97 results about "Cognition" patented technology

The invention discloses an intelligent personalized learning path recommendation system, and relates to the technical field of learning systems, and the system comprises a multi-source data collection module which is used for synchronizing behavior data of students in a cross-platform learning scene in real time; the cognitive feature analysis engine comprises a style recognition sub-engine and a demand prediction sub-engine to predict the potential learning demand intensity of students for unmastered knowledge points and generate a priority list comprising knowledge gaps; the personalized path generator generates a three-dimensional path plan; the proportion of guided questioning, example demonstration and autonomous exploration is automatically configured according to knowledge difficulty; the dynamic self-adaptive adjustment unit is used for designing a reward function including short-term progress speed and long-term ability growth potential by taking real-time performance of students as a state space and taking path adjustment action as a decision space based on a reinforcement learning framework; and a double-loop feedback mechanism is realized. According to the method, the dimension limitation of traditional learning analysis is broken through, the difficulty of stiffness of a static course template is broken through, and meanwhile, the semantic gap of subject cognition is broken through.

The invention provides a large language model long-term memory method based on human cognition inspiration, which comprises the following steps of: calculating semantic similarity distribution of historical interaction text vectors according to a sliding window, and calculating local information entropy of the window; determining an event boundary based on the local information entropy difference of the adjacent windows; taking each discrete event as the memory of the model, and performing partition management on the memory; when the event memory partition is full, the memory event with the low retention rate is transferred to a long-term memory area; when the interactive content relates to historical information, retrieving related historical events in the event memory partition by using a contextualized memory retrieval method; and the large language model generates content for the user in combination with the current context and the retrieved related historical events. According to the method, memory coding and storage, multi-level dynamic memory management and event-level situational memory retrieval methods based on the event cutting theory are adopted, and therefore the problem that an existing large language model long-term memory method lacks dynamic memory and situational memory is solved.

The invention provides a human-computer interaction virtual-real simulation method and system of an intestinal nutrition nursing teaching system, and relates to the technical field of virtual interaction teaching. By integrating a force feedback glove, a bioelectric sensor and a pressure detector, multi-mode interaction data acquisition including intubation angle deviation, maximum force application pressure value and step interval time is realized; the operation feedback dimension and immersion are improved; the system is combined with a scene construction and intelligent guide module, when the detection operation goodness of fit is lower than a preset threshold value or the process completion degree is insufficient, force feedback vector correction and cognitive prompt are triggered, and standard attitude and step memory are guided; and the comprehensive evaluation module summarizes key indexes to calculate a total operation score, performs defect positioning and training suggestion matching on low-score behaviors, generates a structured visual learning report, constructs a cognition-operation-feedback closed loop, and solves the problems of single interactive feedback, insufficient teaching structuralization and lack of feedback mechanisms.

Disclosed in the present invention is a general-purpose intelligent agent. The general-purpose intelligent agent comprises: an input module, which is configured to acquire a preprocessed input signal; a consciousness module, which is configured to perform perception and cognitive operations in a transparent and interpretable abstract thinking form; a self-awareness module, which is configured for perception and cognition of an intelligent agent body; a subconsciousness module, which is configured to use a data-driven model to implement perception and cognitive functions at a subconscious level; an information exchange module, which is configured to perform mutual exchange between perception, cognition and other information generated by the consciousness module, the self-awareness module and the subconsciousness module, thereby enabling integration and utilization; and an output module, which is configured to output a processing result on demand. In a machine intelligent agent of the present invention, a machine uses a general-purpose language as an underlying language for thinking and interaction, a thinking process and result are completely transparent, and thus autonomous decision-making, autonomous learning and continuous evolution are realized under fully controllable conditions. Further disclosed is a method for constructing a general-purpose intelligent agent and a general-purpose language. Compared with the prior art in which a natural language is used, the machine intelligent agent based on the general-purpose language has wide versatility in application.

The innovation is the Landscape Syntax Algorithm, a new analytical framework to transcend the age-old limitations of space syntax. It integrates the highly versatile idea of landscape. Space syntax is largely objective and deals with the arrangement of space, along with little or no consideration for the more subjective parts of human aspects and experiences. Thus, the Landscape Syntax Algorithm fills that gap by providing a model that marries objective spatial measures with the more subjective cognitive and experiential aspects of landscape. This is carried out by infusing perceptual cognition and history / contextual layers within spatial analysis. Hence, there is a thorough reading of the relation between human experience and the built world. It opens up the opportunity to unify the most objective spatial configuration and subjective perceptual cognition to provide a powerful resource for the architect, urban planner, or researcher interested in analyzing and designing spaces that connect to the human experience in a more profound and mature sense. This invention enables quantification and analysis of landscape as moving and interactive phenomena towards a much more human-oriented contextual design of space.

Systems and methods for guiding or steering thought processes on a persistent cognitive machine (PCM) that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for guiding or steering thought processes on the thought manifold are disclosed that involve mathematical manipulations of the geometric space of the thought manifold inspired by gravitational lensing.

A system and method for a digital thought architecture, otherwise called a persistent cognitive machine (PCM), that uses a continuous, differentiable, thought manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with thought manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. Not only does the PCM with thought manifold maintain persistent cognitive processes regardless of external interaction, overcoming limitations of existing AI systems that operate within a prompt-response paradigm where they await input, generate output, and return to a waiting state, it also performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. In some embodiments, the thought manifold may be implemented as a neuromorphic platform.

The invention discloses a DIKWP-driven individualized brain-map interactive feedback system, which is used for neural rehabilitation and brain-computer interface training. The system obtains brain activity data of a patient through electroencephalogram acquisition equipment, constructs a personal brain-semantic map in combination with cognitive evaluation, and establishes a mapping relation between semantic units and brain region responses. In the training process, the DIKWP semantic analysis module performs multi-layer analysis on indexes such as reaction time, accuracy and intention achievement, generates multi-mode instant feedback such as visual sense, auditory sense or tactile sense, and performs directional reinforcement on a weak semantic domain. The system has a dynamic target optimization capability, and the difficulty can be automatically adjusted according to training performance; when attention distraction, emotion abnormity or semantic deviation is detected, a safety intervention mechanism is automatically triggered, and training effectiveness and safety are guaranteed. According to the method, closed-loop individualized rehabilitation interaction is realized, the adaptation degree and efficiency of brain-computer interface training are remarkably improved, and the method is suitable for various rehabilitation scenes such as languages, movement and cognition and has a good industrial application prospect.

The invention provides a construction method of an industrial brain system for an industrial chain, and the method comprises the steps: constructing the industrial brain system based on an autonomous cognitive thinking map function module, an autonomous interactive decision function module and a dynamic behavior active regulation and control function module, the robot is used for autonomously executing perception, cognition, decision making and regulation tasks on various situations of the ductile seepage phase change of an industrial chain and supply chain network; the autonomous cognitive thinking map function module is used for sensing, measuring, cognizing and storing various situations of the ductile seepage phase change of the industrial chain and supply chain network in real time; the autonomous interactive decision function module is used for tough situation identification of total elements, accurate event positioning and comprehensive situation research and judgment; and the dynamic behavior regulation and control module is used for regulating and controlling the vulnerability and risk propagation dynamics behaviors of the industrial chain and supply chain network according to the generated decision scheme. Therefore, accurate identification, measurement, catastrophe prediction and active behavior regulation and control can be carried out on various situations of the tough seepage phase change of the complex giant chain type network.

The invention discloses a brain heuristic multi-expert multi-mode emotion recognition method and system, equipment and a medium, and belongs to the technical field of artificial intelligence and biomedical signal processing. The method comprises the following steps: by simulating a brain function partitioning mechanism, dividing an electroencephalogram signal into a plurality of brain regions according to neuroanatomy prior, and designing a special expert network for each region; a global-local double-current encoder is adopted to cooperatively extract spatial-temporal characteristics of each brain region signal, and meanwhile, a multi-scale large-kernel convolution module is utilized to extract peripheral physiological signal characteristics; and finally, dynamically fusing multi-expert features through an adaptive routing network to realize sentiment classification. Expert load balancing and bifurcation regularization joint loss are introduced into the model in training, and effective cooperation and feature diversity of experts are ensured. According to the method, excellent recognition precision is obtained in practice, it is verified through interpretability analysis that the decision-making process conforms to neuroscience cognition, and a high-precision and high-reliability solution is provided for application of brain-computer interfaces, mental health monitoring and the like.

The invention relates to a superconducting power knowledge system construction method and system based on a lightweight large model. According to the method, multi-source data is utilized for unified representation, deep semantic information is obtained through a lightweight large model, knowledge extraction is achieved, and a basic knowledge graph is constructed; further forming a knowledge graph which can be dynamically updated through knowledge fusion; and in combination with real-time data, adaptive multi-hop reasoning is executed, and a diagnosis and decision result with a causal chain is generated. According to the method, the semantic understanding and reasoning ability of a lightweight large model is taken as a core, the structural expression advantage of a knowledge graph is combined, the limitation of updating lagging and fixed rule reasoning rigidness of a traditional static knowledge base is broken through, and precise cognition and causal chain inference of a complex system state are achieved. The method has universality and mobility, and is suitable for knowledge modeling and intelligent diagnosis of various electric power scenes and other complex industrial systems. The method is verified by taking a superconducting power system as an example, and the feasibility and effectiveness of the construction path are proved.

The invention discloses a personalized knowledge tracking method based on a cognitive dynamic graph and multi-expert mixing, and belongs to the technical field of intelligent education, and the method comprises the following steps: constructing a cognitive dynamic structure graph based on learning interaction data of students, the cognitive dynamic structure graph comprising a cognitive dependence knowledge point graph and a question-knowledge point association graph; information transmission is carried out on the cognitive dynamic structure diagram based on a graph convolutional network, and cognitive enhanced interaction feature representation is obtained; constructing a heterogeneous expert pool and a self-adaptive routing mechanism to differentiate cognition of the students based on the interaction feature representation of cognition enhancement, and generating a comprehensive learning state evaluation result; and on the basis of the comprehensive learning state evaluation result, predicting the performance of the student on the next question, and realizing personalized knowledge tracking of the student. According to the invention, through dynamic knowledge dependence modeling and a multi-expert mixing mechanism, accurate tracking of student knowledge states and efficient planning of personalized learning paths are realized.

The invention discloses a language and cognition combined language rehabilitation training system and method. The system comprises an information input unit, an evaluation unit, a training unit and a data analysis unit. Personal information of a patient is input into the system through the information input unit, and the evaluation unit is used for carrying out language and cognition double evaluation on the patient, so that a personalized training scheme for the patient is formed according to an evaluation result of the patient, and the personalized training scheme is used for carrying out language and cognition combined rehabilitation training on the patient. In the rehabilitation training process of the patient, the training data of the patient are collected in real time through the data analysis unit and are analyzed, so that the personalized training scheme is updated in real time, and the accuracy and effectiveness of rehabilitation training are ensured.

The invention discloses a cognitive thinking big model construction method based on neural symbols, and relates to the technical field of artificial intelligence. Comprising the following steps: S1, constructing a thinking cellular machine; s2, constructing a cognitive reasoning function domain; s3, constructing a cognitive interaction motif; s4, constructing a cognitive thinking large model based on the thinking cellular machine, the cognitive reasoning function domain and the cognitive interaction motif; and S5, performing risk early warning on the industrial chain supply chain according to the constructed cognitive thinking big model, and obtaining a risk diffusion path of the industrial chain supply chain. According to the method, situation cognition of an industrial chain-oriented cognitive thinking large model in a complex uncertain multi-modal environment can be enhanced, situation evolution laws, event causal interpretable reasoning and anti-fact intervention processing can be accurately understood, and full-modal understanding and cross-task generalization ability of the model are improved, so that the accuracy of industrial chain risk early warning is improved, and the risk early warning efficiency is improved. The interdisciplinary research is promoted, and the artificial intelligence application field is expanded.

The invention discloses an AI adaptive learning path dynamic generation method and system based on a multi-modal cognitive trajectory. The method comprises the following steps: non-inductively collecting multi-modal data such as visual, auditory and interactive data; performing feature fusion by using a gating multi-mode unit to generate a student state vector; constructing a three-dimensional cognitive trajectory model for representing cognition, emotion and fatigue; dynamically deciding a learning path by adopting a hierarchical reinforcement learning algorithm and taking a track state as input; predicting a knowledge fault risk in real time by using a dynamic Bayesian network; and when the risk exceeds the threshold, actively generating a personalized completion path based on knowledge graph traceability. According to the method, deep perception of the learning state, advanced prediction of the learning risk and accurate dynamic planning of the learning path are realized, and the learning efficiency and experience are remarkably improved.

The invention discloses a magnetic attraction intelligent connection type autonomous operation and maintenance experiment node system, and the system comprises a sensing and modeling layer which is used for constructing a thinking digital twinning environment which is synchronous with a physics laboratory in real time, is centimeter-level in precision, can be interacted with the physics laboratory, and is rich in semantic information; the cognition and decision-making layer is used for upgrading simple and rule-based reactive behaviors into complex, logic-based and prospective active decisions; the collaboration and network layer is used for connecting all the distributed cognitive experiment nodes into an organic super organism capable of emerging'swarm intelligence '; and the interaction and operation and maintenance layer is used for seamlessly and immersively transmitting remote experts to a physical site by introducing generative artificial intelligence and own intelligence, so that the remote experts become a high-level commander and a lifelong learning partner of the whole cognitive experiment node system. By means of the magnetic attraction intelligent linkage type autonomous operation and maintenance experiment node system, the problems that current experiment equipment is lack of cognition on the physical environment, blank in complex logical reasoning, stagnant in adaptive capacity to dynamic tasks and the like can be solved, and the intelligent and systematic operation capacity of the experiment equipment is remarkably improved.

Systems and methods for latent slice budgeting on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Methods for latent slice budgeting on the cognitive manifold are disclosed that foliation of the cognitive manifold into time slices and budgeting change between the time slices.

A system and method for implementing experiential manifold cognition that extends persistent cognitive machines beyond discrete thought caching to continuous geometric representation of experience. The system maintains an experiential manifold comprising a differentiable manifold with Riemannian metric tensor encoding semantic relationships, compression pressure field governing memory consolidation, and potential field encoding goals and attention. Input data is projected onto the manifold through adaptive geometric diffusion preserving semantic structure. The system executes geometric transformations including metric evolution, geodesic computation, and curvature estimation. During non-interactive periods, autonomous evolution occurs through trajectory recombination and selective pruning. A user interface enables visualization and direct manipulation of manifold geometry, translating navigation into geodesic traversal and edits into metric modifications. The system maintains persistence across sessions and enables controlled federation between multiple manifolds through consent-bounded synchronization. Applications include persistent narrative worlds, collaborative cognitive spaces, and experiential intelligence systems that learn through geometric evolution.

The invention discloses an interactive task generation method and device and a storage medium, and relates to the field of man-machine interaction, and the method comprises the steps: obtaining context information, task knowledge and a task target; constructing a long-term preference map and a heterogeneous task knowledge base; finely adjusting the large language model; obtaining interaction state information; short-term emotional state memory is constructed, and a preference result is analyzed; generating a query vector; generating a retrieval result; generating a memory enhanced template; generating a current interaction task; collecting behavior data when the current interaction task is executed; analyzing the behavior data, and constructing a multi-dimensional reward; and updating the long-term preference map and the short-term emotional state memory. By constructing a long-term preference map and a short-term emotion recognition mechanism, the modeling ability for the individual state of a user and the environment context is improved, and the space consistency, semantic rationality and personalized adaptive recommendation of a task generation process are realized; and the comprehensive requirements on cognition authenticity, execution accessibility and user experience continuity in a multi-modal interaction scene are met.

The invention discloses a large language model agent collaborative decision-making method for a complex dynamic game scene, and the method comprises the steps: achieving the environment perception, experience accumulation and knowledge calling functions, and supporting the cognitive modeling and strategy generation of an agent; based on cognitive information, intelligent agent role division and labor division are achieved through an action characterization device and a role selector, and the intelligent agents are guided to perform their own functions in the collaboration process. Cognitive information and role information are input into a large language model, hierarchical analysis is performed on a game situation depending on natural language understanding and thinking chain reasoning ability, key game nodes are identified, opponent strategies are predicted, and foresight collaborative decisions are generated in combination with teammate intentions. And through a semantic matching and action mapping mechanism, converting a natural language decision generated by reasoning into a structured executable instruction, and performing rationality verification. The intelligent agent executes actions and interacts with the environment, the system updates short-term memory based on feedback and periodically integrates the short-term memory into long-term memory, and a closed-loop process of'cognition-role allocation-reasoning-execution-updating 'is formed. According to the method disclosed by the invention, a distributed collaborative decision-making architecture based on a large language model is constructed, so that the intelligent agent has stronger autonomous perception, reasoning and collaboration capabilities, and the collaboration efficiency, game adaptability and strategy generalization capabilities of the intelligent agent in a complex dynamic game environment are remarkably improved.

The invention discloses an intelligent contract logic vulnerability detection method and system based on a thinking enhancement large model, and the method comprises the steps: converting a complete intelligent contract project source code into a structured context which is easy to understand and analyze of the large model, and achieving the preprocessing of the intelligent contract project source code; a thinking knowledge base is extracted through a real world security audit report and is used for generating subsequent vulnerability detection thinking; multiple types of agents are designed for collaborative detection, a focus context mechanism is supplemented, multiple processes researched by a security officer are simulated, and vulnerability detection is carried out in an iteration mode.

The invention relates to an intelligent deep questioning method and system, and belongs to the technical field of natural language processing and intelligent evaluation. The method comprises the following steps: acquiring a candidate response, converting the candidate response into a standardized text, analyzing the text in combination with multi-dimensional monitoring data, positioning competency missing elements, generating a missing identification set, and marking a sorting question-chasing point; constructing a dynamic self-adaptive score matrix in which competency is linked with a questioning point, fusing monitoring data to calibrate scores and confidence, and generating a questioning technology intervention scheme; a virtual interviewer role library is preset, roles are dynamically matched, and the intervention logic and the missing identification set are combined to generate specialized questioning content; and when the core element capability element characterization is insufficient or the confidence coefficient does not reach the standard, generating a target element cognition questioning problem, and after element extraction and fusion, outputting a multi-dimensional competency score and a confidence coefficient report. According to the invention, the upgrading of the questioning from mechanical questioning to intelligent technical intervention is realized, the questioning targeting and the evaluation accuracy are improved, and reliable support is provided for accurate talent selection.

The invention discloses an interpretable teaching cognition interaction method and system based on a knowledge graph, and a medium, and relates to the field of education management information systems, and the method specifically comprises the steps: collecting answer data of a learner, calculating a knowledge mastering state vector, mapping the knowledge mastering state vector to a knowledge graph node, and carrying out the feature decomposition; obtaining a cognitive feature matrix and a knowledge node cognitive difficulty coefficient; constructing a cognitive path graph based on the cognitive difficulty coefficient, and calculating the migration probability of adjacent nodes to generate a cognitive migration path; and constructing a multi-layer knowledge network on the path, calculating node weights and knowledge circulation coefficients, generating a teaching knowledge point sequence, and encoding the sequence to generate stepped teaching content. According to the method, personalized content recommendation and scientific allocation of teaching resources can be provided for an education platform, and intelligent optimization and management transparency of the teaching process are realized.

The invention discloses a personalized homework intelligent generating and pushing method based on multi-modal learning data, and belongs to the technical field of education. The method aims at overcoming the defects that in the prior art, the emotion state of a learner is ignored, multi-modal data fusion is shallow, and homework generation is static. The method comprises the following steps: acquiring multi-modal data, acquiring behavior data, physiological data and expression data in real time, evaluating cognitive and emotional states, respectively outputting knowledge point mastery degrees and emotional states through sub-models, and correlating and calibrating; dynamic homework generation: a decision engine generates and pushes a personalized homework package through question bank combination or real-time generation according to a dual-state matching regulation rule; feedback optimization and privacy protection are conducted, and desensitization processing is conducted on the physiological data at the same time on the basis of an operation feedback iteration model and rule. According to the method, double regulation and control of cognition and emotion are realized, the double-state evaluation accuracy is high, the homework completion rate of the learner is effectively improved, and the method is suitable for K12 and vocational education scenes.

The application provides a text-priority multimodal sentiment analysis method and system, which respectively constructs private expert networks of text, video and audio, is used for capturing unique sentiment expression features of each mode, simultaneously introduces a shared expert network to model general sentiment semantics across modes, so that collaborative modeling of multimodal information is realized, then a text mode is used as a leading mode to start a sentiment analysis process, a preliminary judgment is given based on text information, according to a confidence degree, video and audio information are gradually introduced according to needs to perform incremental supplement for sentiment classification prediction, a need-decoding strategy consistent with human cognition is realized, and a final sentiment category is obtained. Through simulating a human cognitive process of integrating multi-source information according to needs, the application reduces reasoning time and resource consumption, makes the model decision process more natural and interpretable, and is closer to the characteristics of different modal information being unbalanced and non-equivalent in actual application.

Systems and methods for persistence of memory on a persistent cognitive machine (PCM) that uses a continuous, differentiable, cognitive manifold in geometric space to allow a computer to engage in human-like thought processes. The PCM with cognitive manifold represents a fundamental advancement in artificial intelligence beyond current probabilistic AI system such as large language models (LLMs) and similar reasoning models. A PCM with cognitive manifold performs cognition on a thought manifold in a continuous, differentiable, thought manifold in geometric space as opposed to probabilistic prediction in a discontinuous, anisotropic, and topologically fractured vector space. Persistence of memory is reflected on the cognitive manifold through relative displacements between geodesics after a reasoning trajectory has been calculated in a manner analogous to gravitational wave echoes in general relativity physics.

This document relates to the enhancement of cognition and memory through stimulation by a plurality of odorants, disclosing a system for improving the remote transfer effect of cognition in a human, a device for use in the system, and a method of delivering an aroma. The system for improving the remote transfer effect of cognition in a human comprises: a module for establishing a daily treatment schedule comprising a plurality of time intervals separated by intervals; a module for inputting the daily treatment schedule into an aroma delivery device such that the device delivers one of a plurality of aromas during each time interval and ceases delivery of the aroma at the end of the time interval; wherein the aromas delivered during successive time intervals are sufficiently different from one another so as to be distinguished by the human; wherein the daily treatment schedule is repeated each day for a predetermined treatment duration.

This invention discloses a method and system for power operation safety monitoring and question answering based on a multimodal large model. The method includes the following steps: a data acquisition step, acquiring visual data and safety regulation text data from the power operation site; a multimodal semantic fusion step, generating a unified fused semantic vector through feature extraction and cross-modal alignment fusion technology; a knowledge reasoning step, matching the fused semantic vector with a pre-constructed power safety knowledge graph and performing compliance reasoning based on a graph neural network; and an intelligent question answering generation step, inputting the fused semantic vector and the reasoning results into a large language model to generate natural language question answering information. This invention, through the synergistic innovation of multimodal semantic fusion, knowledge graph reasoning, and a large language model, solves the technical problems of insufficient semantic understanding, disconnect between the question answering system and the field, and unstructured knowledge representation in existing technologies, achieving fully automated and interpretable intelligent safety supervision from perception to cognition.

The invention discloses a large model construction method based on self-cognition, a task planning method and equipment, and the method comprises the steps: obtaining a training data set, and reading a business type corresponding to the training data set; obtaining a service task corresponding to the service type, constructing at least one piece of task description data for the obtained service task, and adding the task description data to the training data set to obtain a target training data set; and training a preset large model based on the target training data set to obtain a trained large language model. According to the method, the task description data is constructed for the business task to perform task description, so that the large language model can learn thinking and logical reasoning capabilities of the professional field from the task description data, and the model effect of the large language model in the professional field can be improved through a small amount of instruction data and task description data.

This invention discloses a heatstroke prevention and monitoring system based on conscious cognitive behavioral characteristics, belonging to the field of intelligent medical technology. This invention addresses the problems of existing technologies lacking intelligent analysis and exhibiting a certain degree of subjectivity, making it difficult to ensure the effectiveness of heatstroke prevention and monitoring. It analyzes the patient's physiological signals and behavioral data using a linear discriminant analysis model to predict whether the patient has heatstroke. After predicting heatstroke, a weighted average method is used to further calculate the patient's risk index, and different prevention and monitoring strategies are implemented based on the risk index. Based on this, it can effectively identify whether a patient has heatstroke and the risk index, thereby enabling targeted preventative measures to avoid further disease progression. Simultaneously, through intelligent analysis and judgment, it can effectively reduce the incidence of heatstroke, reduce the consumption of medical resources, and lower the socioeconomic burden.