Visual smart city display method

Abstract

The invention relates to the technical field of smart city data visualization, in particular to a visual smart city display method, which comprises the following steps: S1, collecting traffic flow data, energy consumption data, environment 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 timestamp; s4, superposing the dynamic data layer to the reference three-dimensional model according to space-time coordinates to generate a fused dynamic scene; s5, receiving deduction parameters input by a user, and calculating a corresponding evolution path in real time; and S6, converting the evolution path into a dynamic visualization effect, and outputting the dynamic visualization effect to a display terminal. According to the invention, through integrated processing of multi-source dynamic data fusion, space-time path deduction and visual output, continuous expression and dynamic evolution display of the operation state of the smart city are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city data visualization, and in particular to a method for visualizing a smart city. Background Art

[0002] With the continuous advancement of urban digitalization and intelligent construction, the types of data involved in the urban operation and management process are becoming more complex, covering a variety 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, which lack the ability to integrate multi-source heterogeneous data and cannot fully reflect the urban operation situation; at the same time, in recent years, some platforms have attempted to introduce three-dimensional modeling and visualization technology to build smart city display systems, but most of them remain at the stage of static scene presentation or single-category indicator visualization, lacking dynamic expression of spatiotemporal evolution processes, and unable to support simulation needs for multi-variable deduction and prediction based on user input conditions.

[0003] Current technologies suffer from the following common problems: First, there's a lack of a unified spatiotemporal coordinate system to integrate diverse dynamic data types, leading to inaccurate data layer overlays. Second, the generation of evolutionary paths lacks the ability to jointly model historical trends and intervening variables, making it difficult to achieve precise control over time-series deductions. Third, the lack of a structured control mechanism for visualization output makes it difficult to efficiently render and interactively display dynamic content. Therefore, a visual smart city display method is urgently needed to address these issues. Summary of the Invention

[0004] Based on the above objectives, the present invention provides a method for visualizing a smart city.

[0005] A method for visualizing a smart city includes the following steps: S1: Real-time collection of traffic flow data, energy consumption data, environmental monitoring data and urban three-dimensional geographic information data; S2: Construct a benchmark 3D model with a spatial coordinate system based on the city’s 3D geographic information data; S3: Map traffic flow data, energy consumption data, and environmental monitoring data into dynamic data layers with timestamps; S4: superimpose the dynamic data layer onto the reference 3D model according to the temporal and spatial coordinates to generate a fused dynamic scene; S5: Receive the deduction parameters input by the user and calculate the corresponding evolution path in real time based on the data layers superimposed in the fusion dynamic scene; S6: Convert the spatiotemporal change process reflected in the evolution path into a dynamic visualization effect and output it to a display terminal.

[0006] Optionally, the S1 specifically includes: S11: Traffic flow monitoring cameras and geomagnetic vehicle detectors deployed at intersections of urban arterial roads collect real-time data on vehicle frequency, average speed, and queue length; S12: By connecting to the municipal energy dispatch system and the building energy consumption monitoring network, the real-time power usage values ​​of electricity, gas and heating of various building units are obtained; S13: Collect air quality index, temperature and humidity, PM2.5 concentration and noise level data through the deployed environmental monitoring stations; S14: By accessing the city CIM platform, the vector topographic maps, 3D building models, and underground pipeline network data provided by the city management unit are called up, and multi-source fusion is performed in combination with satellite remote sensing images to construct a 3D urban geographic information dataset containing spatial coordinate information.

[0007] Optionally, the S2 specifically includes: S21: Analyze the feature types of the urban three-dimensional 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 uniformly convert the coordinates of each layer data to the WGS-84 geographic coordinate system; S22: performing block reconstruction processing on the extracted three-dimensional building model to generate a closed three-dimensional structure based on the building boundary line and height information; S23: Surface models are generated from surface cover elements such as water bodies and green spaces using a surface fitting algorithm. Terrain elevation information is integrated using a terrain reconstruction method based on a TIN structure to form a surface base that seamlessly connects with the building model. S24: Reconstruct the 3D topological network structure based on the spatial layout relationship between various pipeline nodes and connected paths in the underground pipeline network data, and use the voxel interpolation algorithm to generate a visual path representation; S25: All three-dimensional models processed by S22 to S24 are uniformly registered and fused according to their spatial coordinate positions to construct a reference three-dimensional model with a spatial index structure.

[0008] Optionally, the S23 specifically includes: S231: performing a node extraction operation on the input terrain vector data, extracting all terrain sampling points with elevation value attributes, and recording the spatial coordinates of each sampling point in the form of a triple; S232: Based on all sampling point sets, a two-dimensional Delaunay triangulation structure is constructed to generate a set of triangular facets composed of nodes, each of which is a convex triangle formed by three coplanar points; S233: Use the three elevation points of each triangle as vertices for linear interpolation to construct a continuous elevation patch; S234: Continuously stitch all triangular facets to form a complete three-dimensional surface model. For the intersection area between the terrain edge and the building base, boundary lines are extracted and edge nodes are aligned based on the coordinates of the overlapping area. The minimum error fitting method is used to adjust the coordinate difference of the overlapping area.

[0009] Optionally, the S24 specifically includes: S241: Perform structured analysis on the original data of the underground pipe network, identify the three-dimensional coordinate positions of all predetermined nodes and connection paths, construct an initial topological relationship diagram, and form the basic structure of the node set and path set; S242: Establishing a three-dimensional topological network structure based on the connection relationship between nodes, representing all nodes as point objects with spatial coordinates, representing connection paths as spatial curve segments, and assigning corresponding type identification, geometric attributes, and connectivity information to each node and path; S243: Discretize the entire urban area into a regular voxel grid structure, where each voxel unit has a uniform spatial size and records its index position in the three-dimensional space; S244: For each pipe network path segment, based on the coordinate relationship between its starting point and end point, a linear interpolation method or a B-spline interpolation method is used to generate a continuous set of intermediate sampling points; S245: Map the sampled path points to the corresponding voxel grid, assign occupation mark values ​​to the voxel units occupied by the path, and form a voxel path function ; S246: The Marching Cubes algorithm is used to perform isosurface reconstruction on voxel units, converting discrete paths into a continuous visual geometric mesh model.

[0010] Optionally, the S3 specifically includes: S31: perform structured preprocessing on the traffic flow data, energy consumption data and environmental monitoring data collected in S1, classify and identify them according to the collection source, 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 build a basic data structure with triples as space-time index; S33: establishing each type of data as an independent dynamic data layer, wherein each data element in the layer represents the state value of a specified spatial position at a specified time point; S34: Perform layer format conversion and data encoding on the constructed dynamic layer, encapsulate the layer content in spatiotemporal data format, and generate a visualization slice file.

[0011] Optionally, the 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 embedded position in the 3D model and timeline position ; S43: Build a spatiotemporal overlay rendering buffer, group all dynamic data layers by data category and time series, load them in sequence 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: establishing a binding relationship for the area where there is an interactive relationship between the layer and the three-dimensional model; S45: Calling the 3D visualization engine, performing frame-level rendering in the order of layer timestamps, and finally generating a fused dynamic scene.

[0012] Optionally, the S5 specifically includes: S51: receiving simulation parameters input by the user, including target area range, simulation start and end time, simulation time step, intervention variable selection, and output indicator type; S52: Based on the target area range input by the user, extract the numerical records of all dynamic layers in the area within the deduction 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 multidimensional data set; S53: Perform time series slicing on the extracted data set according to the simulation time step specified by the user, dividing the continuous time period into a number of equally spaced step intervals, and extracting the corresponding traffic, energy, and environmental data in each time interval as input variables; S54: In each time interval, based on the type of intervention variable selected by the user, analyze its influence on the relevant factors in the target area under the current state, and perform progressive deduction operations based on historical change trends to calculate the spatial state transition process between adjacent time steps; S55: During the entire deduction period, all calculated spatial state transfer results are gradually connected, and a continuous evolution path is constructed according to the order of position change and time evolution.

[0013] Optionally, the S54 specifically includes: S541: According to the intervention variable type selected by the user, the current state value of the layer corresponding to the target area in the fused dynamic scene is retrieved, and other related variables affected by it are extracted to establish an influence relationship matrix; S542: Based on the impact relationship matrix, the spatial diffusion model of the current state value of the intervention variable is performed. The distribution of local impact gain coefficients of all spatial grid cells in the target area is calculated by using the interaction method of geographically adjacent cells to form an intervention-driven impact increment map; S543: extracting historical time series data corresponding to the current prediction variable, constructing a time series within the user-set lookback time window, calculating the trend component of the variable using the triple exponential smoothing method, and obtaining the historical trend prediction increment; S544: Add the state value of the current position at the current time step, the historical trend prediction increment, and the intervention impact increment to calculate the predicted state value of the next step; S545: Repeat the process from S541 to S544, advance step by step according to the time step, build a continuous spatial state transfer sequence, and integrate the predicted values ​​of all spatial units into a state transfer map in chronological order.

[0014] Optionally, the 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 a visual representation for each path segment, and set the corresponding graphic type, color, width, transparency, and animation parameters; S63: Converting the spatial position and time information of the path node into visual key frame data, and inserting interpolation points for inter-frame transition; S64: Loading each frame into the 3D reference model in the order of the path, and controlling the display level, transparency and playback order to achieve layer organization; S65: Outputting the processed dynamic visualization content to the display terminal, and loading control instructions to implement play, pause and jump operations.

[0015] Beneficial effects of the present invention: The present invention solves the problem of difficulty in spatiotemporal superposition of multi-source heterogeneous data in the existing technology 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 superimposing the above layers onto the three-dimensional model, a unified expression of the city's operating status in time and space dimensions is achieved, thereby improving the integrity and visual accuracy of data display.

[0016] The present invention introduces deduction parameters input by the user, combines intervention variable analysis with historical trend modeling, gradually calculates the spatiotemporal evolution path, and outputs dynamic visualization results in the form of key frame sequences, thus realizing a closed loop of the entire process from data collection to simulation deduction to dynamic presentation. It also improves the interactivity, predictive ability and operational efficiency of the urban simulation display system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A schematic diagram of a visual smart city display method according to an embodiment of the present invention; Figure 2 Schematic diagram of the process of constructing a reference three-dimensional model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0020] It should be noted that references in the specification to "one embodiment," "an embodiment," "exemplary embodiments," "some embodiments," etc. indicate that the described embodiments may include specific features, structures, or characteristics, but not necessarily every embodiment will include such specific features, structures, or characteristics. Furthermore, when specific features, structures, or characteristics are described in conjunction with an embodiment, it is within the knowledge of persons skilled in the relevant art to implement such features, structures, or characteristics in conjunction with other embodiments (whether or not explicitly described).

[0021] In general, terms can be understood, at least in part, from their use in context. For example, depending at least in part on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending at least in part on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0022] like Figure 1-Figure 2 As shown, a visual smart city display method includes the following steps: S1: Real-time collection of traffic flow data, energy consumption data, environmental monitoring data and urban three-dimensional geographic information data; S2: Construct a benchmark 3D model with a spatial coordinate system based on the city’s 3D geographic information data; S3: Map traffic flow data, energy consumption data, and environmental monitoring data into dynamic data layers with timestamps; S4: superimpose the dynamic data layer onto the reference 3D model according to the temporal and spatial coordinates to generate a fused dynamic scene; S5: Receive the deduction parameters input by the user and calculate the corresponding evolution path in real time based on the data layers superimposed in the fusion dynamic scene; S6: Convert the spatiotemporal change process reflected in the evolution path into a dynamic visualization effect and output it to a display terminal.

[0023] S1 specifically includes: S11: Traffic flow monitoring cameras and geomagnetic vehicle detectors deployed at intersections of urban arterial roads collect real-time data on vehicle frequency, average speed, and queue length, and upload it to the central processing system with a sampling period of 5 seconds. S12: By connecting to the municipal energy dispatch system and the building energy consumption monitoring network, we can obtain the real-time power usage values ​​of electricity, gas, and heating for various building units, collect energy consumption data at a 1-minute granularity, and store it in the database simultaneously. S13: Air quality index, temperature and humidity, PM2.5 concentration, and noise level data are collected through deployed environmental monitoring stations. All stations use multi-parameter sensors certified by national standards. Samples are taken every 10 seconds and pre-processed locally before uploading. S14: By accessing the city CIM (City Information Modeling) platform, vector topographic maps, 3D building models, and underground pipeline network data provided by urban management units are called upon, and multi-source fusion is combined with satellite remote sensing imagery to construct a 3D urban geographic information dataset containing spatial coordinate information. The above steps achieve real-time perception of various dynamic elements in the city by uniformly collecting and uploading transportation, energy, environment, and spatial geographic data at high frequencies, ensuring the timeliness and accuracy of subsequent 3D displays and providing a solid data foundation for the system to build dynamic visualization scenes that are consistent in time and space.

[0024] S2 specifically includes: S21: Analyze the feature types of the urban three-dimensional 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 uniformly convert the coordinates of each layer data to the WGS-84 geographic coordinate system; S22: performing block reconstruction processing on the extracted three-dimensional building model to generate a closed three-dimensional structure based on the building boundary line and height information; S23: Surface models are generated from surface cover elements such as water bodies and green spaces using a surface fitting algorithm. Terrain elevation information is integrated using a terrain reconstruction method based on a TIN (Triangulated Irregular Network) structure to form a surface base that seamlessly connects with the building model. S24: Reconstruct the 3D topological network structure based on the spatial layout relationship between various pipeline nodes and connected paths in the underground pipeline network data, and use the voxel interpolation algorithm to generate a visual path representation; S25: All kinds of 3D models processed by 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, and finally output a 3D scene dataset that meets the GIS engine parsing standards. The above steps realize the construction of a benchmark 3D model with consistent spatial relationships and complete element expression through unified coordinate conversion, model reconstruction and spatial registration of multi-source geographic spatial information, which is conducive to the subsequent high-precision overlay of multiple types of dynamic data and accurate visualization of urban simulation scenes.

[0025] S23 specifically includes: 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 the form of a triplet ,in and Represents geographic plane position coordinates, Indicates the corresponding elevation value; S232: Based on all sampling point sets, a two-dimensional Delaunay triangulation structure is constructed 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. S233: Use the three elevation points of each triangle as vertices for linear interpolation to construct continuous elevation patches using the linear interpolation formula: , where the coefficient Calculated by solving the following linear equations: , Among them, three points are the coordinates of the current patch vertices respectively; S234: Continuously stitch all triangular facets to form a complete three-dimensional surface model. For the intersection area between the terrain edge and the building base, boundary lines are extracted and edge nodes are aligned based on the coordinates of the overlapping area. The minimum error fitting method is used to adjust the coordinate difference of the overlapping area to ensure seamless splicing of the surface surface and the building model. The above steps achieve a geometric continuity connection between high-precision terrain modeling and building models by adopting the TIN triangulation method and integrating linear elevation interpolation and boundary alignment technology, ensuring that the urban three-dimensional model does not have elevation mutations or visual faults during the superposition of multi-source data, thereby improving the authenticity and engineering applicability of the visualization scene.

[0026] Boundary line extraction and minimum error fitting in S234 specifically include: S2341: Extract the boundary triangle facets of the terrain model constructed by TIN, identify the triangulated area that overlaps with the projected boundary of the building model, and set the boundary overlap buffer width to , extract the conditions that meet The surface node set of is the terrain triangulation node, is the building boundary line coordinate set, represents the Euclidean distance function; S2342: Performing a node pairing operation on the ground surface node set and the building model base edge point set, using a nearest neighbor search algorithm to establish a one-to-one correspondence relationship, and generating a pairing set; S2343: Perform elevation difference analysis on each pair of nodes and construct the objective function: ,in is the surface node elevation, is the elevation of the edge point of the corresponding building base, represents the total fitting error; S2344: Use the least squares optimization method to adjust the elevation values ​​of the surface nodes within the boundary buffer zone so that Minimum and maintain topological continuity constraints between nodes; S2345: Perform surface reconstruction on the adjusted boundary area and reconstruct the boundary triangulation fragments to ensure that there are no sudden elevation changes or overlaps between the terrain surface and the building edges, thereby forming a continuous and smooth visual base boundary transition zone.

[0027] S24 specifically includes: S241: Perform structured analysis on the raw data of the underground pipe network to identify the three-dimensional coordinate positions of all predetermined nodes (including inspection wells, valves, pumping stations, etc.) and connection paths (including water supply and drainage pipes, cable channels, etc.), construct an initial topological relationship diagram, and form the basic structure of node sets and path sets; S242: Establishing a three-dimensional topological network structure based on the connection relationship between nodes, representing all nodes as point objects with spatial coordinates, representing connection paths as spatial curve segments, and assigning corresponding type identification, geometric attributes, and connectivity information to each node and path, thereby constructing a directed graph structure in the three-dimensional space; 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 the three-dimensional space. The voxel index position of any spatial point in the grid is calculated using the following formula: ,in, Represents a spatial point The voxel index where it is located; Represents the side length of the voxel unit; Indicates floor operation; S244: For each pipe network path segment, based on the coordinate relationship between its starting point and end point, a continuous set of intermediate sampling points is generated using a linear interpolation or B-spline interpolation method, so that the path has a high-resolution representation in space and forms a discrete point sequence that can be used for voxel mapping; S245: Map the sampled path points to the corresponding voxel grid, assign occupation mark values ​​to the voxel units occupied by the path, and form a voxel path function , which is used to record the spatial distribution relationship of the three-dimensional path in the grid. Its expression is as follows: ;in, is the path voxel occupancy function; is the index coordinate of the voxel in three-dimensional space; a value of 1 indicates that there is a path point in the voxel, and a value of 0 indicates an empty voxel; S246: The Marching Cubes algorithm is used to perform isosurface reconstruction operations on all voxel units with a label value of 1, converting discrete paths into continuous visual geometric mesh models, enabling simulation rendering and interactive visualization of path structures in three-dimensional space. Through the above steps, the underground pipeline network data is abstracted from a topological structure into a three-dimensional graph model, and the voxelized interpolation method is used to achieve precise mapping and visual expression of the path in space. This not only improves the integrity and interactivity of the three-dimensional model, but also provides a data foundation and modeling support for the visual monitoring and simulation deduction of urban underground facilities.

[0028] S3 specifically includes: S31: Perform structured preprocessing on the traffic flow data, energy consumption data, and environmental monitoring data collected in S1, classify and identify them according to the collection source, and determine the geographic projection coordinates of each type of data , to achieve spatial correspondence between data and target locations in the three-dimensional urban model; S32: Introduce time attributes for each type of data and assign a unique timestamp to each data record according to the actual collection time , construct a triple It is the basic data structure of space-time index. 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 the time accuracy matches the collection frequency. S33: Each type of data is established as an independent dynamic data layer. Each data element in the layer represents the state value of a specified spatial position at a specified time point and is represented by a mapping function in the following form: ,in, For the Class dynamic data layer in spatial location , time point The state value function under ; is the corresponding state value, including vehicle speed, vehicle density, energy consumption power, air quality index or PM2.5 concentration, etc.; : Corresponding to traffic flow, energy consumption and environmental monitoring layers respectively; S34: Perform layer format conversion and data encoding on the constructed dynamic layer, encapsulate the layer content using spatiotemporal data formats such as GeoTIFF, NetCDF, or GeoJSON, and generate visualization slice files for loading three-dimensional scenes to support on-demand calling 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 with timestamp as the core and uniformly formatting and mapping various types of perception data according to the layer structure, so that dynamic data can be accurately superimposed and continuously presented in the three-dimensional city model, laying a data foundation for subsequent simulation calculations and visualization deductions.

[0029] S4 specifically includes: S41: Reads the benchmark 3D model constructed in S2 and various dynamic data layers generated in S3, uniformly adopting the WGS-84 spatial reference coordinate system and UTC time format to ensure the consistency of the coordinate systems of the model and data in space and time; S42: For each data unit in the dynamic data layer, according to its spatial coordinates With timestamp , perform spatial projection and time matching processing to calculate its embedded position in the 3D model and timeline position , the following coordinate transformation formula is used to complete the model matching: ,in, It is the plane projection coordinate recorded in the layer data; Record values ​​for layers used to visualize height-mapped values ​​(e.g., energy consumption, concentration, or traffic intensity); is a spatial coordinate conversion function used to map two-dimensional plane coordinates and values ​​into three-dimensional space coordinates; The actual rendering coordinates in the 3D model after conversion; S43: Build a spatiotemporal overlay rendering buffer, group all dynamic data layers by data category and time series, load and assign each type of layer a unique layer ID and color / texture mapping strategy, and establish a layer-time-position ternary overlay table to achieve an orderly combination of layers in spatial position and time dimensions; Table 1 Layer-time-location ternary overlay table In Table 1 above, the layer ID represents the unique identifier of the layer to which the data belongs, which is 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 elevation or building surface position after mapping, which is used for rendering positioning; the display value is the business data value of the current layer element, such as traffic density, energy consumption power, AQI value, etc.; the rendering type represents the visual expression of the data in the 3D scene, such as heat map, bar chart, area coloring, icon point cloud, etc.; the display parameters are used to specify rendering details, such as color mapping, transparency, shape scale, dynamic effects, etc.

[0030] S44: Establish binding relationships between layers and areas of the 3D model where there is an interactive relationship (such as road surfaces, building facades, and ground environment areas), so that the layer values ​​at the corresponding moment can drive the real-time changes of the bound area properties (color, transparency, brightness, animated textures, etc.) in the 3D model; S45: Call the 3D visualization engine and perform frame-level rendering in the order of layer timestamps to finally generate a fused dynamic scene. Different layers are superimposed based on spatial position, 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 on the 3D benchmark model using frame-level temporal rendering. This achieves the precise fusion and realistic reproduction of multiple types of dynamic urban operation data in 3D space, significantly improving the dynamic perception capability and decision-making assistance value of the visualization analysis of urban operation status.

[0031] S5 specifically includes: S51: receiving simulation parameters input by the user, including the target area range, simulation start and end time, simulation time step, intervention variable selection, and output indicator type, where the intervention variables include traffic flow intensity, building energy consumption change trend, and air quality level evolution; S52: Based on the target area range input by the user, extract the numerical records of all dynamic layers in the area within the deduction start and end time period in the fused dynamic scene, classify and organize them according to layer type, time series and spatial position, and construct a multidimensional data set with spatial index and time axis structure; S53: Perform time series slicing on the extracted data set according to the simulation time step specified by the user, dividing the continuous time period into a number of equally spaced step intervals, and extracting the corresponding traffic, energy, and environmental data in each time interval as input variables; S54: In each time interval, based on the type of intervention variable selected by the user, analyze its influence on the relevant factors in the target area under the current state, and perform progressive deduction operations based on historical change trends to calculate the spatial state transition process between adjacent time steps; S55: During the entire deduction time period, all calculated spatial state transfer results are gradually connected, and a continuous evolution path is constructed according to the order of position change and time evolution. The path consists of several data nodes containing spatial coordinates and time labels, and the path is finally output as the deduction result for visual presentation. The above steps realize the calculation of continuous evolution paths that integrate multiple types of urban operation data by constructing a dynamic data extraction process based on user-defined input and a causal deduction strategy for each time period. It can efficiently simulate future state change trends and provide intuitive visual prediction support for city managers.

[0032] S54 specifically includes: S541: Based on the intervention variable type selected by the user, the current state value of the layer corresponding to the target area in the fused dynamic scene is retrieved, and other related variables affected by it are extracted to establish an influence relationship matrix, in which each item represents the quantitative effect intensity of the current intervention variable on the unit change of the adjacent variable at a certain spatial position; S542: Based on the influence relationship matrix, the spatial diffusion model of the current state value of the intervention variable is performed. The distribution of the local influence gain coefficient of all spatial grid cells in the target area is calculated by the interaction of geographically adjacent cells. The intervention-driven influence increment map is formed to reflect the expected disturbance of the intervention variable on other variables within this step. S543: Extract the historical time series data corresponding to the current prediction variable, construct a time series within the user-set lookback time window, calculate the trend component of the variable using the triple exponential smoothing method, and obtain the historical trend prediction increment ; This is achieved through the following steps: First, for each position The historical value sequence is subjected to three exponential smoothing processes to obtain the first-level smoothing value Secondary smoothing value and the third-level smoothing value ; Then calculate the increment of the trend forecast value at the current moment, the formula is: ,in, Indicates the increment of the historical trend item to the predicted value of the variable; : are the variable values ​​after 1st, 2nd and 3rd order exponential smoothing respectively; : is the smoothing trend weight coefficient, satisfying , used to regulate the degree of trend response; S544: Add the state value of the current position at the current time step, the historical trend prediction increment, and the intervention impact increment to calculate the predicted state value of the next step. The formula is: ,in, Indicates location In time The predicted value of the state; is the state value of the current position at the current time step; for the incremental impact of the intervention; S545: Repeat the process from S541 to S544, advancing step by step according to the time step, constructing a continuous spatial state transition sequence, and integrating the predicted values ​​of all spatial units into a state transition map in chronological order, which serves as the basic basis for subsequent evolution path calculation and visualization output; the above steps combine intervention variable driving with trend component modeling to achieve the integration of causal analysis of spatial state changes and time series prediction, so that the generation of evolution path has both local response capabilities and global trend prediction capabilities, thereby improving the timeliness and accuracy of urban operation status deduction.

[0033] 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 a visual representation for each path segment, and set the corresponding graphic type, color, width, transparency, and animation parameters; S63: Converting the spatial position and time information of the path node into visual key frame data, and inserting interpolation points for inter-frame transition; S64: Loading each frame into the 3D reference model in the order of the path, and controlling the display level, transparency and playback order to achieve layer organization; S65: Outputting the processed dynamic visualization content to the display terminal, and loading control instructions to implement play, pause and jump operations.

[0034] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0035] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A visual smart city display method, characterized in that: The following steps are involved: S1: Real-time collection of traffic flow data, energy consumption data, environmental monitoring data and urban three-dimensional geographic information data; S2: Construct a benchmark 3D model with a spatial coordinate system based on the city’s 3D geographic information data; S3: Map traffic flow data, energy consumption data, and environmental monitoring data into dynamic data layers with timestamps; S4: superimpose the dynamic data layer onto the reference 3D model according to the temporal and spatial coordinates to generate a fused dynamic scene; S5: Receive the deduction parameters input by the user and calculate the corresponding evolution path in real time based on the data layers superimposed in the fusion dynamic scene; S6: Convert the spatiotemporal change process reflected in the evolution path into a dynamic visualization effect and output it to a display terminal.

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

3. A visual smart city display method according to claim 1, characterized in that: The S2 specifically includes: S21: Analyze the feature types of the urban three-dimensional 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 uniformly convert the coordinates of each layer data to the WGS-84 geographic coordinate system; S22: performing block reconstruction processing on the extracted three-dimensional building model to generate a closed three-dimensional structure based on the building boundary line 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 a TIN structure to form a surface base that seamlessly connects with the building model. S24: Reconstruct the 3D topological network structure based on the spatial layout relationship between various pipeline nodes and connected paths in the underground pipeline network data, and use the voxel interpolation algorithm to generate a visual path representation; S25: Various 3D models processed by S22 to S24 are uniformly registered and fused according to their spatial coordinate positions to construct a reference 3D model.

4. A visual smart city display method according to claim 3, characterized in that: The S23 specifically includes: S231: performing a node extraction operation on the input terrain vector data, extracting all terrain sampling points with elevation value attributes, and recording the spatial coordinates of each sampling point in the form of a triple; S232: Based on all sampling point sets, a two-dimensional Delaunay triangulation structure is constructed to generate a set of triangular facets composed of nodes, each of which is a convex triangle formed by three coplanar points; S233: Use the three elevation points of each triangle as vertices for linear interpolation to construct a continuous elevation patch; S234: Continuously stitch all triangular facets to form a complete three-dimensional surface model. For the intersection area between the terrain edge and the building base, boundary lines are extracted and edge nodes are aligned based on the coordinates of the overlapping area. The minimum error fitting method is used to adjust the coordinate difference of the overlapping area.

5. A visual smart city display method according to claim 4, characterized in that: The S24 specifically includes: S241: Perform structured analysis on the original data of the underground pipe network, identify the three-dimensional coordinate positions of all predetermined nodes and connection paths, construct an initial topological relationship diagram, and form a basic structure of node sets and path sets; S242: Establishing a three-dimensional topological network structure based on the connection relationship between nodes, representing all nodes as point objects with spatial coordinates, representing connection paths as spatial curve segments, and assigning corresponding type identification, geometric attributes, and connectivity information to each node and path; S243: Discretize the entire urban area into a regular voxel grid structure, where each voxel unit has a uniform spatial size and records its index position in the three-dimensional space; S244: For each pipe network path segment, based on the coordinate relationship between its starting point and end point, a linear interpolation method or a B-spline interpolation method is used to generate a continuous set of intermediate sampling points; S245: Map the sampled path points to the corresponding voxel grid, assign occupation mark values ​​to the voxel units occupied by the path, and form a voxel path function ; S246: The Marching Cubes algorithm is used to perform isosurface reconstruction on voxel units, converting discrete paths into a continuous visual geometric mesh model.

6. A visual smart city display method according to claim 1, characterized in that: The S3 specifically includes: S31: perform structured preprocessing on the traffic flow data, energy consumption data and environmental monitoring data collected in S1, classify and identify them according to the collection source, 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 build a basic data structure with triples as space-time index; S33: establishing each type of data as an independent dynamic data layer, wherein each data element in the layer represents the state value of a specified spatial position at a specified time point; S34: Perform layer format conversion and data encoding on the constructed dynamic layer, encapsulate the layer content in spatiotemporal data format, and generate a visualization slice file.

7. A visual smart city display method according to claim 1, characterized in that: The 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 embedded position in the 3D model and timeline position ; S43: Build a spatiotemporal overlay rendering buffer, group all dynamic data layers by data category and time series, load them in sequence 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: establishing a binding relationship for the area where there is an interactive relationship between the layer and the three-dimensional model; S45: Calling the 3D visualization engine, performing frame-level rendering in the order of layer timestamps, and finally generating a fused dynamic scene.

8. A visual smart city display method according to claim 1, characterized in that: The S5 specifically includes: S51: receiving simulation parameters input by the user, including target area range, simulation start and end time, simulation time step, intervention variable selection, and output indicator type; S52: Based on the target area range input by the user, extract the numerical records of all dynamic layers in the area within the deduction start and end time period in the fused dynamic scene, classify and organize them according to layer type, time series and spatial location, and construct a multidimensional data set; S53: Perform time series slicing on the extracted data set according to the simulation time step specified by the user, dividing the continuous time period into several equally spaced step intervals, and extracting the corresponding traffic, energy, and environmental data in each time interval as input variables; S54: In each time interval, based on the type of intervention variable selected by the user, analyze its influence on the relevant factors in the target area under the current state, and perform progressive deduction operations based on historical change trends to calculate the spatial state transition process between adjacent time steps; S55: During the entire deduction period, all calculated spatial state transfer results are gradually connected, and a continuous evolution path is constructed according to the order of position change and time evolution.

9. A visual smart city display method according to claim 8, characterized in that: The S54 specifically includes: S541: According to the intervention variable type selected by the user, the current state value of the layer corresponding to the target area in the fused dynamic scene is retrieved, and other related variables affected by it are extracted to establish an influence relationship matrix; S542: Based on the impact relationship matrix, the spatial diffusion model of the current state value of the intervention variable is performed. The distribution of local impact gain coefficients of all spatial grid cells in the target area is calculated by using the interaction method of geographically adjacent cells to form an intervention-driven impact increment map; S543: extracting historical time series data corresponding to the current prediction variable, constructing a time series within the user-set lookback time window, calculating the trend component of the variable using the triple exponential smoothing method, and obtaining the historical trend prediction increment; S544: Add the state value of the current position at the current time step, the historical trend prediction increment, and the intervention impact increment to calculate the predicted state value of the next step; S545: Repeat the process from S541 to S544, advance step by step according to the time step, build a continuous spatial state transfer sequence, and integrate the predicted values ​​of all spatial units into a state transfer map in chronological order.

10. A visual smart city display method according to claim 1, characterized in that: The 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 a visual representation for each path segment, and set the corresponding graphic type, color, width, transparency, and animation parameters; S63: Converting the spatial position and time information of the path node into visual key frame data, and inserting interpolation points for inter-frame transition; S64: Loading each frame into the 3D reference model in the order of the path, and controlling the display level, transparency and playback order to achieve layer organization; S65: Outputting the processed dynamic visualization content to the display terminal, and loading control instructions to implement play, pause and jump operations.

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