196 results about "Learning abilities" patented technology

The invention discloses a fine-grained multi-modal prompt learning method based on a visual language pre-training model, and relates to the technical field of visual language pre-training models.The method comprises the steps that a multi-modal prompt learning vector used for learning visual features and text features is constructed, and a fine-grained multi-modal prompt learning model is constructed based on the fine-grained multi-modal prompt learning model; global differences, subtle distinguishing visual features and category difference text features among different visual categories are generated, and a multi-modal prompt learning vector is optimized based on a cosine similarity loss function. According to the invention, through combination of dual-granularity visual prompt and text prompt, the problems that in an existing prompt learning tuning method, cooperative reasoning cannot be carried out in a dual-mode prompt representation space, and fine distinguishing features are difficult to capture are effectively solved; and superior migration generalization ability and few-sample learning ability are verified on a plurality of main image identification reference data sets, and the performance is remarkably improved in downstream tasks.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a voice generation method which comprises the following steps: constructing a multi-language voice synthesis model, obtaining plain text data and paired voice text data, and constructing an expansion vocabulary; updating a language perception embedding layer and model parameters, and converting an input text into a mark sequence; and the encoder extracts context semantic features, extracts pronunciation rule features, and the decoder fuses the features to generate an acoustic feature sequence, and converts the acoustic feature sequence into target voice data. According to the invention, the multi-language speech synthesis model is combined with the language perception embedding layer, so that the speech generation capability of a low-resource language is improved; the text conversion accuracy is improved by expanding the vocabulary, the target language learning ability is enhanced by unsupervised training, the low data environment adaptability is optimized by supervised training, and the speech naturalness and fluency are improved by feature fusion.

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 relates to the technical field of educational resource recommendation, and discloses an educational resource intelligent recommendation method and system based on big data driving, and the system comprises a data fusion processing module, a portrait modeling module, a resource feature engine module, an intelligent recommendation core module and a closed-loop feedback module. By fusing knowledge state, cognitive ability and interest preference three-dimensional portraits, capturing user learning ability evolution in real time, dynamically updating knowledge mastery by adopting a knowledge tracking model, and perceiving interest migration in combination with an attention mechanism, the problem of learning cold start in a new field is solved, recommendation coverage range and accuracy are improved, and user experience is improved. The cognitive load sensitive ant colony optimization algorithm is designed, the learning path continuity is guaranteed through a heuristic function, the learning path structure reasonability is optimized, the cognitive burden of a user is reduced, a personalized knowledge attenuation model is constructed by fusing an Ebbinghaus forgetting curve, the knowledge long-term retention rate is increased, and the review resource release accuracy is enhanced.

The invention discloses a multi-dimensional learning condition statistical analysis method based on deep learning, and relates to the technical field of learning condition statistical analysis, and the method captures the learning performance of students in hearing, speaking, reading and writing dimensions, analyzes the learning behavior learning condition, measures the learning progress and quality of the students from multiple aspects, and improves the learning efficiency. The learning ability of students is graded by using Kmeans clustering and SVM, learning behavior changes of the students are tracked in real time in combination with an LSTM time sequence model, ability grading of the students is dynamically adjusted according to performance of the students in a specific learning stage, and a personalized learning report is generated for each student based on a multi-dimensional grading result of the students. According to the method and the system, the ability analysis and behavior data of the students are combined, a customized learning route map is conveniently provided for the students, meanwhile, the LSTM time sequence model is combined with the answer data and video learning behavior data of the students, subtle changes in the learning process are captured, the grading results of the students are updated in real time, and real-time and personalized learning support is provided for the students.

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 relates to the technical field of education, in particular to an English personalized learning recommendation method based on big data, which comprises the following steps: collecting original data of English homework completion speed and answer accuracy of a learner, calculating average completion time and accuracy by using a statistical analysis method, identifying the deviation between learning ability and interest points, and recommending the learning ability to the learner. And obtaining a learning ability evaluation result. According to the invention, through predicting learning content demands, not only is the progress of a learner captured, but also future demands can be predicted, so that education resources are prepared in advance, prospective matching of teaching contents is realized, and through optimizing a teaching material sequence and contents, high matching of teaching materials and personalized learning demands is ensured; the use efficiency of educational resources and the maximization of the learning effect are remarkably improved, the learning path is dynamically adjusted, the learning adaptability is evaluated, the learning process is optimized, the flexible application of course content is enhanced, and more personalized learning experience and higher learning achievement are achieved.

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 provides a multi-element ability evaluation system and evaluation method for children education, and relates to the field of children education systems. According to the multi-element ability evaluation system for children education, the multi-element ability evaluation system for children education is realized through a multi-element ability evaluation system, and the multi-element ability evaluation system comprises three functional modules, namely an evaluation module, a management module and an evaluation analysis module, the evaluation module comprises a learning ability evaluation module, a physical health evaluation module and an emotion and mental state evaluation module, the evaluation module, the evaluation analysis module and the management module are in communication connection through the Internet, and the multi-element ability evaluation system comprises an evaluation report generation module. According to the invention, comprehensive test evaluation can be carried out on three aspects of intelligence quotient and physical ability of the children, so that multiple abilities of the children can be known more comprehensively through the evaluation result, the children can be better taught according to the case and the materials, and the education of the children is more efficient.

The invention belongs to the technical field of English education, and discloses an English learning system and method based on mobile equipment. A learning data set and a student data set are collected and preprocessed, the learning data set comprises score data, unit data and duration data, and the student data set comprises age data, gender data and preference data; analyzing the learning data set and the student data set to obtain a learning reference value, and classifying the learning reference value to obtain a learning classification result; analyzing the learning classification result to obtain a user decision result; in general, the method has the remarkable advantages of being high in user learning ability monitoring performance, large in auxiliary decision making effect and high in rapid adjustment accuracy.

The invention provides a learning performance precise teaching management method based on time sequence behavior modeling. The learning performance precise teaching management method comprises the following steps: S1, carrying out multi-modal learning behavior data acquisition; s2, aligning and segmenting the time sequence data; s3, performing knowledge retention rate differential modeling; s4, carrying out time sequence feature extraction; s5, constructing a mixed time sequence model; s6, performing dynamic learning ability evaluation; s7, carrying out adaptive resource recommendation; s8, carrying out cross-correction knowledge diffusion optimization; the method has the following advantages: the method is real-time and accurate; the data is acquired to acquire the intervened closed-loop delay lt; compared with the traditional method, the time is increased by 40 times. And cost optimization: the workload of teachers is reduced by 58% and the resource purchase cost is reduced by 42% through an automation strategy. The scale effect is that federal learning supports ten-thousand-person-level concurrence, and the model updating period is shortened to the hour level from the quarter level. Education fairness: cross-school knowledge diffusion enables the superior rate of weak schools to be improved by 29%.

The invention discloses a physical, chemical and student experiment AI teaching assistant for adaptive learning path planning and an implementation method, and the method comprises the steps: carrying out the planning of a first learning path for a student, carrying out the analysis of the hand tracking data of the student in an experiment process, obtaining the scoring data, and carrying out the re-planning of the learning path, analysis can be carried out according to the conditions of students and the operation performance in the experiment, so that adaptive change of subsequent experiments is carried out according to the experiment quality of the students, it is guaranteed that the students can obtain experiment guidance conforming to the students, and the defect that in the prior art, targeted experiment guidance cannot be carried out according to the students is overcome; the first learning path is used for adjusting the preset standard value of each experiment of the student, the preset standard value is in positive correlation with the academic record, the learning ability of the student is matched by adjusting the preset standard value, and the teaching strategy can be dynamically adjusted according to the learning progress and the academic record change of the student so as to adapt to the constantly changing learning requirements of the student.

The invention discloses a learning effect evaluation system and method for artificial intelligence image generation training courses, and relates to the technical field of computers. In order to solve the technical problem that in the prior art, a learning management system cannot feed back learning conditions of students in time, the technical scheme provided by the invention is that the system comprises a plurality of modules used for collecting and analyzing data in the learning process of the students, and comprises a student single course homework quality evaluation module, the quality evaluation module is used for performing quality evaluation on a single homework of a student in an artificial intelligence image generation course to obtain a quality score; the overall learning ability evaluation module is used for carrying out comprehensive learning state analysis on the quality scores and the homework process logs of the multiple homeworks of the students to obtain overall learning ability scores of the students; and the visual feedback module is used for generating learning diaries of the students according to the change of the homework quality and the learning ability of the students. The method is suitable for being applied to learning effect evaluation work of artificial intelligence image generation courses.

The invention discloses a self-adaptive interdisciplinary learning path recommendation method based on deep learning. The method comprises the following steps: S1, constructing a historical learning data set; s2, constructing a knowledge point difficulty scoring model based on a deep learning algorithm; s3, generating a student learning ability portrait; s4, generating a preliminary interdisciplinary learning path according to the learning ability portrait of the student and the output of the knowledge point difficulty scoring model; s5, dynamically adjusting a knowledge point sequence and a difficulty level in the interdisciplinary learning path; s6, the system evaluates the performance of the students on each knowledge point in the learning process in real time through a knowledge point difficulty scoring model, and dynamically adjusts the difficulty level of the knowledge points; and S7, after the student completes the learning task, the system optimizes the recommendation strategy of the interdisciplinary learning path according to the learning performance and the learning feedback. According to the method, the actual learning ability of the student can be reflected, and personalized learning path recommendation which better conforms to the current state of the student is provided for the student through the portrait, so that the learning efficiency and effect are 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 relates to the technical field of data processing, in particular to a hyper-relation extraction method based on collaboration of multiple large language models and application of the hyper-relation extraction method. According to the method, the extraction cue word serving as the extraction rule is optimized by means of the strong learning ability of the large language model, a correction mechanism is given to the extraction cue word to correct the generated hyper-relationship, and rapid and accurate text hyper-relationship extraction can be realized without performing fine adjustment on parameters of the model through supervised learning. The method aims at solving the problem of how to improve the super-relation extraction efficiency of the text.

The invention discloses a collaborative training method, medium and device based on an LLM large and small model, and the method comprises the steps: S1, obtaining a huge knowledge system through the combination of the language understanding capability and pre-training capability of a general large model, and carrying out the cold start of an AI service 0 sample needed by a business scene, and enabling the AI service 0 sample to be online; s2, performing a small amount of annotation on sample data generated by a business scene, performing fine tuning on the general large model to form a scene large model, performing continuous learning to enable the scene large model to have knowledge in the field, and quickly improving an algorithm effect; and S3, distilling knowledge in the field obtained by the scene large model into a plurality of small models, and fusing results of the plurality of small models by using a scoring mechanism to realize collaborative training of the large and small models. According to the method, any scene text service can be subjected to cold start online under the condition of limited hardware resources, large and small model cooperative training of large model knowledge can be obtained through a small amount of labeling, and the model learning ability and the working efficiency are greatly improved.

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 teacher teaching ability improvement training method and system, and the method comprises the steps: determining a plurality of evaluation dimensions for teacher teaching ability, obtaining a quantitative evaluation index of each evaluation dimension, and obtaining a plurality of high-quality training course resources according to the quantitative evaluation indexes; historical teaching state parameters input by each teacher are obtained, and teaching ability evaluation is carried out on each teacher; analyzing a teaching short board index and short board state description of each teacher according to an evaluation result, and determining capability improvement demand parameters; and screening out a target training course resource from the plurality of high-quality training course resources according to the ability improvement demand parameters, making a training plan based on the learning ability information of each teacher, and carrying out teaching ability improvement training on each teacher according to the training plan. According to the invention, accurate short board related ability training can be accurately carried out for teachers with different teaching levels, the training efficiency and stability are improved, the adaptability of training resources is ensured, and the practicability is 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 present invention relates to the technical field of knowledge tracing, and in particular to a method and system for predicting learning conditions of learners based on meta-paths, and an electronic device. In the method, after learner data, exercise book data, and learner answer interaction sequence data are obtained, firstly, a learner behavioral graph is constructed, and learning content preferences of learners are extracted; secondly, learning abilities of the learners are calculated; thirdly, features of the learning content preferences and features of the learning abilities are fused, and a learner heterogeneous information network based on meta-paths is constructed; next, embedding learning is performed on the leaner heterogeneous information network by using a layered graph attention mechanism, to extract leaner fused features; and finally, learning conditions of the learners are predicted by using a GRU-based knowledge tracing model. Compared with the prior art, the present invention has the advantages of more accurately predicting the probability that a learner will answer a next exercise correctly, evaluating and predicting a learning outcome of the learner, and thus providing the learner with multi-dimensional feedback and guidance.

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 embodiment of the invention provides a model training method, a text error correction method and a related product, and relates to the technical field of machine learning. According to the model training method provided by the invention, by introducing the model training data including various error types such as spelling errors, grammar errors, logic errors and format errors, various errors possibly existing in the to-be-corrected text can be comprehensively covered and recognized. Moreover, the word segmentation result of the text data is subjected to masking processing through a first sub-model of the machine learning model, the word segmentation result of the text data is subjected to disordering processing through a second sub-model, and the machine learning model is trained according to the annotation data, the result after masking processing and the result after disordering processing; the recognition capability of the machine learning model for the error mode is enhanced, the learning capability of the machine learning model for the correct text structure is also promoted, and the text error correction model shows higher accuracy during text error correction.

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 discloses an intelligent learning asking method and system based on a large language model, and relates to the technical field of generative artificial intelligence and intelligent education, and the method comprises the steps: guiding a user to carry out deep interaction through a human-intelligent interaction module, so as to generate a question and answer set containing multi-party intelligence; a teacher user purifies a question and answer set through a question and answer filing unit to obtain high-quality data, and the system automatically extracts knowledge from dialogues by using the complete interaction process to dynamically update a multilevel knowledge base composed of a personalized knowledge graph and a global knowledge graph; meanwhile, model fine adjustment is conducted on an AI intelligent agent based on a cognitive framework and a reasoning framework through archived high-quality data, and a closed loop of double evolution of a knowledge base and model parameters is formed. According to the invention, the professional ability of AI in the vertical field can be significantly improved, and the high-order learning ability of students can be effectively cultivated.

The invention belongs to the field of resource recommendation, and particularly relates to a learning resource recommendation method, system and device and a storage medium, the learning time of a user is divided into a fragmented time range and a long-time learning time range, the learning ability of the user is calculated, and according to the learning ability and the association degree of each chapter in a knowledge graph, the learning resource is recommended. When the current time is in the fragmented learning time range, a recommendation emergency value is obtained according to the current learning urgency degree and the knowledge gap risk degree, when the recommendation emergency value is smaller than a preset threshold value, a chapter with a maximum recommendation strategy is matched in the knowledge graph for recommendation, and if the recommendation emergency value is larger than the preset threshold value, the recommendation strategy is matched with the chapter with the maximum recommendation strategy. And if yes, optimizing and recommending chapters corresponding to the urgently needed learning resource type for learning. According to the method and the device, the corresponding learning resource recommendation is carried out in consideration of the time fragments of user learning, and the content which is urgently needed to be learned at present is also considered during recommendation to carry out preferential learning, so that the examination score is improved.

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 belongs to the technical field of post matching, and particularly relates to a production and teaching integrated post intelligent matching system and method based on natural language processing, a post uploading interface and a resume uploading interface are provided, a post specification and a personal resume are uploaded by related personnel, and then the system splits and matches the skills of the post specification and the personal resume. A comprehensive post comprehensive matching model is formed by combining dynamic change analysis and learning ability evaluation of post demand skills, and the comprehensive matching model not only can select students with the skills required by the current post, but also can ensure that the students have the ability of adapting to future technical reform. Therefore, more stable and efficient talent support is provided for enterprises, meanwhile, after the non-conforming resumes are screened out based on the comprehensive matching model, the deficient skills of the non-conforming resumes are recognized and labeled, graduates can clearly know which aspects have defects, and learning and improvement are conducted in a targeted mode.