331 results about "Interactive Learning" patented technology

The invention discloses a multi-mode instruction analysis system for a humanoid robot. Comprising a voice input module, a visual input module, a voiceprint feature extraction module, an object recognition and pose estimation module, a multi-modal alignment network based on a space-time attention mechanism, a scene semantic tree construction module, an instruction node mapping module, a confidence evaluation module and a decision module. According to the system, accurate alignment of voice and visual information is realized through a space-time attention mechanism, environment information is represented in combination with a scene semantic tree structure, and the instruction analysis accuracy is improved. And dynamically evaluating the confidence coefficient by adopting a fuzzy instruction backtracking algorithm, and if the confidence coefficient is lower than a threshold value, starting multi-round dialogue clarification to reduce misoperation. According to the method, multi-modal data are fused, the historical interaction learning ability is optimized, the understanding efficiency and interaction robustness of complex instructions are remarkably improved, the method is suitable for scenes such as family service and logistics storage, and the intelligent level of man-machine cooperation is enhanced.

The invention discloses a multi-modal fusion rumor detection method and system based on a dynamic graph convolutional neural network. According to the method, a dynamic feature graph of a language propagation path is constructed, and potential features in the language propagation process are extracted and analyzed by utilizing time sequence changes and key node relations between nodes in a propagation graph. A neural network is adopted to extract and enhance image data, text semantic features are extracted in combination with a text feature modeling network, text feature vectorization expression is achieved based on a BERT model, and rich semantic information is obtained. And a gating mechanism is introduced to dynamically adjust fusion weights of different modal features, and an information fusion strategy is optimized. A collaborative attention mechanism is further adopted for deep fusion, interactive learning of text, image and propagation path features is enhanced, and the relevance of cross-modal and time series data is improved. And finally, inputting the fused feature vectors into a classifier for accurate classification, thereby realizing accurate detection of the social media rumors. According to the method, the multi-modal features are effectively integrated, and the false information identification efficiency is remarkably improved.

The invention discloses an intelligent network transaction credit risk assessment system and method based on multi-modal data fusion and deep learning, and particularly relates to the technical field of network transaction risk assessment, and the system comprises a multi-modal data collection, fusion and deep reinforcement learning agent, and a large model interaction and risk assessment and supervision execution module. The multi-modal data acquisition module acquires various data in real time; the fusion module realizes semantic alignment and interactive learning through multiple strategies; the deep reinforcement learning agent module optimizes a monitoring and supervision strategy based on an Actor-Critic framework; the large model interaction module provides multi-aspect support by means of various technologies; and the risk assessment and supervision execution module identifies illegal behaviors and executes supervision actions. The evaluation method comprises the steps of data acquisition and preprocessing, feature vector generation, agent training, large model deployment optimization, real-time monitoring decision and the like. The system can improve the monitoring and supervision efficiency, protects the rights and interests of consumers, constructs a healthy transaction environment, and promotes the development of related technologies.

The invention relates to the technical field of XR extension, and discloses an XR augmented reality-based educational culture multi-mode interactive learning system, which comprises a culture scene intelligent construction module used for constructing an interactive historical scene through LiDAR scanning, ancient books and literature and AI generation technologies, dynamically associating cultural relics, buildings, historical events and scientific principles based on a knowledge graph technology, and establishing a multi-mode interactive learning model based on the knowledge graph technology; the multi-modal interactive learning system comprises an AR (Augmented Reality) subsystem, a VR (Virtual Reality) deep learning workshop, an AI (Artificial Interaction) real-time error correction function and an interdisciplinary fusion education platform, wherein the AR subsystem supports scanning textbooks to trigger a 3D (Three-Dimensional) scene and explores an internal structure of a cultural relic through gesture operation, and the VR deep learning workshop provides force feedback gloves to simulate non-abandoned tool operation and AI (Artificial Interaction) real-time error correction function. According to the educational culture multi-mode interactive learning system based on the XR augmented reality, visual interaction of historic building mechanical analysis and process mathematical modeling is achieved, a cross-region cooperation space is achieved, the educational culture multi-mode interactive learning system based on the XR augmented reality forms a dynamic optimization closed loop, and the teaching efficiency and the learning effect are remarkably improved.

The invention discloses a multi-modal data fusion network attack detection method, and relates to the technical field of computer network security, and the method comprises the following steps: 1, network flow data collection and attribute analysis; step 2, multi-dimensional feature extraction and attack path drawing; 3, cross-modal data fusion and model construction integration are carried out; 4, model activation enabling and attack route evaluation; and 5, real-time data access and attack detection judgment. According to the multi-modal data fusion network attack detection method, through detailed analysis of multi-dimensional attributes of network flow data, potential feature information in the data is comprehensively mined, and multi-source information such as an attack path diagram, terminal equipment log data and threat intelligence data is further fused; and cross-modal interactive learning is carried out to construct a multi-modal attack detection model, so that the limitation of traditional single data source detection is broken through, and the relevance and complementarity among data are fully utilized.

The invention provides an integrated energy system dynamic scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: constructing an agent for the scheduling of an integrated energy system based on a Markov decision interaction architecture, and the agent comprises a state space, an action space and a reward function; dividing the integrated energy system into multiple environments with different characteristics in different time periods by using K-means clustering; capturing environmental characteristics of the integrated energy system in a scheduling period by using a prediction model, and matching an intelligent agent of a corresponding environment; setting an economical depth deterministic strategy gradient model for each environment to carry out interactive learning and decision making, and forming a distributed learning-distributed execution architecture; and carrying out dynamic scheduling on the integrated energy system by utilizing the distributed learning-distributed execution architecture. Compared with other deep reinforcement learning methods and traditional optimization methods, the method provided by the invention can obtain a more economic and stable scheduling result.

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.

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

The invention relates to the technical field of teaching management, discloses an intelligent teaching system based on a knowledge graph and virtual simulation and a construction method, and aims to effectively solve the difficulty in integration and management of teaching resources and provide personalized teaching experience. The system comprises a knowledge graph construction module which is used for integrating and standardizing teaching resources and constructing a knowledge graph containing knowledge points and association relationships thereof; the virtual simulation teaching module is used for displaying knowledge points in a virtual simulation form by constructing a knowledge presentation model and an interactive learning model, and evaluating learning conditions of students; the intelligent teaching control module is used for constructing a teaching strategy model by adopting a reinforcement learning algorithm and dynamically adjusting a teaching instruction according to the learning progress and the interaction effect of the students; and the execution presentation module is responsible for receiving and executing the teaching instruction and presenting the teaching content in the virtual teaching environment. The system improves the pertinence and effectiveness of teaching, and stimulates the learning interest of students.

The invention discloses a small molecule ligand drug screening method and system based on affinity prediction, and belongs to the technical field of biological medicine. The invention aims to solve the technical problem of low drug screening precision caused by molecular expression limitation, geometric invariance deficiency and insufficient multi-modal information fusion when virtual drug screening is carried out by using protein-ligand affinity. Comprising the following steps: acquiring ligand and protein structure information, and preprocessing to obtain coordinates and a feature matrix of ligand / pocket / residue; performing comprehensive representation, multi-feature flow self-adaption, geometric algebraic multi-layer perception and feature alignment processing on the feature matrix to obtain corresponding feature space representation; performing cross attention fusion and multi-scale interactive learning processing on the feature space representation in sequence to obtain fusion features; inputting the fused features into a multi-scale interactive learning module, and outputting final features; and finally, predicting the binding affinity of the ligand and the protein according to the fusion characteristics to obtain a binding affinity value.

The invention belongs to the field of computer vision image signal processing, and relates to a multi-robot collaborative indoor scene semantic segmentation method and system based on multivariate interactive learning. The method comprises the steps of adopting an image feature encoder based on a double attention integration module to realize cross-modal feature fusion on an RGB image and a depth image; a dimension anisotropy attention enhancement module is adopted, and feature optimization is carried out through a multi-dimensional direction attention mechanism; a cross-task collaborative interaction module is adopted, and information is shared among different tasks by utilizing an interaction mechanism based on a feature level; in a multi-task heterogeneous decoding stage, each robot uses a decoder with a specific task to execute a specific task, and semantic segmentation, instance segmentation and scene classification are realized; and a joint optimization loss function is adopted to perform end-to-end joint training on multiple tasks, so that overall optimization and collaborative awareness performance improvement are realized. According to the method, the precision and robustness can be improved when complex indoor scene semantic segmentation and other visual tasks are executed.

The invention relates to the technical field of computer vision and fire monitoring, and provides a fire image detection and segmentation method based on an end-to-end unified framework and physical knowledge embedding. Aiming at the problems of computation redundancy, feature segmentation and strong dependence on visible light caused by traditional staged processing, the invention provides the following technical scheme: constructing an end-to-end network comprising an Officient Hybrid Ender encoder and a mask-dino decoder, and realizing global modeling and cross-scale feature fusion through a single-layer Transform; thermal imaging physical knowledge embedding is innovatively introduced, and three fusion modes of pixel-level addition, feature-level Embedding and interactive learning are adopted; a lightweight single-layer Transform architecture and a multi-task loss function are designed, and GIOU, Dice and a joint detection segmentation hybrid matching strategy are combined. According to the method, a bounding box and mask prediction are synchronously generated through a unified query mechanism, the adaptability of a low-illumination scene is enhanced by using thermal imaging data, and the training efficiency is improved by decoupling bounding box loss. According to the invention, the real-time performance, robustness and precision of fire monitoring are significantly improved in a complex scene.

The invention discloses an interactive English teaching method, which comprises the following steps: S1, constructing a virtual learning environment by using a virtual reality technology, S2, comprehensively evaluating the English levels of students through an artificial intelligence algorithm, and generating a personalized learning path for each student according to an evaluation result; s3, constructing interactive learning activities and feeding back the interactive learning activities in real time, S4, collecting and analyzing interactive learning activity data of students, selecting and establishing online communities, and issuing cooperative learning tasks in the communities; s5, collecting learning data of the students in all the learning paths, and analyzing the learning data; and S6, according to a learning data analysis result, a teaching strategy is adjusted in time. The virtual reality technology and the artificial intelligence technology are combined, brand-new, personalized and immersive English learning experience is provided for students, and the English teaching quality and the learning effect of the students are expected to be remarkably improved.

The invention discloses a scenarized hierarchical management and control method for an education application in a data space, and the method comprises the steps: obtaining a multi-modal input content in a corresponding preset period after the starting of the current time in response to a starting instruction of the education application in a target data space; calling a corresponding analysis strategy according to the content type of the multi-modal input content; extracting target features from the multi-modal input content based on an analysis strategy; according to an education scene corresponding to the target data space, obtaining a matching result of the target feature and a target database; determining a corresponding data space risk level according to a matching result; and calling and executing an application management and control strategy corresponding to the data space risk level. According to the method, education application hierarchical management and control adaptive to different scenes can be provided, the management and control precision is high, meanwhile, automatic analysis and management and control processes are achieved, and the method is suitable for scenes such as classroom teaching management, after-class and extracurricular training, remote examination and family interactive learning.

The invention provides a low-altitude economic education intelligent question answering method and system for an interactive learning platform, and relates to the technical field of natural language process.The low-altitude economic education intelligent question answering method comprises the steps that firstly, a low-altitude economic education question data set containing multiple pieces of user interaction data is obtained, and a question feature set of the user interaction data is extracted; comprising semantic vector features, domain association features and context association features, retrieving a domain knowledge feature set in a low-altitude economic education knowledge base based on a question feature set, performing cross-dimensional feature fusion processing on the question feature set and the domain knowledge feature set, generating an optimized response statement of the user question statement, and sending the optimized response statement to the low-altitude economic education knowledge base. Finally, according to the difference characteristics of the optimized response statement and the historical response statement, the response service strategy of the interactive learning platform is dynamically adjusted, and the question and answer accuracy and the platform adaptability are improved.

The invention discloses a multi-modal English learning interaction system and a vocabulary memory training method, and relates to the technical field of English learning, the system comprises the following components: a data acquisition module, a data analysis module, a strategy adjustment module, a resource push module and an interaction learning module; multi-modal learning behavior data, including text input, voice reading, handwritten notes, video learning behaviors, interactive operation and the like, of learners are collected through the data acquisition module, the learners are subjected to group division by applying a group intelligent algorithm, and the behavior pattern and performance of each group in vocabulary learning are analyzed for each group, so that the learning efficiency of the learners is improved. Based on the analysis, the system can automatically adjust teaching strategies and push customized multi-modal learning resources and training methods, so that personalized requirements of different learners are met, and the learning effect and experience are remarkably improved.

The invention discloses a system and device based on interactive learning of multiple doll intelligent agents, and belongs to the technical field of large model intelligent agents, and the system comprises an entity doll which is provided with an NFC label and is used for being placed on a central base and establishing NFC connection with the central base, and the central base carries out NFC induction and recognition on the entity doll and integrates radio and loudspeaker functions. The method comprises the following steps: acquiring and playing an audio signal, performing voice or touch interaction with a user, and establishing communication connection with a service terminal, wherein the service terminal selects a chat theme in combination with a portrait of the user, establishes a virtual chat room, performs role attribute setting on an identified entity doll, identifies the intention of the user, and performs chat interaction with the user; the interactive discussion is output to the central base for playing in combination with the chat theme, so that a plurality of intelligent agents can be supported to deeply discuss around a specific theme, the logic continuity and education consistency of multi-role interactive contents are ensured, real group discussion experience is simulated, and an interactive atmosphere similar to group discussion is formed, thereby improving the interactive quality and learning depth.

The invention relates to the technical field of intelligent traffic management, and discloses a multi-modal time series data analysis-driven public traffic scheduling optimization method, which comprises the following steps of: acquiring multi-modal traffic data, performing time-space alignment preprocessing, and extracting a feature vector; fusing various feature vectors based on a cross-modal interactive learning method; and performing real-time prediction of the public traffic condition based on the multi-modal fusion feature vector to obtain a public traffic flow prediction value, and making a public traffic scheduling strategy based on the obtained public traffic flow prediction value. According to the method, multiple feature vectors are fused based on a cross-modal interactive learning method, the mutual relation between different types of data is effectively captured, the public transport scheduling strategy is formulated based on the particle swarm optimization algorithm, the global search and local search capabilities are effectively balanced, the performance of the PSO algorithm in the public transport vehicle scheduling problem can be effectively improved, and the public transport vehicle scheduling efficiency is improved. And a more efficient scheduling scheme is realized.

The invention relates to the field of industrial automation control, in particular to an intelligent monitoring system oriented to complex working conditions and a reinforcement learning optimization method thereof. Comprising a data acquisition module used for acquiring operation data of industrial equipment under complex working conditions; the data preprocessing module is used for preprocessing the operation data acquired by the data acquisition module and then acquiring standard data; the feature extraction module is used for acquiring key feature vectors capable of reflecting the operation state of the industrial equipment; the intelligent monitoring model building module is used for building an intelligent monitoring model, and the intelligent monitoring model takes the key feature vector as input and outputs a prediction result of the operation state of the industrial equipment after interactive learning; and the alarm module is used for judging whether the industrial equipment is in an abnormal state according to the prediction result, and sending an alarm signal if the industrial equipment is in the abnormal state. According to the method, the problems of low disk monitoring efficiency, poor accuracy, insufficient model adaptability, incomplete alarm mechanism and the like under complex working conditions in the prior art are solved.

The invention provides an imaging department doctor training method and system based on generative artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: calling a predetermined case parameterization strategy to carry out the parameterization processing of a real-time case, and obtaining a real-time parameter feature; extracting a first case in the image database, and obtaining a first parameter feature of the first case; generating a virtual case in combination with the real-time parameter features and the first parameter features, and introducing medical knowledge graph analysis to obtain a case reasonable coefficient of the virtual case; if the case reasonable coefficient is in a preset coefficient threshold value, constructing a virtual clinical environment of the virtual case; in a virtual clinical environment, image training is carried out on a target doctor in combination with a virtual case, and the problems that an existing image department doctor training system depends on fixed textbooks and standardized training cases and lacks a dynamic adjustment mechanism based on a real-time case, so that dynamic deduction and immersive interactive learning of doctors are difficult to realize, and the training efficiency is poor are solved. And the technical problem that the image diagnosis skill is difficult to improve efficiently is solved.

The invention discloses an algae abundance dynamic feature selection method based on multi-agent reinforcement learning, which belongs to the technical field of artificial intelligence and data mining, and comprises the steps of multi-source heterogeneous data acquisition and convergence, time sequence data cleaning and standardization processing, driving factor identification and weight calculation key driving factor set establishment, and dynamic feature selection. Convergence and determination of an optimal feature subset selected by multi-agent reinforcement learning and dynamic features: an MARLN system performs multi-round iteration and interactive learning, and each agent continuously optimizes a decision strategy thereof according to a global reward signal fused with a specification item; according to the method, the problems that a traditional static feature selection method cannot adapt to data changes and neglects interaction among features are solved, the most critical driving factors, namely the features, for algae abundance prediction are automatically recognized from multi-source heterogeneous data through a dynamic feature selection method, and the accuracy of algae abundance prediction is improved. And an optimal feature subset is constructed to improve the precision and robustness of the prediction model.

The invention relates to an intelligent identification and evaluation technology for tornado ground disasters, and the technology comprises a data collection and preprocessing module which collects image data and text data of tornado ground disasters from a plurality of data sources, carries out the preprocessing and cleaning of the data, and generates a structured data set; the multi-modal disaster assessment model is based on a VLLM and an advanced visual identification technology, adopts a double-flow network structure and comprises an image feature extraction branch and a text processing branch, the image feature extraction branch extracts key disaster features from a high-resolution image, and the text processing branch deeply analyzes text information related to disaster grade assessment; and the interactive learning mechanism allows the multi-modal disaster assessment model to request feedback to a user after preliminary analysis, and performs self-adjustment and optimization according to the feedback. The invention further provides a map display method. According to the method, the problems of data scarcity, image diversity, low damage evaluation efficiency, rapid processing requirements and model generalization ability can be effectively solved.

The invention discloses an unmanned aerial vehicle synchronous navigation and radio mapping method based on reinforcement learning and region adaptive integrated learning, and the method comprises the following steps: S1, constructing a radio environment, and carrying out region division according to geographical location information; the method comprises the following steps: S1, detecting a flight area of an unmanned aerial vehicle by adopting an area detector, S4, exploring an environment by the unmanned aerial vehicle through interactive learning, and simultaneously collecting and learning radio frequency data, S5, carrying out strategy training by adopting an experience pool jointly constructed based on actual experience and simulation experience, and S6, storing experience, calculating a return and updating a network. According to the unmanned aerial vehicle synchronous navigation and radio mapping method based on reinforcement learning and regional adaptive ensemble learning, through synchronous navigation and mapping, the unmanned aerial vehicle can be accurately navigated in an unknown or interference environment, meanwhile, a high-precision radio coverage map is established, and the method is particularly suitable for unmanned aerial vehicle communication and navigation tasks in a complex urban environment.

Systems and methods of the present invention for constructing an interactive skills map on a graphical user interface include: determining a first skill of a user; displaying the first skill on the graphical user interface; displaying one or more first possible skills adjacent to the first skill, the one or more first possible skills related to the first skill, wherein the one or more first possible skills are inactivated; receiving a first user input to select a possible skill among the one or more first possible skills on the graphical user interface; activating the selected possible skill among the one or more first possible skills to be a second skill; and in response to the first user input, dynamically displaying one or more second possible skills adjacent to the second skill, the one or more second possible skills related to the second skill.

A protein interface prediction method based on three-track coding comprises the following steps: combining a fine-tuned protein language model SiteT5 with evolutionary, geometric and statistical features extracted from a sequence, sending the combined features into a three-track coding network, and integrating a cyclic gating module, a multi-resolution aggregation module and a long sequence deformation module to obtain a protein interface prediction model SiteT5; the method comprises the following steps: respectively capturing a time sequence relation, a local mode and long-range dependence among residues, respectively mapping the three codes into different weights, carrying out point multiplication on the three codes, and carrying out aggregation through a multi-view cross attention module; then the protein residues are sent to a three-layer hierarchical interactive learning module, local structure and global dependency are cooperatively mined through an eight-head gating self-attention module and a position-by-position feedforward module, and finally the probability that each protein residue is an interface is obtained through a classifier. According to the invention, a protein-DNA interface, a protein-RNA interface, a protein-protein interface and an antibody-antigen interface can be effectively captured. And the robustness is ensured, and meanwhile, relatively high prediction precision is also shown.

The invention discloses a building safety education training system based on interactive experience, relates to the technical field of construction site safety education, and aims to solve the problems that traditional building worker safety education lacks pertinence and is difficult to meet specific safety requirements of various types of work, and the teaching mode is relatively single. The system comprises a database, a personalized learning recommendation module, an interactive learning module and a learning progress and achievement tracking module. The database is used for pre-storing safety knowledge of various construction work types; the personalized learning recommendation module is used for screening and recommending learning content suitable for the worker from a pre-stored database covering safety knowledge of various construction work types according to the work type information and learning history of the construction worker, and the learning content comprises a document and a video course. The method has the advantages that precise education content conforming to the work type and the knowledge level of each worker is provided for the worker, and the safety awareness and the skill level of the building worker are improved.

The invention discloses a religious teaching AR virtual reality experience system. According to the system, a 3D virtual environment module is constructed, a 3D environment conforming to the characteristics of a religious place is designed, and a model is established by using 3D modeling software. And through the perception interaction module, facial expressions, voices and physiological states of the user are captured and analyzed in real time. And through the intelligent feedback module, the virtual environment and the teaching content are dynamically adjusted according to the emotional state of the user, and personalized learning experience is provided. The system further comprises a multi-modal fusion module which provides a more comprehensive and accurate result for emotion recognition. The voice and text understanding module in the specific religious field enables the system to capture the emotion and demand of the user more accurately. Based on the cloud computing technology, the system realizes multi-user online interactive learning, shares a virtual environment and teaching resources, can perform deep analysis and evaluation optimization of user learning behaviors, and greatly improves the efficiency and effect of religious teaching.

The invention relates to the field of intelligent temperature control, and provides a refrigeration house PID control parameter optimization method and device, equipment and a storage medium. The method comprises the steps of collecting temperature data and energy consumption data of the refrigeration house at a constant-temperature stage; based on the temperature data and the energy consumption data, a mathematical model representing the mapping relation between the temperature and the energy consumption of the refrigeration house is established; constructing a deep reinforcement learning model based on a preset reward function and a mathematical model giving consideration to the temperature stability and energy consumption optimization of the refrigeration house; and through interactive learning of the intelligent agent of the deep reinforcement learning model and the refrigeration house environment, determining optimization parameters used for adjusting refrigeration house PID control. According to the refrigeration house PID control parameter optimization method, device and equipment and the storage medium provided by the invention, the mathematical model of the temperature and energy consumption mapping relation of the refrigeration house can be effectively established, the deep reinforcement learning model is constructed through the reward function and the mathematical model, and the optimization parameters of refrigeration house PID control are adjusted through deep reinforcement learning.

The invention belongs to the field of multi-room negative pressure gradient control, and discloses a multi-room negative pressure gradient automatic control method and system based on reinforcement learning. An environment model containing multi-dimensional dynamic parameters such as an environment pressure state, an access control state, a fan air volume and a room coupling relation is constructed, so that the method can directly obtain an optimal control strategy through interactive learning on the premise of not needing an accurate physical model. Through the self-adaptive decision-making capability of reinforcement learning, linkage adjustment of the exhaust air volume and the fresh air volume of each room and the power of a total exhaust fan and a fresh air fan of the whole system is achieved, so that the stability of gradients of a corridor-5Pa, a ward-10Pa, a semi-polluted area-15Pa, a polluted area-20Pa and the like is kept, and it is ensured that the pressure difference of all areas is always within the safety range of-7Pa to-2Pa.

The invention belongs to the technical field of auxiliary interactive learning, and particularly relates to a battery-grade lithium carbonate industrial chain monitoring and cognitive deduction system based on industrial intelligence, which comprises a resource management and control database, a knowledge graph modeling library, a situation deduction processing module, a service collaborative application module and a safety protection module, and the resource management and control database is used for storing cross-domain multidisciplinary chain type ubiquitous resource data. According to the battery-grade lithium carbonate industrial chain monitoring and cognitive deduction system based on industrial intelligence, through the resource management and control database and the multi-task distributed scheduling engine, the resource integration and dynamic scheduling capability, the cloud side computing power adaptive scheduling and multi-task parallel processing mechanism are significantly improved; the computing power distribution of the edge end lightweight chip and the cloud high-performance cluster is optimized, the problems of low resource access efficiency and delayed real-time data analysis are reduced, the collaborative management and control of large-scale enterprise resources are realized, and the real-time dynamic monitoring requirement of a complex industrial chain is met.