A method and system for generating a three-dimensional digital map of a city based on spatial entropy

Abstract

This invention provides a method and system for generating 3D digital maps of cities based on spatial entropy, relating to the field of artificial intelligence-based urban design. The method includes constructing a 3D spatial digital map based on land use function plots, measuring and displaying the spatial entropy of these plot units, collecting data on population activity and public sentiment, identifying and displaying plots requiring secondary updates and warnings, adjusting and reviewing the spatial entropy before updating, stitching together the adjusted 3D digital maps, and printing them out for reference by planning, design, and management personnel. It focuses on the production and display technology of large-scale 3D digital map images based on spatial entropy, achieving accurate expression of large-scale urban landscape features across a wide area, enabling real-time display of urban landscape features and rapid revelation of basic patterns, thus improving the efficiency and accuracy of determining urban landscape features.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence urban design technology, specifically to a method and system for generating three-dimensional digital maps of cities based on spatial entropy. Background Technology

[0002] Urban landscape plays a guiding role in establishing a city's image, enhancing its quality, and optimizing its spatial environment. Current research and practice on urban landscape focus on two aspects: first, analyzing and summarizing the constituent content and elements of urban landscape to guide urban planning, construction, and architectural design; and second, studying specific practical experiences in urban landscape construction. Spatial entropy, as an important variable characterizing the complexity of urban space, can be introduced into a systematic and accurate measurement of multidimensional urban landscape characteristics of land parcels, guiding the efficient selection of urban landscape improvement units, aiming to achieve a practical breakthrough in urban landscape feature identification and improvement unit selection. Summary of the Invention

[0003] (a) Technical problems to be solved

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for generating three-dimensional digital maps of cities based on spatial entropy. The urban landscape update based on spatial entropy provides an automated and intelligent image production and display technology, which realizes the accurate expression of large-scale urban landscape features with a wide range of areas and improves the efficiency and accuracy of determining urban landscape features.

[0005] (II) Technical Solution

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] Firstly, a method for generating 3D digital urban maps based on spatial entropy is provided, including:

[0008] Acquire 3D oblique photogrammetry data of the target city, translate it into geographic information vector data and input it into the geographic information platform; after data verification, perform data overlay processing to construct a basic sand table of the city's 3D digital map; and identify the land parcel unit type through POI data to divide the target city into geographic spatial units based on land use function parcels.

[0009] A spatial entropy measurement method is constructed to measure the entropy of building volume scale, building structure form, and building facade surface of different types of land parcel units. The natural discontinuity method is used to obtain the spatial entropy value types of different types of land parcel units, and different types of spatial entropy digital map entropy value result layers and entropy value type layers are established. The image results of the target city's three-dimensional digital map are displayed according to the functional parameters of the digital layers.

[0010] Detailed urban design schemes at the plot level in the target area are collected and vectorized to construct a spatial entropy sample library. Samples in the spatial entropy sample library are automatically matched according to the spatial attributes of plot units. Six types of spatial entropy values ​​of the samples are calculated. The range of sample spatial entropy values ​​is used as the spatial entropy threshold judgment standard. Urban plots that do not meet the spatial entropy threshold are marked as plots that may need to be updated and displayed in a hierarchical manner. Population vitality data and public sentiment data in the target area are collected as secondary judgment conditions for updating plots. Plots that need to be updated are identified and labeled for display.

[0011] A 3D digital map sensor for the city is built to recognize user voice and actions, construct a spatial entropy adjustment instruction library, adjust building height, building area, and building color, and review whether the adjustment content meets the relevant standards and conditions of the target city's design style, and update the adjusted spatial entropy in the 3D digital map.

[0012] The adjusted 3D digital map of the city will be stitched together and printed out, including the entropy values ​​of various spatial entropy units before and after adjustment, bird's-eye views before and after the 3D digital map update, and the spatial entropy values ​​and bird's-eye views of the spatial entropy units that exceed the threshold after adjustment, for reference by planning, design and management personnel.

[0013] Preferably, the acquisition of 3D oblique photographic data of the target city, the translation of the resulting geographic information vector data, and the input into the geographic information platform; after data verification, data overlay processing is performed to construct a basic sand table of the city's 3D digital map; and the target city is divided into geographic spatial units based on land use function plots by identifying the type of land parcel through POI data, specifically including:

[0014] Geographic information basic data collection: using a LiDAR point cloud data collection system, a surveying drone acquires 3D oblique photogrammetry data of the target city, and loads the deep learning digital interpretation interface of the digital map to input the geographic information vector data obtained by translating the 3D oblique photogrammetry data into the geographic information platform.

[0015] The construction of a 3D digital map of the city involves verifying geographic information by converting the 3D oblique photography data of the target city and the geographic information vector data that has been verified to a unified CGCS2000 coordinate system, performing data overlay processing based on geographic coordinates, and creating a 3D digital map of the city based on a geographic information platform.

[0016] The delineation of land parcel unit types involves collecting POI data from the target city via the POI data collection and land use identification interface of the digital map. Different POI reference land use area values ​​are assigned to different types of POI data. POI types are then categorized according to land parcel unit type. The dominant land parcel type with the largest proportion of the total area of ​​the corresponding POI type within the land parcel unit is determined as the land use function of that land parcel unit. The specific calculation formula is as follows:

[0017]

[0018] Where i represents the type of POI, Fi represents the number of POIs of the i-th type within the plot unit, and S i This represents the assigned POI reference land area value, where m represents the total number of POI types within the land parcel unit, and n represents the total number of POI types under the corresponding type of the land parcel unit. The land use function of the land parcel unit is finally determined by comparing the C values ​​of different types.

[0019] Preferably, the method for constructing spatial entropy measurement measures the entropy of building volume scale, building structural form, and building facade surface of different types of land parcel units; it uses the natural discontinuity method to obtain the spatial entropy value types of different types of land parcel units, and establishes digital map entropy value result layers and entropy value type layers for different types of spatial entropy. Based on the functional parameters of the digital layers, it displays the image results of the target city's 3D digital map, specifically including:

[0020] Spatial entropy index measurement measures three types of spatial entropy indices closely related to architectural style: building volume scale entropy, building structural form entropy, and building facade surface entropy. The index calculation module is loaded to calculate the entropy of various plot units in the target city.

[0021] The entropy of building volume includes building height entropy and building base area entropy; the entropy of building structure form includes building main structure form entropy and building roof form entropy; and the entropy of building facade skin includes building facade color entropy and building facade material entropy.

[0022] The basic formula for the spatial entropy index system is:

[0023]

[0024] Where H(X) represents the result of the spatial entropy measurement of the object, n represents the total number of types of the corresponding measurement standard, and P i This represents the probability of X taking the value i under the corresponding category;

[0025] Spatial entropy type identification and display: For different types of spatial entropy values ​​obtained from the calculation, the land parcel units are divided into three categories: "high entropy", "medium entropy" and "low entropy" using the natural discontinuity method; The spatial entropy database interface is loaded into the digital map, and spatial matching of the geographic information vector data and spatial entropy data of the target city is established based on the geographic coordinates. Entropy value result layers and entropy value type layers of digital maps of different types of spatial entropy are established, and the image results of the three-dimensional digital map of the target city are displayed according to the functional parameters of the digital layers;

[0026] The functional parameters of the digital layer include: displaying the digital map layer in layers or overlays through the display settings; linking and viewing entropy value units of the same result or type through the hyperlink function of the digital map layer; recording the statistical feature chart results of a certain type of entropy value results through the chart statistics function of the digital map layer; and roaming to specific plot units to view the corresponding type features under the 3D oblique photogrammetry real-world image when viewing different types of spatial entropy layers through the real-world scene retracing function of the digital map layer.

[0027] Preferably, the process involves collecting detailed urban design schemes at the plot level in the target area and vectorizing them to construct a spatial entropy sample library. Samples within the spatial entropy sample library are automatically matched based on the spatial attributes of the plot units. Six types of spatial entropy values ​​are calculated for the samples. The range of sample spatial entropy values ​​is used as the spatial entropy threshold judgment standard. Urban plots that do not meet the spatial entropy threshold are marked as potentially needing updates and displayed in a tiered manner. Population activity data and public sentiment data in the target area are collected as secondary judgment conditions for updating plots. Plots requiring updates are identified and tagged for display. Specifically, this includes:

[0028] The spatial entropy sample library is constructed by collecting detailed urban design schemes at the plot level in the target area, intelligently translating the design scheme content into three-dimensional vector data, inputting it into a three-dimensional digital map, constructing a spatial entropy sample library for the target area, calculating the land use function of all plots and the normalized boundary shape index, perimeter, and area of ​​the three spatial attributes, and labeling them in the form of tags.

[0029] Spatial entropy threshold matching and determination involves calculating the three major spatial attribute values ​​of the target land parcel unit, automatically matching the unique sample data in the spatial entropy sample library that has the same land use function and the smallest difference in spatial attributes, and then calculating the six types of spatial entropy values ​​of the matching sample using the spatial entropy measurement method. The maximum and minimum values ​​of the six types of spatial entropy values ​​of the sample are used as the determination criteria for the six types of spatial entropy thresholds of the target land parcel unit.

[0030] The formula for the spatial attribute difference is as follows:

[0031]

[0032] Where Cn, Ln, and Sn are the normalized shape index, perimeter, and area values ​​of the sample plots, respectively, and Cx, Lx, and Sx are the normalized shape index, perimeter, and area values ​​of the sample plots, respectively.

[0033] The extraction of land parcels exceeding the threshold and the display of potentially updated land parcel units are achieved by using six types of spatial entropy thresholds obtained through spatial entropy threshold matching and judgment as the judgment criteria. Land parcels exceeding the threshold range are extracted and labeled with spatial entropy type tags. The spatial entropy values ​​of the extracted land parcels exceeding the range are standardized. Then, the severity of exceeding the six types of spatial entropy thresholds is divided into three levels: severe, moderate, and slight, using the natural discontinuity method. The extracted land parcels are labeled with severity tags and marked as potentially updated land parcel units. They are then classified and displayed in the 3D digital map using different colors.

[0034] The results of the verification and early warning update of the population location and emotion data show that the collected population activity data and public sentiment data are loaded into the 3D digital map through spatial correlation. Based on these two types of data, the potential plot units to be updated are re-evaluated. Plots that meet the evaluation criteria are marked as early warning plot units to be updated and displayed on the 3D digital map.

[0035] Preferably, the step of building a city 3D digital map sensor, recognizing user voice and actions, constructing a spatial entropy adjustment instruction library, adjusting building height, building area, and building color, and reviewing whether the adjusted content meets the relevant design style specifications of the target city, and updating the adjusted spatial entropy in the 3D digital map, specifically includes:

[0036] User command voice and action recognition is used to load a VR interactive adjustment module based on urban landscape update interactive commands onto a 3D digital map. This module includes a voice recognition system, a wearable motion recognition device equipped with an inertial sensor, and a handheld control system. It calculates and recognizes user voice and user behavior through transmitted data. The user voice recognition includes commands such as "Forward," "Turn Right," "Turn Left," "View Update Warning Plots," "View Update Request Level," "Adjust Building Color Space Entropy," "Adjust Building Area Space Entropy," "Adjust Building Height Space Entropy," "Confirm," "View," "Yes," and "No." The user behavior includes four actions: walking, eye movement, arm pointing, and finger touch controls.

[0037] A spatial entropy adjustment command library is constructed based on recognized speech and actions. This library enables users to freely walk and browse space on a 3D digital map, select and view detailed information about plots, display spatial entropy values, view update warning information for plots, view update requirement levels, adjust the spatial entropy of building volume scale, adjust the spatial entropy of building structure form, and adjust the spatial entropy of building facade. When a user confirms a certain type of spatial entropy adjustment, the constructed spatial entropy sample library is used as the training set for deep learning. Based on the threshold range of the spatial entropy for that type of plot, new building model data within the threshold range is automatically and randomly generated. If the user is not satisfied, they can repeat the update through adjustment commands.

[0038] The process involves verifying and providing feedback on the standards and specifications for urban landscape design in the target area. For data that is randomly generated after user selection and adjustment, the system performs intelligent review based on the standards and specifications. If the review is passed, the data is entered into the 3D digital map data update system. If the review fails, the data is randomly generated again until it passes the review and is then fed back to the user. The urban landscape design standards and specifications include the requirements for building height, building density, plot ratio, building roof type, building color, and building material control in the overall urban design of the target area.

[0039] Preferably, the process of stitching together and printing the adjusted 3D digital map of the city includes entropy values ​​of various spatial entropy units before and after adjustment, bird's-eye views before and after updating the 3D digital map, and spatial entropy values ​​and bird's-eye views of the 3D digital map units exceeding the threshold after adjustment, for reference by planning, design, and management personnel. Specifically, this includes:

[0040] The process involves updating and standardizing the city's 3D digital map generation. The adjusted land parcel data is combined with the unadjusted land parcel data through spatial overlay to generate updated 3D digital map data. The combined data is then subject to final verification based on the building daylighting spacing and fire protection spacing specifications in the control detailed plan.

[0041] The city's 3D digital map data output involves combining the updated and verified city 3D digital map data with the data before the update and then printing it out. The output data includes bird's-eye views of the city's 3D digital map before and after the update, bird's-eye views of the landscape plot units before and after the update, spatial entropy values ​​of the landscape plot units before and after the adjustment, and the types of spatial entropy of plot units that need to be updated that exceed the threshold, for reference by planning, design and management personnel.

[0042] Secondly, a system for generating three-dimensional digital maps of cities based on spatial entropy is provided, the system comprising:

[0043] The basic data acquisition and display module is used to acquire 3D oblique photography data of the target city, translate it into geographic information vector data and input it into the geographic information platform; after completing data verification, the data is overlaid to construct the basic sand table of the city's 3D digital map; and the target city is divided into geographic spatial units based on land use function plots by identifying the type of land parcel through POI data.

[0044] The spatial entropy measurement and display module is used to construct a spatial entropy measurement method to measure the entropy of building volume scale, building structure form, and building facade surface of different types of land parcel units. The natural discontinuity method is used to obtain the spatial entropy value types of different types of land parcel units, and different types of spatial entropy digital map entropy value result layers and entropy value type layers are established. The image results of the target city's 3D digital map are displayed according to the functional parameters of the digital layers.

[0045] The spatial entropy threshold determination and early warning display module is used to collect detailed urban design schemes at the plot level in the target area and perform vectorization processing to build a spatial entropy sample library. It automatically matches samples in the spatial entropy sample library according to the spatial attributes of the plot unit, calculates six types of spatial entropy values ​​of the samples, uses the range of sample spatial entropy values ​​as the spatial entropy threshold judgment standard, marks urban plots that do not meet the spatial entropy threshold as plots that may need to be updated and displays them in a hierarchical manner, collects population activity data and public sentiment data in the target area as secondary judgment conditions for updating plots, determines the plots that need to be updated and adds tags for display.

[0046] The interactive display and adjustment module for the city's 3D space sand table is used to build a city 3D digital map sensor, recognize user voice and actions, construct a spatial entropy adjustment instruction library, adjust building height, building area, and building color, and review whether the adjustment content meets the relevant standards and conditions of the target city's design style, and update the adjusted spatial entropy in the 3D digital map.

[0047] The results output module is used to stitch together and print out the city's 3D digital map after adjusting the spatial entropy. It includes the entropy values ​​of various spatial entropy units before and after adjustment, the bird's-eye view before and after updating the 3D digital map, and the spatial entropy values ​​and bird's-eye view of the spatial entropy units that exceed the threshold after adjustment, for reference by planning, design and management personnel.

[0048] The system is used to implement the method for generating a three-dimensional digital map of a city based on spatial entropy.

[0049] Thirdly, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that the memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs the aforementioned method for generating a three-dimensional digital map of a city based on spatial entropy.

[0050] Fourthly, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the aforementioned method for generating a three-dimensional digital map of a city based on spatial entropy.

[0051] (III) Beneficial Effects

[0052] (1) This invention provides a method and system for generating urban 3D digital maps based on spatial entropy. In terms of content expression, it focuses for the first time on the production and display technology of large-scale urban 3D digital map images based on spatial entropy. It combines the building volume scale, building structure form, and building facade of the target urban plot unit with urban 3D oblique photography data and geographic information vector data in the form of spatial entropy to construct a 3D digital map. This achieves accurate expression of large-scale urban landscape features with a wide range and large quantity, and increases the expression range from the original 1 square kilometer to more than 10 square kilometers.

[0053] (2) The present invention provides a method and system for generating a three-dimensional digital map of a city based on spatial entropy. In terms of applicable scenarios, it constructs a complete set of rigorous and implemented operation procedures, from the collection of basic geographic information data and the measurement of spatial entropy index system to the determination and early warning of spatial entropy threshold, and then to the interactive display and adjustment of three-dimensional spatial sand table. It then performs spatial positioning, coupling analysis, threshold monitoring and interactive adjustment of large-scale urban landscape features, changes the basic functions of general urban maps that are only readable, difficult to interact with and have low information content, and expands the scope of application of large-scale three-dimensional digital maps of cities based on spatial entropy.

[0054] (3) The present invention provides a method and system for generating three-dimensional digital maps of cities based on spatial entropy. In terms of practical efficiency, it realizes the real-time display of the urban landscape features of the entire region and the rapid revelation of basic laws, avoiding the large amount of ineffective manpower and time costs invested in the basic survey of urban landscape. It reduces the work of determining urban landscape features from the original week to within four hours, avoiding the uncontrollability and time consumption of the previous urban construction part of screening urban landscape improvement units, and also improving the accuracy of data.

[0055] (4) The present invention provides a method and system for generating a three-dimensional digital map of a city based on spatial entropy. In terms of interactive means, it is geared towards the fields of urban planning and architectural design. It incorporates the main structure of the building, the form of the building roof, the building height, the building base area, the building facade color, and the building facade material into the visualization VR interactive adjustment module of the three-dimensional digital map of the city. This enables users to have an instant experience and command adjustment of the urban landscape features when roaming the three-dimensional digital map of the city. The interactive methods involve voice, eye contact, pointing and touch, making it more humanized and the display effect more intuitive and obvious. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the process for generating a large-scale urban 3D digital map based on spatial entropy according to the present invention.

[0057] Figure 2 This is a schematic diagram of land use coding for a three-dimensional digital map of a city according to the present invention;

[0058] Figure 3 This is a schematic diagram showing the spatial entropy value and type of the urban three-dimensional digital map of the present invention;

[0059] Figure 4 This is a schematic diagram of the spatial entropy early warning unit of the urban three-dimensional digital map of the present invention;

[0060] Figure 5 This is a schematic diagram of the interactive adjustment of the city's three-dimensional digital map according to the present invention;

[0061] Figure 6 This is a schematic diagram of the spatial entropy adjustment instruction library for the city's three-dimensional digital map according to the present invention. Detailed Implementation

[0062] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for generating a three-dimensional digital map of a city based on spatial entropy, including the following steps:

[0064] Step S1: Basic Data Acquisition and Display

[0065] Acquire 3D oblique photogrammetry data of the target city, translate it into geographic information vector data and input it into the geographic information platform; after data verification, perform data overlay processing to construct a basic sand table of the city's 3D digital map; and identify the land parcel unit type through POI data to divide the target city into geographic spatial units based on land use function parcels.

[0066] S11, Geographic Information Basic Data Collection

[0067] A surveying drone equipped with a lidar point cloud data acquisition system with an accuracy of less than 10mm is used to acquire 3D oblique photogrammetry data of the target city. The geographic information vector data obtained by translating the 3D oblique photogrammetry data is then loaded into the geographic information platform using a deep learning digital interpretation interface for digital maps.

[0068] In this embodiment, a surveying drone equipped with a LiDAR point cloud data acquisition system with an accuracy within 10mm is used to acquire 3D oblique photogrammetry data of the target city. A deep learning digital interpretation interface for digital maps is then loaded, and the geographic information vector data translated from the 3D oblique photogrammetry data is stored in the ArcGIS spatial data storage database. The geographic information vector data includes topographic information, road information, land parcel boundary information, building information, and facility information of the target city. The building information data includes building height, base area, main structural form, roof type, facade color, and facade material data.

[0069] S12, Construction of 3D Digital Map of the City

[0070] A backpack-mounted laser scanner with a scanning range of up to 200 meters and a camera resolution of over 4K is used to perform geographic information verification in an electric vehicle. The 3D oblique photogrammetry data of the target city and the geographic information vector data that has been verified are converted into a unified CGCS2000 coordinate system. The data is overlaid based on the geographic coordinates and a 3D digital map of the city is created based on the geographic information platform.

[0071] In this embodiment, a backpack-mounted laser scanner with a scanning range of up to 200 meters and a camera resolution of 4K or higher is used in an electric vehicle to perform geographic information verification. Using the spatial adjustment tool in GIS, the 3D oblique photogrammetry data of the target city and the geographic information vector data that has completed data verification are converted into a unified CGCS2000 coordinate system. The data is overlaid according to the geographic coordinates, and a 3D digital map of the city is generated based on the GIS geographic information platform.

[0072] S13. Delineation of Plot Unit Types

[0073] By loading the POI data collection and land use identification interface of the digital map, POI data of the target city is collected, and different POI reference land use area values ​​are assigned to different types of POI data. POI types are classified according to the type of land parcel unit, and the dominant land parcel type with the largest proportion of the total area of ​​the corresponding POI type in the land parcel unit is the land use function of that land parcel unit.

[0074] In this embodiment, POI data collection and land use identification interfaces of digital maps are used to collect POI data for the city. Different POI reference land use area values ​​are assigned to different types of POI data. POI types are classified according to land parcel unit type. The dominant land parcel type with the largest proportion of the total area of ​​the corresponding POI type in the land parcel unit is the land use function of that land parcel unit. The specific calculation formula is as follows:

[0075]

[0076] Where i represents the type of POI, Fi represents the number of POIs of the i-th type within the plot unit, and S i This represents the assigned POI reference land area value, where m represents the total number of POI types within the land parcel unit, and n represents the total number of POI types under the corresponding type of the land parcel unit. The land use function of the land parcel unit is finally determined by comparing the C values ​​of different types.

[0077] After classifying land parcel units based on the identification results, all land parcel units in the city are uniquely coded using a "land use code + number" format. The POI reference land area is determined by averaging statistical data on urban construction land area for different types of cities in the target city and structural sampling data. The land parcel unit types are based on the "Urban Land Use Classification and Planning Construction Land Standards," including five categories: residential land, public service land, commercial land, industrial land, and logistics warehousing land.

[0078] Step S2: Spatial Entropy Measurement and Display

[0079] A spatial entropy measurement method is constructed to measure the entropy of building volume scale, building structure form, and building facade surface of different types of land parcel units. The natural discontinuity method is used to obtain the spatial entropy value types of different types of land parcel units, and different types of spatial entropy digital map entropy value result layers and entropy value type layers are established. The image results of the target city's three-dimensional digital map are displayed according to the functional parameters of the digital layers.

[0080] S21, Spatial Entropy Index Measurement

[0081] The study measures three types of spatial entropy indicators closely related to architectural style: building volume scale entropy, building structural form entropy, and building facade entropy. The calculation is performed on various plot units in the target city by loading the index calculation module.

[0082] In this embodiment, three spatial entropy indicators closely related to architectural style are measured: building volume scale entropy, building structural form entropy, and building facade entropy. The calculation is performed on various plots within the city using an index calculation module. Specifically, the building volume scale entropy includes building height entropy and building base area entropy; the building structural form entropy includes the main building structural form entropy and the roof form entropy; and the building facade entropy includes building facade color entropy and building facade material entropy.

[0083] The basic formula of the spatial entropy index system is as follows:

[0084]

[0085] Where H(X) represents the result of the spatial entropy measurement of the object, n represents the total number of types of the corresponding measurement standard, and P i This represents the probability of X taking the value i under the corresponding category.

[0086]

[0087]

[0088]

[0089] S22. Spatial Entropy Type Recognition and Display

[0090] For the different types of spatial entropy values ​​obtained from the calculation, the land parcel units are divided into three categories: "high entropy", "medium entropy" and "low entropy" using the natural discontinuity method. The spatial entropy database interface is loaded into the digital map, and spatial matching of the geographic information vector data and spatial entropy data of the target city is established based on the geographic coordinates. Entropy value result layers and entropy value type layers of digital maps of different types of spatial entropy are established. The image results of the three-dimensional digital map of the target city are displayed according to the functional parameters of the digital layers.

[0091] In this embodiment, the calculated spatial entropy values ​​of different types are classified into three categories—"high entropy," "medium entropy," and "low entropy"—using the natural discontinuity method. A spatial entropy database interface is loaded into the digital map. Based on geographic coordinates, spatial matching is established between the city's geographic information vector data and spatial entropy data. This creates different types of spatial entropy digital map entropy result layers and entropy type layers. The functional parameters of the digital layers are set to display the image results of the target city's 3D digital map. These functional parameters include: displaying the digital map layers in layers or overlays using display settings; linking to view entropy units with the same result or type using hyperlinks; recording statistical chart results of a specific type of entropy result using charts; and navigating to specific land parcels to view the corresponding type characteristics under the 3D oblique photogrammetry imagery when viewing different types of spatial entropy layers.

[0092] Step S3: Spatial Entropy Threshold Determination and Early Warning Display

[0093] Detailed urban design schemes at the plot level in the target area are collected and vectorized to construct a spatial entropy sample library. Samples in the spatial entropy sample library are automatically matched according to the spatial attributes of plot units. Six types of spatial entropy values ​​of the samples are calculated. The range of sample spatial entropy values ​​is used as the spatial entropy threshold judgment standard. Urban plots that do not meet the spatial entropy threshold are marked as plots that may need to be updated and displayed in a hierarchical manner. Population vitality data and public sentiment data in the target area are collected as secondary judgment conditions for updating plots. Plots that need to be updated and are warned are identified and labeled for display.

[0094] S31. Construction of Spatial Entropy Sample Library

[0095] Detailed urban design plans for the target area at the plot level were collected over the past 5 years. The design content was intelligently translated into three-dimensional vector data using a scanner with a resolution of 1000 dpi or higher. The data was then entered into the three-dimensional digital map generated in step S1 to construct a spatial entropy sample library for the target area. Subsequently, the land use function of all plots and the normalized boundary shape index, perimeter, and area were calculated and labeled.

[0096] In this embodiment, detailed urban design plans at the plot level for the city over the past 5 years are collected, and the design content is intelligently translated into three-dimensional vector data using a scanner with a resolution of 1000 dpi or higher. This data is then entered into the three-dimensional digital map generated in step S1 to construct a spatial entropy sample library for the city. Subsequently, the land use function of all plots and the normalized boundary shape index, perimeter, and area are calculated and labeled. The spatial entropy sample library collects new data once a year and deletes data older than 5 years for dynamic data updates.

[0097] S32. Spatial Entropy Threshold Matching and Judgment

[0098] Calculate the three major spatial attribute values ​​of the target land parcel unit, automatically match the unique sample data in the spatial entropy sample library that has the same land use function and the smallest difference in spatial attributes, and then calculate the six types of spatial entropy values ​​of the matching sample according to the method described in step S2. The maximum and minimum values ​​of the six types of spatial entropy values ​​of the sample are used as the criteria for determining the six types of spatial entropy thresholds of the target land parcel unit.

[0099] In this embodiment, the three major spatial attribute values ​​of each plot unit in the city are calculated. A unique sample data with the same land use function and the smallest spatial attribute difference is automatically matched from the spatial entropy sample library. Then, the six types of spatial entropy values ​​of the matched sample are calculated according to the method described in step S2. The maximum and minimum values ​​of the six types of spatial entropy values ​​of this sample are used as the criteria for determining the six types of spatial entropy thresholds of the target plot unit.

[0100] The formula for the spatial attribute difference is as follows:

[0101]

[0102] Where Cn, Ln, and Sn are the normalized shape index, perimeter, and area values ​​of the sample plots, respectively, and Cx, Lx, and Sx are the normalized shape index, perimeter, and area values ​​of the sample plots, respectively.

[0103] S33. Extraction of land parcels exceeding the threshold and possible update of land parcel unit display.

[0104] Using the six types of spatial entropy thresholds obtained by the method described in step S32 as the judgment criteria, plots exceeding the threshold range are extracted, and spatial entropy type labels are added for plots exceeding the threshold. The spatial entropy values ​​of the extracted plots exceeding the range are standardized. Then, the severity of exceeding the six types of spatial entropy thresholds is divided into three levels: severe, moderate, and slight, using the natural discontinuity method. The extracted plots are labeled with severity labels and marked as plot units that may need to be updated. They are then classified and displayed in the three-dimensional digital map using different colors.

[0105] Using the six types of spatial entropy thresholds obtained by the method described in step S32 as the judgment criteria, plots exceeding the threshold range are extracted, and spatial entropy type labels are added for plots exceeding the threshold. The spatial entropy values ​​of the extracted plots exceeding the range are standardized. Then, the severity of exceeding the six types of spatial entropy thresholds is divided into three levels: severe, moderate, and slight, using the natural discontinuity method. The extracted plots are labeled with severity labels and marked as plot units that may need to be updated. They are then classified and displayed in the three-dimensional digital map using different colors.

[0106] S34, Crowd Location and Emotion Data Verification and Early Warning Update Plot Judgment Results Show

[0107] Collect crowd activity data and public sentiment data, load them into the three-dimensional digital map constructed in step S1 through spatial correlation, and perform a second judgment on the possible plot units to be updated obtained in S3.3 based on these two types of data. Plots that meet the judgment conditions are marked as warning plot units to be updated and displayed in the three-dimensional digital map.

[0108] In this embodiment, crowd activity data and public sentiment data are collected and loaded into the 3D digital map constructed in step S1 through spatial correlation. Based on these two types of data, the potential land parcels to be updated obtained in step S3.3 are further judged. Land parcels that meet the judgment criteria are marked as warning land parcels to be updated and displayed on the 3D digital map. Specifically, crowd activity data is collected from location-based services at 10:00, 14:00, 18:00, and 20:00 daily in the city over the past year. This data is then converted into annual crowd quantity data through spatial correlation and quantity overlay, and processed using kernel density to obtain coordinate-based crowd activity data representing spatial activity. Public sentiment data is collected from facial expression data collected from traffic cameras and public place cameras in the city over the past year. More than 100,000 sets of emotion words are translated into emotion values, and combined with corresponding facial image data as a deep learning training set. Deep learning is used to recognize and translate public facial expression data into emotion values, and kernel density processing is applied to obtain coordinate-based public sentiment data representing public emotion values. Specifically, the population activity data and public sentiment data of each plot are grouped together according to the same land use function. After normalization, the data of each group is divided into five levels according to the order of magnitude: high, medium-high, medium, medium-low, and low using the natural discontinuity method. Different colors are used to display them. Plots with high population activity values ​​and high public sentiment values ​​that are likely to be updated are identified as normal plots. The remaining plots that are likely to be updated are identified as plots with warnings that are likely to be updated, and labels are added for display.

[0109] Step S4: Interactive display and adjustment of the city's 3D spatial sand table

[0110] A 3D digital map sensor system is built to recognize user voice and actions, construct a spatial entropy adjustment instruction library, adjust building height, building area, and building color, and review whether the adjustment content meets the relevant design style specifications of the target city. The adjusted spatial entropy is then updated in the 3D digital map.

[0111] S41, User command voice and motion recognition

[0112] A VR interactive adjustment module based on urban landscape update interactive commands is loaded into a 3D digital map. This module includes a voice recognition system, a motion recognition wearable device equipped with an inertial sensor, and a handheld control system. It calculates and recognizes user voice and user behavior by transmitting data.

[0113] In this embodiment, a VR interactive adjustment module based on urban landscape update interactive commands is loaded into a 3D digital map. This module includes a voice recognition system, a motion recognition wearable device equipped with an inertial sensor, and a handheld control system. The module calculates and recognizes user voice and user behavior through transmitted data. The user voice recognition includes commands such as "forward," "turn right," "turn left," "view update warning plots," "view update demand level," "adjust building color space entropy," "adjust building area space entropy," "adjust building height space entropy," "confirm," "view," "yes," and "no." The user behavior includes four actions: walking, eye movement, arm pointing, and finger touch control.

[0114] S42. Construction of the Spatial Entropy Adjustment Instruction Library

[0115] Based on the voice and actions recognized in S41, a spatial entropy adjustment instruction library is constructed to enable functions such as free walking and browsing space in a 3D digital map, selecting to view detailed information of plots, displaying spatial entropy values, viewing update warning plot information, viewing update demand levels, adjusting the spatial entropy of building volume scale, adjusting the spatial entropy of building structure form, and adjusting the spatial entropy of building facade. When the user confirms the adjustment of a certain type of spatial entropy, the spatial entropy sample library constructed in step S3 is used as the training set for deep learning. According to the threshold range of the spatial entropy of that type corresponding to the plot, new building model data within the threshold range is automatically and randomly generated. If the user is not satisfied, the adjustment instruction can be repeated to update.

[0116] In this embodiment, a spatial entropy adjustment instruction library is constructed based on the voice and actions identified in S41. This library enables users to freely walk and browse the space in a 3D digital map, select to view detailed information about a plot, display spatial entropy values, view updated warning plot information, view update requirement levels, adjust the spatial entropy of building volume scale, adjust the spatial entropy of building structure form, and adjust the spatial entropy of building facade. When a user confirms a certain type of spatial entropy adjustment, the spatial entropy sample library constructed in step S3 is used as the training set for deep learning. Based on the threshold range of the spatial entropy for that type of plot, new building model data within the threshold range is automatically and randomly generated. If the user is not satisfied, they can repeatedly update the data using the adjustment instruction.

[0117] S43. Verification and Feedback of Standard Conditions

[0118] Collect relevant design specifications and conditions for the urban landscape of the target area. For the randomly generated data selected and adjusted by the user in step S42, conduct intelligent review according to the specifications and conditions. If the review is passed, it is input into the data update system of the 3D digital map. If the review is not passed, it is randomly generated again until it is approved and then fed back to the user.

[0119] In this embodiment, the relevant design specifications for the urban landscape of the target area are collected. For the data randomly generated after the user selects and adjusts it in step S42, it is intelligently reviewed according to the specifications. If the review is passed, it is input into the data update system of the three-dimensional digital map. If the review is not passed, it is randomly generated again until it is approved and then fed back to the user. The relevant design specifications for the urban landscape include the building height, building density, plot ratio, building roof type, building color, and building material control requirements in the overall urban design of the target area.

[0120] Step S5, Result Output

[0121] The adjusted 3D digital map of the city will be stitched together and printed out, including the entropy values ​​of various spatial entropy units before and after adjustment, bird's-eye views before and after the 3D digital map update, and the spatial entropy values ​​and bird's-eye views of the spatial entropy units that exceed the threshold after adjustment, for reference by planning, design and management personnel.

[0122] S51. Update and standardize the generation and verification of the city's 3D digital map.

[0123] In this embodiment, the adjusted land parcel data in step S4 is combined with the unadjusted land parcel data through spatial overlay to generate updated three-dimensional digital map data. The combined data is then finally verified according to the building daylighting spacing and building fire protection spacing specifications in the control detailed plan.

[0124] The adjusted land parcel data from step S4 is combined with the unadjusted land parcel data through spatial overlay to generate updated 3D digital map data. The combined data is then finally verified according to the building daylighting spacing and building fire protection spacing specifications in the control detailed plan.

[0125] S52, Output of 3D Digital Map Data for Cities

[0126] The updated urban 3D digital map data after being merged and verified in step S51 is combined with the data before the update and printed out. The output data includes bird's-eye view of the urban 3D digital map before and after the update, bird's-eye view of the model before and after the update of the landscape plot unit, the spatial entropy value of the landscape update plot unit before and after the adjustment, and the spatial entropy type of the plot unit to be updated that exceeds the threshold, for reference by planning, design and management personnel.

[0127] In this embodiment, the updated urban 3D digital map data after being merged and verified in step S51 is summarized with the data before the update and then printed out. The output data includes bird's-eye view of the model before and after the update of the urban 3D digital map, bird's-eye view of the model before and after the update of the landscape plot unit, spatial entropy value of the landscape update plot unit before and after adjustment, and spatial entropy type of the plot unit to be updated that exceeds the threshold, for reference by planning, design and management personnel.

[0128] As another embodiment of the present invention, a system for generating three-dimensional digital maps of cities based on spatial entropy is provided, the system comprising:

[0129] The basic data acquisition and display module is used to acquire 3D oblique photography data of the target city, translate it into geographic information vector data and input it into the geographic information platform; after completing data verification, the data is overlaid to construct the basic sand table of the city's 3D digital map; and the target city is divided into geographic spatial units based on land use function plots by identifying the type of land parcel through POI data.

[0130] The spatial entropy measurement and display module is used to construct a spatial entropy measurement method to measure the entropy of building volume scale, building structure form, and building facade surface of different types of land parcel units. The natural discontinuity method is used to obtain the spatial entropy value types of different types of land parcel units, and different types of spatial entropy digital map entropy value result layers and entropy value type layers are established. The image results of the target city's 3D digital map are displayed according to the functional parameters of the digital layers.

[0131] The spatial entropy threshold determination and early warning display module is used to collect detailed urban design schemes at the plot level in the target area and perform vectorization processing to build a spatial entropy sample library. It automatically matches samples in the spatial entropy sample library according to the spatial attributes of the plot unit, calculates six types of spatial entropy values ​​of the samples, uses the range of sample spatial entropy values ​​as the spatial entropy threshold judgment standard, marks urban plots that do not meet the spatial entropy threshold as plots that may need to be updated and displays them in a hierarchical manner, collects population activity data and public sentiment data in the target area as secondary judgment conditions for updating plots, determines the plots that need to be updated and adds tags for display.

[0132] The interactive display and adjustment module for the city's 3D space sand table is used to build a city 3D digital map sensor, recognize user voice and actions, construct a spatial entropy adjustment instruction library, adjust building height, building area, and building color, and review whether the adjustment content meets the relevant standards and conditions of the target city's design style, and update the adjusted spatial entropy in the 3D digital map.

[0133] The results output module is used to stitch together and print out the city's 3D digital map after adjusting the spatial entropy. It includes the entropy values ​​of various spatial entropy units before and after adjustment, the bird's-eye view before and after updating the 3D digital map, and the spatial entropy values ​​and bird's-eye view of the spatial entropy units that exceed the threshold after adjustment, for reference by planning, design and management personnel.

[0134] The system is used to implement a method for generating a three-dimensional digital map of a city based on spatial entropy, as described in the above embodiments.

[0135] As another embodiment of the present invention, a device is provided, the device comprising:

[0136] One or more processors;

[0137] Memory, used to store one or more programs.

[0138] When the one or more programs are executed by the one or more processors, the one or more processors execute a method for generating a three-dimensional digital map of a city based on spatial entropy as described in the above embodiments.

[0139] As another embodiment of the present invention, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The device is characterized in that the memory stores the computer program capable of running on the processor, and when the processor loads and executes the computer program, it employs a method for generating a three-dimensional digital map of a city based on spatial entropy as described in the above embodiment.

[0140] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for generating a three-dimensional digital map of a city based on spatial entropy, characterized in that, include: Acquire 3D oblique photogrammetry data of the target city, translate it into geographic information vector data, and input it into the geographic information platform; After data verification is completed, the data is overlaid to construct the basic sand table of the city's three-dimensional digital map. By identifying land parcel unit types through POI data, the target city is divided into geospatial units based on land use function parcels; A spatial entropy measurement method is constructed to measure the entropy of building volume scale, building structural form, and building facade surface of different types of land parcels. The spatial entropy value types of different types of land parcel units are obtained by using the natural discontinuity method, and different types of spatial entropy digital map entropy value result layers and entropy value type layers are established. The image results of the target city's three-dimensional digital map are displayed according to the functional parameters of the digital layers. Detailed urban design schemes at the plot level in the target area are collected and vectorized to construct a spatial entropy sample library. Samples in the spatial entropy sample library are automatically matched according to the spatial attributes of plot units. Six types of spatial entropy values ​​of the samples are calculated. The range of sample spatial entropy values ​​is used as the spatial entropy threshold judgment standard. Urban plots that do not meet the spatial entropy threshold are marked as plots that may need to be updated and displayed in a hierarchical manner. Population vitality data and public sentiment data in the target area are collected as secondary judgment conditions for updating plots. Plots that need to be updated are identified and labeled for display. A 3D digital map sensor for the city is built to recognize user voice and actions, and a spatial entropy adjustment instruction library is constructed to adjust building height, building area, and building color. The adjustment content is reviewed to see if it meets the relevant standards and conditions of the target city's design style, and the adjusted spatial entropy is updated in the 3D digital map. The adjusted 3D digital map of the city will be stitched together and printed out, including the entropy values ​​of various spatial entropy units before and after adjustment, bird's-eye views before and after the 3D digital map update, and the spatial entropy values ​​and bird's-eye views of the spatial entropy units that exceed the threshold after adjustment, for reference by planning, design and management personnel.

2. The method for generating a three-dimensional digital map of a city based on spatial entropy according to claim 1, characterized in that: The process involves acquiring 3D oblique photography data of the target city, translating it into geographic information vector data, and inputting it into the geographic information platform; after data verification, data overlay processing is performed to construct a basic sand table of the city's 3D digital map. Furthermore, by identifying land parcel unit types using POI data, the target city is divided into geospatial units based on land use function, specifically including: Geographic information basic data collection: using a LiDAR point cloud data collection system, a surveying drone acquires 3D oblique photogrammetry data of the target city, and loads the deep learning digital interpretation interface of the digital map to input the geographic information vector data obtained by translating the 3D oblique photogrammetry data into the geographic information platform. The construction of a 3D digital map of the city involves verifying geographic information by converting the 3D oblique photography data of the target city and the geographic information vector data that has been verified to a unified CGCS2000 coordinate system, performing data overlay processing based on geographic coordinates, and creating a 3D digital map of the city based on a geographic information platform. The delineation of land parcel unit types involves collecting POI data from the target city via the POI data collection and land use identification interface of the digital map. Different POI reference land use area values ​​are assigned to different types of POI data. POI types are then categorized according to land parcel unit type. The dominant land parcel type with the largest proportion of the total area of ​​the corresponding POI type within the land parcel unit is determined as the land use function of the land parcel unit. The specific calculation formula is as follows: in, i Indicates the type represented by POI. F i This represents the number of POIs of type i within a plot unit. S i This represents the assigned POI reference land area value, where m represents the total number of POI types within the land parcel unit, and n represents the total number of POI types under the corresponding type of the land parcel unit. The land use function of the land parcel unit is finally determined by comparing the C values ​​of different types.

3. The method for generating a three-dimensional digital map of a city based on spatial entropy according to claim 2, characterized in that: The proposed spatial entropy measurement method measures the entropy of building volume scale, building structural form, and building facade surface of different types of land parcel units. The spatial entropy value types of different types of land parcel units are obtained using the natural discontinuity method. Entropy value result layers and entropy value type layers for different types of spatial entropy digital maps are then established. Based on the functional parameters of the digital layers, the imagery results of the target city's 3D digital map are displayed. Specifically, this includes: Spatial entropy index measurement measures three types of spatial entropy indices closely related to architectural style: building volume scale entropy, building structural form entropy, and building facade surface entropy. The index calculation module is loaded to calculate the entropy of various plot units in the target city. The entropy of building volume includes building height entropy and building base area entropy; the entropy of building structure form includes building main structure form entropy and building roof form entropy; and the entropy of building facade skin includes building facade color entropy and building facade material entropy. The basic formula for the spatial entropy index system is: Where H(X) represents the result of measuring the spatial entropy of the object. q This indicates the total number of types of corresponding measurement standards. P j This represents the probability of X taking the value j within the corresponding category; Spatial entropy type identification and display: For different types of spatial entropy values ​​obtained from the calculation, the land parcel units are divided into three categories: "high entropy", "medium entropy" and "low entropy" using the natural discontinuity method; The spatial entropy database interface is loaded into the digital map, and spatial matching of the target city's geographic information vector data and spatial entropy data is established based on geographic coordinates. Entropy value result layers and entropy value type layers of digital maps of different types of spatial entropy are established, and the image results of the target city's three-dimensional digital map are displayed according to the functional parameters of the digital layers; The functional parameters of the digital layer include: displaying the digital map layer in layers or overlays through the display settings; linking and viewing entropy value units of the same result or type through the hyperlink function of the digital map layer; recording the statistical characteristics of entropy value results of a certain type through the chart statistics function of the digital map layer; and navigating to specific plot units to view the corresponding type characteristics under the 3D oblique photogrammetry real-world image when viewing different types of spatial entropy layers through the real-world scene retracing function of the digital map layer.

4. The method for generating a three-dimensional digital map of a city based on spatial entropy according to claim 3, characterized in that: The process involves collecting detailed urban design schemes at the plot level in the target area and vectorizing them to construct a spatial entropy sample library. Samples within the library are automatically matched based on the spatial attributes of the plot units. Six types of spatial entropy values ​​are calculated for each sample. The range of sample spatial entropy values ​​is used as the spatial entropy threshold judgment standard. Urban plots that do not meet the spatial entropy threshold are marked as potentially needing updating and displayed in a tiered manner. Population activity data and public sentiment data in the target area are collected as secondary judgment conditions for updating plots. Plots requiring updating and warning are identified and tagged for display. Specifically, this includes: The spatial entropy sample library is constructed by collecting detailed urban design schemes at the plot level in the target area, intelligently translating the design scheme content into three-dimensional vector data, inputting it into a three-dimensional digital map, constructing a spatial entropy sample library for the target area, calculating the land use function of all plots and the normalized boundary shape index, perimeter, and area of ​​the three spatial attributes, and labeling them in the form of tags. Spatial entropy threshold matching and judgment: Calculate the three major spatial attribute values ​​of the target plot unit, automatically match the unique sample data in the spatial entropy sample library that has the same land use function and the smallest difference in spatial attributes, and then calculate the six types of spatial entropy values ​​of the matching sample according to the spatial entropy measurement method. The maximum and minimum values ​​of the six types of spatial entropy values ​​of the sample are used as the judgment criteria for the six types of spatial entropy thresholds of the target plot unit. The extraction of land parcels exceeding the threshold and the display of potentially updated land parcel units are achieved by using six types of spatial entropy thresholds obtained through spatial entropy threshold matching and judgment as the judgment criteria. Land parcels exceeding the threshold range are extracted and labeled with spatial entropy type tags. The spatial entropy values ​​of the extracted land parcels exceeding the range are standardized. Then, the severity of exceeding the six types of spatial entropy thresholds is divided into three levels: severe, moderate, and slight, using the natural discontinuity method. The extracted land parcels are labeled with severity tags and marked as potentially updated land parcel units. They are then classified and displayed in the 3D digital map using different colors. The results of the verification and early warning update of the population location and emotion data show that the collected population activity data and public sentiment data are loaded into the 3D digital map through spatial correlation. Based on these two types of data, the potential plot units to be updated are re-evaluated. Plots that meet the evaluation criteria are marked as early warning plot units to be updated and displayed on the 3D digital map.

5. The method for generating a three-dimensional digital map of a city based on spatial entropy according to claim 4, characterized in that: The process involves building a 3D digital map sensor for the city, recognizing user voice and actions, constructing a spatial entropy adjustment instruction library, adjusting building height, building area, and building color, and reviewing whether the adjustments meet the relevant design style standards of the target city. The adjusted spatial entropy is then updated and reflected in the 3D digital map. Specifically, this includes: User command voice and action recognition is used to load a VR interactive adjustment module based on urban landscape update interactive commands onto a 3D digital map. This module includes a voice recognition system, a wearable motion recognition device equipped with an inertial sensor, and a handheld control system. It calculates and recognizes user voice and user behavior through transmitted data. The user voice commands include "forward," "turn right," "turn left," "view update warning plots," "view update demand level," "adjust building color space entropy," "adjust building area space entropy," "adjust building height space entropy," "confirm," "view," "yes," and "no." The user behavior includes four actions: walking, eye movement, arm pointing, and finger touch controls. A spatial entropy adjustment command library is constructed based on recognized speech and actions. This library enables users to freely walk and browse space on a 3D digital map, select and view detailed information about plots, display spatial entropy values, view update warning information for plots, view update requirement levels, adjust the spatial entropy of building volume scale, adjust the spatial entropy of building structure form, and adjust the spatial entropy of building facade. When a user confirms a certain type of spatial entropy adjustment, the constructed spatial entropy sample library is used as the training set for deep learning. Based on the threshold range of the spatial entropy of that type corresponding to the plot, new building model data within the threshold range is automatically and randomly generated. If the user is not satisfied, they can repeat the update through adjustment commands. The process involves verifying and providing feedback on the standards and specifications for urban landscape design in the target area. For data that is randomly generated after user selection and adjustment, the system performs intelligent review based on the standards and specifications. If the review is passed, the data is entered into the 3D digital map data update system. If the review fails, the data is randomly generated again until it passes the review and is then fed back to the user. The urban landscape design standards and specifications include the requirements for building height, building density, plot ratio, building roof type, building color, and building material control in the overall urban design of the target area.

6. The method for generating a three-dimensional digital map of a city based on spatial entropy according to claim 5, characterized in that: The process involves stitching together and printing the adjusted 3D digital map of the city, including entropy values ​​of various spatial entropy units before and after adjustment, bird's-eye views before and after the 3D digital map update, and spatial entropy values ​​and bird's-eye views of the warning plot units exceeding the threshold after adjustment. This data is provided for reference by planning, design, and management personnel. Specifically, it includes: The process involves updating and standardizing the city's 3D digital map generation. The adjusted land parcel data is combined with the unadjusted land parcel data through spatial overlay to generate updated 3D digital map data. The combined data is then subject to final verification based on the building daylighting spacing and fire protection spacing specifications in the control detailed plan. The city's 3D digital map data output involves combining the updated and verified city 3D digital map data with the data before the update and then printing it out. The output data includes bird's-eye views of the city's 3D digital map before and after the update, bird's-eye views of the landscape plot units before and after the update, spatial entropy values ​​of the landscape plot units before and after the adjustment, and the types of spatial entropy of plot units that need to be updated that exceed the threshold, for reference by planning, design and management personnel.

7. A system for generating three-dimensional digital maps of cities based on spatial entropy, characterized in that, The system includes: The basic data acquisition and display module is used to acquire 3D oblique photography data of the target city, translate it into geographic information vector data and input it into the geographic information platform; after completing data verification, the data is overlaid to construct the basic sand table of the city's 3D digital map; and the target city is divided into geographic spatial units based on land use function plots by identifying the type of land parcel through POI data. The spatial entropy measurement and display module is used to construct a spatial entropy measurement method to measure the entropy of building volume scale, building structure form, and building facade surface of different types of land parcel units. The natural discontinuity method is used to obtain the spatial entropy value types of different types of land parcel units, and different types of spatial entropy digital map entropy value result layers and entropy value type layers are established. The image results of the target city's 3D digital map are displayed according to the functional parameters of the digital layers. The spatial entropy threshold determination and early warning display module is used to collect detailed urban design schemes at the plot level in the target area and perform vectorization processing to build a spatial entropy sample library. It automatically matches samples in the spatial entropy sample library according to the spatial attributes of the plot unit, calculates six types of spatial entropy values ​​of the samples, uses the range of sample spatial entropy values ​​as the spatial entropy threshold judgment standard, marks urban plots that do not meet the spatial entropy threshold as plots that may need to be updated and displays them in a hierarchical manner, collects population activity data and public sentiment data in the target area as secondary judgment conditions for updating plots, determines the plots that need to be updated and adds tags for display. The interactive display and adjustment module for the city's 3D space sand table is used to build a city 3D digital map sensor, recognize user voice and actions, construct a spatial entropy adjustment instruction library, adjust building height, building area, and building color, and review whether the adjustment content meets the relevant standards and conditions of the target city's design style, and update the adjusted spatial entropy in the 3D digital map. The results output module is used to stitch together and print out the city's 3D digital map after adjusting the spatial entropy. It includes the entropy values ​​of various spatial entropy units before and after adjustment, the bird's-eye view before and after updating the 3D digital map, and the spatial entropy values ​​and bird's-eye view of the spatial entropy units that exceed the threshold after adjustment, for reference by planning, design and management personnel. The urban 3D digital map generation system based on spatial entropy is used to implement the urban 3D digital map generation method based on spatial entropy as described in any one of claims 1-6.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it employs a method for generating a three-dimensional digital map of a city based on spatial entropy, as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When executed by a processor, the program implements a method for generating a three-dimensional digital map of a city based on spatial entropy as described in any one of claims 1-6.

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