680 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 provides a network security risk assessment system and method, and relates to the technical field of network security. The network security risk assessment system comprises a data acquisition layer, the data acquisition layer is connected with an analysis processing layer, the analysis processing layer is connected with an assessment decision-making layer, the assessment decision-making layer is connected with a display interaction layer, and the data acquisition layer acquires business scene data, network topology data, user behavior logs and social network conditions. According to the system, through highly integrated module design, 'information isolated island 'between traditional security tools is broken, comprehensive security situation awareness is realized, novel and unknown threats are dealt with by utilizing situational analysis and dynamic adjustment capability, false alarms and missing alarms are remarkably reduced, and the system is based on machine learning and behavior analysis. Intelligent priority ordering and automatic response can be realized, and the speed and accuracy of risk identification and response are improved.

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 discloses a storage security protection and fire-fighting early warning method based on an edge AI mode, and belongs to the technical field of safety monitoring, and the early warning method employs an edge AI artificial intelligence video data processing analysis method to carry out intelligent security protection behavior identification early warning and intelligent fire-fighting behavior identification early warning; the edge AI artificial intelligence video data processing analysis method comprises the steps that a data center pushes a trained model to an edge AI server, and the edge AI server analyzes streaming data collected by edge Internet of Things equipment in real time and carries out AI reasoning; the edge AI server uploads the received new condition related data to the cloud to train the model, and the trained model is pushed to the edge AI server again; according to the invention, security and protection and fire-fighting applications are deeply fused, an information island is thoroughly broken, data sharing and interconnection are realized, unified output of early warning signals is ensured, and the emergency response speed and accuracy are improved.

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 water conservancy knowledge base construction method and system based on a large model, and relates to the technical field of intelligent water conservancy. Water conservancy related knowledge is integrated and managed, water conservancy knowledge dispersed in different regions and different departments is organically integrated through a large model, an information island is broken, and knowledge sharing and collaboration are achieved. Through data cleaning, standardization processing and other technologies, the large model can convert disordered data into standard and ordered knowledge, and more accurate and reliable information services are provided; the water conservancy knowledge base is constructed based on the knowledge graph, structured, unstructured and semi-structured water conservancy data can be flexibly and conveniently accessed, and multiple data types are compatible; massive water conservancy data and information are efficiently integrated and analyzed by using a large model, valuable information and knowledge are effectively extracted through deep mining of multi-source heterogeneous data, and water conservancy related technicians are effectively assisted in managing water conservancy projects.

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 an informatization accounting archive management method and system, and relates to the technical field of archive management, and the method comprises the steps: constructing a double-layer intelligent analysis structure, achieving the intelligent recognition and classification of accounting archives, and carrying out the feature extraction and fusion through a deep learning model; a subject relation network is constructed based on time sequence similarity, an accounting entry association model is established through multiple matrixes, and a basic intelligent framework is formed; based on an intelligent framework, constructing a multi-channel feature matrix and performing tensor projection operation to generate a multi-dimensional sub-graph structure; a double-layer knowledge graph is constructed based on a subject relation network, and automatic discovery of implicit association between archives is realized through interlayer mapping; and based on the double-layer knowledge graph, constructing a risk monitoring matrix to realize risk early warning, integrating a multi-dimensional retrieval strategy to carry out association analysis, and carrying out hierarchical storage on archives. The information island limitation of traditional archive management is broken through, a collaborative sharing mechanism of archives is established, and the archive management efficiency is greatly improved.

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 discloses a power grid operation risk prediction and collaborative treatment system based on digital twinning. The system comprises a basic support layer, a service management and control layer, an intelligent analysis layer and a collaborative treatment layer. Wherein the basic support layer constructs a power grid equipment operation state real-time mapping model through a digital twinning technology; the business management and control layer realizes cooperation of operation monitoring and maintenance management; the intelligent analysis layer analyzes the data based on an improved Bayesian network to realize power grid situation assessment and risk prediction; and the collaborative governance layer executes hierarchical response according to an analysis result. The technical problems of information island, inaccurate risk prediction, untimely response and the like in traditional power grid operation management are solved, collaborative linkage of accurate sensing of the power grid operation state and intelligent risk prediction and disposal is realized, the intelligent and automatic level of power grid operation management is remarkably improved, and the power grid operation management efficiency is improved. And a powerful guarantee is provided for safe and stable operation of a power grid.

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 an industrial silicon intelligent power distribution and control system and method. The system comprises a data acquisition and processing module, a shared feature extraction module, an intelligent control module and a power distribution module which are connected in sequence. Multi-mode operation data is collected in real time, a shared feature extraction module is adopted to perform feature extraction on the operation data, data input is provided for subsystems of the intelligent control module, and information isolated island is avoided; through cooperative work of all the modules, global optimization control over the industrial silicon production process is achieved. The intelligent control module comprises an intelligent industrial silicon submerged arc furnace baking system, an automatic electrode lifting system, an automatic submerged arc furnace transformer gear adjusting system and an automatic electric energy quality adjusting system, and each system performs task prediction based on corresponding characteristics by adopting a pre-trained model and outputs optimized parameters; and the power distribution module generates a power distribution instruction based on the optimization parameters, so that intelligent distribution of power resources is realized, and dynamic adjustment and optimal distribution of energy consumption are realized.

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 provides a supply chain optimization method and system for cargo import and export, and relates to the technical field of supply chain optimization, and the method comprises the steps: building a distributed account book based on block chain nodes; determining a trigger condition and execution logic, and generating a smart contract; installing an Internet of Things sensor, obtaining cargo environment parameters generated based on a monitored cargo environment, and carrying out information synchronous updating with the distributed account book to obtain whole-process monitoring data; carrying out transportation analysis according to the cargo demand prediction model to obtain predicted logistics data; carrying out transportation planning, and generating a supply chain strategy; and executing the supply chain strategy based on the intelligent contract, generating supply chain data, and performing visual display of the supply chain data. Through the method and the device, the technical problems of serious information island phenomenon and incapability of efficiently sharing data in the prior art can be solved, the technical targets of supply chain data transparency and automatic management are realized, and the technical effect of improving the overall efficiency and the strain capacity of the supply chain is achieved.

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 discloses an intelligent comprehensive management method and system for a safe community, and the method comprises the steps: carrying out the data aggregation and storage through a distributed big data platform for massive community data resources accessed to a unified management platform, extracting key community management indexes and business elements through the operation of data cleaning, correlation analysis and the like, and carrying out the data collection and storage. A command and dispatch-oriented multi-dimensional data model is constructed, and flexible data query and analysis are supported; on the basis of a multi-dimensional data model, a rule-based expert system technology is adopted, experience knowledge and decision logic of managers are subjected to formalized expression, a community management knowledge base and a rule base are constructed, automatic execution of business logic is achieved through a rule engine, and a command and dispatch scheme is dynamically generated according to the real-time state of a community. The problems of information isolated island, low data utilization rate, low commanding and dispatching efficiency and the like in community management are effectively solved, and intelligence and refinement of community management are realized.

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 relates to the technical field of intelligent switch cabinet monitoring, and particularly discloses a multi-mode fusion monitoring system for an intelligent switch cabinet. The system is provided with a use environment data analysis module, a monitoring error item matching module, a first monitoring signal analysis module, a second monitoring signal analysis module, a fusion monitoring factor acquisition module and a fusion monitoring result output module. The problems that a traditional single-mode monitoring method is difficult to comprehensively and accurately evaluate the overall state of the intelligent switch cabinet, information islands exist in the single-mode monitoring method, and relevance among various monitoring information cannot be fully utilized are solved. By monitoring electrical parameters, vibration characteristics and the like and combining and fusing monitoring error terms, the errors can be quantized and compensated, the system can evaluate the state of the switch cabinet more comprehensively, the limitation of single-factor evaluation is avoided, and association among different modes can be mined.

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.