46008results about "Inference methods" patented technology

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

In embodiments, systems and methods for improving machine-learning systems are disclosed. In embodiments, a system includes a data pool system that is configured to receive data from a plurality of different data sources and maintain a training data set that is used to train a specific machine-learning model based on the data from the plurality of different data sources. In embodiments, the system further includes a data scoring system that determines a data reliability score corresponding to the new data based on a set of intrinsic features of the new data and a data scoring model, wherein the data pool system selectively adds the new data to the training data set based on the reliability score of the new data. The system also includes a machine learning system that trains the specific machine-learning model based on the training data set.

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The present invention provides a system and method for causality-augmented generative intelligence capable of autonomously discovering non-obvious actionable insights from heterogeneous and multimodal data sources. The system integrates a data ingestion unit for semantic and temporal harmonization of structured and unstructured datasets, a causal inference processor for constructing a dynamically evolving directed causal knowledge representation using perturbation-based validation, a latent representation processor that combines multimodal semantic embeddings with causal parameters to generate fused latent vectors, and a generative insight processor utilizing causally constrained generative reasoning to synthesize hypotheses anchored to verified cause-effect dependencies. A validation processor performs counterfactual assessment and observational verification to ensure retention of only those insights that remain consistent with causal ground truth.

The invention relates to the field of log fault analysis, in particular to a log aggregation fault diagnosis method and system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal heterogeneous log, carrying out sliding time sequence slicing processing, carrying out time sequence association sequence reconstruction, and constructing a time sequence reconstruction log data stream; log event deep semantic analysis is carried out on the time sequence reconstruction log data stream, event semantic topological evolution is carried out, and a multi-dimensional event topological representation matrix is constructed; performing routine event behavior analysis and abnormal fault mode inference based on the multi-dimensional event topology representation matrix, and marking abnormal fault points; and the occurrence timestamp and the abnormal propagation rate of the abnormal fault point are calculated, fault space-time diffusion evolution is carried out, and a dynamic fault propagation path map is constructed. Through efficient and accurate fault traceability analysis, the fault diagnosis efficiency is greatly improved, and the stability and reliability of log data are improved.

The invention relates to the technical field of financial data processing, in particular to a multi-source heterogeneous fund data processing method and system, and the method comprises the steps: recording source information through building a data source registry, and marking a unique identifier for data; performing differential analysis on the fund data in different formats to generate standardized column type storage data; traversing column type storage data to extract statistical characteristics, performing field classification through metadata analysis and financial dictionary matching, analyzing business connotations of fields difficult to classify in combination with a localized large language model, and mapping the business connotations to a header fusion knowledge graph; generating a mapping rule from the source field to the enterprise-level data model by applying a rule engine template on the basis of field classification and semantic recognition results; converting the data structure according to the mapping rule and executing standardization processing; the quality is further optimized through data cleaning; and finally, the data are verified, standardized fund data supporting data traceability are output, and the strict requirements of financial supervision application are met.

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention discloses a knowledge base enhancement generation method and system based on hybrid retrieval and fact verification. The method comprises the following steps: receiving an original query of a user; performing intention analysis and rewriting on the query, identifying an intention type and generating a sub-query adapted to retrieval; executing mixed retrieval of vector retrieval, keyword retrieval and selectable knowledge graph retrieval based on the sub-query, and recalling related knowledge fragments; performing deduplication clustering, fact conflict recognition processing and correlation reordering on the knowledge fragments; according to the intention type and the reordered knowledge fragment, dynamically selecting a prompt template to construct an enhanced prompt; and sending the enhanced prompt into the large language model, and generating a target response with the reference source. According to the method, knowledge recall comprehensiveness is improved through mixed retrieval, knowledge reliability is ensured through fact verification, generation logicality is enhanced in combination with dynamic prompt construction, and response accuracy and credibility are improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a multi-modal causal reasoning and interpretation method, device, equipment and medium, and the method comprises the steps: obtaining original data streams of at least two different modals, and extracting modal features; a cross-modal attention mechanism is utilized to fuse modal features, and causal features are extracted through feature distillation; constructing a dynamic causal graph based on causal features, and updating an edge weight through a causal intensity function; identifying the causal relationship in the dynamic causal graph and performing anti-factual reasoning verification to evaluate the reliability of the causal relationship; and generating a causal interpretation result in combination with the dynamic causal graph and the causal relationship reliability. According to the method, the multi-modal data are fused, the causal features are extracted, and dynamic causal graph updating and anti-factual reasoning verification are combined, so that reliable modeling and explanation of the causal relationship in a complex scene are realized, the defects of single modal or simple fusion in the prior art are overcome, and the accuracy and interpretability of causal reasoning are improved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

An example may receive at least one input via at least one device. An example may use the at least one input to determine an entity identity. An example may use the entity identity to create an automated agent and load context data associated with the entity identity into at least one layer of a multi-layer memory of the automated agent. An example may cause the automated agent to machine-learn a supervision level via the context data. The machine-learned supervision level may indicate a level of supervision of the automated agent by an entity associated with the entity identity. An example may configure the automated agent to execute a task on behalf of the entity and in accordance with the machine-learned supervision level.

The invention relates to the technical field of data center intelligent management, in particular to a data center construction and intelligent operation and maintenance management system, which comprises a dynamic environment sensing module, a heterogeneous equipment protocol adaptation module, a multi-dimensional resource dynamic scheduling module, a hidden fault prediction module and an energy efficiency optimization execution module. Physical environment data such as temperature gradient, current harmonic component and optical fiber strain rate are acquired by deploying a multi-mode sensor, and an environment characteristic matrix is constructed; standard semantic mapping of the heterogeneous protocol is realized by using a semantic slot migration algorithm; establishing a resource topological graph based on the hypergraph neural network and dynamically updating the resource topological graph; a dual-channel space-time convolutional network is adopted to realize fault prediction; and combining the fault probability matrix to generate a dynamic tuning strategy of dimensions such as cooling, electric power, network and the like, and forming closed-loop optimization control. According to the invention, integrated collaboration of multi-source information fusion, equipment intelligent control and energy efficiency adaptive optimization is realized, and the intelligence, reliability and energy efficiency level of data center operation and maintenance are improved.

The invention provides an intelligent SQL generation method and system for business query, and belongs to the technical field of artificial intelligence. Related data of query statements are acquired, and a corresponding query intention knowledge graph is constructed by semantic clustering; the method comprises the following steps: analyzing a historical SQL statement structure, extracting a natural language template and an SQL template, and expanding through a large language model to generate a feed-shot example set; and constructing a composite cue word template by combining task setting guidance, a feed-shot example and CoT chain thinking reasoning guidance. An intention completion module is arranged in a large language model, a natural language query statement of a user is combined with a composite cue word template context, a structured query statement is generated through entity recognition, semantic completion, parameter filling and fuzzy intention training, and the structured query statement is converted into a standard SQL statement through a knowledge graph and a template. According to the method, the use threshold of business personnel is remarkably reduced, and efficient conversion from natural language questions to SQL statements is realized.

Systems and methods for integrating generative artificial intelligence (AI) within Software-as-a-Service (SaaS) platforms to automate data operations, synchronize cross-platform workflows, and enable intent-based interactions. A platform displays table structures of items and characteristics linked to a common objective, provides input interfaces, and enrolls AI agents as credentialed users with read / write privileges. The system prompts agents with column types, structural relations, and role profiles to generate and execute editing instructions that progress workflow objectives, detect missing or inconsistent data, and notify users or request information as needed. Hierarchical access schemes permit multiple agent instances with inherited privileges and resource limits managed through an AI center. Agents can operate as autonomous team members, analyze outputs, and support natural-language explanation sessions. Additional embodiments coordinate inter-service updates, maintain deviation detection tools, and construct tailored products and platform elements. These capabilities improve robust automation, decision support, and operational efficiency in complex SaaS environments.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

The invention discloses an accounting data intelligent processing method and system for enterprise financial auditing, and relates to the technical field of accounting data intelligent processing, and the method comprises the steps: obtaining multi-mode enterprise financial data, and carrying out the preprocessing; carrying out multi-modal semantic understanding analysis on the unstructured text and image data; constructing an enterprise financial space-time knowledge graph containing time attributes; inputting into an anomaly analysis model, extracting spatial structure characteristics of the financial entity in the topological network, and extracting dynamic characteristics of the financial relationship evolved along with the time sequence; identifying an abnormal source, evaluating a systematic risk value and generating an abnormal propagation path; and integrating to generate a final audit report. According to the method, structured and bill images are fused, identifiers and time calibers are unified, abnormal source and propagation are positioned based on the space-time knowledge graph, closed-loop counter-knock and cross-period anomalies are identified, the auditing accuracy and coverage rate are remarkably improved, the workload of false report, missing report and manual recheck is reduced, and a traceable structured report is quickly generated.

The invention provides an industrial agent decision-making method and device, electronic equipment and a storage medium. The method comprises the following steps: loading multi-source industrial data to a knowledge graph, and constructing a domain ontology and an MCP ontology; performing parameter fine tuning on a preset base large language model; receiving an industrial process planning demand input by a user, and performing semantic analysis on the industrial process planning demand; processing the query result by utilizing an MCP context strategy to generate a prompt context; inputting the prompt context and the industrial process planning demand into a fine tuning large language model to generate a first process planning scheme; performing rule verification on the first process planning scheme based on the knowledge graph, and judging whether the first process planning scheme meets a preset rule or not according to a verification result; and converting the final process planning scheme into a control script or an interface calling sequence which can be analyzed by an industrial execution system, and outputting the control script or the interface calling sequence. According to the method, the cross-software system compliance process planning with unified knowledge, automatic generation and rule verification can be realized.

The invention discloses a dynamic intention recognition and GraphRAG fusion method and system for open domain questions and answers, and relates to the technical field of intelligent and natural language processing. The method comprises the following steps: constructing and dynamically updating a heterogeneous knowledge graph; performing dual-stage intention recognition on user query; subtasks are disassembled based on an intention result, and cooperative execution is carried out through multiple agents; service standardization integration is realized by means of a model context protocol; and closed-loop optimization is carried out in combination with user feedback. The system comprises an image knowledge base, an intention recognition module, an agent collaboration module, an MCP service integration module and a feedback optimization module. The system is a novel open domain question-answering system integrating structured knowledge modeling, dynamic intention understanding, intelligent collaborative reasoning and standardized service interaction, and through fusion of structured knowledge modeling and intelligent collaborative reasoning, question-answering accuracy and complex intention understanding ability are improved, system adaptability and efficiency are enhanced, multi-system collaboration is promoted, and the system has a wide application prospect. The method is suitable for scenes such as knowledge services and intelligent assistants.

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 a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

The invention discloses an industrial data analysis and mining system based on a knowledge graph, relates to the technical field of data mining, and aims to solve the problems of difficulty in cross-process parameter association and low abnormal traceability precision. The system calculates the correlation strength of the oven temperature in the coating process and the direct-current internal resistance of the battery cell in the formation and capacity grading process through a time-lag mutual information algorithm, generates an influence relation edge with a time-varying weight in combination with a temperature-internal resistance negative correlation process rule, and constructs an industrial knowledge graph; taking a direct-current internal resistance abnormal batch as a starting point, reversely traversing the knowledge graph along a process path, fusing a parameter Z-score deviation degree and a dynamic weight to generate a root cause parameter list, and realizing quality root cause positioning; instantiating abnormal parameters into event nodes, identifying star-type and chain-type propagation topologies through subgraph isomorphism detection, and extracting a standardized fault mode; based on an FCI causal discovery algorithm, a real causal structure is identified, the weight of the knowledge graph is dynamically adjusted, adaptive evolution of the graph is realized, and the accuracy and the intelligent level of complex process quality analysis are improved.

The invention relates to the technical field of intelligent retrieval and matching, in particular to a property intelligent retrieval and matching system based on a knowledge graph. The method comprises the following steps: a data acquisition and processing unit acquires property right data and preprocesses the property right data; the knowledge modeling and graph construction unit constructs the preprocessed property right data into a knowledge graph based on entity extraction, relation extraction and attribute labeling; the retrieval analysis and semantic understanding unit is used for receiving a retrieval request input by a user, converting the retrieval request into a structured retrieval statement matched with the knowledge graph based on a dynamic multi-hop reasoning intention path discovery scheme, and analyzing a retrieval intention of the retrieval request through a context modeling and semantic disambiguation method. According to the method, a semantic understanding mechanism combining a dynamic multi-hop reasoning intention path discovery scheme with context modeling and semantic disambiguation is introduced, so that a complex retrieval request input by a user in a natural language form can be accurately analyzed.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision-making method, device, equipment and medium based on multi-modal semantic alignment. Executing cross-modal alignment by taking the voice semantic map as a reference to generate associated information, fusing the voice features, the visual features, the action features and the associated information to generate a fusion feature vector, inputting a decision network to generate a decision feature vector and generate a task execution instruction, obtaining execution feedback information of the task execution instruction, and updating the decision network. According to the method, input is dominated by voice instructions, visual features, action features and semantic map depth alignment and fusion are combined, input naturalness and multi-modal data analysis and decision-making efficiency are improved, and interaction adaptability and decision-making accuracy of the model in a complex scene are improved.

The invention belongs to the field of artificial intelligence, particularly relates to an intelligent regulation and control system for an injection molding process of an industrial control system, and aims to solve the problem that high-precision cooperative regulation and control are difficult under material batch fluctuation, mold state change and environmental disturbance. The system comprises a multi-source sensing module, a dynamic modeling module, a self-adaptive decision-making module, an execution feedback module and a knowledge evolution module, and high-stability and high-adaptability intelligent regulation and control of the injection molding process are achieved through a mixed digital twin model integrating a physical mechanism and data driving, confidence-guided multi-objective optimization and continuous evolution of a process knowledge graph.

The invention discloses a power equipment fault diagnosis method and system based on a dynamic knowledge graph and large model collaborative reasoning, and the method comprises the steps: achieving the automatic extraction of an entity relationship through a weak supervision entity relationship extraction mechanism in combination with a power field dictionary and a remote supervision technology, and obtaining a weak supervision entity relationship; constructing a time sequence knowledge graph to capture a dynamic evolution rule of the fault propagation chain; structured knowledge graph embedded representation is fused with a large model input layer through a knowledge injection layer, a two-stage reasoning process is generated by adopting a graph retrieval enlarged model, and finally a diagnosis conclusion containing a structured evidence chain is generated. According to the method, the fusion of weak supervised learning and sequential relation modeling is realized, and the automatic extraction and dynamic updating capability of the knowledge in the electric power field is remarkably improved; through a knowledge injection layer and a two-stage joint reasoning mechanism, the structured reasoning advantage of a knowledge graph and the semantic generation capability of a large model are effectively combined, and the diagnosis accuracy, the time sequence reasoning capability and the interpretability are greatly enhanced.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention relates to a power distribution system optimization method considering a space-time game under vehicle-station-network interaction, and the method employs a double-layer optimization framework integrating the space-time game and dynamic electricity price, an upper decision maker is a power distribution system operator, and the power distribution system operation cost is minimized and the renewable energy consumption rate is maximized. Generating a dynamic electricity price signal in a time-sharing and partition manner; the lower-layer main body comprises an electric vehicle user and a charging station operator, and the electric vehicle user optimizes a charging time period and a charging station and forms charging demand distribution through a non-cooperative game based on a dynamic electricity price signal and a charging service charge by taking the maximum self-charging decision utility as a target; and a charging station operator optimizes and adjusts a charging service fee decision by taking charging demand distribution as input and taking revenue maximization as a target. Compared with the prior art, the method can collaboratively optimize the economic operation of the power grid and the consumption of renewable energy sources, promotes the efficient flow and balanced distribution of resources among regions, and provides decision support for the dispatching of the power distribution network.

The invention discloses an accurate health risk early warning analysis system and method based on multi-modal medical data fusion. The system comprises a multi-source data acquisition module, a preprocessing module, a dynamic fusion module, a risk assessment module, an interpretability module and a dynamic early warning module. According to the method, multi-modal data are collected, feature vectors are generated through preprocessing and cross-modal fusion, a comprehensive health risk index is calculated through a double-flow model (time sequence LSTM + static GNN), abnormal association is analyzed in combination with causal reasoning, a threshold value is dynamically adjusted, grading early warning is triggered, and finally the model is optimized through reinforcement learning. According to the scheme, deep fusion and dynamic evaluation of multi-modal data are achieved, the accuracy, timeliness and interpretability of risk early warning are improved, the method is suitable for scenes such as chronic disease management and intensive care, and powerful support is provided for clinical decision making.

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.