27589results about "Knowledge representation" patented technology

The invention relates to the technical field of power equipment, and discloses a digital twin operation monitoring system for power equipment, which comprises a data sensing and acquisition system for acquiring key operation parameters of temperature, current, voltage, partial discharge, vibration and humidity of the power equipment in real time, and performing multi-dimensional data acquisition through a sensor and a data transmission module; the state evaluation and prediction system is used for performing equipment health evaluation and residual life prediction by using a prediction model LSTM based on the collected data, and updating a prediction result in real time; provided is a digital twin modeling system. Through the combination of edge calculation, an LSTM model and a digital twinning technology, the precision and real-time performance of health management of power equipment are improved, data quality is optimized through edge calculation, the LSTM model captures an equipment degradation trend, virtual-real fusion is realized through digital twinning, and accurate monitoring and early warning of the health state of the equipment are ensured, so that intelligent operation and maintenance decisions are optimized, the failure rate is reduced, and the safety of power equipment health management is improved. The equipment life is prolonged.

The invention discloses an auditing decision support system and method based on a dynamic knowledge graph, relates to the technical field of computers, and aims to solve the problems that auditing data are heterogeneous and complex, risk identification is not timely and causal interpretation is lacked. According to the system, multi-modal audit data is collected in real time through a streaming event processing framework, and a dynamic audit knowledge graph with timeliness weight is constructed. Based on a graph calculation engine and cross-domain rule mining, identifying a high-frequency risk mode, and generating a risk conduction path graph; further fusing a multi-modal graph attention network, identifying and positioning abnormal entities, and outputting abnormal nodes and risk links thereof; and finally, the abnormal node embedding representation is dynamically updated through the time sequence diagram attention network, an interpretable audit causal map is generated in combination with a structural causal model, and closed-loop support from data acquisition and risk identification to interpretive audit decision is realized. The intellectualization and transparency of audit decision making are improved, and an efficient and traceable decision making basis is provided for a complex audit scene.

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

A system and method for extending AI-enhanced decision platforms with deontic and normative reasoning capabilities that enhance adjustably autonomous decision-making through a novel integration of symbolic and neural approaches alongside quantum-inspired token management. The invention uses hierarchical and fuzzy deontic logic implementations and quantum-inspired state representations that combine complex amplitudes and phase information to manage obligations, permissions, and prohibitions while maintaining observer awareness to achieve complex goals while incorporating knowledge across multiple expert domains. The system employs dynamic event and spatio-temporal knowledge graphs along with debate mechanisms, enabling high-assurance automated reasoning while preserving explainability through neuro-symbolic integration and information-theoretic metrics. The platform is capable of operating through a federated distributed computational graph architecture that allows for arbitrary scaling while maintaining system coherence and logical consistency using quantum-inspired token operations and phase alignment transformations for optimizing information transfer between states.

The invention relates to the field of security surveying and mapping, in particular to a network security space surveying and mapping method, system and device based on multi-source data fusion, and the method comprises the steps: obtaining network security data in real time, and constructing a dynamic network topological graph; calculating a time-varying vulnerability score based on the topological graph and a historical attack log, and predicting an attack path and a propagation probability through a Bayesian network; performing cross-domain fusion on equipment, service and user behavior characteristics by adopting a federated learning framework to generate a dynamic asset portrait; generating a risk thermodynamic diagram in combination with spatial autocorrelation analysis and a multi-index fusion algorithm; a defense strategy effect is simulated based on an attack graph reconstruction engine, a Pareto optimal strategy combination is generated through an NSGA-II algorithm, and closed-loop verification and dynamic parameter correction are realized by utilizing honeypot deployment and flow traction. Therefore, the problems of topology update lag, single risk assessment dimension, cross-domain threat association fracture, defense strategy static stiffness, non-closed loop of a verification system and the like in the traditional technology are solved.

The invention discloses a hydraulic engineering equipment data intelligent management system based on digital twinning, and belongs to the technical field of hydraulic engineering. Comprising an intelligent perception and data fusion module for realizing real-time acquisition and standardized processing of cross-modal data; the knowledge graph construction and causal reasoning module is used for constructing an intelligent knowledge system capable of autonomously learning and semantic reasoning; the digital twin modeling and simulation module is used for realizing dynamic simulation and scene deduction of a full life cycle and providing limit working condition simulation and risk assessment support; the intelligent prediction and health management module is responsible for performing real-time monitoring, fault early warning and residual service life prediction on the equipment state, and generating personalized intelligent maintenance strategies for different working conditions; the visualization and decision support module is used for visually presenting the equipment operation data and the analysis result and providing intelligent decision recommendation; and the cloud edge collaboration and system integration module realizes cross-platform interoperation and continuous integration through distributed computing and micro-service architecture.

The invention belongs to the technical field of equipment management, and discloses an industrial equipment fault prediction and health management method based on multi-sensor fusion, and the method comprises the steps: obtaining multi-source sensing data of industrial equipment, carrying out the signal decoupling analysis, and obtaining a decoupling characteristic spectrum; performing frequency domain conversion and modulation analysis to form a multi-dimensional characteristic spectrum system; analyzing the modal correlation of the multi-dimensional feature pedigree to obtain a fault feature mapping network; a mixed time sequence prediction model is constructed, residual life prediction and degradation trend evaluation are carried out, and an equipment health trend graph is obtained; establishing a health state evaluation index system, and performing reliability evaluation to obtain an equipment health state report; and generating a maintenance decision suggestion, and realizing real-time anomaly detection and maintenance suggestion pushing through edge calculation. Through multi-sensor data fusion and advanced analysis technologies, early warning and accurate prediction of industrial equipment faults are realized, and the operation reliability and production efficiency of the industrial equipment are remarkably improved.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

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.

The invention discloses a computer equipment fault monitoring system and method based on artificial intelligence, and relates to the technical field of computer equipment fault monitoring. The system comprises a data access module, a semantic analysis module, a knowledge graph construction module, a dynamic semantic association module, a data fusion processing module, a decision output module and an adaptive optimization module. The data access module collects and standardizes hardware, software and network data; the semantic analysis module extracts and enhances semantic tags; the knowledge graph construction module forms a data semantic relation network; the dynamic semantic association module screens potential semantic relationships; the data fusion processing module generates a multi-dimensional feature vector; the decision output module triggers fault early warning; and constructing a feedback knowledge graph of the self-adaptive optimization module. According to the method, through event-driven interpolation, dynamic weight fusion, closed-loop feedback optimization and the like, the problems of multi-source data alignment, semantic fusion and dynamic adaptation are solved, the fault monitoring accuracy and the system adaptability are improved, and the method is suitable for fault monitoring and early warning of computer equipment.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a content generation and optimization method based on a knowledge graph, which comprises the following steps: constructing a multi-source knowledge database, extracting core concepts and knowledge contents, and constructing the knowledge graph. Performing semantic analysis to generate semantic vector representation and a keyword list; retrieving the associated text fragment based on the semantic vector and the keyword list, and inputting the associated text fragment into a generation model to generate initial answer content; and utilizing the knowledge graph to match the domain entity and the knowledge graph node, generating a logical reasoning path, optimizing the initial answer content, and generating the final answer content. According to the method, content generation of accurate retrieval, deep knowledge association and logical reasoning enhancement is realized by fusing a multi-source knowledge database, knowledge graph reasoning and generation optimization; semantic vector matching and keyword retrieval are combined, so that the accuracy of knowledge acquisition is improved; and through knowledge graph reasoning path construction, the answer logic is coherent.

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

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 invention provides a multi-modal enterprise credit risk assessment method based on a knowledge graph, which integrates data such as enterprise relationships, industry policies and supply chain information by constructing an enterprise financial knowledge graph, processes entity static attributes and associated information by using a multi-modal embedding technology, captures the associated information in combination with a heterogeneous graph neural network, and evaluates the credit risk of an enterprise. And the dynamic space-time attention mechanism mines time and space features of the time series data, identifies a core risk conduction path based on an attention weight, and finally fuses graph-level features, dynamic space-time features and business rules to output a structured evaluation result. According to the method, multi-modal data is effectively integrated, the problem of incidence relation modeling deficiency is solved, deep fusion of enterprise multi-source data and accurate extraction of risk features are realized, and the accuracy and interpretability of enterprise credit risk assessment can be effectively improved.

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 technical field of information data analysis. The invention provides a public policy case analysis knowledge graph fusion reasoning method and system. The method comprises the following steps: generating standardized preprocessing data; performing extraction processing on the standardized pre-processed data to generate a structured triple set; processing the structured triple set to generate a multi-dimensional knowledge graph; constructing a hybrid inference engine, and processing the multi-dimensional knowledge graph to generate inference result data; performing incremental updating processing of nodes and relationships on the multi-dimensional knowledge graph, and performing parameter optimization processing on the hybrid inference engine to generate an updated knowledge graph and an optimized inference engine; and reasoning result data are processed, and a visual analysis result is output, so that the problems of limitation of a rule engine on causal reasoning, semantic fuzziness and logic illusion of a large language model in the policy field and insufficiency of a single-field knowledge graph on cross-field interaction influence revelation are solved.

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph construction method and system based on a large language model technology. The method comprises the following steps: receiving a multi-source heterogeneous data stream, and completing semantic space mapping and cross-modal feature fusion to generate a unified semantic representation vector set; constructing an initial knowledge graph skeleton; performing incremental optimization on the skeleton, and performing entity relationship disambiguation and conflict detection; and iteratively updating the knowledge representation, and outputting a target knowledge graph meeting semantic consistency. The system comprises a data receiving module, a semantic fusion module, a skeleton construction module, an optimization module and a knowledge updating module. According to the method, multi-source heterogeneous data is effectively processed, the accuracy, the dynamic updating capability and the semantic consistency of the knowledge graph are improved, and the method has wide application prospects in the fields of intelligent question answering, information retrieval and the like.

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

The invention relates to a knowledge graph construction method and system based on a large language model, and the method and system achieve the automatic construction and dynamic maintenance of a knowledge graph through multi-modal data fusion, reinforcement learning and comparative learning joint optimization, teacher-student model knowledge migration, time sequence dynamic analysis and an incremental updating mechanism. Constructing a reinforcement learning framework, and taking accuracy and integrity as reward indexes to train a large model to extract an entity relationship; a large-scale knowledge graph is used as a teacher model, and conflict resolution and semantic alignment of newly added knowledge and an existing graph are realized through a graph attention network; a verification rule is dynamically generated based on historical data and domain knowledge, and relation periodicity and mutation points are detected in combination with Fourier transform and a CUSUM algorithm; and finally, generating a traceable knowledge graph through incremental updating and version control. And the multi-modal data processing precision, the entity relationship extraction dynamic adaptability and the knowledge graph maintenance efficiency are improved.

Disclosed in the present invention are an aviation equipment reliability evaluation method and system based on a knowledge graph and model inference. The method comprises: acquiring data of human factors, equipment systems, and a working environment of aviation equipment; carrying out preprocessing and text labeling on the acquired data; inputting the labeled text information into a constructed entity relationship joint extraction model to form a high-quality structured triple of the knowledge graph; constructing an elastic knowledge graph for the aviation equipment, wherein the elastic knowledge graph comprises an online knowledge graph and an offline knowledge graph which has aviation equipment reliability; and extracting semantic features, and analyzing the similarity between the extracted features to realize indirect inference of the aviation equipment reliability. The present invention fully fuses expert experience and knowledge data, and exerts respective advantages of a human brain and machine intelligence, so as to achieve accurate analysis and prediction of aviation equipment reliability, thereby providing intelligent risk analysis, early warning and optimization suggestions for command and control personnel, and reducing a fault occurrence rate.

A federated distributed computational system enables secure collaboration across multiple institutions for biological data analysis. The system consists of interconnected computational nodes managed by a centralized or decentralized federation manager, depending on the deployment model. Each node contains specialized components that work together to process biological data while preserving privacy. These components include a local computational engine that handles data processing, a privacy preservation module that protects sensitive information, a knowledge integration component that manages biological data relationships by connecting various data sources, and a communication interface that enables secure information exchange between nodes. The federation manager coordinates all computational activities across the network while ensuring data privacy is maintained throughout the process. This architecture allows research institutions to collaborate on complex biological analysis tasks without compromising their sensitive data, enabling breakthrough discoveries through shared computational resources and expertise while maintaining the security, compliance, and confidentiality required in biological research.

The invention discloses a construction engineering risk assessment method and system for multi-source anomaly monitoring, and particularly relates to the field of construction engineering risk assessment, comprising the following steps: analyzing equipment-to-component node mapping and return delay characteristics based on multi-source monitoring data received in an idle task period; establishing a cross-device time alignment model and a pseudo-synchronization section time migration trajectory, identifying a potential abnormal path set through a time delay drift trend, and providing a time feature basis for subsequent structure anomaly reasoning and risk assessment; deducing a node triggering sequence and a time causal chain based on the final pseudo-synchronization segment index matrix and channel path mapping; multi-source monitoring data is received in an idle task period, and a cross-device time alignment model is established, so that the problem of time sequence dislocation caused by mechanisms such as cache delay and packet loss re-sending is effectively identified, and historical abnormal data is prevented from being misjudged as a future risk from the source.

The invention relates to an intelligent question-answering system optimization method and device based on a knowledge graph, and the method comprises the steps: obtaining original knowledge data of a target knowledge domain, and constructing a knowledge graph structure model; extracting term information of entity nodes in the knowledge graph structure model, and constructing an entity term set; receiving a natural language question input by a user, executing a semantic understanding operation based on the standardized expression set to obtain a structured question semantic representation, and matching the question semantic representation with the case training set to obtain context semantic features; constructing a cue word template, and executing a query instruction generation operation to obtain a target query statement of the graph database; submitting the target query statement to a graph database to execute data retrieval operation, and obtaining query result data corresponding to the question semantic representation; and performing personalized rendering processing on the query result data based on the user portrait information to generate final question and answer return content. The method has the effect of improving the query accuracy.

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses a multi-agent collaborative task planning method, and is applied to the technical field of artificial intelligence. The method comprises the steps of decomposing a task into a plurality of sub-tasks through semantic recognition and generating corresponding semantic coding vectors; meanwhile, a preset agent resource library is called, and quantitative evaluation capability vectors of all agents in multiple skill dimensions are obtained; dynamically allocating the most adaptive target agent to execute the corresponding subtask based on matching calculation of the subtask coding vector and the agent capability vector; then parallelly driving the target agent to execute the subtasks, fusing processing results output by the target agent, and integrating to generate a task response text; and finally returning the response text to the user. According to the method, the task is split into the coding vectors corresponding to the sub-tasks through semantic recognition, and dynamic matching is performed in combination with the multi-dimensional capability vector of each agent, so that adaptation of task requirements and agent resources is realized, and the resource scheduling efficiency and execution reliability of a multi-agent system in a complex task scene are improved.

The invention discloses a large model enhanced code security detection method. The method comprises the following steps: dynamically associating CVE vulnerability features with code semantic representation through a knowledge graph construction module; extracting vulnerability features from a CVE vulnerability library by utilizing a knowledge graph construction module, and dynamically associating the vulnerability features with code semantics; based on the CodeQL standard, a query language is generated by adopting a large model and input into CodeQL for AST analysis, and code structure features are extracted; the large model generates a query language meeting the CodeQL standard, codes are analyzed through CodeQL, and key structure features are extracted; integrating a static analysis tool chain to carry out multi-dimensional credible verification on model output; integrating an SAST tool and a symbolic execution tool, and verifying the model output from different dimensions; and summarizing and fusing detection results of the enhancement layers, and determining a final detection result by adopting a decision optimization method. According to the method, through technical fusion and dynamic optimization, the problems of rule stiffness, one-sided detection and low result credibility are systematically solved.

Systems and methods are provided for improving generative artificial intelligence (AI). Systems and methods can integrate more reliable data sources and enhance generative AI training and inference processes for complex tasks. The integration of real-time data and expert input can be included as crucial steps in aligning AI outputs with improved accuracy. Similarly, fine-tuning methodologies and augmentation algorithms can be used to focus on minimizing the occurrence of fabricated content, thereby significantly increasing the chances that the information generated is both current and credible.

The invention belongs to the technical field of knowledge maps and artificial intelligence, and discloses a building safety risk identification method of a big language model assisted knowledge map. According to the technical scheme, the overall process comprises the steps of data collection and preprocessing, construction of urban building structured table data, construction of an urban building safety knowledge graph, integration of the structured table data and knowledge graph data, model fine adjustment, model training, model performance evaluation and model recognition effect verification. According to the technical scheme, the large language model with the high semantic modeling capacity and the knowledge graph with the high graph structure expression capacity are integrated, the related knowledge of urban building safety is automatically extracted, constructed and integrated, high-risk events such as fire disasters and structural hidden dangers are recognized, and the informatization level and the intelligent level of urban building safety management are improved.

The invention relates to the technical field of power systems and automation thereof, in particular to a distribution network auxiliary decision-making method and system considering source load fluctuation relevance and a medium. The method comprises the following steps: firstly, collecting related information of a distribution network area, quantifying a synchronization and hysteresis association rule of multi-source heterogeneous data fluctuation, and constructing a composite feature vector and a standardized risk perception data set; defining a state space and an action space of a reinforcement learning algorithm based on the composite feature vector, and realizing auxiliary decision-making optimization of the distribution network; constructing a scene feature library, calculating the fluctuation relevance similarity between a new scene and a historical scene, and multiplexing a deep reinforcement learning model architecture and carrying out transfer learning; building a power grid digital twinborn simulation platform, designing evaluation indexes, generating candidate schemes, deducing the candidate schemes, selecting recommendation strategies and storing the recommendation strategies in a strategy knowledge base.

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.