Digital twin construction method and system for urban planning

Abstract

The present invention relates to the technical field of digital twin construction, and in particular to a digital twin construction method and construction system for urban planning. The method obtains the initial core strength and current core strength of a functional area; for any functional area, a functional area with a current core strength less than that of the functional area is used as a reference functional area, and the current planning influence of the functional area is obtained based on the core reinforcement change difference between the functional area and the reference functional area, the change in the current association, and the current population change of the functional area; the current planning effectiveness of the functional area is obtained based on the change in the current planning influence of the corresponding functional area as the distance from the functional area increases; the current rendering detail level is obtained based on the current planning influence level and the current planning effectiveness level; and a digital twin is constructed based on the current rendering detail level. The present invention effectively improves the accuracy of digital twin construction for urban planning by obtaining the current rendering detail level of the functional area.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin construction, and in particular to a digital twin construction method and construction system for urban planning. Background Art

[0002] With the rapid development of smart city construction, digital twin technology, as a key means of deep integration of physical cities and virtual space, has been widely used in the field of urban planning.

[0003] Existing technologies are usually based on building information models and urban three-dimensional geographic information systems. They use Internet of Things technology to digitally map people, objects, events and other elements of the physical city to form a virtual city model. Such models provide basic support for urban planning by integrating multi-source data (such as geospatial data and infrastructure information). However, in existing technologies, different functional areas are usually rendered to a uniform degree, without combining the mapping influence relationship between functional areas and taking into account the dynamic and complex population and social factors, which in turn affects the accuracy and precision of the construction of the digital twin of urban planning, and easily leads to a disconnect between planning schemes and actual needs. Summary of the Invention

[0004] In order to solve the technical problem in the prior art of using uniform rendering for different functional areas, resulting in inaccurate construction of digital twins for urban planning, the present invention aims to provide a method and system for constructing digital twins for urban planning. The technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a method for constructing a digital twin for urban planning, the method comprising the following steps:

[0006] Obtain the initial core strength and current core strength of each functional area in the urban model;

[0007] For any functional area, other functional areas with lower current core strength than this functional area are taken as reference functional areas. The current planning impact of this functional area is obtained based on the difference between the current core strength and the initial core strength between this functional area and each reference functional area, the change in the current association between this functional area and each reference functional area, and the current population change of this functional area.

[0008] Taking the functional area as the center, the effectiveness of the current planning of the functional area is obtained according to the change of the current planning influence of the corresponding functional area as the distance from the functional area increases;

[0009] According to the current planning impact and effectiveness of the functional area, as well as the difference between the current population change of the functional area and other functional areas, the current rendering detail level of the functional area is obtained;

[0010] Build digital twins for urban planning based on current rendering levels of detail.

[0011] Furthermore, the method for obtaining the current planning impact degree is:

[0012] Obtaining a first influence degree of the functional area according to a difference between a current core strength of the functional area and each reference functional area and an initial core strength difference, and a change in a current association between the functional area and each reference functional area;

[0013] The difference in heat values ​​between the current predicted population distribution heat map and the initial population distribution heat map for the functional area is used as the second impact level of the functional area;

[0014] The result of normalizing the product of the first impact degree and the second impact degree is taken as the current planning impact degree of the functional area.

[0015] Furthermore, the method for obtaining the first impact degree is:

[0016] The difference between the current core strength of the functional area and the initial core strength is used as the first difference of the functional area;

[0017] For any reference functional area of ​​the functional area, the ratio of the first difference between the functional area and the reference functional area is used as the intensity change difference between the functional area and the reference functional area;

[0018] Obtaining the initial correlation degree between the functional area and the reference functional area according to the initial core strength, distance and initial number of people between the functional area and the reference functional area;

[0019] Obtaining a current correlation degree between the functional area and the reference functional area according to the current core strength, distance, and current number of people between the functional area and the reference functional area;

[0020] Obtaining the difference between the current correlation degree and the initial correlation degree as the current correlation change degree between the functional area and the reference functional area;

[0021] The product of the intensity change difference and the current association change degree is used as the influence reference value between the functional area and the reference functional area;

[0022] The result of adding the influence reference values ​​of the functional area and all reference functional areas and performing normalization is taken as the first influence degree of the functional area.

[0023] Furthermore, the method for obtaining the degree of association is:

[0024] For any two functional areas, the core strengths of the two functional areas are compared, and the functional area with the larger core strength is taken as the first functional area, and the functional area with the smaller core strength is taken as the second functional area;

[0025] Obtaining the distance between the first functional area and the second functional area as a first distance;

[0026] The product of the core strength of the first functional area, the number of people in the second functional area and the reciprocal of the first distance is taken as the association strength between the two functional areas.

[0027] Furthermore, the method for obtaining the initial number of people is:

[0028] The number of people in each functional area in the initial population distribution heat map is used as the initial number of people in each functional area;

[0029] The method for obtaining the current number of people is:

[0030] The number of people in each functional area in the current predicted population distribution heat map is used as the current number of people in each functional area.

[0031] Furthermore, the method for obtaining the effectiveness of the current plan is:

[0032] Obtaining a first distance between the functional area and each other functional area, and arranging the functional areas other than the functional area in ascending order according to the corresponding first distances to obtain a functional area sequence;

[0033] Arrange the current planning influence degree of each functional area in the functional area sequence according to the position of the corresponding functional area in the functional area sequence to obtain a planning influence degree sequence;

[0034] Arrange the first distance corresponding to each functional area in the functional area sequence according to the position of the corresponding functional area in the functional area sequence to obtain a distance sequence;

[0035] The result of the negative correlation between the Pearson correlation coefficient of the planning impact degree series and the distance series is taken as the effectiveness of the current planning of the functional area.

[0036] Furthermore, the method for obtaining the current rendering detail level is:

[0037] Obtain the difference between the second influence degree of the functional area and each other functional area, and use them as the first value;

[0038] The result of adding up all the first values ​​and performing normalization is used as the planning analysis value of the functional area;

[0039] The result of normalizing the product of the current planning impact degree, the current planning effectiveness degree and the planning analysis value of the functional area is used as the current rendering detail level of the functional area.

[0040] Furthermore, the method for obtaining the first distance is:

[0041] For any two functional areas, the centroids of the two functional areas are respectively obtained, and the Euclidean distance between the centroids of the two functional areas is used as the first distance between the two functional areas.

[0042] Furthermore, the method for obtaining the initial core strength and the current core strength is:

[0043] Build a city's building and infrastructure management model based on the city's static geospatial data, and use the core strength of each functional area in the building and infrastructure management model as the initial core strength;

[0044] Conduct real-time review and evaluation of building and infrastructure management models based on dynamic perception data and business rule data, taking the core strength of each functional area at the current moment as the current core strength;

[0045] The method for obtaining the initial population distribution heat map and the current predicted population distribution heat map is:

[0046] Based on the building and infrastructure management model, the city's population distribution heat map is obtained by collecting population data as the initial population distribution heat map;

[0047] By simulating the core strength of each functional area and coupling multi-agent simulation to simulate different group behavior rules, we use spatiotemporal graph convolutional networks and federated learning to combine multi-city data to break down data silos and obtain real-time predicted population distribution heat maps.

[0048] The predicted population distribution heat map at the current moment is used as the current predicted population distribution heat map.

[0049] In the second aspect, another embodiment of the present invention provides a digital twin construction system for urban planning, which includes: a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods.

[0050] The present invention has the following beneficial effects:

[0051] For any functional area, the present invention takes other functional areas with current core strength less than that of the functional area as reference functional areas, which is conducive to the subsequent accurate and efficient analysis of the plannability of the functional area; and then obtains the current planning impact of the functional area according to the difference between the current core strength and the initial core strength of the functional area and each reference functional area, the change of the current association between the functional area and each reference functional area, and the current population change of the functional area, and accurately reflects the planning impact of the functional area, which is conducive to the subsequent accurate analysis of the corresponding rendering detail level of the functional area when constructing the digital twin; in order to more accurately analyze the rendering details of the functional area and improve the construction accuracy of the digital twin, further according to the distance from the functional area, The farther the area is from the functional area, the more changes in the current planning influence of the corresponding functional area are detected, the current planning effectiveness of the functional area is obtained, the feasibility of the current planning of the functional area is accurately reflected, and the current rendering details of the functional area are further analyzed; and then according to the current planning influence and current planning effectiveness of the functional area, as well as the difference in current population changes between the functional area and other functional areas, the current rendering details of the functional area are accurately obtained, so that the space of the functional area in the digital twin can be accurately constructed subsequently; and then based on the current rendering details, the digital twin for urban planning is accurately constructed, which effectively improves the accuracy and precision of the construction of the digital twin for urban planning, and is conducive to more accurate and reasonable institutional urban planning schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A schematic flow chart of a method for constructing a digital twin for urban planning provided by one embodiment of the present invention;

[0054] Figure 2 A flow chart of a method for obtaining the current planning impact level provided by one embodiment of the present invention;

[0055] Figure 3 A structural diagram of a digital twin construction system for urban planning provided by one embodiment of the present invention;

[0056] Figure 4 A schematic diagram of a computer device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation methods, structures, features and effects of the digital twin construction method and construction system for urban planning proposed by the present invention. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0058] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0059] The specific scheme of the digital twin construction method and construction system for urban planning provided by the present invention is described in detail below with reference to the accompanying drawings.

[0060] Example 1:

[0061] The specific scenario of this embodiment is: in the process of constructing digital twins for urban planning, spatial planning requires multi-source data fusion analysis. It is known that geographic information, population distribution and traffic data will have an impact on the spatial planning of the city, that is, there is a coupling relationship between human social behavior and entity and spatial relationships in spatial planning. If it is constructed only based on the development focus intensity of functional areas, it will affect the accuracy and precision of the construction of digital twins for urban planning. In this embodiment, in order to accurately construct the digital twin, and then analyze the mapping influence relationship between functional areas and the impact of population flow on functional area planning, the rendering detail level of each functional area is determined from multiple aspects, which effectively improves the accuracy of the construction of digital twins for urban planning, which is conducive to accurately setting the city's planning scheme.

[0062] This paper proposes a digital twin construction method for urban planning. Figure 1 , which shows a schematic flow chart of a method for constructing a digital twin for urban planning provided by one embodiment of the present invention, the method comprising the following steps:

[0063] Step S1: Obtain the initial core strength and current core strength of each functional area in the urban model.

[0064] Specifically, the construction of digital twins for urban planning follows the closed-loop logic of "data collection-modeling-simulation-decision-making-optimization", based on the fusion of multi-source heterogeneous data, and realizes the dynamic mapping of physical entities and virtual spaces through hierarchical modeling.

[0065] First, static geospatial data (such as GIS terrain, BIM building models, and regulatory vector boundaries), dynamic perception data (such as population distribution heat maps), and business rule data (such as floor area ratio thresholds and ecological red line constraints) are integrated to build a comprehensive digital base covering "land-building-material-event";

[0066] Then, based on static geospatial data, a city's building and infrastructure management model is constructed, which includes lifecycle management, condition monitoring and maintenance strategies for buildings and infrastructure, from planning, design, construction to maintenance. The Internet of Things and big data analysis are used to monitor and optimize the energy efficiency, equipment operating conditions and environmental impact of buildings to ensure the rational use of resources. When constructing a building and infrastructure management model, it is necessary to first establish a centralized data platform to add attribute tags to building entities, including materials, construction years, energy consumption, equipment maintenance records and space usage, and then establish an association with the GIS terrain coordinate system to form a "single-district-city" multi-scale model to manage the entire life cycle of buildings and infrastructure in the city. On the other hand, historical maintenance data is analyzed based on AI algorithms to predict equipment failure cycles (such as elevator maintenance intervals); at the same time, the carbon footprint of building materials from production to demolition is tracked through digital threads. In addition, the blockchain distributed ledger is used to record the change history of buildings and infrastructure to ensure that the data cannot be tampered with and is traceable.

[0067] After completing the building and infrastructure management model, real-time planning review and evaluation are conducted based on dynamic perception data and business rule data within the building and infrastructure management model to ensure that urban planning complies with relevant regulations, standards, and sustainable development goals. The planning review and evaluation process involves multi-source data integration, the establishment of a rule engine, and 3D visual review. Rule indicators (floor area ratio, setbacks), ecological red lines, and cultural heritage protection areas are digitized to form a structured database. Data from planning, environmental protection, and transportation departments are integrated to enable multi-source data sharing. This can aggregate various planning documents, environmental impact assessment reports, and socioeconomic analyses, providing a comprehensive foundation for subsequent reviews. Further, based on the building and infrastructure management model, audit logic (e.g., building spacing > 30 meters) is defined using tools such as Drools to automatically compare indicators. Illegal areas are marked in the real-world model for 3D visual review. Finally, a multi-dimensional planning evaluation is conducted, facilitating the subsequent accurate construction of a digital twin for urban planning.

[0068] It is known that in the building and infrastructure management model, the urban space is divided into multiple functional areas, wherein the functional areas include building entities and roads, etc. At the same time, the core strength of each functional area is directly obtained and is assumed to be known in this embodiment. The core strength reflects the information of different functional areas. The greater the core strength, the more attractive the corresponding functional area is in urban development. In this embodiment, the core strength initially corresponding to each functional area after the construction of the building and infrastructure management model is used as the initial core strength of each functional area. In the subsequent real-time review and evaluation based on the building and infrastructure management model, the core strength of the functional area will change because there are changes in dynamic factors in the city. In order to accurately construct the digital twin of the city in real time, the core strength of each functional area at the current moment is used as the current core strength.

[0069] Population mobility is known to influence urban planning. For example, functional areas with high population mobility are more significant for urban planning. Therefore, analyzing a city's population mobility is necessary. Furthermore, this embodiment uses collected population counts based on the building and infrastructure management model to obtain a city's population distribution heatmap as an initial population distribution heatmap. Considering the mobility of people within a city, this embodiment simulates different group behavior rules (e.g., employment groups with a commuting threshold of less than 30 minutes and commercial groups driven by the energy level of the business district) based on the core strength of each functional area and coupled multi-agent simulation. This employs a spatiotemporal graph convolutional network (ST-GCN) to capture the spatiotemporal dependencies between road network topology and commuting cycles, supplemented by an XGBoost regressor to predict grid-level density (R² > 0.85). Federated learning combines multi-city data to break down data silos (blockchain encryption gradient parameters), resulting in a real-time predicted population distribution heatmap for the city model. The predicted population distribution heatmap at the current moment is used as the current predicted population distribution heatmap. The methods for obtaining the initial and predicted population distribution heatmaps are both well-known techniques and will not be elaborated upon here.

[0070] Step S2: For any functional area, other functional areas whose current core strength is smaller than that of the functional area are taken as reference functional areas. The current planning impact of the functional area is obtained based on the difference between the current core strength and the initial core strength of the functional area and each reference functional area, the change in the current association between the functional area and each reference functional area, and the current population change of the functional area.

[0071] Specifically, in order to accurately construct a digital twin of a city and accurately and reasonably define the city's planning, this embodiment analyzes each functional zone at the current moment. The greater the planning impact of a functional zone on other functional zones at the current moment, the more likely that the functional zone has planning significance. It is known that functional zones with low core strength will hardly have an impact on functional zones with high core strength, that is, functional zones with high core strength will only have an impact on functional zones with low core strength. Therefore, for any functional zone, this embodiment uses other functional zones with current core strengths lower than that of the functional zone as reference functional zones. The greater the change in the core strength of the functional zone relative to the reference functional zone and the stronger the correlation between the functional zone and the reference functional zone, the greater the current planning impact of the functional zone. This embodiment then preliminarily analyzes the planning impact of the functional zone based on the difference between the current core strength of the functional zone and each reference functional zone and the difference between the initial core strength and the current correlation between the functional zone and each reference functional zone, and the change in the current correlation between the functional zone and each reference functional zone.

[0072] On the other hand, considering that changes in population size will also affect the planning of functional areas, that is, the more the population of a functional area increases, the greater the planning impact of the functional area; therefore, this embodiment further analyzes the current population changes of the functional area to more accurately obtain the current planning impact of the functional area. Furthermore, this embodiment obtains the current planning impact of the functional area based on the difference between the current core strength and the initial core strength of the functional area and each reference functional area, the changes in the current association between the functional area and each reference functional area, and the current population changes of the functional area. The greater the current planning impact, the greater the planning impact of the functional area.

[0073] Preferably, in one possible implementation of this embodiment, the method for obtaining the current planning impact degree is as follows: Figure 2 , which shows a flow chart of a method for obtaining the current planning impact provided by this embodiment, the method comprising the following steps:

[0074] Step S201: obtaining a first influence degree of the functional area according to the difference between the current core strength and the initial core strength of the functional area and each reference functional area, and the change of the current association between the functional area and each reference functional area.

[0075] When the difference between the current core strength and the initial core strength of the functional zone is significantly greater than the difference between the current core strength and the initial core strength of the reference functional zone, it indicates that the change in the functional zone is greater, which indirectly indicates that the current planning of the functional zone has a greater impact. At the same time, when the current correlation between the functional zone and each reference functional zone is greater, it indicates that the planning impact of the functional zone is greater. In this embodiment, the first impact degree of the functional zone is obtained based on the difference between the current core strength and the initial core strength of the functional zone and each reference functional zone, and the change in the current correlation between the functional zone and each reference functional zone. The greater the first impact degree, the greater the planning impact of the functional zone.

[0076] In one possible implementation of this embodiment, the method for obtaining the first degree of influence is: taking the absolute value of the difference between the current core strength and the initial core strength of the functional area as the first difference of the functional area; for any reference functional area of ​​the functional area, taking the ratio of the first difference between the functional area and the reference functional area as the strength change difference between the functional area and the reference functional area; the greater the strength change difference, the greater the change of the functional area relative to the reference functional area, which indirectly indicates that the functional area has a greater planning impact on the reference functional area; further, based on the initial core strength, distance and initial number of people of the functional area and the reference functional area, the initial degree of association between the functional area and the reference functional area is obtained; in this embodiment, the number of people corresponding to each functional area in the initial population distribution heat map is taken as the initial number of people in each functional area. Based on the current core strength, distance and current number of people of the functional area and the reference functional area, the current degree of association between the functional area and the reference functional area is obtained; in this embodiment, the number of people corresponding to each functional area in the current predicted population distribution heat map is taken as the current number of people in each functional area;

[0077] When the current correlation degree is greater than the initial correlation degree, it means that the functional area has a greater planning influence on the reference functional area. Then, this embodiment obtains the difference between the current correlation degree and the initial correlation degree as the current correlation change degree between the functional area and the reference functional area; then, the product of the intensity change difference and the current correlation change degree is used as the influence reference value between the functional area and the reference functional area; the larger the influence reference value, the greater the planning influence of the functional area on the reference functional area; in order to comprehensively analyze the planning influence of the functional area on the reference functional area, the influence reference values ​​of the functional area and all reference functional areas are added and normalized, and the result is used as the first influence degree of the functional area. This embodiment normalizes the sum of the influence reference values ​​of the functional area and all reference functional areas using the norm normalization function.

[0078] In one possible implementation of this embodiment, the degree of association is determined by comparing the core strengths of any two functional areas, determining the functional area with the greater core strength as the first functional area and the functional area with the lesser core strength as the second functional area; and determining the distance between the first and second functional areas as the first distance. The specific method for determining the first distance is as follows: determining the centroids of any two functional areas, respectively, and determining the Euclidean distance between the centroids of the two functional areas as the first distance between the two functional areas. The methods for determining the centroids and the Euclidean distance are both well-known techniques and will not be further described. It is known that the greater the core strength of a functional area, the stronger its attraction to its reference functional area. Furthermore, the smaller the distance between a functional area and its reference functional area and the greater the number of people in its reference functional area, the greater its association with its reference functional area. Furthermore, this embodiment uses the product of the core strength of the first functional area, the number of people in the second functional area, and the inverse of the first distance as the degree of association between the two functional areas. It should be noted that when the core strength of the two functional areas is the same, the functional area with more people will be used as the first functional area; if the core strength and number of people of the two functional areas are the same, any one of the functional areas will be selected as the first functional area.

[0079] Step S202: The difference in heat value between the functional area in the current predicted population distribution heat map and the initial population distribution heat map is used as the second influence degree of the functional area.

[0080] The greater the difference in heat values ​​between the current predicted population distribution heat map and the initial population distribution heat map for a functional area, the greater the population of the functional area is increasing, indirectly indicating that the functional area has greater planning significance and planning impact. Therefore, in this embodiment, the difference in heat values ​​between the current predicted population distribution heat map and the initial population distribution heat map for the functional area is used as the second impact level of the functional area. The greater the second impact level, the greater the planning impact of the functional area.

[0081] Step S203: normalizing the product of the first influence degree and the second influence degree as the current planning influence degree of the functional area.

[0082] It is known that a greater first degree of influence and a greater second degree of influence indicate a greater planning impact for the functional area. To accurately obtain the current planning impact of the functional area, the product of the first degree of influence and the second degree of influence is normalized to serve as the current planning impact of the functional area. In this embodiment, the product of the first degree of influence and the second degree of influence is normalized using the norm normalization function.

[0083] At this point, the current planning impact level of each functional area is obtained.

[0084] Step S3: Taking the functional area as the center, the current planning effectiveness of the functional area is obtained according to the change of the current planning influence of the corresponding functional area as the distance from the functional area increases.

[0085] Specifically, assuming that the functional area is planned, the closer the functional area is to the functional area, the greater the planning impact it receives, and the farther the functional area is from the functional area, the smaller the planning impact it receives. It should be noted that the current planning impact level can characterize the current planning impact level of the corresponding functional area. Therefore, with the functional area as the center, as the current planning impact level of the corresponding functional area becomes smaller as the distance from the functional area increases, it means that the planning level of the functional area is greater. In this embodiment, the current planning effectiveness level of the functional area is obtained based on the change in the current planning impact level of the corresponding functional area as the distance from the functional area increases. The greater the current planning effectiveness level, the greater the degree to which the functional area can be planned.

[0086] Preferably, in a method that can be implemented in this embodiment, the method for obtaining the current planning effectiveness is: obtain the first distance between the functional area and each other functional area, arrange the functional areas other than the functional area in ascending order according to the corresponding first distance, and obtain a functional area sequence; arrange the current planning influence of each functional area in the functional area sequence according to the position of the corresponding functional area in the functional area sequence, and obtain a planning influence sequence; arrange the first distance corresponding to each functional area in the functional area sequence according to the position of the corresponding functional area in the functional area sequence, and obtain a distance sequence; when the Pearson correlation coefficient between the planning influence sequence and the distance sequence is smaller, that is, the closer it is to -1, the greater the degree to which the functional area can be planned; therefore, this embodiment uses the result of the negative correlation between the planning influence sequence and the Pearson correlation coefficient of the distance sequence as the current planning effectiveness of the functional area. This embodiment is achieved through The Pearson correlation coefficient of the planning impact degree sequence and the distance sequence is negatively correlated; It is the Pearson correlation coefficient between the planning influence degree sequence and the distance sequence. It should be noted that the method for obtaining the Pearson correlation coefficient is a well-known technology and will not be described in detail.

[0087] At this point, the current planning effectiveness of each functional area is obtained.

[0088] Step S4: obtaining the current rendering detail level of the functional area according to the current planning impact and effectiveness of the functional area, and the difference in current population changes between the functional area and other functional areas.

[0089] Specifically, when the current planning influence and the current planning effectiveness of the functional area are greater, it means that the functional area is more plannable. On the other hand, considering that among all functional areas, the greater the current population increase of the functional area is, the more planning significance the functional area has, that is, the greater the degree to which the functional area can be planned. In order to ensure that the functional area is currently accurately planned, the current rendering detail level of the functional area should be greater. Therefore, this embodiment obtains the current rendering detail level of the functional area based on the current planning influence and the current planning effectiveness of the functional area, as well as the difference in current population changes between the functional area and other functional areas.

[0090] Preferably, in one possible implementation of this embodiment, the current rendering detail level is obtained by obtaining the difference between the second influence level of the functional area and each other functional area, each of which is used as a first value; when the first values ​​are larger, it indicates that the current population increase of the functional area is more significant, and then all the first values ​​are added together and normalized to obtain the planning analysis value of the functional area; the larger the planning analysis value, the greater the planning level of the functional area, which indirectly indicates that the rendering detail of the functional area should be greater; wherein, this embodiment normalizes the sum of all the first values ​​using a norm normalization function. It is known that the greater the current planning influence level and the greater the current planning effectiveness of the functional area, the greater the current rendering detail level of the functional area should be. Therefore, this embodiment normalizes the product of the current planning influence level, the current planning effectiveness, and the planning analysis value of the functional area as the current rendering detail level of the functional area. wherein, this embodiment normalizes the product of the current planning influence level, the current planning effectiveness, and the planning analysis value of the functional area using a norm normalization function.

[0091] At this point, the current rendering detail level for each functional area is obtained.

[0092] Step S5: Construct a digital twin for urban planning based on the current rendering detail level.

[0093] Specifically, the system deeply integrates multi-source data, including geographic information, population mobility, infrastructure carrying capacity, and policy-driven factors. By constructing a dynamic evolutionary model based on machine learning, it simulates the coordinated evolution of land use change, transportation network expansion, and building density distribution. The system accurately constructs the spatial representation of each functional area within the digital twin using the current rendering detail of each functional area, visualizing the development process of urban functional zones (such as commercial centers, residential clusters, and ecological corridors). Real-time ray tracing rendering is enabled for high-intensity connection pairs (such as CBD-subway stations), while simplified models and textures are used for low-intensity connections (such as industrial areas-parks). By integrating IoT sensor data, dynamic feedback on urban operating status (such as peak energy consumption and traffic congestion index) is provided, driving adaptive optimization of simulation parameters. Ultimately, a multi-scenario planning sandbox with real-time forecasting and early warning capabilities is generated, providing decision makers with a three-dimensional simulation tool covering spatial morphological evolution, resource allocation efficiency, and socioeconomic benefits. Based on the forecast results, local models are refreshed in real time, generating a digital twin based on spatial planning in real time.

[0094] In summary, this embodiment obtains the initial core strength and current core strength of a functional zone; for any functional zone, a functional zone with a current core strength less than that of the functional zone is used as a reference functional zone, and the current planning impact of the functional zone is obtained based on the difference in core reinforcement changes between the functional zone and the reference functional zone, the changes in the current association, and the current population changes of the functional zone; the current planning effectiveness of the functional zone is obtained based on the changes in the current planning impact of the corresponding functional zone as the distance from the functional zone increases; the current rendering detail level is obtained based on the current planning impact level and the current planning effectiveness level; and a digital twin is constructed based on the current rendering detail level. By obtaining the current rendering detail level of the functional zone, the present invention effectively improves the accuracy of constructing digital twins for urban planning.

[0095] Example 2:

[0096] This invention also proposes a digital twin construction system for urban planning, please refer to Figure 3 , which shows a structural diagram of a digital twin construction system for urban planning provided by an embodiment of the present invention. The system includes: a data acquisition module 10, a current planning impact acquisition module 20, a current planning effectiveness acquisition module 30, a current rendering detail acquisition module 40 and a data processing module 50.

[0097] The data acquisition module 10 is used to obtain the initial core strength and current core strength of each functional area in the city model.

[0098] The current planning impact degree acquisition module 20 is used to, for any functional area, take other functional areas whose current core strength is smaller than that of the functional area as reference functional areas, and obtain the current planning impact degree of the functional area based on the difference between the current core strength and the initial core strength of the functional area and each reference functional area, the change in the current association between the functional area and each reference functional area, and the current population change of the functional area.

[0099] The current planning effectiveness acquisition module 30 is used to acquire the current planning effectiveness of the functional area based on the change in the current planning influence of the corresponding functional area as the distance from the functional area increases.

[0100] The current rendering detail level acquisition module 40 is used to acquire the current rendering detail level of the functional area according to the current planning impact and effectiveness of the functional area, and the difference in current population changes between the functional area and other functional areas.

[0101] The data processing module 50 is used to construct a digital twin for urban planning based on the current rendering detail level.

[0102] It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the digital twin construction system for urban planning and the digital twin construction method for urban planning provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0103] Example 3:

[0104] The present invention also proposes a device for constructing a digital twin for urban planning, comprising a memory and a processor, wherein the memory stores executable program code, and the processor is configured to call and execute the executable program code to perform a method for constructing a digital twin for urban planning provided in an embodiment of the present application. The device can be a chip, component, or module, and the chip can include a connected processor and memory; wherein the memory is configured to store instructions, and when the processor calls and executes the instructions, the chip can execute the method for constructing a digital twin for urban planning provided in the above embodiment.

[0105] In addition, the present application also protects a computer device, see Figure 4The computer device includes a memory 401, a processor 402, and a computer program 403 stored in the memory 401 and running on the processor 402, wherein when the processor 402 executes the computer program 403, the computer device can execute any one of the digital twin construction methods for urban planning introduced above.

[0106] Example 4:

[0107] This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code runs on a computer, the computer executes the above-mentioned related method steps to implement a digital twin construction method for urban planning provided by the above embodiment.

[0108] Example 5:

[0109] This embodiment also provides a computer program product. When the computer program product runs on a computer, it enables the computer to execute the above-mentioned related steps to implement a digital twin construction method for urban planning provided by the above embodiment.

[0110] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0111] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A digital twin construction method for urban planning, characterized in that: The method comprises the following steps: Obtain the initial core strength and current core strength of each functional area in the urban model; For any functional area, other functional areas with lower current core strength than this functional area are taken as reference functional areas. The current planning impact of this functional area is obtained based on the difference between the current core strength and the initial core strength between this functional area and each reference functional area, the change in the current association between this functional area and each reference functional area, and the current population change of this functional area. Taking the functional area as the center, the effectiveness of the current planning of the functional area is obtained according to the change of the current planning influence of the corresponding functional area as the distance from the functional area increases; According to the current planning impact and effectiveness of the functional area, as well as the difference between the current population change of the functional area and other functional areas, the current rendering detail level of the functional area is obtained; Build digital twins for urban planning based on the current rendering level of detail; The method for obtaining the current planning impact degree is: Obtaining a first influence degree of the functional area according to a difference between a current core strength of the functional area and each reference functional area and an initial core strength difference, and a change in a current association between the functional area and each reference functional area; The difference in heat values ​​between the current predicted population distribution heat map and the initial population distribution heat map for the functional area is used as the second impact level of the functional area; Normalize the product of the first impact degree and the second impact degree to obtain the result as the current planning impact degree of the functional area; The method for obtaining the effectiveness of the current plan is: Obtaining a first distance between the functional area and each other functional area, and arranging the functional areas other than the functional area in ascending order according to the corresponding first distances to obtain a functional area sequence; Arrange the current planning influence degree of each functional area in the functional area sequence according to the position of the corresponding functional area in the functional area sequence to obtain a planning influence degree sequence; Arrange the first distance corresponding to each functional area in the functional area sequence according to the position of the corresponding functional area in the functional area sequence to obtain a distance sequence; The result of the negative correlation between the Pearson correlation coefficient of the planning impact degree series and the distance series is taken as the effectiveness of the current planning of the functional area; The method for obtaining the current rendering detail level is: Obtain the difference between the second influence degree of the functional area and each other functional area, and use them as the first value; The result of adding up all the first values ​​and performing normalization is used as the planning analysis value of the functional area; The result of normalizing the product of the current planning impact degree, the current planning effectiveness degree and the planning analysis value of the functional area is used as the current rendering detail level of the functional area.

2. A digital twin construction method for urban planning according to claim 1, characterized in that: The method for obtaining the first impact degree is: The difference between the current core strength of the functional area and the initial core strength is used as the first difference of the functional area; For any reference functional area of ​​the functional area, the ratio of the first difference between the functional area and the reference functional area is used as the intensity change difference between the functional area and the reference functional area; Obtaining the initial correlation degree between the functional area and the reference functional area according to the initial core strength, distance and initial number of people between the functional area and the reference functional area; Obtaining a current correlation degree between the functional area and the reference functional area according to the current core strength, distance, and current number of people between the functional area and the reference functional area; Obtaining the difference between the current correlation degree and the initial correlation degree as the current correlation change degree between the functional area and the reference functional area; The product of the intensity change difference and the current association change degree is used as the influence reference value between the functional area and the reference functional area; The result of adding the influence reference values ​​of the functional area and all reference functional areas and performing normalization is taken as the first influence degree of the functional area.

3. The method for constructing a digital twin for urban planning according to claim 2, wherein: The method for obtaining the degree of association is: For any two functional areas, the core strengths of the two functional areas are compared, and the functional area with the larger core strength is taken as the first functional area, and the functional area with the smaller core strength is taken as the second functional area; Obtaining the distance between the first functional area and the second functional area as a first distance; The product of the core strength of the first functional area, the number of people in the second functional area and the reciprocal of the first distance is taken as the association strength between the two functional areas.

4. The method for constructing a digital twin for urban planning according to claim 2, wherein: The method for obtaining the initial number of people is: The number of people in each functional area in the initial population distribution heat map is used as the initial number of people in each functional area; The method for obtaining the current number of people is: The number of people in each functional area in the current predicted population distribution heat map is used as the current number of people in each functional area.

5. The method for constructing a digital twin for urban planning according to claim 1, wherein: The method for obtaining the first distance is: For any two functional areas, the centroids of the two functional areas are respectively obtained, and the Euclidean distance between the centroids of the two functional areas is used as the first distance between the two functional areas.

6. The method for constructing a digital twin for urban planning according to claim 4, characterized in that: The method for obtaining the initial core strength and the current core strength is: Build a city's building and infrastructure management model based on the city's static geospatial data, and use the core strength of each functional area in the building and infrastructure management model as the initial core strength; Conduct real-time review and evaluation of building and infrastructure management models based on dynamic perception data and business rule data, taking the core strength of each functional area at the current moment as the current core strength; The method for obtaining the initial population distribution heat map and the current predicted population distribution heat map is: Based on the building and infrastructure management model, the city's population distribution heat map is obtained by collecting population data as the initial population distribution heat map; By simulating the core strength of each functional area and coupling multi-agent simulation to simulate different group behavior rules, we use spatiotemporal graph convolutional networks and federated learning to combine multi-city data to break down data silos and obtain real-time predicted population distribution heat maps. The predicted population distribution heat map at the current moment is used as the current predicted population distribution heat map.

7. A digital twin construction system for urban planning, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When executing the computer program, the processor implements the steps of a method for constructing a digital twin for urban planning as described in any one of claims 1 to 6.

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