Smart city data intelligent fusion system

Abstract

The application discloses a smart city data intelligent fusion system. A smart city data intelligent fusion system comprises a map information acquisition device configured to acquire digital elevation model data of a target area and construct a three-dimensional scene base model based on the digital elevation model data; the system unifies the formats of multi-source heterogeneous city management information through an information standardization processing device, and accurately renders the standardized traffic, population, economic, energy and other information to the corresponding preset data layers by using a multi-dimensional scene rendering device, and finally superimposes these layers on the unified three-dimensional scene base model, effectively breaking the "data island" barriers between different departments and realizing the deep integration and spatial coupling of cross-field data.

Description

Technical Field

[0001] This application relates to the field of urban management technology, and more specifically, to a smart city data intelligence fusion system. Background Technology

[0002] In the construction of smart cities, massive amounts of multi-source and heterogeneous data (such as traffic flow videos, energy consumption records, and air quality monitoring values) are continuously generated in various fields such as transportation, energy, environmental protection, population, and economy. However, these data are scattered across different departments and systems, and there are problems such as different formats, inconsistent interface standards, and different update frequencies, forming serious "data silos".

[0003] This "island" state makes it difficult to effectively coordinate and integrate cross-domain data. For example, the transportation department cannot easily use real-time air quality data from the environmental protection department to analyze its impact on traffic patterns, and data from different departments cannot be correlated for analysis. This makes it difficult for city managers to obtain a comprehensive, multi-dimensional panoramic view of city operations, resulting in biased and inefficient decision-making, and hindering the overall effectiveness of smart city management. Summary of the Invention

[0004] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0005] As a first aspect of this application, in order to solve the technical problems mentioned in the background section above, some embodiments of this application provide a smart city data intelligent fusion system, including:

[0006] A map information acquisition device is used to acquire digital elevation model data of a target area and to construct a basic three-dimensional scene model based on the digital elevation model data.

[0007] The management information collection device is used to collect multi-source urban management information, including traffic information representing traffic and pedestrian flow, population information representing the distribution of permanent residents, economic information representing economic activities, and energy information representing energy use.

[0008] An information standardization processing device is connected to the management information collection device and is used to perform standardization processing on the collected urban management information to generate standardized information with a unified format.

[0009] A multi-dimensional scene rendering device is connected to the map information acquisition device and the information standardization processing device. The multi-dimensional scene rendering device has multiple data layers corresponding to the types of urban management information. It is used to render the standardized information to the corresponding data layers and overlay the rendered layers on the three-dimensional scene base model to generate a three-dimensional scene model that integrates multi-dimensional data.

[0010] The model interaction calling device is connected to the multi-dimensional scene rendering device and is used to respond to the request information input by the administrator, and to retrieve and generate corresponding visualization model information from the three-dimensional scene model that integrates multi-dimensional data based on the request information.

[0011] This application unifies the format of multi-source, heterogeneous urban management information through an information standardization processing device, and accurately renders standardized information such as traffic, population, economy, and energy onto corresponding preset data layers using a multi-dimensional scene rendering device. These layers are then superimposed on a unified 3D scene base model, effectively breaking down the "data silos" barriers between different departments and achieving deep integration and spatial coupling of cross-domain data. The resulting 3D scene model, incorporating multi-dimensional data, provides city managers with an intuitive, unified, and spatiotemporally correlated panoramic view of urban operations. This allows for convenient correlation and analysis of previously fragmented data (such as traffic patterns and air quality), significantly improving the comprehensiveness of decision-making and cross-domain collaborative analysis capabilities. This overcomes the problems of fragmented and inefficient decision-making caused by scattered and inconsistent data formats in the previous technologies, enhancing the overall effectiveness of smart city management.

[0012] Furthermore, the map information collection device includes:

[0013] Map information collection devices include:

[0014] The data acquisition module is used to acquire raw digital elevation model data of the target area from external data sources;

[0015] The coordinate transformation module is used to perform spatial reference system unification processing on the raw DME data acquired by the data acquisition module and transform it to a preset geographic coordinate system.

[0016] The 3D modeling module is used to construct a basic 3D scene model describing the continuous undulation of the target area's surface based on DME data processed by the coordinate transformation module and placed in a unified coordinate system.

[0017] Furthermore, constructing the basic model of the 3D scene includes the following steps:

[0018] Receives a two-dimensional regular grid array in a unified geographic coordinate system after processing by the coordinate transformation module;

[0019] For each grid point p in the input two-dimensional regular grid array, calculate its planar coordinates (x, y), read the elevation value z of grid point p, and generate the vertex coordinates (x, y, z) of grid point p;

[0020] Connect all grid points p to form a continuous network of triangular facets covering the entire region:

[0021] For each triangular facet network, calculate the normal vector of the plane containing the triangle based on the three-dimensional coordinates of its three vertices;

[0022] Construct a vertex array, an index array, and a normal array, where:

[0023] The vertex array stores the three-dimensional coordinates of all unique vertices;

[0024] The index array stores the indices of the three vertices of each triangular mesh network;

[0025] The normal array stores the normal vector for each vertex or face.

[0026] Construct a basic 3D scene model based on the vertex array, index array, and normal array.

[0027] Furthermore, the management information collection device includes:

[0028] The multi-source interface adaptation module is configured with multiple standardized data interfaces. Based on the type of the target data source and access requirements, the corresponding interface is called to access the raw data of various urban management information.

[0029] The protocol parsing and conversion module performs protocol parsing and preliminary format conversion on the incoming raw data to generate intermediate data;

[0030] Data extraction and encapsulation module: Extracts core information fields related to urban management from the intermediate data parsed by the protocol as needed, and encapsulates them into structured urban management information.

[0031] Furthermore, the information standardization processing device includes:

[0032] The data cleaning and verification module performs missing value processing, outlier detection and correction, and format consistency verification on the input urban management information.

[0033] The spatiotemporal benchmark unification module standardizes the spatial location and aligns the temporal benchmark of urban management information;

[0034] The numerical normalization and grading module converts urban management information from different sources and with different dimensions into standardized values ​​that are comparable and can be directly mapped to visual attributes.

[0035] The visual encoding mapping rule generation module defines standardized numerical and spatial location information and transforms it into specific visualization instructions to generate standardized information.

[0036] Furthermore, the geographic coordinates in the urban management information are obtained and converted into coordinates of the three-dimensional scene base model;

[0037] The timestamps of municipal management information are unified to the same time base, and time granularity is aggregated.

[0038] Furthermore, the multi-dimensional scene rendering device includes:

[0039] The layer management module draws the received standardized information onto the corresponding preset layer according to its information type.

[0040] The rendering module spatially overlays and merges the basic 3D scene model with all rendered preset data layers to generate a final, unified 3D scene model that integrates multi-dimensional data.

[0041] Furthermore, the model interaction invocation device includes:

[0042] The interactive interface module is used to input the administrator's required information;

[0043] The model parsing and data extraction module receives requirement information and performs real-time parsing and intelligent data extraction on the 3D scene model that integrates multi-dimensional data to generate a model subset.

[0044] The dynamic view generation module generates highly focused and easy-to-understand visual model information based on the extracted model subset.

[0045] Furthermore, the model parsing and data extraction module includes:

[0046] Demand information receiving unit, used to receive demand information;

[0047] The demand information processing unit is used to generate a subset of models;

[0048] The model subset encryption unit encrypts the geographic coordinates of the model subset based on the administrator's identity information to generate an encrypted model subset.

[0049] The beneficial effects of this application are as follows:

[0050] (1) Effectively break down “data silos” and achieve deep integration of multi-source heterogeneous data: Through information standardization processing devices, original urban management information from multiple fields, formats and interfaces such as transportation, population, economy and energy is processed in a unified manner to generate standardized information with consistent formats, which fundamentally solves the problem of different data formats and different interface standards, and lays the foundation for data integration.

[0051] (2) The multi-dimensional scene rendering device has multiple preset data layers, which provide independent carrying space and a unified rendering framework for data from different fields (such as traffic layer, population layer, economic layer, and energy layer). The standardized information is accurately rendered to the corresponding layer and superimposed on the unified three-dimensional scene base model, realizing the seamless integration and visualization fusion of cross-departmental, cross-domain, and heterogeneous data under the same spatiotemporal benchmark, and completely breaking the original "data silo" status.

[0052] (3) The system generates a three-dimensional scene model that integrates multi-dimensional data, presenting geospatial information (topography, buildings) and dynamic urban management information (people flow, vehicle flow, economic activities, energy consumption) in a clear and three-dimensional space. Managers can simultaneously observe and analyze multi-dimensional information such as traffic congestion, surrounding population density distribution, regional economic activity, and energy consumption intensity, as well as their interrelationships, on a unified, geospatially accurate platform, obtaining an unprecedented panoramic view of urban operations.

[0053] (4) Data is integrated and overlaid in a unified three-dimensional scene, making the potential correlations between previously scattered data readily apparent (e.g., the spatiotemporal correlation between traffic flow changes and surrounding commercial activities, peak energy consumption, or population density in a specific area). Managers can easily discover cross-domain influencing factors and linkage effects (e.g., analyzing the impact of environmental data on traffic patterns), providing strong data support and visualization analysis tools for formulating comprehensive and collaborative urban management strategies (e.g., linking traffic management with commercial activity guidance, and combining energy allocation with population flow prediction).

[0054] (5) The model interaction and retrieval device allows administrators to accurately and quickly retrieve the required data dimensions and spatial ranges from the massive fusion model based on specific decision-making or analysis needs (requirement information). The system can dynamically generate visualized model information for specific issues (such as the comprehensive situation of traffic-population-energy during peak hours in a certain area, or the simulation of the impact of a major project on the surrounding environment and economy), which greatly improves the efficiency and relevance of information acquisition and meets the decision support needs in different scenarios. Attached Figure Description

[0055] The accompanying drawings, which form part of this application, are used to provide a further understanding of the application and to make other features, objects, and advantages of the application more apparent. The illustrative embodiments and descriptions of this application are used to explain the application and do not constitute an undue limitation of the application.

[0056] Furthermore, throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the elements are not necessarily drawn to scale.

[0057] In the attached diagram:

[0058] Figure 1 This is a schematic diagram of the structure of a smart city data intelligence fusion system. Detailed Implementation

[0059] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0060] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0061] The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0062] Reference Figure 1 The smart city data intelligence fusion system includes: a map information acquisition device, a management information acquisition device, an information standardization processing device, a multi-dimensional scene rendering device, and a model interaction and calling device. The map information acquisition device is used to acquire digital elevation model data of the target area and construct a basic three-dimensional scene model based on the digital elevation model data. The management information acquisition device is used to collect multi-source urban management information, including traffic information representing traffic and pedestrian flow, population information representing the distribution of the resident population, economic information representing economic activities, and energy information representing energy use. The information standardization processing device is signal-connected to the management information acquisition device and is used to perform standardization processing on the collected urban management information to generate standardized information with a unified format.

[0063] A multi-dimensional scene rendering device is connected to the map information acquisition device and the information standardization processing device. The multi-dimensional scene rendering device has multiple data layers corresponding to the types of urban management information. It is used to render the standardized information to the corresponding data layers and overlay the rendered layers on the three-dimensional scene base model to generate a three-dimensional scene model that integrates multi-dimensional data.

[0064] The model interaction calling device is connected to the multi-dimensional scene rendering device and is used to respond to the request information input by the administrator, and to retrieve and generate corresponding visualization model information from the three-dimensional scene model that integrates multi-dimensional data based on the request information.

[0065] Furthermore, the map information acquisition device includes: a data acquisition module, a coordinate transformation module, and a 3D modeling module. The data acquisition module is used to obtain the original digital elevation model data of the target area from external data sources.

[0066] The external data sources are: national or local basic geographic information databases. Digital elevation model data consists of regular grid data of the surface elevation information of the target area.

[0067] The coordinate transformation module is used to perform spatial reference system unification processing on the raw DME data acquired by the data acquisition module and transform it to a preset geographic coordinate system.

[0068] The coordinate transformation module processes the raw DME data from its inherent coordinate system to a system-preset, unified geographic coordinate system, such as the WGS-84 coordinate system, thereby enabling accurate processing of all geographic location information within the target area.

[0069] In this way, it can be ensured that all spatial data (including subsequent urban management information) are overlaid and integrated within the same precise geospatial framework, guaranteeing the consistency and accuracy of spatial location.

[0070] The 3D modeling module is used to construct a basic 3D scene model describing the continuous undulation of the target area's surface based on DME data processed by the coordinate transformation module and placed in a unified coordinate system.

[0071] Furthermore, constructing the basic model of the 3D scene includes the following steps:

[0072] S1: Receives a two-dimensional regular grid array in a unified geographic coordinate system after processing by the coordinate transformation module;

[0073] For each grid point p in the input two-dimensional regular grid array, calculate its planar coordinates (x, y) and read the elevation value z of grid point p to generate the vertex coordinates (x, y, z) of grid point p.

[0074] Regular grid elevation data consists of raster data arranged at fixed intervals, with each raster cell (grid point) storing an elevation value (z).

[0075] S2: Connect all grid points p to form a continuous triangular patch network covering the entire area.

[0076] Specifically: Each raster cell is treated as a grid point, its spatial position (x, y) determined by the row and column number and resolution, and its elevation value (z) is directly taken from the raster data. Adjacent grid points are connected to form regularly arranged triangular or quadrilateral patches. Then, within the regular grid cells, linear or bilinear interpolation algorithms are applied to calculate and fill the elevation values ​​of any point inside the triangles / grids based on the elevation values ​​of the vertices / grid points, generating a continuous and seamless 3D surface.

[0077] For a two-dimensional regular grid array, four adjacent vertices (e.g., points located at (i,j), (i,j+1), (i+1,j), (i+1,j+1)) are divided into two triangular patches:

[0078] Triangle 1: Vertex (i,j)->(i,j+1)->(i+1,j); Triangle 2: Vertex (i,j+1)->(i+1,j+1)->(i+1,j).

[0079] S3: For each triangular facet network, calculate the normal vector of the plane containing the triangle based on the three-dimensional coordinates of its three vertices;

[0080] Construct a vertex array, an index array, and a normal array, where:

[0081] The vertex array stores the three-dimensional coordinates of all unique vertices;

[0082] The index array stores the indices of the three vertices of each triangle mesh network. Vertex reuse by index avoids redundant storage, saves memory, and improves rendering efficiency.

[0083] The normal array stores the normal vector for each vertex or face. These normal vectors are used during rendering to determine how light strikes the surface, enhancing the three-dimensionality of the terrain. The normal vectors are calculated based on the 3D coordinates of their three vertices.

[0084] S4: Construct a basic 3D scene model based on the vertex array, index array, and normal array.

[0085] The vertex array, index array, and normal array are constructed and defined as the basic model of the 3D scene and output, clearly defining them as the spatial basis for carrying multi-dimensional data.

[0086] Furthermore, the management information acquisition device includes: a multi-source interface adaptation module, a protocol parsing and conversion module, and a data extraction and encapsulation module.

[0087] The multi-source interface adaptation module is configured with multiple standardized data interfaces. Based on the type of the target data source and access requirements, the corresponding interface is called to access the raw data of various types of urban management information.

[0088] Standardized data interfaces include: API interface, database connection interface, message queue interface, file transfer interface, and IoT sensor protocol interface.

[0089] For example:

[0090] Real-time traffic information such as traffic flow, vehicle speed, and congestion index can be obtained through the traffic management platform API.

[0091] Population information such as the number, density, and age structure of permanent residents at the community / grid level can be obtained by connecting to a population statistics database.

[0092] Economic information such as business registration, taxation, and consumer activity in commercial districts can be obtained through e-government data exchange platforms or data interfaces of commercial institutions.

[0093] Energy information, such as regional / building-level energy consumption data, can be obtained through smart meter / water meter / gas meter data acquisition platforms or energy management system (EMS) interfaces.

[0094] The protocol parsing and conversion module performs protocol parsing and preliminary format conversion on the incoming raw data to generate intermediate data.

[0095] The processing procedure of the protocol parsing and conversion module is as follows:

[0096] Parse native data protocols (such as HTTP / JSON) from different interfaces. Extract the parsed data and convert it into an internally unified intermediate representation format.

[0097] Data extraction and encapsulation module: Extracts core information fields related to urban management from the intermediate data parsed by the protocol as needed, and encapsulates them into structured urban management information.

[0098] The data extraction and encapsulation module processes data as follows:

[0099] Based on a predefined information schema, identify and extract key data items. For example:

[0100] Extract timestamps, locations (road segment / intersection IDs or coordinates), traffic flow, and average vehicle speed from traffic data.

[0101] Extract statistical time, geographic region code, population size, and population density from population data.

[0102] Extract information from economic data such as company registration location, turnover, industry classification, and foot traffic in the business district.

[0103] Extract metering point locations, timestamps, electricity / water / gas consumption, and peak loads from energy data.

[0104] Furthermore, the information standardization processing device includes: a data cleaning and verification module, a spatiotemporal benchmark unification module, a numerical normalization and classification module, and a visual coding mapping rule generation module.

[0105] The data cleaning and verification module performs missing value processing, format consistency verification, outlier detection and correction, and visual encoding mapping rule generation on the input urban management information.

[0106] Missing value handling: Identify and fill missing values ​​in key fields (such as location, timestamp, core metric values) (e.g., using nearest neighbor imputation, average imputation, or labeling as a specific value).

[0107] Outlier detection and correction: Based on statistical rules or business rules (such as population density cannot be negative, and traffic flow has a reasonable upper limit), detect, correct, or remove obviously abnormal data points.

[0108] Format consistency check: Ensure that the timestamp format is consistent (e.g., consistent UTC timestamp), the geographic location identifier format is consistent (e.g., consistent latitude and longitude coordinates or standard area code), and the numerical unit is consistent (e.g., energy consumption is consistent in kilowatt-hours).

[0109] Logical consistency check: Check the logical relationships between data (e.g., the population of a certain area should be consistent with the sum of the populations of its subordinate communities).

[0110] The spatiotemporal benchmark unification module standardizes the spatial location and aligns the temporal benchmarks of urban management information.

[0111] The alignment is as follows:

[0112] Obtain geographic coordinates from urban management information and convert them into coordinates for a 3D scene base model; unify the timestamps of urban management information to the same time base and aggregate them at the time granularity.

[0113] Specifically:

[0114] If the collected urban management information includes geographic coordinates (latitude and longitude), verify whether its coordinate system is consistent with the basic model of the 3D scene (e.g., both are WGS-84). If they are inconsistent, perform coordinate system conversion.

[0115] If the urban management information contains regional identifiers (such as administrative division codes), it is associated with predefined spatial boundary data corresponding to the basic model of the 3D scene, and converted into the center point coordinates, centroid coordinates, or geometric patches covering the region, which serve as its spatial anchor points in the 3D scene.

[0116] When aligning times, the timestamps of all city management information are unified to the same time base (such as UTC), and time granularity aggregation may be performed as needed (such as aggregating second-level data into minute-level or hour-level averages) to match rendering requirements and analysis objectives.

[0117] The numerical normalization and grading module converts urban management information from different sources and with different dimensions into standardized values ​​that are comparable and can be directly mapped to visual attributes.

[0118] The original numerical indicators (such as population, traffic flow, energy consumption, and turnover) are mapped to a preset, uniform numerical range [0 to 255].

[0119] The visual encoding mapping rule generation module defines standardized numerical and spatial location information and transforms it into specific visualization instructions to generate standardized information.

[0120] Standardized information includes the following steps in its generation:

[0121] Z1: Preset visual encoding scheme:

[0122] For each type of urban management information (traffic, population, economy, energy), predefine its visualization format (i.e., which layer to render to and how to render it).

[0123] The representation methods include color mapping, height / size mapping, and icon / symbol mapping.

[0124] Color mapping is as follows: Population density: Normalized values ​​of 0 (low) are mapped to blue, 0.5 (medium) to yellow, and 1 (high) to red; or interpolation is performed on a continuous color band (e.g., blue->yellow->red gradient). Air quality: Pollutant concentrations are classified and mapped to different colors.

[0125] Height / size mapping is as follows: Regional economic activity: Normalized turnover is mapped to the height of the corresponding 3D histogram of the region. Traffic flow: Normalized traffic flow is mapped to the density or velocity of flowing particles on the road model.

[0126] Icon / symbol mapping: Energy facility location: Render a specific 3D icon (such as power plant or substation icon) at the corresponding coordinate point.

[0127] Z2: Generate rendering instruction metadata:

[0128] For each processed urban management information, standardized values ​​are combined with spatial location information.

[0129] For example, a standardized rendering instruction can be generated based on traffic conditions, including the rendering location and visual attribute values ​​(such as RGB color values, height scaling factors, and icon type). These instructions themselves, or sets thereof, constitute standardized information.

[0130] This standardized information is intermediate data decoupled from the rendering logic and formatted uniformly. Its core includes:

[0131] In a 3D scene, the rendering location (Where), the information it represents (What - information type and original meaning), the strength / category of the information (Value - normalized value / grading), and how it should be visually presented (How - pointing to the preset visual encoding scheme or containing specific rendering parameters).

[0132] Furthermore, the multi-dimensional scene rendering device includes a layer management module and a rendering processing module. The layer management module draws the received standardized information onto the corresponding preset layers according to its information type.

[0133] The data layer setup method includes the following steps:

[0134] Step 1: During system initialization or configuration, pre-create independent data layers corresponding one-to-one with the types of urban management information (traffic, population, economy, energy). For example: Traffic information layer: used to render road traffic flow and congestion status. Population information layer: used to render population density distribution and heat maps. Economic information layer: used to render regional economic activity and commercial hotspots. Energy information layer: used to render energy consumption intensity and facility locations.

[0135] Step 2: Associate each layer with its corresponding information type identifier and exclusive rendering strategy (such as color mapping rules, height mapping rules, particle effect rules, etc.).

[0136] The rendering process of a preset layer includes the following steps:

[0137] Step 1: Receive standardized information. Based on the information type identifier carried by each piece of standardized information, send it to the corresponding preset data layer (e.g., population density information -> population information layer).

[0138] Step 2: Based on the precise spatial location information (coordinates, associated geometric regions) contained in the standardized information: For point information (such as specific sensor locations, facility points): Perform rendering operations at the corresponding coordinate points. For area / region information (such as administrative district population density, grid energy consumption): Perform rendering operations within the corresponding geographic boundary (polygon). This boundary must precisely match the basic 3D scene model.

[0139] Step 3: Perform rendering operations along the corresponding linear geometry (road centerline). For each preset layer, independently execute all rendering instructions contained therein to generate the corresponding 2D / 3D graphic elements. These graphic elements contain only their own specific urban management information visualization content, and their spatial positions have been precisely registered.

[0140] The rendering module spatially overlays and merges the basic 3D scene model with all rendered preset data layers to generate a final, unified 3D scene model that integrates multi-dimensional data.

[0141] The fusion process is as follows:

[0142] S1: Receive and load the basic 3D scene model (3D terrain mesh) generated by the map information acquisition device.

[0143] S2: Treat the preset layer as an independent rendering layer, and accurately overlay it onto the geographical location corresponding to the basic model of the 3D scene according to the preset rendering order and transparency settings.

[0144] S3: Utilizes a unified geographic coordinate system and precise spatial positioning already achieved during layer rendering to ensure that all visual elements on all layers are perfectly aligned with their actual locations on the terrain model.

[0145] S4: Integrates the basic 3D scene model, which overlays all preset layers, into a single, coherent, and interactive 3D scene object—a 3D scene model that integrates multi-dimensional data. This model retains all geometric and visual features of the basic terrain. At the corresponding geographical locations, it overlays and displays visualized urban management information (traffic, population, economy, energy) carried by each preset layer. It supports overall rotation, scaling, translation, and other 3D operations.

[0146] Furthermore, the model interaction calling device includes: an interaction interface module, a model parsing and data extraction module, and a dynamic view generation module.

[0147] The interactive interface module is used to input the administrator's required information;

[0148] The required information includes the administrator's identity, the layer information requested, and the corresponding location information. For example, Administrator A needs the pedestrian density information for the entire area. Administrator B needs the energy information for the entire area.

[0149] The model parsing and data extraction module receives requirement information and performs real-time parsing and intelligent data extraction on the 3D scene model that integrates multi-dimensional data to generate a model subset.

[0150] The processing procedure includes the following steps:

[0151] Step 1: Based on the target spatial range in the query, quickly trim the model, retaining only the basic terrain and overlay layer visualization elements within that geographic area.

[0152] Step 2: Based on the type of information you are interested in, activate and extract only the relevant preset data layers (e.g., activate only the traffic and population layers, and hide the economy and energy layers).

[0153] The dynamic view generation module generates highly focused and easy-to-understand visual model information based on the extracted model subset.

[0154] The main function of the dynamic view generation module is to highlight some key information by changing the color depth and increasing the brightness, making it easier for administrators to understand.

[0155] The model analysis and data extraction module includes:

[0156] Demand information receiving unit, used to receive demand information;

[0157] The demand information processing unit is used to generate a subset of models;

[0158] The model subset encryption unit encrypts the geographic coordinates of the model subset based on the administrator's identity information to generate an encrypted model subset.

[0159] In this solution, the model parsing and data extraction module further includes an encryption unit. This unit can determine whether to encrypt the geographic location information based on the user's permissions, thereby reducing the risk of urban management information leakage. Generally, the acquired geographic information is distorted, especially for critical energy facilities. Therefore, to avoid the leakage of critical geographic location information, this solution needs to encrypt and distort the geographic location information in the model subset to reduce the risk of geographic information leakage.

[0160] The encryption process is as follows:

[0161] Z1: The model subset encryption unit obtains the administrator identity information from the requirement information and obtains the administrator permission level based on the administrator identity information;

[0162] Z2: The model subset encryption unit pre-configures the key k according to the administrator's permission level;

[0163] k is generated using PBKDF2-HMAC-SHA256 based on user identity information S and a random number r.

[0164] User identity information S is stored by the user, and the random number r is a random number generated based on the timestamp and a random function.

[0165] The process of generating random number r is as follows:

[0166] Define a random function f(t) for each time interval. The random function is an inverse proportional function f(t) = a / (t+1).

[0167] Where t is the sum of all numbers in the timestamp, for example, t = 2 + 0 + 2 + 4 + 0 + 2 + 0 + 1 + 2 + 3 + 6 + 0 for January 2, 2024, 1:23:60. a is a random number that is different for each time period.

[0168] Thus, for key k, the model subset encryption unit can obtain the corresponding random function based on the generation time of the user's identity information and requirement information, and directly calculate it.

[0169] For the interactive interface module, the corresponding random function can be obtained based on the user's identity information and the generation time of the request information, and then directly calculated. Therefore, for users within the same system, without knowing the identity information of other users, it is impossible to obtain their keys. Furthermore, for malicious attackers, even if they illegally obtain user identity information and certain model subsets, they cannot obtain the key k because they lack the generation time of the request information corresponding to the model subset, and thus cannot obtain the true geographical coordinates.

[0170] Z3: The model subset encryption unit distorts and encrypts the coordinate information in the model subset according to k.

[0171] Z3 includes the following steps:

[0172] Z31: Obtain the original geographic coordinates P = {p} in the model subset. i =(x i ,y i ) |i=1,2,…,N};i represents the index of the coordinate point, and N represents the total number of coordinate points;

[0173] Z32: Initialize a pseudo-random number generator using the hash value of key k. The pseudo-random number generator generates M parameters, each set of parameters including: Amplitude: a j ,b j ,frequency and phase

[0174] Z33: Construct each point p i =(x i ,y i The perturbation function of ).

[0175]

[0176] Z34: Encrypt p based on the perturbation function i =(x i ,y i );

[0177]

[0178] If the encryption process is known, the interactive interface module can directly decrypt the encryption, and the decryption process is omitted.

[0179] The above description is merely a selection of preferred embodiments of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this application.

Claims

1. A smart city data intelligence fusion system, characterized in that, include: A map information acquisition device is used to acquire digital elevation model data of a target area and construct a basic three-dimensional scene model based on the digital elevation model data. The management information collection device is used to collect multi-source urban management information, including traffic information representing traffic and pedestrian flow, population information representing the distribution of permanent residents, economic information representing economic activities, and energy information representing energy use. An information standardization processing device is connected to the management information collection device and is used to perform standardization processing on the collected urban management information to generate standardized information with a unified format. A multi-dimensional scene rendering device is connected to the map information acquisition device and the information standardization processing device. The multi-dimensional scene rendering device has multiple data layers corresponding to the types of urban management information. It is used to render the standardized information to the corresponding data layers respectively, and to overlay the rendered layers on the three-dimensional scene base model to generate a three-dimensional scene model that integrates multi-dimensional data. The model interaction calling device is connected to the multi-dimensional scene rendering device, including an interaction interface module, a model parsing and data extraction module, and a dynamic view generation module; The interactive interface module is used to input the administrator's requirements, which include the administrator's identity information, the required layer information, and the corresponding location information. The model parsing and data extraction module is used to receive the requirement information, trim the 3D scene model that integrates multi-dimensional data based on the target spatial range corresponding to the location information, retain only the basic terrain and overlay layer visualization elements within the target spatial range, and activate and extract only the relevant data layers according to the layer information of the requirement to generate a model subset; The model parsing and data extraction module includes a requirement information receiving unit, a requirement information processing unit, and a model subset encryption unit. The model subset encryption unit is used to obtain the administrator's identity information, obtain the administrator's permission level based on the administrator's identity information, and pre-configure a key k based on the administrator's permission level. The key k is generated using PBKDF2-HMAC-SHA256 based on user identity information S and random number r. The user identity information S is stored by the user, and the random number r is a random number generated based on the timestamp of the requirement information and a random function. The model subset encryption unit initializes the pseudo-random number generator using the hash value of the key k, and the pseudo-random number generator generates M sets of parameters, each set of parameters including amplitude. ,frequency and phase Based on the M sets of parameters, a perturbation function is constructed for each original geographic coordinate in the model subset, and the original geographic coordinates are distorted and encrypted according to the perturbation function to generate an encrypted model subset; The interactive interface module is used to calculate the key k based on the generation time of the user identity information and the demand information, and to decrypt the encrypted model subset to obtain the model subset; The dynamic view generation module is used to generate visual model information based on the model subset, and to focus the key information by changing the color depth and increasing the brightness of the key information.

2. The smart city data intelligent fusion system according to claim 1, characterized in that, The generation of the random number r includes: Define a random function for each time period Where t is the sum of all numbers in the timestamp of the demand information, and a is a random number that takes different values ​​in different time periods; Based on the generation time of the demand information, obtain the random function corresponding to the current time period, and generate the random number r based on the random function.

3. The smart city data intelligent fusion system according to claim 1, characterized in that, The original geographic coordinates in the model subset constitute a coordinate set. Where i is the index of the coordinate point, and N is the total number of coordinate points; The perturbation function includes: The original geographic coordinates are encrypted according to the perturbation function. Obtain encrypted geographic coordinates in: The encrypted geographic coordinates constitute a subset of the encrypted model.

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