14922 results about "Metadata" patented technology

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

The invention discloses an NL2SQL optimization method and device based on a large model, equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: constructing a target metadata knowledge base, and obtaining an initial natural language query request; determining each target entity corresponding to the initial natural language query request, and determining missing target SQL elements in the initial natural language query request based on each target entity; generating a first cue word based on the initial natural language query request, the target SQL element and the target metadata knowledge base, and complementing the target SQL element based on the first cue word by utilizing the target large model to obtain a target natural language query request; and generating a plurality of candidate SQL statements corresponding to the target natural language query request by using the target large model, verifying each candidate SQL statement, and determining a target SQL statement from each candidate SQL statement based on a verification result. According to the method, the accuracy of the NL2SQL can be improved by utilizing a large model.

The invention provides a multi-source heterogeneous data integration and governance-oriented data weaving method, which comprises the following steps of: performing data acquisition from an accessed multi-source heterogeneous data source to generate an original multi-source heterogeneous data stream; performing standardization processing on the original multi-source heterogeneous data stream to generate a standardized multi-source heterogeneous data set; performing active content scanning processing on the standardized multi-source heterogeneous data set to determine business metadata, and performing consanguinity tracking processing on the business metadata to generate enhanced business metadata; calling a domain ontology framework to carry out standardized constraint on the enhanced service metadata to obtain standardized service metadata without cross-data source semantic ambiguity, and carrying out implicit association mining processing on the standardized service metadata based on a graph neural network to generate a semantic knowledge graph containing core entities and relationships; and performing logic abstraction processing on the distributed data resources according to the semantic knowledge graph to generate a unified data access interface.

The invention discloses a cloud desktop security access control method based on a zero-trust architecture, belongs to the technical field of network security, and is used for solving the problems of difficulty in hidden attack detection, cross-cloud attack chain breakage and conflict between security control and service continuity in a multi-cloud environment. Firstly, user identity attributes, session metadata and service call logs are aggregated, an identity-resource-behavior triple dynamic graph is constructed, cross-session association features are extracted, and a multi-dimensional behavior baseline is generated. And quantifying the access deviation degree based on the behavior baseline, triggering sensitive operation traceability analysis, constructing a time sequence risk propagation model, identifying latent attack features, predicting a penetration path and outputting a risk propagation coefficient. And finally, dynamically generating a process-level micro-isolation strategy according to a risk result, gradually adjusting the authority through a nonlinear authority attenuation function, inserting a secondary authentication node when unexpected resource jump is detected, reconstructing a communication white list, and realizing collaborative optimization of security protection and service continuity.

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

The invention discloses a report verification batch parallel execution optimization method based on a pluggable rule engine framework, and relates to the technical field of report verification, and the method comprises the following steps: receiving compliance rules, converting the compliance rules into a uniform data structure model, analyzing to generate metadata indexes, and supporting business rule set configuration; loading the matched business rule set based on the meta-information of the to-be-verified report, and registering the matched business rule set to an execution container; s3, receiving a to-be-verified report, analyzing a report structure, and generating a report field index table; performing field dependency analysis based on the report field index and the loaded business rule set, and constructing a rule field dependency graph; graph partition optimization and topological sorting are executed according to the dependency graph, and rule batches capable of being executed in parallel are generated; and uniformly collecting and outputting all rule execution results, hit states, abnormal records and performance indexes, and generating a structured verification report and an audit chain log. The financial statement compliance check execution efficiency can be improved.

The invention relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

A portable agent package apparatus for coupling to one or more environment, energy or water infrastructure or water body sensors produce timestamped or temporal process data, includes a physics surrogate world model trained to predict at least one hydraulic, chemical, or biological state variable of the sensed water system, a connection memory that stores metadata describing data source identifiers, units, and sampling cadence, pointers to available analytical tools or peer agent packages, or streams of operational experience or a hierarchical options library, an emotion tensor continuously encodes normalized metrics comprising at least one of model accuracy, computational load, data quality, latency, and uncertainty, or further including an exploration bonus channel, a value estimate error, an anomaly score, or an alignment divergence flag, and a bidirectional, authenticated communication interface that receives the temporal or timestamped process data from the one or more sensors, transmits Memo updates, and accepts goal directives.

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 present invention discloses an artificial intelligence-based adaptive big data storage and retrieval optimization system and method designed to intelligently manage and optimize large-scale distributed data environments. The system integrates data acquisition, distributed storage, metadata processing, adaptive learning, and retrieval optimization units configured to work collaboratively for continuous self-optimization. The invention employs deep reinforcement learning and predictive neural network techniques to dynamically analyze system telemetry, workload behavior, and data access patterns in real time, enabling proactive adjustment of data placement, caching, replication, and compression parameters across distributed nodes. The metadata processing framework utilizes graph-based dependency modeling to maintain semantic and contextual relationships among datasets, facilitating intelligent and context-aware data retrieval. The retrieval optimization unit interprets user queries semantically and computes the optimal retrieval route using latency prediction models and dynamic routing techniques.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

In one implementation, a computer-implemented method involves receiving a user message corresponding to a query or a statement to AI chatbot, performing preprocessing operations resulting in generation of initial context of the user message by extracting text of the user message, metadata of the user message, and a conversation identifier, obtaining historical context pertaining to the user message from a plurality of storage mechanisms provided in differing formats including a knowledge graph, a vector database comprised of vector embeddings, and a database comprising text summaries of prior conversations between the user and the AI chatbot, generating a prompt for a LLM that instructs the LLM to generate a response to the user message that is based on and consistent with the user message, the initial content, and the historical context, and providing a final response to the user that is corresponds to an LLM-generated response.

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

The invention provides a method and system for achieving CUDA call tracking based on an eBPF, and relates to the field of GPU call tracking of the eBPF. The method comprises the following steps: dynamically mounting an eBPF program at an API function entry and an API function exit of a user mode CUDA runtime library through an uprobe technology of the eBPF; the eBPF program captures metadata of the CUDA API calling event in a kernel mode and packages the metadata into a predefined structural body; asynchronously transmitting the structural body data to a user mode through an annular buffer area; and calling a standard function library by a user mode program to load an eBPF program, reading data in the annular buffer area in real time, and completing analysis and visual display of a CUDA calling event. According to the method, the calling parameter, the return value and the nanosecond timestamp are effectively captured, and the observability and the debugging efficiency of the GPU application program are improved.

The invention discloses an enterprise data asset intelligent analysis method and system based on AI recognition, and the method comprises the steps: receiving an enterprise multi-source heterogeneous data stream, carrying out the joint feature extraction and semantic alignment through a pre-trained multi-modal fusion recognition model, and generating a structured data asset recognition result; constructing a dynamic enterprise data asset atlas according to the structured data asset identification result in combination with the data access trajectory and authority metadata collected in real time; performing spatio-temporal evolution analysis on the dynamic enterprise data asset map, and extracting potential data value density features and risk exposure features; inputting the data value density features and the risk exposure features into a self-organizing mapping network to generate a data asset grading topological graph; and based on the data asset grading topological graph, through strategy constraint reinforcement learning, generating an executable data governance action sequence. According to the embodiment of the invention, the identification precision and real-time analysis capability of special assets of enterprises can be improved.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention provides an SQL (Structured Query Language) statement generation method and system based on a large language model. Target database metadata are collected, a structured dictionary is constructed, cue words adaptive to the large language model are generated in combination with a user query strategy, and accurate SQL generation is realized through a closed-loop execution optimization mechanism after model reasoning and SQL analysis. Through a large language model intelligent analysis technology, a multi-source heterogeneous database metadata dynamic adaptation technology and a closed-loop optimization mechanism for SQL statement generation, execution and feedback, the efficient and accurate SQL statement automatic generation function is achieved through the characteristics of cross-database grammar automatic adaptation, complex service requirement accurate analysis, intelligent error correction and the like. The system not only improves the working efficiency of developers, but also reduces the resource occupation and operation and maintenance cost of the system, and improves the accuracy and safety of data query and processing.

Methods, systems, apparatuses, devices, and computer program products are described. A system may obtain a set of documents associated with a knowledge base for retrieval-augmented generation (RAG). The system may generate multiple representations of the information included in the documents using multiple knowledge extraction pipelines. For example, the system may generate a set of metadata-based vector embeddings based on the documents, a set of knowledge graphs based on the documents, and a set of hierarchical tree representations based on the documents. The system may receive a user query and may retrieve contextual information from the set of vector embeddings, the set of knowledge graphs, and the set of hierarchical tree representations to augment the user query for a large language model (LLM) prompt. The system may input the prompt to the LLM, and the LLM may output a response based on the user query and the contextual information.

The embodiment of the invention provides a task scheduling method, system and device based on cloud computing and a storage medium, and belongs to the technical field of computers. According to the method, task priorities are initialized according to task resource requirements, task types and task timeliness requirements, task scheduling strategies are determined according to task metadata and computing resource information, the task metadata comprise the task priorities and the task resource requirements, and the task scheduling strategies are used for representing task scheduling sequences and task allocation objects; task scheduling is carried out according to the task scheduling strategy, load fluctuation information and task demand change information of each computing node are obtained, the task scheduling strategy is adjusted according to the load fluctuation information and the task demand change information, and the step of task scheduling according to the task scheduling strategy is repeatedly executed, so that the task scheduling efficiency is improved in the task execution process. Task scheduling and allocation are adaptively adjusted based on load changes and task demand changes, and the task execution efficiency and the system resource utilization rate are improved.

The invention relates to the cross technical field of business intelligence and natural language processing, in particular to an interactive AI report generation method and system based on intelligent semantic driving, and the method comprises the steps: (1) a large language model analyzes user question semantics, calculates the matching degree with a report subject, and dynamically selects a question correction or report generation process; (2) the fuzzy query is converted into a deterministic request based on the domain knowledge base, and semantic ambiguity is eliminated; (3) analyzing field association in combination with database metadata, and generating an executable SQL (Structured Query Language); (4) dynamically rewriting SQL (Structured Query Language) according to user roles, adding permission filtering conditions, executing query and desensitizing sensitive data; (5) analyzing result features, automatically matching a visual form and generating natural language interpretation; and (6) collecting user feedback, and optimizing a semantic model and a query strategy. According to the scheme, the accuracy of natural language query and the adaptive capacity of the system can be remarkably improved, and the security of data access is guaranteed.

A system for breakage early warning of high-voltage transmission lines based on acoustic prints, including a data acquisition unit, an edge processing unit, a cloud server, a warning execution unit, and a communication unit. The data acquisition unit is configured to collect environmental information of a high-voltage transmission tower in real time through a multimodal sensing device. The edge processing unit includes an adaptive filtering module configured to eliminate wind noise interference in the environmental information and compensate for frequency response deviation. The cloud server can generate a warning instruction based on the environmental information. The warning execution unit can perform warning and alarming actions based on the warning instruction. The communication unit is configured to enable data and information transmission among the units. A method implemented by such system is also provided.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The present invention relates to an intelligent, context-aware battery management system embedded within a mobile device that dynamically allocates power resources based on real-time user behavior, system state, and environmental context. It incorporates a smart recognition engine that analyzes sensor-derived telemetry data to compute behavioral deviation scores, enabling the system to anticipate abnormal or emergency-prone conditions. A continuous activity classification module contextualizes user motion and geolocation to inform power policy decisions. Upon detecting significant behavioral anomalies or critically low battery conditions, an emergency mode subsystem is triggered, restricting device operations to essential functionalities while preserving energy for critical communication and navigation tasks. The system also establishes a secure, lightweight emergency communication tunnel for relaying essential metadata, including GPS and behavioral indicators, to predefined response servers.

The invention relates to the technical field of intelligent retrieval, in particular to an enterprise RAG intelligent question answering system based on vector database reordering. The system specifically comprises: a document recall module, which retrieves a vector database to obtain candidate document blocks containing business metadata; the comprehensive scoring module is used for calculating a semantic correlation score by adopting a later-stage interaction architecture based on bidirectional token importance weighting, performing path semantic matching and context sensing rule evaluation according to a preset metadata ontology graph to obtain a service attribute score, and analyzing the evidence sub-graph to obtain a fact path score; fusing the semantic correlation score, the service attribute score and the fact path score to generate a comprehensive correlation score; and the sorting output module performs optimization resorting based on a preset punishment mechanism and the comprehensive correlation score to generate an optimized context set, and calls a generation model to output answers based on the optimized context set. According to the method, semantic accuracy, business compliance and fact reliability can be considered, and more trustworthy high-quality enterprise-level answers can be generated.

The invention provides an information transmission device and implementation method based on multi-protocol dynamic conversion and data fusion, and belongs to the technical field of information transmission, and the device comprises a multi-protocol communication interface module, a data analysis unit, a dynamic protocol conversion engine, a data fusion unit, a data assembly reporting unit and a log storage module. According to the method, information transmission is realized by initializing an interface, analyzing multi-protocol original data, dynamically converting the multi-protocol original data into a uniform format, fusing de-duplicated data, packaging, reporting and recording logs. The device and the method solve information interaction obstacles of different protocol devices, support bidirectional conversion of multiple industrial protocols, realize cross-protocol device collaboration and centralized management, improve the system intelligence level, and are suitable for scenes requiring multi-device collaboration, such as electric power, intelligent transportation, intelligent buildings and the like.

The invention discloses an insurance claim settlement-oriented multi-modal image video evidence analysis method and system. The method comprises the following steps of: acquiring video / image and multi-source data such as metadata, audio, IMU (Inertial Measurement Unit), GPS (Global Positioning System), OBD (On-Board Diagnostic) and the like; calculating content Hash of the video and the audio according to frames, connecting the content Hash with time information in series to form chained Hash, and adding a verification digital signature and a credible timestamp; realizing cross-modal time sequence alignment based on self-adaptive time anchor-attitude coupling; tampering detection is carried out in combination with PRNU fingerprints, noise field consistency, dual compression, copy-movement and the like; multi-view geometry and monocular depth are fused, IMU scale constraint and micro rendering are introduced, three-dimensional reconstruction and re-projection optimization are completed, and collision dynamics verification is carried out; and constructing an event cause and effect graph, judging responsibility in combination with traffic rules, outputting a confidence coefficient vector and a structured report, and generating a verifiable evidence packet. The scheme has the advantages of high efficiency and traceability in the aspects of space-time restoration and interpretable responsibility judgment.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.