111 results about "Cognitive level" patented technology

The invention discloses an educational scene-oriented knowledge graph enhanced large model personalized learning recommendation method and system, and the core thought of the method is that through the coupling of a knowledge dominant structure and a large model semantic capability, personalized learning resource pushing of triple driving of structure + semantics + behavior is realized. And deep, multi-dimensional and dynamic feedback-driven learning resource personalized intelligent recommendation can be realized in combination with student individual differences, learning paths, cognitive levels and teaching resource semantic structures.

The invention provides a service robot cross-modal user intention analysis method and system. The method comprises the following steps: firstly, acquiring and analyzing a voice instruction sequence and a visual flow containing a gesture, fusing a task target object and a gesture pointing direction to generate an intention hypothesis, and then projecting the intention hypothesis to a knowledge graph of an environment with a body; the method comprises the following steps: acquiring an intent hypothesis of a user, querying a candidate object set simultaneously matched with spatial constraints and semantic constraints of the intent hypothesis, when ambiguity exists in the candidate object set, generating an inquiry instruction for eliminating the ambiguity according to a relation path in a knowledge graph of the body environment, and finally analyzing and confirming a final user intent according to the response of the user to the inquiry instruction; according to the technical scheme provided by the invention, the ability of the robot to cope with uncertainties such as unknown reference and environment shielding in a real scene is enhanced, the interactive experience closer to the human cognitive level is realized, and the interactive efficiency and satisfaction of the user in a complex environment are also improved.

The invention discloses a personalized teaching content generation method and system based on a digital portrait, and the method comprises the steps: collecting the multi-dimensional learning data of a student in real time, analyzing the data keyword of the student, constructing the portrait of the student, generating a teaching target based on the portrait of the student, and enabling the teaching target to comprise a teaching theme, exercises related to the theme, and knowledge points related to the theme. Converting the teaching target into an executable instruction of a large language model through a structured prompt engineering technology; the large language model generates initial teaching content according to the teaching instruction; verifying and optimizing the generated initial teaching content to ensure that the generated teaching content conforms to a teaching target and can adapt to the current cognitive level and learning requirements of students; and outputting the verified and optimized teaching content to the students and automatically adapting to the content presentation form according to the learning style preference of the students.

The invention provides a knowledge graph-based low-altitude economic domain question and answer teaching interaction method and system, and the method comprises the steps: firstly receiving a low-altitude economic domain teaching problem which is inputted by a learner and comprises domain concept terms and association relation description, and then carrying out the semantic analysis of the low-altitude economic domain teaching problem; the method comprises the following steps: extracting a core concept unit and logic association characteristics between concepts, calling a pre-configured domain association knowledge graph to carry out knowledge matching, generating a question answering candidate set containing a multi-level interpretation structure, carrying out teaching adaptability adjustment on the question answering candidate set, and generating teaching guidance contents conforming to the cognitive level of a learner. And finally, the teaching guidance content is presented to the learner, and feedback information is received to carry out teaching interaction in the next stage, so that the quality and efficiency of question and answer teaching in the low-altitude economic field are improved, and the learner is assisted in systematically mastering related knowledge.

The invention provides an intelligent interaction system and method based on a multi-stage cognitive mode, and the system comprises a multi-modal data collection module which is used for collecting user interaction data through a multi-modal sensor, and the data comprise language input, non-language behaviors, interface operation data, expressions, eye movement tracks and the like; and the cognitive feature analysis module is used for calling a deep learning model to perform feature extraction on the interaction data. According to the method, language, behavior, interaction, physiology and other data are fused through the multi-modal sensor, the cognitive driving vector is generated by using the deep learning model, the real-time cognitive state of the user is effectively captured, then the probability distribution of the cognitive stage is constructed in combination with Bayesian reasoning, the problems that in the prior art, only the cognitive level can be statically judged, and real-time updating is difficult are solved, and the user experience is improved. The accuracy and timeliness of user state perception are remarkably improved, and dynamic accurate recognition and continuous modeling in the cognitive stage are achieved.

The invention relates to the technical field of education intelligent analysis, and particularly discloses a learner cognition level fine-grained tracking method and system based on a state space model. The method aims at solving the problems that an existing knowledge tracking method is low in cognitive level modeling granularity, weak in long sequence processing capacity, poor in educational interpretation and the like. According to the method, a Bloom cognitive classification system and a state space modeling technology are combined, and fine-grained and multi-level dynamic modeling and future learning performance prediction of the knowledge mastering state of the learner are achieved. The core steps of the method comprise: constructing a semantic mapping relationship between knowledge points and cognitive hierarchies (S101); collecting and encoding multi-source learning behavior features (S102); learning a cognitive state evolution trajectory based on the state space model (S103); and outputting the cognitive hierarchy classification and the answer performance prediction (S104). According to the method, by fusing the multi-dimensional learning behavior data and the state space modeling capability, the accuracy and personalized analysis depth of cognitive tracking are improved, and technical support is provided for precise teaching and intelligent decision making.

The invention belongs to the technical field of education data mining and personalized learning, discloses a learner knowledge cognition level diagnosis method and system based on cross-scale learning performance dynamic modeling, and has higher accuracy in the aspects of learner cognition state prediction and knowledge point difficulty assessment. Through a selective state space modeling mechanism and cross-scale historical learning income feature engineering, cognitive change tracks of students in various learning scenes can be accurately captured; and the robustness, convergence efficiency and long sequence processing capability of the model in learner performance prediction are improved. The method can be widely applied to a personalized education platform, a self-adaptive learning system and an intelligent teaching auxiliary tool, provides accurate student learning state analysis for teachers, optimizes learning path design, and improves the teaching effect.

The invention relates to the technical field of artificial intelligence, and particularly provides an intelligent proposition method, system and device for simulating expert propositions and a storage medium, and the method comprises the steps: constructing a structured knowledge network fusing knowledge points, cognitive levels and proposition specifications; establishing a material vector database associated with the knowledge point system; according to the target knowledge point, proposition constraint information is extracted from the knowledge network, related materials are retrieved from a vector library, and the related materials are combined into a generative prompt to be input into a large language model to generate a test question first draft; inputting the test question first draft into at least one large language model serving as a simulation answering agent, evaluating the quality of the test questions by analyzing the answering process and result, and outputting the test questions reaching the standard. According to the method, the proposition efficiency and consistency can be remarkably improved, absolute dependence on expert experience is reduced, and automatic output of high-quality test questions is achieved.

The embodiment of the invention provides an intelligent recommendation method and system based on child digital picture book reading. The method comprises the following steps: constructing a recommendation model based on self-supervised learning and a convolutional neural network CNN; in the self-supervised learning module, feature representation of a comparison mechanism optimization model combined with user cognition is introduced; constructing a dynamically updated user portrait, wherein the user portrait comprises a current cognitive level label of the user; in response to a reading request of a user, self-adaptive fusion is carried out on a user portrait and the reading request, an age-worthiness evaluation factor of the picture book is introduced in the fusion process, and the age-worthiness evaluation factor is determined based on the matching degree of the character difficulty, the picture complexity and the user cognitive level of the picture book. And taking the fused label vector as the input of a trained recommendation model to obtain a recommendation list. According to the embodiment of the invention, the feature representation of the model is dynamically optimized in combination with the cognitive development stage of the user, the reading ability difference of different age groups is accurately adapted, and the recommendation accuracy and adaptability are improved.

The invention discloses a medical record data mining and potential risk prediction method and system based on deep learning, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a first semantic vector set containing the current symptom multi-role chief complaint information of a target child, multi-modal physiological data, basic treatment information and initial cognitive level model parameters, a conflict vector set is generated through conflict recognition, initial cognitive level model parameters are adjusted in combination with multi-modal physiological data, individual cognitive level model parameters are obtained, and then a questioning text set is generated according to the conflict vector set and the individual cognitive level model parameters; after feedback is received, conflict resolution is carried out on the first semantic vector set to obtain a second semantic vector set, the second semantic vector set is fused with basic doctor seeing information to generate medical record combination feature data, and a potential disease risk prediction result of the target child is generated according to the medical record combination feature data; the method has the beneficial effects that the multi-role chief complaint information conflict in the child medical record can be effectively processed, and the potential disease risk prediction accuracy is improved.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

The invention relates to a wharf safety intelligent monitoring method, device and equipment based on digital twinning and a medium. The method comprises the following steps: firstly, carrying out space-time alignment fusion on multi-source heterogeneous original data of a wharf site to generate a dynamic digital twinborn scene; extracting and matching entity relationships and events based on the dynamic digital twinborn scene and a pre-constructed wharf operation knowledge graph, and generating a dynamic knowledge situation sub-graph; performing graph neural network coding processing on the dynamic knowledge situation sub-graph to obtain coding features of nodes in the graph; risk and conflict analysis is carried out based on the coding features, and a current risk list and a potential conflict prediction list are generated; and generating security alarm information based on the list. By adopting the method, safety monitoring can be improved from a perception level based on simple rule matching to a cognition level fusing semantic understanding and context association reasoning, so that false alarm and missing alarm are remarkably reduced, and accurate early warning of potential risks in a complex working environment is realized.

The application discloses a robot cognitive development method based on ontology semantics, and comprises the following steps: constructing a robot article identification professional knowledge base based on attribute function and ontology information representation of article definition; information determination based on attribute discrimination and semantic search; and robot cognitive development based on attribute information addition. The application simulates the process of human memory, learning and cognition of articles based on an ontology semantic knowledge base, and through machine learning and sensor attribute information, the robot can actively cognize, learn, expand and accumulate learned knowledge and experience according to information data; the robot can continuously develop its cognitive ability through learning, automatically construct a robot article identification professional knowledge base of unknown articles, and based on a semantic structure of triplets, share knowledge and exchange operation logic between man and machine, so that the cognitive level of the robot is improved and the operation experience of an operator is improved.

The application belongs to the technical field of personalized learning, and discloses a multi-level intelligent cognitive tracking method, system, storable medium and terminal, the method comprising: introducing Bloom cognitive domain education target classification, constructing test question knowledge cognitive tensor TKC, collecting learning resources and answer data of learners, and generating a sequence of learner time sequence answer pairs; introducing a multi-attribute cognitive diagnosis method, combining a deep neural network, and constructing a cognitive level mining model; sorting and encoding the cognitive level mining results of the learners to obtain deep representation features, combining a self-attention mechanism, constructing a multi-level intelligent cognitive tracking model, and further predicting the answer performance of the learners on the test questions. The application is beneficial to accurately and finely modeling the overall knowledge structure and specific level of the learners, thereby promoting personalized learning of the learners and providing a new idea for mining and tracking the cognitive state and level of the learners in an online learning platform.

The application discloses a mental health evaluation method and system based on brain area electric stimulation, relates to the technical field of medical and health information science, and creatively integrates brain area electric stimulation into mental health evaluation. The basic cognitive level and neural plasticity potential of a measured person are objectively quantified through comprehensive scores before and after brain area electric stimulation, so that corresponding suitable mental health evaluation test questions are selected based on the basic cognitive level and neural plasticity potential, and the precision, sensitivity of corresponding mental tests and the mental comfort of the measured person are significantly improved. A decay quantization model of a stimulating current from an electrode to the brain is given, the decay quantization model is used to accurately control the electric quantity, and the maximum beneficial effect is realized under the condition of guaranteeing health and safety. Historical data accumulated in the application process can be effectively used to solve medical problems, such as the influence of brain area electric stimulation on child neural development evaluation, early warning of mental illness and brain function reinforcement.

The invention relates to the field of intelligent course recommendation, and discloses an intelligent course recommendation system and method combined with cognitive level recognition, and the method comprises the steps: constructing a multi-dimensional operation task scene through an interactive game task, and generating an initial cognitive state sequence; mapping the initial cognitive state sequence, and guiding an adaptive feature screening mechanism to highlight key behavior features in a specific emotional state so as to construct a cognitive state map; based on the cognitive state map, capturing short-term cognitive fluctuation and extracting core cognitive indexes in different time periods; fusing the cognitive level evolution trajectory with the historical learning trajectory of the student, the knowledge point mastering condition, the individual preference and the emotional state to generate a personalized cognitive level judgment result which reflects the real ability of the student in the current task and the learning demand of emotion adaptation; and dynamically generating a course recommendation strategy according to a personalized cognitive level judgment result. The method has the advantage of improving the judgment precision of the current cognitive level of the student.

The invention relates to the technical field of education informatization, and discloses a teaching system for middle vocational ideological and political education, which comprises a student emotional state acquisition module, a personalized learning path generation module, a virtual reality practice teaching module, an intelligent interaction guide module, a multi-dimensional teaching effect evaluation module and a teacher decision module, a special cognitive development model of the secondary vocational students is established through a secondary vocational student cognitive feature analysis unit, and the particularities of relatively weak cultural basis, insufficient learning initiative, cognitive feature concrete deviation and the like of the secondary vocational students are fully considered. The learning content adaptive unit can dynamically adjust the depth and presentation mode of the ideological and political education content according to the cognitive level and value tendency of the students, and ensures that the teaching content is matched with the acceptance of the secondary vocational students, thereby improving the adaptability and teaching effect of the teaching content.

The invention discloses a simulation method and system for students with different cognitive levels based on a large language model. According to the method, the cognitive prototype is constructed based on the past learning records of the students, and the mastering degrees of the students on different knowledge concepts are clearly expressed through the knowledge graph. The cognitive prototype can be used for accurately predicting the performance of the student in a new task, including whether the problem can be solved or not and specific errors possibly caused. Meanwhile, a self-evaluation-self-optimization loop process based on beam search is introduced, and a student answer consistent with a prediction behavior is iteratively generated, so that student simulation which is more real, natural and consistent in cognition is realized on the premise that additional training is not needed. Related results show that the staged and cognitive prototype-driven student simulation framework provided by the invention is superior to the existing mainstream method in multiple dimensions, and behavior characteristics of students with different cognitive levels can be reproduced more truly.

The invention discloses an intelligent feature cognition detection device for visual detection. The intelligent feature cognition detection device comprises a multi-modal sensing module, a dynamic stimulation generation unit, an intelligent feature analysis unit and a dynamic cognition evaluation unit, according to the method, eye movement, pupil and electroencephalogram three-mode data are integrated, the sampling rate and precision are better than those of the prior art, environment self-adaptive calibration is combined, data integrity is improved, and the nerve and behavior process of visual feature cognition can be comprehensively reflected. Based on a linkage mechanism of a cognitive state and stimulation parameters, four types of stimulants close to an actual detection scene are generated, real-time adjustment of complexity, duration and interval is supported, the method is adaptive to testees with different cognitive levels, and compared with a fixed stimulation scheme, the relevance between a detection result and actual post ability is improved.

The invention belongs to the technical field of English teaching methods, and particularly relates to an intelligent English teaching method and system and a storage medium, and the method comprises the steps: constructing a multi-dimensional linguistic feature analysis model, and extracting lexical features, syntactic relationship features and semantic deviation features in a text input by a student through a natural language processing technology; dynamic student portraits are established based on the cognitive psychology theory, cognitive level labels are updated according to real-time learning data, and the data comprise grammar error clustering distribution, spoken language fluency indexes and vocabulary association response time; and generating a personalized teaching path, matching teaching materials from the hierarchical resource library according to the cognitive level label, and dynamically adjusting the complexity and presentation form of a teaching strategy. Lexical, syntactic and semantic deviation features are extracted through a natural language processing technology, student error types can be accurately positioned, the problem that traditional error correction only stays on surface modification is avoided, and cognitive tags are updated based on real-time learning data.

The invention discloses a cognitive evaluation method and system based on three-mode time sequence data, and relates to the technical field of data analysis, comprising the steps of: respectively performing overall analysis and single-point analysis on a behavior change curve and a physiological change curve, performing overall and partial cognitive representation analysis on behaviors and physiology, and analyzing the cognitive representation condition of the behavior change curve and the physiological change curve. A reliable basis is provided for subsequent multi-modal data fusion, and the reasonability and adaptability of fusion are ensured. Different fusion weights are respectively given to the behavior data, the physiological data and the test result data in combination with the cognitive overall embodiment degree and the cognitive single-point embodiment degree, so that the accuracy of data fusion is improved. The cognitive level of the cognitive impairment patient is evaluated by analyzing the fusion data, the accuracy and adaptability of cognitive evaluation are improved, the reasonability of cognitive task setting is ensured, and the requirement for personalized formulation of cognitive tasks of rehabilitation training of the cognitive impairment user is met.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized English grammar learning system based on artificial intelligence. Comprising a multi-modal data acquisition module, a deep user portrait construction module, a dynamic association knowledge graph module, a multi-modal intelligent diagnosis module, a meta-cognitive guide type learning path planning module, a personalized content generation module, an emotional real-time feedback module, a meta-cognitive training module, a social cooperation module and a closed-loop optimization module. The multi-modal data acquisition module is used for acquiring multi-dimensional and multi-modal data of a learner, and covering related data such as basic information. According to the personalized English grammar learning system and method based on artificial intelligence, by setting a multi-modal data acquisition module and a deep user portrait construction module, basic information, cognitive features, meta-cognitive levels and emotional preferences of learners are captured in all directions; and constructing a dynamically updated deep user portrait by means of a multi-dimensional data fusion algorithm and a time sequence analysis algorithm.

The invention relates to the technical field of education management, in particular to a health education content management system for gynecological nursing, which is constructed by the following steps: S1, analyzing original medical content to generate a professional expression structure, and identifying a core medical entity and a logic relation chain between entities; s2, on the basis of the professional expression structure and the user cognition level label, outputting a cognition adaptation expression through a hierarchical conversion engine; and S3, binding the cognitive adaptation expression with a professional expression structure, and constructing a bidirectional version topological relation in a content storage library, so that a reversible mapping relation is formed between the professional expression and the adaptation expression. According to the method, not only is the insufficient adaptation of a general medical model in terms of gynecology ambiguity and multi-stage dependence overcome, but also the subsequent conversion process is ensured to keep consistent in terms, relationships and contexts, and an interpretable and traceable basic expression framework is provided for cognitive adaptation.

The invention belongs to the technical field of intelligent education and personalized learning, and provides a personalized learning path recommendation method based on a course knowledge point sequence. The method aims at solving the problems that existing learning path recommendation logic association is insufficient, the personalized matching degree is low, and the optimization efficiency is not high. The method comprises the following steps: constructing a knowledge point sequence model by adopting a topological sorting algorithm on the basis of a curriculum knowledge point pre-modification and post-modification relationship, a difficulty level and dependence intensity, and generating a structured knowledge relationship graph; a learner feature model and a knowledge resource model are established, learner features are described from the dimensions of learning targets, cognitive levels, learning styles and the like, and the matching degree with knowledge points is calculated; and constructing an optimization model taking learning cost minimization, mastering degree maximization and path continuity optimization as targets, performing iterative optimization by using an improved intelligent optimization algorithm, and generating a learning path with logic rationality and personalized adaptability. According to the method, the scientificity and learning efficiency of learning path recommendation can be effectively improved.

The invention discloses a smart travel public service system based on digital travel, particularly relates to the field of digital travel, and is used for solving the problems of dynamic grading and synchronous regulation and control of language difficulty of existing digital travel services. The tourist portrait construction module extracts the age of the tourist based on the reservation information, and matches a corresponding education stage; the cognition grade evaluation module is used for extracting a text travel knowledge point keyword, calculating a cognition coverage degree based on a teaching material corpus in an education stage and generating a cognition grade label; the model training module is used for training a generative language model according to corpora corresponding to different cognitive levels; the multi-difficulty version generation module is used for constructing a semantic map of explanation contents of the exhibition items and generating a multi-difficulty version; the accompanying tourist division module is used for binding explanation equipment and tourist identifiers and dividing tourist groups according to reservation numbers; and the voice rhythm synchronization module identifies the cognitive level of the main group, performs semantic number alignment and rhythm adjustment on non-main group equipment, and realizes group synchronous explanation.

The invention discloses a learner cognitive diagnosis method oriented to an intelligent education scene, and belongs to the technical field of cognitive diagnosis and education data analysis. According to the method, concept-level multi-dimensional modeling is carried out on the ability and exercise difficulty of a learner by constructing multi-dimensional representation of concept perception so as to describe mastering characteristics of the learner on different cognitive levels; and meanwhile, distinguishing a pre-correction dependency relationship and a semantic approximation relationship, establishing a relationship-perceived concept dependency model, and deducing a potential exercise-concept association structure according to the relationship-perceived concept dependency model. Further, the annotated exercise-concept incidence matrix and the inference incidence matrix are fused in a unified diagnosis layer, and comprehensive evaluation of the knowledge mastering state of the learner is achieved. According to the method, an end-to-end mode is adopted for optimization, the fineness, knowledge coverage and stability of cognitive diagnosis can be effectively improved, better prediction performance and generalization ability are shown on multiple real education data sets, and the method is suitable for intelligent education application scenes such as learning analysis and personalized teaching.

The present invention provides a method and system for interactive question-answering teaching in the field of low-altitude economy based on knowledge graph. First, the method receives the low-altitude economy teaching questions input by the learner, which contain domain concept terms and descriptions of association relationships. Then, the method performs semantic analysis on the low-altitude economy teaching questions, extracts the core concept units and the logical association features between concepts, and then calls the pre-configured domain-related knowledge graph for knowledge matching, generates a candidate set of question answers containing a multi-level explanation structure, and then adjusts the teaching adaptability of the candidate set of question answers to generate teaching guidance content that meets the learner's cognitive level. Finally, the teaching guidance content is presented to the learner, and the learner's feedback information is received to carry out the next stage of teaching interaction, thereby improving the quality and efficiency of question-answering teaching in the field of low-altitude economy and helping learners to systematically master relevant knowledge.

The application provides a cognitive diagnosis method and system based on causal inference to eliminate problem difficulty bias, relates to the technical field of educational data mining, and specifically includes the following steps: preprocessing the historical record of a student's answer to a problem to obtain a training set composed of a student, a problem, problem difficulty and a score; introducing a problem difficulty variable on the basis of an existing diagnosis model, eliminating bias by using causal inference, constructing a cognitive diagnosis model, and training the cognitive diagnosis model by using the training set; inputting a student to be diagnosed, a problem and problem difficulty into the trained cognitive diagnosis model to obtain a cognitive diagnosis result of the student output by the diagnosis model; the application provides a general cognitive diagnosis framework, creates a cognitive diagnosis model based on an existing diagnosis model, eliminates the adverse effects of problem difficulty bias by using causal inference technology, and performs unbiased training on the model to diagnose the cognitive level of the student, thereby improving the accuracy and efficiency of cognitive diagnosis.

The development process of the elderly psychological health attainment evaluation tool provided by the embodiment of the invention comprises the following steps: carrying out literature review and quality research, and preliminarily combing dimensions and an entry pool of the elderly psychological health attainment evaluation tool; an expert consultation method is adopted, and the dimensionality and the entry pool of the elderly psychological health attainment evaluation tool are improved; writing a first draft of a scale according to the perfected dimensions and entries, and modifying and optimizing the scale to form a first version of elderly mental health attainment evaluation tool; and based on the modified and optimized scale, measuring indexes of the elderly psychological health attainment evaluation tool are detected, and a formal elderly psychological health attainment evaluation tool is formed. According to the method, a COSMIN standard is used as a methodology basis, and a new concept framework of mental health attainment is used as a basis for compiling evaluation tool entries. Through scientific question design and data collection and analysis methods, scientificity and reliability of the evaluation tool are ensured. The entry in the invention adopts an expression mode suitable for the cognitive level of the elderly, so that the elderly can accurately understand the evaluation content.

This invention relates to the field of cognitive health companionship technology, specifically to a multimodal companionship system for patients with cognitive impairment based on AI technology. The invention receives memory materials from a family member's terminal, constructs an interactive virtual scene, configures guiding questions, records the patient's answers to all guiding questions, sets the initial difficulty level of cognitive training based on an assessed memory recall integrity score, and collects performance data after the patient completes cognitive training tasks to determine if the initial difficulty level needs adjustment. This achieves precise matching of cognitive training difficulty to the patient's current cognitive level. Simultaneously, based on the performance data of cognitive training tasks within a subsequently set training period, a cognitive ability report is generated and compared with historical cognitive ability reports in multiple dimensions to assess the patient's cognitive ability improvement. This reflects the dynamic changes and improvement trajectory of the patient's cognitive ability, effectively improving the targeting and effectiveness of cognitive training.