604 results about "Information silo" patented technology

The invention relates to the technical field of construction engineering cost management, and discloses an intelligent engineering cost calculation system based on multi-source heterogeneous data fusion, and the system comprises a multi-source data collection module which is used for collecting structured data and unstructured data from a design file, a market database, a construction monitoring system, a contract document, and a historical project library; and the heterogeneous data fusion module is connected with the multi-source data acquisition module and analyzes the risk terms in the contract text by adopting a natural language processing technology. According to the invention, the multi-source data acquisition module is used for widely collecting data in multiple aspects of design, market, construction, contract and the like, the problems of data splitting and information isolated island in traditional cost management are solved, integration of multi-source heterogeneous data is realized, and the heterogeneous data fusion module utilizes advanced technologies of natural language processing, image recognition and the like, so that the cost management efficiency is improved. Contract texts and design drawings can be efficiently analyzed, the processing capacity of unstructured data is improved, and the error rate and omission rate of manual interpretation are reduced.

The invention discloses a commerce and trade circulation supply chain optimization method based on multi-modal data fusion, and the method comprises the following steps: 1, multi-modal data collection: collecting the multi-modal data of the whole supply chain in real time through an Internet of Things device, an enterprise ERP system, a logistics tracking system and a third-party platform, the multi-modal data comprises text data, image data, voice data, time sequence data and spatial data. When the scheme is implemented, multi-source heterogeneous data of the Internet of Things, ERP and the like are integrated, so that the problem of an information island in a traditional supply chain is effectively solved; position coding and a cross-modal attention mechanism are introduced into the multi-modal fusion model, so that the data alignment precision is remarkably improved, for example, when'emergency order adding 'in a voice communication record is associated with a corresponding order image, the weight distribution accuracy is improved; the application of the block chain technology ensures the transparency and traceability of the supply chain decision.

The invention provides a business data sharing fusion system and method based on a trusted data space, and relates to the technical field of data sharing. According to the method, sharing and fusion of service data are realized by constructing a trusted data space infrastructure. The method comprises the following steps: firstly, establishing a node path planning model for priority evaluation and data matching; then forming a model collaborative deployment scheme to distribute tasks to edge nodes; then perception scoring and resource scheduling are carried out on the tasks, and a multi-task concurrent transmission scheme is generated; constructing a multi-domain network service level protocol evaluation model to perform service decomposition and business collaborative management; and finally carrying out heterogeneous processing and data fusion on the business data. According to the method, the problems of information islands, multi-source heterogeneous data fusion and cross-domain business collaboration among regional mechanisms are solved, and the system performance and the user experience are improved.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention discloses an order-driven cross-factory collaborative production system, and relates to the technical field of intelligent manufacturing and supply chain collaboration, and the system obtains the productivity data, logistics cost and tax policies of a plurality of production bases such as Ningbo, Thailand and America in real time, and carries out the intelligent splitting and distribution of orders through a multi-base productivity game algorithm. And dynamic optimal matching of the order and the productivity is realized. Meanwhile, the system integrates WMS inventory data and third-party logistics real-time quotation, a transportation scheme with the lowest total cost is generated by adopting a genetic algorithm, and cross-border logistics and tax expenditure are remarkably reduced. The system overcomes the problems of information isolated island, response lag, extensive cost control and the like in traditional multi-factory production, realizes global productivity collaborative optimization and supply chain integrated intelligent decision, and improves the enterprise order performance efficiency and the overall resource utilization rate.

The invention provides an enterprise financial comprehensive management system based on an intelligent technology, and relates to the technical field of financial management. The accounting intelligent subsystem is used for acquiring financial data, performing semantic analysis and conversion on the financial data through a preset rule and a multi-stage data mapping mechanism, generating a standardized accounting service instruction and issuing the standardized accounting service instruction to an automatic process engine, thereby automatically generating a financial document meeting an accounting criterion; the financial management subsystem is used for integrally realizing execution of backlog management, compliance guidance manual query and financial data structured storage according to a preset standardized process; and the business service subsystem is used for providing tax-related business service, asset management service, fund management service and operation management service processing and guidance for enterprises through modular design. The invention aims to improve the operation efficiency, reduce the error rate, break information islands, realize effective integration and utilization of resources, and provide powerful support for decision making of enterprises.

The invention provides a multi-park energy consumption prediction scheduling method based on digital twinning and a control system, and systematically solves the problem of the pain point of multi-park energy consumption management by constructing a technical chain from data perception to closed-loop optimization. The method comprises the following steps: firstly, by constructing a global unified digital twinborn model, standardized integration of dispersed and heterogeneous park assets and data is realized, and the problem of information islands is solved; secondly, prediction is carried out by adopting federated learning, cross-park knowledge sharing and joint modeling are realized on the premise of ensuring data privacy and security of each park, and the prediction precision of a single park under the condition of limited data is remarkably improved; and finally, through a'prediction-decision-execution-update 'closed-loop process, traditional passive and static energy consumption management is converted into active and dynamic prediction scheduling, so that the energy consumption peak can be stabilized prospectively, the energy distribution can be optimized, and the comprehensive energy consumption cost and carbon emission can be effectively reduced.

The invention discloses a drug risk monitoring method based on multi-source data fusion, and relates to the technical field of data analysis. Through multi-source data fusion and processing, the problems of data isomerism and information islands are solved, a comprehensive and high-quality data basis is provided for drug risk monitoring, a weighted collaborative filtering algorithm and a Bayesian network are adopted, potential risk signals are accurately mined, real-time dynamic monitoring and early warning of drug risks are achieved, and the drug risk monitoring and early warning efficiency is improved. The timeliness and the reliability of monitoring are obviously improved; meanwhile, the real-time stream processing framework is used for carrying out incremental updating on newly-added data, dynamically adjusting a risk assessment result, and carrying out automatic early warning when a risk value exceeds a standard, so that the contradiction between a complex process and a rapid early warning demand is effectively solved, the efficiency and accuracy of drug safety supervision are improved, and powerful support is provided for guaranteeing the public drug safety.

The invention discloses a server processing system and method, electronic equipment and a storage medium, and relates to the technical field of electric digital data process.The server processing system comprises an interrupt processor, a management processor and a data processor, and the interrupt processor, the management processor and the data processor can communicate through corresponding links; and after the interrupt signal is triggered, the interrupt processor can determine the current interrupt level according to the interrupt signal so as to determine the target processor and control the target processor to carry out fault processing based on the shared information, so that the problems that the data synchronization delay among the processors of the server is relatively high and the data synchronization time is short in the prior art are solved. The technical problem that fault processing is difficult to effectively carry out when a server breaks down due to disconnection of hardware signals and software states is solved, and the technical effects that through a special framework, state synchronization and data sharing can be carried out among processors in time, fault information islands can be broken, and faults can be quickly responded and processed are achieved.

The invention relates to the technical field of supply chain management, in particular to a digital visual management system and method applied to a building material supply chain. The method comprises the following steps: acquiring real-time position data, material attribute data and time sequence node data; performing data preprocessing and integration on the real-time position data, the material attribute data and the time sequence node data to obtain standard building material supply chain data; obtaining a construction drawing; basic building materials of the standard building material supply chain data are extracted based on the construction drawing, wherein the basic building materials comprise concrete, reinforcing steel bars and building blocks; building pouring abnormity monitoring is carried out based on the concrete, the steel bars and the building blocks, and pouring abnormity monitoring data is generated. Through multi-source data integration, ternary coupling analysis, scheduling optimization and three-dimensional dynamic visualization, the problems of information island, scheduling conflict and low management efficiency of the building material supply chain are solved, and intelligent, efficient and transparent management of the supply chain is realized.

The invention relates to the technical field of the Internet of Things, and discloses a campus smart life terminal comprehensive management and control system based on big data, and the system comprises a data collection module, a big data analysis module, a comprehensive management and control module and an execution feedback module. Operation data of different systems are integrated in real time, cross-platform data sharing and consistency of a canteen, an access control and an energy terminal are guaranteed, the problem of information isolated island caused by independent operation of terminal data is avoided, meanwhile, association analysis is carried out on collected environmental factors and user behavior information, the risk of terminal operation abnormity and resource conflict can be found in real time, and the user experience is improved. The method is advantaged in that high efficiency and reliability of terminal cooperation management are guaranteed, fault response delay is further reduced, a shunting scheme is automatically generated when sudden demand increase occurs, a condition of student waiting time prolonging is prevented, and dynamic adaptive capability and service quality of the campus life terminal are guaranteed.

The invention discloses a bank intelligent risk control system based on a block chain and multi-modal artificial intelligence. A data acquisition module acquires and integrates bank internal data, third-party data and cross-chain data; the edge computing module is provided with a plurality of edge computing nodes, and high-frequency transaction data is processed in real time by using a lightweight model; the block chain network module adopts a relay chain to connect a bank chain and a supply chain, and performs verification based on zk-SNARK zero knowledge proof; the artificial intelligence module constructs a multi-modal risk assessment model, and performs risk assessment in combination with an XGBoost integrated learning algorithm, GNN deep learning and federated learning; and the application decision module performs risk early warning, dynamic processing, visual monitoring and on-chain auditing according to the evaluation result of the artificial intelligence module. According to the scheme, the problems of information isolated island, difficulty in guaranteeing data authenticity, risk identification lag and the like in the existing bank risk control system are effectively solved, and the bank risk prevention and control capability and the market competitiveness are improved.

The invention belongs to the technical field of engineering progress tracking and early warning, and particularly discloses an AI-based engineering progress tracking and early warning method and system. According to the method, the problem of information islands in multi-modal data integration of a traditional method is solved by constructing the engineering space-time knowledge graph, and the comprehensiveness of construction site state sensing is improved; an intelligent mechanism of deviation analysis and trend deduction is established, and accurate prediction of the engineering state evolution trend is realized through multi-dimensional comprehensive evaluation of geometric morphology, construction process topology, resource in-place time sequence and quality compliance attributes; through structured early warning generation and multi-level risk conduction path modeling, the risk source and the influence range thereof are clarified, and a subsequent construction scheme is optimized in combination with a dynamic adjustment strategy, so that the scientificity and execution efficiency of project progress management are improved.

The invention relates to the field of traffic control, in particular to an intelligent network connection inductive control platform for road traffic safety facilities. The traffic data acquisition module is used for acquiring a data flow of a traffic sensor and outputting traffic data with a traffic data source identifier through an identification technology; the traffic data processing module is used for obtaining standardized traffic parameters according to a traffic data source identifier adaptive analysis protocol; evaluating a data reliability index according to the index system; the situation fusion module is used for forming a vehicle driving track through a graph neural network, generating a global traffic situation map by using a data reliability index, and constructing a traffic network digital twinborn model; and the decision and control module is used for analyzing traffic states from the traffic network digital twin model, predicting traffic events and collision risks and generating traffic control instructions. According to the platform, through the equipment fingerprint and protocol reverse technology, an information island is broken, the comprehensiveness and high credibility of a data source are ensured, and the road traffic safety and passing efficiency are remarkably improved.

The invention discloses an automobile part multi-source fusion tracing method and system based on a distributed block chain. Firstly, a block chain network and a tracing platform are constructed; a microscopic visual feature image of the surface of the automobile part is collected, the texture type is judged, features are extracted, electromagnetic field intensity distribution features are extracted in combination with an RFID tag, and electromagnetic fingerprints are recognized through a neural network; secondly, multi-source feature data and production and transportation information are bound with unique identifiers and then stored in a distributed block chain network, and a five-layer Merkel tree storage structure is adopted to ensure that the data cannot be tampered; and finally, constructing a tracing query module of a three-layer architecture, and automatically managing data writing and query through an intelligent contract. According to the invention, through combination of multi-feature fusion and the block chain technology, the problems of information island and weak traceability of a traditional traceability system are solved, credible traceability and authenticity identification of automobile parts in the whole life cycle are realized, and the supply chain data security and query efficiency are improved.

The invention relates to the technical field related to medical systems, in particular to a medical teaching and research integrated system based on a common bottom layer, and aims to break through information islands among medical treatment, teaching and scientific research and realize closed-loop linkage and dynamic knowledge conversion of a whole process. The system comprises a diagnosis and treatment decision subsystem, a scientific research queue management subsystem and a teaching subsystem, integrates data through a three-in-one data engine, and realizes information sharing and cooperative work. The diagnosis and treatment decision subsystem provides diagnosis and treatment support for severe diseases based on an expert thinking chain. And the scientific research queue management subsystem is responsible for screening cases, sorting data, implementing quality control and statistical analysis, and adjusting an expert thinking chain according to scientific research results. And the teaching subsystem carries out teaching by using a standardized case presentation mode. Through a feedback closed loop of clinical discovery, scientific research verification, knowledge updating and teaching feedback, rapid propagation and application of medical knowledge are promoted, and the medical service quality and the medical education level are improved.

The invention relates to a power grid project multi-dimensional digital supervision method based on big data, and belongs to the technical field of power grid project digital supervision, and the method comprises the following steps: S1, carrying out the real-time collection and fusion of multi-dimensional data, and constructing a supervision system through four supervision layers and a digital twinborn body; s2, multi-dimensional supervision indexes are intelligently calculated, and a supervision dimension index system is constructed; s3, risk dynamic early warning and root cause tracing; and S4, decision optimization and closed-loop control. According to the method, multi-source heterogeneous data integration is carried out by fusing multi-dimensional data sources of BIM model, GIS positioning, Internet of Things sensing data, environment monitoring and business system data, so that single data source deviation is avoided, information islands are broken, multi-target collaborative optimization is realized, a'supervision-decision-execution 'closed loop is realized, an optimal regulation and control strategy is convenient to calculate, and the system performance is improved. An optimal regulation and control strategy can be generated based on the reinforcement learning model, and an early warning instruction is directly connected to a field terminal, so that the power grid project monitoring and early warning efficiency can be effectively improved.

The invention belongs to the technical field of medical material management, and particularly relates to a medical material intelligent supply chain management scheme and system based on multi-modal AI. According to the invention, through multi-modal data fusion, full-life-cycle and full-link tracking of materials is realized, information islands are eliminated, management transparency is improved, unexpected consumption and physical damage risks are identified in an early stage, a large amount of overstock or clinical supply interruption is avoided, operation cost is reduced, and based on a dynamic loss threshold, replenishment and cross-warehouse allocation can be realized just in need. The inventory turnover rate and the supply guarantee rate are considered, environment monitoring and validity period information are processed, it is ensured that temperature and humidity sensitive materials are stored under the optimal condition, losses caused by expiration or environment abnormity are reduced, the waste rate and the excessive stockpiling risk are comprehensively reduced, meanwhile, timely availability of key materials is clinically guaranteed, and the service life of the materials is prolonged. And the economic benefit and safety of the medical material supply chain are improved.

The invention provides an oil field information asset automatic discovery and management method and system, and the method comprises the steps: carrying out the flow reduction, carrying out the active detection, carrying out the asset identification label marking of all types of attribute information and components of assets according to the asset information and feature information obtained through the active detection and flow reduction, and carrying out the asset marking. And for unidentified assets found by active detection and flow reduction scanning, inputting corresponding information of the unidentified assets according to the asset identification tag, and carrying out security monitoring on the identified asset information. And the asset information is fed back to the security platform, and the found asset information is compared with alarm information in the security platform, so that non-asset alarm information and alarm information inconsistent with the asset category are removed, and the security platform is assisted to remove invalid information. Through flow reduction and active detection, docking of various network products and automatic identification of asset information in the existing network, asset management ecology of part of third-party products can be integrated, information islands can be broken through, and a clear asset ledger can be established.

The invention provides a building engineering data information intelligent management platform which comprises an intelligent sensing module, an edge calculation module, a data management module, an AI decision module, a business application module and a safety standard module. Through the intelligent sensing module, the edge calculation module, the data management module, the AI decision module, the service application module and the safety standard module, the position of a constructor and the equipment state can be tracked in real time, personnel are prevented from entering a dangerous area by mistake and the mechanical overload risk is prevented, operation safety is guaranteed, AI quality inspection and fault diagnosis are locally processed, cloud dependence is reduced, and the working efficiency is improved. According to the method, the system and the device, real-time early warning of potential safety hazards can still be ensured when the network is interrupted, BIM, IoT and service data can be integrated in a standardized manner, information islands are eliminated, and cross-stage quality tracing and responsibility definition are realized.

The invention provides a digital twin smart park security and protection management system based on artificial intelligence, and relates to the field of security and protection management, and the system comprises a multi-mode sensing network which is used for collecting park environment data through a multi-mode sensor network, and carrying out the preprocessing of the data, and obtaining an environment data set; the digital twin modeling module is used for constructing a park three-dimensional virtual model based on the environment data set; the semantic alignment module is used for defining a dynamic security semantic rule based on the three-dimensional virtual model and carrying out semantic alignment of multi-modal features on the environment data set; the threat assessment module is used for performing threat assessment on the aligned semantics based on a knowledge graph and a Bayesian network; and the resource scheduling execution end is used for generating a disposal plan according to the evaluation result and realizing security response through an execution terminal. The method is used for solving the problems of information isolated island, response lag, high false alarm rate, fuzzy situation awareness and the like of a park security system in the prior art.

The invention relates to the technical field of information fusion, in particular to a power equipment state information fusion system and method. The method comprises the following steps: obtaining time sequence signal data and infrared thermal image data, and constructing a power equipment heterogeneous fusion data set; acquiring power equipment position and power equipment environment data, performing multi-layer cross-mode data coupling in combination with the power equipment heterogeneous fusion data set, and constructing a power equipment information evaluation model based on the multi-layer cross-mode data coupling; therefore, through multi-source data fusion and cross-level evaluation model construction, the problems of a traditional power equipment monitoring system in the aspects of information island and prediction accuracy are solved, and the health management capability and fault early warning efficiency of power equipment are improved.

The invention discloses an urban risk prediction method and system based on multivariate data fusion, and relates to the technical field of urban tunnel risk prediction.The method comprises the steps that data such as the water flow velocity, pipeline structure parameters and real-time rainfall of a plurality of monitoring areas in an urban drainage tunnel are comprehensively monitored through the first step, the second step, the third step and the fourth step; the system can carry out comprehensive risk assessment and timely identify potential risks of blockage, sewage backflow and gas explosion. According to the comprehensive evaluation, the problem of single information island caused by a traditional single water level monitoring method is improved, a multi-level early warning mechanism is constructed, early warning signals of different levels are automatically generated when risks are recognized, and management personnel are prompted to take necessary countermeasures. The flexible early warning mechanism enables urban managers to respond quickly, improves the efficiency of emergency treatment, reduces possible loss, and ensures safe operation of the sewage discharge tunnel in various environments.

The invention discloses a multi-modal big data intelligent cleaning and fusion method based on a knowledge graph and deep learning, and relates to the technical field of artificial intelligence and multi-modal data processing. According to the method, a cross-modal semantic similarity matrix is constructed through a multi-modal semantic association mining algorithm, the problem that in a traditional method, an effective semantic association mechanism is lacked between modal data is effectively solved, the quality and consistency of the data are remarkably improved, and a hierarchical feature extraction and weighted fusion strategy is further adopted, so that the method is more efficient and efficient. The multi-modal data is subjected to feature extraction by using a convolutional neural network and a Transform model, and the fusion weight of each modal feature is dynamically adjusted through an attention mechanism, so that the information island phenomenon is effectively solved, the quality and effectiveness of the fusion feature are remarkably improved, and through a semantic consistency verification mechanism and a continuous learning updating mechanism, the accuracy of the feature fusion is improved. High quality of fusion features is ensured, the model can adapt to new data in real time, and adaptability and expansibility of the model are remarkably improved.

The invention discloses an intelligent prediction and transaction management system for carbon quotas and carbon assets, and relates to the technical field of carbon transaction systems. The intelligent prediction and transaction management system for the carbon quota and the carbon assets comprises a carbon transaction mode portrait construction module, a dynamic module matching and initializing module, a multi-source data fusion and dynamic updating module, a hybrid prediction model operation module and a dynamic transaction strategy generation and optimization module. Through cooperative work of the five modules, enterprise carbon transaction whole-process intelligent management is realized. The portrait construction module accurately classifies enterprise modes and provides a basis for dynamic matching; a module library and a parameter initialization mechanism ensure strategy customization flexibility and deployment efficiency; the multi-source data fusion module breaks an information island and improves the input quality of a prediction model; outputting a multi-time scale prediction result and a risk index by the hybrid prediction model in combination with a statistics and machine learning method; and the dynamic strategy module automatically generates and optimizes a transaction strategy according to the prediction result and the enterprise risk preference.

The invention discloses a multi-source information real-time identification and positioning method for a coal mine rock burst risk area. The method comprises the following steps: integrating a micro-seismic system, a rock noise system, a stress system and a geological system to construct an acquisition network; extracting multi-source characteristic indexes and carrying out space-time unification; establishing a dynamic weighted risk fusion model to calculate a comprehensive risk index RI of a three-dimensional grid unit, wherein the weight of the comprehensive risk index RI is dynamically and adaptively adjusted along with mining; based on the RI value, a high-risk area is identified and positioned through three-dimensional visualization, and graded early warning information is generated; according to the invention, a time-space synchronous multi-source information acquisition network is constructed, and uniform preprocessing and feature extraction are carried out on four types of data of microseism, rock noise, stress and geologic structure, so that deep fusion and collaborative analysis of multi-source information are realized, and an information island phenomenon existing in traditional monitoring is overcome; and the judgment on the rock burst risk is more comprehensive.

The invention discloses a multi-modal data retrieval method and device under dynamic collaboration and a medium, belongs to the technical field of information retrieval and natural language processing, and is used for solving the problems that unstructured data is difficult to process efficiently in traditional data retrieval, an information island phenomenon exists, and the data retrieval efficiency is low. And an effective connection and integration mechanism is lacked between data from different sources. The method comprises the following steps: performing task allocation processing on a corresponding Agent cluster on a question type to be queried to obtain an Agent task list; performing data query processing on the Agent task list under different data sources, and obtaining query result data; performing data primary processing on the query result data to determine initial retrieval data; performing multi-modal consistency check on the initial retrieval data, and integrating verified integrated retrieval data; and performing information strategy generation processing based on a fact verification mode and an explanation enhancement mode on the integrated retrieval data to obtain final answer information.

The invention relates to the technical field of traffic scheduling, and particularly discloses a hidden channel emergency response intelligent collaborative perception and decision-making system, which comprises a multi-level heterogeneous module, a data linkage query module, a channel parameter prediction module, a traffic state evaluation module, an emergency defibering scheduling module and an information playback and display module. According to the method, through the functions of rapid query, such as keyword retrieval, advanced retrieval of complex query conditions, one-stop full-text retrieval and batch retrieval, it is ensured that workers of different departments and levels can efficiently obtain needed information, circulation and sharing of information can be promoted, and the efficiency of information query is improved. All related departments can make decisions based on newest and comprehensive information, the information island phenomenon is reduced, the overall emergency rescue efficiency is improved, and the whole process of recording and return visit of event awareness-comprehensive decision-rescue guarantee can be achieved based on a comprehensive geographic information system (GIS).

The invention relates to the technical field of information management systems, in particular to a full-process intelligent office system which comprises a data layer, an AI engine layer, a service engine layer, an application layer, an interface layer, a central data bus, a unified identity authentication center, a safety protection center, a system monitoring center, an intelligent workflow engine (1000), a real-time notification pushing mechanism and a multi-terminal adaptation framework. Data barriers of all departments of an enterprise are broken through through the AI technology, full-process data intercommunication is achieved, enterprise management forms a complete closed loop, and the management complexity and the information island problem are reduced; all functional modules are coordinated through a central AI agent, and data consistency and business process continuity are ensured. By realizing the full-process digital management of the enterprise, the system obviously improves the office efficiency of the enterprise, reduces the management cost, enhances the competitiveness of the enterprise, and is particularly suitable for the intelligent operation and management requirements of small and medium-sized enterprises and entrepreneurship teams.

The invention relates to the technical field of engineering data management, in particular to a method for performing engineering data management and application by using a large model knowledge base, which can perform unified data management on engineering data by combining a large model and a knowledge base and using a unified semantic modeling model URDF and a graph database Neo4J. Therefore, the requirement of modern engineering for data management can be met, and an information'island 'between the engineering is broken through. And meanwhile, data application of the unified integrated engineering data management system is realized by utilizing technologies including RAG, large model fine tuning and the like. Therefore, the user can use the natural language to apply the data, so that the data value is explored, and the value is stimulated to change. The problems that an existing method is generally only suitable for a specific field, manual intervention is needed, interaction between data and a user is not fully utilized, and implementation of the data value is limited are solved.