A visual smart city display method

Abstract

The present application relates to the technical field of smart city data visualization, and particularly relates to a visualized smart city display method, comprising the following steps: S1: collecting traffic flow data, energy consumption data, environmental monitoring data and city three-dimensional geographic information data in real time; S2: constructing a reference three-dimensional model with a space coordinate system; S3: mapping the data into a dynamic data layer with a time stamp; S4: superimposing the dynamic data layer on the reference three-dimensional model according to space-time coordinates to generate a fusion dynamic scene; S5: receiving user input deduction parameters, and calculating corresponding evolution paths in real time; S6: converting the evolution paths into dynamic visualization effects, and outputting to a display terminal. Through integrated processing of multi-source dynamic data fusion, space-time path deduction and visualized output, the present application realizes continuous expression and dynamic evolution display of the operation state of a smart city.

Description

Technical Field

[0001] This invention relates to the field of smart city data visualization technology, and in particular to a method for visualizing smart cities. Background Technology

[0002] With the continuous advancement of urban digitalization and intelligentization, the types of data involved in urban operation and management are becoming increasingly complex, covering multiple types of dynamic information such as traffic flow, energy consumption, environmental monitoring, and geographic information. Traditional information display methods are mostly based on two-dimensional charts or static maps, lacking the ability to integrate multi-source heterogeneous data and making it difficult to comprehensively reflect the urban operation status. At the same time, in recent years, some platforms have attempted to introduce 3D modeling and visualization technologies to build smart city display systems, but most of them remain at the stage of static scene presentation or visualization of single-category indicators, lacking dynamic expression of spatiotemporal evolution processes and unable to support the simulation needs of multivariate inference and prediction based on user input conditions.

[0003] Current technologies generally suffer from the following problems: First, the lack of a unified spatiotemporal coordinate system to integrate multiple types of dynamic data leads to inaccurate data layer overlay; second, the generation method of evolutionary paths lacks joint modeling of historical trends and intervention variables, making it difficult to achieve fine-grained control of time-series extrapolation; third, the visualization output lacks a structured control mechanism, making it difficult to achieve efficient rendering and interactive display of dynamic content. Therefore, there is an urgent need for a visualization method for smart cities to address these issues. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a method for visualizing smart city displays.

[0005] A method for visualizing smart cities includes the following steps:

[0006] S1: Real-time collection of traffic flow data, energy consumption data, environmental monitoring data, and urban 3D geographic information data;

[0007] S2: Construct a benchmark 3D model with a spatial coordinate system based on urban 3D geographic information data;

[0008] S3: Map traffic flow data, energy consumption data, and environmental monitoring data into a dynamic data layer with timestamps;

[0009] S4: Overlay the dynamic data layer onto the baseline 3D model according to the spatiotemporal coordinates to generate a fused dynamic scene;

[0010] S5: Receives the inference parameters input by the user and calculates the corresponding evolution path in real time based on the data layers superimposed in the fused dynamic scene;

[0011] S6: Transform the spatiotemporal changes reflected in the evolution path into dynamic visualization effects and output them to the display terminal.

[0012] Optionally, S1 specifically includes:

[0013] S11: Real-time data on vehicle frequency, average speed and queue length are collected by traffic flow monitoring cameras and geomagnetic vehicle detectors deployed at intersections of main urban roads.

[0014] S12: By connecting to the municipal energy dispatch system and the building energy consumption monitoring network, obtain the real-time power consumption values ​​of electricity, gas and heating of various building units;

[0015] S13: Collect air quality index, temperature and humidity, PM2.5 concentration and noise level data through deployed environmental monitoring stations;

[0016] S14: By accessing the city's CIM platform, the vector topographic maps, 3D building models, and underground pipe network data provided by the city management unit are called, and multi-source fusion is performed in combination with satellite remote sensing images to construct a city 3D geographic information dataset containing spatial coordinate information.

[0017] Optionally, S2 specifically includes:

[0018] S21: Perform element type analysis on the urban 3D geographic information data obtained in S1, extract the corresponding vector layers according to the classification attributes of buildings, roads, water bodies, green spaces and underground pipe networks, and perform coordinate transformation of each layer data to the WGS-84 geographic coordinate system.

[0019] S22: Perform block reconstruction processing on the extracted 3D building model to generate a closed 3D structure based on the building boundary lines and height information;

[0020] S23: The surface cover elements such as water bodies and green spaces are used to generate a surface model through a surface fitting algorithm. The terrain reconstruction method based on TIN structure is used to integrate the terrain elevation information to form a surface base that is seamlessly connected with the building model.

[0021] S24: Based on the spatial layout of various pipeline nodes and connecting paths in the underground pipeline network data, reconstruct the three-dimensional topological network structure, and use voxel interpolation algorithm to generate a visual path representation;

[0022] S25: Register and fuse the various 3D models processed by S22 to S24 according to their spatial coordinate positions to construct a reference 3D model with a spatial index structure.

[0023] Optionally, S23 specifically includes:

[0024] S231: Perform node extraction operation on the input terrain vector data, extract all terrain sampling points with elevation value attributes, and record the spatial coordinates of each sampling point in the form of triplets;

[0025] S232: Based on the set of all sampling points, construct a two-dimensional Delaunay triangulation structure to generate a set of triangular facets composed of nodes. Each triangular facet is a convex triangle formed by three coplanar points.

[0026] S233: Linear interpolation is performed using the three elevation points of each triangular patch as vertices to construct a continuous elevation patch;

[0027] S234: All triangular facets are continuously stitched together to form a complete three-dimensional surface model. For the area where the terrain edge intersects with the building base, the boundary line is extracted and the edge node is registered based on the coordinates of the overlapping area. The coordinate difference of the overlapping area is adjusted by using the minimum error fitting method.

[0028] Optionally, S24 specifically includes:

[0029] S241: Perform structured analysis on the raw data of the underground pipeline network, identify the three-dimensional coordinates of all predetermined nodes and connecting paths, construct an initial topology graph, and form the basic structure of the node set and path set;

[0030] S242: Establish a three-dimensional topological network structure based on the connection relationship between nodes, represent all nodes as point objects with spatial coordinates, represent the connection path as spatial curve segment, and assign each node and path the corresponding type identifier, geometric attributes and connectivity information;

[0031] S243: Discretize the entire urban area into a regular voxel grid structure. Each voxel unit has a uniform spatial size and records its index position in three-dimensional space.

[0032] S244: For each pipeline route segment, based on the coordinate relationship between its starting point and ending point, a continuous set of intermediate sampling points is generated using linear interpolation or B-spline interpolation methods;

[0033] S245: Map the sampled path points to the corresponding voxel grid, assign occupancy flag values ​​to the voxel cells occupied by the path, and form the voxel path function. ;

[0034] S246: The Marching Cubes algorithm is used to perform isosurface reconstruction on voxel elements, converting discrete paths into continuous visual geometric mesh models.

[0035] Optionally, S3 specifically includes:

[0036] S31: Perform structured preprocessing on the traffic flow data, energy consumption data and environmental monitoring data collected in S1, classify and label them according to their collection sources, and determine the geographic projection coordinates of each type of data;

[0037] S32: Introduce a time attribute for each type of data, assign a unique timestamp to each data record according to the actual collection time, and construct a basic data structure with triples as the space-time index;

[0038] S33: Create an independent dynamic data layer for each type of data, where each data element in the layer represents the state value of a specified spatial location at a specified time point;

[0039] S34: Perform layer format conversion and data encoding on the constructed dynamic layer, encapsulate the layer content using a spatiotemporal data format, and generate a visual slice file.

[0040] Optionally, S4 specifically includes:

[0041] S41: Read the baseline 3D model constructed in S2 and various dynamic data layers generated in S3;

[0042] S42: For each data unit in the dynamic data layer, perform spatial projection and time matching processing based on its spatial coordinates and timestamp to calculate its embedding position in the 3D model. relative to time axis position ;

[0043] S43: Construct a spatiotemporal overlay rendering buffer, group all dynamic data layers according to data category and time series, load them sequentially and assign each type of layer a unique layer ID and color / texture mapping strategy, and establish a layer-time-position ternary overlay table;

[0044] S44: Establish binding relationships between layers and 3D models where there is interaction.

[0045] S45: Call the 3D visualization engine, perform frame-level rendering according to the layer timestamp order, and finally generate a blended dynamic scene.

[0046] Optionally, S5 specifically includes:

[0047] S51: Receives simulation parameters input by the user, including the target area range, simulation start and end time, simulation time step, selection of intervention variables, and type of output index;

[0048] S52: Based on the target area range input by the user, extract the numerical records of all dynamic layers within the area during the inference start and end time period in the fused dynamic scene, and classify and organize them according to layer type, time series and spatial location to construct a multi-dimensional data set.

[0049] S53: Perform time-series slicing on the extracted dataset according to the simulation time step specified by the user, divide the continuous time period into several equally spaced intervals, and extract the corresponding traffic, energy and environmental data as input variables in each time interval;

[0050] S54: Within each time interval, based on the type of intervention variable selected by the user, analyze its influence on relevant factors in the target area under the current state, and perform progressive extrapolation operations in combination with historical change trends to calculate the spatial state transition process between adjacent time steps.

[0051] S55: Throughout the entire simulation period, all calculated spatial state transition results are gradually connected, and a continuous evolution path is constructed according to the order of positional changes and the order of temporal evolution.

[0052] Optionally, S54 specifically includes:

[0053] S541: Based on the type of intervention variable selected by the user, retrieve the current state value of the corresponding layer of the target area in the fused dynamic scene, extract other related variables affected by it, and establish an influence relationship matrix;

[0054] S542: Based on the influence relationship matrix, spatial diffusion modeling is performed on the current state value of the intervention variable. The interaction of geographically adjacent units is used to calculate the local influence gain coefficient distribution of all spatial grid units in the target area, forming an influence increment map driven by the intervention.

[0055] S543: Extract historical time series data corresponding to the current predictor variable, construct a time series within the user-defined backtracking time window, calculate the trend component of the variable using triple exponential smoothing, and obtain the historical trend prediction increment.

[0056] S544: Add the current position's state value at the current time step, the historical trend prediction increment, and the intervention impact increment to calculate the predicted state value for the next step.

[0057] S545: Repeat the process from S541 to S544, advancing step by step according to the time step, to construct a continuous spatial state transition sequence, and integrate the predicted values ​​of all spatial units into a state transition map in chronological order.

[0058] Optionally, S6 specifically includes:

[0059] S61: Sort the node sequence in the evolution path by time label and divide it into several segments according to spatial continuity to construct a temporal path list;

[0060] S62: Specify the visual representation for each path segment, and set the corresponding graphic type, color, width, transparency, and animation parameters;

[0061] S63: Convert the spatial location and temporal information of path nodes into visual keyframe data and insert interpolation points for inter-frame transitions;

[0062] S64: Loads the data frame by frame into the 3D baseline model according to the path order, and controls the display level, transparency and playback order to achieve layer organization;

[0063] S65: Output the processed dynamic visualization content to the display terminal and load control commands to enable playback, pause and jump operations.

[0064] The beneficial effects of this invention are:

[0065] This invention solves the problem of spatiotemporal overlay difficulties of multi-source heterogeneous data in existing technologies by constructing a unified three-dimensional geographic coordinate system, integrating multiple types of real-time data such as traffic flow, energy consumption, and environmental monitoring, and mapping them into dynamic data layers with timestamps. At the same time, by orderly overlaying the above layers onto the three-dimensional model, a unified expression of the city's operational status in time and space dimensions is achieved, improving the completeness and visual accuracy of data display.

[0066] This invention, by introducing user-input simulation parameters and combining intervention variable analysis with historical trend modeling, gradually calculates the spatiotemporal evolution path and outputs dynamic visualization results in the form of keyframe sequences, realizing a closed-loop process from data acquisition to simulation and then to dynamic presentation; it improves the interactivity, predictive ability and operational efficiency of the urban simulation display system. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 This is a schematic diagram of a visualized smart city display method according to an embodiment of the present invention;

[0069] Figure 2 This is a schematic diagram illustrating the process of constructing a baseline 3D model according to an embodiment of the present invention. Detailed Implementation

[0070] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0071] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0072] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0073] like Figures 1-2 As shown, a method for visualizing smart cities includes the following steps:

[0074] S1: Real-time collection of traffic flow data, energy consumption data, environmental monitoring data, and urban 3D geographic information data;

[0075] S2: Construct a benchmark 3D model with a spatial coordinate system based on urban 3D geographic information data;

[0076] S3: Map traffic flow data, energy consumption data, and environmental monitoring data into a dynamic data layer with timestamps;

[0077] S4: Overlay the dynamic data layer onto the baseline 3D model according to the spatiotemporal coordinates to generate a fused dynamic scene;

[0078] S5: Receives the inference parameters input by the user and calculates the corresponding evolution path in real time based on the data layers superimposed in the fused dynamic scene;

[0079] S6: Transform the spatiotemporal changes reflected in the evolution path into dynamic visualization effects and output them to the display terminal.

[0080] S1 specifically includes:

[0081] S11: By deploying traffic flow monitoring cameras and geomagnetic vehicle detectors at intersections of main urban roads, real-time data on vehicle frequency, average speed and queue length are collected and uploaded to the central processing system with a sampling period of 5 seconds.

[0082] S12: By connecting to the municipal energy dispatch system and the building energy consumption monitoring network, obtain the real-time power consumption values ​​of electricity, gas and heating of various building units, collect energy consumption data in 1-minute increments and synchronize them into the database.

[0083] S13: Collect air quality index, temperature and humidity, PM2.5 concentration and noise level data through deployed environmental monitoring stations. All stations use multi-parameter sensors with national standard certification, and the data is sampled every 10 seconds and pre-processed locally before being uploaded.

[0084] S14: By accessing the city's CIM (City Information Modeling) platform, the vector topographic maps, 3D building models, and underground pipeline data provided by the city management unit are called, and multi-source fusion is performed in combination with satellite remote sensing imagery to construct a city 3D geographic information dataset containing spatial coordinate information. The above steps realize the real-time perception of various dynamic elements of the city by uniformly collecting and frequently uploading traffic, energy, environment and spatial geographic data, ensuring the timeliness and accuracy of subsequent 3D display, and providing a solid data foundation for the system to build a dynamic visualization scene that is consistent in time and space.

[0085] S2 specifically includes:

[0086] S21: Perform element type analysis on the urban 3D geographic information data obtained in S1, extract the corresponding vector layers according to the classification attributes of buildings, roads, water bodies, green spaces and underground pipe networks, and perform coordinate transformation of each layer data to the WGS-84 geographic coordinate system.

[0087] S22: Perform block reconstruction processing on the extracted 3D building model to generate a closed 3D structure based on the building boundary lines and height information;

[0088] S23: The surface cover elements such as water bodies and green spaces are used to generate a surface model through a surface fitting algorithm. The terrain reconstruction method based on the TIN (triangular irregular network) structure is used to integrate the terrain elevation information to form a surface base that is seamlessly connected with the building model.

[0089] S24: Based on the spatial layout of various pipeline nodes and connecting paths in the underground pipeline network data, reconstruct the three-dimensional topological network structure, and use voxel interpolation algorithm to generate a visual path representation;

[0090] S25: The various 3D models processed from S22 to S24 are uniformly registered and fused according to their spatial coordinate positions to construct a benchmark 3D model with a spatial index structure. The final output is a 3D scene dataset that meets the parsing standards of the GIS engine. The above steps, through unified coordinate transformation, model reconstruction and spatial registration of multi-source geospatial information, realize the construction of a benchmark 3D model with consistent spatial relationships and complete feature expression, which helps to achieve high-precision overlay of multiple types of dynamic data and accurate visualization of urban simulation scenes.

[0091] S23 specifically includes:

[0092] S231: Perform node extraction on the input terrain vector data, extract all terrain sampling points with elevation value attributes, and record the spatial coordinates of each sampling point in triplet form. ,in and Represents geographic planar location coordinates. Indicates the corresponding elevation value;

[0093] S232: Based on the set of all sampling points, construct a two-dimensional Delaunay triangulation structure to generate a set of triangular facets composed of nodes. Each triangular facet is a convex triangle formed by three coplanar points, satisfying the maximum and minimum angle optimization principle.

[0094] S233: Using the three elevation points of each triangular patch as vertices, linear interpolation is performed to construct continuous elevation patches, employing the following linear interpolation formula: , where the coefficient The following system of linear equations was obtained:

[0095] ,

[0096] Among them, three points These are the coordinates of the vertices of the current face patch;

[0097] S234: All triangular facets are continuously stitched together to form a complete 3D surface model. For the intersection of the terrain edge and the building base, boundary lines are extracted and edge nodes are registered based on the coordinates of the overlapping area. The coordinate difference of the overlapping area is adjusted using the minimum error fitting method to ensure seamless stitching between the surface and the building model. The above steps, by using the TIN triangulation reconstruction method and integrating linear elevation interpolation and boundary registration technology, achieve a high-precision geometric continuity connection between the terrain model and the building model. This ensures that the urban 3D model does not experience elevation abrupt changes or visual discontinuities during the overlay of multi-source data, improving the realism and engineering applicability of the visualization scene.

[0098] Boundary line extraction and minimum error fitting in S234 specifically include:

[0099] S2341: Extract the set of boundary triangular facets from the terrain model constructed by TIN, identify triangular mesh regions that spatially overlap with the projected boundaries of the building model, and set the width of the boundary overlap buffer zone to [value missing]. Extract those that meet the conditions The set of surface nodes, in which For terrain triangulation nodes, For the coordinate set of the building boundary line, Represents the Euclidean distance function;

[0100] S2342: Perform node pairing operation on the set of surface nodes and the set of building model base edge points, and use the nearest neighbor search algorithm to build a one-to-one correspondence and generate a pairing set;

[0101] S2343: Perform elevation difference analysis on each pair of paired nodes and construct the objective function:

[0102] ,in Elevation of surface nodes. To correspond to the elevation of the edge point of the building foundation, This represents the total fitting error;

[0103] S2344: Using the least squares optimization method, the elevation values ​​of surface nodes within the boundary buffer zone are adjusted to make... Minimize, while maintaining topological continuity constraints between nodes;

[0104] S2345: Perform surface reconstruction on the adjusted boundary area, reconstruct the boundary triangular mesh fragment, and ensure that there are no elevation changes or overlapping phenomena between the terrain surface and the building edge, thereby forming a continuous and smooth visual base boundary transition zone.

[0105] S24 specifically includes:

[0106] S241: Perform structured analysis on the raw data of the underground pipe network, identify the three-dimensional coordinates of all predetermined nodes (including manholes, valves, pumping stations, etc.) and connection paths (including water supply and drainage pipes, cable channels, etc.), construct an initial topology graph, and form the basic structure of the node set and path set;

[0107] S242: Based on the connection relationship between nodes, a three-dimensional topological network structure is established, all nodes are represented as point objects with spatial coordinates, the connection path is represented as a spatial curve segment, and each node and path is assigned a corresponding type identifier, geometric attributes and connectivity information, thereby constructing a directed graph structure in three-dimensional space.

[0108] S243: Discretize the entire urban area into a regular voxel grid structure. Each voxel unit has a uniform spatial size, and its index position in three-dimensional space is recorded. The voxel index position of any spatial point in the grid is calculated by the following formula: ,in, Representing a spatial point The voxel index in which it is located; Indicates the side length of a voxel unit; This indicates the floor function;

[0109] S244: For each pipeline path segment, based on the coordinate relationship between its starting point and ending point, a continuous set of intermediate sampling points is generated using linear interpolation or B-spline interpolation methods, so that the path has a high-resolution representation in space, forming a discrete point sequence that can be used for voxel mapping.

[0110] S245: Map the sampled path points to the corresponding voxel grid, assign occupancy flag values ​​to the voxel cells occupied by the path, and form the voxel path function. This is used to record the spatial distribution of the 3D path within the mesh, and its expression is as follows:

[0111] ;in, The path voxel occupancy function; The index coordinates of a voxel in three-dimensional space; a value of 1 indicates that there is a path point within the voxel, and a value of 0 indicates that the voxel is empty;

[0112] S246: The Marching Cubes algorithm is used to perform isosurface reconstruction on all voxel units with a label value of 1, converting discrete paths into continuous visual geometric mesh models, realizing simulation rendering and interactive visualization of path structures in three-dimensional space; through the above steps, underground pipeline data is abstracted from topological structure into a three-dimensional graph model, and the accurate mapping and visual representation of paths in space is realized based on the voxel interpolation method, which not only improves the integrity and interactivity of the three-dimensional model, but also provides a data foundation and modeling support for the visualization monitoring and simulation of urban underground facilities.

[0113] S3 specifically includes:

[0114] S31: Perform structured preprocessing on the traffic flow data, energy consumption data, and environmental monitoring data collected in S1, classify and label them according to their data sources, and determine the geographic projection coordinates of each type of data. To achieve spatial correspondence between data and target locations in the city's 3D model;

[0115] S32: Introduce a time attribute for each type of data, and assign a unique timestamp to each data record according to the actual collection time. Constructing a triplet This is the basic data structure for spatial-temporal indexing; traffic flow data uses second-level timestamps, energy consumption data uses minute-level timestamps, and environmental monitoring data uses 10-second-level timestamps to ensure that time accuracy matches the collection frequency.

[0116] S33: Establish each type of data as an independent dynamic data layer. Each data element in the layer represents the state value of a specified spatial location at a specified time point, represented by a mapping function of the following form:

[0117] ,in, For the first Dynamic data layers in spatial position Time point The state value function is defined as follows: the corresponding state value includes vehicle speed, vehicle density, energy consumption, air quality index, or PM2.5 concentration. These correspond to traffic flow, energy consumption, and environmental monitoring layers, respectively.

[0118] S34: Perform layer format conversion and data encoding on the constructed dynamic layers, encapsulate the layer content using spatiotemporal data formats such as GeoTIFF, NetCDF, or GeoJSON, and generate visualization tile files for loading 3D scenes to support on-demand access and real-time rendering of time-series data. The above steps improve the spatiotemporal organization efficiency of urban operation data by establishing a dynamic layer indexing mechanism based on timestamps and uniformly formatting and mapping various types of sensing data according to the layer structure. This enables dynamic data to be accurately superimposed and continuously presented in the 3D urban model, laying a data foundation for subsequent simulation calculations and visualization inferences.

[0119] S4 specifically includes:

[0120] S41: Read the baseline 3D model constructed in S2 and various dynamic data layers generated in S3, and uniformly adopt the WGS-84 spatial reference coordinate system and UTC time format to ensure the consistency of the coordinate system of the model and data in space and time.

[0121] S42: For each data unit in the dynamic data layer, based on its spatial coordinates... With timestamp Perform spatial projection and time matching processing to calculate its embedding position in the 3D model. relative to time axis position The following coordinate transformation formula is used to complete model matching:

[0122] ,in, These are the planar projection coordinates recorded in the layer data; Record values ​​for the layer to visualize the height mapping (such as energy consumption, concentration, or traffic flow intensity). This is a spatial coordinate transformation function used to map two-dimensional planar coordinates to three-dimensional spatial coordinates; These are the actual rendering coordinates in the 3D model after conversion;

[0123] S43: Construct a spatiotemporal overlay rendering buffer, group all dynamic data layers according to data category and time series, load them sequentially and assign each type of layer a unique layer ID and color / texture mapping strategy, establish a layer-time-position ternary overlay table, and realize the orderly combination of layers in spatial position and time dimension;

[0124] Table 1: Layer-Time-Location Triad Overlay Table

[0125]

[0126] In Table 1 above, the layer ID represents the unique identifier of the layer to which the data belongs, used for layer classification and rendering scheduling; the timestamp represents the precise time of data collection; the coordinate index represents the overlay position of the data in the 3D scene, where X and Y are geographic plane coordinates, and Z is the mapped elevation or building surface position, used for rendering positioning; the display value is the business data value of the current layer element, such as traffic density, energy consumption, AQI value, etc.; the rendering type represents the visualization form of the data in the 3D scene, such as heat map, bar chart, region shading, icon point cloud, etc.; the display parameters are used to specify rendering detail parameters, such as color mapping, transparency, shape ratio, dynamic effects, etc.

[0127] S44: Establish binding relationships between layers and 3D models in areas where there is interaction (such as road surfaces, building facades, and ground environment areas), so that the layer values ​​at the corresponding time can drive the real-time changes of the properties (color, transparency, brightness, animation texture, etc.) of the bound areas in the 3D model.

[0128] S45: The 3D visualization engine is invoked to perform frame-level rendering in the order of layer timestamps, ultimately generating a fused dynamic scene. Different layers are superimposed based on spatial location, time nodes, and visual parameters to form a dynamic 3D city display with spatiotemporal evolution characteristics. The above steps establish a spatial coordinate mapping and time synchronization mechanism for dynamic layers and superimpose them onto the 3D baseline model in a frame-level temporal rendering manner. This achieves accurate fusion and realistic reproduction of multiple types of dynamic city operation data in 3D space, significantly improving the dynamic perception capability and decision support value of the visualization analysis of city operation status.

[0129] S5 specifically includes:

[0130] S51: Receives simulation parameters input by the user, including the target area range, simulation start and end time, simulation time step, selection of intervention variables and output index type, among which intervention variables include traffic flow intensity, building energy consumption change trend and air quality level evolution;

[0131] S52: Based on the target area range input by the user, extract the numerical records of all dynamic layers within the fused dynamic scene during the inference start and end time period, and classify and organize them according to layer type, time series and spatial location to construct a multi-dimensional data set with spatial index and time axis structure.

[0132] S53: Perform time-series slicing on the extracted dataset according to the simulation time step specified by the user, divide the continuous time period into several equally spaced intervals, and extract the corresponding traffic, energy and environmental data as input variables in each time interval;

[0133] S54: Within each time interval, based on the type of intervention variable selected by the user, analyze its influence on relevant factors in the target area under the current state, and perform progressive extrapolation operations in combination with historical change trends to calculate the spatial state transition process between adjacent time steps.

[0134] S55: Throughout the entire simulation period, all calculated spatial state transition results are gradually connected, and a continuous evolution path is constructed according to the order of location changes and the order of time evolution. The path consists of several data nodes containing spatial coordinates and time labels. Finally, the path is output as the simulation result for visualization. The above steps realize the calculation of continuous evolution path by integrating multiple types of urban operation data through the construction of a dynamic data extraction process based on user-defined input and a time-by-time causal inference strategy. It can efficiently simulate future state change trends and provide intuitive visualization prediction support for urban managers.

[0135] S54 specifically includes:

[0136] S541: Based on the type of intervention variable selected by the user, retrieve the current state value of the corresponding layer of the target area in the fused dynamic scene, extract other related variables affected by it, and establish an influence relationship matrix, where each item represents the quantitative effect strength of the current intervention variable on the unit change of adjacent variables at a certain spatial location;

[0137] S542: Based on the influence relationship matrix, spatial diffusion modeling is performed on the current state value of the intervention variable. The interaction of geographically adjacent units is used to calculate the local influence gain coefficient distribution of all spatial grid units in the target area, forming an intervention-driven influence increment map to reflect the expected perturbation of other variables by the intervention variable within this step.

[0138] S543: Extract historical time-series data corresponding to the current predictor variable, construct a time series within the user-defined backtracking time window, calculate the trend component of the variable using triple exponential smoothing, and obtain the historical trend prediction increment. Specifically, this is achieved through the following steps:

[0139] First, for each position The historical value sequence is subjected to three exponential smoothing processes to obtain the first-level smoothed value. Secondary smoothing value and level 3 smoothing value ;

[0140] Then calculate the increment of the trend prediction value at the current moment, using the following formula:

[0141] ,in, This represents the increment of the historical trend term on the predicted value of the variable; : These are the variable values ​​after smoothing by the first, second, and third exponents, respectively; : represents the trend smoothing weight coefficient, satisfying , used to regulate the degree of trend response;

[0142] S544: Add the current position's state value at the current time step, the historical trend prediction increment, and the intervention impact increment to calculate the predicted state value for the next step. The formula is:

[0143] ,in, Indicates position In time State prediction value; This represents the state value of the current position at the current time step. To intervene and affect the increment;

[0144] S545: Repeat the process from S541 to S544, advancing step by step according to the time step, to construct a continuous spatial state transition sequence. The predicted values ​​of all spatial units are then integrated into a state transition map in chronological order, serving as the basis for subsequent evolution path calculation and visualization output. The above steps, by combining intervention variable-driven and trend component modeling, realize the fusion of causal analysis of spatial state changes and time series prediction, enabling the generation of evolution paths to have both local response capabilities and global trend prediction capabilities, thereby improving the timeliness and accuracy of urban operation status simulation.

[0145] S6 specifically includes:

[0146] S61: Sort the node sequence in the evolution path by time label and divide it into several segments according to spatial continuity to construct a temporal path list;

[0147] S62: Specify the visual representation for each path segment, and set the corresponding graphic type, color, width, transparency, and animation parameters;

[0148] S63: Convert the spatial location and temporal information of path nodes into visual keyframe data and insert interpolation points for inter-frame transitions;

[0149] S64: Loads the data frame by frame into the 3D baseline model according to the path order, and controls the display level, transparency and playback order to achieve layer organization;

[0150] S65: Output the processed dynamic visualization content to the display terminal and load control commands to enable playback, pause and jump operations.

[0151] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0152] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for visualizing smart cities, characterized in that, Includes the following steps: S1: Real-time collection of traffic flow data, energy consumption data, environmental monitoring data, and urban 3D geographic information data; S2: Constructing a baseline 3D model with a spatial coordinate system based on urban 3D geographic information data; specifically including: S21: Perform element type analysis on the urban 3D geographic information data obtained in S1, extract the corresponding vector layers according to the classification attributes of buildings, roads, water bodies, green spaces and underground pipe networks, and perform coordinate transformation of each layer data to the WGS-84 geographic coordinate system. S22: Perform block reconstruction processing on the extracted 3D building model to generate a closed 3D structure based on the building boundary lines and height information; S23: The surface cover elements of water bodies and green spaces are used to generate a surface model through a surface fitting algorithm. The terrain elevation information is integrated using a terrain reconstruction method based on TIN structure to form a surface base that is seamlessly connected with the building model. S24: Based on the spatial layout of various pipeline nodes and connecting paths in the underground pipeline network data, reconstruct the three-dimensional topological network structure, and use voxel interpolation algorithm to generate a visual path representation; S25: Register and fuse the various 3D models processed by S22 to S24 according to their spatial coordinates to construct a baseline 3D model; S3: Maps traffic flow data, energy consumption data, and environmental monitoring data into a dynamic data layer with timestamps; specifically including: S31: Perform structured preprocessing on the traffic flow data, energy consumption data and environmental monitoring data collected in S1, classify and label them according to their collection sources, and determine the geographic projection coordinates of each type of data; S32: Introduce a time attribute for each type of data, assign a unique timestamp to each data record according to the actual collection time, and construct a basic data structure with triples as the space-time index; S33: Create an independent dynamic data layer for each type of data, where each data element in the layer represents the state value of a specified spatial location at a specified time point; S34: Perform layer format conversion and data encoding on the constructed dynamic layer, encapsulate the layer content using a spatiotemporal data format, and generate a visual tile file; S4: Overlay the dynamic data layer onto the baseline 3D model according to the spatiotemporal coordinates to generate a fused dynamic scene; S5: Receives the inference parameters input by the user and calculates the corresponding evolution path in real time based on the data layers superimposed in the fused dynamic scene; S6: Transform the spatiotemporal changes reflected in the evolution path into dynamic visualization effects and output them to the display terminal.

2. The method for visualizing a smart city according to claim 1, characterized in that, S1 specifically includes: S11: Real-time data on vehicle frequency, average speed and queue length are collected by traffic flow monitoring cameras and geomagnetic vehicle detectors deployed at intersections of main urban roads. S12: By connecting to the municipal energy dispatch system and the building energy consumption monitoring network, obtain the real-time power consumption values ​​of electricity, gas and heating of various building units; S13: Collect air quality index, temperature and humidity, PM2.5 concentration and noise level data through deployed environmental monitoring stations; S14: By accessing the city's CIM platform, the vector topographic maps, 3D building models, and underground pipe network data provided by the city management unit are called, and multi-source fusion is performed in combination with satellite remote sensing images to construct a city 3D geographic information dataset containing spatial coordinate information.

3. The method for visualizing a smart city according to claim 1, characterized in that, S23 specifically includes: S231: Perform node extraction operation on the input terrain vector data, extract all terrain sampling points with elevation value attributes, and record the spatial coordinates of each sampling point in the form of triplets; S232: Based on the set of all sampling points, construct a two-dimensional Delaunay triangulation structure to generate a set of triangular facets composed of nodes. Each triangular facet is a convex triangle formed by three coplanar points. S233: Linear interpolation is performed using the three elevation points of each triangular patch as vertices to construct a continuous elevation patch; S234: All triangular facets are continuously stitched together to form a complete three-dimensional surface model. For the area where the terrain edge intersects with the building base, the boundary line is extracted and the edge node is registered based on the coordinates of the overlapping area. The coordinate difference of the overlapping area is adjusted by using the minimum error fitting method.

4. The method for visualizing a smart city according to claim 3, characterized in that, S24 specifically includes: S241: Perform structured analysis on the raw data of the underground pipeline network, identify the three-dimensional coordinates of all predetermined nodes and connecting paths, construct an initial topology graph, and form the basic structure of the node set and path set; S242: Establish a three-dimensional topological network structure based on the connection relationship between nodes, represent all nodes as point objects with spatial coordinates, represent connection paths as spatial curve segments, and assign each node and path a corresponding type identifier, geometric attributes, and connectivity information; S243: Discretize the entire urban area into a regular voxel grid structure. Each voxel unit has a uniform spatial size and records its index position in three-dimensional space. S244: For each pipeline route segment, based on the coordinate relationship between its starting point and ending point, a continuous set of intermediate sampling points is generated using linear interpolation or B-spline interpolation methods; S245: Map the sampled path points to the corresponding voxel grid, assign occupancy flag values ​​to the voxel cells occupied by the path, and form the voxel path function. ; S246: The Marching Cubes algorithm is used to perform isosurface reconstruction on voxel elements, converting discrete paths into continuous visual geometric mesh models.

5. The method for visualizing a smart city according to claim 1, characterized in that, S4 specifically includes: S41: Read the baseline 3D model constructed in S2 and various dynamic data layers generated in S3; S42: For each data unit in the dynamic data layer, perform spatial projection and time matching processing based on its spatial coordinates and timestamp to calculate its embedding position in the 3D model. With respect to the time axis position T; S43: Construct a spatiotemporal overlay rendering buffer, group all dynamic data layers according to data category and time series, load them sequentially and assign each type of layer a unique layer ID and color / texture mapping strategy, and establish a layer-time-position ternary overlay table; S44: Establish binding relationships between layers and 3D models where there is interaction. S45: Call the 3D visualization engine, perform frame-level rendering according to the layer timestamp order, and finally generate a blended dynamic scene.

6. The method for visualizing a smart city according to claim 1, characterized in that, S5 specifically includes: S51: Receives simulation parameters input by the user, including the target area range, simulation start and end time, simulation time step, selection of intervention variables, and type of output index; S52: Based on the target area range input by the user, extract the numerical records of all dynamic layers within the fused dynamic scene during the inference start and end time period, and classify and organize them according to layer type, time series and spatial location to construct a multi-dimensional data set; S53: Perform time-series slicing on the extracted dataset according to the simulation time step specified by the user, divide the continuous time period into several equally spaced intervals, and extract the corresponding traffic, energy and environmental data as input variables in each time interval; S54: Within each time interval, based on the type of intervention variable selected by the user, analyze its influence on relevant factors in the target area under the current state, and perform progressive extrapolation operations in combination with historical change trends to calculate the spatial state transition process between adjacent time steps. S55: Throughout the entire simulation period, all calculated spatial state transition results are gradually connected, and a continuous evolution path is constructed according to the order of positional changes and the order of temporal evolution.

7. The method for visualizing a smart city according to claim 6, characterized in that, Specifically, S54 includes: S541: Based on the type of intervention variable selected by the user, retrieve the current state value of the corresponding layer of the target area in the fused dynamic scene, extract other related variables affected by it, and establish an influence relationship matrix; S542: Based on the influence relationship matrix, spatial diffusion modeling is performed on the current state value of the intervention variable. The interaction of geographically adjacent units is used to calculate the local influence gain coefficient distribution of all spatial grid units in the target area, forming an influence increment map driven by the intervention. S543: Extract historical time series data corresponding to the current predictor variable, construct a time series within the user-defined backtracking time window, calculate the trend component of the variable using triple exponential smoothing, and obtain the historical trend prediction increment. S544: Add the current position's state value at the current time step, the historical trend prediction increment, and the intervention impact increment to calculate the predicted state value for the next longer step. S545: Repeat the process from S541 to S544, advancing step by step according to the time step, to construct a continuous spatial state transition sequence, and integrate the predicted values ​​of all spatial units into a state transition map in chronological order.

8. The method for visualizing a smart city according to claim 1, characterized in that, S6 specifically includes: S61: Sort the node sequence in the evolution path by time label and divide it into several segments according to spatial continuity to construct a temporal path list; S62: Specify the visual representation for each path segment, and set the corresponding graphic type, color, width, transparency, and animation parameters; S63: Convert the spatial location and temporal information of path nodes into visual keyframe data and insert interpolation points for inter-frame transitions; S64: Loads the data frame by frame into the 3D baseline model according to the path order, and controls the display level, transparency and playback order to achieve layer organization; S65: Output the processed dynamic visualization content to the display terminal and load control commands to enable playback, pause and jump operations.

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