119 results about "Real time learning" patented technology

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

The invention provides a wisdom education method and system based on student behavior sequence recommendation, and relates to the technical field of wisdom education. According to the method and the system, through an AI autonomous identification knowledge graph construction and innovative knowledge fusion method, a course knowledge system is structurally and hierarchically displayed, and a knowledge framework is clearly presented, so that knowledge graph framework construction of all-platform courses is facilitated, pre-repaired courses and subsequent courses are efficiently associated, and the construction efficiency is improved. And a clear and complete course learning path is provided for students. By adopting a recommendation algorithm based on a student behavior sequence, combining SASRec and introducing a self-attention mechanism of a sparse attention module, behavior data of students can be deeply analyzed, a learning path is adjusted according to real-time learning data, and personalized learning suggestions are provided for the students. According to the method and the system, multi-dimensional functions such as knowledge graph construction, a student behavior sequence recommendation algorithm and intelligent learning path optimization are integrated, and the method and the system have wide coverage and strong comprehensiveness.

The invention provides a personalized English education system and method based on multi-modal sentiment analysis. The system comprises a multi-modal interaction module for receiving and processing multi-modal data of students, an emotion recognition module for performing emotion analysis on the input multi-modal data, and a personalized module for evaluating real-time learning states of the students based on historical learning data of the students, and the core brain module is used for dynamically adjusting interactive feedback according to the emotional state and the real-time learning state. The emotion recognition module comprises emotion information fusion, the emotion information fusion adopts a weighting strategy, and the final output emotion state is adjusted through emotion consistency constraint and a conflict correction mechanism. According to the invention, through an emotion consistency loss function, a conflict correction mechanism and a knowledge graph-based super-outline control mechanism, the emotion and cognitive states of the students are accurately identified.

The invention discloses a teaching display system and method based on a knowledge graph, and a medium, and relates to the technical field of artificial intelligence and education, and the method comprises the steps: regularly collecting multi-modal original teaching data, and constructing a teaching knowledge graph; dynamically updating the relation weight of the teaching knowledge graph; according to the potential capability coefficient of each learning terminal, the distinction degree coefficient and the difficulty coefficient of the plurality of questions corresponding to each knowledge point and the relationship weight between the knowledge points, obtaining a cognitive dependency coefficient between the knowledge points corresponding to each learning terminal; generating a personalized learning path of each learning terminal according to the cognitive dependency coefficient and the learning target; performing interaction attribute addition on nodes in the teaching knowledge graph according to the personalized learning path, and generating a VR virtual scene and visual data; and real-time learning monitoring is performed on the personalized learning path of each learning terminal, and real-time learning feedback operation is performed according to the real-time learning monitoring result, so that the overall teaching quality is remarkably improved.

The invention relates to an electrical equipment fault monitoring and positioning method and system based on data analysis, and belongs to the technical field of electrical equipment monitoring. The method comprises the following steps: sensing a transient traveling wave signal of a fault current by adopting a traveling wave detection technology to determine an initial fault area; acquiring state data of the secondary equipment in the fault area through mapping of the secondary equipment in the fault area; constructing a secondary loop connection model based on the configuration file, and designing an action sequence rule base to verify the action logic and time sequence matching of a regional data centralized protection device, a circuit breaker and a communication link in real time; based on logic verification, action triggering conditions are extracted to be fused with the regional data set; and inputting the fault feature vector into a classification model for fault identification by obtaining the classification model. According to the invention, through traveling wave detection, EEMD decomposition, data synchronization and logic verification, feature information related to the fault is accurately extracted, and through data fusion and real-time learning, the fault identification precision and efficiency are improved.

The invention relates to the technical field of data center interaction, and particularly discloses an information interaction method and system for computing power and electric power collaborative scheduling of a data center, which integrates multi-source heterogeneous data and constructs a space-time correlation model to realize accurate prediction and dynamic modeling of resource demands of the data center. The real-time learning ability of the DRL and the discrete decision advantage of the MIP are utilized to realize multi-objective synchronous optimization, the energy efficiency and economy of the data center are significantly improved, the multi-objective optimization can realize "computing power-electric power-heating power" coupling, the block chain technology ensures that data cannot be tampered and is transparent and credible, the security and auditing performance of the system are improved, and the system performance is improved. Carbon footprint tracking, green power authentication and PUE index dynamic energy efficiency optimization are supported, green power use is directly stimulated, the carbon footprint and operation cost is reduced, thermal data is used for cooling control, power data is used for task scheduling, resource conflicts are avoided, the limitation of traditional single resource scheduling is broken through, and the response speed is increased.

The invention discloses a heat supply load prediction system, and relates to the technical field of heat supply prediction. The data acquisition and fusion module is used for acquiring multi-source heterogeneous data in real time and integrating the acquired multi-source heterogeneous data into a data set with a uniform format; the feature engineering module extracts key features influencing the heat supply load, and a feature subset with the highest prediction value is screened out through a feature selection method and serves as input of a deep learning model; constructing a deep long-short-term memory network model based on an attention mechanism, and performing model training through the training data set; the real-time prediction and adaptive adjustment module receives new data acquired by the data acquisition and fusion module in real time, and inputs the new data into the trained deep long and short term memory network model based on the attention mechanism for real-time prediction of the heat supply load; by integrating multi-source heterogeneous data and utilizing an advanced deep learning model, accurate prediction of the heat supply load is realized, and the method has real-time learning and adaptive adjustment capabilities.

The invention relates to the field of resource optimization, and discloses a supply chain full-link-oriented logistics resource optimization and matching method and system, and the method comprises the steps: generating a multi-dimensional logistics state feature vector according to full-link multi-source data of a target supply chain, constructing a multi-target optimization model according to a fuzzy inference rule and a fuzzy weight system, and carrying out the optimization of the multi-target logistics state feature vector; performing preliminary matching degree analysis on the multi-dimensional logistics state feature vector, performing resource allocation on a target supply chain by using a preliminary logistics resource allocation scheme generated according to a comprehensive matching score, and performing weight online learning and dynamic self-adaptive adjustment on a fuzzy weight system according to a performance error value of an actual performance parameter value, so as to obtain a multi-dimensional logistics resource allocation scheme; and obtaining a target matching weight system, optimizing the preliminary logistics resource allocation scheme, and matching the logistics resources of the target supply chain to obtain a target resource matching result. According to the invention, in a dynamic scene of a full link of a supply chain, real-time learning of a multi-target tradeoff relation and adaptive updating of a fuzzy weight can be realized.

The invention relates to the technical field of data processing, in particular to a learning behavior data analysis system and method based on an agent, and the method comprises the steps: determining knowledge points in learning content, generating a visual knowledge point graph, testing the mastering degree of a learner for the knowledge points, generating a visual knowledge point mastering graph, and analyzing the knowledge points. The method comprises the steps of generating an optimal learning task which is most adaptive to the state of a learner based on the mastery degree of the learner and a learning behavior library, collecting real-time learning feature data of the learner during task execution, sensing the emotion and attention state of the learner in real time, and generating a personalized learning path in combination with a knowledge point graph, learning behavior analysis and the state of the learner. Learning tasks are dynamically adjusted according to real-time data, and learning strategies are optimized according to feedback of learners. Through dynamic perception and adjustment capability, the problem that a learning behavior analysis system in the prior art lacks real-time perception and dynamic adjustment of the state of a learner can be effectively solved.

The invention discloses a pollution source monitoring method and system based on a neural network, and relates to the technical field of pollution source monitoring, and the method comprises the steps: dividing a preset range of a target drinking water source into a plurality of grid sub-regions through a GIS means, setting water quality monitoring point positions in the grid sub-regions, and obtaining the water quality monitoring data of each point position; constructing a digital twinborn model, judging whether a water quality abnormity alarm signal and a water quality abnormity early warning signal are generated or not according to the water quality monitoring result of each water quality monitoring point, and obtaining a correlation coefficient between the water quality abnormity early warning signal generated by each water quality monitoring point and the scene sub-sequence; adding a pollution source monitoring model through a neural network on the basis of the digital twin model; according to the method, the pollution source monitoring model is learned in real time, real-time monitoring and control are performed by using the sewage treatment whole-process model according to the prediction result of the pollution source monitoring model, effective early warning of sudden water source water quality pollution events such as natural disasters in the drinking water source is realized, and the pollution monitoring timeliness is ensured.

The invention provides an artistic course personalized learning path method and system based on deep learning. Firstly, an artistic course learning basic information set of a learner and a preset artistic course module library are acquired; calling a pre-trained deep learning course dynamic association model to generate association strength description of the learning basis of the learner and each course module; screening and sorting based on association strength description to form an initial learning module sequence; acquiring a real-time learning behavior data set of the first course module learned by the learner, and generating a module adjustment signal; and adjusting the initial learning module sequence according to the module adjustment signal to obtain a personalized learning path adaptive to the real-time learning state of the learner, and generating a personalized learning path document comprising a course module learning sequence and module connection guidance, thereby realizing accurate customization and dynamic optimization of the learning path, and improving the learning efficiency. And the learning effect of artistic courses is improved.

The invention relates to the technical field of intelligent evaluation, and particularly discloses an intelligent evaluation model dynamic optimization method based on reinforcement learning, which is characterized in that a basic intelligent evaluation model and general parameters thereof are finely adjusted according to historical interaction data of a student object, and current interaction data of the student object is predicted by updated model parameters. A reference prediction error between a reference prediction result and real interaction is calculated, recent performance statistical characteristics and current personalized adjustment parameters of a student object are integrated as a current reinforcement learning (RL) state, an RL Agent strategy network is used for selecting an adjustment action, and the personalized adjustment parameters of the student object are calculated and updated based on the adjustment action. According to the method, personalized adjustment is performed on the real-time learning state of each student individual, so that the stability and generalization ability of the basic model are ensured, and quick response and targeted adjustment on the state change of the student individual are realized.

The invention provides a power grid standard intelligent recommendation method based on reinforcement learning and post system management. The power grid standard intelligent recommendation method comprises the following steps of 1, collecting and cleaning power grid standard data; step 2, post-standard system knowledge graph construction; 3, designing a reward function for the power grid standard recommendation system, and realizing a personalized recommendation strategy by taking post responsibilities and skill levels as dynamic weight parameters; and step 4, post-driven online incremental training is carried out. According to the method, a reinforcement learning dynamic optimization engine is adopted to learn user behaviors (click rate and task completion score) in real time, and a standard recommendation strategy is adjusted; and when the standard is updated, the post capability weight is automatically adjusted, closed-loop feedback optimization is realized, and the problem of updating delay of a traditional system is solved.

The invention discloses an electro-hydraulic system adaptive neural network preset time control method, which integrates a neural network real-time learning technology, innovatively introduces a preset time performance function, and designs an adaptive neural network preset time performance controller. For the electro-hydraulic system position tracking control problem, it can be ensured that system output transient performance and steady-state performance converge to a specified performance index range within a preset time, safe and reliable operation of the system is ensured, the unknown dynamic state of the system can be learned in real time through a neural network, high-precision motion control performance is achieved, and the control precision is improved. And the problem of differential explosion in traditional backstepping control of the electro-hydraulic system can be avoided, and the influence of measurement noise on the control precision is reduced.

The invention discloses an AIGC enabled Chinese and foreign culture comparative auxiliary teaching system and teaching method. The system dynamically generates texts, images and audio and video data closely related to foreign culture by means of the AIGC technology according to user requirements and learning levels. Meanwhile, the system can fully consider the difference of education ideas and learning habits under different cultures, formulate a personalized learning scheme for each user, and provide real-time learning effect evaluation and feedback. According to the method, deep learning, natural language processing and multimedia display technologies are applied, and the functions of user interaction, multi-modal learning, resource updating management, system optimization and upgrading and the like are integrated. The language skills of the user are improved; and foreign culture connotation is deeply explored. On the level of Chinese and foreign culture comparison, comprehensive, interactive and personalized culture learning experience is provided for users, the method is suitable for multiple culture teaching scenes, communication and understanding of Chinese and foreign culture are promoted, learners are helped to cross culture differences, and more effective cross-culture communication is achieved.

The present invention discloses an optimization method for dynamically generating a learning path of a large language model based on RAG, which relates to the technical field of data processing and includes: obtaining a predetermined learning path in a predetermined learning syllabus; obtaining an RAG model; performing information retrieval and analysis on a target dynamic learning portrait to obtain a target real-time learning state, and performing a correlation comparison analysis with a predetermined knowledge base to obtain a candidate knowledge sequence; performing knowledge fusion analysis on the target predetermined knowledge and the candidate knowledge sequence to obtain a target knowledge sequence, and dynamically adjusting the predetermined learning path in combination with a target period to generate a target dynamic learning path for a target student. The present invention solves the technical problem that the prior art cannot perform intelligent dynamic adjustment of the subsequent learning path according to the real-time learning situation of students, resulting in a lack of pertinence in the learning process of students, unsatisfactory learning quality and effect, and low efficiency, and achieves the technical effect of improving the personalization and flexibility of the learning path and enhancing the overall learning effect of students.

The invention relates to the technical field of large language models, in particular to an AI large model course recommendation and adaptive learning path optimization system for multi-dimensional learning condition analysis. Comprising a learning condition data processing unit; a multi-dimensional learning condition analysis unit; an AI large model course recommendation unit; and an adaptive learning path optimization unit. According to the method, through a matching degree calculation module and a parameter optimization module of an AI large model course recommendation unit, cosine similarity is calculated based on course joint features and learning condition features, dynamic matching degrees are obtained in combination with course-geographical attention weights, courses with the dynamic matching degrees larger than a threshold value are screened, and the first N courses are taken in a descending order to generate a personalized recommendation list; and then real-time learning effect data when the user learns the recommendation list is received, the model parameters are iteratively updated by adopting an Adam optimizer after the optimization loss is constructed, and the problem of lack of iterative optimization of the model parameters based on the real-time learning effect data in the prior art is solved.

The invention relates to the technical field of intelligent education planning, and discloses a dynamic course planning method based on a knowledge graph. The method comprises the following steps: acquiring real-time learning data including knowledge point mastering degree, learning behavior and progress; and performing association analysis on the data by using the subject knowledge graph to generate a learning state representation vector reflecting knowledge point association strength and path dependency degree. And inputting the vector into a course planning model for calculation to obtain an adjustment scheme containing the knowledge points to be strengthened, the path rearrangement sequence and the resource index. According to the scheme, the node weight of the corresponding learner in the knowledge graph is updated, and a dynamically evolved personalized knowledge graph is formed. And generating a next-stage learning task instruction based on the graph, wherein a logic mapping relationship between the to-be-learned knowledge point sequence and a preset learning target is clearly revealed. According to the method, structured traceability and diagnosis of learning weak links are realized, and an interpretable personalized learning path with a clear logic basis can be provided.

The invention discloses a large language model generative recommendation method based on self-adaptive reflection mechanism driving, and relates to the technical field of recommendation. The method comprises the steps of performing online real-time learning on behavior data of a user, and determining an initial recommendation result through a large language model; performing automatic feedback learning through the initial recommendation result, and calculating a three-layer reflection score and a comprehensive reflection score; according to the three-layer reflection score and the comprehensive reflection score, executing total regeneration or local regeneration on the initial recommendation network, and regenerating an initial recommendation result; the newly generated initial recommendation result is evaluated again, a new comprehensive reflection score is determined, and if the new comprehensive reflection score is smaller than a comprehensive score threshold value, local fragments of the initial recommendation result are optimized, and a local generation result is obtained; and carrying out weighted fusion on the local generation result and the newly generated initial recommendation result to obtain a global recommendation result. According to the method, the recommendation efficiency can be improved while the recommendation precision is ensured.

The invention provides a dynamic intelligent target detection system and method based on a reasoning, namely a training normal form, and the system comprises a real-time learning module which carries out the real-time reasoning of input data through a YOLOX model, monitors a model reasoning result in real time, and triggers and activates the online learning according to the needs; the parameter fusion engine is used for training a model needing online learning, calculating parameter sensitivity based on Hessian matrix approximation by applying a gradient importance sorting algorithm, filtering abnormal node gradients through a difference perception aggregation protocol and by using cosine similarity, and realizing stable parameter transition through momentum updating by adopting progressive knowledge fusion; the multi-stage verification subsystem is used for carrying out automatic inspection on the trained model; and the resource awareness controller is used for dynamically adjusting resource allocation and gradient sparse transmission by acquiring hardware resource conditions in real time. The real-time performance, the accuracy and the environment adaptability of target detection can be remarkably improved, and it is ensured that the model efficiently runs on different hardware platforms.

The invention discloses a medical instrument supply chain tracing management system, and relates to the technical field of supply chain management, and the system comprises a supply chain management platform which is in communication connection with the following modules: a twinning perception intelligent control module which integrates a digital twinning and depth Q network, constructs a dynamic virtual mapping of a medical instrument supply chain transportation environment, and provides a dynamic virtual mapping module for a medical instrument supply chain. Environmental parameter changes are learned in real time, the in-transit state of the medical instrument is simulated, and risks are predicted. According to the method, the transportation environment virtual model is established, real-time and dynamic mapping and visual monitoring of the in-transit state of the medical instrument are achieved, a deep reinforcement learning algorithm is integrated, the quality risk in transit can be autonomously predicted based on historical and real-time environment data, an accurate regulation and control instruction is generated, and the transportation efficiency is improved. According to the method, cold chain equipment is actively adjusted, an alarm is triggered or a transportation path is optimized, a traditional passive and lagged monitoring mode is converted into self-adaptive intelligent regulation and control, and the quality guarantee capability and risk response efficiency of a transportation link are improved.

The invention discloses an intelligent self-adaptive learning method and system based on a course knowledge graph, and aims to construct a comprehensive knowledge graph through deep analysis of course contents, and the comprehensive knowledge graph comprises knowledge points, dependency relationships among the knowledge points, difficulty levels and learning sequences. By collecting and analyzing the learning behavior data of the students, the system can evaluate the mastering condition of the students on each knowledge point in real time, and further dynamically adjust learning paths and recommendation resources according to the individual requirements of the students. The system adopts an adaptive algorithm, optimizes a learning path and provides personalized learning resources according to the real-time learning progress and feedback of the students, so as to help the students to efficiently master knowledge in the shortest time. Meanwhile, the system further integrates a learning effect evaluation and feedback mechanism, and the accuracy and the intelligent degree of the learning scheme are gradually improved by continuously monitoring the learning process of the students.

The invention relates to the technical field of learning activity optimization, and discloses an interactive learning activity dynamic optimization method and system, and the method comprises the steps: obtaining learning data of a user, extracting an interest label and a learning capability label of the user through a preset user behavior analysis model, and generating a user state label; generating a corresponding activity generation path based on the user state tag in combination with a preset course knowledge base; matching a corresponding learning activity according to the activity generation path to obtain an initial learning activity; optimizing the activity generation path by adding a path branch, trimming the path branch or replacing a path node in combination with a real-time learning state of the user to obtain an activity optimization path; optimizing the initial learning activity through the activity optimization path to obtain an optimized learning activity so as to dynamically optimize the interactive learning activity; according to the method, the learning activity can be dynamically optimized, and the state change condition of the user in the learning process can be responded in real time.

The invention discloses a real-time learning intervention method and system based on dynamic cognitive load evaluation, and relates to the technical field of learning intervention. Comprising the following steps: extracting a feature vector sequence representing a cognitive state; dividing the cognitive state into multi-level interval cognitive states, and establishing a cognitive state coding sequence; constructing a cognitive state transition trend matrix according to the cognitive state switching frequency; extracting a state jump amplitude between two continuous cognitive states, taking the state jump amplitude as a dynamic adjustment factor to adjust an opening threshold, and marking an initial intervention node; constructing an intervention record chain table, and performing linkage comparison with the cognitive state transition trend matrix; and when the continuous intervention failure accumulatively exceeds a set tolerance frequency, calculating an amplitude difference of the cognitive state before and after intervention, and adaptively adjusting a cognitive state transfer damping factor according to the amplitude difference. The real-time performance, the stability and the decision robustness of the intelligent intervention system in the learning process are improved.

The invention relates to the technical field of nuclear power plant automatic control and fluid regulation, in particular to a nuclear power plant condensation water regulating valve automatic control system which comprises a sensor data acquisition and synchronization module which acquires and synchronizes parameters in a nuclear power plant condensation water system; the reinforcement learning control module reads parameters of the sensor data acquisition and synchronization module, and generates a regulating valve control strategy by using a reinforcement learning algorithm; the real-time prediction and control decision module controls and predicts the opening degree of the condensed water regulating valve according to the regulating strategy of the reinforcement learning control module; and the anomaly detection and safety protection module reads the parameters acquired by the data acquisition and synchronization module, monitors the parameters and gives an alarm according to an anomaly monitoring result. According to the system, valve oscillation and unreasonable adjustment in a traditional method can be avoided while a valve adjustment strategy is learned and optimized in real time, and the precision and stability of condensed water flow adjustment are improved.

The invention discloses an enterprise human resource performance evaluation method based on a knowledge graph, and the method comprises the following steps: S1, collecting employee daily work data, task cooperation data, process tracking data, enterprise knowledge base data and external market dynamic data through a multi-source heterogeneous data interface, and obtaining initial multi-modal data; s2, carrying out cleaning and structured processing on the initial multi-modal data; s3, automatically extracting performance semantic features of the employees on the basis of an improved Mamba framework; s4, constructing a multi-granularity logical reasoning knowledge graph library, and learning and updating the weight of logical reasoning rules in real time; s5, performing neural logic reasoning by using the dynamic logic rule set to obtain employee performance grade scores; s6, realizing cross-department employee performance data collaborative analysis through a federal knowledge distillation method; and S7, optimizing the performance evaluation result according to the deviation between the evaluation result and the actual performance feedback data. According to the invention, objective, dynamic and comprehensive performance evaluation is realized.

A cloud-integrated, adaptive honeypot system comprising: a multitude of adaptive honeypot nodes configured to emulate vulnerable services and deployed within cloud-native workloads; an interaction monitoring unit to capture and analyze attacker behavior in real time; a central orchestration module for dynamically adapting honeypot behavior and managing the deployment lifecycle; a live learning unit on updating threat detection models based on real-time telemetry data; and a cloud integration interface that works with cloud service provider APIs to trigger automated responses, including firewall updates and workload quarantines.

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 present application relates to the technical field of large language model, specifically, to an AI large model course recommendation and adaptive learning path optimization system for multi-dimensional academic situation analysis, comprising: an academic situation data processing unit; a multi-dimensional academic situation analysis unit; an AI large model course recommendation unit; and an adaptive learning path optimization unit. Through the matching degree calculation module and the parameter optimization module of the AI large model course recommendation unit, the cosine similarity is first calculated based on the course joint features and the academic situation features, the dynamic matching degree is obtained by combining the course-geographical attention weight, the courses with a dynamic matching degree greater than a threshold value are screened, the top N personalized recommendation list is generated in descending order, and then the real-time learning effect data of the user learning the recommendation list is received, the model parameters are iteratively updated by using the Adam optimizer after constructing the optimization loss, and the problem of lacking iterative optimization of model parameters based on real-time learning effect data in the prior art is solved.

The invention provides an intelligent temperature control method for an injection molding machine. The intelligent temperature control method comprises the following steps: S1, collecting data to obtain a data set; s2, training a miniature neural network model according to the data set; s3, exporting the trained miniature neural network model, and deploying the miniature neural network model to the temperature controller; s4, reading data and inputting the data into the miniature neural network model; and S5, the micro neural network model outputs a duty ratio and outputs the duty ratio to a PWM controller, and then a heating ring on a charging barrel of the injection molding machine is automatically regulated and controlled to accurately operate according to the duty ratio. The micro neural network model is mainly introduced on the basis of a traditional injection molding machine temperature control system, and accurate control over heating of the injection molding machine charging barrel is achieved through real-time learning and adjustment. According to the system, by automatically adjusting the duty ratio and combining the actual temperature and the target temperature, the limitation that a traditional control method depends on fixed parameters is overcome, and therefore the heating process can be stably operated under various complex environment conditions.