A dynamic linkage method and system based on artificial intelligence early warning and digital twin technology

Abstract

The application discloses a dynamic linkage method and system based on artificial intelligence early warning and digital twin technology, and relates to the technical field of artificial intelligence.The application constructs a full-process automatic linkage closed loop through five modules of AI early warning triggering and analysis, digital twin scene dynamic updating, early warning associated element intelligent labeling, early warning attribute and scene correlation mapping, and linkage control and data management: multi-source early warning data is output as space, attribute and time key parameters through structured analysis, drives the digital twin three-dimensional engine to incrementally update the scene and automatically label associated elements such as monitoring and emergency forces; early warning levels and scene elements are dynamically adapted by relying on a mapping rule engine, and full-link data traceability is completed by combining state machine time sequence control and a spatial database.The application can eliminate artificial mediation links, shorten the linkage response time, realize scene dynamic fidelity, element accurate matching and level self-adaptive adjustment, and reduce operation and expansion costs.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence, and more particularly to a dynamic linkage method and system based on artificial intelligence early warning and digital twin technology. Background Technology

[0002] In the fields of emergency response and public safety, real-time risk management relies heavily on the collaboration between AI-powered early warning systems and digital twin scenarios to build a closed-loop system encompassing early warning, decision-making, and response. Existing technologies suffer from significant bottlenecks: AI early warning systems and digital twin scenario data are fragmented; after an early warning is triggered, manual retrieval and updating of static scenes, manual labeling of monitoring points, and manual identification of available resources are required, resulting in severe delays in information acquisition and response. Traditional manual intervention processes are time-consuming, taking approximately 5–10 minutes, exceeding the 3–5 minute critical window for emergency response. Furthermore, digital twin scenarios only present basic spatial information, lacking precise correlation with the AI-driven early warning risk level and impact range, and cannot adaptively adjust element labeling logic according to the rise and fall of warning levels. The overall architecture suffers from low automation and weak correlation, severely hindering the engineering implementation and large-scale application of an intelligent closed-loop system for "early warning-decision-response" in emergency response and public safety. Summary of the Invention

[0003] Addressing the core pain points of existing AI early warning and digital twin technologies in emergency response and public safety scenarios, such as data fragmentation, reliance on manual coordination, weak information correlation, and insufficient adaptability, this invention aims to provide a dynamic linkage method and system for AI early warning and digital twins. This system constructs a fully intelligent closed-loop linkage mechanism, breaking through the bottleneck of "disconnection between early warning and scenario information," and solving the problems of low automation and weak correlation. It provides support for the engineering implementation of an intelligent closed-loop "early warning-decision-response" system in the field. The technical solution adopted by this invention is as follows: In a first aspect of the present invention, a dynamic linkage method based on artificial intelligence early warning and digital twin technology is provided, comprising the following steps: S1. Integrate and structure the multi-source AI early warning data, extract the spatial parameters, attribute parameters and time parameters of the early warning data, and generate a list of key early warning parameters; S2. Based on the list of key early warning parameters, incremental real-time data acquisition and 3D rendering updates are performed on the digital twin scene to locate the incident area and mark abnormal devices and dynamically changing areas. S3. Based on the coordinates and impact range of the early warning space, perform spatial matching and labeling of the related elements of monitoring equipment and emergency response forces in the digital twin scenario; S4. Call the mapping rule engine between early warning attributes and scene elements, and dynamically adapt related elements according to early warning type and risk level, including filtering scope, labeling style and display priority; S5. A state machine model is used to execute linkage timing control, output linkage results, and store the entire link data in a spatial database to achieve traceable management.

[0004] Preferably, the structured parsing uses a combination of natural language processing and a rule engine to convert the latitude and longitude of the incident area and its boundaries into spatial coordinates usable in the digital twin scene, and to identify the warning type, risk level, scope of impact, and trigger time.

[0005] Preferably, the digital twin scene update adopts an incremental update strategy, updating only the terrain, building and equipment status data of the incident area and the affected area.

[0006] As a preferred approach, a spatial indexing algorithm is used to filter video surveillance, emergency personnel, and emergency vehicles within the affected area, classify and label them by type, and display attribute information and real-time status.

[0007] As a preferred option, the mapping rule engine associates the element type, coverage radius, and display priority with the preset warning type and warning level configuration, and adds, deletes, hides, or highlights the corresponding elements when the warning level is raised or lowered.

[0008] Preferably, the state machine model defines a fixed sequence of early warning reception, parameter parsing, scene updating, feature labeling, and attribute mapping, and includes anomaly retry and status reporting functions.

[0009] Preferably, the spatial database is a PostGIS spatial database, used to store the basic coordinates of the digital twin, the list of early warning parameters, the location attributes of related elements, and the full-process linkage log.

[0010] In another aspect of the present invention, a dynamic linkage system based on artificial intelligence early warning and digital twin technology is also provided, applied to the aforementioned dynamic linkage method based on artificial intelligence early warning and digital twin technology, comprising: The AI-powered early warning triggering and analysis module is used for multi-source early warning data access and structured analysis, and outputs a list of key early warning parameters. The digital twin scene dynamic update module is used to incrementally acquire terrain and equipment data to drive the 3D engine to render in real time; The intelligent labeling module for early warning related elements is used to integrate monitoring and force data to complete spatial matching and automatic labeling; The AI ​​early warning attribute and twin scene association mapping module is used to dynamically adapt the early warning level and elements according to the rule engine; The linkage control and data management module is used for timing control, exception handling, and spatial database storage and querying.

[0011] Preferably, the association mapping module has built-in visual configuration mapping rules stored in a relational database, which adjust the association range, annotation style, and display priority according to the warning level. Preferably, the hierarchical feedback module connects to an instant messaging tool to achieve real-time alarm push for blocking-level failures.

[0012] Preferably, the linkage control and data management module adopts state machine timing control to store the entire link data into the PostGIS spatial database.

[0013] Compared with the prior art, the present invention has the following significant advantages: This invention, through an AI-driven early warning and digital twin fully automated linkage architecture, constructs a closed-loop collaborative mechanism encompassing early warning analysis, scene updates, element labeling, and attribute adaptation. This overcomes the core shortcomings of existing technologies, such as the disconnect between early warning and scene data, reliance on manual intervention, static scene lag, susceptibility to errors in manual element labeling, and the need for manual reconfiguration upon changes in early warning levels. It achieves multi-dimensional breakthroughs: Firstly, by automatically triggering analysis through AI-driven early warning, incremental updates of the digital twin, and intelligent labeling of related elements, it completely eliminates manual intervention, shortening the early warning-scene linkage response time and fully covering 3-5 days of emergency response. The system addresses the issues of delayed response and missed opportunities inherent in traditional methods by utilizing a golden window of opportunity. Furthermore, it leverages real-time dynamic data acquisition and an early warning attribute mapping rule engine to achieve dynamic scene fidelity and precise element matching, avoiding blind judgments and element omissions common in traditional scenarios. Simultaneously, through visualized rule configuration and incremental adaptation logic, element annotations can be automatically added or deleted when adjusting early warning levels, eliminating the need for full-process reconstruction. This reduces manual configuration workload and lowers scenario expansion costs compared to the high costs of existing secondary development. Combined with a spatial database, it enables full-link data traceability, supporting decision optimization. Ultimately, this results in technological advantages such as efficient linkage, precise matching, flexible adaptation, and low-cost operation and maintenance. It provides underlying support for the intelligent closed-loop implementation of early warning-decision-response in emergency response, public safety, and other fields, promoting the large-scale application of related scenarios. Attached Figure Description

[0014] Figure 1 This is a flowchart of a dynamic linkage method based on artificial intelligence early warning and digital twin technology in a specific embodiment of the present invention; Figure 2 This is a simplified flowchart of AI early warning parameter parsing in a specific embodiment of the present invention; Figure 3 This is a simplified flowchart of the dynamic update process of the digital twin scenario in a specific embodiment of the present invention; Figure 4 This is a flowchart of the element adaptation sub-flow when the warning level changes in a specific embodiment of the present invention; Figure 5This is a framework diagram of a dynamic linkage system based on artificial intelligence early warning and digital twin technology in a specific embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] refer to Figure 1-4 As shown, this embodiment provides a dynamic linkage method based on artificial intelligence early warning and digital twin technology. The specific steps are as follows, taking a "park security scenario" as an example for detailed explanation: S1: Integrate and structure the multi-source AI early warning data to extract spatial, attribute, and temporal parameters, generating a list of key early warning parameters, specifically including: Multi-source early warning data access: When the smoke-detecting IoT device in Warehouse No. 1 of Area A in the park detects that the smoke concentration exceeds the standard, it pushes an early warning signal to the system via the MQTT protocol; at the same time, the AI ​​video surveillance model identifies an open flame in the warehouse window and outputs the alarm identification result synchronously through the API interface. The two trigger a dual verification early warning to avoid false alarms.

[0017] Structured parsing of early warning parameters: Early warning data is parsed using a BERT-based NLP model to extract key parameters. Spatial parameters: The GPS coordinates of the smoke detector are converted into the coordinate system used in the digital twin scenario. In the example, the coordinates are X=325120m and Y=456890m, and the incident area is clearly identified as Warehouse No. 1 in Area A of the park. Attribute parameters: Warning type: warehouse fire; Risk level: Level 2; Impact range: 500-meter radius. Time parameters: The warning trigger time is 2024-05-20 14:00:00. The estimated duration will be updated after the situation is handled. A list of key warning parameters will be generated.

[0018] S2: Based on the aforementioned list of key early warning parameters, incremental real-time data acquisition and 3D rendering updates are performed on the digital twin scene to locate the incident area and mark abnormal devices and dynamically changing areas, specifically including: Based on the analyzed early warning parameters, the park's digital twin 3D engine is driven to complete real-time scene updates: Real-time acquisition of basic scene data: Topographic data: By connecting to the park's GIS system, the latest topographic data around Warehouse No. 1 was retrieved. There is a temporary construction fence 30 meters to the east of the warehouse and the main road of the park is 10 meters to the west. The data transmission adopts GeoJSON format to ensure that the topographic accuracy reaches 0.5 meters. Equipment status data: Connects to the video surveillance platform to obtain the status of 12 cameras within a 500-meter range—CAM-002 is offline due to network fluctuations, while CAM-005 is online normally; connects to the IoT device management platform to obtain data from temperature sensing devices in the warehouse; Incremental update execution: Only update the terrain and equipment data of Warehouse 1 and a 500-meter radius to avoid full scene reconstruction.

[0019] Real-time rendering and updating of 3D scenes: The digital twin engine automatically locates the coordinates of the incident, loads a temporary construction site model, and marks offline cameras with flashing red icons. Differentiated rendering of the warehouse model: Add dynamic flame effects to the warehouse roof and window areas, and draw a semi-transparent red outline within a 500-meter radius around the warehouse to visually indicate the scope of the warning impact.

[0020] S3: Based on the coordinates and impact range of the early warning space, perform spatial matching and labeling of the related elements of monitoring equipment and emergency response forces in the digital twin scenario, specifically including: Based on the warning spatial parameters, the R-tree spatial indexing algorithm is used to filter related features and complete automatic labeling: Video surveillance elements: Retrieve a list of online cameras within a 500-meter range from the video surveillance platform, and record the device number, coverage angle, and pixel focal length; Dispatchable force elements: Data obtained from the park's security force allocation system—fire patrol vehicles, security patrol teams.

[0021] Spatial matching and intelligent annotation: In a digital twin scenario, three online cameras are marked with yellow camera icons. Clicking the icon will display a pop-up window showing the real-time video stream. Fire patrol vehicles are marked with red fire truck icons, and security patrol teams are marked with blue personnel icons. Text labels are attached next to the icons, and dotted lines connect the elements to the incident location to visually show the distance relationship.

[0022] S4: Invokes the mapping rule engine between warning attributes and scene elements, dynamically adapting related elements according to warning type and risk level, including filtering scope, annotation style, and display priority, specifically including: Based on a preset rule engine, dynamic adaptation between warning attributes and scene elements is achieved: Attribute mapping rule call: By matching the preset rules corresponding to the level 2 fire warning, the fire-fighting forces and video surveillance within 500 meters are associated, and non-security elements are blocked. The rule parameters are read from the MySQL database. Specifically, the warning level is level 2 → the force type includes fire / security, and the range is 500m; the warning level is level 1 → the force type is fire / security / medical, and the range is 800m.

[0023] Dynamic adaptation and adjustment: At 14:00:20, the warehouse temperature sensor detected a temperature rise to 92℃. The AI ​​model upgraded the warning level to Level 1 (high risk), automatically triggering the rule engine. Range expansion: The associated range has been expanded from 500 meters to 800 meters, and new medical points within the park within 800 meters have been added, marked with green medical point icons, and accompanied by labels for first aid kits and stretchers; Priority adjustment: Enlarge the fire patrol vehicle icon to 1.2 times its original size and add a flashing effect to highlight the core response force; hide non-core elements within a range of 500-800 meters to avoid cluttering the screen.

[0024] S5: Employs a state machine model to execute linkage timing control, outputs linkage results, and stores end-to-end data in a spatial database for traceable management. Specifically, this includes: The linkage process is controlled through a state machine model, and data storage across the entire chain is completed: Linked timing control: The system executes the process according to a preset timing sequence. The key node timestamps are as follows: 14:00:00: The smoke detector triggers an alarm, and the system receives the data; 14:00:03: Complete the analysis of early warning parameters and generate the "List of Key Early Warning Parameters"; 14:00:03-14:00:11: Incremental update of digital twin scenarios; 14:00:11-14:00:15: Complete intelligent annotation of related elements; 14:00:20: Warning level upgraded; 14:00:22: Dynamic adaptation of elements completed. Anomaly Handling: At 14:00:08, a data transmission timeout was detected for CAM-004. The system automatically retried 3 times. If the retry failed, an audible and visual alarm was triggered.

[0025] Spatial database storage: The entire data chain is stored using a PostGIS spatial database. Examples of core data tables and records are as follows: Early warning data table (early_warning): ID=WARN-20240520-001, Type=Fire, Level=Level 1, Incident coordinates=POINT (325120 456890), Trigger time=2024-05-20 14:00:00; Element data table (related_element): ID=ELE-001, Type=Camera, Device number=CAM-005, Coordinates=POINT(325090 456890), Related warning ID=WARN-20240520-001; Linked log table (link_log): ID=LOG-001, Warning ID=WARN-20240520-001, Stage=Scene Update, Start Time=14:00:03, End Time=14:00:11, Status=Success; It supports quick queries by coordinate range (X∈[325000,325200], Y∈[456800,457000]) and warning time (2024-05-20 14:00-14:05), providing data support for subsequent statistical analysis.

[0026] Please see Figure 2 As shown, in a second aspect of the present invention, a dynamic linkage system based on artificial intelligence early warning and digital twin technology is proposed, applied to the aforementioned dynamic linkage method based on artificial intelligence early warning and digital twin technology, comprising: The AI-powered early warning triggering and analysis module is used for multi-source early warning data access and structured analysis, and outputs a list of key early warning parameters. The digital twin scene dynamic update module is used to incrementally acquire terrain and equipment data to drive the 3D engine to render in real time; The intelligent labeling module for early warning related elements is used to integrate monitoring and force data to complete spatial matching and automatic labeling; The AI ​​early warning attribute and twin scene association mapping module is used to dynamically adapt the early warning level and elements according to the rule engine; The linkage control and data management module is used for timing control, exception handling, and spatial database storage and querying.

[0027] The above embodiments are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A dynamic linkage method based on artificial intelligence early warning and digital twin technology, characterized in that, Includes the following steps: S1. Integrate and structure the multi-source AI early warning data, extract the spatial parameters, attribute parameters and time parameters of the early warning data, and generate a list of key early warning parameters; S2. Based on the list of key early warning parameters, incremental real-time data acquisition and 3D rendering updates are performed on the digital twin scene to locate the incident area and mark abnormal devices and dynamically changing areas. S3. Based on the coordinates and impact range of the early warning space, perform spatial matching and labeling of the related elements of monitoring equipment and emergency response forces in the digital twin scenario; S4. Call the mapping rule engine between early warning attributes and scene elements, and dynamically adapt related elements according to early warning type and risk level, including filtering scope, labeling style and display priority; S5. A state machine model is used to execute linkage timing control, output linkage results, and store the entire link data in a spatial database to achieve traceable management.

2. The dynamic linkage method based on artificial intelligence early warning and digital twin technology according to claim 1, characterized in that, The structured analysis uses a combination of natural language processing and a rule engine to convert the latitude and longitude of the incident area and its boundaries into spatial coordinates usable in the digital twin scenario, and to identify the warning type, risk level, scope of impact, and trigger time.

3. The dynamic linkage method based on artificial intelligence early warning and digital twin technology according to claim 1, characterized in that, The digital twin scene update adopts an incremental update strategy, updating only the terrain, building, and equipment status data within the incident area and its affected range.

4. The dynamic linkage method based on artificial intelligence early warning and digital twin technology according to claim 1, characterized in that, A spatial indexing algorithm is used to filter video surveillance, emergency personnel, and emergency vehicles within the affected area, and the data is categorized, labeled, and displayed with attribute information and real-time status.

5. The dynamic linkage method based on artificial intelligence early warning and digital twin technology according to claim 1, characterized in that, The mapping rule engine associates feature types, coverage radius, and display priority with preset warning types and warning levels. When the warning level is raised or lowered, the corresponding features can be added, deleted, hidden, or highlighted.

6. The dynamic linkage method based on artificial intelligence early warning and digital twin technology according to claim 1, characterized in that, The state machine model defines a fixed sequence of events for early warning reception, parameter parsing, scene updating, feature labeling, and attribute mapping. The state machine model includes exception retry and status reporting functions.

7. The dynamic linkage method based on artificial intelligence early warning and digital twin technology according to claim 1, characterized in that, The spatial database is a PostGIS spatial database used to store the basic coordinates of the digital twin, the list of early warning parameters, the location attributes of related elements, and the full-process linkage log.

8. A dynamic linkage system based on artificial intelligence early warning and digital twin technology, used to implement the method of any one of claims 1-7, characterized in that, include: The AI-powered early warning triggering and analysis module is used for multi-source early warning data access and structured analysis, and outputs a list of key early warning parameters. The digital twin scene dynamic update module is used to incrementally acquire terrain and equipment data to drive the 3D engine to render in real time; The intelligent labeling module for early warning related elements is used to integrate monitoring and force data to complete spatial matching and automatic labeling; The AI ​​early warning attribute and twin scene association mapping module is used to dynamically adapt the early warning level and elements according to the rule engine; The linkage control and data management module is used for timing control, exception handling, and spatial database storage and querying.

9. A dynamic linkage system based on artificial intelligence early warning and digital twin technology according to claim 8, characterized in that, The association mapping module has built-in visual configuration mapping rules, which are stored in a relational database. It adjusts the association range, annotation style, and display priority according to the warning level.

10. A dynamic linkage system based on artificial intelligence early warning and digital twin technology according to claim 8, characterized in that, The linkage control and data management module adopts state machine timing control to store the entire link data into the PostGIS spatial database.

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