Green infrastructure evolution visualization method

Abstract

The application provides a green infrastructure evolution visualization method; green infrastructure is extracted from a map; the best distance threshold of connectivity analysis is determined according to connectivity index data of the green infrastructure, and key nodes of the green infrastructure are identified according to the best distance threshold; the resistance value of migration activities between the green infrastructures is determined; a green infrastructure network is constructed according to the key nodes of the green infrastructure and the resistance value of the migration activities between the green infrastructures; modeling is performed according to spatial data of the green infrastructure network, and green infrastructure 4D-BIM model source data are obtained; a green infrastructure evolution visualization system is constructed based on the green infrastructure 4D-BIM model source data, and green infrastructure evolution information is displayed through the green infrastructure evolution visualization system; continuous, immersive and dynamic visualization of a city green infrastructure network is realized; and the problem of insufficient dynamic expression is solved.

Description

Technical Field

[0001] This application relates to the field of architectural planning, and more particularly to a method for visualizing the evolution of green infrastructure. Background Technology

[0002] Urban green infrastructure is a systematic network composed of interconnected ecological landscape elements, including natural spaces such as forests, rivers, and wetlands, as well as semi-natural blue-green spaces such as urban parks and reservoirs. With the acceleration of urbanization, problems such as rapid expansion of construction land and imbalanced resource allocation easily arise, leading to the fragmentation and functional degradation of urban green infrastructure, thereby weakening the city's ecosystem service supply capacity. Therefore, it is necessary to build an urban green support system based on green infrastructure and promote the organic integration of urban development and green space networks.

[0003] Existing technologies primarily identify green infrastructure nodes based on urban land use types or by directly identifying nodes within important ecological spaces such as urban forest parks, nature reserves, and ecological red lines. The method of directly identifying green infrastructure based on land use types broadens the scope of urban green infrastructure, significantly increasing the workload for identifying key nodes and constructing networks. Conversely, methods based on important ecological spaces like urban forest parks, nature reserves, and ecological red lines narrow the scope of green infrastructure, which is also detrimental to its protection and optimization. Existing technologies often determine optimal thresholds by referencing parameters from neighboring regions or by analyzing indicators such as landscape connectivity, landscape component count, the number of patches of the largest landscape component, and the area ratio of the largest landscape component. However, they have not yet established a localized framework for optimal distance thresholds in green infrastructure connectivity analysis. This results in insufficient model accuracy and inadequate matching with real-world green infrastructure.

[0004] The existing forms of expression for the historical evolution of urban green infrastructure are too simplistic and lack intuitive three-dimensional / four-dimensional dynamic expression capabilities. Most of them are limited to two-dimensional plan maps, static charts and statistical reports, which make it difficult to present the spatiotemporal dynamic evolution process of urban green infrastructure in the real three-dimensional urban environment in an intuitive and immersive way. Their dynamic expression capabilities are insufficient. Summary of the Invention

[0005] The following is an overview of the topics described in detail in this article.

[0006] The purpose of this application is to at least partially solve one of the technical problems existing in the related technologies. The embodiments of this application provide a method for visualizing the evolution of green infrastructure.

[0007] An embodiment of this application provides a method for visualizing the evolution of green infrastructure, comprising: Extracting green infrastructure from maps; The optimal distance threshold for connectivity analysis is determined based on the connectivity index data of green infrastructure, and key nodes of green infrastructure are identified based on the optimal distance threshold. Determine the resistance values ​​for migration activities between green infrastructures; Construct a green infrastructure network based on the key nodes of the green infrastructure and the resistance values ​​of migration activities between the green infrastructures; Based on the spatial data of the green infrastructure network, the source data for the 4D-BIM model of green infrastructure is obtained; A green infrastructure evolution visualization system is constructed based on the source data of the 4D-BIM model of green infrastructure, and the green infrastructure evolution visualization system displays green infrastructure evolution information.

[0008] According to certain embodiments of this application, determining the optimal distance threshold for connectivity analysis based on connectivity index data of green infrastructure includes: Based on connectivity index data such as the number of landscape connections, the number of landscape components, the number of patches of the largest landscape component, and the area ratio of the largest landscape component, we analyze the changing trends of each connectivity index data under different threshold values ​​and preliminarily determine the distance threshold range for connectivity analysis. Within the specified distance threshold range, the distance is refined according to a preset step size, and the optimal distance threshold for connectivity analysis is determined by combining the trend consistency of the overall connectivity index and the probability connectivity index.

[0009] According to certain embodiments of this application, identifying key nodes of green infrastructure based on the optimal distance threshold includes: The overall connectivity index and the probability connectivity index are calculated based on the optimal distance threshold. Key nodes of green infrastructure are identified using the overall connectivity index and the probability connectivity index through a patch area average weighting method.

[0010] According to certain embodiments of this application, determining the resistance value for migration activities between green infrastructures includes: Land use type, transportation network, population density, slope, altitude, and distance to key nodes were selected as construction factors. A preliminary resistance surface was constructed based on the weights of each factor and the classification and grading method. The resistance value for migration activities between green infrastructures is obtained by correcting the initial resistance surface with a correction factor.

[0011] According to certain embodiments of this application, constructing a green infrastructure network based on the resistance values ​​of key nodes of the green infrastructure and migration activities between the green infrastructures includes: Construct a minimum cumulative resistance model based on the resistance values ​​of migration activities between the green infrastructures; The minimum cumulative resistance model is used to simulate the cumulative shortest path between the starting point and the target point of a key node in green infrastructure. The cumulative shortest path is used to characterize the resistance that the migration activity between the starting point and the target point needs to overcome. Edges connecting key nodes of green infrastructure are generated based on the cumulative shortest path between the starting point and the target point, and a green infrastructure network is constructed through the connection between the edges and key nodes.

[0012] According to certain embodiments of this application, after constructing a green infrastructure network, the method includes: A gravity model is used to calculate the interaction force matrix between key nodes of green infrastructure. The interaction force matrix is ​​used to evaluate the interaction strength of key nodes of green infrastructure, and the interaction strength is used to characterize the importance of the connecting edges between key nodes. Network analysis is used to assess the connectivity of green infrastructure networks, which characterizes the stability of the green infrastructure networks.

[0013] According to certain embodiments of this application, the step of modeling based on spatial data of the green infrastructure network to obtain source data for a 4D-BIM model of green infrastructure includes: Convert the spatial data of the green infrastructure network into a common format for source data; Set the LOD level for the source data of 4D-BIM models of green infrastructure; Configure spatial attribute data values ​​for the source data of the 4D-BIM model of green infrastructure.

[0014] According to certain embodiments of this application, after obtaining the source data of the 4D-BIM model of green infrastructure, the method further includes: The source data of the 4D-BIM model of the green infrastructure is subjected to surface reduction optimization processing; According to the scene import rules, the source data of the green infrastructure 4D-BIM model after surface reduction optimization is divided according to file size and hierarchy, and stored in the target folder for source data management and retrieval.

[0015] According to certain embodiments of this application, the green infrastructure evolution visualization system is configured with a visualization module and a historical evolution module; The visualization module is equipped with a green infrastructure overall network connectivity analysis dashboard. The data indicators of the analysis dashboard include the total area of ​​urban green infrastructure, network coverage, obstacle area, connectivity index, and cumulative minimum cost path. The historical evolution module binds the 4D-BIM models of green infrastructure at different times with the time data in the green infrastructure historical evolution plan table, uses a timeline tool to control the historical evolution information of green infrastructure, and displays 4D simulation images of the green infrastructure network as well as projection images of the planning and development of green infrastructure.

[0016] According to certain embodiments of this application, the green infrastructure evolution visualization system is configured with an interaction and information query module; the interaction and information query module is used to receive user interaction data and query and display green infrastructure information based on the interaction data.

[0017] The above-mentioned scheme has at least the following beneficial effects: It achieves accurate identification of key nodes in urban green infrastructure. Based on land use type, and using morphological spatial pattern analysis methods, it analyzes spatial patterns, identifies landscape types, and further filters urban green infrastructure based on the landscape element identification results, enabling the scientific identification of key nodes in urban green infrastructure. It constructs a localized framework for optimal distance thresholds based on indicator evaluation and trend analysis, providing an effective reference framework for the localization of optimal thresholds for green infrastructure connectivity analysis. Combining 3D modeling and historical evolution, it allows for a macroscopic grasp of the overall layout of urban green infrastructure and a microscopic view of the detailed relationship between a specific green space and surrounding buildings, achieving continuous, immersive, and dynamic visualization of the urban green infrastructure network; it also solves the problem of insufficient dynamic expression. By deeply integrating macroscopic GIS spatial analysis, microscopic BIM model information, and four-dimensional spatiotemporal simulation technology, it assesses the ecological service functions and network connectivity. It not only allows for a three-dimensional, dynamic replay of the changes in urban green infrastructure over the years, but also enables data visualization analysis of the rationality and convenience of the layout of urban green infrastructure, contributing to scientific planning decisions. Attached Figure Description

[0018] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0019] Figure 1 This is a step-by-step diagram of a method for visualizing the evolution of green infrastructure; Figure 2 This is a diagram showing the sub-steps for determining the optimal distance threshold for connectivity analysis based on connectivity index data of green infrastructure; Figure 3 It is a sub-step diagram for identifying key nodes of green infrastructure based on the optimal distance threshold; Figure 4 This is a diagram showing the sub-steps for determining the resistance values ​​of migration activities between green infrastructures; Figure 5 It is a sub-step diagram for constructing a green infrastructure network based on the resistance values ​​of key nodes in green infrastructure and migration activities between green infrastructures. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, or the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0022] Embodiments of this application provide a method for visualizing the evolution of green infrastructure.

[0023] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0024] Reference Figure 1 A visualization method for the evolution of green infrastructure includes the following steps: Step S100: Extract green infrastructure from the map; Step S200: Determine the optimal distance threshold for connectivity analysis based on the connectivity index data of green infrastructure, and identify key nodes of green infrastructure based on the optimal distance threshold; Step S300: Determine the resistance value for migration activities between green infrastructures; Step S400: Construct a green infrastructure network based on the key nodes of the green infrastructure and the resistance values ​​of migration activities between green infrastructures; Step S500: Model the green infrastructure network based on its spatial data to obtain source data for the 4D-BIM model of green infrastructure. Step S600: Construct a green infrastructure evolution visualization system based on the source data of the green infrastructure 4D-BIM model, and display the green infrastructure evolution information through the green infrastructure evolution visualization system.

[0025] For step S100, based on high-precision land use types, typical green facility land such as woodland, grassland, wetland, and water area are extracted as landscape analysis elements, while other land uses are set as background elements. Morphological spatial pattern analysis methods are used to extract bridging areas, island patches, and branch lines of green facility land core areas. Edge area The ring road area and the porous area will serve as alternative nodes for green infrastructure in the core area.

[0026] Reference Figure 2 For step S200, determining the optimal distance threshold for connectivity analysis based on the connectivity index data of green infrastructure includes the following steps: Step S211: Based on connectivity index data such as the number of landscape connections, the number of landscape components, the number of patches of the largest landscape component, and the area ratio of the largest landscape component, analyze the changing trends of each connectivity index data under different threshold values, and preliminarily determine the distance threshold range for connectivity analysis. Step S212: Within the distance threshold range, refine the data according to a preset step size, and determine the optimal distance threshold for connectivity analysis by combining the trend consistency of the overall connectivity index and the probability connectivity index.

[0027] Specifically, based on connectivity index data such as the number of landscape connections, the number of landscape components, the number of patches of the largest landscape component, and the area ratio of the largest landscape component, the changing trends of each connectivity index data under different threshold values ​​are analyzed to preliminarily determine the reasonable distance threshold range for connectivity analysis. On this basis, the distance threshold is refined with step sizes of 100m / 50m, and the trend consistency of the Integral Index of Connectivity (IIC) and the Possible Index of Connectivity (PC) is superimposed to determine the optimal distance threshold for connectivity analysis.

[0028] Reference Figure 3 Identifying key nodes of green infrastructure based on optimal distance thresholds includes the following steps: Step S221: Calculate the overall connectivity index and the probability connectivity index based on the optimal distance threshold; Step S222: Based on the overall connectivity index and the probability connectivity index, the key nodes of the green infrastructure are identified by the patch area average weighting method.

[0029] Reference Figure 4 For step S300, determining the resistance value for migration activities between green infrastructures includes the following steps: Step S310: Select land use type, transportation network, population density, slope, altitude, and distance to key nodes as construction factors, and construct a preliminary resistance surface according to the weight of each factor and the classification and grading method. Step S320: Correct the initial resistance surface according to the correction factor to obtain the resistance value of migration activities between green infrastructures.

[0030] Specifically, based on the comprehensive situation of urban socio-economic development and natural ecological protection, six factors—land use type, transportation network, population density, slope, altitude, and distance to key nodes—were selected as construction factors. Weights and classification methods for each factor were designed simultaneously to construct a preliminary resistance surface. Using factors such as nighttime light pollution and GDP as correction factors, the constructed preliminary resistance surface was corrected to obtain reasonable resistance values ​​for migration activities between green infrastructure components, thus characterizing the accessibility and connectivity of green infrastructure components.

[0031] Reference Figure 5 For step S400, constructing a green infrastructure network based on the key nodes of the green infrastructure and the resistance values ​​of migration activities between green infrastructures includes the following steps: Step S410: Construct a minimum cumulative resistance model based on the resistance values ​​of migration activities between green infrastructures; Step S420: Simulate the cumulative shortest path between the starting point and the target point of the key node of the green infrastructure based on the minimum cumulative resistance model. The cumulative shortest path is used to characterize the resistance that the migration activity between the starting point and the target point needs to overcome. Step S430: Generate edges connecting key nodes of green infrastructure based on the cumulative shortest path between the starting point and the target point, and construct a green infrastructure network through the connection between the edges and key nodes.

[0032] Then, the interaction force matrix between key nodes of green infrastructure is calculated using a gravity model. The interaction force matrix is ​​used to evaluate the interaction strength of key nodes of green infrastructure, and the interaction strength is used to characterize the importance of the connection edges between key nodes. Network analysis is used to evaluate the connectivity of green infrastructure network, and the connectivity is used to characterize the stability of green infrastructure network.

[0033] Output data results of the urban green infrastructure network, including GI patches, pinch points, obstacle points, administrative boundaries, topography, and cumulative minimum cost paths, all in .shp format.

[0034] For step S500, modeling is performed based on the spatial data of the green infrastructure network to obtain the source data for the 4D-BIM model of green infrastructure, including the following steps: Transform spatial data of green infrastructure networks into a common format for source data; Set the LOD level for the source data of 4D-BIM models of green infrastructure; Configure spatial attribute data values ​​for the source data of the 4D-BIM model of green infrastructure.

[0035] Specifically, the data results (including spatial data) of the urban green infrastructure network are converted into the common source data formats .ifc and .rfa for urban green infrastructure 4D-BIM models. The main source data for urban green infrastructure includes GI patches, pinch points, obstacle points, administrative boundaries, terrain, and cumulative minimum cost paths, all in .shp format.

[0036] The LOD levels of the source data for the 4D-BIM model of urban green infrastructure are set, with GI patches, pinch points, obstacle points, administrative boundaries, terrain, and cumulative minimum cost paths classified as LOD 100; building groups, road groups, and bridge groups classified as LOD 200; and forest parks and their ancillary facilities, wetland parks and their ancillary facilities, urban green squares, reservoirs and their hydraulic structures classified as LOD 300.

[0037] The source data of the 4D-BIM model of urban green infrastructure is modeled using professional BIM software, and the level of detail of the 4D-BIM model source data is reviewed in accordance with the established LOD level principle of urban green infrastructure 4D-BIM model source data.

[0038] Based on the technical specifications for land spatial information modeling, spatial attribute data values ​​are added to the source data of the established 4D-BIM model of urban green infrastructure. These values ​​include the center coordinates (XY values), land area, land use category, elevation, perimeter, land spatial use classification, protection level, and construction year. The corresponding values ​​are filled in as actual data to serve as the basis for data visualization.

[0039] Then, the source data of the 4D-BIM model of urban green infrastructure is preprocessed.

[0040] Preprocessing includes: reducing the polygon count of the source data of the 4D-BIM model of green infrastructure; dividing the source data of the 4D-BIM model of green infrastructure after the reduction of polygon count according to the scene import rules, and storing it in the target folder for source data management and retrieval.

[0041] Specifically, the source data of the 4D-BIM model of urban green infrastructure undergoes polygon reduction optimization. While ensuring visual performance, the number of polygons in the model is reduced to control the overall memory size of the 4D-BIM model source data file. The polygon-reduced 4D-BIM model source data file is exported, and its file size and hierarchy are divided according to the scene import rules. It is then stored in a designated folder for source data management and retrieval.

[0042] In step S600, the preprocessed 4D-BIM model source data of urban green infrastructure is imported into the development engine software step by step for program development. The imported 4D-BIM model source data of urban green infrastructure is registered according to the WGS84 geodetic coordinate system. A green infrastructure evolution visualization system is then constructed to display green infrastructure evolution information.

[0043] The green infrastructure evolution visualization system is equipped with a visualization module, a historical evolution module, and an interactive and information query module.

[0044] The visualization module includes a dashboard for analyzing the overall network connectivity of green infrastructure. The dashboard's data metrics include the total area of ​​urban green infrastructure, network coverage, area of ​​obstructions, connectivity index, and cumulative minimum cost path.

[0045] The historical evolution module binds the 4D-BIM models of green infrastructure at different times to the time data in the green infrastructure historical evolution plan table. It uses a timeline tool to control the historical evolution information of green infrastructure and displays 4D simulation images of the green infrastructure network as well as projection images of the planning and development of green infrastructure.

[0046] The interaction and information query module receives user interaction data and queries and displays information about green infrastructure based on this data. For example, by clicking on the 4D-BIM model of urban green infrastructure in a scene, a user can quickly locate and focus on the selected target object. The target object includes GI patches, pinch points, obstacle points, administrative boundaries, terrain, cumulative minimum cost paths, building clusters, road clusters, bridge clusters, forest parks and their ancillary facilities, wetland parks and their ancillary facilities, urban green plazas, reservoirs and their hydraulic structures. Simultaneously, detailed information about the 4D-BIM model of urban green infrastructure is dynamically displayed on the platform's UMG interface, such as the center coordinates (XY values), land area, land use category, elevation, perimeter, land use classification, protection level, and construction year.

[0047] This visualization method for the evolution of green infrastructure enables precise identification of key nodes in urban green infrastructure. Based on land use types, and using morphological spatial pattern analysis methods, it analyzes the patterns of blue-green spaces such as woodlands, grasslands, water bodies, and wetlands, identifying core areas, bridging areas, island patches, and branch lines. Edge area The city identifies seven landscape types, including ring roads and pores. Based on the landscape element identification results, it further filters urban green infrastructure, which can not only avoid the shortcomings of existing technologies, but also scientifically identify key nodes of urban green infrastructure.

[0048] A localized framework for optimal distance thresholds based on indicator evaluation and trend analysis was constructed. Based on indicators such as the number of landscape connections, the number of landscape components, the number of patches of the largest landscape component, and the area ratio of the largest landscape component, the distance thresholds for connectivity analysis were initially defined. Then, by combining the consistency of the changing trends of the potential connectivity index and the overall connectivity index, the optimal distance threshold was determined. This framework can provide an effective reference for the localization of optimal thresholds for connectivity analysis of green infrastructure in different cities or regions.

[0049] By precisely overlaying vector data (patches, paths) of urban green infrastructure, reflecting its macroscopic structure, with BIM models depicting the detailed urban form, urban green infrastructure analysis is no longer an abstract planar diagram but is conducted within a realistic 3D urban environment. This allows users to grasp the overall layout of urban green infrastructure macroscopically while also examining the detailed relationships between specific green spaces and surrounding buildings microscopically. Furthermore, by assigning time stamps to urban green infrastructure models from different periods and driving their dynamic changes within a game engine, a continuous and immersive dynamic visualization of the urban green infrastructure network—from its inception to its current scale, and from isolated to connected—is achieved, resolving the issue of insufficient dynamic representation.

[0050] By deeply integrating macro-level GIS spatial analysis, micro-level BIM model information, and four-dimensional spatiotemporal simulation technology, the ecological service functions and network connectivity of urban green space infrastructure can be assessed. This not only allows for a three-dimensional, dynamic replay of the changes in urban green space infrastructure over the years, but also enables data visualization analysis to assess the rationality and ease of use of the layout of urban green space infrastructure, thereby helping planners make more informed decisions.

[0051] This application also provides an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described green infrastructure evolution visualization method.

[0052] This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0053] The processor can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory can be implemented using read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory, and the processor calls and executes the data storage method or data reading method of the embodiments of this application.

[0054] Input / output interfaces are used to implement information input and output; communication interfaces are used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); the bus transmits information between various components of the device (such as processor, memory, input / output interfaces and communication interfaces); among them, the processor, memory, input / output interfaces and communication interfaces are connected to each other within the device through the bus.

[0055] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described green infrastructure evolution visualization method.

[0056] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0057] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0058] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0059] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0060] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0061] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0062] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0064] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] The above is a detailed description of the preferred embodiments of this application, but this application is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for visualizing the evolution of green infrastructure, characterized in that, include: Extracting green infrastructure from maps; The optimal distance threshold for connectivity analysis is determined based on the connectivity index data of green infrastructure, and key nodes of green infrastructure are identified based on the optimal distance threshold. Determine the resistance values ​​for migration activities between green infrastructures; Construct a green infrastructure network based on the key nodes of the green infrastructure and the resistance values ​​of migration activities between the green infrastructures; Based on the spatial data of the green infrastructure network, the source data for the 4D-BIM model of green infrastructure is obtained; A green infrastructure evolution visualization system is constructed based on the source data of the 4D-BIM model of green infrastructure, and the green infrastructure evolution visualization system displays green infrastructure evolution information.

2. The green infrastructure evolution visualization method according to claim 1, characterized in that, The process of determining the optimal distance threshold for connectivity analysis based on connectivity index data of green infrastructure includes: Based on connectivity index data such as the number of landscape connections, the number of landscape components, the number of patches of the largest landscape component, and the area ratio of the largest landscape component, we analyze the changing trends of each connectivity index data under different threshold values ​​and preliminarily determine the distance threshold range for connectivity analysis. Within the specified distance threshold range, the distance is refined according to a preset step size, and the optimal distance threshold for connectivity analysis is determined by combining the trend consistency of the overall connectivity index and the probability connectivity index.

3. The green infrastructure evolution visualization method according to claim 1, characterized in that, The process of identifying key nodes of green infrastructure based on the optimal distance threshold includes: The overall connectivity index and the probability connectivity index are calculated based on the optimal distance threshold. Key nodes of green infrastructure are identified using the overall connectivity index and the probability connectivity index through a patch area average weighting method.

4. The green infrastructure evolution visualization method according to claim 1, characterized in that, The determination of resistance values ​​for migration activities between green infrastructures includes: Land use type, transportation network, population density, slope, altitude, and distance to key nodes were selected as construction factors. A preliminary resistance surface was constructed based on the weights of each factor and the classification and grading method. The resistance value for migration activities between green infrastructures is obtained by correcting the initial resistance surface with a correction factor.

5. The method for visualizing the evolution of green infrastructure according to claim 1, characterized in that, The construction of the green infrastructure network based on the key nodes of the green infrastructure and the resistance values ​​of migration activities between the green infrastructures includes: Construct a minimum cumulative resistance model based on the resistance values ​​of migration activities between the green infrastructures; The minimum cumulative resistance model is used to simulate the cumulative shortest path between the starting point and the target point of a key node in green infrastructure. The cumulative shortest path is used to characterize the resistance that the migration activity between the starting point and the target point needs to overcome. Edges connecting key nodes of green infrastructure are generated based on the cumulative shortest path between the starting point and the target point, and a green infrastructure network is constructed through the connection between the edges and key nodes.

6. The method for visualizing the evolution of green infrastructure according to claim 1, characterized in that, After constructing a green infrastructure network, the method includes: A gravity model is used to calculate the interaction force matrix between key nodes of green infrastructure. The interaction force matrix is ​​used to evaluate the interaction strength of key nodes of green infrastructure, and the interaction strength is used to characterize the importance of the connecting edges between key nodes. Network analysis is used to assess the connectivity of green infrastructure networks, which characterizes the stability of the green infrastructure networks.

7. The method for visualizing the evolution of green infrastructure according to claim 1, characterized in that, The process of modeling based on spatial data of the green infrastructure network to obtain source data for the 4D-BIM model of green infrastructure includes: Convert the spatial data of the green infrastructure network into a common format for source data; Set the LOD level for the source data of 4D-BIM models of green infrastructure; Configure spatial attribute data values ​​for the source data of the 4D-BIM model of green infrastructure.

8. The method for visualizing the evolution of green infrastructure according to claim 1, characterized in that, After obtaining the source data of the 4D-BIM model of green infrastructure, the method further includes: The source data of the 4D-BIM model of the green infrastructure is subjected to surface reduction optimization processing; According to the scene import rules, the source data of the green infrastructure 4D-BIM model after surface reduction optimization is divided according to file size and hierarchy, and stored in the target folder for source data management and retrieval.

9. The method for visualizing the evolution of green infrastructure according to claim 1, characterized in that, The green infrastructure evolution visualization system is equipped with a visualization module and a historical evolution module; The visualization module is equipped with a green infrastructure overall network connectivity analysis dashboard. The data indicators of the analysis dashboard include the total area of ​​urban green infrastructure, network coverage, obstacle area, connectivity index, and cumulative minimum cost path. The historical evolution module binds the 4D-BIM models of green infrastructure at different times with the time data in the green infrastructure historical evolution plan table, uses a timeline tool to control the historical evolution information of green infrastructure, and displays 4D simulation images of the green infrastructure network as well as projection images of the planning and development of green infrastructure.

10. The method for visualizing the evolution of green infrastructure according to claim 1, characterized in that, The green infrastructure evolution visualization system is equipped with an interaction and information query module; the interaction and information query module is used to receive user interaction data and query and display green infrastructure information based on the interaction data.

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