A Smart Community Management Method and System Based on 3D Scenes

Abstract

This invention discloses a smart community management method and system based on a 3D scene. The invention relates to the field of smart community management and includes the following steps: S1: Dynamic twin construction of the 3D scene, creating a dynamic 3D scene synchronized with the physical community in seconds, rather than a static model, ensuring accurate implementation of management actions; S2: Triggered perception and linkage of the 3D scene; S3: Dynamic resource scheduling optimization of the 3D scene, dynamically adjusting the community management resource configuration based on the real-time status of the 3D scene; S4: Iterative optimization of the 3D scene management effect. This invention's smart community management method and system based on a 3D scene, through dynamic twin modeling and scene-linked perception and early warning, constructs a synchronization system between the physical community and the 3D scene, replacing the manual inspection trigger mode. Abnormal situations can be automatically located, instructions generated, and synchronously fed back, thus significantly shortening the early warning and response time and improving technical linkage.

Description

Technical Field

[0001] This invention relates to the field of smart community management, and in particular to a smart community management method and system based on a three-dimensional scene. Background Technology

[0002] Current technologies related to 3D management of smart communities are mostly based on static 3D modeling, which can only realize the visualization of community scenes and the overlay of basic data, but lack the ability to link with the physical community in real time.

[0003] In existing technologies, 3D scenes, sensing devices, and management resources are disconnected, requiring manual inspections to detect anomalies and trigger management actions, resulting in delayed warnings and inefficient responses. Furthermore, resource scheduling often employs a fixed partition allocation model, failing to dynamically adjust based on real-time scene conditions, easily leading to resource idleness or localized shortages. In addition, existing systems lack self-iterative optimization mechanisms, making it difficult for 3D models to adapt to changes in community physical conditions and upgrades in management needs. Inadequate data security protection and interactive experience design further limit overall management efficiency, preventing the shift from passive response to proactive prediction and failing to meet the actual needs of refined and efficient smart community management.

[0004] Therefore, it is necessary to propose a smart community management method and system based on three-dimensional scenes to solve the above problems. Summary of the Invention

[0005] The main objective of this invention is to provide a smart community management method and system based on a three-dimensional scene, which can effectively solve the problems in the background technology.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A smart community management method based on a 3D scene includes the following steps: S1: Dynamic twin construction of 3D scenes, creating dynamic 3D scenes that are synchronized with the physical community in seconds, rather than static models, to ensure the precise implementation of management actions; S2: Three-dimensional scene-triggered perception linkage, relying on the three-dimensional scene data fluctuations of S1 to trigger management actions, realizing two-way linkage between physical facilities and three-dimensional scenes; S3: Dynamic resource scheduling optimization for 3D scenes, dynamically adjusting the configuration of community management resources based on the real-time status of the 3D scene; S4: Iterative optimization of 3D scene management effect. By accumulating management data in 3D scenes, the threshold setting and resource configuration are optimized in reverse.

[0007] Preferably, step S1 specifically includes the following steps: S101: Distributed micro-sensing node deployment. Low-power micro-sensing nodes are deployed in key locations of the smart community. The nodes only collect three types of core data: physical spatial location, environmental parameters, and facility operation vibration values. They are directly connected to the 3D scene processing terminal via the LoRa protocol. S102: Real-time calibration and modeling of 3D scene. Based on the CAD drawings of community buildings, a basic 3D framework is built. The community panorama is taken by drone every 2 hours. Combined with micro-sensing node data, the 3D scene is dynamically calibrated, focusing on correcting the growth status of greenery, the positional offset of facilities, and changes in temporary obstacles. S103: Hidden risk point scene annotation. The hidden facilities in the community are visualized and annotated through a 3D scene. Combined with historical failure data, high-risk areas are marked with different colors in the 3D scene.

[0008] Preferably, step S2 specifically includes the following steps: S201: Abnormal data threshold setting. In the 3D scene processing terminal, a baseline threshold is set for various types of perception data. If the baseline threshold is exceeded, a 3D scene warning is triggered. S202: Precise positioning and action generation of 3D scene. After an anomaly is triggered, the 3D scene automatically locks the anomaly location and generates a visual linkage instruction. The instruction includes the 3D coordinates of the anomaly location, related facility information, and standardized processing procedures. It is simultaneously pushed to the handheld terminal of the corresponding management personnel. At the same time, the processing progress is marked in the 3D scene, including pending, processing, and closed loop.

[0009] Preferably, S2 also includes physical facility linkage feedback: after the management personnel arrive at the site, they scan the site sensing nodes with a handheld terminal, and the three-dimensional scene automatically synchronizes and processes the process data.

[0010] Preferably, step S3 specifically includes the following steps: S301: Real-time recording of resource status. All resources of the smart community are recorded into the 3D scene, and their specific locations, availability status, and responsible persons are marked. When resources are moved, the location information in the 3D scene is updated in real time through the handheld terminal to ensure that the resources are visible and controllable. S302: 3D path optimization calculation. When multiple anomalies are triggered simultaneously, the 3D scene automatically calculates the optimal scheduling path based on the location of each anomaly and the distribution of resources, and prioritizes the allocation of resources that are closest and have the strongest adaptability. S303: Dynamic resource load balancing, real-time statistics of the workload of each resource in the 3D scene, and synchronous updates of resource allocation labels in the 3D scene.

[0011] Preferably, step S4 specifically includes the following steps: S401: Contextualized management data accumulation, which associates the data from each anomaly handling with the corresponding location in the 3D scene to form a historical data archive. Historical management records for any area can be queried through 3D scene location. S402: Threshold and strategy adaptive adjustment, based on historical data, automatically optimizes the abnormal threshold in the 3D scene; at the same time, it optimizes the resource scheduling strategy. S403: 3D Scene Model Upgrade: Every quarter, the 3D scene model is iteratively upgraded based on historical management data and changes in the community's physical state. New risk point annotations are added, and resource scheduling algorithms are optimized to ensure that the method adapts to the dynamic needs of community management.

[0012] A smart community management system based on a 3D scene includes a dynamic twin modeling module, a scene linkage perception and early warning module, an intelligent resource scheduling module, an iterative optimization module, and a data security and interaction module. The dynamic twin modeling module specifically includes: Micro-sensing node access submodule: responsible for connecting to distributed low-power micro-sensing nodes within the community, realizing real-time data acquisition and transmission through the LoRa protocol, filtering and integrating three core data types: location, environment, and vibration, eliminating redundant information, and ensuring efficient data transmission; Scene modeling and calibration submodule: Based on CAD drawings, a basic 3D framework is built, and drone aerial images and perception node data are integrated to complete a dynamic scene calibration every 2 hours to correct scene deviations caused by changes in physical space; The hidden risk scenario labeling submodule links to historical fault data in the community, visualizes and labels hidden facilities, presents risk levels through color grading, generates risk heat maps that are embedded in the 3D scene, and provides targeted basis for subsequent early warning and dispatch.

[0013] Preferably, the scene-linked perception and early warning module specifically includes: Multi-dimensional threshold management submodule: Presets and stores various types of sensing data benchmark thresholds, automatically corrects unreasonable thresholds based on historical data, and improves the accuracy of early warnings; Anomaly location and instruction generation submodule: Real-time monitoring of data synchronized by the modeling module; triggering an alert when the threshold is exceeded; quickly locating the three-dimensional coordinates of the anomaly location; automatically generating standardized linkage instructions containing processing flow and related facility information; pushing them to the management personnel terminal and marking the processing progress. Physical scene feedback synchronization submodule: By scanning sensing nodes with handheld terminals of management personnel, real-time data collection and processing are carried out, and the status annotations of abnormal areas in the 3D scene are updated synchronously.

[0014] Preferably, the intelligent resource scheduling module specifically includes: Resource Information Management Submodule: Input resource information for smart communities, and mark real-time location, availability status and adaptability; Optimal Path Planning Submodule: When multiple anomalies are triggered simultaneously, the optimal scheduling route is calculated and allocated by combining the anomaly locations and resource distribution in the 3D scene through a path algorithm. Load balancing adjustment submodule: Real-time statistics of the workload of each resource, analysis of load saturation through data modeling, automatic adjustment of cross-regional resource support schemes, and updating of resource allocation annotations in the 3D scene.

[0015] Preferably, the iterative optimization module specifically includes: The data accumulation management submodule associates data with corresponding locations in the 3D scene, builds historical data archives, and supports query and statistics by region and time dimension. The strategy self-optimization submodule automatically adjusts the anomaly threshold and resource scheduling rules based on historical data, optimizes the early warning mechanism for high-frequency fault areas, dynamically increases the configuration of inspection resources during peak periods, and enhances the system's proactive management capabilities. Scene Model Iteration Submodule: Every quarter, based on changes in the physical community and management data, optimize the 3D scene model structure, add new risk point annotations, and upgrade path planning and load balancing algorithms; The data security and interaction module's data security protection sub-module encrypts collected data and command information, and sets hierarchical access control. Human-Computer Interaction Submodule: Optimizes the 3D scene operation interface, supports drag-and-drop, zoom, precise positioning and other functions, simplifies the operation process for managers, and provides data visualization reports.

[0016] Compared with existing technologies, the present invention provides a smart community management method and system based on three-dimensional scenes, which has the following beneficial effects: This smart community management method and system based on 3D scenes constructs a synchronous system between the physical community and the 3D scene through dynamic twin modeling and scene linkage perception and early warning. It replaces the manual inspection trigger mode. Abnormal situations can be automatically located, instructions can be generated and feedback can be provided synchronously. Therefore, it can significantly shorten the early warning and response time, improve the technical linkage, and avoid the problem of response lag.

[0017] This smart community management method and system based on 3D scenes relies on real-time data from the 3D scene to achieve dynamic adaptation and allocation of management resources through optimal path planning and load balancing adjustment. This avoids resource idleness and local shortages, reduces unnecessary round-trip time, and can effectively improve resource utilization efficiency compared to fixed partition scheduling mode.

[0018] This smart community management method and system based on 3D scenes can automatically adjust the abnormal threshold, scheduling rules and 3D model through data accumulation and strategy self-optimization. It can adapt to the physical changes and high-frequency failure scenarios of the community without manual intervention, and can continuously improve the initiative and adaptability of management.

[0019] This smart community management method and system based on 3D scenes features a data security protection submodule that enables hierarchical control and data encryption to prevent information leakage and tampering. The optimized human-computer interaction interface lowers the operating threshold, and the combination of visual reports assists in decision-making. It balances system stability and practicality, and is tailored to the actual application scenarios of community management. Through the labeling of hidden risk scenarios and the presentation of heat maps, it enables the visual management of hidden facilities such as underground pipe networks, predicts fault risks in advance, and reduces losses caused by sudden failures. At the same time, through full-process automated management, it significantly reduces the cost of manual inspection and scheduling. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0022] Example 1: like Figure 1 As shown, a smart community management method based on a 3D scene includes the following steps: S1: Dynamic twin construction of 3D scenes, creating dynamic 3D scenes that are synchronized with the physical community in seconds, rather than static models, to ensure the precise implementation of management actions. This includes the following steps: S101: Distributed micro-sensing node deployment. Low-power micro-sensing nodes are deployed in key locations of smart communities, such as building walls, pipe network interfaces, green areas, and public facilities. The nodes only collect three types of core data: physical spatial location, environmental parameters (temperature, humidity, water depth), and facility operation vibration values, avoiding data redundancy. They are directly connected to the 3D scene processing terminal via the LoRa protocol. S102: Real-time calibration and modeling of 3D scene. Based on the CAD drawings of community buildings, a basic 3D framework is built. The community panorama is taken by drone every 2 hours. Combined with micro-sensing node data, the 3D scene is dynamically calibrated. The focus is on correcting the changes in the growth status of greenery, the positional deviation of facilities, and the changes in temporary obstacles (such as construction fences and illegally parked vehicles) to ensure that the error between the 3D scene and the physical community does not exceed 5 centimeters. S103: Hidden Risk Point Scene Labeling. This feature uses a 3D scene to visually label hidden facilities in the community, such as underground pipe networks and cable trenches. Combined with historical fault data, high-risk areas are marked with different colors in the 3D scene, such as aging pipe network sections marked in red and normal areas marked in blue, providing targeted guidance for subsequent management actions.

[0023] S2: 3D scene-triggered perception linkage, relying on the 3D scene data fluctuations of S1 to trigger management actions, realizes two-way linkage between physical facilities and the 3D scene, specifically including the following steps: S201: Abnormal data threshold setting. In the 3D scene processing terminal, a baseline threshold is set for various types of sensing data. For example, if the vibration value of the pipeline exceeds 0.3g, the water depth in the green area exceeds 5cm, or the facility position offset exceeds 10cm, it is judged as abnormal. If the baseline threshold is exceeded, a 3D scene warning is triggered. S202: Precise positioning and action generation of 3D scene. After an anomaly is triggered, the 3D scene automatically locks the anomaly location and generates a visual linkage instruction. The instruction includes the 3D coordinates of the anomaly location, related facility information, and standardized processing procedures. It is simultaneously pushed to the handheld terminal of the corresponding management personnel. At the same time, the processing progress is marked in the 3D scene, including pending processing, processing, and closed loop. It also includes physical facility linkage feedback: After the management personnel arrive at the site, they scan the on-site sensing nodes with a handheld terminal, and the 3D scene automatically synchronizes the processing data. For example, when repairing the pipeline, the nodes provide real-time feedback on the changes in vibration values, and the 3D scene updates the label color synchronously, changing from red to yellow (in progress), and automatically turning to blue after the fault is resolved, forming a linkage closed loop of abnormal triggering, action execution, and status feedback.

[0024] S3: Dynamic resource scheduling optimization for 3D scenes. Based on the real-time status of the 3D scene, the community management resource configuration is dynamically adjusted, specifically including the following steps: S301: Real-time recording of resource status. All resources of the smart community, such as cleaning tools, maintenance equipment, emergency supplies, and management personnel, are recorded into the 3D scene, with specific locations, availability, and responsible person information marked. When resources are moved, the location information in the 3D scene is updated in real time through the handheld terminal to ensure that resources are visible and controllable. S302: 3D path optimization calculation. When multiple anomalies are triggered simultaneously, the 3D scene automatically calculates the optimal scheduling path based on the location of each anomaly and the distribution of resources. It prioritizes the allocation of resources that are closest and have the strongest adaptability. For example, if a pipeline leak and a street light failure occur at the same time, the 3D scene plans a series route for maintenance personnel to avoid repeated round trips. S303: Dynamic balancing of resource load. The 3D scene statistically analyzes the workload of each resource in real time. If a cleaning staff member's area is frequently experiencing failures, the system will automatically adjust the support of cleaning staff in adjacent areas and update the resource allocation labels in the 3D scene in sync to ensure balanced management of resource load and avoid local shortages of manpower and equipment.

[0025] S4: Iterative optimization of 3D scene management effects. By accumulating and managing data from 3D scenes, threshold settings and resource configurations are optimized in reverse. This includes the following steps: S401: Contextualized management data accumulation, which associates the time, process, resource consumption, and effect of each exception handling with the corresponding location in the 3D scene to form a historical data archive. Historical management records in any area can be queried through 3D scene location. S402: Threshold and strategy adaptive adjustment. Based on historical data, the abnormal threshold is automatically optimized in the 3D scene. For example, if the pipeline network in a certain area frequently fails due to vibration value of 0.25g, the threshold for that area is automatically lowered to 0.2g to provide early warning. At the same time, the resource scheduling strategy is optimized, such as automatically increasing the inspection resources of public areas during peak hours. S403: 3D Scene Model Upgrade: Every quarter, the 3D scene model is iteratively upgraded based on historical management data and changes in the community's physical state. New risk point annotations are added, and resource scheduling algorithms are optimized to ensure that the method adapts to the dynamic needs of community management.

[0026] Example 2: A smart community management system based on a 3D scene includes a dynamic twin modeling module, a scene linkage perception and early warning module, an intelligent resource scheduling module, an iterative optimization module, and a data security and interaction module. The dynamic twin modeling module specifically includes: Micro-sensing node access submodule: responsible for connecting to distributed low-power micro-sensing nodes within the community, realizing real-time data acquisition and transmission through the LoRa protocol, filtering and integrating three core data types: location, environment, and vibration, eliminating redundant information, ensuring efficient data transmission, and also having node status monitoring function, with automatic alarm for abnormal nodes; Scene modeling and calibration submodule: Based on CAD drawings, a basic 3D framework is built, and drone aerial images and perception node data are integrated. The scene dynamic calibration is completed every 2 hours to correct scene deviations caused by changes in physical space. Information such as temporary obstacles and facility position offsets is updated synchronously to ensure that the error between the scene and the physical community is controlled within 5 centimeters. The hidden risk scenario labeling submodule links historical fault data of the community to visualize and label hidden facilities such as underground pipe networks and cable trenches. It presents the risk level through color grading and generates a risk heat map to be embedded in the 3D scene, providing targeted basis for subsequent early warning and dispatch.

[0027] The scene-linked perception and early warning module specifically includes: Multi-dimensional threshold management submodule: Presets and stores various sensing data benchmark thresholds, supports custom adjustment of threshold parameters according to community area and facility type, and has threshold validity verification function. It automatically corrects unreasonable thresholds based on historical data to improve the accuracy of early warning. Anomaly location and instruction generation submodule: Real-time monitoring of data synchronized by the modeling module; triggering an alert when the threshold is exceeded; quickly locating the three-dimensional coordinates of the anomaly location; automatically generating standardized linkage instructions containing processing flow and related facility information; pushing them to the management personnel terminal and marking the processing progress. Physical scene feedback synchronization submodule: By scanning sensing nodes with handheld terminals of management personnel, the system collects on-site processing data in real time, and synchronously updates the status annotations of abnormal areas in the 3D scene, realizing the visual tracking of the processing process and ensuring two-way synchronization between early warning and execution actions.

[0028] The intelligent resource scheduling module specifically includes: Resource Information Management Submodule: Input smart community resource information such as cleaning tools, maintenance equipment, emergency supplies and management personnel, mark real-time location, availability and adaptability, support real-time updates of resource movement trajectory and information traceability, and ensure that the entire life cycle of resources is visible and controllable; Optimal Path Planning Submodule: When multiple anomalies are triggered simultaneously, the optimal scheduling route is calculated and allocated by combining the anomaly location and resource distribution in the 3D scene and using a path algorithm. Resources with strong adaptability and the closest distance are prioritized for allocation, reducing unnecessary round trips and improving processing efficiency. Load balancing adjustment submodule: Real-time statistics of the workload of each resource, analysis of load saturation through data modeling, automatic adjustment of cross-regional resource support schemes, updating of 3D scene resource allocation labels, ensuring balanced management of resource load, and avoiding local shortages of manpower and equipment.

[0029] The iterative optimization module specifically includes: The data accumulation management submodule associates data such as anomaly handling time, resource consumption, and handling effect with the corresponding location in the 3D scene, builds historical data archives, supports query and statistics by region and time dimension, and provides data support for optimization decision-making. The strategy self-optimization submodule automatically adjusts the anomaly threshold and resource scheduling rules based on historical data, optimizes the early warning mechanism for high-frequency fault areas, dynamically increases the configuration of inspection resources during peak periods, and enhances the system's proactive management capabilities. Scene Model Iteration Submodule: Every quarter, based on changes in the physical community and management data, the 3D scene model structure is optimized, new risk point annotations are added, and path planning and load balancing algorithms are upgraded to ensure that the system iterates in sync with the needs of community management. Data Security and Interaction Module: Data Security Protection Submodule: Encrypts collected data and command information, sets hierarchical access control to prevent data leakage and tampering, and also has data backup and recovery functions to ensure stable system operation; Human-Computer Interaction Submodule: Optimizes the 3D scene operation interface, supports drag-and-drop, zoom, precise positioning and other functions, simplifies the operation process for managers, and provides data visualization reports.

[0030] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A smart community management method based on a three-dimensional scene, characterized in that: The following steps are included: S1: Dynamic twin construction of 3D scenes, creating dynamic 3D scenes that are synchronized with the physical community in seconds, rather than static models, to ensure the precise implementation of management actions; S2: Three-dimensional scene-triggered perception linkage, relying on the three-dimensional scene data fluctuations of S1 to trigger management actions, realizing two-way linkage between physical facilities and three-dimensional scenes; S3: Dynamic resource scheduling optimization for 3D scenes, dynamically adjusting the configuration of community management resources based on the real-time status of the 3D scene; S4: Iterative optimization of 3D scene management effect. By accumulating management data in 3D scenes, the threshold setting and resource configuration are optimized in reverse.

2. The smart community management method based on a three-dimensional scene according to claim 1, characterized in that: S1 specifically includes the following steps: S101: Distributed micro-sensing node deployment. Low-power micro-sensing nodes are deployed in key locations of the smart community. The nodes only collect three types of core data: physical spatial location, environmental parameters, and facility operation vibration values. They are directly connected to the 3D scene processing terminal via the LoRa protocol. S102: Real-time calibration and modeling of 3D scene. Based on the CAD drawings of community buildings, a basic 3D framework is built. The community panorama is taken by drone every 2 hours. Combined with micro-sensing node data, the 3D scene is dynamically calibrated, focusing on correcting the growth status of greenery, the positional offset of facilities, and changes in temporary obstacles. S103: Hidden risk point scene annotation. The hidden facilities in the community are visualized and annotated through a 3D scene. Combined with historical failure data, high-risk areas are marked with different colors in the 3D scene.

3. The smart community management method based on a three-dimensional scene according to claim 2, characterized in that: S2 specifically includes the following steps: S201: Abnormal data threshold setting. In the 3D scene processing terminal, a baseline threshold is set for various types of perception data. If the baseline threshold is exceeded, a 3D scene warning is triggered. S202: Precise positioning and action generation of 3D scene. After an anomaly is triggered, the 3D scene automatically locks the anomaly location and generates a visual linkage instruction. The instruction includes the 3D coordinates of the anomaly location, related facility information, and standardized processing procedures. It is simultaneously pushed to the handheld terminal of the corresponding management personnel. At the same time, the processing progress is marked in the 3D scene, including pending, processing, and closed loop.

4. The smart community management method based on a three-dimensional scene according to claim 3, characterized in that: The S2 also includes physical facility linkage feedback: after the management personnel arrive at the site, they scan the site sensing nodes with a handheld terminal, and the three-dimensional scene automatically synchronizes and processes the process data.

5. The smart community management method based on a three-dimensional scene according to claim 1, characterized in that: S3 specifically includes the following steps: S301: Real-time recording of resource status. All resources of the smart community are recorded into the 3D scene, and their specific locations, availability status, and responsible persons are marked. When resources are moved, the location information in the 3D scene is updated in real time through the handheld terminal to ensure that the resources are visible and controllable. S302: 3D path optimization calculation. When multiple anomalies are triggered simultaneously, the 3D scene automatically calculates the optimal scheduling path based on the location of each anomaly and the distribution of resources, and prioritizes the allocation of resources that are closest and have the strongest adaptability. S303: Dynamic resource load balancing, real-time statistics of the workload of each resource in the 3D scene, and synchronous updates of resource allocation labels in the 3D scene.

6. The smart community management method based on a three-dimensional scene according to claim 5, characterized in that: S4 specifically includes the following steps: S401: Contextualized management data accumulation, which associates the data from each anomaly handling with the corresponding location in the 3D scene to form a historical data archive. Historical management records for any area can be queried through 3D scene location. S402: Threshold and strategy adaptive adjustment, based on historical data, automatically optimizes the abnormal threshold in the 3D scene; at the same time, it optimizes the resource scheduling strategy. S403: 3D scene model upgrade. Every quarter, the 3D scene model is iterated and upgraded based on historical management data and changes in the community's physical state. New risk point annotations are added, and resource scheduling algorithms are optimized to ensure that the method adapts to the dynamic needs of community management.

7. A smart community management system based on a three-dimensional scene, employing a smart community management method based on a three-dimensional scene as described in any one of claims 1-6, characterized in that: It includes a dynamic twin modeling module, a scene linkage perception and early warning module, an intelligent resource scheduling module, an iterative optimization module, and a data security and interaction module. The dynamic twin modeling module specifically includes: Micro-sensing node access submodule: responsible for connecting to distributed low-power micro-sensing nodes within the community, realizing real-time data acquisition and transmission through the LoRa protocol, filtering and integrating three core data types: location, environment, and vibration, eliminating redundant information, and ensuring efficient data transmission; Scene modeling and calibration submodule: Based on CAD drawings, a basic 3D framework is built, and drone aerial images and perception node data are integrated to complete a dynamic scene calibration every 2 hours to correct scene deviations caused by changes in physical space; The hidden risk scenario labeling submodule links to historical fault data in the community, visualizes and labels hidden facilities, presents risk levels through color grading, generates risk heat maps that are embedded in the 3D scene, and provides targeted basis for subsequent early warning and dispatch.

8. A smart community management system based on a three-dimensional scene according to claim 7, characterized in that: The scene-linked perception and early warning module specifically includes: Multi-dimensional threshold management submodule: presets and stores various types of sensing data benchmark thresholds, automatically corrects unreasonable thresholds based on historical data, and improves the accuracy of early warnings; Anomaly location and instruction generation submodule: Real-time monitoring of data synchronized by the modeling module; triggering an alert when the threshold is exceeded; quickly locating the three-dimensional coordinates of the anomaly location; automatically generating standardized linkage instructions containing processing flow and related facility information; pushing them to the management personnel terminal and marking the processing progress. Physical scene feedback synchronization submodule: By scanning sensing nodes with handheld terminals of management personnel, real-time data collection and processing are carried out, and the status annotations of abnormal areas in the 3D scene are updated synchronously.

9. A smart community management system based on a three-dimensional scene according to claim 7, characterized in that: The intelligent resource scheduling module specifically includes: Resource Information Management Submodule: Input resource information for smart communities, and mark real-time location, availability status and adaptability; Optimal Path Planning Submodule: When multiple anomalies are triggered simultaneously, the optimal scheduling route is calculated and allocated by combining the anomaly locations and resource distribution in the 3D scene through a path algorithm. Load balancing adjustment submodule: Real-time statistics of the workload of each resource, analysis of load saturation through data modeling, automatic adjustment of cross-regional resource support schemes, and updating of resource allocation annotations in the 3D scene.

10. A smart community management system based on a three-dimensional scene according to claim 7, characterized in that: The iterative optimization module specifically includes: The data accumulation management submodule associates data with corresponding locations in the 3D scene, builds historical data archives, and supports query and statistics by region and time dimension. The strategy self-optimization submodule automatically adjusts the anomaly threshold and resource scheduling rules based on historical data, optimizes the early warning mechanism for high-frequency fault areas, dynamically increases the configuration of inspection resources during peak periods, and enhances the system's proactive management capabilities. Scene Model Iteration Submodule: Every quarter, based on changes in the physical community and management data, optimize the 3D scene model structure, add new risk point annotations, and upgrade path planning and load balancing algorithms; The data security and interaction module's data security protection sub-module encrypts collected data and command information, and sets hierarchical access control. Human-Computer Interaction Submodule: Optimizes the 3D scene operation interface, supports drag-and-drop, zoom, precise positioning and other functions, simplifies the operation process for managers, and provides data visualization reports.

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