618 results about "Graph spectra" patented technology

The invention relates to the technical field of artificial intelligence, in particular to an automatic construction method of an end-to-end agent based on graph structure semantic fusion. The method comprises the following steps: receiving business demand data input by a user; business target and demand constraint condition analysis is carried out on the business demand data, and a core workflow framework of the intelligent agent is generated; performing end-to-end execution path analysis on the core workflow framework of the intelligent agent to obtain an end-to-end workflow; constructing a dynamic evolution semantic map; and constructing an end-to-end call chain execution strategy based on the end-to-end workflow, and performing agent instance packaging and agent instance reinforcement learning enhancement processing according to the dynamic evolution semantic map, thereby automatically constructing an end-to-end agent. According to the invention, by fusing the graph structure knowledge and the generation capability of the large language model, an efficient, accurate and extensible agent automatic construction scheme is provided for various complex business scenes.

The invention discloses a wide-area landslide rapid identification method based on an interpretable intelligent algorithm, and relates to the field of remote sensing science and technology, and the method comprises the steps: building a dual-channel feature extraction architecture through multi-source spatio-temporal data fusion and knowledge graph dynamic weighting: capturing image local textures through a lightweight CNN, modeling geological spatial correlation through a graph convolutional network, and carrying out the recognition of the landslide. Combining the SHAP value and causal reasoning to generate an interpretable contribution degree thermodynamic diagram and a rule chain; a knowledge graph bidirectional verification system is introduced, spatial logic contradictions are verified by using prior rules, and co-evolution of a model and a rule base is triggered based on misjudgment samples; outputting a multi-dimensional credibility report, quantifying uncertainty by Monte Carlo Dropout, and customizing interpretation granularity according to roles; a terrain-adaptive block-stream processing architecture is adopted, and edge lightweight deployment and federated learning are combined, so that wide-area real-time early warning and model dynamic updating are realized. According to the scheme, the limitation of a traditional black box model is broken through, and a disaster prevention closed loop with physical driving, transparent decision and second-level response is formed.

The invention provides a knowledge graph construction and product recommendation method and system based on user data, and relates to the technical field of artificial intelligence, and the method comprises the steps: constructing an initial knowledge graph through user historical behavior data, generating an optimization graph through a multi-layer neural network comprising a multi-granularity layer, a graph attention layer and a graph convolution layer, and performing bidirectional random walk sampling based on real-time behavior data of a target user to obtain a related sub-graph, calculating a product node importance score, and performing sorting pushing by adopting a multi-target optimization algorithm. According to the method, multi-dimensional accurate description of user interests can be realized, the recommendation accuracy and diversity are improved, and meanwhile, the commercial value is considered.

The invention relates to a multi-modal AI data fusion processing method, device and equipment and a medium, and the method comprises the steps: firstly extracting visual, auditory and text modal features through a pre-training encoder, executing dimension alignment, and generating a standard data feature set with unified dimensions; a cross-modal semantic graph is constructed based on a cosine similarity algorithm, and the problem of semantic mismatch of heterogeneous data is solved; residual enhancement is carried out on the map nodes, and noise interference is eliminated; fusing the optimized features and the semantic topology in combination with a graph convolutional network to generate aggregation graph representation; the fusion features are mapped to a low-dimensional semantic space through a variational auto-encoder, and cross-modal correlation essence is captured; the key dimension contribution degree is quantified, a visual report is generated, and semantic association rules among modals are disclosed, so that the dimension isomerism limitation of a traditional fusion technology is broken through, quantifiable cross-modal semantic mapping is established, the whole process traceability from feature fusion to decision interpretation is realized, and the method is suitable for popularization and application. And the multi-modal decision black box problem in the fields of medical diagnosis, automatic driving and the like is effectively solved.

The invention discloses a multi-modal knowledge graph construction and intelligent fault diagnosis method and system for a communication power supply system. The method comprises the steps of multi-modal knowledge graph construction, graph increment updating and intelligent fault diagnosis. The multi-modal knowledge graph construction adopts a unified data acquisition semantic specification and a heterogeneous data fusion strategy, a dynamic evolution heterogeneous graph is established, and equipment full life cycle state perception and causal link modeling are realized; the efficient, atomicity and consistency updating of the atlas is realized by the hierarchical atlas increment through shadow composition, structural difference rate calculation and a subgraph replacement mechanism; according to the intelligent fault diagnosis, an alarm propagation sub-graph is constructed, a path convergence and multi-dimensional attribute scoring mechanism is adopted, and deep joint verification is carried out by using a multi-modal evidence fusion network; and in combination with a reinforcement learning optimization strategy embedded based on a graph structure, adaptive scheduling and diversity constraint of a diagnosis path are realized, and the fault positioning accuracy and the system intelligence in a complex scene are remarkably improved.

The invention relates to the technical field of battery management, in particular to a battery module early abnormality early warning method and system fused with a time sequence knowledge graph, and the method comprises the steps: obtaining static parameters and dynamic sensor data of each battery node in a battery module, and constructing the time sequence knowledge graph; performing node-relation importance evaluation of topology embedding, and screening out key nodes and relations; constructing heterogeneous knowledge unified mapping of multi-manifold representation, and projecting battery voltage, temperature and state manifolds to a unified representation space; performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space, and performing spectrogram-driven adaptive Transform structure optimization on the data of the uniform representation space; bidirectional coupling graph-sequence information flow cooperative processing is realized, graph structure information is converted into sequence representation, and a graph structure is fed back and updated; carrying out multi-scale feature hierarchy fusion and integration, and integrating hierarchy feature information of a battery monomer, a module and a system; and evaluating the health state of the battery module based on the multi-scale fusion feature information, and generating early warning information in an abnormal state or an abnormal state.

The invention discloses an engineering safety progress intelligent monitoring method based on multi-source data collaboration, which relates to the technical field of intelligent engineering monitoring, and comprises the following steps of: mapping multi-source engineering monitoring data into nodes and edges of a graph in real time by utilizing an incremental graph updating algorithm, generating a dynamic knowledge graph, and generating a dynamic mapping result; performing graph traversal on the dynamic knowledge graph through an association subgraph extraction algorithm, extracting a security event matrix and a project progress matrix, calculating an SPI index value by using a dynamic weighted fusion algorithm, synchronously performing multi-threshold interval grading on the SPI index value, generating an SPI early warning level, performing state coding on the SPI early warning level, and generating a comprehensive feature vector; according to the method, data of different types can be effectively integrated and a basis is provided for formulating a targeted engineering safety progress solution through an incremental graph updating algorithm and a Bayesian causal graph model, and node probability distribution characteristics are extracted by using a forward propagation layer. The method is advantaged in that the incremental graph updating algorithm and the Bayesian causal graph model are utilized to effectively integrate data of different types and provide a basis for formulating a targeted engineering safety progress solution.

The invention, which relates to the technical field of network awareness anomaly detection, discloses a network awareness anomaly detection system based on big data, comprising a data acquisition module, a feature fusion module, a map construction module, a model calculation module, a root cause reasoning module, a threshold decision module and a response control module. The data acquisition module receives network flow data, equipment state data and system log data and outputs a standardized feature set; the feature fusion module is connected with the data acquisition module, dynamically calculates a weight coefficient of each data source based on information entropy, performs feature aggregation of privacy protection through a federated learning framework, and outputs a fusion feature vector; the atlas construction module is connected with the feature fusion module, maintains a network equipment node set and a communication edge set in real time, and updates a space-time association atlas according to a topology change event; and the model calculation module is connected with the atlas construction module, extracts topological features through a space-time diagram convolutional network, and updates a detection model based on an incremental learning mechanism.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention relates to the technical field of big language models, and discloses a big language model training method based on knowledge graph enhancement, comprising the following steps: S1, constructing a multi-source heterogeneous knowledge graph; s2, coding the mixed attention heterogeneity map; s3, bidirectionally mapping a pre-training task; and S4, position specific gating fusion. According to the big language model training method and system based on knowledge graph enhancement, a same proton graph is established for a structured triple and text entity description, nodes are connected across graph edges to form a heterogeneous graph, associated edges are established through entity linking and syntactic analysis, multi-source knowledge is modeled in a unified mode, and the problem of low fusion efficiency is solved; mixed attention coding adopts a layering mechanism, a semantic level calculates weights according to type compatibility, a node level calculates similarity aggregation features through cosine distance and path length, entity vectors are generated through pooling, map structures and semantics are explicitly learned, reasoning accuracy is improved, and the problem of knowledge understanding superficial layer is solved.

The invention discloses a supply chain risk identification method and system based on a knowledge graph, belongs to the technical field of supply chain management and artificial intelligence crossing, and aims to solve the technical problem of how to realize dynamic monitoring, accurate identification and active early warning of supply chain risks, improve full star, real-time performance and interpretability of supply chain risk identification, and improve the risk identification efficiency. According to the technical scheme, the method comprises the following steps: collecting and treating multi-source data: collecting static background information and dynamic risk information of a supplier, and carrying out highly intelligent data treatment on the collected static background information and dynamic risk information of the supplier through a data treatment engine to ensure data quality and consistency; constructing a dynamic knowledge graph; intelligent risk identification: based on a graph topological structure and dynamic attributes, identifying key risk nodes and communities, tracing in time, marking risks, and performing early warning; decision support and visualization are carried out; and dynamically optimizing and feeding back.

The invention relates to the technical field of industrial internet security and artificial intelligence crossing, in particular to an industrial production process APT attack detection method and system based on a knowledge graph, and the method comprises the steps: obtaining industrial production data, carrying out the preprocessing of the obtained industrial production data, and obtaining an APT attack detection result; the preprocessed industrial production data are used as input for dynamic construction of a knowledge graph, known attack mode reasoning is carried out based on the knowledge graph, the known attack mode reasoning comprises the steps that a known attack chain is recognized through multi-hop matching of graph embedding, a time sequence graph convolutional network and an attention mechanism are fused to detect unknown abnormal behaviors, and the known attack chain is subjected to known attack mode reasoning. Data fusion is performed based on the topological relation of the knowledge graph, an attack entry node, an associated entity and a propagation path are positioned according to a data fusion result, and real-time detection and traceability of the hidden attack chain are realized by constructing the equipment-protocol-data stream three-dimensional semantic dynamic knowledge graph and fusing a graph embedding technology and a graph convolutional network.

The invention discloses a power distribution cabinet maintenance system based on artificial intelligence, and belongs to the technical field of intelligent power distribution cabinet diagnosis. Normalization, time sequence feature extraction and denoising are carried out on the collected data, an electrical topological graph is automatically constructed, node states are vectorized, text semantics are coded by utilizing a BERT class model, and a maintenance knowledge graph is generated; through fusion of multi-source data, a multi-branch neural network is constructed, state perception and risk determination are realized, and a fault trend and structure degradation are identified. Calculating a potential fault risk coefficient, and triggering a structure health monitoring mechanism; analyzing the structural health based on a topological graph and a graph neural network, and starting semantic strategy retrieval when the structure is abnormal; comparing the semantic conformity between the state and the historical strategy, and assisting in generating a precise maintenance strategy; according to the system, dynamic monitoring, intelligent early warning and strategy recommendation of the operation state of the power distribution cabinet are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention discloses a track representation enhancement method based on a dynamic subgraph. A historical sign-in track of the user is given, semantic track representation aims at learning low-dimensional vectors capable of revealing user behavior patterns and semantic intentions from user track data, and convenience is provided for subsequent prediction tasks. The method comprises the following steps: firstly, constructing a city knowledge graph based on a space-time semantic tetrad, and explicitly modeling the association between a user track and a space-time scene; secondly, extracting a semantic sub-graph sequence taking a user sign-in track as a core from the user sign-in track by using a relation perception dynamic sub-graph extraction mechanism; and then, learning representation of a sub-graph sequence based on an attention aggregation network of a cross-sub-graph, and further obtaining knowledge-enhanced user track representation. And finally, verifying the characterization quality based on a prediction module, and outputting K sign-in points which the user is most likely to be interested in as results. Through the modeling method of the dynamic subgraph sequence, time-space sensitive trajectory semantics are effectively modeled, and the accuracy of a prediction task is improved.

The invention discloses a multi-modal-based software architecture intelligent design and optimization system, which relates to the field of intelligent design and optimization, and comprises the steps of performing modal perception and preprocessing on architecture design demand information input by a user, converting the architecture design demand information into structured semantic data, and optimizing and enhancing semantic expression by introducing a reinforcement learning strategy, forming a structured user intention vector set; through a semantic mapping and reasoning processing unit, user intention vectors in the user intention vector set are constructed into a semantic-component alignment graph, and graph structure modeling and confrontation generation algorithm combination are adopted to generate a candidate structure graph set; a structure diagram generation step in the structure generation module effectively improves the rationality, diversity and adaptation capability of an automatically generated structure, and provides core support for realizing automatic construction of a target-demand-oriented architecture structure.

The invention relates to the technical field of knowledge graph construction, in particular to a node attenuation and edge similarity weight fused knowledge graph construction method and system, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the preprocessing of the multi-source heterogeneous data; identifying entities in the preprocessed multi-source heterogeneous data, and extracting a relationship between the entities; establishing an initial graph structure based on the entities and the relationship between the entities, and determining the representation mode of nodes and edges in the graph; fusing time decay and space correlation factors to correct representation of nodes and edges in the constructed graph structure, and fusing semantic similarity on the basis of node and edge weight correction to further adjust an edge connection relation; after the graph structure is corrected and optimized, final knowledge graph data representation is organized and generated, and unified storage and graph calculation structured packaging are completed. According to the method, the defect that an existing map construction scheme only depends on time attenuation and neglects space factors can be effectively overcome.

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The embodiment of the invention provides a multi-modal knowledge graph construction method in cross-media retrieval. The method comprises the steps that extracted multi-modal features are mapped to a multi-modal feature space through a linear transformation layer; in the multi-modal feature space, the intra-modal attention weight of each modal feature is calculated according to a self-attention mechanism, the cross-modal attention weight of different modal features is calculated according to a cross attention mechanism, the two weights are fused to obtain a final fusion weight, each modal feature is weighted and input into a graph attention network, and the multi-modal feature is obtained. Obtaining a multi-modal fusion graph structure; performing semantic analysis on each modal feature, matching with a preset multi-modal semantic knowledge base, determining potential semantic association, performing semantic alignment on the multi-modal fusion graph structure according to a preset graph matching algorithm and the potential semantic association to obtain a multi-modal knowledge graph, performing cross-modal data retrieval according to the multi-modal knowledge graph, and performing cross-modal data retrieval according to the multi-modal knowledge graph. The multi-modal data cross-media retrieval method and device can improve the efficiency and accuracy of multi-modal data cross-media retrieval.

The invention discloses a power generation equipment state fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: actively injecting a mechanical excitation signal of a preset frequency spectrum into a key part according to a physical topological structure of power generation equipment, and carrying out the fusion to generate a time-space-frequency three-dimensional data volume; inputting the three-dimensional data volume into a physical embedded variational auto-encoder, and outputting an equipment state pure feature tensor; inputting the pure feature tensor into a graph space-time causal reasoning network to generate a fault propagation causal graph with probability weight; performing multi-agent diagnosis on the fault propagation causal atlas, and outputting a fault diagnosis report which has a credibility interval and comprises fault positioning and root cause analysis; and mapping the fault diagnosis report to the digital twin of the equipment in real time, and outputting a self-adaptive maintenance strategy sequence which minimizes the expected value of the whole life cycle operation and maintenance cost. According to the embodiment of the invention, the accuracy and anti-interference capability of fault diagnosis can be improved, and the operation and maintenance cost can be effectively reduced.

The invention discloses a lightweight incremental question answering system based on graph structure index and double-layer retrieval. Comprises: a graph-based incremental index module for segmenting an input document into text blocks, extracting entities and relationships through a large language model (LLM), and constructing a dynamic knowledge graph; the double-level retrieval module is used for mapping user query into a graph structure, adopting a double-strategy retrieval mechanism associated with local keyword matching and global theme and combining graph vectors for collaborative query; and the path constraint sub-graph generation module is used for generating an identifier sub-graph based on an n-hop reasoning path on the basis of the retrieved content, and performing deep semantic extension and answer generation. During work, through the modular and progressive collaborative design, the system realizes full-process optimization from data acquisition to knowledge representation and from query response to deep reasoning.

The invention discloses a tunnel full-period construction feature information fusion and quality tracing method and system, and the method comprises the steps: converting the construction data of a tunnel full period into construction feature information through constructing a digital twinborn model of tunnel construction, and carrying out the dynamic weighting according to the importance of different construction feature information, performing fusion to generate high-dimensional construction feature data; representing the high-dimensional construction feature data as a graph structure; analyzing the graph structure by using a graph neural network algorithm GNN to obtain an anomaly detection result of the construction equipment; and constructing the construction data into a knowledge graph, tracing quality data in the construction process by using a knowledge graph reasoning technology, and analyzing potential quality risks in combination with the anomaly detection result, so as to realize risk early warning and full life cycle quality management of the construction process. According to the embodiment of the invention, more comprehensive and accurate data support can be provided for construction management, and the safety of the construction process and the efficiency of quality management are improved.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention discloses a vehicle transaction risk intelligent early warning and credit assessment method based on big data analysis. The method comprises the steps of S1, collecting an original transaction data set; s2, constructing a heterogeneous graph structure based on the original transaction data set; s3, dividing the heterogeneous graph structure into a graph snapshot sequence according to a fixed time window, and generating a time evolution graph sequence with time evolution characteristics; s4, constructing low-dimensional feature representations of the user nodes and the transaction event nodes under each time window; s5, constructing a causal path graph according to the time sequence and behavior dependency relationship of the vehicle transaction record data, and extracting a high-frequency transaction causal chain from the causal path graph; s6, performing joint modeling on the low-dimensional feature representation of the user node and the transaction event node and the high-frequency transaction causal chain; and S7, outputting the risk level label of the target transaction and the credit scoring result of the corresponding user. The method has the advantages of being high in structure expression ability, clear in causal logic and high in risk assessment accuracy.

The invention discloses a network security intrusion detection system and method fusing a graph neural network, and the method comprises the steps: collecting multi-source flow data in a network operation process, constructing an induction topological graph through employing a graph convolution modeling technology, and revealing a deep incidence relation between network nodes; a hidden attack mode is identified by adopting a multi-layer graph propagation and resonance enhancement technology, and high-quality node embedding representation is generated through a graph attention mechanism and abnormal resonance amplification; an attack behavior map is extracted by combining depth map learning and a map pooling technology, and a self-adaptive protection strategy is generated through map inversion mapping and dynamic topological transformation; an interlaced detection sequence is constructed by adopting a multi-layer detection rule decomposition and graph optimization sorting technology, and a self-adaptive response signal is generated through active defense prediction and time difference bottleneck analysis, so that intelligent detection, accurate analysis and self-adaptive protection of network intrusion behaviors are realized, and the intelligent level and protection effect of network security protection are improved.

The embodiment of the invention provides a bill collaborative management method and system based on multi-source heterogeneous data fusion, and belongs to the technical field of data processing. The method comprises the following steps: performing hash calculation according to standardized bill data to obtain a unique identifier; and obtaining a map node corresponding to the standardized bill data to obtain a target map node. The path from the map root node to the target map node is a target structure path. And obtaining a complete behavior sequence of the standardized bill data according to the unique identifier and the behavior log. And constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence. And inputting the behavior structure joint sequence into the structure perception embedding model to obtain a sequence embedding vector. And obtaining a structure behavior inconsistency score and an abnormal type label according to the structure behavior consistency evaluation function, the behavior structure joint sequence and the sequence embedding vector. And generating a suggestion text according to the structure behavior inconsistency score and the abnormal type label, and displaying the suggestion text. Accurate identification of abnormal bills is realized.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention discloses an information reasoning method, system and equipment based on knowledge enhancement and a medium. The method comprises the following steps: constructing a target knowledge graph and a target knowledge graph index according to a target entity, a target attribute and a target constraint condition in to-be-reasoned information; determining an atlas sub-graph of the target entity through the first-level entity hash index; screening an entity attribute set matched with the target attribute from the atlas sub-graph based on the secondary attribute classification index; filtering the entity attribute set matched with the target attribute according to the three-level space-time dimension index to obtain a target dynamic attribute; recalling a target retrieval fact from the target knowledge graph according to the graph sub-graph, the entity attribute set and the target dynamic attribute; the to-be-reasoned information and the target retrieval facts are input into the preset large language model to obtain the target reasoning result, knowledge enhancement reasoning can be carried out by constructing the multi-level index target knowledge graph and fusing the dynamic attribute constraints in combination with the large language model, and then the accuracy, the real-time performance and the field adaptability of the reasoning result are improved.

The invention provides a water conservancy field retrieval enhancement generation method and device based on a knowledge graph and a medium. The method comprises the steps of extracting entity relationships in a water conservancy field document; constructing a directed unweighted graph by utilizing the entities and the relationships; summarizing description information of each entity and relation description between the entity and other entities by using a large model to generate an entity abstract, performing multi-level semantic modeling on the entity abstract, and converging semantic information of adjacent entities as graph embedding representation of the entities; associating the entity abstract and the graph embedding representation thereof with entity nodes in the directed unweighted graph to obtain an optimized graph; dividing the entities into a plurality of communities according to the modularity among the entities in the optimization graph; carrying out community summarization on each community by utilizing the large model to obtain a community abstract; when a user query request is received, carrying out knowledge graph recall by utilizing multi-graph inquiry and multi-path sorting fusion; and according to the sorting score of the retrieved atlas information, optimizing the task cue word to guide the generation model to optimize the answer.

The invention relates to the technical field of computers, and discloses a chip mounter material tracing management method and system based on a knowledge graph. The method comprises the following steps: constructing a dynamic knowledge graph covering the whole process of a patch; injecting multi-source data in real time to drive dynamic updating of the atlas; when the materials are abnormal, reverse tracing and forward influence double-path reasoning are started; isolation, rework and reset instructions are automatically generated and pushed to a manufacturing execution system; and synchronously updating the atlas state to form an auditable record. The system comprises a dynamic knowledge graph construction module, a real-time data injection module, a bidirectional inference engine module, an automatic processing instruction generation module and a graph state synchronization module. According to the scheme, millisecond-level error positioning and closed-loop processing are realized, and the tracing efficiency and the quality guarantee capability are improved.