106 results about "Learning abilities" patented technology

The invention discloses an intelligent programming assistance method and system combining a large model and a knowledge graph. The method comprises the steps that the system is initialized, and a programming domain knowledge graph body and an education programming large language model are loaded; constructing a learner initial portrait and associating the learner initial portrait with a knowledge graph; personalized programming learning experience is provided, including real-time programming tutoring, and thinking chain-based heuristic guidance is generated through combination of a large language model and knowledge graph information; and interactive feedback and system iteration are carried out, and a reinforcement learning mechanism based on human feedback is utilized to continuously optimize the large language model. Through deep fusion of structured knowledge of a knowledge graph and understanding generation ability of a large language model, the method aims at solving the problems that an existing programming auxiliary tool is insufficient in individuation and heuristic, heterogeneous knowledge is difficult to fuse, teaching resource modes are single, and an effective evolution mechanism is lacked. More accurate, personalized, heuristic and sustainable-evolution intelligent programming assistance can be provided, and the programming thinking and autonomous learning ability of learners can be effectively improved.

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.

The invention discloses a reward model training method, a large language model optimization method and correlation, and the reward model training method comprises the steps: obtaining a preference training sample pair and a to-be-trained reward model, the preference training sample pair comprising a preferred response sample and a non-preferred response sample; calculating an award score difference between the preferred response sample and the non-preferred response sample based on a to-be-trained award model; constructing a cost matrix based on the reward score difference and the semantic association degree between the preferred response sample and the non-preferred response sample; calculating a loss margin based on the cost matrix; and based on the loss margins, carrying out calculation to obtain paired preference loss values of the band margins, and updating parameters of the to-be-trained reward model by taking minimization of the loss values based on the band margins as an optimization target to obtain a trained reward model. The learning ability and overall generalization performance of the model for difficult samples are improved, excessive dependence on simple samples is avoided, and then the generation quality of the large language model in complex tasks is improved.

The invention discloses a multi-dimensional capability construction knowledge tracking method based on learning behavior decoupling, and belongs to the technical field of knowledge tracking. The method comprises the steps of obtaining test question embedding and test question-response embedding, and obtaining learning deviation behavior characterization and multi-scale behavior characterization based on the test question-response embedding; inputting the learning deviation behavior representation and the multi-scale behavior representation into a binary state model to obtain a knowledge construction representation; obtaining a test question sequence structure representation based on test question embedding, and performing multi-scale association on the test question sequence structure representation to obtain a multi-scale test question association representation; the multi-scale test question association representation and the knowledge construction representation are subjected to student ability modeling through frequency domain perception fusion, and differential learning ability is obtained; and predicting the student test question performance based on the differentiated learning ability to obtain a student test question performance prediction value. The method improves the accuracy of student test question performance prediction.

The invention discloses an automatic game testing method and system, and the method achieves the automatic generation and execution of testing logic through the building of a knowledge base and an event base, and the cooperative management of the knowledge base and the event base, and reduces the manual intervention. Converting a natural language demand input by a user side into a semantic feature, and matching the semantic feature with an operation instruction in an event library to obtain an executable test logic so as to reduce a test threshold; triggering the event in the event library according to the executable test logic, pushing the execution result of the event to the user side, and updating the knowledge base and the event library by using the execution feedback data of the event, thereby forming an execution-feedback-update closed loop, and improving the self-learning ability of the test system. In this way, the logic rules in the game development kit are converted into the knowledge base, intelligent analysis and dynamic feedback optimization of natural language requirements are achieved in combination with the preset operation instruction of the event base, and the test efficiency and the adaptive capacity are remarkably improved.

The invention discloses a method, a system, equipment and a medium for automatically updating cue words of a large language model system, belongs to the technical field of cue word optimization of large language models, and aims to solve the technical problem of how to optimize cue words of large language models on the basis of recent interaction historical sessions between users and large language models or agents. Automatic updating of cue words of a large language model system is achieved, the continuous and lifelong learning ability of the large language model is improved, and then the thinking depth of the large language model is improved. According to the technical scheme, the method comprises the steps of obtaining a system cue word updating model with an interactive historical text compression memory function through SFT supervised fine tuning or enhanced post-training based on a system cue data set, constructing a system cue word cue experience entry knowledge base based on the system cue word updating model and in combination with long-time interactive dialogue history of a user and a large language model, and obtaining the system cue word cue experience entry knowledge base. And on the basis of a system cue word updating model, the current dialogue history is interacted by combining the user and the large language model, so that the system cue word is automatically updated.

The invention relates to the technical field of heart failure auxiliary diagnosis and treatment, in particular to a heart failure auxiliary diagnosis and treatment knowledge distillation method and system.The knowledge distillation method comprises the steps that an age-crossing fusion data set is built by combining ancient medical case text data related to traditional Chinese medicine heart failure; performing field fine tuning on the pre-training model to generate a basic teacher model; performing semantic alignment, and outputting a cross-time contrast database; constructing a curative effect scoring model by using the time sequence model, and outputting a quantitative association database; using a graph neural network to output an intelligent heart failure knowledge graph, and generating a comprehensive teacher model; performing knowledge distillation on the comprehensive teacher model to generate a clinical student model; the knowledge distillation system is applied to the knowledge distillation method, has a dynamic learning ability, can fuse ancient and modern trans-time knowledge and combine traditional Chinese medicine ancient book wisdom to carry out heart failure auxiliary diagnosis and treatment, and can give consideration to model performance and deployment efficiency through knowledge distillation.

The invention relates to the technical field of natural language processing, in particular to a data enhancement and feature enhancement-based Chinese named entity recognition method, which is based on a feature enhancement double-attention named entity recognition model, and is characterized in that the model comprises an embedded layer, a data enhancement module, a multi-scale convolution fusion attention layer and a prediction layer; the embedded layer uses a dual-channel attention fusion module to process texts in parallel, fuses multi-dimensional information features of Chinese characters, combines an error correction type mask language model, a pre-training model and a bidirectional gating loop unit, fuses local and global text features, and obtains text representation from multiple dimensions and multiple levels; according to the method, by introducing an innovative model mechanism or training strategy, challenges such as label sparsity and text noise existing in a Chinese named entity recognition task can be effectively handled, the learning ability and generalization performance of the model for long-tail entity categories are improved, and the robustness of the model in a real and non-ideal data environment is enhanced.

The invention discloses an efficient simultaneous interpretation method based on an expert routing threshold, and relates to the field of voice processing, the method is based on a classical Transform architecture model, an expert routing strategy model based on the expert routing threshold is constructed, and multi-language streaming translation is realized, the model comprises a streaming voice encoder, and the streaming voice encoder adopts a hybrid design and is connected with the expert routing threshold. The block-by-block autoregression block is composed of an autoregression block and a non-autoregression block; the text decoder is used for simultaneously processing the complete offline voice and the randomly truncated voice prefix to generate a hidden state; the routing threshold module is realized by a feedforward network and projects the final hidden state into a scalar value to determine an expert weight; and the hybrid expert post-processing module shares a language model head with the text decoder, and predicts a target translation sequence in combination with prefix information and global information. According to the method, a mixed expert threshold scheme is adopted to learn the strategy, the self-learning ability of the neural network is fully played, good effects are achieved in streaming translation and streaming TTS, and the method can be used for generating more streaming sequences.

The invention provides a method and system for cognitive diagnosis and attribution analysis of teacher teaching, and belongs to the technical field of teaching management. The method specifically comprises the following steps: constructing a hybrid multi-dimensional capability vector for a student agent; acquiring a teaching instruction of a teacher, analyzing the teaching instruction, determining a teaching mode and analyzing teaching content; the intelligent agent responds to the teaching mode and the teaching content, calls an ability probe algorithm to evaluate the performance of the intelligent agent for completing the task, and calculates and updates a multi-dimensional ability vector of the intelligent agent; and comparing state differences of the multi-dimensional capability vector before and after evolution, modifying a teaching instruction based on an anti-fact attribution algorithm, carrying out anti-fact inference, and generating an evaluation report containing diagnosis information. By constructing a hybrid multi-dimensional ability vector, accurate description and dynamic tracking of the learning ability of the student are realized; and evaluating task performance in real time and updating a multi-dimensional capability vector through a capability probe algorithm to form a closed-loop feedback mechanism.

The invention discloses a reinforcement learning intrusion detection method based on space-time attention and dynamic courses, and the method comprises the steps: carrying out the dynamic weight distribution of input features in space and time dimensions through a multi-level space-time attention mechanism, and enhancing the expression capability of key features; a dynamic curriculum learning strategy is adopted to realize progressive difficulty adjustment of training samples, and the learning ability of the model is gradually improved through initial difficulty estimation, dynamic threshold adjustment and curriculum sample selection; designing a composite reward mechanism to optimize a reward signal, and combining a basic classification reward and a stability reward to promote stable convergence of the strategy; a self-evolution target network module is introduced, and dynamic update management of a target network is realized through performance monitoring, emergency update triggering and frequency self-adaption. A space-time cooperative detection closed loop is constructed, and an end-to-end intrusion detection process is realized through environment interaction, strategy optimization and online detection. The method effectively improves the accuracy and robustness of intrusion detection, and adapts to a complex network attack scene.

The invention relates to an RDFS ontology-oriented large language model evaluation and cue word optimization method. The method comprises the following steps of 1, constructing a positive example data set and a negative example data set; 2, designing cue word templates of multiple levels; 3, test cases in the standardized test data set are combined with different types of cue word templates, an input sequence is generated and sent to a to-be-evaluated large language model, and an output judgment result and an explanation text are obtained; 4, analyzing the judgment result and the explanation text to generate a comprehensive evaluation report; and 5, based on a data analysis result of the comprehensive evaluation report, forming a system performance map by constructing a'cue word type-evaluation dimension-axiom type 'ternary mapping relationship, and implementing an optimization process according to the system performance map. According to the scheme, the problems of incomplete evaluation system, imperfect test data, single evaluation dimension and the like in the prior art are solved, and systematic and precise evaluation and improvement of the model RDFS ontology learning ability are realized.

The invention discloses an intelligent foreign language teaching resource planning method and system based on a knowledge graph, and relates to the technical field of foreign language teaching and intelligence. The planning method comprises the following steps: constructing a knowledge system and data acquisition: collecting foreign language teaching knowledge, carrying out preprocessing, constructing a foreign language knowledge graph by means of a graph database, and carrying out data acquisition; the method comprises the following steps: establishing an association relationship between knowledge, acquiring learning feature data of learning basis, style, progress and preference of students through student registration information, learning behavior data acquisition and learning ability test, and acquiring daily curriculum schedule and idle time data of the students in real time; the foreign language knowledge graph is constructed, scattered teaching knowledge is systematically integrated, network association among knowledge is formed, and learning requirements of students can be accurately positioned and intelligent screening and efficient adaptation of teaching resources can be realized in combination with learning feature data, including learning basis, style, progress and preference, of the students.

The invention provides a training method of a face recognition model, and a face recognition method and system, and relates to the technical field of face recognition, and the method comprises the steps: pre-training a teacher network; constructing a student network and establishing a knowledge distillation framework; dividing the feature vector output by the teacher network into blocks, and applying different weights according to the importance of each block so as to form weighted block mean square error loss; the cosine similarity of feature vectors output by the teacher network and the student network is used as a difficult sample indicator; the angle interval in additive angle interval loss is dynamically adjusted according to the indicator, and differential constraints are applied to samples with different difficulty levels. According to the method, the learning ability for key features is enhanced through weighted block mean square error loss, attention distribution for difficult and simple samples is optimized through dynamic additive angle interval loss, the identification precision, convergence speed and model compression effect of a student network are effectively improved, and the method is suitable for resource-limited edge device deployment.

The invention provides an adversarial learning-based data value protection method facing transverse federated learning, and aims to prevent a model from being illegally migrated to an unauthorized task for use. The method comprises the following steps: locally extracting intermediate features by a client, and inputting the intermediate features into a main task classifier and a plurality of adversarial task classifiers; a gradient inversion layer is introduced, so that the feature extractor enhances the identification capability of a main task in training, and weakens the generalization capability of an unauthorized task at the same time; a composite loss function is constructed, and optimization balance among tasks is realized through dynamic weight adjustment; and finally, carrying out model parameter aggregation and iterative updating under a federated learning framework. According to the method, on the premise that the performance of a main task is not affected, the learning ability of the model for sensitive attributes is effectively limited, so that unexpected leakage of data values is inhibited, and the safety and specificity of the model are improved.

The invention is suitable for the technical field of post matching, and provides a multi-dimensional intelligent matching method and system for employment posts, and the method comprises the following steps: carrying out the comprehensive skill matching of posts and talents, and determining a comprehensive skill matching score; calling educational background information and certificate information in the talent resume, and reasoning to determine the learning ability of the talent; extracting post skills which are matched unsuccessfully in the employment posts, determining the demand emergency degree and learning difficulty of each post skill which is matched unsuccessfully, and determining the association degree between the post skills and talent skills and the association degree between the post skills and professions; and based on the learning ability, the demand emergency degree, the learning difficulty and the correlation degree, determining the cultivation cost cardinal number of each post skill which fails in matching. According to the invention, the learning ability of talents is incorporated into a matching evaluation system, so that potential suitable talents with relatively strong learning ability and development potential although the current skills are not completely matched with posts can be identified, the talent selection range is expanded for enterprises, and the talent recruitment efficiency is improved.

The application discloses a kind of teacher teaching ability promotion training method and system, its method includes: determining multiple evaluation dimensions of teacher teaching ability, obtaining the quantitative evaluation index of each evaluation dimension, obtain multiple high-quality training course resources according to quantitative evaluation index;The historical teaching state parameter of each teacher is input and the teaching ability of each teacher is evaluated;According to the evaluation result, the teaching short board index and short board state description of each teacher are analyzed and the ability promotion demand parameter is determined;According to the ability promotion demand parameter, the target training course resource is filtered from multiple high-quality training course resources, and the training plan is formulated based on the learning ability information of each teacher, and the teaching ability promotion training is carried out according to the training plan for each teacher.It can accurately aim at different teaching level teachers for accurate short board related ability training, which improves the efficiency and stability of training, and also guarantees the adaptability of training resources, improves the practicability.

An object of the system according to the embodiment is to support creation of a reading comment, improve academic ability of a child, and reduce a burden on a guardian.SOLUTION: A system includes a basic rule presentation unit, a support unit, a correction unit, and a recommendation unit. A basic rule presentation part presents a basic rule and a writing method for creating a reading comment. The support unit supports creation of a comment based on the basic rule and the writing style presented by the basic rule presentation unit. The correction unit corrects the comment created by the support unit. The recommendation unit provides a recommendation of a writing style according to the content based on the information of the book registered in the database.SELECTED DRAWING: Figure 1

Embodiments of the present application provide a BERT model fine-tuning method and device, equipment and a storage medium, and relate to the technical field of computers. The method comprises: generating an original token list according to a text corpus, and obtaining word frequency statistical information of the original token list; determining a mask generation strategy based on the word frequency statistical information, and performing mask processing on the original token list according to the mask generation strategy to obtain a mask processing result; and performing iterative learning on a pre-trained BERT model based on the mask processing result until a preset condition is met, to obtain a fine-tuned BERT model. The present application statistically obtains word frequency information corresponding to a text corpus, and adaptively adjusts a mask generation probability according to the word frequency, so that the mask distribution generated by tokens of different frequencies is more uniform, and the semantic information of the text corpus is better preserved, thereby improving the learning ability of the BERT model for masked corpus.

The invention provides an attack technology prediction method and device based on a large language model. The method comprises the steps of obtaining historical attack data of an attacker and performing data modeling to construct a bipartite graph; generating a structured context according to the bigraph, and inputting the structured context into a large language model; and constructing a prediction cue word, guiding the large language model to carry out attack technology prediction according to the structured context by the prediction cue word to obtain a preliminary prediction result, and carrying out attack stage filtering and sorting screening on the preliminary prediction result according to a set standard to obtain a final attack technology prediction result. By applying the method, the recommendation ability of the large language model can be stimulated, the adaptability and learning ability of the large language model to a specific security task can be improved, the scene learning ability of the large language model can be fully utilized, a new attack mode can be effectively dealt with, the dependence on a large amount of annotation data is reduced, and the user experience is improved. Particularly, under the condition that data is scarce or the cold start problem is serious, the accuracy of the generated result can be improved.

The invention relates to the technical field of foreign language ability evaluation, and discloses a foreign language ability dynamic evaluation system and method based on multi-dimensional learning data, and the system comprises a multi-dimensional data acquisition module, a data analysis module, a data evaluation module and a comprehensive feedback module. The multi-dimensional data acquisition module is used for acquiring learning ability data of other subjects of students, foreign language special learning data of the students and foreign language culture literacy data of the students, the data analysis module is used for calculating self-learning ability, foreign language special ability and foreign language culture literacy ability of the students, and the data evaluation module is used for evaluating foreign language comprehensive ability. The system has the advantages that through a multi-dimensional, dynamic and personalized evaluation framework, the actual foreign language ability of the learner is accurately described, a comprehensive basis is provided for teaching optimization and student development, the one-sidedness of single evaluation is avoided, and the actual foreign language ability of the learner is accurately reflected.

The invention discloses a learning ability dynamic evaluation method and system based on a stylus writing track. The method comprises the steps that N sample objects with object IDs are selected; constructing N writing behavior sequences; encoding N sequence representation vectors; performing unsupervised clustering on the N sequence representation vectors; determining the most similar behavior cluster of the target object; extracting a standard writing behavior sequence of the most similar behavior cluster, and calculating a sequence deviation between the standard writing behavior sequence and the real writing behavior sequence; if the sequence deviation is greater than a first threshold value, determining that the writing track of the target object is unqualified; otherwise, extracting the writing rhythm ratio of the target object to perform learning abnormity label evaluation; according to the method, the standard sequence can better represent the stable behavior pattern of the excellent students of the cluster, and the sequence deviation can more accurately reflect the deviation degree of the target object and the normal behavior, so that misjudgment caused by standard distortion is avoided, and the evaluation effectiveness of the learning ability is improved.

The invention relates to an intelligent reading recommendation method and system based on dynamic learning ability evaluation, and relates to the field of intelligent education technology.The method comprises the steps that current scanning content is extracted based on scanning behavior data, knowledge content mastered by a user is recognized in combination with historical learning records, and the knowledge content is removed to obtain an unmastered knowledge range; analyzing the current scanning content to identify knowledge entries and evaluating the learning difficulty of the entries; based on historical learning records and knowledge entries, evaluating expected time required for understanding the current scanned content; determining an actual understanding time based on the scanning behavior data; the real-time learning ability of the user is generated in combination with the actual understanding time, the expected time and the entry learning difficulty; and generating learning recommendation content based on the real-time learning ability and the unmastered knowledge range, and performing content recommendation based on the learning recommendation content. The method has the effect of improving the learning efficiency and experience of the user.

The invention discloses a radiation source individual identification method and system based on large language model reprogramming. The radiation source individual identification method comprises the steps that received radio frequency signals are preprocessed and partitioned; through a signal reprogramming module, by utilizing a trainable text prototype library and a multi-head cross attention mechanism, mapping the signal fragments into a representation sequence aligned with the semantic space of the large language model; constructing a natural language prompt, converting the natural language prompt into a vector, splicing the vector with the signal representation, and inputting the spliced vector into a parameter-frozen large language model for feature extraction and aggregation; and finally, outputting an identification result through the lightweight trainable classification layer. According to the method, the general mode recognition and few-sample learning ability of a large language model are utilized, only a few parameters such as a reprogramming module and a classification layer need to be trained, and the problems that a traditional method is poor in generalization, depends on a large amount of annotated data and lacks knowledge guidance are effectively solved; rapid, accurate and explainable identification of radiation source individuals in a high-dynamic electromagnetic environment is realized.

The present disclosure belongs to the technical field of nuclear power and specifically relates to a vulnerability information identification method and device based on a large language model. The present disclosure collects PoC information of certain CVEs and manually labels part of the data, thereby enhancing the accuracy and reliability of the training data in a manual labeling manner, so as to improve the learning ability of the subsequent model. The GPT-3.5-turbo model is used to identify and summarize the CVE and PoC data, each piece of information is described based on the TTP content form, the CVE and PoC information are unified into the TTP standard format, the noise interference in the original information is eliminated, the identification and learning ability of the large language model for the key information features can be enhanced, and the instruction fine-tuning technology is used to improve the task completion effect of the GPT model. Based on the Llama-2 model after fine-tuning, the input target CVE and PoC information are identified and processed, and the correlation between the CVE and the PoC is determined based on the output. Thus, the problem of relying on manual analysis in the CVE and PoC correlation identification is solved.

The present invention relates to a method for providing a learner-personalized language learning service and, more specifically, to a method for providing a learner-personalized language learning service, which is performed by a server and provides a Korean learning service to a foreign learner. The method comprises the steps of: preparing, by the server, an evaluation corpus including a learning corpus for allowing a learner to learn Korean and an error corpus in which an error made by the learner is expressed; receiving learner information input using a learner terminal; setting a Korean language ability level of the learner on the basis of the learner information; and allowing the learner to learn by using the evaluation corpus according to the set Korean language ability level of the learner. The present invention provides language learning data according to an individual language learning ability level of a learner and a subject desired by the learner, and enables personalized language learning to be performed for each learner, thereby improving learning efficiency and satisfaction.

The application belongs to the field of intelligent education, and particularly relates to an artificial intelligence education system based on education big data, which comprises a student terminal and a server, and is characterized in that a user verification module, a learning module, a handwork module, an entertainment module, a feedback module and an analysis module are arranged in the student terminal; the student terminal comprises a main body and a time module; the time module is used for controlling the working time of the learning module, the handwork module and the entertainment module; the learning module and the entertainment module are arranged to allow students to have proper rest during the learning process, so as to achieve the combination of work and rest; the handwork module is arranged to improve the attention and the hand-on ability of the students, so as to improve the learning ability of the students; the learning result, the attention and the hand-on ability of the students are tested to measure the personal comprehensive level of the students, and the personal comprehensive level of the students is compared with the level of the students of the same age, so that the personal characteristics of the students can be better judged, and the teaching plan can be formulated in a targeted manner, so as to improve the education effect.

The invention discloses a mathematical question answering auxiliary system based on artificial intelligence, and relates to the technical field of artificial intelligence, the mathematical question answering auxiliary system comprises a management center, and the management center is connected with a question input module, a language processing module, an intelligent engine module and an answering output module; performing solution decomposition on the collected to-be-solved mathematical question to obtain analysis knowledge points, and setting learning reference points to perform resource supplementary matching to obtain recommended learning materials; performing question detection on the solving process of the to-be-answered mathematical question by visiting students to obtain question learning points, and feeding back the question learning points to the management center for feedback supplement to obtain question supplement data; performing learning recording on the visited students through the question supplement data to obtain a learning feedback result, and performing optimization answering on the to-be-answered mathematical question through the learning feedback result to obtain an optimal answering process; the accuracy and efficiency of mathematical question answering are improved, and the self mathematical literacy and learning ability are improved by supplementing learning resources.

The present application relates to the technical field of network data processing, in particular to a self-evolving online learning personalized recommendation method, comprising: collecting behavior data of learners in real time during online learning; constructing a simplified knowledge graph of each subject knowledge system by using a hierarchical decoupling method; obtaining the learning behavior of learners at each knowledge point based on behavior data mapping by using a behavior continuity merging algorithm; fitting the learning ability of learners at each knowledge point based on behavior data by using a time sequence ability vector fitting algorithm; periodically determining the first recommended learning mode of each knowledge point based on the learning behavior and learning ability of group learners at each knowledge point; and correcting the first recommended learning mode of each knowledge point in the current period based on the learning behavior and learning ability of individual learners to serve as a personalized recommendation result, realizing double-driven recommendation of "group effectiveness + individual adaptation", and simultaneously realizing dynamic standard correction and real-time response, improving the logical coherence and accuracy of the recommendation.