A future community digital management system and method based on a real three-dimensional map

Abstract

The application discloses a kind of future community digitization management system and method based on real scene three-dimensional map.The system includes perception layer, data layer, twin layer, intelligent layer, application layer and system bus;Method is, S1, real scene three-dimensional visualization;S2, dynamic monomerization query;S3, space-time situation prediction;S4, cross-modal fusion;S5: real scene three-dimensional visualization, dynamic monomerization query, space-time situation prediction and cross-modal fusion capability based on steps S1-S4 are constructed, provide community governance, public service and emergency command business collaborative service.The application introduces three-dimensional Gaussian sputtering efficient representation, point cloud semantic segmentation based on Transformer, space-time graph neural network prediction and cross-modal attention fusion mechanism, solve the problems of low rendering efficiency, poor monomerization accuracy, weak prediction ability and difficult data fusion in prior art.

Description

Technical Field

[0001] This invention relates to the field of smart city digital community technology, and in particular to a future community digital management system and method based on real-scene 3D maps. Background Technology

[0002] Digitalization is a crucial component of the comprehensive development of future communities. The digital cockpit for future communities, based on a real-world 3D map system, integrates various indicators and information from community management applications, providing vital support for refined and modernized community governance.

[0003] A digital cockpit, also known as a data cockpit or command cockpit, refers to a decision support interface that integrates and displays the real-time operational status, early warning information, and business indicators of a community through a large screen or multi-screen linkage.

[0004] The existing technology has the following main drawbacks:

[0005] 1. Low efficiency of 3D scene representation: Traditional NeRF-based implicit neural representation requires point-by-point ray sampling, resulting in slow rendering speed (10-15 FPS), which cannot meet the real-time interaction requirements of the cockpit; while traditional mesh models are difficult to handle the fine geometry and appearance modeling of complex urban scenes.

[0006] 2. Insufficient accuracy in individual building segmentation: Existing segmentation methods based on regular geometry or traditional CNNs are unable to handle issues such as building facade occlusion and topological complexity in real-world 3D scenes. The accuracy of individual building boundary recognition is low, and it cannot support fine-grained attribute attachment.

[0007] 3. Weak spatiotemporal prediction capabilities in communities: Existing management systems mostly rely on statistical reports for trend analysis, lacking in-depth modeling of spatiotemporal data such as community population flow, energy consumption, and events, and thus unable to provide accurate prediction, early warning, and extrapolation capabilities;

[0008] 4. Lack of multi-source data fusion mechanism: Real-world 3D, IoT sensing, and business system data lack a unified semantic alignment and attention fusion mechanism, resulting in serious data silos and making it difficult to form a unified community cognitive view. Summary of the Invention

[0009] The purpose of this invention is to provide a future community digital management system and method based on real-scene 3D maps. This invention addresses the problems of low rendering efficiency, poor individualization accuracy, weak prediction capabilities, and difficulty in data fusion in existing technologies by introducing efficient 3D Gaussian sputtering representation, Transformer-based point cloud semantic segmentation, spatiotemporal graph neural network prediction, and cross-modal attention fusion mechanisms.

[0010] The technical solution of the present invention:

[0011] A future community digital management system based on a real-scene 3D map includes a perception layer, a data layer, a twin layer, an intelligence layer, an application layer, and a system bus.

[0012] The perception layer includes oblique photography equipment, lidar, IoT sensors, video surveillance equipment, and government data interfaces, used to collect community spatial data, real-time operational data, and business data.

[0013] The data layer includes a spatiotemporal database, a data governance engine, and a multimodal feature extractor. The spatiotemporal database adopts a hierarchical storage structure to store real-world 3D data, IoT time-series data, and thematic business data.

[0014] The twin layer includes a 3D Gaussian sputtering engine, a dynamic unitization engine, and a physical simulation module; the 3D Gaussian sputtering engine uses an explicit 3D Gaussian set to represent the community scene and achieves real-time rendering through differentiable rasterization; the dynamic unitization engine automatically identifies the boundaries of individual buildings and constructs semantic indexes based on a Transformer point cloud segmentation network.

[0015] The intelligent layer includes a spatiotemporal graph neural network prediction engine, a cross-modal attention fusion module, and a decision reasoning engine; the spatiotemporal graph neural network prediction engine is used for spatiotemporal prediction of community pedestrian flow, energy consumption, and events; the cross-modal attention fusion module is used to fuse real-scene 3D, time-series prediction, and business semantic data;

[0016] The application layer includes a visualization module, a community management business module, and a mobile service module. The visualization module uses layered rendering technology to achieve the integrated display of two-dimensional data panels and three-dimensional scenes, including: a real-time rendering submodule, a thematic layer overlay submodule, and a two-dimensional and three-dimensional interactive submodule.

[0017] The system bus adopts a microservice architecture message bus to achieve asynchronous and decoupled communication between different layers.

[0018] In the aforementioned future community digital management system based on real-scene 3D maps, the 3D Gaussian sputtering engine optimizes Gaussian parameters. Minimize rendering loss Furthermore, an adaptive density control strategy is employed to dynamically adjust the number of Gaussians.

[0019] In the aforementioned future community digital management system based on real-scene 3D maps, the dynamic individualization engine adopts a query-based Transformer architecture. It predicts instance categories, 3D bounding boxes, and segmentation masks through a cross-attention mechanism of instance queries and point cloud features, and constructs an R-tree spatial index to support fast queries.

[0020] In the aforementioned future community digital management system based on real-scene 3D maps, the spatiotemporal graph neural network prediction engine constructs a community spatiotemporal map. It captures spatial dependencies through a graph attention mechanism and temporal dependencies through a gated recurrent unit, thereby enabling multi-task situation prediction.

[0021] In the aforementioned future community digital management system based on real-scene 3D maps, the cross-modal attention fusion module uses visual modality as Query, temporal prediction modality and business semantic modality as Key and Value, calculates fusion features through scaling dot product attention, and realizes two-dimensional and three-dimensional linkage interaction based on attention weights.

[0022] A digital management method for future communities based on real-world 3D maps, the process of which is as follows:

[0023] S1. Real-world 3D visualization: Construct a real-world 3D representation of the community based on 3D Gaussian sputtering, and optimize the Gaussian distribution through adaptive density control;

[0024] S2. Dynamic Individual Query: Based on the Transformer point cloud segmentation network, dynamic individual recognition is performed on the real-world 3D scene to construct a semantic index;

[0025] S3. Spatiotemporal Situation Prediction: Spatiotemporal prediction of community multi-source data based on spatiotemporal graph neural network;

[0026] S4. Cross-modal fusion: Based on the cross-modal attention mechanism, it fuses real-world 3D, temporal prediction, and business semantic data to achieve layered visualization;

[0027] S5: Based on the real-scene 3D visualization, dynamic individual query, spatiotemporal situation prediction and cross-modal fusion capabilities built in steps S1-S4, it provides collaborative services for community governance, public services and emergency command.

[0028] In the aforementioned digital management method for future communities based on real-scene 3D maps, step S1 uses an explicit 3D Gaussian set to represent the community scene. ,

[0029] in, The center of Gauss is located at... Let covariance matrix be the variance matrix. For opacity, These are the spherical harmonic coefficients (used for view-dependent colors).

[0030] In the aforementioned digital management method for future communities based on real-world 3D maps, the covariance matrix is ​​scaled by the scaling matrix. and rotation matrix Parameterization:

[0031] ;

[0032] To avoid numerical instability caused by directly optimizing the covariance matrix, quaternions are used. Represents rotation, 3D vector Indicates scaling:

[0033] ;

[0034] Differentiable rasterization rendering: Projecting a 3D Gaussian image onto a 2D image plane, and synthesizing pixel colors through alpha blending; for pixels... Its color is obtained by mixing all Gaussians within the view frust, sorted by depth. ;

[0035] in, ,

[0036] , These are the two-dimensional mean and covariance after projection.

[0037] In the aforementioned digital management method for future communities based on real-scene 3D maps, the adaptive density control in step S1 includes: cloning or splitting Gaussians with gradient norms greater than a threshold, and deleting Gaussians with opacity lower than a threshold, wherein the Gaussian covariance matrix is ​​parameterized using quaternions and scaling vectors; specifically...

[0038] Using an alternating optimization strategy, each Density control is performed in the next iteration:

[0039] Cloning: Gradient Norm Furthermore, smaller-scale Gaussian clones are used;

[0040] Splitting: on the gradient norm Furthermore, the larger-scale Gaussian splits into two, with the new scale being... ;

[0041] Pruning: Remove opacity Gauss.

[0042] loss function combination Photometric loss and SSIM structural similarity loss,

[0043] ,

[0044] in, .

[0045] In the aforementioned digital management method for future communities based on real-world 3D maps, the Transformer point cloud segmentation network in step S2 includes a local Transformer encoder, a global Transformer interaction module, and a query-based instance segmentation decoder, and employs a multi-head attention mechanism to calculate the relationships between points.

[0046] In the aforementioned digital management method for future communities based on real-scene 3D maps, the dynamic individual query described in step S2 specifically refers to:

[0047] Convert the 3D Gaussian point cloud generated by S1 into a point cloud representation. ,in Let the coordinates be the points. Features aggregated from Gaussian properties.

[0048] The aforementioned method for digital management of future communities based on real-world 3D maps employs a query-based Transformer architecture for semantic segmentation and instance recognition.

[0049] 1. Local-Global Feature Encoding:

[0050] 1.1 Local Transformer: Performs farthest point sampling (FPS) on the point cloud to obtain the center point set. For each center point neighborhood Self-attention encoding is performed on the points within.

[0051] ;

[0052] Where Q, K, and V are the query, key, and value matrices, To encode the relative position matrix, geometric relationships between points are introduced.

[0053] ,

[0054] For multilayer perceptron (MLP);

[0055] 1.2 Global Transformer: Achieves global feature interaction through the Induced Set Attention (ISAB) mechanism.

[0056] ,

[0057] in, This is a characteristic of voxel aggregation. In voxel coordinates, These are learnable parameters; long-range spatial dependencies are captured through iterative ISAB.

[0058] ;

[0059] 2. Dynamically monolithic head network:

[0060] A query-based instance segmentation framework is used for initialization. Query for learnable examples It interacts with point cloud features through the Transformer decoder.

[0061] ,

[0062] Each query predicts the instance category c, bounding box parameters. and mask coefficients m; the mask is generated by the dot product of point features and mask coefficients.

[0063] ;

[0064] 3. Loss function:

[0065] ;

[0066] in, Focal Loss is used for classification. The regression loss for the 3D bounding box is GIoU. For mask BCE loss, For Dice coefficient loss;

[0067] 4. Construction of single-item semantic indexes:

[0068] For the identified individual buildings, construct an R-tree spatial index:

[0069] Each leaf node entry: ;

[0070] , The diagonal point of the bounding box of a single entity in three-dimensional space;

[0071] A unique identifier for a single entity, associated with an attribute database;

[0072] During the query, coordinates are determined by ray detection, R-tree range query returns a candidate set, and then the target single entity is determined by precise geometric inclusion judgment.

[0073] In the aforementioned digital management method for future communities based on real-scene 3D maps, the spatiotemporal graph neural network in step S3 uses alternating stacks of spatial graph attention layers and time-gated recurrent units to learn the attention weights between nodes through a multi-head mechanism.

[0074] In the aforementioned digital management method for future communities based on real-scene 3D maps, step S3 specifically includes:

[0075] 1. Construct a community spatiotemporal map :

[0076] node This includes spatial entities such as buildings, roads, public facilities, and grid units.

[0077] side : Constructed based on spatial adjacency (distance < threshold) and functional association (people flow, goods flow);

[0078] feature Multimodal feature vectors, including:

[0079] Structural features: building attributes, POI category;

[0080] Dynamic characteristics: IoT sensing timing (energy consumption, pedestrian flow, environment);

[0081] Semantic features: Deep learning embeddings extracted in the S2 stage;

[0082] 2. Prediction is performed using a Spatiotemporal Graph Convolutional Network (ST-GCN):

[0083] 2.1 Spatial Convolution (Graph Attention Mechanism):

[0084] ,

[0085] Attention coefficient is calculated using a multi-head mechanism:

[0086] ,

[0087] 2.2 Temporal Convolution (Gated Recurrent Unit):

[0088] ,

[0089] ,

[0090] ,

[0091] ;

[0092] 3. Multi-task prediction head:

[0093] Crowd flow prediction: Predicting the crowd density at each node at time T based on historical crowd flow map sequences;

[0094] Energy consumption prediction: Predict building-level energy consumption curves and identify abnormal patterns;

[0095] Incident Risk: Predicting the probability distribution of community security incidents.

[0096] In the aforementioned digital management method for future communities based on real-scene 3D maps, the cross-modal attention fusion in step S4 uses the visual modality as the Query, the temporal prediction modality and the business semantic modality as the Key and Value, calculates the fusion features through scaling dot product attention, and realizes two-dimensional and three-dimensional linkage interaction based on attention weights.

[0097] In the aforementioned digital management method for future communities based on real-scene 3D maps, step S4 specifically includes:

[0098] 1. Establish a unified representation space for real-world 3D data, IoT data, and business data; encode each modal data:

[0099] Visual modality (3D scene): (CNN feature map);

[0100] Temporal modalities (IoT data): (GRU encoding);

[0101] Semantic modality (business data): (Embedding vector);

[0102] 2. Cross-modal attention fusion:

[0103]

[0104] Specifically, the visual modality serves as the query, while the temporal and semantic modalities serve as the key and value:

[0105] ,

[0106] ;

[0107] 3. Layered visual rendering:

[0108] L1 layer (base map): Real-time rendering of a 3D scene using 3D Gaussian sputtering;

[0109] L2 layer (thematic): Vector thematic layer (grid, building attributes) based on the S2 individualization results;

[0110] L3 layer (dynamic): spatiotemporal thermodynamic body of S3 prediction results, IoT real-time location (particle system).

[0111] L4 layer (fusion): Visualization of cross-modal attention weights (overlay of saliency heatmaps);

[0112] 4. Two-dimensional and three-dimensional linkage mechanism:

[0113] Establish a coordinate mapping from screen space to world space:

[0114] ;

[0115] When a user selects an indicator in the 2D panel, the attention weight of that indicator relative to the spatial entity is calculated:

[0116] ,

[0117] Highlight the top-N entities by weight and trigger camera flight positioning.

[0118] In the aforementioned digital management method for future communities based on real-scene 3D maps, step S5 specifically includes:

[0119] Based on the S1-S4 framework, it provides capabilities such as real-scene 3D visualization, dynamic individual query, spatiotemporal situation prediction, and cross-modal fusion, offering:

[0120] Digital Cockpit: A comprehensive visualization interface integrating real-time rendering, situation prediction, and business metrics;

[0121] Event handling: Directly mark the event location in the 3D scene and automatically associate it with surrounding resources (personnel, equipment);

[0122] Scheme simulation: Based on physical simulation and AI prediction, the effects of the transformation scheme are simulated in a three-dimensional environment.

[0123] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0124] 1. Significantly improved rendering efficiency: Compared with the NeRF method, 3D Gaussian sputtering can achieve real-time rendering of 60+ FPS at 1080p resolution, and the training time can be shortened from 48 hours to a few hours, meeting the real-time interaction needs of the cockpit.

[0125] 2. Breakthrough in individual building accuracy: Based on the Transformer-based point cloud segmentation network, the mAP of building recognition is improved by 15-20% in complex urban scenarios, and it supports automated fine-grained attribute attachment.

[0126] 3. Enhanced predictive ability: The spatiotemporal graph neural network effectively models the complex spatiotemporal dependencies of communities, improving the accuracy of pedestrian flow prediction and achieving an F1-score of 0.89 for energy consumption anomaly detection;

[0127] 4. Cross-modal deep fusion: The attention mechanism enables the alignment and fusion of 3D vision, temporal sensing, and business semantics to form a unified community cognitive view, supporting "one-map overview" decision-making. Attached Figure Description

[0128] Figure 1 This is a schematic diagram of the system framework of the present invention;

[0129] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0130] The present invention will be further described below with reference to the accompanying drawings and embodiments, but this should not be construed as limiting the present invention.

[0131] Example. A future community digital management system based on a real-scene 3D map, such as... Figure 1 As shown, it includes a perception layer, a data layer, a twin layer, an intelligence layer, an application layer, and a system bus;

[0132] The perception layer includes oblique photography equipment, lidar, IoT sensors, video surveillance equipment, and government data interfaces, used to collect community spatial data, real-time operational data, and business data.

[0133] The data layer includes a spatiotemporal database, a data governance engine, and a multimodal feature extractor. The spatiotemporal database adopts a hierarchical storage structure to store real-world 3D data, IoT time-series data, and thematic business data.

[0134] The twin layer includes a 3D Gaussian sputtering engine, a dynamic unitization engine, and a physical simulation module; the 3D Gaussian sputtering engine uses an explicit 3D Gaussian set to represent the community scene and achieves real-time rendering through differentiable rasterization; the dynamic unitization engine automatically identifies the boundaries of individual buildings and constructs semantic indexes based on a Transformer point cloud segmentation network.

[0135] The intelligent layer includes a spatiotemporal graph neural network prediction engine, a cross-modal attention fusion module, and a decision reasoning engine; the spatiotemporal graph neural network prediction engine is used for spatiotemporal prediction of community pedestrian flow, energy consumption, and events; the cross-modal attention fusion module is used to fuse real-scene 3D, time-series prediction, and business semantic data;

[0136] The application layer includes a visualization module, a community management business module, and a mobile service module. The visualization module uses layered rendering technology to achieve the integrated display of two-dimensional data panels and three-dimensional scenes, including: a real-time rendering submodule, a thematic layer overlay submodule, and a two-dimensional and three-dimensional interactive submodule.

[0137] The system bus adopts a microservice architecture message bus to achieve asynchronous and decoupled communication between different layers.

[0138] A digital management method for future communities based on real-scene 3D maps, such as Figure 2 As shown, the process is as follows:

[0139] S1. Real-world 3D visualization: Construct a real-world 3D representation of the community based on 3D Gaussian sputtering, and optimize the Gaussian distribution through adaptive density control;

[0140] S2. Dynamic Individual Query: Based on the Transformer point cloud segmentation network, dynamic individual recognition is performed on the real-world 3D scene to construct a semantic index;

[0141] S3. Spatiotemporal Situation Prediction: Spatiotemporal prediction of community multi-source data based on spatiotemporal graph neural network;

[0142] S4. Cross-modal fusion: Based on the cross-modal attention mechanism, it fuses real-world 3D, temporal prediction, and business semantic data to achieve layered visualization;

[0143] S5: Based on the real-scene 3D visualization, dynamic individual query, spatiotemporal situation prediction and cross-modal fusion capabilities built in steps S1-S4, it provides collaborative services for community governance, public services and emergency command.

[0144] Specific verification example:

[0145] Example 1: Real-world 3D representation based on 3D Gaussian sputtering

[0146] Five thousand oblique photographic images of a future community (planned area of ​​1.2 square kilometers) were collected at a resolution of 6000×4000. Structure-of-motion (SfM) reconstruction was performed using COLMAP to obtain an initial sparse point cloud. (about 100,000 points).

[0147] 1. Initialize the 3D Gaussian set:

[0148] Each SfM point corresponds to a three-dimensional Gaussian, and its position... ,

[0149] color Initialize using the zeroth-order term of the spherical harmonic coefficients.

[0150] covariance Based on the local density estimation of the point cloud, the initial density is an isotropic Gaussian;

[0151] 2. Optimize parameter settings:

[0152] Optimizer: Adam, learning rate 10 -4 ;

[0153] Loss function: ;

[0154] Density control cycle: Executed once every 100 iterations

[0155] Gradient threshold: ;

[0156] Opacity pruning threshold ;

[0157] 3. Adaptive density control operation:

[0158] Cloning: Gradient Norm And maximum scaling value A Gaussian is copied along the gradient direction;

[0159] Splitting: on the gradient norm and The Gaussian splits into two along the direction of maximum variance, with the new scale being... ;

[0160] Pruning: Delete Transparent Gaussian;

[0161] 4. Implementation Results:

[0162] Training iterations: 7000;

[0163] Final number of Gaussians: approximately 500,000;

[0164] Rendering performance: RTX 4090 graphics card, 1080p resolution, 120 FPS;

[0165] Video memory usage: 4GB;

[0166] Compared to the NeRF method, the training time is reduced from 48 hours to 3 hours, and the rendering speed is increased by 10 times.

[0167] Example 2: Dynamic Single-Unit Recognition Based on Transformer

[0168] The 3D Gaussian point cloud generated in Example 1 is downsampled to 1 million points and used as input to construct a Transformer point cloud segmentation network.

[0169] Network architecture configuration:

[0170] Local Transformer encoder: 4 layers, 4 attention heads per layer, hidden dimension 256, feedforward dimension 1024;

[0171] FPS (Farthest Point Sampling): Sampling rate 0.1, obtaining 100,000 center points;

[0172] Neighborhood query: Each center point queries points within a radius of 0.5m, up to a maximum of 32 points;

[0173] Global Transformer interaction: 3 layers of ISAB (induced attention blocks), 256 induced points;

[0174] Voxel size: 0.5m × 0.5m × 0.5m;

[0175] Instance segmentation decoder:

[0176] Number of learnable instance queries: (The maximum number of building instances in the corresponding community);

[0177] Transformer decoder layers: 6;

[0178] Prediction head: Shared MLP (256→128→C+7+1), outputting class probabilities, 3D bounding box parameters (center point, size, rotation angle) and mask coefficients respectively;

[0179] Training configuration:

[0180] Training data: 5000 labeled community building instances, including residential, commercial, and public facilities;

[0181] Optimizer: AdamW, initial learning rate 10 -4 The weight decays by 0.05.

[0182] Learning rate scheduling: cosine annealing, 200 epochs;

[0183] Loss weights: , , , ;

[0184] R-tree index construction:

[0185] For each identified building unit, calculate its axial bounding box (AABB).

[0186] Leaf node entries: ;

[0187] Node capacity: 16;

[0188] Tree height: 4 layers;

[0189] Implementation results:

[0190] Semantic segmentation mIoU: 82.3%;

[0191] Instance segmentation AP@0.5: 78.6% ;

[0192] Single-frame inference time: 45ms (RTX 4090);

[0193] R-tree query response time: average 12ms (candidate set < 5).

[0194] Example 3: Community Situation Prediction Based on Spatiotemporal Graph Neural Network

[0195] Constructing a community spatiotemporal map .

[0196] Graph structure construction:

[0197] Node set There are 800 buildings, 1200 road sections, and 200 grid units, totaling 2200 nodes.

[0198] Edge construction rules:

[0199] Spatial adjacency edges: If the Euclidean distance between nodes is less than 50m, an edge is established, totaling approximately 15,000 edges;

[0200] Functional association edges: Based on the pedestrian flow OD matrix, an edge is established if the flow rate is >10 people / hour, with a total of approximately 3200 edges;

[0201] Node features :

[0202] Structural characteristics (static): building age, type code, area, number of floors (4-dimensional);

[0203] Dynamic characteristics (time series): Energy consumption, population flow, temperature, and humidity over the past 24 hours (24×4=96 dimensions);

[0204] Semantic features: Instance embeddings extracted from Instance 2 (256 dimensions);

[0205] Network configuration:

[0206] Spatial graph attention layer: 2 layers, 8-head attention, hidden dimension 128, output dimension 64;

[0207] Temporal GRU layer: 2 layers, hidden dimension 128, input dimension 64 (spatial layer output).

[0208] Prediction head:

[0209] Crowd forecasting: MLP (128→64→24), predicts hourly crowd flow over the next 24 hours;

[0210] Energy consumption forecast: MLP (128→64→24), predicts hourly energy consumption for the next 24 hours;

[0211] Event Risk: MLP (128→32→3), predicting safety, fire, and public health risks; probability.

[0212] Training configuration:

[0213] Training data: Historical data from the past 6 months, samples are constructed using a sliding window;

[0214] Optimizer: Adam, learning rate 10 -3 ;

[0215] Loss functions: MSE is used for pedestrian / energy consumption prediction, and BCE is used for event risk;

[0216] Batch size: 32 graph sequences;

[0217] Implementation results:

[0218] RMSE for predicted pedestrian flow: 8.2 people / hour (a 28% reduction compared to the LSTM baseline of 11.3);

[0219] Predicted energy consumption MAPE: 6.5% (34% lower than the ARIMA baseline of 9.8%).

[0220] Event risk prediction AUC-ROC: 0.87;

[0221] Single-step inference time: 18ms.

[0222] Example 4: Cross-modal attention fusion and visualization

[0223] Establish a unified representation and fusion of three modal data: real-scene 3D, time-series prediction, and business semantics.

[0224] Modal coding:

[0225] Visual modality Example 1: Feature map of 3D Gaussian rendering, extracted using ResNet-50, size... ;

[0226] Time series prediction mode Example 3: The predicted output of the spatiotemporal graph neural network, encoded by GRU, with dimensions... ;

[0227] Business semantic modality Community population, housing, events, and other business data are encoded through the Embedding layer, with varying sizes. ;

[0228] Cross-modal attention fusion:

[0229] by For Query, and Concatenate them into a Key and Value;

[0230] Multi-head attention: 8 heads, dimensions ;

[0231] Calculation formula:

[0232] ,

[0233] ;

[0234] Layered visual rendering:

[0235] L1 layer (base map): Example 1 3D Gaussian sputtering real-time rendering, L3 level simplified model (face count reduced by 80%) is loaded when the view distance is >800m, L2 level standard model is loaded when the view distance is 200-800m, and L1 level fine model is loaded when the view distance is <200m.

[0236] L2 layer (special topic): Instance 2 single-unit result, with overlay of community grid boundary (semi-transparent blue) and building attribute labels (white text);

[0237] L3 layer (dynamic): The prediction results of Instance 3 are displayed as a spatiotemporal thermal body to show the crowd density (highlighted in red), and the IoT points are rendered as a particle system (green normal / red alarm flashing).

[0238] L4 layer (fusion): Visualization of cross-modal attention weights The saliency mapping is a superposition of semi-transparent heatmaps;

[0239] 2D and 3D interactive linkage:

[0240] Transformation from screen coordinates to world coordinates:

[0241] ,

[0242] When an indicator is selected in the 2D panel, the attention weight is calculated. Highlight the Top-5 entities and locate them in flight;

[0243] When an entity is selected in a 3D scene, the associated business data is retrieved in reverse and dynamically loaded into the 2D panel.

[0244] Implementation results:

[0245] Feature dimensionality: 256.

[0246] 2D / 3D linkage delay: <100ms;

[0247] Number of entities rendered simultaneously: >2000;

[0248] Stable frame rate: 60 FPS.

[0249] Example 5: Collaboration of Community Management Business

[0250] Based on the technical capabilities built in Examples 1-4, community management business functions are provided.

[0251] Digital cockpit interface:

[0252] Main screen: Example 4 uses layered visualization rendering to display the real-time comprehensive situation of the community;

[0253] Side screen: Chart of prediction results from Example 3, showing the trend for the next 24 hours;

[0254] Bottom bar: Key indicator dashboard (population density, total energy consumption, number of events);

[0255] Incident handling procedures:

[0256] When a user clicks on a building in a 3D scene, Example 2: R-tree query returns the building ID (12ms).

[0257] The system automatically associates with resources within a 100m radius: 3 cameras, 2 security personnel, and 1 fire hydrant;

[0258] A pop-up response panel supports one-click calling, video access, and contingency plan activation;

[0259] Solution simulation function:

[0260] Draw the renovation plan (such as adding roads) in the 3D scene;

[0261] The spatiotemporal graph neural network of Example 3 was used to simulate the redistribution of pedestrian flow after the modification.

[0262] Heat maps are used to compare and show the predicted differences before and after the modification;

[0263] Implementation results:

[0264] Average incident response time: reduced from 15 minutes to 3 minutes;

[0265] Simulation time: Real-time (<500ms);

[0266] Concurrent users: >100.

Claims

1. A future community digital management system based on a real-scene 3D map, characterized in that: It includes the perception layer, data layer, twin layer, intelligence layer, application layer, and system bus; The perception layer includes oblique photography equipment, lidar, IoT sensors, video surveillance equipment, and government data interfaces, used to collect community spatial data, real-time operational data, and business data. The data layer includes a spatiotemporal database, a data governance engine, and a multimodal feature extractor. The spatiotemporal database adopts a hierarchical storage structure to store real-world 3D data, IoT time-series data, and thematic business data. The twin layer includes a 3D Gaussian sputtering engine, a dynamic unitization engine, and a physical simulation module; the 3D Gaussian sputtering engine uses an explicit 3D Gaussian set to represent the community scene and achieves real-time rendering through differentiable rasterization; the dynamic unitization engine automatically identifies the boundaries of individual buildings and constructs semantic indexes based on a Transformer point cloud segmentation network. The intelligent layer includes a spatiotemporal graph neural network prediction engine, a cross-modal attention fusion module, and a decision reasoning engine; the spatiotemporal graph neural network prediction engine is used for spatiotemporal prediction of community population flow, energy consumption, and events; The cross-modal attention fusion module is used to fuse real-scene 3D, temporal prediction, and business semantic data; The application layer includes a visualization module, a community management business module, and a mobile service module; The visualization module is based on layered rendering technology to achieve the integrated display of two-dimensional data panels and three-dimensional scenes, including: real-time rendering sub-module, thematic layer overlay sub-module, and two-dimensional and three-dimensional interactive sub-module; The system bus adopts a microservice architecture message bus to achieve asynchronous and decoupled communication between different layers.

2. The future community digital management system based on a real-scene 3D map according to claim 1, characterized in that: The three-dimensional Gaussian sputtering engine optimizes Gaussian parameters. Minimize rendering loss Furthermore, an adaptive density control strategy is employed to dynamically adjust the number of Gaussians.

3. The future community digital management system based on a real-scene 3D map according to claim 1, characterized in that: The dynamic monolithization engine adopts a query-based Transformer architecture, predicts instance categories, 3D bounding boxes, and segmentation masks through a cross-attention mechanism of instance queries and point cloud features, and constructs an R-tree spatial index to support fast queries.

4. The future community digital management system based on a real-scene 3D map according to claim 1, characterized in that: The spatiotemporal graph neural network prediction engine constructs a community spatiotemporal graph. It captures spatial dependencies through a graph attention mechanism and temporal dependencies through a gated recurrent unit, thereby enabling multi-task situation prediction.

5. The future community digital management system based on a real-scene 3D map according to claim 1, characterized in that: The cross-modal attention fusion module uses the visual modality as the query, the temporal prediction modality and the business semantic modality as the key and value, respectively. It calculates fusion features by scaling dot product attention and realizes two-dimensional and three-dimensional linkage interaction based on attention weights.

6. The future community digital management method of the future community digital management system according to any one of claims 1-5, characterized in that, The process is as follows: S1. Real-world 3D visualization: Construct a real-world 3D representation of the community based on 3D Gaussian sputtering, and optimize the Gaussian distribution through adaptive density control; S2. Dynamic Individual Query: Based on the Transformer point cloud segmentation network, dynamic individual recognition is performed on the real-world 3D scene to construct a semantic index; S3. Spatiotemporal Situation Prediction: Spatiotemporal prediction of community multi-source data based on spatiotemporal graph neural network; S4. Cross-modal fusion: Based on the cross-modal attention mechanism, it fuses real-world 3D, temporal prediction, and business semantic data to achieve layered visualization; S5: Based on the real-scene 3D visualization, dynamic individual query, spatiotemporal situation prediction and cross-modal fusion capabilities built in steps S1-S4, it provides collaborative services for community governance, public services and emergency command.

7. A method for digital management of future communities based on real-scene 3D maps according to claim 6, characterized in that, The adaptive density control in step S1 includes: cloning or splitting Gaussians with gradient norms greater than a threshold, and deleting Gaussians with opacity less than a threshold, wherein the Gaussian covariance matrix is ​​parameterized by quaternions and scaling vectors.

8. A method for digital management of future communities based on real-scene 3D maps according to claim 6, characterized in that: The Transformer point cloud segmentation network described in step S2 includes a local Transformer encoder, a global Transformer interaction module, and a query-based instance segmentation decoder, and employs a multi-head attention mechanism to calculate the relationships between points.

9. A method for digital management of future communities based on real-scene 3D maps according to claim 6, characterized in that: The spatiotemporal graph neural network described in step S3 employs an alternating stacking of spatial graph attention layers and temporally gated recurrent units, and learns the attention weights between nodes through a multi-head mechanism.

10. A method for digital management of future communities based on real-scene 3D maps according to claim 6, characterized in that: The cross-modal attention fusion described in step S4 uses the visual modality as the Query, the temporal prediction modality and the business semantic modality as the Key and Value, respectively. It calculates the fusion features by scaling dot product attention and realizes two-dimensional and three-dimensional linkage interaction based on attention weights.

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