2660 results about "Raw data" patented technology

The invention discloses a geological disaster networking monitoring and early warning method, and relates to the technical field of geological disaster monitoring and early warning, and the method comprises the following steps: S1, collecting the original data of multiple types of monitoring equipment in a monitoring region, extracting a high-amplitude sudden change region and a frequency drift factor according to the time and frequency distribution, constructing a high-frequency disturbance sensing matrix, and carrying out the recognition of the high-frequency disturbance sensing matrix; and generating a disturbance characteristic index map for representing the spatial distribution of the unnatural disturbance source. According to the method, active identification and modeling of non-natural interference are realized by constructing a high-frequency disturbance perception matrix and a disturbance index map, disturbance propagation analysis and residual difference are combined to strengthen precursor signal features, the risk level is accurately judged through trend identification and causal analysis, and finally, early warning model parameters are dynamically optimized based on response regulation factors, so that the early warning accuracy is improved. A closed-loop mechanism of interference identification, signal purification, trend extraction, risk judgment and strategy adjustment is formed, and the early warning stability, accuracy and practicability of the system in a high-interference environment are remarkably 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 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 multi-source data real-time fusion processing method and system for a mobile intelligent device, and relates to the technical field of data processing.The method comprises the steps that 1, multi-dimensional original data streams are collected in real time through a heterogeneous sensor array integrated by the mobile intelligent device, data streams of different sensors are aligned by applying a space-time synchronization mechanism, and the data streams of different sensors are obtained; generating an original data set with consistent time and space; 2, dynamic interpolation compensation operation is executed on the original data set, and a dynamic calibration framework is constructed based on the internal topological relation of the data flow to form a dynamic sensing domain; and generating an evolution sequence according to the data unit evolution behavior of the domain boundary, generating a space correction value through the evolution sequence and the offset feature of the preset reference, and generating preprocessed data fused with the space correction value in combination with real-time data correlation analysis. According to the method, dynamic adjustment is triggered through anomaly detection, the fusion parameters are updated through the sliding window, and real-time efficient fusion processing of multi-source data of the mobile intelligent device is achieved.

The disclosed embodiments provide systems and methods for automated mapping of raw data into a data fabric. An innovative approach leveraging Artificial Intelligence (AI)-powered tools and a data fabric to automate the ingestion, transformation, and integration of raw data into a unified model is introduced. By automating the data mapping process, organizations can reduce reliance on manual methods and accelerate their ability to utilize robust insights for exposure management and attack surface reduction. The disclosed solution provides a scalable architecture for unifying cybersecurity signals across cloud and hybrid environments, enabling real-time decision-making and improved organizational resilience against cyber threats

The invention discloses an unmanned aerial vehicle monitoring and countering integrated system, and relates to the technical field of unmanned aerial vehicle monitoring, and the system comprises a sensing module which is used for carrying out the monitoring of an environment through a plurality of sensors, and obtaining original data; the fusion module is used for processing the original data by adopting a data fusion algorithm to obtain target information; the identification module is used for outputting the type and behavior mode of the unmanned aerial vehicle; the analysis module is used for evaluating the type and behavior mode of the unmanned aerial vehicle by adopting a dynamic threat evaluation method and generating early warning information; the strategy module is used for formulating a dynamic countering strategy; the execution module is used for implementing corresponding countering measures and monitoring the countering effect in real time; and the recording module is used for recording all results in the whole process. Through the technical means of multi-sensor cooperative monitoring, data fusion processing, classification identification, dynamic threat assessment, game decision making, intelligent countering and the like, omnibearing perception, accurate identification, intelligent assessment and efficient disposal of the unmanned aerial vehicle are realized.

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal threat perception method and system based on a space-time diagram neural network, and the method comprises the steps: obtaining a multi-modal original data set in a vehicle insurance claim settlement link, and carrying out the business relation mining and space-time dynamic analysis, and obtaining an entity space-time relation diagram; inputting the entity space-time relation graph into a space-time graph neural network for space-time fusion to obtain a node threat embedding vector; performing graph contrast learning and cross-modal feature discrimination on the node threat embedding vector to obtain a vehicle insurance threat feature vector; and carrying out fraud space-time propagation modeling based on the vehicle insurance threat feature vector, and generating a vehicle insurance threat blocking strategy, the method can accurately predict a propagation path and an influence boundary of gang fraud in a vehicle insurance ecological network, and identifies potential threats and starts prevention measures before a fraud behavior is completely displayed.

Various embodiments relate to normalizing raw data by mapping the raw data to a computer-readable tag. A computer-readable tag may be an identifier that at least partially represents a category (e.g., a department) and / or the raw data itself. In response to receiving the raw data, some embodiments perform the mapping by, for example, performing natural language processing (NLP) on each particular department's raw data to associate natural language words in the raw data to its corresponding computer-readable tag and then populating, at a data structure that includes the computer-readable tag, an entry with data (representing the raw data) in a standardized format. In this way, regardless of whether different sets of raw data come from disparate sources that have diverse formats, protocols, or structures relative to each other, the normalized data and standardized form makes the data compatible.

The invention relates to an experimental data processing method and device, an AI analysis module and computer equipment, and belongs to the field of data processing.The method comprises the steps that multi-dimensional original data are partitioned according to types, and formats are unified; generating a similarity matrix based on time and space neighborhood information, and marking abnormal fluctuation points; effective signals are separated through a time-frequency feature matching noise library; extracting multi-layer features of basic statistics, time sequence correlation and trend change; and dynamically screening core features to update the tracking type experimental model. The matched AI analysis module integrates hardware circuits of data partitioning, similarity calculation, anomaly marking, noise matching, feature extraction and model updating, and whole-process acceleration is achieved. According to the method, through multi-dimensional data compatibility processing, accurate anomaly detection, multilayer feature fusion and model adaptive optimization, the experimental data processing efficiency and conclusion reliability are remarkably improved, and the method is suitable for real-time analysis of multiple scenes such as scientific research and industry.

In a described embodiment, a multi-agent system for processing information is provided including a data processing agent configured to ingest and normalize raw data inputs to produce standardized data and a standards integration agent configured to apply reporting standards into the standardized data thereby generating integrated reporting standards. The system further includes a performance alignment agent configured to align performance indicators based on the standardized data and the integrated reporting standards and an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standards. An orchestration framework configured to manage operations of the data processing agent, the standards integration agent, and the performance alignment agent to produce a regulatory repot compliant with regulatory requirements is further provided. The orchestration framework is further executable by a large language model.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

A computer-implemented method, computer program product and computing system for receiving a message concerning an event within a computer platform, wherein the message concerns a technology type and includes raw data; defining a cipher for the technology type, thus defining an associated cipher; processing the raw data included within the message using the associated cipher to define supplemental data for the technology type; and forming enriched data for the technology type based, at least in part, upon the raw data and the supplemental data.

The invention, which relates to the technical field of intelligent interaction, discloses an AI-based adaptive scene intelligent interaction system comprising a multi-modal data acquisition module, a modal preprocessing module, a multi-modal embedded coding module, an intention fusion and representation module, a service scene matching module and a service execution and reinforcement learning module. The method comprises the following steps: acquiring multi-modal original data in a user interaction process, including voice signals, text input and user behavior tracks, and synchronously recording an acquisition timestamp; according to the method, through a multi-modal unified embedding and dynamic weighting mechanism, the problem of characteristic dimension imbalance is effectively solved, and the user intention recognition accuracy is improved; meanwhile, reinforcement learning and a multi-factor scoring model are combined, personalized scene matching and dynamic response are achieved, the adaptive capacity and service accuracy of the system in a complex environment are improved, and therefore the stability and user experience of the intelligent interaction system are remarkably optimized.

Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g., vulnerability hypergraph) that encompasses identified relevant data providing a holistic view of a contextual landscape.

The invention relates to the technical field of environment monitoring, in particular to a farmland environment intelligent monitoring system based on multi-source data fusion, which comprises a multi-source data acquisition module for synchronously acquiring original data; the dynamic compensation preprocessing module is used for receiving the original data and generating standard data with aligned time axes through a dynamic timestamp compensation mechanism; the spatial-temporal feature decoupling module is used for performing multi-scale feature extraction on the standardized data and separating spatial distribution features, time sequence features and environment coupling features; the multi-modal fusion analysis module is used for fusing the spatial distribution characteristics, the time sequence characteristics and the environment coupling characteristics by adopting an evidence chain fusion algorithm to generate farmland environment state evaluation parameters; and the edge decision optimization module is used for dynamically adjusting a resource allocation strategy of an edge computing node according to the farmland environment state evaluation parameters. Priority processing of key tasks is achieved under limited computing power, and the response efficiency and the resource utilization rate of the system are effectively improved.

The invention relates to the technical field of target detection and tracking, and discloses a multi-modal real-time target detection and tracking system, which is characterized in that a multi-modal data acquisition module integrates a high-definition camera, a millimeter-wave radar, a laser radar and an infrared sensor and is used for acquiring target information from multiple dimensions such as visual images, distance, speed and angle, three-dimensional point cloud and thermal radiation; and the data preprocessing module is used for carrying out denoising, enhancement, normalization, filtering, coordinate conversion and temperature correction processing on the original data acquired by the multi-modal data acquisition module. Multiple sensors are integrated to collect multi-dimensional data, after preprocessing, efficient fusion is achieved through hierarchical fusion and an attention mechanism, the detection module is combined with an improved algorithm and a dynamic threshold value, the tracking module fuses multiple features and has the online learning ability, and the system control module achieves intelligent management. The system greatly improves the accuracy, the real-time performance and the stability of detection and tracking, has remarkable advantages of an innovative technology, and provides a new scheme for related fields.

The invention provides a multi-sensor dynamic calibration method in rotary geosteering drilling, and relates to the technical field of petroleum engineering and drilling, and the method comprises the following steps: collecting original sensing data in real time through a multi-source sensor array deployed on a rotary geosteering drilling tool, wavelet packet decomposition is combined with an improved sliding window algorithm to carry out online denoising processing on original data, and a three-dimensional feature matrix containing environmental interference factors is established; and based on the three-dimensional feature matrix, constructing a dynamic calibration model based on depth time sequence association, respectively processing sensor body feature flow and environment interference feature flow by using a dual-channel LSTM network, performing dynamic weight distribution and fusion on dual-channel features by means of a gating attention mechanism, and outputting a dynamic deviation compensation coefficient of each sensor. The parameter set is generated and reconstructed through the three-dimensional feature matrix, the two-channel network and cooperative calibration, the drilling efficiency and precision are improved, errors are reduced, and intelligent development is promoted.

The invention discloses an intelligent lighting management system and method based on multi-source data, and relates to the technical field of intelligent lighting management, and the system comprises a multi-source data collection module, an environment feature anchoring module, a dynamic error modeling and calibration module, an edge-cloud collaborative decision module and a lighting strategy execution module. The multi-source data acquisition module fuses environment sensor, visual perception and laser radar data to construct a'sensor original data + environment characteristic data 'dual-input system; the environment feature anchoring module extracts stable features from the visual / laser radar data to serve as a calibration reference; the dynamic error modeling and calibration module quantifies the error in real time based on the reference and generates calibration parameters; the edge-cloud collaborative decision-making module and cloud long-term optimization form a hierarchical collaborative architecture through edge node real-time control; and the lighting strategy execution module dynamically adjusts lighting parameters according to the calibration data and feeds back an effect to form a closed loop.

The invention discloses an industrial inspection intelligent decision-making method and system based on large and small model collaboration. The industrial inspection intelligent decision-making method comprises the following steps: dividing an inspection task into a plurality of sub-tasks by using a cloud large model, and performing dynamic task planning based on the sub-tasks; calling an edge small model to execute the subtask to obtain multi-modal original data; performing cross-modal feature fusion on the multi-modal original data by using a cloud large model to obtain cross-modal fusion features; and reasoning based on the cross-modal fusion features by using a cloud large model, extracting abnormal factors, and formulating a new inspection task based on analysis of the abnormal factors. Through the comprehensive method of comprehensive multi-mode perception, conditional diffusion model enhancement and cloud-edge collaborative decision, the problems of fault sample scarcity and perception deviation in an extreme environment are effectively solved, high precision and low time delay are considered, and a new breakthrough of intelligent inspection is brought.

The invention provides a sewage AI intelligent management and control system based on a neural network algorithm, which comprises multi-source data acquisition units, an algorithm processing unit and an execution feedback unit, and is characterized in that the multi-source data acquisition units are deployed at a water inlet and a water outlet of a sewage treatment assembly line and in a biological reaction tank; comprising a fast parameter detection array composed of a pH sensor, a conductivity sensor, a dissolved oxygen sensor, a turbidity sensor, a temperature sensor and an ORP sensor. The process state monitoring group consists of an aeration equipment rotating speed sensor, a reflux pump flowmeter and a sludge concentration meter; the fast parameter detection array and the process state detection group are connected with the edge computing node through an industrial bus, perform sliding window mean filtering preprocessing on original data, generate regulation and control instructions of aeration rate, reflux ratio and sludge discharge frequency through model calculation, and send the regulation and control instructions to the execution unit; the complete refined AI intelligent control sewage treatment system based on the neural network algorithm is realized.

The invention relates to the technical field of attitude measurement, and discloses a gradienter attitude real-time calibration method based on multi-modal data fusion, which comprises the following steps: acquiring multi-modal original data and completing unified preprocessing to obtain multi-modal data; reconstructing a liquid surface form in a physical domain neural operator layer, and outputting a physical domain attitude and a residual error; noise and drift are deduced in a sensing domain neural operator layer, and a sensing domain attitude estimation value, an offset parameter, a scale parameter and a residual error are output; establishing a deviation memory bank, updating by using residual errors and historical results, generating a long-term drift compensation amount, and superposing a sensing domain result; inputting a physical domain and a compensated sensing domain result into a dynamic constraint reversible transformation model, and outputting a fusion attitude and uncertainty; and executing slow variable refining compensation on the updated parameters, and outputting final real-time calibration attitude and quality information. According to the invention, by introducing multi-modal data fusion and double-layer reversible neural operator modeling, real-time, high-precision and long-term stable calibration of the attitude of the gradienter is realized.

The invention relates to the technical field of wind field simulation, and provides a wind field numerical simulation method based on multiple meteorological data sources. The objective of the invention is to solve the problems of large simulation error, rough terrain boundary processing and poor turbulence model parameter adaptability caused by non-uniform coverage of a single data source, insufficient precision and unscientific multi-source fusion. The method is characterized by comprising the following steps: step 1, constructing a CFD three-dimensional grid based on a geometric model of a target area; 2, collecting original data of a ground station, satellite remote sensing, numerical forecasting and the like; 3, performing standardized preprocessing (abnormal value elimination, missing interpolation, radiation / geometric correction and resampling), determining a multi-source fusion weight by combining historical data analysis, and generating comprehensive meteorological data by adopting a weighted average method; and 4, inputting the comprehensive data into a CFD-RANS model, dynamically adjusting turbulence parameters, accurately setting terrain boundary conditions, and obtaining a wind field space-time distribution result through numerical solution. According to the invention, through combination of multi-source data fusion and CFD simulation, the wind field simulation precision and stability are improved.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The invention relates to the field of high-voltage cable fault diagnosis, and discloses a high-voltage cable insulation fault intelligent diagnosis method and system based on multi-information fusion, and the method comprises the following steps: S1, obtaining the operation multi-source data of a high-voltage cable, and constructing a multi-source original data matrix; s2, preprocessing to obtain a time-space aligned standardized data matrix; s3, performing feature extraction to obtain a multi-dimensional feature vector, and learning internal association between features by using a multi-modal deep network to obtain a joint multi-modal feature; s4, constructing a fault type identification model based on Bayesian reasoning and Monte Carlo sampling, and obtaining a fault type classification result; s5, in combination with deep learning and a physical model, obtaining a fault occurrence interval, positioning information and a fault level; and S6, based on a fault type classification result, a fault occurrence interval and positioning information, obtaining a fault level, and carrying out early warning pushing on a generated diagnosis report. According to the invention, high-precision identification, positioning and risk assessment of high-voltage cable insulation faults are realized.

A federated learning system (100) for data protection in data sharing and collaboration, consisting of: a module for data acquisition and local preprocessing that is configured to clean, normalize and standardize local data sets at each participating node without transferring raw data externally; a local model training module configured to train a machine learning model on the pre-processed local dataset; a secure model update and encryption module configured to encrypt and secure model parameters or updates before transmission using privacy protection techniques; a federated aggregation and coordination module configured to aggregate encrypted updates from multiple participating nodes into a global model; a module for monitoring and ensuring data protection compliance, configured to enforce data protection budgets and audit protocols and to ensure compliance with data protection regulations; a performance optimization and resource management module configured to optimize communication, computation, and resource utilization across all nodes; and a module for global model delivery and feedback, configured to redistribute the aggregated global model to participants and integrate performance feedback for iterative improvements.

The invention relates to the technical field of power transformation equipment state monitoring, in particular to a power transformation equipment multi-parameter intelligent sensing and state monitoring system which comprises a sensing unit, a data processing unit, a state analysis and diagnosis unit and an upper computer monitoring and management unit. Through data collection preprocessing, lightweight AI model anomaly preliminary screening and hierarchical edge cloud cooperative transmission strategies, efficient cleaning of original data, rapid edge end anomaly identification and optimal utilization of network resources are realized, data transmission bandwidth occupation is greatly reduced, monitoring real-time performance is improved, and the method is suitable for large-scale popularization and application. The method integrates technologies such as digital twinning, federated learning and a time-space attention network, realizes equipment cross-time-space fault accurate positioning, fault type reliable identification and residual life dynamic prediction in combination with a quantification algorithm, triggers hierarchical early warning through hierarchical health assessment, and provides scientific and accurate decision support for refined operation and maintenance of power transformation equipment.

Disclosed in the present invention are a large language model federated fine-tuning method and apparatus based on gradient compression. The method comprises the following steps: constructing, on the basis of a gradient tensor generated during fine tuning of a large language model, a raw data set having a time series relationship, performing inference by means of an autoencoder to obtain a reconstructed gradient data set, and constructing a reconstruction loss function to optimize the autoencoder; and initializing a base model of the large language model as a global model at a server end, the server end updating the global model to a client, using a pre-trained encoder to obtain a compressed gradient at the client, and at the server end, using a pre-trained decoder to decode and aggregate the compressed gradient, and then updating the global model. The present invention can improve the fine-tuning efficiency of the large language model and reduce computing resource requirements while ensuring data privacy protection, and is suitable for application scenarios such as communication optimization improvement and privacy protection enhancement in the process of scientific computing-oriented large model fine-tuning and training.

The invention provides an API behavior prediction and security policy management and control method based on machine learning, and relates to the field of computer network security, and the method comprises the steps: obtaining API original data in real time, preprocessing the original data, obtaining the preprocessed original data, obtaining API dimension features in real time based on the preprocessed original data, and fusing the API dimension features to obtain unstructured data, establishing a dynamic feature validity verification rule to pre-process the unstructured data to obtain a standardized feature tensor of a unified dimension; joint modeling of API behavior spatio-temporal characteristics is carried out through a collaborative architecture of a multi-head time attention mechanism and a dynamic graph neural network, API behavior prediction is carried out, an API dynamic security management and control strategy is generated in real time through a strategy engine, strategy execution is carried out, a security strategy execution effect is monitored in real time, and iterative optimization is carried out. According to the invention, the problems that the security control strategy of the traditional API gateway is fixed and rigid, and the security strategy is difficult to dynamically adjust according to the real-time access condition and behavior are solved.

The invention relates to a knowledge graph construction method, device and equipment based on multi-source data. The method comprises the following steps: acquiring original data from different data sources; preprocessing the original data to obtain target text data; wherein the preprocessing comprises format conversion, text cleaning and normalization and / or sentence segmentation and segmentation; performing knowledge extraction on the target text data through a pre-optimized large language model to obtain original structured data including an original entity, an original relationship and an original attribute; post-processing the original structured data to obtain target structured data including a target entity, a target relationship and a target attribute; wherein the post-processing comprises format analysis, entity standardization and ambiguity elimination, and relation and attribute verification; and updating nodes and edges of the current knowledge graph according to the target structured data. The method can adapt to multi-source heterogeneous data, and the accuracy and consistency of the knowledge graph are improved.