4 results about "Controlled vocabulary" patented technology

The invention provides an English vocabulary cognition training method and system based on an interval repetition memory method, and relates to the technical field of language cognition training, abnormal value elimination and normalization preprocessing are performed on collected data, a memory intensity coefficient is calculated, a memory intensity level is judged, and a final review interval is generated in combination with a vocabulary basic interval and a special adjustment rule; each layer is matched with a differentiated multi-modal training task, and the vocabulary adding proportion, cognitive layer dynamic switching and fatigue relieving links in the training process are controlled. According to the method, multi-dimensional data such as learning duration, memory performance and vocabulary difficulty and dynamic review intervals are fused, compared with fixed period review, the one-week memory retention rate of vocabularies is increased to 85%, the two-week memory retention rate reaches 72%, the long-term memory effect is remarkably enhanced, and the problem that multi-dimensional data such as intervals, non-fused reaction time and cognitive states are adjusted only based on correct rate is solved. And personalized and efficient vocabulary cognition training requirements cannot be met.

The invention discloses a teacher teaching and research knowledge recommendation method and device based on deep learning and a medium, and relates to the technical field of information retrieval and recommendation, and the method comprises the steps: collecting English teaching and research resources, segmenting the English teaching and research resources according to paragraphs, mapping the paragraphs into themes, summarizing the themes into a theme set, extracting a pre-repair relationship between the themes through deep learning, and carrying out the pre-repair relationship between the themes; constructing a pre-constructed directed acyclic graph; based on the pre-modified directed acyclic graph and the theme set, performing controlled word list analysis on the English query text, determining a retrieval constraint, and performing closure expansion on the theme set in the retrieval constraint on the pre-modified directed acyclic graph to obtain a target theme set; and strictly filtering the English teaching and research resources according to the retrieval constraint to obtain a candidate literature set, and performing minimum set coverage selection on the candidate literature set based on the target topic set to obtain a literature set. The problems that a research and repair path organization depends on empirical weights and is difficult to recheck and directly apply are solved by combining topological order and difficulty smoothing with fixed role bit organization.

The invention discloses a large language model-based digital twin entity modeling method and related equipment, and the method comprises the steps: carrying out the knowledge extraction of preset text data, and obtaining a controlled word list, an ontology, a rule base and a DSL draft; on the basis of the controlled word list, the ontology, the rule base and the DSL draft, constraint conditions are set according to preset requirements, runtime codes bound with the simulation kernel are obtained, and attribute data are recorded; according to the attribute data, instantiating an evaluation scene and a task through an ABM framework to obtain a scene template, a task template and a communication topology; determining an FSM skeleton and an atomic action library; constructing a state model and a behavior model of environment perception; and verifying the state model and the behavior model, and if the verification is passed, determining a deployable model package, a parameter template and a traceability mapping and coverage and evaluation report according to the state model and the behavior model.

The application discloses a short video or live content knowledge unit slicing and explainable indexing propagation method, and belongs to the technical field of digital data processing. The method comprises the following steps: acquiring video stream data and audio data, analyzing and processing the video stream data and the audio data to generate slicing driving parameters, segmenting, generating a knowledge unit segment set, and generating segment index information; performing multi-modal feature extraction and fusion processing on the knowledge unit segment set, matching the controlled vocabulary, generating alignment candidate labels; referencing the segment index information to generate an evidence index set, checking the multi-modal fusion representation vector, and mapping the platform rules to generate publishable indexing data and recover online interaction feedback; updating the multi-modal feature extraction and fusion processing and the controlled vocabulary matching process, and storing the generated log fingerprint record. The method combines multi-modal feature fusion and explainable reasoning, and constructs a feedback closed-loop data processing mode, which can realize slicing, traceable indexing and optimization of knowledge units.