90 results about "Collaborative knowledge" patented technology

The invention discloses a sparse reward environment-oriented collaborative knowledge fusion reinforcement learning method, and relates to the field of collaborative knowledge fusion reinforcement learning methods. By constructing a lightweight collaborative knowledge fusion model and a dynamic reward remodeling mechanism, the problems of low intelligent agent exploration efficiency and difficulty in strategy convergence in a sparse reward environment are solved. The method comprises the following steps: constructing a reinforcement learning framework comprising a policy network and a value network; designing an action space mutation supervision mechanism and a lightweight collaborative knowledge fusion model, and generating a smooth substitution action when a strategy is detected to be unstable; and a reward function is designed in combination with the task target and the dynamic constraint, and reward remodeling is realized by activating rewards through sub-target potential energy difference and knowledge fusion. According to the method, effective intermediate feedback can be provided for the agents in the sparse reward environment, the exploration efficiency is improved, the convergence time is shortened, the stability and cross-scene migration ability of the strategy are enhanced, and an effective solution is provided for sparse reward scenes such as robot control and multi-agent game.

The invention provides a multi-model collaborative knowledge graph construction method, system and device and a storage medium, and the method comprises the steps that a routing engine receives a knowledge graph construction request, matches a preset rule base according to target domain parameters, and generates a node assembly sequence; the scheduling engine constructs a task execution directed acyclic graph according to the task execution directed acyclic graph; the execution engine preprocesses the original document according to the graph to generate a structured document block set with metadata; calling a pre-training model in the small model resource pool to perform entity and relation extraction to form a preliminary entity set and an association relation set; attribute completion and implicit relation reasoning are carried out on the preliminary entity set, and a completion entity attribute set and a newly-added relation set are generated; performing entity alignment on the preliminary entity set and the complemented entity attribute set by the large model to obtain a fused entity set; and performing conflict resolution on the incidence relation set and the newly-added relation set to obtain a fusion relation set. And storing the fusion entity set and the fusion relationship set to a knowledge graph database.

The invention relates to the technical field of federated learning, and discloses a federated learning method and system based on multiple agents and knowledge distillation, and a medium, and the method comprises the steps: S1, training a local model; s2, knowledge distillation based on decoupling; s3, decentralized knowledge sharing based on a block chain: packaging the standardized distillation knowledge fragments extracted in S2 and metadata thereof into block chain transactions, submitting the block chain transactions to a block chain network, verifying the legality of the transactions, and writing the transactions into a block chain account book to realize decentralized distribution; s4, performing multi-agent collaborative knowledge management based on a large language model, and updating a student model; and S5, repeatedly executing the steps S1 to S4 until a training termination condition is met. According to the method, personalized selection, adaptive filtering and efficient sharing of knowledge in the federated learning process are realized, the client drift phenomenon caused by data non-independent identical distribution characteristics is effectively relieved, the convergence speed, the overall performance and the personalized level of the model in a heterogeneous data environment are remarkably improved, and the communication overhead between clients is optimized.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention provides an information recommendation method and device based on collaborative sampling of a knowledge graph and an adjacent graph, and relates to the technical field of information recommendation. According to the method, the triple is constructed by obtaining the historical interaction data of the user, and the triple is fused with the project attribute graph to construct the collaborative knowledge graph; performing expression alignment through projection transformation to obtain initial embedding expression; performing multi-hop neighbor sampling through biased random walk to obtain a multi-hop neighbor set; determining a neighbor which is most similar to the embedding representation of the positive sample item as an anchor neighbor; then linear interpolation is carried out, and the embedding representation of the difficult negative sample is synthesized; on the basis of the multi-head graph attention network, training by adopting a positive sample and a synthesized difficult negative sample, and aggregating to obtain an updated embedded representation; and calculating an interaction probability based on the updated embedded representation, and generating a recommended item for the user. According to the method, the problems of user-project interaction sparsity and false negative example interference are effectively solved, and stronger semantic comprehension and generalization ability are provided for a recommendation system.

The invention relates to the technical field of mechanical arm optimization learning, and discloses a sparse reward environment optimization learning method and system based on demonstration data enhancement, and the method comprises the steps: obtaining expert demonstration data of a grabbing task of a mechanical arm, generating an enhanced demonstration sample through track segmentation time sequence interpolation and state space neighborhood extension, and carrying out the reconstruction of the enhanced demonstration sample; constructing a demonstration experience playback buffer area; building a reinforcement learning framework containing a strategy network and a value network, and learning an optimal grabbing strategy by utilizing reinforcement demonstration; designing a lightweight collaborative knowledge fusion model to monitor an action change rate, and outputting a smooth action when the action change rate exceeds a threshold value; and a reward function containing a task target and dynamic constraints is constructed, knowledge fusion rewards are superposed when the fusion model is activated, and reward sparsity compensation and strategy optimization are realized. According to the method, the executing stability and safety of the mechanical arm are improved, and the parameter updating accuracy and the training convergence speed are improved.

The invention provides a team cooperation training method and system based on scene reproduction and multi-agent cooperation, and relates to the technical field of team cooperation, and the method comprises the steps: building an interaction scene through building a team cooperation knowledge base, training an agent initial behavior mode through deep reinforcement learning, and building a knowledge distillation enhanced multi-agent cooperation learning network. Anti-fact reasoning is adopted to analyze cooperation behavior influence, a capability evolution track is dynamically tracked, and a cooperation optimization scheme is output. According to the method, the team cooperation efficiency can be effectively improved, cooperation short boards are accurately identified, and continuous optimization of team capability is realized.

The invention discloses a multi-agent collaborative knowledge reasoning system based on a large language model, and relates to the technical field of intelligent manufacturing and artificial intelligence. Natural language output of a large language model is converted into rules, facts and ontology fragments which can be directly consumed by an inference engine through knowledge obtaining and compiling, continuous increment updating of cross-domain knowledge is achieved in cooperation with metadata with sources and timestamps, and the limitation that a traditional static knowledge base is difficult to cover dynamic faults is overcome; secondly, a blackboard and agenda mechanism is used as a cooperative carrier, intermediate assertions of intelligent agents such as vibration, circuits and logs are published and subscribed in a structured mode, and a conflict resolution and consistency verification module carries out unified judgment according to specificity, time freshness and source credibility; therefore, delay and uncertain accumulation caused by long-chain natural language dialogues are avoided in a strong real-time scene.

The invention discloses a collaborative distillation personalized federal learning method for multi-user semantic communication, and belongs to the technical field of artificial intelligence and wireless communication. The semantic consistency in a task group is ensured through a clustering method, meanwhile, personalized training is performed on a semantic encoder in the task group, the adaptability of a semantic communication model is improved in combination with a partial parameter sharing strategy, and finally, global semantic knowledge is shared through inter-group collaborative knowledge distillation; the method comprises the following specific steps: clustering and grouping clients participating in training, constructing a hierarchical semantic communication model, performing hierarchical personalized federated learning training in a task group, and performing knowledge sharing based on collaborative distillation to obtain a semantic communication model of collaborative distillation personalized federated learning. The method can accurately reflect preferences, and is high in individuation performance, high in semantic recovery accuracy, high in semantic communication model generalization ability, high in data processing heterogeneity and high in task diversity.

The invention discloses a method and a device for constructing a multilevel power field knowledge base, relates to the field of artificial intelligence, and mainly aims to solve the problem that the field knowledge base constructed based on an existing method is poor in effect of improving the application reliability of a large language model. Comprising the steps of performing text image separation on original data to obtain a domain knowledge original text and a domain knowledge original image; performing adaptive semantic text block segmentation on the original text to construct a semantic text block knowledge base; performing dual-channel collaborative knowledge extraction on the text blocks, fusing extraction results, and constructing a knowledge graph knowledge base; performing adaptive text description on the domain knowledge original image to generate a constructed image and a text description knowledge base; vector conversion and mapping alignment are carried out on the domain knowledge text blocks, the knowledge graph triple, the domain knowledge original image and a combination of corresponding text descriptions to construct a semantic vector knowledge base; and constructing a multi-level domain knowledge base based on the plurality of knowledge bases.

The invention discloses an innovative resource collaborative matching method and system oriented to distributed operation, and relates to the field of data processing recommendation, and the method comprises the steps: carrying out the standardization processing of a heterogeneous resource portrait description text through a large language model, constructing and forming a resource attribute knowledge graph, generating extension description information of the resources in the target industrial chain through the large language model, and constructing and forming an industrial chain knowledge graph; performing semantic alignment and hidden associated information mining on the fused graph by using a pre-trained SACN model, and constructing a resource collaborative knowledge graph representing resource attributes and collaborative logic in an industrial chain; and performing semantic fusion on the original description text representation of the resource and the resource collaborative knowledge graph representation based on a large language model, generating comprehensive vector representation through multi-modal fusion, and outputting a resource matching recommendation result by adopting a collaborative filtering recommendation model. According to the method, the cross-organization and cross-industry-chain innovative resource collaborative knowledge graph is constructed, instant and clear demands of users are accurately responded, and the resource allocation efficiency is improved.

The invention discloses a multi-source feature collaborative knowledge mining and semantic association method, and belongs to the technical field of full-life-cycle knowledge processing of railway track engineering. According to the method, field-related documents are screened, an element range and a processing sequence are limited in combination with a knowledge graph, the documents are customized to generate a term candidate set, term statistical weights are calculated based on a full-life-cycle corpus, dynamic semantic vectors are generated by utilizing a field self-adaptive pre-training model, and composite concept representation is obtained through fusion of an attention mechanism. And linking the knowledge graph to construct a heterogeneous graph, mining association through a graph attention network, analyzing, querying and verifying sequential logic, and then outputting structured knowledge. According to the method, the problems of railway track engineering data islands and cross-stage semantic segmentation can be solved, low-frequency key terms are accurately recognized, the result interpretability is enhanced, applications such as design optimization and construction management and control are directly supported, and the method is adaptive to multiple engineering professions and high in practicability.

The invention relates to the technical field of knowledge tracking, and discloses a graph enhanced double-memory collaborative knowledge tracking model based on an ACT-R cognitive architecture. Comprising a static knowledge structure coding module based on hypergraph projection, a batch-level dynamic learning track construction and coding module, a cross-graph gating fusion mechanism, a sequence modeling module and an expert hybrid prediction module. According to the method, long-term stable structured semantic association between concepts in declarative memory is modeled through a static knowledge structure diagram, a dynamic learning trajectory diagram based on batch reconstruction is designed to accurately capture an evolution rule of a behavior sequence in programmed memory, and on the basis, a cross-diagram gating fusion mechanism and a hybrid expert mechanism are introduced, so that the evolution rule of the behavior sequence in the programmed memory is accurately captured. And self-adaptive fusion and multi-path decision of double-graph features are realized.

The invention discloses a construction safety risk intelligent control system and method based on a knowledge graph, and the system comprises a data collection layer which is used for collecting construction model data, process parameters, real-time monitoring data and historical risk event data of a construction site; the multi-library collaborative knowledge graph layer is used for constructing a knowledge graph based on the data acquired by the data acquisition layer; the intelligent analysis processing layer is used for analyzing the association network of the scheme, the risk and the hidden danger and generating risk early warning information, an optimized construction scheme and a quantitative evaluation report; and the visual decision-making layer is used for receiving and displaying the risk early warning information, the optimized construction scheme and the quantitative evaluation report which are generated by the intelligent analysis processing layer. According to the method, deep and cross-professional coupling risks can be mined, automatic and quantitative optimization of the construction scheme is realized, the dynamic response capability to field changes is enhanced, and the scientificity and efficiency of power transmission construction risk management and control are remarkably improved.

The invention relates to the technical field of plant management, and discloses a chaenomeles speciosa remote management and data modeling method based on a cloud collaborative architecture. The method comprises the following steps: firstly, receiving begonia growth environment data collected by terminal equipment, and carrying out standardized preprocessing and multi-source data fusion analysis to obtain a growth feature entity candidate list and a non-feature data fragment; after a first to-be-modeled data entity is extracted from the candidate list, a plurality of matched candidate data entity entries are extracted from the cloud collaborative knowledge base, the candidate data entity entries and the non-feature data fragments are input into a collaborative disambiguation model, semantic-level collaborative aggregation analysis is carried out with the non-feature data fragments as contexts, semantic aggregation coding features are obtained and decoded, and the semantic aggregation coding features are obtained; and obtaining a disambiguation data entity. And circularly executing the steps to obtain a disambiguation data entity list, and further generating a remote management strategy. The method can improve data processing accuracy and management pertinence, and is suitable for large-scale and distributed planting scenes of begonia.

A collaborative knowledge enhancement method for multi-behavior recommendation combines cascade correlation learning and parallel interaction enhancement through a collaborative data fusion module, captures a hierarchical high-order relationship between behaviors by using a graph neural network and residual connection, and independently extracts each behavior feature to avoid high-frequency behavior interference. Multi-stream representation is fused through a projection mechanism; sharing and unique information are separated based on a decoupling projection mechanism to generate behavior exclusive expert representation, and multi-behavior data are integrated in combination with a gating aggregation strategy to relieve gradient conflicts and negative migration problems; and finally, a prediction target behavior interaction probability is output through fusion features and an expert network, and a recommendation result is optimized. According to the method, the problems of unbalanced behavior distribution and multi-task interference are effectively solved, the target behavior prediction precision is enhanced while the low-frequency behavior representation capability is improved, and personalized recommendation and model stability are considered.

The invention relates to the field of knowledge tracking, and discloses a time-guided attention and mixed expert collaborative knowledge tracking method and system, and the method comprises the steps: obtaining a historical answer interaction sequence of a student; constructing and training a knowledge tracking model; and inputting the historical answer interaction sequence into the knowledge tracking model to complete prediction of the answer condition of the student. According to the method, the individuation of knowledge tracking and the time sequence modeling capability are effectively improved through a time-guided attention and mixed expert cooperation mechanism. Wherein the time-guided attention module is combined with multi-scale forgetting bias, so that stable modeling for long-term dependence is enhanced; and the hybrid expert module dynamically activates an adaptive expert according to the learning rhythm of the student, thereby realizing adaptive capture of heterogeneous learning behaviors. The method does not need to depend on complex external semantic information, can achieve high-quality prediction only based on basic interaction data, and remarkably improves the robustness and prediction accuracy of irregular time intervals.

The application belongs to the technical field of recommendation system, and provides a knowledge graph recommendation method based on multi-view contrast learning. Four different views are comprehensively considered and constructed from different perspectives, including a collaborative knowledge graph composed of a knowledge graph and a user-item interaction graph, a user-item interaction graph, and a user-user graph and a project-project graph constructed based on the user-item interaction graph. In addition, the intersection and union ratio is applied to reasonably construct the user-user graph and the project-project graph, and a receptive field is designed to avoid introducing more noise. Then, the four views are subjected to contrast learning at the local and global levels, aiming to mine the collaborative information between users and projects, between users, and between projects, and the global structure information in a self-supervised manner, thereby alleviating the problem of sparse supervision signals.

The application provides a construction scheme intelligent generation method and system based on multi-agent cooperation, and relates to the field of building engineering digitization technology. The method comprises the following steps: constructing a project knowledge base containing vector index and knowledge graph index; retrieving similar historical schemes based on project characteristics and learning chapter structure characteristics; generating a construction scheme outline; performing knowledge-enhanced retrieval on the chapter to be generated to obtain structured technical data; using a double-channel generation mechanism to process general chapters and construction method chapters respectively; receiving natural language fine-tuning instructions to complete content correction; converting the generated content into a standard Word document and applying an enterprise template. The system comprises a knowledge base construction module, a template retrieval module, an outline generation module, a knowledge retrieval module, a multi-agent cooperation module, a full-text integration module, and a document output module. The application significantly improves the efficiency and technical accuracy of construction scheme preparation through multi-agent division of labor, knowledge-enhanced retrieval, and double-channel generation, and solves the problems of low efficiency, case residue, and insufficient targeting in traditional scheme preparation.

The application relates to a collaborative knowledge graph recommendation model construction method enhanced by contrast learning. Three contrast learning tasks are designed to supplement the recommendation supervision task, so that the problem that the node representation learned by the graph neural network is inaccurate due to the problems of sparse supervision signal, long tail effect, noise interference and the like can be alleviated. Meanwhile, in order to make the contrast learning more beneficial to the recommendation task, a multilayer perceptron containing two linear layers is introduced between the node representation and the contrast learning to help the contrast learning training. The application promotes the development of the knowledge graph-based recommendation system and has practical significance.

This invention relates to the fields of artificial intelligence and software testing technology, and provides a content compliance review method for a "testing brain" based on KG and RAG collaborative knowledge enhancement. The method includes: acquiring a set of reference documents and obtaining the knowledge graph and high-dimensional vector corresponding to each reference document to construct a knowledge base; receiving a report to be reviewed; on the one hand, comparing the similarity between the retrieval vector of the report to be reviewed and the high-dimensional vector of the knowledge base to retrieve highly relevant reference documents from the knowledge base; on the other hand, selecting highly relevant subgraphs from the knowledge graph based on the entities in the report to be reviewed; fusing the highly relevant reference documents and highly relevant subgraphs to form structured knowledge, and generating structured prompts based on the structured knowledge; and outputting the compliance review results of the report to be reviewed based on the structured prompts and structured knowledge. This invention can significantly improve the efficiency, accuracy, and interpretability of software testing compliance review.

The invention relates to the technical field of network security, and discloses a social robot collaborative group detection method based on a multi-order collaborative knowledge graph. According to the method, user data in a Chinese social platform is collected through a crawler, a multi-order collaborative interaction graph is constructed based on the interaction relation of users of the social platform and then input into a graph encoder module, node feature vectors are obtained and input into a time perception module and a graph perception module respectively, and a time sequence prediction task and a side link prediction task are executed. Meanwhile, the model is constructed based on a time-graph semantic consistency perception module of a self-attention mechanism. And finally, inputting a node feature vector obtained by training under the guidance of double tasks into a group detection module constructed based on a Gaussian mixture model and a K-means clustering method for detection. According to the method, the complex interaction relationship among the users in the social robot group and the abnormal mode in the time sequence activity can be effectively captured, and efficient detection of the social robot collaborative group in the social network platform is realized.

The present invention discloses an item recommendation method, device, storage medium and electronic device. It relates to the field of artificial intelligence, and in particular to the field of recommendation systems. The method includes: constructing an initial implicit collaborative knowledge graph; based on the initial implicit collaborative knowledge graph, constructing multiple embedding representations, the multiple embedding representations include account embedding representations, item embedding representations and entity embedding representations; based on the multiple embedding representations, constructing an adaptive implicit collaborative knowledge graph, wherein the adaptive implicit collaborative knowledge graph is used to represent the account's potential interest in items, as well as the implicit preferences and associations between items and entities; based on the adaptive implicit collaborative knowledge graph, using an item recommendation model, obtaining item recommendation results corresponding to a target account, wherein the target account is an account in the adaptive implicit collaborative knowledge graph. The present invention solves the technical problem of poor item recommendation results caused by the large sparsity of interaction data and knowledge graphs.

The invention relates to a deep learning-based breast cancer rehabilitation question and answer method, apparatus and device, and a medium. According to the method, patient demands are accurately captured through multi-modal feature analysis to generate knowledge demand vectors, and medical knowledge sub-graphs and general knowledge fragments are retrieved in parallel to form a knowledge basis; based on demand vector parameter dynamic weighting fusion of the two types of knowledge, a collaborative knowledge unit is constructed, and then importance distribution is performed on the medical knowledge by using a graph attention neural network to generate attention distribution; semantic integration is performed in combination with attention distribution and general knowledge to form fusion representation, and conflicts are arbitrated through a rule engine to ensure safety and reliability; finally, the conflict-free reasoning path and the patient demand vector are combined to generate professional, accurate and living personalized rehabilitation guidance, and seamless cooperation of medical preciseness and life practicability is achieved.

This invention discloses a method, apparatus, and non-volatile storage medium for generating attack paths. The method includes: acquiring attack information, wherein the attack information includes an attack target, an attack object, a vulnerability, and an attack result; constructing a task model based on the attack information; constructing multiple collaborative knowledge graphs based on preset semantic anchors; retrieving the multiple collaborative knowledge graphs according to the task model to construct a task subgraph; querying attack paths in the task subgraph according to the attack target to obtain candidate paths; calculating the scores corresponding to the candidate paths; and selecting candidate paths whose scores meet preset conditions as the target attack path. This invention solves the technical problem that existing AI attack path generation methods rely on static graphs, lack real-time environmental feedback and dynamic replanning capabilities, resulting in generated attack paths that cannot respond to execution deviations, alarm triggers, or asset changes, lack a closed-loop feedback mechanism, and cannot dynamically replan or adaptively repair.

The invention relates to the technical field of knowledge viewpoint classification, in particular to a feature custom weight enhancement-based viewpoint classification method and system. Comprising the following steps: S1, collecting viewpoint text data; s2, preprocessing the collected data, and extracting key feature words; s3, carrying out vector representation on the key feature words by adopting Word2Vec; s4, a TF-IDF superposition enhancement weight algorithm is used for the vectorized key feature words; and S5, constructing a viewpoint classifier by adopting a machine learning algorithm model, and verifying the effect of extracting the key feature words in classification. The machine learning-driven online collaborative knowledge construction viewpoint classification method comprising six links is designed, the Word2Vec is adopted to carry out vector representation on feature words, the SMOTE oversampling technology is adopted to solve the problem of sample size imbalance, four machine learning algorithm models are adopted to construct a viewpoint classifier, and particularly, the viewpoint classifier is used for distinguishing the importance of different features. According to the method, the superposition enhancement weight algorithm is customized based on the TF-IDF, and the accuracy of machine learning viewpoint classification is improved.

The invention discloses a real-time image fusion method and device based on knowledge distillation and a learnable lookup table. The method comprises the following steps: acquiring a data set, wherein the data set comprises an infrared image and a visible light image; inputting the infrared image and the visible light image into a pre-trained fusion network to obtain a fusion image; extracting specific features of infrared light and visible light, taking the features as search elements, constructing a multi-modal fusion lookup table, and searching in the multi-modal fusion lookup table to obtain a search result; taking the pre-trained fusion model as a teacher network, taking the multi-modal fusion lookup table as a student network, and carrying out collaborative knowledge distillation training to obtain an optimized multi-modal fusion lookup table; and deploying the trained multi-modal fusion lookup table into the device to realize real-time rapid fusion of the infrared and visible light images. According to the method, the multi-modal fusion lookup table is trained through knowledge distillation and deployed into the device, so that the fusion speed can be remarkably increased while the fusion quality is ensured.

The application discloses a method and device for constructing a multi-level power field knowledge base, and relates to the field of artificial intelligence, and aims to improve the field knowledge base constructed based on the existing method, which is not good for improving the reliability of large language model application. The method comprises the following steps: separating text and images from original data to obtain field knowledge original text and field knowledge original images; performing adaptive semantic text block segmentation on the original text to construct a semantic text block knowledge base; performing double-channel collaborative knowledge extraction on the text blocks, and fusing the extraction results to construct a knowledge graph knowledge base; performing adaptive text description generation on the field knowledge original images to construct an image and text description knowledge base; performing vector conversion and mapping alignment on the combination of the field knowledge text blocks, the knowledge graph triples, the field knowledge original images and the corresponding text descriptions to construct a semantic vector knowledge base; and constructing a multi-level field knowledge base based on the above multiple knowledge bases.

The invention provides an AI agent implementation method based on a big language model collaborative knowledge graph, and relates to the technical field of AI.The AI agent implementation method comprises the steps that a task instruction is received to obtain an initial response of a big language model, related entities and relations are extracted from the knowledge graph, an attention fusion network is constructed for collaborative analysis, response content is optimized, and an execution action sequence is generated; and through reinforcement learning real-time adjustment and optimization, self-adaptive learning of the intelligent agent is realized, and new knowledge is updated to the knowledge graph at the same time. According to the invention, the knowledge accuracy and interaction efficiency of the AI agent are improved, and the system adaptability and knowledge expansion capability are enhanced.

The application discloses an industrial intelligent agent system and method fusing digital twin and a four-element core, belongs to the field of artificial intelligence, and comprises a digital twin perception layer, a four-element fusion core layer, a hybrid reasoning engine and an interpretable report generator.The digital twin perception layer is used for acquiring real-time state data of a physical entity; the four-element fusion core layer integrates OWL ontology modeling, graph database storage, large language model interaction and multi-physical field agent model, and generates a four-element collaborative knowledge processing result; the hybrid reasoning engine performs parallel rule reasoning, graph algorithm mining, large language model reasoning and agent model reasoning and fuses to generate a comprehensive reasoning conclusion; and the interpretable report generator generates a multi-modal report containing a reasoning path and physical evidence.The application realizes interpretable tracing of a decision-making process through four-element collaboration, realizes millisecond-level physical quantity prediction through an agent model, realizes continuous evolution of knowledge through a dynamic evolution module, and significantly improves the reliability, real-time performance and adaptability of an industrial intelligent system.