1696 results about "Adaptive learning" patented technology

An AI-based Cybersecurity System and Method enable real-time detection, analysis, and mitigation of cyber threats within computing networks using adaptive artificial intelligence. The system continuously monitors network traffic, extracts behavioral and contextual attributes, and applies deep learning-based inference to identify anomalous activities indicating security breaches. The method integrates several computational units, including a network monitoring unit, feature extraction unit, artificial intelligence processor, contextual reasoning processor, and decision synthesis unit, to compute a composite risk index quantifying threat likelihood and severity. A classification processor categorizes detected threats into types such as ransomware, phishing, or unauthorized access, while a mitigation control processor initiates automated response actions to isolate compromised nodes and restore network integrity. An adaptive learning processor updates AI models using feedback from confirmed incidents. This provides a scalable, self-evolving cybersecurity framework that minimizes human intervention and enhances resilience against dynamic and zero-day threats.

The invention discloses an unmanned aerial vehicle route automatic planning method for electric power inspection, and relates to the technical field of unmanned aerial vehicle inspection. Comprising the following steps: starting an unmanned aerial vehicle, carrying out environment perception initialization, calculating the total cruise mileage, evaluating the interference risk, carrying out real-time obstacle avoidance, dynamically optimizing an inspection route, carrying out energy monitoring management, generating a return flight strategy, recording an inspection task and carrying out adaptive learning. Through dynamic electromagnetic interference modeling, multi-modal fusion perception, self-adaptive risk decision and cloud collaborative learning, the problem of insufficient adaptability of a traditional electric power inspection unmanned aerial vehicle in a complex electromagnetic environment and a dynamic obstacle scene is solved, the safety and the inspection efficiency are improved, the robustness is enhanced, and the method is suitable for popularization and application. Intelligent upgrading is carried out through continuous learning and multi-machine cooperation, the overall operation and maintenance cost of the system is reduced, a high-reliability and full-automatic inspection solution is provided for intelligent power grid construction, and the industrial application value is remarkable.

The invention discloses a self-adaptive learning question answering system and method based on multi-modal interaction, and particularly relates to the technical field of self-adaptive learning question answering. By constructing a modal recognition and preprocessing module, standardization and structuralization of multi-modal input of texts, voices, images and the like are realized; establishing a cross-round modal memory map through time sequence coding and map modeling; dynamically updating a user portrait in combination with user interaction history and current modal characteristics; in the question and answer generation process, current input, a user portrait and a historical graph are fused, intermediate semantic representation is generated through a context enhancement module, and the intermediate semantic representation is combined with a knowledge base to generate answers; meanwhile, a modal confidence degree dynamic evaluation mechanism is introduced, and weights are distributed according to the input quality, the user adaptation degree and the context correlation; and finally, incremental optimization is carried out on the graph structure, the user portrait and the question and answer strategy through a user feedback driving system, and multi-round, multi-mode and self-adaptive intelligent question and answer interaction is realized.

The present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

The invention belongs to the technical field of smart home control, and particularly relates to a smart home generation strategy optimization system based on adaptive learning, and the system comprises the steps: collecting the operation history, sensor data and equipment use modes of a user in a smart home system, and determining an evaluation standard and an optimization direction; extracting features according to user behaviors, environment changes and equipment states, and constructing a feature matrix; selecting a reinforcement learning method for modeling according to an optimization problem, designing an adaptive learning mechanism, and enabling the model to be continuously adjusted according to real-time data in an operation process; according to the method, historical data is used for training the model, a reward mechanism in reinforcement learning is used for guiding the implementation of an optimization target, a control strategy adapting to the current environment and demand is generated based on model prediction, and the method has the effects of better meeting the use demand of a user for the smart home and improving the intelligent level of the system and the user experience.

The invention relates to a supporting structure stress state monitoring method based on artificial intelligence, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring and marking strain data of a supporting structure; after abnormal values are removed, normalizing the multi-sensor data to generate a normalized strain sequence; a state monitoring model is constructed, a deep time sequence neural network architecture is adopted, and the state monitoring model comprises an input layer, a self-adaptive wavelet attention feature mapping layer, a time domain gating convolution module, a global maximum pooling layer, a dynamic feature importance reweighting layer and a full-connection classification layer; inputting a normalized data training model; optimizing a loss function through a quantile interval adaptive learning rate and a momentum updating strategy; after real-time monitoring data is processed, inputting the data into the training model according to time window slices, outputting four types of probabilities, and taking the maximum value as a prediction state; and if a plurality of continuous windows are early-warning and dangerous, triggering the terminal to give an alarm. The accuracy of monitoring the stress state of the supporting structure can be improved.

The invention discloses an intelligent auxiliary teacher lesson preparation system and method based on an education large model. The method comprises the following steps: S1, generating a standardized teacher lesson preparation data set; s2, constructing a teaching knowledge graph; s3, generating preliminary intelligent lesson preparation content; s4, presenting the preliminarily generated intelligent lesson preparation content, and providing an interactive interface combining a natural language and voice recognition; s5, generating the adjusted intelligent lesson preparation content; and S6, according to historical lesson preparation data of the teacher and the interactive feedback data, performing adaptive learning and optimization on the intelligent lesson preparation content, adjusting weights of key knowledge points in the teaching knowledge graph, updating a content generation strategy of the large education model, and generating a final intelligent lesson preparation scheme. According to the method, the response capability of the intelligently generated content to the teaching intention of a teacher is remarkably improved, the teaching content and the test question style are supported to dynamically cooperate with classroom feedback along with the teaching rhythm, and an intelligent decision support basis is provided for large-scale personalized teaching.

The invention discloses a multi-modal data fused large model Agent intelligent decision-making method and system, belongs to the technical field of artificial intelligence, multi-modal data processing, deep learning, reinforcement learning and intelligent decision-making, and aims to solve the technical problem of how to improve the performance and adaptability of intelligent decision-making in processing complex tasks and dynamic environments. According to the technical scheme, the method comprises the steps of multi-modal data fusion, wherein text, image and audio data from different modals are integrated, and unified feature representation is generated through feature extraction and feature fusion technologies; intelligent decision-making: decision-making reasoning is carried out based on the fused feature representation, and a final decision-making result is generated by adopting a deep learning model and a reinforcement learning algorithm; adaptive learning: monitoring data changes and decision-making effects in real time, and dynamically adjusting deep learning model parameters and strategies; and feedback optimization: further optimizing the performance of the deep learning model by collecting the feedback information of the decision result.

The invention provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

The invention discloses an interpretability prediction analysis system for multivariable time series data. According to the method, the prediction precision and the decision support capability of the complex time series data are remarkably improved through multi-module cooperation. Firstly, an adaptive learning optimization module dynamically adjusts model parameters and a prediction strategy, so that the model can quickly adapt to time-varying characteristics of data distribution, for example, when a causal relationship between variables suddenly changes, the weight of latest data is automatically enhanced, and historical noise interference is reduced. The dynamic causal interpretation engine tracks the influence intensity and hysteresis effect of key variables in real time, converts traditional black box prediction into a traceable causal relationship chain, and helps a user to intuitively understand driving factors of a prediction result, for example, it is identified that prediction value sudden increase in a certain period is mainly derived from hysteresis effect accumulation of an upstream variable A.

The invention relates to the technical field of AI interaction, and discloses an AI multi-modal emotion interaction memory terminal, which realizes microsecond-level synchronization of voice, facial expression and text data through a multi-thread acquisition engine, dynamically allocates each modal weight by adopting a multi-head cross attention mechanism, and adaptively adjusts modal importance based on a conversation context hidden state; when the cross-modal confidence difference exceeds a threshold value, a gating LSTM conflict resolution module is activated, and the multi-source data collaboration problem is solved; the emotional memory modeling constructs an emotional state transition topology based on a graph convolutional network, protects user privacy in combination with a differential privacy mechanism, and realizes associated event storage of millisecond backtracking of short-term memory and long-term memory. The technology integrates multi-modal dynamic perception, privacy security calculation and adaptive learning ability, significantly improves the real-time performance and personification degree of emotion interaction, and can be applied to the fields of intelligent customer service, emotion accompanying, health monitoring and the like.

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention relates to the technical field of collaborative optimization, in particular to a geometric parameter collaborative optimization method of a taper hole machining cutter, which comprises the following steps: by constructing a high-fidelity digital twin model, integrating multi-physics field coupling and machine tool dynamic characteristics based on a finite element method, generating a geometric parameter-performance data mapping set and training and calculating an agent model; outputting a Pareto solution set through multi-target global optimization; and constructing a constraint range based on the solution set, and calling a digital twin model to carry out local optimization to obtain an optimal geometric parameter combination. The method comprises a self-correction mechanism: correcting a material constitutive relation and a friction coefficient through experimental data; staged adaptive learning, NSGA-II and DBSCAN clustering are adopted, and the efficiency is optimized along with the method; and a Pareto stability index and transfer learning are introduced, so that the result robustness and the cross-task reusability are improved. According to the method, high-precision and high-efficiency geometric parameter collaborative optimization of the taper hole machining tool can be realized.

An intelligent anomaly detection system (100) for IoT networks using edge computing and deep learning, comprising: (a) a data collection and pre-processing module configured to collect data from a variety of heterogeneous IoT devices and perform pre-processing operations, including normalization, denoising, and feature extraction; b) an edge intelligence and model deployment module configured to deploy deep learning models optimized for edge devices to perform local data analysis; (c) an anomaly detection and classification module configured to identify abnormal behavior in the processed data using deep neural networks and to classify the anomalies into predefined threat categories; (d) an adaptive learning and model update module configured to update the deployed models through incremental or federated learning mechanisms without transmitting raw data to a centralised server; (e) an alert and response management module configured to generate alerts and execute predefined mitigation actions based on the type and severity of the detected anomalies; f) and a system monitoring and visualization module configured to display real-time insights, alerts and system analysis through a user interface, g) the system operates in a decentralised manner using edge computing to achieve scalable and privacy-preserving anomaly detection in IoT environments in real time.

The invention relates to the technical field of sound wave measurement, in particular to an ultrasonic image diagnosis system and method.According to the ultrasonic image diagnosis system and method, matching between reflection characteristics and tissue characteristics is made to have the self-adaptive learning ability through a deep neural network, the targeted recognition effect is improved in the aspect of signal classification, and based on the judgment result of the reflection characteristics, the diagnosis accuracy is improved. When boundary partitioning is carried out on a tissue area, the edge structure is judged by using the combination of three parameters of gray range, gradient direction and texture continuity, contour fuzziness caused by single index judgment is avoided, and the boundary partitioning accuracy is improved by extracting a gray distribution center, an amplitude change track and an edge area continuous change sequence. And the region positioning result is input into a support vector machine, accurate recognition of lesion properties is realized according to boundary classification comparison of morphological structure quantitative features and historical benign and malignant feature data, secondary verification of the structural form is performed after the image is formed, and the diagnosis integrity and the judgment confidence are effectively improved.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention discloses a fatigue driving monitoring and early warning system based on adaptive learning, and the system comprises a data processing module which is used for collecting and preprocessing driving data; the facial feature module is used for constructing a facial key point dynamic trajectory graph; the physiological feature module is used for extracting a heart rate multi-order modal component and a skin electric energy disturbance factor; the behavior characteristic module is used for extracting a periodic disturbance degree, a lane offset curvature fluctuation range and a control rhythm index; the feature fusion module is used for integrating multi-source information and carrying out time domain modeling; the recognition updating module is used for constructing an individualized recognition model and dynamically updating model parameters; the fatigue evaluation module is used for evaluating a fatigue state and generating a corresponding grade output signal; and the early warning intervention module is used for triggering voice prompt, seat vibration or visual prompt according to the output signal. According to the invention, real-time identification and intelligent intervention of the driving fatigue state are realized, and driving safety and response efficiency are improved.

The invention relates to the technical field of farmland nutrient management, and discloses an organic fertilizer application method and system for dynamic management of farmland nutrients, and the method comprises the steps: obtaining and standardizing original data of a farmland environment, extracting features to generate farmland feature vectors, and completing nutrient demand recognition and fertilization type analysis; integrating the data to generate an enhanced farmland matrix, performing multi-dimensional nutrient analysis, calculating application necessity and an environmental influence value, and generating decision data after verification; constructing an enhanced fertilizer feature space based on an organic fertilizer library and the decision data, completing fertilizer matching, proportioning and application path optimization, and outputting pre-inspection report data; and collecting application data to carry out anomaly detection and adaptive learning. The system comprises a processor and a memory, and instructions stored in the memory can implement the method. According to the invention, through multi-dimensional data integration and intelligent decision making, the precision and scientificity of farmland nutrient management are improved, and the system is suitable for precise fertilization requirements of modern agriculture.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention relates to the technical field of bridge construction, in particular to an intelligent cleaning device and method for a box girder side formwork. The device comprises a mobile platform, a control device, a mechanical arm assembly, a cleaning device, an infrared induction sensor, a dust collection device, a multispectral visual recognition module, a 3D environment modeling module, a path planning module, a cleaning quality detection module and a self-adaptive learning database. The multispectral visual recognition module recognizes the stain type and the adhesion strength. The 3D environment modeling module is used for constructing a three-dimensional point cloud model of a working area in real time; the path planning module generates an optimal cleaning path based on a reinforcement learning algorithm; the cleaning quality detection module detects residual stains and triggers secondary cleaning; the adaptive learning database stores historical cleaning data and optimizes a cleaning strategy through a machine learning model. The full-process automatic cleaning operation of the box girder side form can be achieved, and the comprehensive functions of dirt recognition, autonomous path planning, efficient cleaning, real-time detection and the like are achieved.

The invention discloses a non-perpetual culture immersive interactive experience device and system based on a virtual digital human, and relates to the technical field of intangible cultural heritage intelligent interaction, and the device comprises a multi-mode sensing module which collects data such as user actions and voices through a depth camera and the like; the non-perpetual knowledge base module stores a non-perpetual knowledge graph and a case library; the digital human modeling engine fuses the inheritor characteristics and the user data to generate a virtual digital human; the immersion interaction engine constructs a cross-platform rendering environment to realize five-sense fusion experience; and the adaptive learning module analyzes the interaction sequence to optimize the digital human behavior, and all the modules cooperate to realize the intelligent interaction experience of non-genetic culture. Through the multi-modal perception module, the digital human modeling module and the like, the immersive interactive experience of the non-perpetual culture is realized, the user behavior can be accurately captured, the characteristic virtual digital human is generated, the five-sense fusion experience is provided, the content can be optimized based on the user interaction, the inheritance and propagation of the non-perpetual culture are promoted, and the participation degree and the sense of identity of the user are improved.

The invention relates to the technical field of artificial intelligence and language learning, in particular to a multi-modal language learning auxiliary system and method based on artificial intelligence, and the system comprises a multi-modal input module, a cross-modal feature fusion module, a dynamic adaptive learning module and an interactive feedback generation module. Wherein the multi-mode input module is used for receiving original text data, original image / video data and original voice data; the cross-modal feature fusion module is used for extracting a visual feature vector and a semantic coding vector and generating a cross-modal joint feature vector; the dynamic adaptive learning module is used for generating dynamic scene parameters; and the interactive feedback generation module is used for outputting a multi-mode feedback data packet through a prompt learning engine. According to the method, by fusing multi-modal feature alignment and dynamic context parameter modeling, linkage generation of grammar, culture and pronunciation feedback is achieved, and the context understanding ability and interaction feedback precision in the language learning process are improved.

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

The invention relates to an artificial intelligence-based cargo transportation path planning method and system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical transaction data and cargo attributes, and generating service label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation algorithm strategy is called according to the service label, an exclusive transportation path is distributed to the high-value goods, obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and genetic algorithm iterative optimization, generating a global transportation scheme considering both time efficiency and cost; customer preference parameters are updated in a closed loop mode based on execution feedback data, and system self-adaptive learning is achieved. According to the method, the problems that differentiated services, dynamic response to environment changes and low-efficiency resource utilization cannot be achieved in a traditional method are solved, the punctuality rate of high-value orders and common orders can be increased, and meanwhile the transportation cost of the common orders can be reduced.

The invention discloses a construction elevator intelligent dynamic scheduling system and method based on multi-modal deep learning, and relates to the technical field of elevator scheduling management, the system comprises a multi-modal data acquisition module, a multi-modal deep learning processing module, a hierarchical decision module, an adaptive learning module, a hybrid reinforcement learning scheduling module, and a conflict prediction and solution module; time sequence data, image data and text requirements are processed at the same time through a multi-mode deep learning architecture, a three-layer neural network decision architecture is adopted to realize full-process intelligent decision from strategy to execution, and a learning strategy and decision weight are automatically adjusted according to a project progress stage in combination with an adaptive learning system. The optimal scheduling scheme can be automatically generated according to information such as material types, quantity and latest completion time submitted by a team, the use efficiency of the construction elevator is effectively improved, resource waste and scheduling conflicts are reduced, and the method is suitable for scenes such as building construction elevator scheduling and a multi-elevator cooperative working environment.

The invention discloses a method for converting a natural language into a low code based on multi-modal reinforcement learning, which comprises the following steps of: performing word segmentation, embedding and multi-layer feature extraction on a natural language instruction input by a user, combining a self-attention mechanism and a graph attention mechanism, extracting and optimizing an original semantic feature vector, and automatically identifying a business field; semantic relationship triples in the domain knowledge graph are fused, and a multi-modal semantic alignment and context enhancement strategy is adopted, so that the accuracy and stability of semantic representation are remarkably improved; when semantic drift or ambiguity is detected, a multi-candidate correction mechanism is triggered to obtain a better analysis result, and the robustness of semantic understanding is enhanced; and finally, mapping an analysis result into an instruction which can be identified by a low-code platform, automatically generating a code structure, continuously optimizing a semantic model and a knowledge graph based on user feedback, realizing adaptive learning, and improving the conversion efficiency and quality from a natural language to codes.

The invention discloses an unmanned aerial vehicle path planning method and system for a complex urban environment, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a plurality of algorithms are deeply fused, and a combination mode and an information transmission and optimization result inheritance mechanism between stages are beneficial to path planning of the unmanned aerial vehicle; a self-adaptive learning factor with a time-varying period is designed, the global exploration and local development balance capability of PSO is remarkably improved, and the population diversity and the global search capability are enhanced; through rapid global guidance of PSO, population dynamic enhancement of SSA, solution space depth optimization of GA and final precision improvement of LS, the path of the unmanned aerial vehicle can be rapidly and efficiently planned.

The invention relates to the technical field of data sharing, and discloses a multi-department data sharing method and device, equipment and a storage medium. The method comprises the following steps: carrying out acquisition and standardized preprocessing on multi-department data required by enterprise project application to generate an intermediate data packet; establishing an event trigger distributed data control mechanism based on the intermediate data packet, calculating a dynamic trigger threshold, monitoring data change, and generating a target control data stream; performing template-free adaptive learning to generate an optimal data processing flow definition file; executing data classification, self-adaptive compression and increment processing at the edge computing node to generate an optimized data packet and metadata thereof; and inputting the optimized data packet and the metadata thereof into a SaaS cloud server central data warehouse to generate a project declaration data packet, thereby realizing consistency coordination and conflict automatic resolution of data of multiple departments, and realizing unified access and standardized processing of heterogeneous data sources.

The invention relates to the technical field of prefabricated dish detection and traceablility, and discloses a prefabricated dish quality detection device and a safety traceablility method.According to the device, multi-aspect data of prefabricated dishes are collected in real time through a multi-modal data collection module, environmental interference is eliminated through a dynamic calibration module, and multi-dimensional quality indexes are generated through a multi-source data fusion module; the deep learning quality evaluation model outputs a quality level and an abnormal probability, and the traceability data generation module binds related information to generate a unique traceability code. The security traceability method comprises the steps of multi-dimensional data acquisition and binding, distributed encryption storage, dynamic consensus verification, lightweight traceability verification, supply chain reverse tracking and the like. Precise detection and safe traceability of the quality of the prefabricated dishes are achieved, the quality safety of the prefabricated dishes is effectively guaranteed, the credibility of consumers is improved, meanwhile, intelligent optimization and self-adaptive learning capabilities are achieved, and the overall performance and operation efficiency of the system are improved.

The invention relates to the technical field of artificial intelligence education, in particular to an intelligent homework tutoring system and method based on multi-modal interaction and adaptive learning, and the system comprises a multi-modal input analysis module, a cognitive state dynamic evaluation module, an intelligent decision explanation engine, and a learning effect visualization closed-loop module. The method has the beneficial effects that a three-dimensional adaptive system of explanation granularity-presentation form-interaction frequency is used, and teaching strategies such as visual derivation, concept metaphor and the like can be automatically matched according to cognitive styles of students. Secondly, a'backtracking reinforcement-lateral expansion 'double-intervention mechanism is innovatively used, and the recurrence rate of similar errors is effectively reduced through error real-time detection and correlation knowledge contrast teaching. And finally, constructing a dynamic knowledge graph and an interactive learning report, realizing visual tracking of a learning path, and helping students to establish a systematic knowledge framework.