224 results about "Data semantics" patented technology

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 data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a multi-source knowledge processing and querying method, which comprises the following steps: data acquisition, cleaning and standardization processing, and standardized database construction; extracting core concepts and association relationships, and generating knowledge elements; constructing a multi-dimensional knowledge graph based on knowledge elements, and establishing a semantic index to realize data semantic annotation and bidirectional mapping; and analyzing the query intention, extracting a query constraint condition, and executing association reasoning based on the multi-dimensional knowledge graph to generate a query result. According to the method, the standardized database of the multi-source heterogeneous data is constructed, so that the data consistency is improved; through multi-dimensional knowledge graph construction and semantic index establishment, the relevance and structural expression of data are enhanced, so that the relationship between knowledge elements is clear and traceable; and through association reasoning based on query constraint conditions, the query accuracy and efficiency are improved.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

The invention discloses an industrial system automatic fault diagnosis method based on a large language model. According to the method, a three-layer mapping system of industrial data, natural language description and knowledge reasoning is constructed, field multi-source sensor data are subjected to semantic conversion, and a quantitative calculation model based on a large language model is constructed based on historical data and logs. And a fault case is matched in real time with the help of a retrieval-enhancement generation technology to serve as a reference, the fault case and abnormal information are input into a knowledge reasoning model based on a large language model together, a structured logical reasoning chain is generated, and a diagnosis conclusion containing candidate faults, cause analysis and disposal suggestions is further output. Meanwhile, through user feedback and a reinforcement learning mechanism, the model and the knowledge base are adaptively updated, the defects of traditional static rules and expert experience are effectively overcome, the accuracy, interpretability and robustness of fault detection are remarkably improved, and the method adapts to complex and changeable working condition requirements.

The invention discloses a digital financial data sharing system and method, and belongs to the technical field of digital financial data analysis, and the system comprises a data standardization management module, a data access conversion interface module, a cross-mechanism authentication authority management module, a safety audit risk control module and an intelligent analysis cooperation platform module. The data standardization management module is used for unifying a data format to be XBRL, metadata definition and cleaning rules and eliminating data semantic differences between mechanisms, and the data access conversion interface module is used for supporting multi-source heterogeneous data access and providing a standardized output interface. The cross-institution authentication authority management module is used for realizing decentralized identity authentication based on a block chain technology, zero-knowledge proof and a federated learning technology, and controlling a data access range in a hierarchical manner in combination with role authority, and the security audit risk control module is used for monitoring data flow in real time. According to the invention, on the basis of realizing digital financial data sharing, the problem of data islands can be solved, and cross-mechanism data authentication can be facilitated.

The invention relates to an intelligent semantic analysis knowledge graph system for multi-source heterogeneous data. The system comprises a data preprocessing module used for preprocessing to-be-processed multi-source heterogeneous data to obtain format-standardized text data; the semantic analysis module is used for performing natural language processing on the text data to obtain words, and then performing vocabulary layer, syntactic layer and semantic layer analysis on the text data and the words to obtain structured data; the structured data comprises a triple consisting of entities, relationships and events and a network diagram of the entities and the relationships; the model structure mapping module is used for mapping the structured data into a predefined structure of a knowledge graph to obtain structured graph data; and the knowledge graph module analyzes entities and relationships in the graph data, performs reasoning to obtain potential relationships in the graph data by utilizing a logic rule and a machine learning algorithm, realizes cross-data-source and cross-document deep semantic association, and improves the efficiency of large-scale data processing and knowledge management.

The invention discloses a knowledge graph optimization method based on large model and multi-modal data fusion. The method comprises the following steps: S1, constructing a multi-modal data set; s2, forming a preprocessed multi-modal data set; s3, generating a unified semantic vector representation; s4, inputting the unified semantic vector representation into an adaptive Poisson distribution model, performing dynamic modeling on the arrival rate and distribution characteristics of the multi-modal data, and determining adaptive parameters of data sampling and updating; s5, generating a fused semantic representation set; s6, performing entity extraction and relation identification by using the fused data representation, and constructing a preliminary knowledge graph; and S7, carrying out automatic verification, redundant information elimination and structure adaptive adjustment on nodes, edges and attributes of the knowledge graph to form an optimized knowledge graph. According to the method, the data acquisition and atlas updating frequency can be adjusted according to the real-time data semantic change, and it is ensured that the atlas construction process has semantic driving performance and time sensitivity.

The invention discloses a data processing method based on computer software development, which belongs to the technical field of data processing, and comprises the following steps: S1, constructing an adaptive analysis engine driven by a knowledge graph, and carrying out multi-modal data semantic modeling and context labeling by adopting a multi-modal data feature fusion technology; and S2, designing a cross-node distributed data cleaning and dynamic fragmentation optimization strategy based on a differential privacy and feature space alignment technology, and through hierarchical deployment of a national cryptographic algorithm and homomorphic encryption, realizing data full life cycle security protection, ensuring data asset security by static storage encryption, and supporting security calculation requirements by ciphertext operation; attribute-based encryption fine-grained access control accurately matches a data use permission, and a block chain technology ensures traceability and tamper-proofing of data operation; the problem that a traditional encryption scheme is insufficient in flexibility is solved while the compliance requirement is met, and a trusted infrastructure is established for cross-domain data sharing.

The invention discloses a large-model multi-modal data semantic representation alignment method, and the method comprises the steps: carrying out the fine-grained local feature extraction of first modal data and second modal data through employing a data processing technology based on deep learning, the method comprises the following steps: acquiring first modal data and second modal data, calculating similarity between fine-grained local features of the first modal data and the second modal data as an initial alignment loss function value, and then further combining global context semantic information to carry out semantic enhancement coding on the fine-grained local features of the first modal data and the second modal data to obtain an initial alignment loss function value; according to the method, a fine alignment loss function value is obtained through context-level semantic alignment measurement, and then model training is guided based on the fine alignment loss function value and an initial alignment loss function value, so that semantic representation alignment of multi-modal data is realized. In this way, the alignment precision of the multi-modal data on the semantic level can be remarkably improved, and a more accurate feature basis is provided for subsequent data fusion and application.

The invention relates to the technical field of data processing, and discloses a multi-source heterogeneous data semantic alignment method and device and computer equipment.The method comprises the steps that multi-source heterogeneous spatio-temporal data are collected through a center data node and a plurality of edge nodes, spatial attribute feature extraction and inter-unit spatial correlation calculation are carried out, and multi-source heterogeneous spatial data are obtained; obtaining a city data semantic association degree feature matrix; inputting the urban data semantic association degree feature matrix into a multilayer graph attention network for feature extraction to obtain a node feature vector matrix and a task association degree adjacency matrix; generating a distributed scheduling scheme of the data processing tasks according to the node feature vector matrix and the task association degree adjacency matrix; according to the method, data processing tasks are distributed to computing nodes according to a distributed scheduling scheme, alternate iterative optimization is carried out based on a city data semantic association degree feature matrix, a semantic mapping relation set is obtained, and intelligent scheduling of the data processing tasks and efficient semantic alignment in a distributed environment are achieved.

The invention relates to the technical field of data processing, in particular to a cross-unit data management method, which comprises the following steps of: performing format standardization processing on data in a data pool of each organ and unit, converting the data into a uniform standard format, extracting semantic features to construct a semantic association network, and recording a semantic relationship of data items and a dynamic authorization and authority mapping mechanism. Then, according to the inquirer identity information, the unit attributes, the business requirements and the data sensitivity level, dynamically generating inquiry authorization and establishing an authority mapping table; and when a query condition is received, analyzing by utilizing a natural language processing technology, and generating a query route and optimizing a statement in combination with the semantic association network and the permission mapping table. And finally, calling data from each data pool according to the route, performing fusion processing, displaying the data to a querier in real time, and recording a query log. The invention aims to solve the problems of data semantic isolation, authority management staticization, low query efficiency and insufficient cross-unit data conflict resolution capability in the existing cross-unit data management.

The invention discloses a multi-modal data fusion-based intelligent management system for a thoracic surgery monitoring unit, belongs to the technical field of medical information and artificial intelligence, and aims to solve the limitation of an existing thoracic surgery monitoring system in the aspects of multi-modal data fusion, heterogeneous data semantic alignment and intelligent deep analysis and prediction decision. The system is characterized by comprising a multi-modal data acquisition unit, a heterogeneous data fusion and semantic alignment module, an intelligent analysis and prediction decision module, a man-machine interaction and visual presentation module and a secure storage and management module. By the adoption of the technical scheme, comprehensive multi-modal data fusion, high real-time performance, deep intelligent analysis and prospective prediction can be achieved, intelligent decision support, resource optimization, continuous learning and self-adaptive optimization are provided, and the intelligent level and patient management efficiency of the thoracic surgery intensive care unit are remarkably improved.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention provides an aircraft fault early warning method and system based on flight data, and relates to the technical field of aircraft fault early warning. The method comprises the following steps: firstly, mining out an aircraft external data semantic vector and an aircraft internal data semantic vector corresponding to flight data to be processed; secondly, performing semantic enhancement operation on a pre-generated flight data noise vector based on an aircraft external data semantic vector, and outputting a flight data enhancement vector; then, according to the aircraft internal data semantic vector, mining an aircraft internal depth vector; and finally, according to the flight data reinforcement vector, carrying out association mining operation on the aircraft internal depth vector, outputting a target flight data semantic vector, and according to the target flight data semantic vector, analyzing aircraft fault early warning information. On the basis of the content, the problem that in the prior art, the reliability of aircraft fault early warning is relatively low can be solved.

The invention relates to the field of multi-modal semantic alignment, and provides a multi-modal data semantic alignment method and device based on a cross-modal attention mechanism. The method comprises the following steps: acquiring multi-modal data and a category label corresponding to the multi-modal data, and mapping the multi-modal data into a multi-modal embedded vector through a pre-trained multi-modal encoder; generating text descriptions corresponding to the multi-modal data according to the multi-modal large language model, screening the text descriptions in combination with category labels, and constructing a multi-modal knowledge base; constructing a multi-element embedding center based on the multi-modal knowledge base; through a cross-modal attention mechanism, interacting a multi-modal embedding vector with the text description, and generating a multi-modal embedding vector after semantic enhancement; and performing comparative learning on the multi-modal embedding vector after semantic enhancement and a multi-element embedding center to realize semantic alignment of the multi-modal data. In this way, the accuracy of semantic representation is enhanced, and the alignment effect of the multi-modal data in the unified semantic space is remarkable.

The invention discloses an automatic auditing and checking system and method for substation operation safety. The system comprises an automatic data acquisition module, a data semantic fusion analysis module, a dynamic auditing rule generation module, a risk collaborative prediction module, an automatic information pushing module and a visual interaction configuration module. All the modules are connected through an intranet data interface and a system internal communication protocol, aiming at the prominent problems that the auditing process depends on manpower, the information system is split, the system semantics is difficult to understand, the risk identification is not comprehensive, the rule adaptability is poor, the response and push chain is broken and the like commonly existing in the existing substation operation auditing work. The invention provides an automatic auditing and checking system and method for substation operation safety, and aims to construct a full-process closed-loop intelligent auditing platform with semantic comprehension capability, multi-source data fusion capability, risk collaborative prediction capability and dynamic rule evolution capability.

The invention discloses a cross-modal large model construction method and system based on track spatio-temporal characteristics, and belongs to the crossing field of artificial intelligence and dynamic spatio-temporal data processing, and the method comprises the steps: carrying out the sliding sampling and spatial distribution difference judgment through the semantic dynamic segmentation of multi-scale track spatio-temporal data, and generating spatio-temporal data blocks with consistent semantics; designing a space-time encoder of a hybrid architecture, extracting track time sequence association and spatial features, and unifying dimensions; constructing a text space-time fusion mechanism, and dynamically adapting cross-modal features by means of an anchor interface and gating fusion; a staged instruction fine tuning strategy is adopted, semantic alignment of space-time and text features is optimized firstly, then model top-layer parameters are trained cooperatively, complex scene adaptation is enhanced in combination with instruction difficulty progression and hard sample mining, and space-time constraint regular terms are introduced to guarantee output rationality. According to the method, high-precision cross-modal reasoning capability is provided for scenes such as track analysis and track prediction.

The invention provides a multi-language cross-modal information retrieval method, device and equipment. The method comprises the following steps: acquiring first to-be-retrieved data of a first modal; preprocessing the first to-be-retrieved data to obtain first data; inputting the first data into a corresponding type of encoder in a multi-modal information retrieval model for encoding to obtain a first type of encoded data; the encoder comprises a Chinese encoding unit; inputting the first type of coding data into a projection layer of a multi-modal retrieval model for projection processing to obtain a first target coding vector; and according to the first target coding vector, performing at least one type of other modal information retrieval in a vector database to obtain at least one type of other modal retrieval result data with the same semantics as the first data. According to the method, the multi-modal information retrieval efficiency, the semantic interpretation capability and the multi-modal alignment capability can be improved.

The invention discloses a forest management data processing method driven by a large language model, and belongs to the technical field of forest resource management and artificial intelligence data processing crossing. The method comprises the steps of multi-source data acquisition and preprocessing, forest management knowledge graph construction, large language model fine adjustment and retrieval enhancement generation, data semantic fusion and understanding and management strategy reasoning, wherein the large language model performs automatic reasoning based on fused data, management intention and industry knowledge to form a management strategy conforming to a forest growth law and multi-target balance; operation plan text generation: on the basis of completing strategy and space matching, compiling a forest operation plan text meeting forestry industry specifications and management requirements, and man-machine interaction and continuous optimization: collecting feedback information of forestry workers on forest operation plans through a man-machine interaction interface, and systematic evaluation is carried out on the generated operation scheme from forest resource sustainability, ecological function improvement, operation goal achievement degree, risk controllability and policy compliance.

The invention belongs to the technical field of power system data processing, and relates to a data semantic understanding and intelligent cataloguing method based on power industry data, which comprises the following steps: acquiring real-time monitoring data, equipment operation data, historical operation and maintenance data and business system data of a power system; carrying out missing value processing, abnormal value elimination, repeated data deletion and normalization processing on the collected data, carrying out integration through a data fusion algorithm to form a unified data set, carrying out semantic analysis on industry text data by utilizing a natural language processing technology, extracting key entities and relationships, and carrying out semantic analysis on the key entities and relationships; mapping the data into a knowledge graph in combination with a power industry ontology model to generate structured and semantic industry knowledge data; finally, through the semantic analysis result and the knowledge graph, the data classification label is automatically generated, intelligent cataloguing and efficient management are achieved, the intelligent level of data management is improved, the data search efficiency and utilization value are remarkably improved, and the complexity of manual intervention is reduced.

The invention provides an intelligent engineering progress tracking method and system based on the Internet of Things, and relates to the technical field of engineering progress tracking, and the method comprises the steps: binding digital identities to construction members and construction equipment; executing multi-source data acquisition on an Internet of Things sensing layer based on the digital identity label; the construction activity is recognized through multi-source data semantics, and the execution state corresponding to the construction activity is judged; performing progress deviation calculation according to the execution state and the project progress; according to the deviation magnitude of the progress deviation and the job category, generating a scheduling suggestion; and outputting the scheduling suggestion to the construction equipment to dynamically adjust the project progress. According to the invention, the technical problem of poor engineering progress tracking efficiency in the prior art can be solved, and the technical effect of improving the engineering progress tracking efficiency is achieved.

Disclosed in the present application are a data access control method and device based on semantic support, and a medium. The method comprises: a data provider establishing an ontology model of data semantics on the basis of ontology knowledge of each piece of distributed data source data, and uploading the ontology model to a blockchain network; providing rule information of the data source data, and writing a distributed data source data address and the rule information into a smart contract on the blockchain network, wherein the rule information is used for representing an access control policy for the distributed data source data; receiving a data access request uploaded by a data access end by means of the blockchain network, and acquiring authorization information for the data access request on the basis of the rule information in the smart contract; and if it is determined that the authorization information indicates that access is allowed, on the basis of a data access method corresponding to the ontology model, acquiring fine-grained data corresponding to the data access request, encrypting the fine-grained data, and then sending same to the data access end.

The invention discloses a network topic hot spot extraction method based on bullet screen semantic recognition, and aims to solve the problems of bullet screen data semantic sparsity, semantic offset, noise interference and the like. The method is characterized by comprising the following steps: carrying out semantic coding on a bullet screen by utilizing a Transform structure; a dynamic semantic evolution perception model is constructed, semantic offset is measured through KL divergence, and topological correlation analysis is carried out through GCN; constructing a space-time density field in combination with a video time axis to realize space-time coupling feature fusion; automatically extracting a hot spot cluster by adopting an improved density peak clustering algorithm; and predicting a hotspot evolution trend by using an LSTM model. By means of the technical scheme, topic hotspots can be accurately captured, semantic evolution logic can be recognized, and the purity and predictability of hotspot extraction are improved.

The invention discloses a large-scale railway scene point cloud data semantic segmentation method based on deep learning, and the method comprises the steps: carrying out the down-sampling of railway point cloud, constructing a KDTree, splicing three-dimensional coordinates, reflection intensity and semantic tags, and aggregating local features, and forming a multi-dimensional feature tensor; a semantic segmentation network PGA-Net is designed, a global feature aggregation module is introduced in a coding stage based on a KPConv architecture to capture long-range dependence, global and local features are dynamically fused through a gating attention mechanism, and a'global first and local second 'feature learning mechanism is formed; a point-level semantic prototype guiding module is used for calculating the similarity of point features and a semantic prototype, and feature semantic consistency is enhanced; in a decoding stage, a global attention perception module is adopted to strengthen key semantic features; and fine semantic segmentation is realized through up-sampling and category mapping. According to the method, the problems of large point cloud span, complex structure and difficulty in long-range dependence modeling in a railway scene are solved, and the segmentation precision and robustness are improved.

The invention belongs to the technical field of cross-modal semantic alignment, and discloses a multi-modal semantic alignment method oriented to classroom teaching guidance. Semantic modeling is carried out for multi-modal data in the teaching process, semantics are transmitted through an intermediary modal to align data semantics in a cross-modal mode, and the semantic alignment effect of other modal data is enhanced by utilizing the intermediary modal data which is large in scale and easier to process in the teaching process. According to the method, a data prototype semantic space is further constructed, data of different modalities can be described in the unified prototype semantic space, and multi-modal data can be mapped into the prototype semantic space in two modes of prototype projection transformation and self-supervision category label generation. According to the method, efficient alignment and deep analysis of multi-modal data can be realized in a complex classroom environment, so that a teacher is helped to acquire classroom dynamics in real time, a teaching strategy is optimized, and improvement of teaching quality and classroom efficiency is promoted.

The invention provides a mold engineering drawing labeling system, method and equipment, a storage medium and a program product, and belongs to the field of computers. The system comprises an input analysis module used for obtaining three-dimensional model information, two-dimensional engineering drawing information and mapping information of a target mold; packaging the three-dimensional model information, the two-dimensional engineering drawing information and the mapping information into structured data based on a set data format; the semantic understanding module is used for performing semantic understanding based on the structured data by utilizing a large language model to obtain feature functions and annotation requirements; the dynamic annotation engine module is used for generating an annotation strategy based on the feature function and the annotation requirement; and the parameterized annotation generation module is used for generating an annotation instruction code based on an annotation strategy by utilizing a large language model, and writing the annotation instruction code into the target mold engineering drawing. The method is at least used for solving the problems that in an existing method, labeling efficiency is low, deep semantics cannot be understood, and consequently certain limitation exists in feature recognition application.

The invention discloses a dynamic science popularization project management system based on interdisciplinary data fusion. According to the system, a multi-source data warehouse is constructed, astronomy, geography, programming behaviors and evaluation data semantics are aligned into a knowledge graph, and a personalized project type exploration task is generated through semantic path search in combination with a student cognitive state graph; the multi-agent reinforcement learning scheduling engine allocates resources in real time, optimizes task paths, fuses behavior modeling and a fuzzy evaluation model in task execution to generate cognitive feedback and advanced capability evaluation, and realizes automatic task generation, intelligent scheduling and closed-loop evaluation in popular science teaching.

The invention belongs to the field of information processing, discloses an industrial multi-modal data semantic alignment method based on vector space and topological constraints, and aims to solve the problem that multi-modal data semantic segmentation in an industrial scene is difficult to unify and associate. The method comprises the following steps: performing feature extraction and structured analysis on modal data, and mapping the modal data to a unified semantic vector space through a projection layer; on the basis of vector similarity matching, topological structure constraints derived from process drawings and the like are introduced, neighbor relation verification is carried out on candidate entities, context logic verification is carried out in combination with a large model, and therefore the accuracy of cross-source entity alignment is remarkably improved; mixed retrieval is supported by establishing an efficient vector database index, and incremental updating of the knowledge graph is achieved through large-model-assisted reasoning and rule base verification. Vector semantics and specific structural dependency of an industrial system are effectively fused, and high-precision and evolvable cross-modal semantic alignment and intelligent association analysis are achieved.

A collaborative management authority rule optimization system based on community discovery comprises a feature extraction module, an organization role screening module and a data attribute screening and authority distribution control module. Generating a standardized user feature vector and a data vector through structured coding and semantic modeling; the organization role screening module constructs a user collaboration graph based on a community division algorithm, and reasones user optimal role mapping by using a community portrait model; the data attribute screening module is combined with a data classification tree and a structure dependency relationship extraction algorithm to recognize a semantic category and upstream and downstream dependency chains of target data; and the permission allocation control module performs reasoning in combination with a permission knowledge base according to roles, data classification and a dependency relationship, and finally generates a permission allocation table, so that dynamic permission configuration and strategy matching oriented to a collaborative scene are realized. Through automatic role mapping, data semantic association analysis and authority inheritance reasoning, accurate authority distribution and real-time strategy adjustment in a ship collaboration scene are realized, so that data security and collaboration efficiency are improved, and core support is provided for digital transformation of the ship industry.

A nuclear power scene-oriented multi-source heterogeneous data acquisition and processing system realizes thorough separation of equipment access and data understanding through a decoupling architecture of acquisition side plug-in and service side normalization, acquisition plug-in only transmits original data, and semantic analysis is centrally processed by a unified physical model engine; the problem of hard coding coupling of the plug-in and the business logic is avoided; by defining a standardized equipment abstract model and shielding bottom layer manufacturer, protocol and format differences, various kinds of heterogeneous data are standardized on the semantic level, unified modeling and semantic alignment of the multi-source heterogeneous data are achieved, data of various internet of things equipment sources are effectively integrated, and the data processing efficiency is improved. The problems of unclear data semantics and difficulty in unified use caused by diverse equipment manufacturers, different communication protocols and heterogeneous data formats are solved, cross-system linkage is promoted, semantic understanding, structured analysis and standardized presentation of cross-protocol and cross-platform equipment data are realized, and the safety, efficiency and management level of a nuclear power station are improved.