63 results about "Cognitive map" patented technology

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention provides a multi-task printing scheduling optimization method based on AI intelligent strategy generation, and relates to the technical field of production workshop intelligent scheduling, and the method constructs and maintains a dynamic cognitive map, comprehensively perceives each entity state and complex association relationship of printing production in real time, utilizes an artificial intelligence engine, carries out context perception and reasoning based on the map, and improves the printing quality. The system executes the process in real time, analyzes event data, incrementally updates the state attribute of the cognitive map, evaluates the current execution effect, and adjusts the execution path and resource allocation of the flexible workflow in real time by applying a learning strategy; and continuous self-optimization and system self-evolution of a scheduling strategy model are realized through historical execution data, so that the problems of response rigidity, difficulty in realizing multi-target collaborative optimization and lack of fine-grained real-time adjustment in coping with emergencies in traditional scheduling are solved. And the scheduling flexibility, the overall efficiency, the product quality and the capability of coping with a complex and uncertain environment of printing production are remarkably improved.

The invention discloses an AI data warehouse full-link blood relationship tracking method and device, and relates to the technical field of data processing. The method comprises the following steps: obtaining structured log data with business terms and entity tags; establishing a knowledge graph according to the structured log data and the business document; generating blood relationship metadata according to the knowledge graph and the data job execution log; constructing a blood relationship cognition map according to the blood relationship metadata, the data operation log and the business document; generating a governance strategy vector according to the blood relationship cognitive map, the system real-time index and the service SLO, and sending the governance strategy vector to an execution engine, so that the execution engine carries out processing according to the governance strategy vector; and obtaining a governance strategy execution result, user feedback information of the governance strategy execution result and a system log generated in a governance process. According to the method, complex semantics and causal relationships behind data operation can be captured.

The invention provides an RAG knowledge base construction method and system based on hierarchical semantic indexes. The method belongs to the cross technical field of artificial intelligence and information retrieval. The method comprises the following steps: performing multi-level semantic analysis on an input original document set to generate document semantic hierarchical structure data; constructing a hierarchical semantic index tree based on the document semantic hierarchical structure data; performing dynamic knowledge graph initialization according to the hierarchical semantic index tree to generate an initial dynamic cognitive graph; and collecting real-time interaction data through a user feedback interface and a new data access module, and performing incremental updating on the initial dynamic cognitive map to form a knowledge representation system supporting life cycle evolution. Through multi-level semantic analysis and construction of a hierarchical semantic index tree (HSIT), deep semantic analysis can be performed on an original document set, the context integrity of knowledge is ensured, structured storage is realized, the knowledge can be expressed and stored more accurately, and information loss or semantic ambiguity is avoided.

The invention relates to the technical field of intelligent education, and discloses a personalized historical exercise recommendation and automatic correction system based on a large model, and the system comprises a cross-modal data collection module which is used for collecting character input, time behavior and interaction track data of a student in an answering process, and outputting a collection result to a cognitive map construction module; and the cognitive map construction module receives the acquisition result output by the cross-modal data acquisition module, and is used for generating a cognitive map representing the mastering degree of the students on the historical knowledge points and the relationship thereof based on the acquisition result, and sending the cognitive map to a cognitive drive recommendation module and an automatic correction module. The cross-modal data acquisition module is introduced, so that dominant content answered by the students is acquired, diversified recessive behavior data such as answering duration, editing and modifying tracks, mouse hovering positions and answering pauses of the students are further acquired, and the system can surpass traditional simple answering right and wrong judgment due to the multi-dimensional data capture capability.

The invention relates to a science and technology project evaluation method and system, and relates to the technical field of multi-index decision and intelligent modeling. Comprising the steps of collecting multi-dimensional data of finance, intellectual property, teams, historical projects and the like of science and technology projects; constructing an evaluation index system; constructing a cognitive map by taking the indexes as nodes and combining statistical correlation and expert rules; a graph attention network (GAT) is utilized to train the graph, and structural causal weights among indexes are extracted; fusing the structure weight and the original fuzzy relation matrix to form a structure enhanced fuzzy relation matrix; and in combination with a weight vector determined by a fuzzy analytic hierarchy process (FAHP), performing fuzzy weighted comprehensive evaluation, and outputting a project grade and an interpretable result. According to the method, the accuracy, self-adaptability and interpretability of science and technology project evaluation are effectively improved, and the method is suitable for complex project evaluation tasks in a multi-source heterogeneous data environment.

The invention discloses a multi-modal data dynamic reasoning system and method based on a cognitive map, and relates to the technical field of map data management.The method comprises the steps that text, voice and image data input by a user are extracted, user data are analyzed, and a structured interaction record is generated in combination with a user identifier and a timestamp; analyzing historical interaction records, detecting continuous query events of the same user for similar questions, gathering negative feedback signals, analyzing repetition frequencies of repeated events in the used user, and analyzing feedback intensity coefficients of output repeated behaviors; positioning corresponding nodes in the cognitive map according to the repeated behavior characteristics, analyzing attenuation coefficients, activating associated alternative reasoning paths, and outputting a to-be-updated node set and candidate paths; and adjusting the node weight based on the attenuation coefficient, updating the weight according to the semantic relevancy of the candidate path, updating the atlas connection relationship, and outputting the optimized cognitive atlas. Abnormal conditions are identified, the knowledge graph node weight is adjusted, and the analysis accuracy is improved.

The invention discloses a multi-domain constraint emotion recognition system inspired by an adaptive cognitive map and an emotion recognition method, which are applied to the field of electroencephalogram signal processing. According to a traditional emotion classification method, after electroencephalogram signals are subjected to manual feature extraction, key features are screened and input into traditional classifiers such as a support vector machine (SVM), but professional priori knowledge is needed for manual feature extraction; most of the methods mainly focus on feature selection and time information utilization, so that space information is not fully utilized; according to the method, a space-guided dynamic graph constraint learning module is designed, a topological structure is generated in a self-adaptive mode, the function relation of the topological structure is constrained, and complex spatial domain feature extraction overcoming cognitive priori information is achieved; meanwhile, a time-excited sequence attention module is provided, and global time sequence dependence characteristics are captured by paying attention to more important time in the sequence; furthermore, a new multi-domain interaction attention module is designed, traditional static cross-domain interaction is overcome, and constraint of multi-domain difference information and aggregation of complementary information are achieved. Besides, in order to better utilize information migration between different domains, a constraint is introduced to reduce distribution difference between the source domain and target domain features, and similarity and mobility of cross-domain features are improved.

A smart training student data integrated management system of the present invention belongs to the technical field of smart training and education data analysis, and comprises a learning track data acquisition module used for acquiring student interaction data of students in a training process; the dynamic cognition map construction module is used for constructing a dynamic cognition map representing the ability of the trainee by performing quantitative analysis on the dynamic association strength between skills in response to the trainee interaction data; the emergence capability identification and prediction module is used for calculating the emergence index of the skill cluster based on the dynamic cognitive map and identifying a capability emergence cluster; the self-adaptive evaluation engine module is used for dynamically updating the weight of the evaluation dimension in response to the emergence index, and robustness and universality of similarity calculation are guaranteed.

The invention discloses an AI intelligent supervision online education system, and relates to the technical field of online education. The AI intelligent supervision online education system comprises a student interface, the student interface is connected with a parent interface, the parent interface is connected with a teacher interface, the teacher interface is connected with data processing and analysis, and the student interface is connected with a cognitive map engine. According to the system, comprehensive and dynamic learning path planning and real-time feedback are achieved through cognitive map generation and analysis, the emotion intelligent evaluation and support module monitors the emotion state of students in real time and provides personalized psychological support, the adaptive learning engine accurately recommends learning resources, the learning mode is dynamically adjusted, and the learning efficiency is improved. The multi-modal interaction module supports multiple interaction modes and enhances the interestingness and participation degree of learning, the project system learning and community cooperation function promotes deep cooperation and practical application, and the ethical and value education module helps students to establish correct concepts, so that the education efficiency and quality are improved.

The invention provides a student portrait construction method and system based on a large model agent. The method comprises the steps of collecting learning behavior data of students; generating an expert cognitive path map of each subject based on the large model, and constructing a standard path database containing typical tasks; performing semantic alignment on the student path and a standard path, and calculating a cognitive residual feature vector including step redundancy, a path deviation angle and concept confusion frequency; calculating an attention weight matrix by using a bidirectional Transform model, positioning a cognitive interruption point in combination with a residual feature, and generating a sticking point interpretation map containing knowledge fault positioning and repair suggestions; generating a dynamic cognitive map and a strategy evolution tree; and generating a student cognitive portrait containing a sticking point interpretation map, a cognitive map and a strategy evolution tree. According to the method, the student cognition portrait containing the sticking point interpretation map, the cognition map and the strategy evolution tree is generated, the student cognition features, learning obstacles and strategy preferences are comprehensively described, and a visual decision basis is provided for teaching intervention.

The invention discloses a Java enterprise-level rapid development method based on visual modeling. The method comprises the steps that modeling behaviors are collected to generate a cognitive map, a view is constructed, and operation behaviors are collected to generate cognitive feature data; generating a semantic constraint model, and fusing the cognitive features and the node features to construct the semantic constraint model; semantic consistency detection is executed, design and implementation differences are automatically compared, and bidirectional correction is triggered; generating a project engineering structure, and generating a structured code and an interface document according to the correction model; zero-shutdown migration evolution is executed, a migration relation graph is constructed, and seamless switching is carried out in a gray level mode; and performing semantic self-correction updating in the operation period, monitoring semantic offset and backfilling design content for closed-loop synchronization. According to the method, intelligent modeling and uninterrupted evolution of Java enterprise-level applications are realized through cognitive map driving and a multi-mode migration mechanism.

The invention relates to the technical field of artificial intelligence and big data, and discloses a learning disorder assessment and adaptive training system and method based on multi-dimensional behaviors, and the system comprises a multi-modal assessment module, a path generation module, a nerve regulation and control module and an adaptive engine module. By fusing multi-modal data features of language behaviors, electroneurographic signals and task response behaviors and combining a cross-modal attention mechanism to dynamically construct a cognitive state recognition model, learning disorder typing accuracy is improved, multi-dimensional ability defects such as attention, memory, language understanding and execution control are quantified based on a three-dimensional cognitive map, and learning disorder typing accuracy is improved. It is ensured that the assessment result comprehensively covers the core dimension of cognitive impairment; transcranial direct current stimulation is synchronously triggered in the cognitive task starting stage, space-time precise matching of task state nerve regulation and brain region activation is achieved, stimulation target point coordinates and current intensity are dynamically adjusted according to real-time nerve feedback, and the neuroplasticity reconstruction efficiency is improved.

The invention relates to a non-visual clue brain-like SLAM (Simultaneous Localization and Mapping) method based on grid cells, which refers to a neural mechanism of the grid cells in a mammalian brain olfactory cortex, tracks and encodes the position of a robot only by utilizing self-motion information of the robot, and constructs a cognitive map of the robot and an interaction environment of the robot. The method comprises the following steps: firstly, constructing a multi-level grid cell continuous attractor network module, and encoding self-motion information of a mobile robot into cognitive representation of a grid cell module through a cognitive space transformation module; and then constructing a cognitive map in a navigation environment without visual clues by utilizing the cognitive representation of the grid cell modules. The invention provides a robust and efficient brain-like SLAM navigation method without visual clues, which can be used for intelligent autonomous positioning and navigation of the mobile robot in satellite denial and visual clue-free environments, and has important significance for expanding the application scene of the mobile robot.

The invention discloses a large-scale space brain-like navigation method based on high-dimensional cognitive coding, which comprises the following steps: constructing grid cells by using an oscillation interference network with negative feedback, and carrying out path integration on three-axis speed of self-motion information to realize high-frequency rough estimation of the position of an unmanned system; a continuous attractor neural network is utilized to construct position cells, a large-scale space is efficiently represented through a cyclic mapping mechanism, and self-adaptive fusion of self-motion information and environment information is achieved; and the cognitive map is quickly decoded by using an interest iteration mechanism, so that the memory and reasoning functions of the unmanned system on the environment information are realized. According to the invention, self-motion information provided by an inertial navigation system and external environment information provided by a global navigation satellite system and a camera are fused, and an efficient and reliable large-scale space brain-like navigation method is provided for an unmanned system.

The invention discloses a big data-based text travel industry cognitive analysis system, and relates to the technical field of big data, and the system comprises a data collection module which is used for collecting related data, including scenic spot data, tourist behavior data, social media data and traffic data, of the text travel industry from a multi-source channel; the data preprocessing module is used for cleaning, denoising and formatting the collected data and ensuring the data security through a data encryption technology; the cognitive analysis module is used for performing semantic analysis, sentiment analysis and behavior pattern recognition on the preprocessed data based on a deep learning algorithm and a natural language processing technology to generate a cognitive map of the text travel industry; the intelligent decision-making module is used for predicting tourist demands through a machine learning model according to the cognitive map in combination with historical data and real-time data, optimizing resource configuration and providing personalized recommendation services; and the visual display module is used for displaying an analysis result to a user in a visual manner and supporting multi-dimensional data query and real-time monitoring.

The invention relates to a distributed cognitive map construction and dynamic updating system and method. The distributed cognitive map construction and dynamic updating system comprises a map construction module, an incremental updating module, a distributed consistency module and a query optimization module. The graph construction module is used for converting multi-source and multi-mode heterogeneous fashion data into a unified and structured knowledge graph; the incremental updating module is used for realizing dynamic maintenance and real-time performance of the knowledge graph; the distributed consistency module is used for ensuring high consistency, high availability and change traceability of knowledge graph data among a plurality of nodes; and the query optimization module is used for providing efficient and flexible knowledge graph query capability and carrying out rapid retrieval and complex analysis on fashion knowledge. By means of the design, high-availability, high-consistency and semantic-rich fashion cognitive map construction, maintenance and updating are achieved.

The invention discloses a multi-scene intelligent consultation system based on a pre-trained large model, and aims to solve the defects of an existing consultation system in the aspects of multi-modal understanding, semantic consistency, adaptive modeling and expert ability calling. The system comprises a multi-modal input recognition module, a semantic state cognitive map construction module, an instruction translation module, a self-consistent cognitive feedback module, an expert model scheduling module, an instruction response generation module and a system scheduling and resource management module, and can realize multi-modal information fusion, semantic intention analysis, expert knowledge matching and intelligent response generation. The method is widely applicable to multi-scene intelligent interaction application of intelligent customer service, medical inquiry, financial consultation and the like.

The invention discloses a submission message generation method and system. The method comprises the following steps: firstly, constructing a multi-role developer cognitive map, and simulating information attention key points and cognitive modes of different roles; establishing cognitive models and decision preferences of different role developers based on the acknowledgement science theory and the large-scale development behavior data; then designing a code change importance perception algorithm based on an attention mechanism, simulating a human visual scanning and importance judgment process, and dynamically identifying key change contents concerned by a developer; and finally, establishing a context-aware message personalized generation framework, integrating multi-dimensional context information such as project history, team culture and personal preference, and adaptively adjusting a generation strategy and an output format of a submitted message. According to the method, the CMG problem is redefined from a'text generation task 'to a'cognitive assistance system', and an intelligent submission message really conforming to human cognitive habits is generated through a cognitive process and a decision mechanism of a deep modeling developer.

The invention discloses a recognition method for a target weak part under a complex background fused with a cognitive map, and the method comprises the steps: obtaining a to-be-recognized input image, inputting the to-be-recognized input image into a trained target weak part recognition model based on visual perception, carrying out the auxiliary reasoning through combining with a spatial position relation knowledge map of the target weak part, and carrying out the recognition of the target weak part. Obtaining a target weak part identification result; wherein the target weak part identification model comprises a backbone network, a multi-scale sensing network and a candidate region generation network; the multi-scale sensing network is used for fusing the multi-scale feature maps extracted by the backbone network to obtain a fused feature map; the candidate region generation network is used for generating a target candidate region according to the fusion feature map so as to realize classification of target weak parts, and a label distribution strategy based on Gaussian distribution measurement is set so as to improve the positive sample preset anchor frame distribution number of the target; and a target weak part identification multi-task loss function is set, and training of an identification model is realized.

The application relates to the technical field of sales support, and discloses a medicine sales data analysis method, which comprises the following steps: constructing a cognitive map model; performing clinical path complexity grading; applying a clinical path complexity grading algorithm to analyze clinical decision process data of a medical institution, and calculating a clinical path complexity index; implementing cognitive-clinical path matching; adopting a cognitive-clinical path matching algorithm to process sales representative cognitive state data and clinical path complexity data, and calculating a matching degree score; performing hierarchical progressive information presentation; based on a hierarchical progressive information presentation algorithm, processing the matching degree score and the clinical path data, dividing information into different priority levels, and outputting an optimized information presentation sequence; and providing personalized clinical knowledge navigation; the application significantly improves the accuracy and effectiveness of professional information transmission by intelligently adapting the cognitive ability of a sales representative to the complexity of a clinical path.

The invention discloses an equipment maintenance corpus intelligent processing and knowledge extraction method, which comprises the following steps: firstly, preprocessing an original corpus, and constructing a domain corpus; then based on a comparative learning framework, fusing field semantic knowledge to perform semantic enhancement representation learning on the corpora; secondly, an innovative sequence-to-sequence generation neural network model is provided, and parallel high-quality extraction of multi-granularity knowledge of a text level, a sentence segmentation level, an entity level and the like is achieved through special mark guidance; then, based on a cognitive map technology, the extracted multi-source heterogeneous knowledge is fused to construct an interpretable map embedded knowledge base, and flexible knowledge reasoning service is supported; and finally, based on the constructed knowledge base, developing intelligent applications such as an equipment fault diagnosis system, personalized maintenance scheme generation and preventive maintenance decision. Compared with the prior art, breakthrough progress is achieved, multiple artificial intelligence technology innovations are fused, and the method has great contribution in theoretical significance and application value.

This invention discloses a self-evolving multi-agent collaborative method and system based on a dynamic cognitive graph. The method includes constructing a dynamic cognitive graph and modeling the reasoning process as a directed acyclic graph; initializing a multi-agent collaborative network; executing sub-tasks and dynamically reconstructing the cognitive graph using execution feedback; evaluating the execution process using a case-based reasoning evolutionary mechanism, generating structured cases, and encoding and storing the structured cases in a case library for experience reuse in subsequent tasks; the multi-agent collaborative network adopts a hybrid collaborative architecture, communicating between agents through a blackboard system; monitoring metacognition by real-time monitoring of the confidence level of the reasoning process, and using an arbitration mechanism to arbitrate conflicts when multiple agents reach contradictory conclusions; outputting the execution and evaluating the system performance. This invention significantly improves the processing capability of complex long-link reasoning tasks and the system's continuous learning capability.

The invention discloses a large model memory management and interaction method based on a dynamic cognitive map. The interaction method comprises the following steps: receiving information input by a user; performing dialogue generation, intention recognition and information extraction by adopting a large language model inference engine to obtain an information recognition result; and performing dynamic memory management on the information identification result. And through contradiction detection and an active verification mechanism of the reflection engine, continuous pollution of wrong memory is effectively avoided, so that the system memory becomes more and more reliable over time.

The invention discloses a personalized tourist route recommendation method based on tourist trust degree, and relates to the technical field of smart tourism, and the method comprises the steps: obtaining tourist data, calculating a real-time deviation value of the tourist trust degree, and generating a trust crisis event list; according to the trust crisis event list, judging whether the tourist has trust crisis events which continuously and densely occur in the travel, and outputting a fusing control message; inputting the fusing control message into a cognitive map generation engine, and establishing a basic trust relationship network; calling path reference data in the trust model parameter library, and performing root cause matching in combination with an attribution template preset in the event attribution knowledge library to generate a visual trust repair report; and performing route correction based on the visual trust repair report. According to the method, the fusing control message is input into the cognitive map generation engine, and the basic trust relationship network is constructed in combination with the historical interaction data, so that the multi-dimensional association between the tourist and the service provider can be systematically presented.

The invention belongs to the field of tourism recommendation, and provides a tourism recommendation method and system based on multi-modal data, and the method comprises the steps: constructing a multi-modal tourism causality cognitive map; natural interaction information input by a user is collected, and tourism intention variables and implicit preference features of the user are extracted through an intention recognition module; performing forward causal reasoning in the causal cognitive map based on the tourism intention variable, determining a target scenic spot sequence meeting the intention of the user, and performing path feasibility evaluation; according to the target scenic spot sequence, executing an anti-fact reasoning operation in a cognitive map, identifying adverse factors and reasons thereof in a potential path, and performing interpretation enhancement and risk avoidance correction on a recommended path; and a recommendation result is output, the result comprises a recommendation sequence of multiple paths, and each path is attached with user intention interpretation, a causal path diagram, a risk analysis diagram and an accessibility weight score.

The invention relates to the technical field of education management, and discloses a learning cognitive path planning system based on a cognitive map. Comprising a cognitive map construction module for recognizing subject knowledge points, extracting a dependency relationship and constructing a cognitive map; the evaluation portrait construction module is used for constructing a learning evaluation portrait; the difference atlas drawing module is used for identifying a difference atlas in the cognitive atlas; the path calculation and screening module is used for calculating the path value of the feasible path and screening out a learning cognitive path; according to the method, the cognitive dynamic change condition of the learner can be regularly monitored and analyzed, the continuous cognitive updating effect of the learner along the timeline is realized, and the limitation and hysteresis existing when a learning evaluation portrait is constructed by single-dimensional static data are avoided; and the phenomenon that part of associated subject knowledge points are missed and lost due to direct comparison of the real-time cognitive level and the planned cognitive level is avoided, and the planning accuracy of the learning cognitive path is improved.

The invention discloses a mental improvement algorithm based on human-computer interaction, and the algorithm comprises the following steps: S1, collecting the behavior data, cognitive ability indexes and learning preferences of a user through a human-computer interaction interface, and constructing an initial cognitive feature vector containing the attention concentration degree and cognitive style classification; and S2, based on a cognitive challenge module, dynamically generating a multi-dimensional interaction task according to the current cognitive level of the user. According to the method, fusion of cross-domain task complexity and a neuroscience incentive mechanism is dynamically adjusted through the fuzzy logic controller, and accurate balance between cognitive challenge and individual adaptive learning is achieved. Compared with a traditional mental training algorithm, a semantic network analysis technology is introduced into cross-domain knowledge association task generation, and the problems of task staticization, excitation simplification and path linearization in a traditional algorithm are effectively solved in combination with a three-dimensional cognitive map constructed by a map attention network and a deep reinforcement learning optimization strategy.

The invention discloses an AGV cluster distribution scheduling method based on multi-agent cooperative control, and the method comprises the following steps: constructing a self-organizing cognitive map control network based on virtual potential flow, and building a mapping relation between a physical layer and a cognitive layer; establishing a virtual potential flow model in the cognitive layer, and generating an initial virtual potential flow field; potential flow distribution is monitored based on information entropy, a balance area and a disorder area are divided, and a virtual potential flow state diagram is generated; potential flow adjusting nodes are generated in the disorder area, and an adjusted virtual potential flow field and a coordination instruction set are formed; issuing the adjusted potential flow field to a physical layer, and generating a path occupation set; and the cognitive layer performs conflict detection and time sequence coordination to form a closed-loop scheduling control system driven by the virtual potential flow. Based on the virtual potential flow self-organizing cognitive map, autonomous collaborative scheduling of the AGV cluster is achieved, and the method has the advantages of being high in real-time performance, high in coordination and capable of achieving self-adaptive optimization.

The present application relates to the technical field of intelligent identification of oil and gas reservoirs, and in particular to a low-resistance layer intelligent identification method based on a large language model, which comprises: acquiring low-resistance layer small layer multi-source data of a target oilfield, and preprocessing well logging curves, static geological parameters and oil-water layer labels; extracting time series statistical features of well logging curves and fusing them with parameters such as porosity, permeability, shale content and small layer thickness; inputting the fused features into multiple large language models to obtain oil-water layer prediction results, and constructing an error sample set and a sample difficulty label; constructing a supervised contrast learning model based on the sample difficulty label to form a model cognitive map; clustering and rule extraction are performed on the error sample aggregation area to generate an error mode library and a diagnosis rule library, and the large language model is injected for knowledge enhancement reasoning to improve the identification accuracy and interpretability of the low-resistance layer.