4222 results about "Information management" patented technology

The invention provides a multi-agent collaborative question and answer enhancement method and system based on heterogeneous data knowledge, and belongs to the technical field of information management. The planning optimization intelligent agent carries out structured processing on the input complex natural language problem; a multi-modal retrieval mechanism on the heterogeneous knowledge source is constructed based on the problem disassembly and entity recognition result, and a multi-path recall agent passes through a semantic matching model of a double-tower structure; the abstract extraction agent performs semantic fusion and information extraction on the text segments and the knowledge graph sub-graphs output by the multi-path recall module; logic verification and quality evaluation are carried out on the answers generated by the abstract extraction module by the reflection iteration agent; and by setting a threshold mechanism and combining importance weights of the sub-questions, performing scoring and reflection optimization on the generated answers by utilizing a large language model LLM (Language Language Model). The method can effectively cope with cross-domain and multi-level complex question and answer tasks, and has good expandability and intelligent level.

The invention belongs to the technical field of fishery resource informatization management and ecological prediction, and particularly relates to a dynamic prediction method of a resource habitat dynamic prediction system based on multi-source heterogeneous data fusion, and the method comprises the steps: a multi-source heterogeneous data collaborative collection and standardization processing module synchronously collects cross-regional data, and generates a time-space aligned standardized data set; the multi-modal habitat adaptability evaluation and prediction model is used for receiving the standardized data set as input, constructing environmental, biological and social modalities based on multi-source fusion data, and generating a habitat adaptability prediction result; the three-dimensional dynamic visualization and decision support platform is used for receiving the habitat suitability prediction result and generating a habitat thermodynamic diagram, a resource abundance gradient and an environmental parameter dynamic visualization display and decision under multiple spatial and temporal scales; according to the method, an international data collaboration mechanism, an ecological niche model optimization algorithm and a lightweight visualization engine are subjected to system-level integration, and an engineering solution is provided for biological resource protection of a sea area.

A BIM-based engineering information management system of the present invention relates to the field of constructional engineering informatization management, and comprises a data processing module, a model construction module, a block chain evidence storage module, a verification module, a data fusion module and an authority management module. The data processing module receives original engineering data and generates a structured data signal. And the model construction module generates a BIM model according to the structured data signal. And the block chain evidence storage module encrypts and stores the metadata signal, generates an evidence storage completion signal and feeds back the evidence storage completion signal to the model construction module. And the verification module analyzes the dynamic operation log signal, and generates a risk early warning signal and a repair suggestion signal when abnormality is detected. And the data fusion module generates a fusion data signal, transmits the fusion data signal to the model construction module and triggers BIM model updating. And the authority management module adjusts the user authority according to the risk early warning signal. According to the invention, the problems of data islands, safety risks, low cooperation efficiency and insufficient intelligence in engineering information management can be solved.

The invention relates to the technical field of safety monitoring, in particular to a bridge safety monitoring system based on sensor data. The method comprises the steps that a sensor measuring unit monitors key physical parameters of a bridge in real time; the data acquisition and transmission unit reads sensor data and transmits the read sensor data; the data processing and analysis unit extracts key features reflecting the health state of the bridge in the sensor data based on a multi-modal physical perception feature fusion method, identifies an abnormal mode in the sensor data through a support vector machine model, and evaluates the remaining life of the bridge based on a carbonization-corrosion-crack closed-loop feedback control rule; and the information management unit stores the analysis result of the data processing and analysis unit and displays the monitored bridge physical parameters. According to the design of the invention, a multi-modal physical perception feature fusion method is introduced, and adversarial learning, graph wavelet transformation and a three-dimensional convolutional neural network are combined, so that high-precision feature extraction of bridge structure response is realized.

The invention discloses a block chain-based medical data management method, system and device, and a medium, and relates to the field of information management. The method comprises the following steps: acquiring medical data, analyzing content attributes and context information of the medical data through a first smart contract on a block chain, and generating a first sensitivity level identifier; based on the first sensitivity level identifier, performing differential encryption on the medical data through a second smart contract on the block chain, and generating verification information; partitioning the differentially encrypted medical data, constructing a data structure together with the verification information, and storing the data structure into a block chain; in response to the received access request, verifying an authority certificate in the access request and the type of the access request through a third smart contract on the block chain, and generating an access authorization certificate; and outputting target medical data corresponding to the access request based on the access authorization certificate. By implementing the technical scheme provided by the invention, the full-life-cycle safety management and control of the medical data from collection, storage to sharing is realized.

The invention discloses a self-adaptive cloud management platform system based on intelligent resource scheduling and container arrangement, and relates to the field of computer information management. The system comprises a refined resource scheduling and adaptive optimization module, a containerized application life cycle management and dynamic container arrangement module, a high-precision operation and maintenance monitoring and self-healing mechanism module based on big data analysis, and a dynamic resource allocation and elastic scaling strategy module of an intelligent scheduling engine. The system takes a containerization technology as a core, realizes centralized management and monitoring of cloud computing resources, can realize dynamic intelligent resource scheduling and optimization, accelerates application deployment, improves system flexibility, strengthens operation and maintenance monitoring and platform safety guarantee, and comprehensively improves resource optimization and cost effectiveness. The resource scheduling efficiency is improved, the application deployment is simplified, the operation and maintenance monitoring is enhanced, and efficient resource management is realized through an adaptive optimization technology.

The invention relates to the field of information management, and discloses a constructional engineering information management system, which comprises a data acquisition module, an intelligent analysis module, a risk prediction module, a resource optimization module and a feedback learning module, the data acquisition module is used for acquiring multi-source data related to constructional engineering; the intelligent analysis module carries out fusion, preprocessing and feature extraction on the collected data; the risk prediction module predicts possible risks in the future by using a statistical analysis model; the resource optimization module dynamically adjusts and optimizes manpower, equipment, materials and process arrangement of the construction site according to the risk prediction result; and the feedback learning module is used for collecting a system execution result, risk prediction accuracy and user feedback, and recording an optimized execution result. The method has the advantage of realizing efficient engineering management and decision support.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention relates to the technical field of project cost management, in particular to a project cost-oriented information management method and system, and the method comprises the following steps: carrying out multi-source data collection and standardization processing, carrying out the automatic calculation, logic verification and correction of a project amount based on an AI calculation amount engine and a rule engine, and generating a precise project amount list; calling a dynamic pricing model, carrying out deviation analysis on the actual cost and the plan cost based on a earned value analysis method and a machine learning model, predicting the cost hyper-branched risk, and carrying out graded early warning; constructing a multi-participant collaborative platform, and supporting change application submission, associated cost influence calculation, online examination and approval, problem tracking and progress synchronization; the whole-cycle data is classified and archived, an enterprise-level cost index library is generated based on historical project data, cost reference is provided for a new project, and the method is suitable for cost information efficient management and cost control of the whole life cycle of constructional engineering, municipal engineering, installation engineering and the like.

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention discloses a multi-source heterogeneous data synchronization system, and relates to the field of computer information management application. The system fuses key technologies such as flow dynamic regulation and control, self-adaptive load balancing and intelligent semantic mapping. Through a real-time network state monitoring and self-adaptive flow regulation and control algorithm, the data transmission rate is dynamically adjusted according to the server resource utilization rate, the data priority and the network bandwidth, data backlog and system overload are avoided, and the robustness and the real-time performance of a synchronization task are remarkably improved. The intelligent field mapping engine is based on semantic analysis, word vector representation and a machine learning model, automatically recognizes field semantic consistency of heterogeneous data sources, generates an optimal mapping rule and dynamically optimizes a mapping result, the manual configuration cost is reduced, and the mapping accuracy rate reaches 98% or above. The system has high expandability and self-learning ability, is suitable for a large-scale multi-source heterogeneous data integration scene, and provides an efficient, intelligent and self-adaptive full-stack solution for a data synchronization task.

The invention discloses a three-dimensional GIS (geographic information system) system for safety assessment of an urban gas pipeline internet of things. A gas pipe network monitoring and assessment system combining the internet of things, a mobile internet and BIM / GIS information is established. Firstly, an overall framework based on sensing, transmission, knowing and use of the internet of things is established; secondly, a front-end sensing system deployment scheme is established, a database system is built, and index data are collected and stored; thirdly, a gas leakage hazard model is constructed, and possible hazard consequences are evaluated; 4, a pipe network monitoring system application layer module is constructed, and underground space information management, safety analysis, prediction and early warning and an auxiliary decision making module are achieved; and establishing a gas pipe network risk identification module, a basic information checking module, a monitoring and explosion early warning module, a three-dimensional visualization module and an auxiliary decision-making module. The system monitors the operation condition of the gas pipe network in real time, analyzes the operation rule of the gas pipe network, evaluates the risk of the gas pipe network, predicts, early warns and decides in time, changes passive first-aid repair into active protection, and is high in monitoring stability, precision and efficiency.

The invention discloses a database management method and management system, and relates to the technical field of general database management, and the system comprises a user information management module, a work management module, a salary settlement module, an evaluation feedback module, an analysis prediction module, a multi-terminal integration module and a safety guarantee module. In the invention, a role-based access control permission model is constructed in a permission control layer and a module definition, and a field-level data control mechanism is implemented for three roles of a platform administrator, a customer enterprise and an employee, for example, the employee can only check a specific public field in a salary list, and sensitive fields such as a salary calculation formula are dynamically shielded; through a permission inheritance mechanism, the system automatically adds a filtering condition that employee ID = current user ID for a query request of an employee role, data isolation is ensured, a multi-dimensional data relation graph of employee-post-customer-contract is constructed, and a dynamic association path between entities is mapped in real time through a visual view.

The invention discloses a cargo warehouse-in and warehouse-out management method and management system, and relates to the technical field of information management. Through dynamic storage position optimization, the warehouse-in and warehouse-out frequency, the associated sales relationship and the storage demand characteristics of cargos are combined with a warehouse three-dimensional space model; and generating a cargo storage location allocation scheme by using a multi-objective optimization algorithm. According to the scheme, the picking time of high-frequency warehouse-in and warehouse-out commodities is remarkably shortened, the average time consumption of single-time commodity picking is reduced, meanwhile, the combined picking error rate of associated commodities is reduced, the problem that in the prior art, storage positions are disjointed with warehouse-in and warehouse-out requirements is solved, through predictive process scheduling, cross congestion of operation channels is effectively avoided, and the working efficiency is improved. Therefore, smooth operation of the in-out warehouse process is realized, the occurrence frequency of congestion events of the in-out warehouse channel is reduced, the delay time is shortened, and the problem of congestion of the in-out warehouse process in the prior art is solved.

The invention discloses a BIM model-based water conservancy project cost accurate calculation method and system, and relates to the technical field of water conservancy project informatization management, and the method comprises the steps: obtaining BIM model component data, construction scheduling data, construction path data and construction cost data, constructing a scheduling component diagram for conflict recognition, calculating the load value of a path segment, and calculating the load value of the path segment; a construction progress deviation value is determined by combining sensing data of the internet of things, and finally a comprehensive cost estimation value is calculated; the method has the beneficial effects that BIM model component data, construction scheduling data, path data and cost data can be integrated, a three-dimensional scheduling component diagram is constructed for conflict detection, a progress deviation value is dynamically calculated in combination with sensing data of the Internet of Things, quantitative association of construction cost and progress deviation is realized, and the construction efficiency is improved. The method has the advantages of improving the construction cost calculation accuracy, enhancing the dynamic response capability of the construction process and realizing the real-time linkage of the cost and the progress.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention discloses an information management method and system based on an electronic bidding transaction platform, and relates to the technical field of information management. By constructing a multi-dimensional and multi-level information management system, deep mining and intelligent identification of potential association relationships and behavior patterns among bidding units are realized, so that the capability of the electronic bidding platform in the aspects of risk prevention and control and abnormal behavior identification is effectively improved. By fusing multi-source heterogeneous data such as industry and commerce registration information, bidding history, project contracts and the like and introducing atlas modeling and semantic analysis technologies, key risk factors such as legal person penetration, historical cooperation, high bidding document similarity and the like can be accurately identified, then the risk association strength between bidding units is quantitatively evaluated, and the risk assessment efficiency is improved. And a visual map and an auxiliary auditing mechanism are formed, so that the judgment efficiency of supervisors is improved.

An AI-driven, cloud-based system (100) for real-time biomedical and pharmaceutical compliance and risk management, including: (a) a compliance knowledge module configured to ingest, interpret and structure regulatory data using natural language processing (NLP) and generate machine-readable compliance rules; (b) a real-time monitoring and event recording module configured to collect and normalise operational data from distributed biomedical and pharmaceutical systems, including laboratory information management systems (LIMS), manufacturing execution systems (MES) and IoT-enabled devices; (c) an intelligent risk assessment and prediction module configured to correlate operational data with compliance rules, calculate dynamic risk scores and predict potential compliance violations using machine learning models; (d) an automated policy and workflow enforcement module configured to initiate remedial actions, assign tasks and log activities based on predefined standard operating procedures (SOPs); (e) an audit readiness and reporting module configured to generate compliance logs, audit trails and standardised regulatory reports in real time; and (f) an adaptive learning and feedback optimization module configured to refine rule sets and predictive models based on feedback, historical data and regulatory updates; g) the modules are integrated into a cloud infrastructure to enable real-time, scalable and predictive compliance and risk management across biomedical and pharmaceutical processes.

The invention provides an emergency first-aid medical information management method and system based on a block chain and a distribution mode, and relates to the technical field of medical information management, and the method comprises the steps: obtaining first-aid event information, generating an identification code, pushing the identification code to a block chain node through a distributed network, generating a credible record through employing a hierarchical consensus and rule engine, and storing the credible record. And determining an optimal rescue path in combination with real-time data, and triggering a rescue instruction and executing tracking through an intelligent contract. According to the invention, the safety of emergency information transmission is improved, the rescue resource allocation efficiency is optimized, and the traceability of the cross-mechanism cooperative rescue process is ensured.

The invention discloses a verifiable medical data query method based on a DAG block chain architecture, and belongs to the technical field of block chains and medical information management. According to the method, medical events such as diagnosis and treatment records and image reports are modeled into DAG nodes, an on-chain topology with time sequence and causal association is constructed by encrypting a hash pointer, dynamic indexing and multi-keyword verifiable query are achieved in combination with an off-chain authentication data structure, a verification object containing a matching node set and a verification path is received, and the verification object is subjected to dynamic indexing and multi-keyword verifiable query. The authenticity, integrity and timeliness verification of the result is independently completed based on the authentication root stored on the chain without depending on a third party. According to the method, through an on-chain and off-chain collaborative storage and incremental authentication updating mechanism, on the premise of ensuring privacy and tamper-proofing of medical data, query efficiency, verification performance and expandability under high concurrency are remarkably improved, communication and resource expenses are lower, and the method is suitable for large-scale popularization and application. The method is suitable for application scenes such as cross-institution medical data sharing, remote diagnosis and treatment and compliance auditing.

The invention discloses a logistics order management system and method, and belongs to the technical field of logistics information management. The method comprises the following steps: step 1, generating a unique digital fingerprint for each logistics order; step 2, performing multi-level security encryption and state synchronization management on order information; 3, constructing a digital twinborn model corresponding to the order, collecting and preprocessing key data in real time and visually displaying the key data in the logistics process, and realizing dynamic monitoring of the whole process; step 4, based on an order state consensus mechanism, automatically matching optimal logistics resources and driving an order processing flow, and generating a verifiable order execution proof chain at the same time; and step 5, performing intelligent analysis and adaptive optimization on the full-life-cycle data of the order, and continuously improving the overall efficiency and precision of logistics management.

The invention discloses an enterprise information security management method and system based on artificial intelligence, and belongs to the technical field of information management, and the method comprises the steps: inputting original data, integrating the original data into a first behavior graph structure, and constructing an attribute vector for a user node; threat perception and behavior risk assessment are carried out through a graph learning model, and the threat probability of each edge is output; identifying and quantifying attack paths in the graph, and generating an attack path set and a path risk score set; generating a protection strategy candidate set for each path, performing evaluation to obtain a grading result of the protection strategies, and sorting the protection strategies according to the grading result; and a protection strategy is deployed, the deployment state and protection feedback are monitored, after strategy deployment is completed, the state of a deployment target is recorded, a lightweight monitoring assembly is installed on the deployment target, and deployment feedback information is collected. According to the method, potential security threats are identified in real time in a mode of combining intelligent prediction and simulation, and the security policy is dynamically adjusted according to the change of the threats.

The invention discloses a preoperative and postoperative full-process optimization nursing system for the thoracic surgery department, relates to the technical field of medical nursing, and aims to solve the problem that physical execution nursing and information management decision are separated in the prior art; and the nursing intervention is lack of real-time response and closed-loop regulation and control capability to the highly dynamic physiological state of the thoracic surgery patient in the perioperative period. The nursing system is characterized by comprising a multi-mode physiological and environmental perception layer, a dynamic nursing decision engine and a closed-loop physical intervention execution layer. Wherein the sensing layer is responsible for data acquisition, state model construction, risk prediction and optimal intervention planning by a decision engine, and the execution layer accurately implements intervention and feeds back. By the adoption of the technical scheme, individualized, dynamic and prospective management of perioperative nursing of the patient in the thoracic surgery department can be achieved, a refined, individualized and intelligent management means is provided, and the clinical value is remarkable.

The invention discloses an AI technology-based rail transit industry crew member intelligent management system, which comprises a crew member scheduling management module, and is characterized in that the crew member scheduling management module scientifically and reasonably arranges work tasks of crew members by introducing an AI-based intelligent scheduling optimization engine according to a travel diagram and operation requirements of a line; the AI-based intelligent scheduling optimization engine constructs a multi-target crew scheduling model by using traffic operation data, and generates an intelligent and dynamically adjusted scheduling scheme by using the multi-target crew scheduling model. The system has the advantages that the problems of manual shift arrangement, paper document storage, health state monitoring, attendance and attendance information management and the like in the prior art are solved. The efficiency, safety and accuracy of crew management are effectively improved, the risks of manual operation and information lag are reduced, and a more intelligent and flexible solution is provided for rail transit operation.

The invention relates to the technical field of logistics information management, and discloses a cross-border logistics information management method and system based on a logistics management platform, and the method comprises the steps: carrying out the analysis of a received customer order in a domestic logistics system, and obtaining a labeling parameter set; querying equipment specification information of an overseas logistics system, and converting the labeling parameter set based on the equipment specification information to obtain an overseas execution instruction; the overseas execution instruction is transmitted to overseas warehouse labeling equipment of an overseas logistics system for automatic labeling operation, and labeling process data is obtained; state monitoring is conducted on the labeling process data, a labeling state report is obtained, the labeling state report is transmitted back to the domestic logistics system, and synchronous confirmation information is generated. According to the method, full-process automation and intelligentization of cross-border logistics labeling information coordination processing are realized.

The invention discloses an archive data management labeling method and device based on computer application, and relates to the technical field of archive information management and intelligent labeling, and the device comprises a semantic analysis module, an allogenic character normalization module, a context modeling module, a historical semantic alignment module, a context feedback module, a processing module and a model optimization module. The device can automatically monitor file input, clean and segment texts, quickly and normatively replace foreign characters and intelligently convert simple and traditional Chinese characters; a semantic vector is generated based on a Transform model, semantic consistency is detected, and if semantic drift is found, rollback adjustment is carried out; the processing module is combined with a rule engine and a depth model to complete entity labeling and multi-label reasoning; and the optimization module dynamically records and feeds back annotation data, and supports self-learning and continuous improvement of the model. By continuously optimizing the judgment logic, the system has self-learning and evolutionary capabilities, so that the archive labeling process is more and more intelligent, and the labeling result is more and more accurate.

The invention relates to the technical field of archive information management, and discloses an archive metadata intelligent extraction and indexing system, which comprises a multi-modal preprocessing module for obtaining and preprocessing original multi-modal archive data; the context entity recognition module is used for performing entity recognition and standardization according to the context vector; the cross-modal fusion module is used for carrying out confidence weighted multi-modal fusion and logic verification; the archive association module is used for carrying out association identification and consistency detection between archives; the intelligent indexing module is used for carrying out hierarchical intelligent indexing and quality feedback on the consistency constrained file metadata set; the quality evaluation module is used for carrying out metadata quality evaluation and active repair on the standardized indexing result; the knowledge graph module is used for constructing a time sequence knowledge graph and intelligent retrieval service; according to the method, collaborative extraction of cross-modal information is realized by constructing a confidence-weighted multi-modal fusion model and a bidirectional attention mechanism.

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The invention discloses a building engineering data information intelligent management method, and relates to the technical field of information management, and the method comprises the steps: collecting and aligning construction site multi-source heterogeneous data, forming an engineering alignment data set, extracting construction state features based on the alignment data set, and calculating a comprehensive deviation index; according to the method, the multi-source heterogeneous data and the dynamic twinborn model are fused, real-time monitoring, prediction and optimization control of the construction site state are achieved, the dynamic twinborn scene is constructed, the health state mapping of the key component is generated, and the construction optimization strategy is generated based on the health state mapping and the deviation index and is controlled. The structural deviation is quantitatively analyzed, the optimal construction strategy is automatically generated, model self-evolution and parameter self-optimization are achieved through feedback learning, the safety and intelligent level of constructional engineering are remarkably improved, the energy consumption and the construction period delay rate are reduced, and conversion from static monitoring to active decision making is achieved.

The invention relates to the technical field of bridge management, in particular to a bridge informatization management system based on digital twinning. Comprising a stress acquisition device and a traffic flow acquisition device, the stress acquisition device is arranged at a key stress point of a bridge, and the server comprises a bridge modeling module, a data receiving module, a model prediction module and a state analysis module; the bridge modeling module is used for establishing a bridge high-precision three-dimensional model through BIM and constructing a digital twinborn body of the bridge; the data receiving module is used for receiving the sensor measured value uploaded by the stress acquisition device and the traffic flow information uploaded by the traffic flow acquisition device; the model prediction module is used for converting the traffic flow data into a dynamic load and inputting the dynamic load into a digital twinborn body of the bridge for transient analysis to obtain a model prediction value of each bridge key stress point; and the analysis module is used for judging the bridge state according to an error value of the model prediction value and the sensor actual measurement value, and generating bridge state early warning when the error value exceeds an error threshold value.