching evaluation scheme. The method has the beneficial effects that personalized and scientific teaching evaluation and optimization are realized, and the teaching effect and the learning achievement of students are improved.

Apparatuses, systems, and techniques to losslessly compress neural networks via semi-structured sparsity. In at least one embodiment, a weighted average of candidate masks for semi-structured sparsity is learned for each parameter block of a neural network, and a composite mask is determined by selecting candidate masks based on the learned weighted averages. In at least one embodiment, computational resources required for inference are reduced, thereby contributing to more sustainable and environmentally friendly AI applications.

The invention relates to a user behavior analysis method based on machine learning. The method comprises the following steps: acquiring original behavior data generated by a user on an interactive interface, and generating a fine-grained behavior event stream; aggregating the behavior event streams to generate a standardized user behavior sequence; segmenting the behavior sequence through a sliding time window to obtain a plurality of behavior subsequences; extracting a time sequence feature vector of each behavior sub-sequence; inputting the time sequence feature vector into a pre-trained hesitation recognition model, and predicting a hesitation probability value; when the hesitation probability value is greater than or equal to a preset threshold value, judging that the user is in a hesitation state; and according to the current service scene of the user, matching a target intervention strategy from the intervention strategy library, and guiding the user to complete a target conversion behavior. By adopting the method, the hesitant behavior of the user can be accurately identified, effective intervention is provided, and the user experience and the service conversion rate are improved.

The invention provides a language learning dynamic resource configuration and interaction system based on an internet platform, relates to the technical field of data processing systems, and provides a double-layer cascade diagnosis mechanism. Through a first calculation module, a state anomaly score is calculated based on the stability of user interaction behaviors instead of simple correctness, so that beneficial struggling and harmful fatigue are accurately distinguished; when the score exceeds a threshold value, a second calculation module is activated, a specific learning fragment is analyzed in combination with eye movement trajectory data, and a cognitive deviation value for quantifying specific cognitive impairment is calculated; on one hand, through accurate state recognition, wrong intervention during deep thinking of the user is avoided, and the learning heart stream is effectively protected; and on the other hand, through accurate cognitive attribution, the system can provide targeted accurate assistance, the tutoring efficiency and the learning effect are improved, and intelligent teaching upgrading is realized.

The invention discloses an educational resource recommendation method and system based on artificial intelligence. The method comprises the following steps: acquiring audio data, interaction data and task data acquired by a user terminal; performing feature extraction on the audio data, the interaction data and the task data to obtain an emotion feature vector, a learning rhythm vector and a content feature vector; inputting the emotion feature vector and the learning rhythm vector into a pre-constructed emotion recognition model to obtain a psychological state vector; the cognitive load is calculated based on the learning rhythm vector and the content feature vector, and then the cognitive load is corrected through the psychological state vector; matching a state interval of the corrected cognitive load according to a preset threshold interval; and according to the state interval, adjusting a difficulty coefficient of the recommended course, rearranging a course content sequence and an auxiliary learning prompt, generating structured data, and outputting the structured data as an intelligent auxiliary learning recommendation result. According to the invention, online learning interactivity and teaching quality in rural and remote areas are effectively improved.

The invention discloses an adaptive education textbook generation method and device based on a large model, and a medium, and relates to the technical field of intelligent education, and the method comprises the steps: determining a knowledge point set which needs to be expanded currently by a learner through a knowledge graph retrieval and course outline matching algorithm based on a learner portrait data set, and generating a teaching task list; according to the individualized evidence Prompt script, generating individualized teaching content by using a controlled decoding mode of a large language model, performing content consistency verification, and rearranging the content into a customized teaching material; carrying out conflict discovery-retrieval supplement-local regeneration-format recombination on the customized textbook through a ReAct reasoning framework to obtain a high-adaptability education textbook; the teaching material can be continuously corrected and optimized in the use process, so that the generation of the high-adaptability personalized education teaching material which is correct and smooth in teaching, fit and sustainable in evolution is realized.

In one implementation, geometry of a point cloud is encoded / decoded. On the encoder side, the encoder determines a first feature representing a voxel occupancy status of a current level and / or one or more finer levels of the point cloud, based on the voxel occupancy status of the current level and / or the finer levels; determines a second feature representing prediction of the voxel occupancy status of the current level and / or the finer levels of the point cloud; determines a third feature associated with the voxel occupancy status of the current level and / or the finer levels, based on the first feature and the second feature; and encodes the third feature. On the decoder side, the first feature is decoded from a bitstream, the second feature is determined similarly as the encoder side, and the third feature is determined based on the first feature and the second feature.

An online system performs conversations with users of an organization in relation to the organization. The online system presents a chat interface that allows users to ask natural language questions related to the organization or to other users of the organization. The online system generates prompts and sends to a machine learning based language model to get a response. The online system monitors the conversations to generate critical analysis of the conversation, for example, by analyzing the pacing of the conversation, the types of personalities of the participants of the conversation. The system modifies prompts generated for responding to one or more subsequent natural language requests received from the user to cause the machine learning based language model to generate responses that cause the one or more attributes to change

The invention relates to the field of electric digital data processing, in particular to a casting process parameter optimization method and system based on machine learning, and the method comprises the steps: sampling in each preset range of process parameters and combining to generate different parameter groups, forming a candidate pool by the parameter groups, and selecting the parameter groups to carry out a simulation experiment, a training data set composed of the parameter set and the gas entrapment defect rate is obtained; training a Gaussian regression model based on the training data set; outputting a predicted gas entrapment defect rate and a predicted variance corresponding to the parameter group in the new candidate pool according to a Gaussian regression model; and constructing a weighted acquisition function, selecting a parameter group corresponding to a minimum weighted acquisition function value to carry out a simulation experiment, obtaining new training data, training a Gaussian regression model based on the new training data set until the minimum predicted gas entrapment defect rate does not change any more, and obtaining an optimal parameter group. According to the method, more attention can be paid to parameters which have great influence on the gas entrapment defect rate, fine search is carried out, and the optimization precision and efficiency are improved.

A heterogeneous multi-hop wireless network is provided. The network consists of single-link data nodes, multi-link data nodes and data centers. The single-link data node supports one communication interface, the multi-link data node supports two communication interfaces and the data center is considered as multi-link node. The routes from all data nodes to data centers are given. The network is configured to schedule data transmissions from all data nodes to data centers without transmission interference and channel access delay. The routing link scheduling problem is formulated as an optimization problem with constraints. Due to the NP-Hard complexity of the formulated optimization problem, the scheduling policies are then parameterized for the application of graph neural network techniques. The parameterized optimization problem is solved using primal-dual approach with zero duality gap. A heterogeneous graph neural network (HetGNN) algorithm is provided to train the primal-dual problems.

Machine learning based systems and methods are disclosed herein for automatically classifying digital calendar events. A calendar tracking application (app) receives a first set of digital calendar data of a first period. A machine learning model may generate classification data comprising the first set of digital calendar data classified according to predefined meeting categorie(s). The machine learning based system and methods provide various benefits, including: (1) categorizing one or more user digital calendars into multiple pre-defined categories and reporting these in a GUI based dashboard; (2) using a machine learning based model trained on categorized data (as opposed to mechanistic rules only) to improve the predictive accuracy of the categorization; and (3) using the combination of machine learning based model and mechanistic rules to automate the categorization.