A method and system for visualizing architectural planning of a garbage transfer station

Abstract

The present invention relates to the field of architectural planning visualization, and in particular to an architectural planning visualization method and system for a garbage transfer station. The method comprises the following steps: obtaining a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station; reconstructing the image details of the satellite positioning map, fitting the road spatial distribution, and constructing a regional road distribution network; performing surrounding building positioning calculations on the satellite image based on the regional road distribution network to optimize the details and reconstruct the satellite image, and making the optimal address decision, and extracting the optimal address area image; identifying the key feature points of the transfer station on the garbage transfer station design drawing, and then performing three-dimensional point cloud modeling to construct a three-dimensional structural model of the transfer station; accurately matching the corresponding foundation of the three-dimensional structural model of the transfer station according to the optimal address area image, and performing spatial positioning mapping to construct a transfer station address twin model. The present invention improves the operating efficiency of the transfer station and enhances the rationality of architectural planning.

Description

Technical Field

[0001] The present invention relates to the field of architectural planning visualization, and in particular to an architectural planning visualization method and system for a garbage transfer station. Background Art

[0002] As a key link in the urban waste treatment system, garbage transfer stations undertake the important task of transferring various types of garbage from collection points to treatment facilities or final disposal sites. With the acceleration of urbanization and the increasing demand for garbage treatment, the scale and number of garbage transfer stations have gradually increased, and their role in urban garbage classification, transfer and resource recycling has become increasingly important. At the same time, the construction and operation of garbage transfer stations face many challenges such as tight land resources, unreasonable site selection, environmental impact, and traffic flow design.

[0003] In the traditional waste transfer station construction planning process, designers usually rely on two-dimensional floor plans and hand-drawn plans for preliminary planning, but this method has certain limitations. The operation efficiency of the transfer station is often low, and the structural planning is difficult to reasonably fit the local environmental topography. With the continuous advancement of science and technology, especially the deep integration of information technology and architectural design, the complexity of architectural planning requires designers to consider not only the reasonable layout of space, but also factors such as personnel flow, garbage transportation channels, and environmental protection facilities. Therefore, the shortcomings of traditional planning methods in terms of efficiency, visualization, and accuracy are gradually exposed, and a more intelligent and efficient method for waste transfer station construction planning is urgently needed. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a method and system for visualizing the architectural planning of a garbage transfer station to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for visualizing the architectural planning of a garbage transfer station, comprising the following steps:

[0006] Step S1: Obtain a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station; reconstruct image details of the satellite positioning map, perform road spatial distribution fitting, and construct a regional road distribution network;

[0007] Step S2: Calculate the location of surrounding buildings based on the satellite image optimized and reconstructed in detail on the regional road distribution network, make the best site selection decision, and extract the best address area image;

[0008] Step S3: Identify key feature points of the transfer station based on the design drawings of the garbage transfer station, perform three-dimensional point cloud modeling, and construct a three-dimensional structural model of the transfer station;

[0009] Step S4: accurately match the three-dimensional structure model of the transfer station with the corresponding foundation according to the optimal address area image, and perform spatial positioning mapping to construct a transfer station address twin model;

[0010] Step S5: Perform dynamic transfer behavior analysis based on the design drawings of the garbage transfer station, and conduct in-depth mining of the garbage transfer logic to generate dynamic transfer logic;

[0011] Step S6: Based on the dynamic transfer logic, the dynamic transfer behavior of the transfer station address twin model is simulated, and dynamic structure planning optimization is performed to construct a three-dimensional planning visualization model.

[0012] The present invention reconstructs the satellite positioning map in detail and fits the road spatial distribution to construct a regional road distribution network, thereby achieving a clear understanding of the spatial structure and road layout of the area to be developed and providing basic data for subsequent planning. The construction of the regional road distribution network helps to optimize the positioning calculation of surrounding buildings, improve the accuracy and feasibility of site selection decisions, and provide a scientific basis for the planning of garbage transfer stations. By performing surrounding building positioning calculations and optimal site selection decisions on reconstructed satellite images, the optimal address area image is extracted to achieve accurate positioning of the optimal station location and improve the accuracy of building positioning. The extraction of the optimal address area image helps planners to better understand the surrounding environment and provide a reasonable site selection plan for the design and construction of the garbage transfer station. Through the feature point recognition and three-dimensional point cloud modeling of the transfer station design drawings, a three-dimensional structural model of the transfer station is constructed to achieve digital representation of the transfer station design and provide a basis for subsequent spatial positioning and mapping. The establishment of the three-dimensional structural model of the transfer station helps planners to fully understand the design characteristics of the transfer station and provide technical support and reference for planning and design. , through the precise matching and spatial positioning mapping of the optimal address area image and the three-dimensional structural model of the transfer station, a transfer station address twin model is constructed to achieve the precise combination of the transfer station design and the actual environment, and improve the realism and feasibility of the design. The construction of the transfer station address twin model helps planners to better predict the design effect and provide a reliable basis for subsequent planning and simulation. Through dynamic transfer behavior analysis and in-depth mining of garbage transfer logic, dynamic transfer logic is generated to help planners understand the operation mechanism and process of the garbage transfer station and provide logical support for planning. The generation of dynamic transfer logic helps planners optimize the design of the transfer station, improve operational efficiency and environmental protection, and realize scientific and intelligent garbage disposal. Based on the dynamic transfer logic, the dynamic transfer behavior simulation and structural planning optimization of the transfer station address twin model are carried out, and a three-dimensional planning visualization model is constructed to achieve intuitive display and optimization of the overall planning scheme. The establishment of a three-dimensional planning visualization model helps relevant personnel to understand the planning and design effects more intuitively, provide effective reference for planning decisions, and promote the implementation and optimization of planning schemes.

[0013] Preferably, step S1 comprises the following steps:

[0014] Step S11: Obtaining a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station;

[0015] Step S12: reconstructing image details of the satellite positioning map to construct a detailed optimized reconstructed satellite image;

[0016] Step S13: performing regional road visual recognition on the detail optimized and reconstructed satellite image to extract regional road contour lines;

[0017] Step S14: performing road distribution analysis on the regional road contour lines to generate road distribution data;

[0018] Step S15: Perform road spatial distribution fitting on the road distribution data to construct a regional road distribution network.

[0019] The present invention provides necessary basic data and basis for subsequent planning and design by acquiring satellite positioning maps and garbage transfer station design drawings.

[0020] Through image detail reconstruction, the satellite positioning map is clearer and more accurate, providing a more reliable data basis for subsequent analysis and planning. The satellite images after detail optimization and reconstruction help planners better understand the regional landforms and characteristics, provide more refined data for road distribution analysis, ensure the accuracy and feasibility of the planning process, and provide a suitable starting point for the entire project. Through regional road visual recognition and extraction of road contours, the location and form of roads can be accurately captured, providing a basis for subsequent road distribution analysis. The extraction of regional road contours helps planners better understand road layout and traffic flow lines, and provides key data support for road spatial distribution fitting. Road distribution analysis helps planners understand the distribution and density of roads in the region, and provides quantitative data support for road spatial distribution fitting. The generated road distribution data helps planners optimize road planning and traffic design, improve the efficiency and convenience of the road network, and realize the visualization and optimization of the road network through road spatial distribution fitting and construction of regional road distribution network, providing a scientific basis for the site selection and traffic planning of garbage transfer stations. The construction of regional road distribution network helps planners better understand the structure and connection of the road system, and provide spatial analysis and planning suggestions for subsequent planning.

[0021] Preferably, the specific steps of step S12 are:

[0022] Performing grayscale histogram calculation on the satellite positioning map to obtain grayscale histogram parameters;

[0023] Perform image grayscale value distribution analysis based on grayscale histogram parameters to generate image grayscale value distribution data;

[0024] Perform image brightness distribution recognition according to image gray value distribution data to extract image brightness distribution data;

[0025] Performing global pixel brightness enhancement processing on the satellite positioning map based on the image brightness distribution data, thereby obtaining a brightness enhanced positioning map;

[0026] Perform image edge feature recognition on the brightness enhancement positioning map and mark the image edge feature information;

[0027] Perform sharpening filter optimization on the image edge feature information to generate filter-optimized edge features;

[0028] The image details of the brightness enhanced positioning map are reconstructed based on the filtered optimized edge features, and the detail optimized reconstructed satellite image is constructed.

[0029] The present invention calculates the grayscale histogram to understand the grayscale distribution of the image, which is helpful for the subsequent adjustment and optimization of the brightness and contrast of the image. The grayscale histogram parameters provide important information about the overall grayscale distribution of the image, providing a basis for subsequent image processing and analysis. The generation of image grayscale value distribution data is helpful for a deep understanding of the grayscale distribution law of the image, providing data support for subsequent brightness enhancement and feature recognition. By analyzing the grayscale value distribution, a basis is provided for the optimization and adjustment of the image processing algorithm, and the visual effect and quality of the image are improved. By identifying the image brightness distribution, the brightness changes in different areas of the image are captured, providing a basis for subsequent brightness enhancement processing. The extraction of image brightness distribution data is helpful for highlighting the main features and details of the image, laying a foundation for subsequent image processing steps. By brightness enhancement processing, the overall brightness of the image is improved. The brightness enhancement positioning map can make the image clearer and more contrasty, providing a better basis for subsequent feature recognition and edge feature optimization. Through edge feature recognition and optimization, the edge information in the image is highlighted, the image clarity and visual effect are improved, which helps to better capture the details in the image. The sharpening filter optimization of edge features helps to enhance the contour and details of the image, providing a better reference and basis for subsequent image reconstruction and optimization. Through image detail reconstruction, the details and features of the image are further optimized, making the image clearer and layered, providing better image materials for the visualization of architectural planning. The detail optimization and reconstruction of satellite images can make the image more real and specific, provide planners with more intuitive and accurate information, and help to better carry out architectural planning and design.

[0030] Preferably, step S2 specifically comprises the following steps:

[0031] Step S21: performing surrounding building positioning calculation on the detail optimized and reconstructed satellite image to obtain the surrounding building positioning coordinates;

[0032] Step S22: performing regional spacing topological correlation analysis on the surrounding building positioning coordinates based on the regional road distribution network, and extracting surrounding building-road distribution topological correlation data;

[0033] Step S23: performing environmental terrain undulation recognition on the detail optimized and reconstructed satellite image to obtain environmental terrain undulation information;

[0034] Step S24: Analyze the topographic features of the environmental terrain undulation information to extract the environmental terrain features of the area to be developed;

[0035] Step S25: Optimize the site selection decision by optimizing and reconstructing the satellite image according to the environmental topographic features of the area to be developed and the surrounding building-road distribution topological correlation data, and extract the optimal address area image.

[0036] The present invention accurately obtains the location information of buildings around the garbage transfer station through the calculation of surrounding building positioning, and provides basic data for subsequent planning and analysis. The acquisition of surrounding building positioning coordinates helps to consider surrounding environmental factors in the planning process, improves the accuracy and practicality of planning, and better understands the spatial relationship between surrounding buildings and roads through the topological association analysis of regional road distribution network and building positioning coordinates, providing more refined data support for planning and positioning. The extracted surrounding building-road distribution topological association data helps planners to better evaluate the impact of surrounding road networks on building planning and optimize planning schemes. Identifying environmental terrain undulation information helps planners understand the terrain characteristics of the area to be developed, and provides an important reference for considering terrain factors in planning and design. Environmental terrain undulation information The acquisition of information helps planners to more comprehensively evaluate the geographical environment of the area to be developed and provide support for decision-making in the planning process. Through the analysis of topographic and geomorphic features, we can have an in-depth understanding of the topographic features of the area to be developed, including information such as mountains, rivers, and plains, providing more comprehensive geographic data support for planning. The extracted topographic and geomorphic features help planners better evaluate the natural conditions and topographic characteristics of the area, providing an important reference for the formulation of planning schemes. The optimal site location decision based on the environmental topographic and geomorphic features and the surrounding building-road distribution topological correlation data helps determine the most suitable area for building a garbage transfer station, improving the efficiency and accuracy of planning. Extracting the optimal address area image helps planners intuitively understand the geographical environment and surrounding conditions of the best site selection area, providing an intuitive basis for planning decisions.

[0037] Preferably, step S25 specifically comprises the following steps:

[0038] Calculate the area of ​​the transfer station based on the design drawings of the garbage transfer station and extract the area of ​​the transfer station;

[0039] Mining spatial layout features based on the area of ​​the transfer station to generate spatial layout features of the transfer station;

[0040] Based on the surrounding building-road distribution topological correlation data and the environmental topographic features of the area to be developed, the optimal location decision is made for the transfer station spatial layout characteristics, thereby obtaining the optimal transfer station building address;

[0041] According to the optimal transfer station building address, the satellite image is reconstructed through detailed optimization to segment the building address area and extract the optimal address area image.

[0042] The present invention calculates the area occupied by the transfer station, accurately understands the land resources required for the garbage transfer station, and provides basic data for subsequent planning and design. Extracting the area occupied by the transfer station helps planners to better evaluate site requirements, ensure the feasibility of the planning scheme and effectively use land resources. Through spatial layout feature mining, the internal layout characteristics of the transfer station, including information such as building structure and equipment placement, are deeply understood, providing important reference for spatial design. Generating the spatial layout characteristics of the transfer station helps planners optimize the site layout and improve work efficiency and resource utilization efficiency. Combined with the surrounding building-road distribution topological association data and environmental topographic features, more factors are considered in the site selection decision process to find the most suitable location for building the transfer station. The optimal site decision helps to ensure the rationality and convenience of the transfer station in terms of geographical location, improve service efficiency and reduce operating costs. Through the segmentation of the building address area, the optimal site area is subdivided into specific construction areas, providing clear spatial division for subsequent planning, design and construction. Extracting the optimal address area image helps planners to more intuitively understand the geographical environment and surrounding conditions of the construction area, providing an intuitive basis for planning decisions.

[0043] Preferably, step S3 specifically comprises the following steps:

[0044] Step S31: Identify key feature points of the garbage transfer station on the design drawing of the transfer station and mark multiple key feature points;

[0045] Step S32: calculating the distances between the multiple key feature points, and extracting the distances between the multiple feature points;

[0046] Step S33: performing geometric analysis of the transfer station according to the distance between the feature points to generate geometric features of the transfer station;

[0047] Step S34: performing functional area analysis on the garbage transfer station design drawings to generate a plurality of different transfer station functional areas;

[0048] Step S35: performing topological structure association analysis on a plurality of different transfer station functional areas, thereby generating functional area topological structure association features;

[0049] Step S36: Based on the associated features of the functional area topological structure, three-dimensional point cloud modeling is performed on the geometric features of the transfer station to construct a three-dimensional structural model of the transfer station.

[0050] The present invention accurately locates important areas and structures in the design drawings by marking multiple key feature points, providing a basis for subsequent analysis. The identification of key feature points helps planners better understand the design concept and key areas of the transfer station, providing important clues for subsequent analysis. By extracting the distances between multiple feature points, the spatial relationship between each key feature point in the design drawing is quantified, and the proportion and layout in the design are understood. The calculation of the distance between feature points helps to reveal the structural features and layout rules in the design drawings, providing data support for subsequent morphological analysis. Through geometric morphological analysis, the geometric morphological features of the transfer station, including information on shape, size, etc., are extracted from the spatial distribution of feature points. The generated geometric morphological features help planners better understand the overall morphological characteristics of the transfer station, provide guidance for subsequent planning and design, and Through analysis, the design drawings of the transfer station are divided into several different functional areas, such as the receiving area, the processing area, etc., in order to better organize the spatial layout. The generated functional areas help planners understand the functional requirements and connections of each area, and provide a basis for the subsequent topological structure association. Through the topological structure association analysis, the connection and dependency between different functional areas are understood, and guidance is provided for the overall structure of the building space. The generated functional area topological structure association characteristics help planners optimize the spatial layout and ensure the coordination and effective operation of the functional areas. Through 3D point cloud modeling, the topological structure association characteristics of the functional areas are converted into a specific 3D structural model to realize the three-dimensional visualization of the transfer station. 3D point cloud modeling helps planners to more intuitively understand the spatial layout and structural characteristics of the transfer station, and provide intuitive support for subsequent design and evaluation.

[0051] Preferably, the specific steps of step S4 are:

[0052] Step S41: Analyze the bearing points of the building structure of the transfer station on the three-dimensional structural model of the transfer station, and mark the bearing points of the building structure;

[0053] Step S42: Calculate the spatial position of the bearing point of the building structure to obtain the spatial position coordinates of the bearing point;

[0054] Step S43: Performing ground point visual analysis on the optimal address area image to extract the area image ground points;

[0055] Step S44: accurately matching the regional image foundation points with the corresponding foundations according to the spatial position coordinates of the anchoring points, thereby obtaining foundation point matching data;

[0056] Step S45: Based on the foundation point matching data, the three-dimensional structure model of the transfer station is used to spatially locate and map the optimal address area image to construct a transfer station address twin model.

[0057] The present invention accurately locates the key areas of the load-bearing structure in the transfer station by marking the bearing points of the building structure, thus providing a basis for subsequent analysis. The marking of the bearing points helps planners to better understand the structural layout and bearing elements of the transfer station, thus providing important information for subsequent positioning and spatial mapping. By calculating the spatial position coordinates of the bearing points, the positions of the bearing points in three-dimensional space are accurately determined, thus providing an accurate basis for subsequent mapping. The spatial position calculation helps planners to understand the spatial relationship between the bearing points, thus providing necessary data support for subsequent foundation point matching. Through visual analysis of the foundation points, the position information of the foundation points is extracted from the image of the optimal address area, thus providing a data basis for subsequent matching. The extraction of points helps to understand the terrain features and positioning information of the regional image, and provides support for subsequent spatial positioning mapping. By matching the anchor points and foundation points, a spatial correspondence between the two is established to achieve accurate data matching. The matching of anchor points and foundation points helps to associate structural information with terrain information, and provides accurate data support for subsequent spatial positioning mapping. Based on the matching data of anchor points and foundation points, the three-dimensional structural model of the transfer station is spatially mapped with the optimal address area image to construct a transfer station address twin model. Spatial positioning mapping helps to associate the structural information of the transfer station with the specific geographical location, and achieve more accurate building planning and visualization.

[0058] Preferably, the specific steps of step S5 are:

[0059] Step S51: Perform dynamic transfer behavior analysis based on the garbage transfer station design drawings to generate dynamic transfer behavior characteristic data;

[0060] Step S52: performing garbage in-and-out identification on the dynamic transfer behavior characteristic data to obtain garbage in-and-out behavior data;

[0061] Step S53: performing transfer vehicle scheduling analysis based on multiple different transfer station functional areas and extracting transfer vehicle routes;

[0062] Step S54: Evolving the internal flow direction of the transfer station for the garbage in-and-out behavior data, thereby generating internal garbage flow direction data of the transfer station;

[0063] Step S55: Based on the internal garbage flow data of the transfer station, the garbage transfer logic is deeply mined on the transfer vehicle path to generate dynamic transfer logic.

[0064] The present invention simulates the dynamic transfer behavior inside the garbage transfer station by analyzing the design drawings. The generated dynamic transfer behavior characteristic data is helpful to understand the garbage flow situation and possible bottleneck points inside the station. By identifying the in-and-out behavior of garbage, the flow situation and frequency of garbage in the station are understood. The extraction of garbage in-and-out behavior data is helpful to analyze the load situation of the station and optimize the transfer process. By analyzing the transfer stations in different functional areas, the path planning and scheduling of transfer vehicles are optimized. The extracted transfer vehicle path data is helpful to improve transportation efficiency and reduce transportation costs. By analyzing the garbage in-and-out behavior data and the internal flow evolution, the garbage flow direction data inside the station is generated. The generated garbage flow direction data is helpful to optimize the layout and process design inside the station. Based on the garbage flow direction data, the transfer vehicle path is logically mined to generate dynamic transfer logic. The logic mining is helpful to optimize the scheduling plan of the transfer vehicle and improve the transportation efficiency and responsiveness of the station.

[0065] Preferably, the specific steps of step S6 are:

[0066] Step S61: Perform dynamic transfer behavior simulation on the transfer station address twin model based on dynamic transfer logic to generate transfer station transfer behavior simulation data;

[0067] Step S62: simulating available transfer paths for the transfer station transfer behavior simulation data, thereby generating multiple transfer path adjustment plans;

[0068] Step S63: Calculating the transfer operation efficiency of each of the multiple transfer route adjustment plans, thereby generating the operation efficiency of each route plan;

[0069] Step S64: evaluating the optimal path plan according to the operation efficiency of the path plan, and extracting the optimal path plan;

[0070] Step S65: Optimizing the functional area layout based on the optimal path solution to generate functional area layout optimization data;

[0071] Step S66: performing dynamic structural planning optimization on the three-dimensional structural model of the transfer station according to the functional area layout optimization data, thereby obtaining a dynamic structural planning three-dimensional model;

[0072] Step S67: Perform three-dimensional planning visualization on the dynamic structure planning three-dimensional model to construct a three-dimensional planning visualization model.

[0073] The present invention simulates the transfer behavior and the internal operation of the site, including vehicle flow, garbage disposal, etc. The generated simulation data is helpful to evaluate the site operation efficiency and bottleneck points under different situations. By simulating the available transfer paths, multiple transfer path adjustment plans are generated to optimize vehicle scheduling and garbage transfer processes, and different path plans are provided for selection to improve the overall transportation efficiency. The operation efficiency is calculated path by path, and the transportation efficiency of different path plans is quantified, which helps to determine which path plans are most suitable for specific transportation needs and site layouts. According to the operation efficiency evaluation, the best path plan is extracted to improve the overall site operation efficiency, and the best path plan is selected to reduce costs and improve service quality. The functional area layout is optimized based on the optimal path plan, which helps to optimize the internal layout and process of the site. The optimized layout improves work efficiency and reduces possible conflict points. Through dynamic structural planning optimization, the three-dimensional structural design of the transfer station is optimized to make it more in line with actual operation needs. The optimized structural design improves the operation efficiency and sustainability of the site. The three-dimensional model of the dynamic structure planning is visualized, which helps planners and stakeholders to understand the design plan more intuitively. The construction of a three-dimensional planning visualization model improves communication efficiency and the comprehensibility of the design plan.

[0074] In this specification, a system for visualizing the construction planning of a garbage transfer station is provided, which is used to execute the method for visualizing the construction planning of a garbage transfer station as described above, including:

[0075] The road distribution module obtains the satellite positioning map of the area to be developed and the design drawings of the garbage transfer station; reconstructs the image details of the satellite positioning map, performs road spatial distribution fitting, and constructs a regional road distribution network;

[0076] The optimal location module calculates the location of surrounding buildings based on the detailed optimization and reconstruction of satellite images based on the regional road distribution network, makes optimal location decisions, and extracts the optimal address area image;

[0077] The 3D structure module identifies the key feature points of the garbage transfer station based on the design drawings, and then performs 3D point cloud modeling to construct a 3D structure model of the transfer station.

[0078] The spatial positioning and mapping module accurately matches the three-dimensional structure model of the transfer station with the corresponding foundation according to the image of the optimal address area, and performs spatial positioning and mapping to build a twin model of the transfer station address;

[0079] The transfer behavior module conducts dynamic transfer behavior analysis based on the design drawings of the garbage transfer station and conducts in-depth mining of the garbage transfer logic to generate dynamic transfer logic;

[0080] The three-dimensional planning visualization module simulates the dynamic transfer behavior of the transfer station address twin model based on dynamic transfer logic, and performs dynamic structural planning optimization to construct a three-dimensional planning visualization model.

[0081] The present invention reconstructs satellite positioning maps and road spatial distribution fitting to construct a regional road distribution network, which helps to determine the optimal transfer station location. Such analysis improves the accessibility and traffic efficiency of the site, and provides a basis for subsequent planning. Based on the regional road distribution network and surrounding building positioning calculations, the optimal site location decision is made, and the optimal address area image is extracted to ensure the rationality and convenience of the transfer station location. This step helps to optimize the surrounding environment and traffic convenience of the site. By identifying the key feature points of the transfer station design drawings and performing three-dimensional point cloud modeling, a three-dimensional structural model of the transfer station is accurately constructed, providing a visualization basis for subsequent planning. The three-dimensional structural model helps planners better understand Understand the spatial layout and characteristics of the site, build a transfer station address twin model by matching and spatially mapping the optimal address area image and the three-dimensional structural model of the transfer station, achieve precise correspondence between the site location and the design, ensure the accuracy and practicality of site planning, and generate dynamic transfer logic through dynamic transfer behavior analysis and logic mining to help optimize the site transportation process and efficiency. Such analysis helps to improve the operational efficiency of the site and reduce potential problems. Based on dynamic transfer logic and structural planning optimization, build a three-dimensional planning visualization model to more intuitively display the design and operation of the transfer station. This visualization model helps communication and decision-making, and improves the transparency and comprehensibility of planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A schematic flow chart of the steps of a method for visualizing architectural planning of a garbage transfer station according to the present invention;

[0083] Figure 2 Detailed implementation flow chart of step S1;

[0084] Figure 3 Detailed implementation flow chart of step S2;

[0085] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0086] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0087] The present application example provides a method and system for visualizing the architectural planning of a garbage transfer station. The execution subjects of the method and system for visualizing the architectural planning of a garbage transfer station include but are not limited to the following: mechanical equipment, data processing platform, cloud server nodes, network upload equipment, etc. that are equipped with the system can be regarded as general computing nodes of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0088] See also Figures 1 to 4 The present invention provides a method for visualizing the construction planning of a garbage transfer station, and the method for visualizing the construction planning of a garbage transfer station comprises the following steps:

[0089] Step S1: Obtain a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station; reconstruct image details of the satellite positioning map, perform road spatial distribution fitting, and construct a regional road distribution network;

[0090] Step S2: Calculate the location of surrounding buildings based on the satellite image optimized and reconstructed in detail on the regional road distribution network, make the best site selection decision, and extract the best address area image;

[0091] Step S3: Identify key feature points of the transfer station based on the design drawings of the garbage transfer station, perform three-dimensional point cloud modeling, and construct a three-dimensional structural model of the transfer station;

[0092] Step S4: accurately match the three-dimensional structure model of the transfer station with the corresponding foundation according to the optimal address area image, and perform spatial positioning mapping to construct a transfer station address twin model;

[0093] Step S5: Perform dynamic transfer behavior analysis based on the design drawings of the garbage transfer station, and conduct in-depth mining of the garbage transfer logic to generate dynamic transfer logic;

[0094] Step S6: Based on the dynamic transfer logic, the dynamic transfer behavior of the transfer station address twin model is simulated, and dynamic structure planning optimization is performed to construct a three-dimensional planning visualization model.

[0095] The present invention reconstructs the satellite positioning map in detail and fits the road spatial distribution to construct a regional road distribution network, thereby achieving a clear understanding of the spatial structure and road layout of the area to be developed and providing basic data for subsequent planning. The construction of the regional road distribution network helps to optimize the positioning calculation of surrounding buildings, improve the accuracy and feasibility of site selection decisions, and provide a scientific basis for the planning of garbage transfer stations. By performing surrounding building positioning calculations and optimal site selection decisions on reconstructed satellite images, the optimal address area image is extracted to achieve accurate positioning of the optimal station location and improve the accuracy of building positioning. The extraction of the optimal address area image helps planners to better understand the surrounding environment and provide a reasonable site selection plan for the design and construction of the garbage transfer station. Through the feature point recognition and three-dimensional point cloud modeling of the transfer station design drawings, a three-dimensional structural model of the transfer station is constructed to achieve digital representation of the transfer station design and provide a basis for subsequent spatial positioning and mapping. The establishment of the three-dimensional structural model of the transfer station helps planners to fully understand the design characteristics of the transfer station and provide technical support and reference for planning and design. , through the precise matching and spatial positioning mapping of the optimal address area image and the three-dimensional structural model of the transfer station, a transfer station address twin model is constructed to achieve the precise combination of the transfer station design and the actual environment, and improve the realism and feasibility of the design. The construction of the transfer station address twin model helps planners to better predict the design effect and provide a reliable basis for subsequent planning and simulation. Through dynamic transfer behavior analysis and in-depth mining of garbage transfer logic, dynamic transfer logic is generated to help planners understand the operation mechanism and process of the garbage transfer station and provide logical support for planning. The generation of dynamic transfer logic helps planners optimize the design of the transfer station, improve operational efficiency and environmental protection, and realize scientific and intelligent garbage disposal. Based on the dynamic transfer logic, the dynamic transfer behavior simulation and structural planning optimization of the transfer station address twin model are carried out, and a three-dimensional planning visualization model is constructed to achieve intuitive display and optimization of the overall planning scheme. The establishment of a three-dimensional planning visualization model helps relevant personnel to understand the planning and design effects more intuitively, provide effective reference for planning decisions, and promote the implementation and optimization of planning schemes.

[0096] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a method for visualizing the construction planning of a garbage transfer station according to the present invention. In this example, the steps of the method for visualizing the construction planning of a garbage transfer station include:

[0097] Step S1: Obtain a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station; reconstruct image details of the satellite positioning map, perform road spatial distribution fitting, and construct a regional road distribution network;

[0098] In this embodiment, a suitable satellite image data source is selected, which is an open source platform (such as Google Earth, OpenStreetMap) or a commercial satellite image provider (such as DigitalGlobe). According to the coordinate range of the area to be developed, high-resolution satellite images are downloaded from the selected data source to ensure that the image clarity and coverage meet the requirements. After obtaining authorization, the design drawings are obtained from the design unit of the garbage transfer station to ensure that they are obtained in electronic format (such as CAD, PDF or high-resolution images). The format and resolution of the design drawings are confirmed for subsequent processing. If necessary, the format is converted to ensure that the drawings can be read by image processing software (such as AutoCAD, Photoshop, etc.). The satellite images are preprocessed using image processing software (such as OpenCV or Photoshop): Denoising: Apply Gaussian blur or median filtering to remove image noise, enhance contrast: Use histogram equalization to improve image contrast and enhance details, and use image reconstruction technology, such as super-resolution reconstruction algorithm (such as SRCNN or GAN) to improve the details and clarity of satellite images. This step can help better identify features such as buildings and roads. Use Canny The edge detection algorithm extracts the edge of the road. By setting a suitable threshold, ensure that the main contour of the road is extracted. Use contour extraction methods (such as cv2.findContours()) to identify and extract the contour information of the road. Convert the contour information into a polygonal representation to simplify the road shape. Use spatial analysis tools (such as ArcGIS or QGIS) to fit the spatial distribution of the extracted road contours and build a road distribution network. Convert the extracted road contours into a road network model. Use graph theory methods (such as Dijkstra algorithm) to analyze road connectivity and form the topological structure of the road network. Add attributes to the road network based on information such as road width, type (main road, secondary road, etc.) and traffic flow for subsequent analysis. Save the constructed regional road distribution network in GIS data format (such as Shapefile or GeoJSON) for subsequent analysis and visualization. Use GIS software to visualize the constructed road distribution network to ensure that it conforms to the actual situation and make necessary verification and adjustments.

[0099] Step S2: Calculate the location of surrounding buildings based on the satellite image optimized and reconstructed in detail on the regional road distribution network, make the best site selection decision, and extract the best address area image;

[0100] In this embodiment, the satellite image with optimized detail reconstruction and the constructed regional road distribution network are prepared. The two should be in the same coordinate system (such as WGS84). Image processing technology (such as deep learning model Yolo or Faster R-CNN) is used to automatically identify and extract buildings from the satellite image with optimized detail reconstruction. When training the model, open source data sets (such as OpenStreetMap data) can be used as references to ensure that the model can accurately identify buildings. Once the building is identified, its location coordinates are determined by calculating the center point of each building's outline (using the cv2.moments() method). These center points are associated with the regional road distribution network to ensure that the spatial relationship between buildings and roads can be reflected in subsequent analysis. GIS software (such as ArcGIS or QGIS) is used for spatial analysis, and the building location, road network and site selection criteria are comprehensively considered. Buffer analysis (Buffer Analysis) is used to evaluate the distance between buildings and roads, generate an impact area layer, assign weights to each site selection criterion, use the analytic hierarchy process (AHP) or simple weighting method to score each candidate location, evaluate the pros and cons of each candidate address based on the calculated score, and determine the best site among them. Once the best site is determined, the best address area is delineated in the satellite image based on the coordinates of the address, and an image processing tool (such as OpenCV) is used to draw a rectangular box or polygon on the image. The image processing tool is used to crop the image of the best address area, ensuring that the extracted image only contains the part related to the transfer station, and the extracted image of the best address area is saved in a high-resolution format (such as PNG or TIFF), and the relevant spatial information is recorded for subsequent analysis and display.

[0101] Step S3: Identify key feature points of the transfer station based on the design drawings of the garbage transfer station, perform three-dimensional point cloud modeling, and construct a three-dimensional structural model of the transfer station;

[0102] In this embodiment, the design drawings of the garbage transfer station are obtained from the relevant departments or design units, and the drawings are ensured to be in electronic format (such as CAD, PDF or high-resolution images), and the format of the drawings is ensured to be suitable for subsequent processing. If necessary, the CAD drawings are converted into common image formats (such as PNG or TIFF) to facilitate the use of image processing software, and the drawings are preprocessed using image processing software (such as OpenCV): grayscale: converting color drawings into grayscale images, denoising: applying Gaussian blur to remove image noise, edge detection: using the Canny edge detection algorithm to extract edge information in the drawings, and using feature detection algorithms (such as Harris corner detection or SIFT) to identify key feature points in the drawings. These feature points may include: marking the identified key feature points on the drawings, marking the feature points using image drawing tools (such as cv2.circle()), and recording their coordinates, converting the identified two-dimensional feature point coordinates into three-dimensional coordinates, which is achieved by setting a predefined height value (for example, according to the floor height shown in the design drawing), expanding the coordinates of each feature point into three-dimensional coordinates (x, y, z), and using three-dimensional modeling software (such as Blender, MeshLab or CloudCompare) to import 3D feature point data and generate a preliminary 3D point cloud model. The generated point cloud is processed to improve the quality of the model. Smoothing algorithms (such as Moving Least Squares) are used to reduce noise and point cloud downsampling is performed to improve efficiency. In the 3D modeling software, the processed point cloud is meshed and converted into a 3D mesh model using algorithms (such as Delaunay Triangulation or Poisson Surface Reconstruction). The 3D structural model is further refined according to the detailed information in the design drawings, and architectural elements (such as doors, windows, columns, eaves, etc.) are added to improve the accuracy of the model. The generated 3D model is compared with the design drawings to ensure the accuracy and completeness of the model, and adjustments and corrections are made when necessary. The constructed 3D structural model is saved in a commonly used 3D format (such as OBJ, FBX, or STL) for subsequent use and sharing. The key parameters and decisions of the entire modeling process are recorded to form a final report to provide a reference for subsequent analysis and optimization.

[0103] Step S4: accurately match the three-dimensional structure model of the transfer station with the corresponding foundation according to the optimal address area image, and perform spatial positioning mapping to construct a transfer station address twin model;

[0104] In this embodiment, ensure that the 3D structural model of the transfer station (such as OBJ, FBX format) is prepared and loaded in the 3D modeling software (such as Blender, 3ds Max or Unity), use computer vision technology to extract feature points from the optimal address area image, and common methods include: SIFT (Scale Invariant Feature Transform): extract key points and their descriptors in the image, ORB (Oriented FAST and Rotated BRIEF): a fast and efficient feature extraction method suitable for real-time applications, extract corresponding feature points from the 3D structural model of the transfer station, and analyze the geometric features of the model (such as corners, edges, and faces) to achieve this, and use feature matching algorithms (such as FLANN) or BFMatcher will match the feature points extracted from the image and the model, identify the correspondence between the image feature points and the model feature points, use RANSAC (random sampling consensus algorithm) to verify the matching results, exclude false matches to ensure that the matching pairs are accurate, ensure that the coordinate systems of the optimal address area image and the three-dimensional model are consistent, and if necessary, perform coordinate transformation (for example, using homogeneous coordinate transformation) to ensure that the two are in the same reference frame, and use spatial transformation algorithms (such as affine transformation or perspective transformation) to map the feature points in the optimal address area image to the three-dimensional structure model. This step usually involves the following steps: Calculate the transformation matrix (using the matched feature points), apply the transformation matrix to each point in the image to obtain its corresponding point in the three-dimensional space, visualize the mapping results in the three-dimensional modeling software, check the alignment of the optimal address area image and the three-dimensional structure model, ensure the accuracy of the relationship between the two, fuse the matched and mapped optimal address area image with the three-dimensional structure model to form an address twin model of the transfer station. This model should be able to reflect the transfer station and its surrounding environment in the real world. Integrate other relevant information in the twin model, such as traffic flow, environmental impact and facility distribution, to provide support for subsequent decision analysis, save the constructed transfer station address twin model in a suitable file format (such as GLTF, FBX or OBJ) for subsequent visualization, analysis and application, and if conditions permit, conduct on-site verification to ensure the accuracy and practicality of the twin model. Compare it with the model through field measurement, and use the constructed address twin model for subsequent analysis, visualization and decision support to ensure its effectiveness in practical applications.

[0105] Step S5: Perform dynamic transfer behavior analysis based on the design drawings of the garbage transfer station, and conduct in-depth mining of the garbage transfer logic to generate dynamic transfer logic;

[0106] In this embodiment, ensure that the latest design drawings of the garbage transfer station are obtained, including information such as layout, functional areas, entrances and exits, and transfer processes. Use CAD software (such as AutoCAD) to analyze the design drawings, identify key areas (such as loading areas, unloading areas, waiting areas, etc.) and process paths, and determine key indicators related to transfer behavior, such as transfer time, vehicle turnover rate, loading efficiency, waiting time, etc., for subsequent analysis. Extract historical transfer data from the management system of the transfer station, record vehicle entry and exit time, loading and unloading volume, operating time, etc., clean the acquired historical data, remove outliers and duplicate records, ensure data accuracy and consistency, use statistical analysis, machine learning or data mining technology (such as cluster analysis, association rule mining) to analyze the transfer behavior, identify common transfer patterns and behavior characteristics, and then identify the transfer behavior based on the identified data. Transshipment mode, establish a dynamic transshipment behavior model, describe the various links in the transshipment process and their interrelationships, select appropriate deep mining methods, such as process mining (such as ProM tools), decision tree analysis or neural networks, analyze the transshipment logic, use historical transshipment data and dynamic behavior models, train the transshipment logic model to explore the key factors affecting transshipment efficiency, analyze the logical relationship between each link in the transshipment process, identify the main factors affecting transshipment efficiency (such as vehicle scheduling, loading and unloading operations, etc.), integrate the results of dynamic transshipment behavior analysis and deep mining to form a complete dynamic transshipment logic model, optimize the transshipment process based on the key factors and logical relationships discovered, and put forward improvement suggestions to improve efficiency and reduce costs, organize the generated dynamic transshipment logic and optimization suggestions into a report, and provide it to the management and operation team to support subsequent decision-making and implementation.

[0107] Step S6: Based on the dynamic transfer logic, the dynamic transfer behavior of the transfer station address twin model is simulated, and dynamic structure planning optimization is performed to construct a three-dimensional planning visualization model.

[0108] In this embodiment, a dynamic transfer logic model is constructed according to the actual operation logic of the transfer station, and factors including vehicle scheduling, loading and unloading processes, waiting time, etc. are considered. A suitable simulation tool (such as AnyLogic, Simul8, or MATLAB Simulink) is selected to support dynamic simulation, and the historical data of the transfer behavior (such as vehicle entry and exit records, transfer time, etc.) is input into the simulation model so that the model can reflect the actual situation. The dynamic transfer behavior simulation is run to observe various behavioral performances in the transfer process, such as vehicle flow, loading and unloading efficiency, etc., and key indicators (such as transfer time, workload, etc.) are recorded. The simulation results are analyzed to identify the bottlenecks and problems in the transfer process, and improvement suggestions are put forward. According to the simulation results and existing design drawings, the structural planning requirements of the transfer station are identified, and the functional areas that need to be optimized (such as loading and unloading areas, waiting areas, vehicle routes, etc.) are determined. The three-dimensional structural model of the transfer station is established using architectural design software (such as Revit, AutoCAD), and the demand analysis results are summarized. The results are integrated into the model, and a suitable optimization algorithm is selected, such as genetic algorithm, simulated annealing or particle swarm optimization, to perform dynamic structural planning optimization. The three-dimensional structural model is optimized using the selected algorithm, and the position and size of each functional area are adjusted, and the vehicle traffic route is optimized to improve the overall transfer efficiency. The optimized structural model is evaluated to ensure that it can meet the transfer needs in actual operation and improve efficiency. A suitable three-dimensional visualization tool (such as Unity, Blender or SketchUp) is selected to display the optimized structural model. The optimized three-dimensional structural model is imported into the selected visualization tool and prepared for visualization settings. The material, lighting and environmental effects of the model are set in the visualization tool to ensure that the visualization effect of the model is realistic and easy to understand. An interactive three-dimensional planning visualization model is created to allow users to observe the structure and functional areas of the transfer station from different angles and views. The generated three-dimensional visualization model is exported to generate visualization reports and presentation materials to provide support for subsequent decision-making and presentation.

[0109] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0110] Step S11: Obtaining a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station;

[0111] Step S12: reconstructing image details of the satellite positioning map to construct a detailed optimized reconstructed satellite image;

[0112] Step S13: Performing regional road visual recognition on the detail optimized and reconstructed satellite image to extract regional road contour lines;

[0113] Step S14: performing road distribution analysis on the regional road contour lines to generate road distribution data;

[0114] Step S15: Perform road spatial distribution fitting on the road distribution data to construct a regional road distribution network.

[0115] In this embodiment, a suitable satellite image data provider is selected, such as Google Earth, NASA or other geographic information system (GIS) services, to ensure the acquisition of high-resolution satellite positioning maps, use API or crawler technology to download the required satellite positioning maps, ensure that the acquired images cover the area to be developed and the resolution meets the needs of subsequent analysis, coordinate with the design team or relevant departments to obtain the design drawings of the garbage transfer station, which should include the building floor plan, surrounding environment and supporting facilities, store the acquired satellite maps and design drawings in a database or a dedicated file system to ensure the accessibility and security of the data, select a suitable image processing tool, such as OpenCV, GIMP or Photoshop, to reconstruct the image details, and enhance the satellite image, including: denoising: applying Gaussian blur or median filtering to remove noise in the image, contrast adjustment: using histogram equalization to improve the contrast of the image and make the details clearer, using image reconstruction algorithms (such as super-resolution reconstruction, deep learning methods) to reconstruct the details of the satellite image, enhance the resolution and detail performance of the image, perform quality inspection on the reconstructed satellite image to ensure that the details are clear and accurate, and suitable for subsequent visual recognition and analysis, and select a suitable visual recognition algorithm, such as edge detection (Canny Edge detection) or deep learning-based image segmentation algorithms (such as U-Net or Mask R-CNN) are used to pre-process the satellite images after detail optimization and reconstruction, including: Grayscale conversion: converting color images into grayscale images to simplify subsequent processing; Edge detection: using the Canny edge detection algorithm to identify road edges in the image; Extracting the contours of regional roads from the edge detection results, generating a binary image of the road; Recording the extracted road contour data, including coordinate information, for subsequent analysis and processing; Using spatial analysis techniques such as Kriging interpolation, buffer analysis, or density analysis to analyze the extracted road contours; Integrating road contour data with other geographic information (such as land use type, population density, etc.) to obtain a more comprehensive view of road distribution; Generate road distribution data based on the analysis results; Record the length, width, traffic volume, and other information of each road; Select an appropriate spatial distribution fitting model, such as least squares fitting, Gaussian Process regression or other statistical models are used to analyze the spatial distribution characteristics of roads. The road distribution data are input into the selected fitting model to perform parameter estimation and model training. Based on the fitting results, a regional road distribution network is constructed to represent the connection relationship and relative position between roads. The constructed road distribution network is verified and compared with the actual road network. If necessary, the model parameters are adjusted to improve the fitting accuracy.

[0116] In this embodiment, the specific steps of step S12 are:

[0117] Performing grayscale histogram calculation on the satellite positioning map to obtain grayscale histogram parameters;

[0118] Perform image grayscale value distribution analysis based on grayscale histogram parameters to generate image grayscale value distribution data;

[0119] Perform image brightness distribution recognition according to image gray value distribution data to extract image brightness distribution data;

[0120] Performing global pixel brightness enhancement processing on the satellite positioning map based on the image brightness distribution data, thereby obtaining a brightness enhanced positioning map;

[0121] Perform image edge feature recognition on the brightness enhancement positioning map and mark the image edge feature information;

[0122] Perform sharpening filter optimization on the image edge feature information to generate filter-optimized edge features;

[0123] The image details of the brightness enhanced positioning map are reconstructed based on the filtered optimized edge features, and the detail optimized reconstructed satellite image is constructed.

[0124] In this example, an image processing library (such as OpenCV or PIL) is used to read the image file of the satellite positioning map, convert the color image into a grayscale image, and use OpenCV's cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) method to calculate the histogram of the grayscale image and count the number of pixels for each grayscale value (0-255). OpenCV's cv2.calcHist() Function implementation, extract the parameters of the grayscale histogram, such as mean, variance, maximum and minimum values, etc., for subsequent analysis. According to the calculated grayscale histogram, analyze the distribution of each grayscale value in the image, draw a grayscale value distribution graph to visualize the analysis results, record the number of pixels for each grayscale value, and generate the image grayscale value distribution data, which is a dictionary or array containing grayscale values ​​and their corresponding pixel counts. According to the grayscale value distribution data, set the brightness threshold to distinguish the brightness area of ​​the image, select the appropriate threshold by analyzing the peak and valley values ​​of the histogram, identify and extract the brightness distribution data in the image, including high brightness areas and low brightness areas, use binarization methods (such as Otsu's method) to achieve this, select a suitable image enhancement algorithm, such as histogram equalization or contrast limited adaptive histogram equalization (CLAHE), apply the selected enhancement algorithm to the original image, and generate a brightness enhanced positioning map. Use cv2.equalizeHist() or cv2.createCLAHE() method to select a suitable edge detection algorithm, such as Canny edge detection or Sobel Operator, perform edge detection on the brightness enhanced positioning map, extract edge features in the image, use cv2.Canny() method for edge detection, mark edge feature information in the edge detection result, use different colors or lines to mark for subsequent analysis, select a suitable sharpening filter, such as Laplace filter or high-pass filter, apply the selected sharpening filter to optimize the edge features, use cv2.filter2D() method to implement, save the edge feature image after sharpening filter optimization, ensure that the edge features are clearer and the details are more prominent, select a suitable detail reconstruction algorithm, such as non-local mean (NLM) or super-resolution reconstruction, reconstruct the details of the brightness enhanced positioning map based on the edge features after filtering optimization, generate a satellite image with optimized detail reconstruction, and perform quality inspection on the output satellite image with optimized detail reconstruction to ensure that the details are clear and the information is accurate.

[0125] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0126] Step S21: performing surrounding building positioning calculation on the detail optimized and reconstructed satellite image to obtain the surrounding building positioning coordinates;

[0127] Step S22: performing regional spacing topological correlation analysis on the surrounding building positioning coordinates based on the regional road distribution network, and extracting surrounding building-road distribution topological correlation data;

[0128] Step S23: performing environmental terrain undulation recognition on the detail optimized and reconstructed satellite image to obtain environmental terrain undulation information;

[0129] Step S24: Analyze the topographic features of the environmental terrain undulation information to extract the environmental terrain features of the area to be developed;

[0130] Step S25: Optimize the site selection decision by optimizing and reconstructing the satellite image according to the environmental topographic features of the area to be developed and the surrounding building-road distribution topological correlation data, and extract the optimal address area image.

[0131] In this embodiment, the satellite image reconstructed by detail optimization is used, and image segmentation technology (such as threshold-based segmentation or deep learning segmentation model, such as U-Net) is applied to identify and extract the outlines of surrounding buildings, geometric calculations are performed on the extracted building outlines, the centroid coordinates of each building are determined, the cv2.moments() function of OpenCV is used to calculate the centroid position of the outline, the positioning coordinates of the surrounding buildings are recorded in a data structure (such as a list or a dictionary) for subsequent analysis, the regional road distribution network generated previously is integrated with the positioning coordinates of the surrounding buildings to form a comprehensive data set including building coordinates and road network information, regional spacing analysis is performed, the distance between each building and the nearby roads is calculated, spatial analysis tools (such as QGIS or ArcGIS) are used to perform spatial distance calculation and topological relationship modeling, and topological association data of the surrounding buildings and roads are extracted, including information such as the distance, connection relationship and accessibility between the buildings and the roads, and digital elevation model (DEM) data of the area to be developed is obtained, and these data are obtained through satellite data or geographic information system (GIS), terrain analysis algorithms (such as slope and terrain profile analysis) are applied to identify the undulations of the environmental terrain, and GIS software (such as ArcGIS) is used to analyze the terrain. or QGIS) to extract undulation information and record the undulation information of environmental terrain, including high and low undulation, slope change and other features for subsequent analysis; use terrain analysis techniques (such as terrain index calculation and terrain feature extraction) to analyze environmental terrain undulation information and extract geomorphic features; integrate the extracted environmental terrain undulation information with other geographic information (such as soil type, vegetation cover, etc.) to form a comprehensive environmental terrain and geomorphic feature data set for the area to be developed; record the analysis results, including feature description, terrain type (such as mountain, plain, hill, etc.) and its distribution; construct the optimal site selection decision model based on the surrounding building-road distribution topological association data and environmental terrain and geomorphic features; use multi-criteria decision analysis methods (such as hierarchical analysis method AHP or comprehensive evaluation method) to take environmental features and topological association data as input to evaluate the advantages and disadvantages of each candidate address area; extract the optimal address area image based on the output of the decision model, and mark or highlight it on the satellite image reconstructed by detail optimization so that decision makers can conduct visual analysis and verify the site selection results; combine the actual situation and expert opinions to ensure the rationality and effectiveness of the site selection decision.

[0132] In this embodiment, the specific steps of step S25 are:

[0133] Calculate the area of ​​the transfer station based on the design drawings of the garbage transfer station and extract the area of ​​the transfer station;

[0134] Mining spatial layout features based on the area of ​​the transfer station to generate spatial layout features of the transfer station;

[0135] Based on the surrounding building-road distribution topological correlation data and the environmental topographic features of the area to be developed, the optimal location decision is made for the transfer station spatial layout characteristics, thereby obtaining the optimal transfer station building address;

[0136] According to the optimal transfer station building address, the satellite image is reconstructed through detailed optimization to segment the building address area and extract the optimal address area image.

[0137] In this example, the design drawings of the garbage transfer station are obtained, and the drawings are ensured to be in a high-resolution digital format (such as PDF or CAD files). If the design drawings are in CAD format, CAD software (such as AutoCAD) is used to convert the drawings into a format suitable for analysis (such as PNG or TIFF). Image processing tools (such as OpenCV) are used to process the transfer station design drawings: the image is converted into a grayscale image, binarization is applied, the transfer station area is separated from the background, and a contour detection algorithm (such as cv2.findContours()) is used to extract the contour of the transfer station, and the area of ​​the contour is calculated through cv2.contourArea() Method implementation, extract and record the area of ​​the transfer station, determine the spatial layout characteristics of the transfer station, including: the shape of the building, boundaries, the location of the access channels, the relative position of each functional area (such as garbage storage area, sorting area, office area), analyze the results of the area calculation, extract the spatial layout characteristics, use geometric analysis methods, such as: calculate the aspect ratio, the proportional relationship between the perimeter and the area, analyze the spatial relationship and accessibility between the functional areas, record the spatial layout characteristics of the transfer station, and form a feature data set for subsequent analysis. Based on the surrounding building-road distribution topological association data and environmental topographic features, a site selection decision model is constructed, and a multi-criteria decision analysis (MDA) is used. The CDA method, such as the analytic hierarchy process (AHP) or the fuzzy comprehensive evaluation method, integrates the spatial layout characteristics of the transfer station, the surrounding buildings and road information, and the environmental topographic features into the decision model, assigns weights to each feature, and scores them according to their importance and impact. The decision model is run to evaluate the pros and cons of different candidate addresses, and finally the optimal transfer station building address is determined. The site selection results are verified in combination with expert opinions and actual conditions to ensure the rationality of the site selection. The satellite images reconstructed with detail optimization are used to process the optimal transfer station building address. A suitable image segmentation algorithm is selected, such as region-based segmentation (such as region growing method) or edge-based segmentation (such as Canny edge detection). The selected segmentation algorithm is applied to identify and extract the optimal transfer station building address area. Image preprocessing, such as denoising and contrast enhancement, may be required to improve the segmentation effect. The image of the optimal address area is extracted and saved to ensure that the features of the area are clear and convenient for subsequent analysis and display.

[0138] In this embodiment, refer to Figure 4, is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0139] Step S31: Identify key feature points of the garbage transfer station on the design drawing of the transfer station and mark multiple key feature points;

[0140] Step S32: calculating the distances between the multiple key feature points, and extracting the distances between the multiple feature points;

[0141] Step S33: performing geometric analysis of the transfer station according to the distance between the feature points to generate geometric features of the transfer station;

[0142] Step S34: performing functional area analysis on the garbage transfer station design drawings to generate a plurality of different transfer station functional areas;

[0143] Step S35: performing topological structure association analysis on a plurality of different transfer station functional areas, thereby generating functional area topological structure association features;

[0144] Step S36: Based on the associated features of the functional area topological structure, three-dimensional point cloud modeling is performed on the geometric features of the transfer station to construct a three-dimensional structural model of the transfer station.

[0145] In this embodiment, the design drawings of the garbage transfer station are obtained, and the drawings are ensured to be in high-resolution digital format. The design drawings are analyzed using image processing tools (such as OpenCV): the drawings are converted into grayscale images, and edge detection algorithms (such as Canny) are applied to the images. Edge detection) Identify the edges in the drawing, extract obvious feature points (such as corners, intersections, etc.) in the design drawing through contour detection, mark the identified feature points on the drawing, use image drawing functions (such as cv2.circle()) to mark the position of the feature points, and record their coordinates. For multiple marked feature points, use the Euclidean distance calculation method to calculate the distance between any two feature points, traverse the feature point list, calculate the distance between each pair of feature points, and record the calculated distance between feature points in a data structure (such as a list or dictionary) for subsequent analysis. Define the geometric features of the transfer station based on the distance between feature points, including: the shape features of the building (such as aspect ratio, area, etc.), the distribution features of feature points (such as symmetry, uniformity), generate geometric feature data by calculating the relative position and distance between feature points, use statistical analysis methods (such as mean, variance) to evaluate the morphological features, and record the geometric shape of the transfer station. The functional features of the transfer station are used for future reference and application. According to the functional layout of the design drawings, the different functional areas of the transfer station (such as storage area, sorting area, office area, etc.) are identified. The functional areas on the design drawings are divided by image segmentation technology (such as threshold-based segmentation or region growing algorithm). This is achieved by defining the color or texture features of the area. The divided functional areas are recorded, including the boundary coordinates and functional descriptions of each area. The topological relationship between the functional areas is defined, mainly considering the adjacent relationship and connection relationship between the areas. The adjacency matrix or adjacency table in graph theory is used to represent the topological structure of the functional area. The connection relationship between each area and other areas is calculated to form a topological structure model of the functional area. The topological structure association features of the functional area are recorded, including the connectivity and adjacent relationship of each area, for subsequent modeling and analysis. The topological structure association features and geometric morphological features of the functional area are used to generate three-dimensional point cloud data. Software (such as MeshLab or CloudCompare) to convert the 2D functional area information and geometric features into a 3D model. Use 3D modeling software (such as Blender or SketchUp) to build a 3D structural model of the transfer station. Add details such as doors, windows, imports and exports, and signs to the model to ensure that the 3D model is as close to the actual design as possible. Export the generated 3D structural model for subsequent visualization and analysis.

[0146] In this embodiment, step S4 includes the following steps:

[0147] Step S41: Analyze the bearing points of the building structure of the transfer station on the three-dimensional structural model of the transfer station, and mark the bearing points of the building structure;

[0148] Step S42: Calculate the spatial position of the bearing point of the building structure to obtain the spatial position coordinates of the bearing point;

[0149] Step S43: Performing ground point visual analysis on the optimal address area image to extract the area image ground points;

[0150] Step S44: accurately matching the regional image foundation points with the corresponding foundations according to the spatial position coordinates of the anchoring points, thereby obtaining foundation point matching data;

[0151] Step S45: Based on the foundation point matching data, the three-dimensional structure model of the transfer station is used to spatially locate and map the optimal address area image to construct a transfer station address twin model.

[0152] In this embodiment, a three-dimensional structural model of the transfer station is obtained to ensure that the geometric details and material properties of the model are complete. A structural analysis software (such as SAP2000, ANSYS or Rhino) is used to perform load analysis on the three-dimensional model to identify the bearing points of the building structure. These bearing points are usually the parts of the structure that bear the greatest stress. The identified bearing points are marked in the three-dimensional model through the annotation tool or marking function of the software to ensure that the positions of the bearing points are clearly recorded. The spatial coordinates of the marked bearing points in the three-dimensional model are obtained. The coordinate information of the points is usually extracted through the geometric query function of the model analysis software. The spatial position coordinates of the bearing points are recorded in a data structure (such as an array or a dictionary) to prepare for subsequent matching and mapping processes. A high-resolution image of the optimal address area is obtained to ensure that the image is clear and contains sufficient details. Image processing technology (such as feature detection algorithm) is used to identify foundation points in the regional image. Harris corner detection, SIFT or ORB algorithm is used to extract obvious foundation features. The extracted foundation point coordinates are recorded in a data structure to facilitate subsequent matching and analysis. A suitable point matching algorithm is selected, such as nearest neighbor matching or RANSAC. An algorithm is used to match the coordinates of the anchor points with the coordinates of the foundation points, accurately match the extracted foundation points and anchor points, calculate the distance between each foundation point and the nearest anchor point, determine the best matching pair, and record the matching results (including the relationship between the foundation points and the corresponding anchor points) in a data structure to form foundation point matching data. Based on the foundation point matching data, a spatial positioning mapping method is selected, such as affine transformation or perspective transformation. The three-dimensional structural model of the transfer station and the spatial position of its anchor points are used to perform spatial positioning mapping on the optimal address area image. This is achieved by mapping the coordinates of the foundation points to the corresponding anchor point positions in the three-dimensional model. According to the matching and mapping results, a twin model of the transfer station address is constructed. This model will include a combination of three-dimensional structural information and actual ground image information.

[0153] In this embodiment, the specific steps of step S5 are:

[0154] Step S51: Perform dynamic transfer behavior analysis based on the garbage transfer station design drawings to generate dynamic transfer behavior characteristic data;

[0155] Step S52: performing garbage in-and-out identification on the dynamic transfer behavior characteristic data to obtain garbage in-and-out behavior data;

[0156] Step S53: performing transfer vehicle scheduling analysis based on multiple different transfer station functional areas and extracting transfer vehicle routes;

[0157] Step S54: Evolving the internal flow direction of the transfer station for the garbage in-and-out behavior data, thereby generating internal garbage flow direction data of the transfer station;

[0158] Step S55: Based on the internal garbage flow data of the transfer station, the garbage transfer logic is deeply mined on the transfer vehicle path to generate dynamic transfer logic.

[0159] In this embodiment, dynamic data related to the garbage transfer station is collected, including the entry and exit records of transfer vehicles, the time at the station, the transfer operation records, etc. The data is obtained through sensors, surveillance cameras or manual records, and the characteristic data of dynamic transfer behavior is defined, such as the frequency of vehicle entry and exit, the amount of garbage transferred each time, and the transfer behavior patterns in different time periods. Data analysis tools (such as Python's pandas and NumPy) are used to analyze the dynamic transfer behavior. The collected data is sorted and analyzed by a database (such as a database or database), and dynamic transfer behavior feature data is extracted. Data mining algorithms (such as cluster analysis or classification algorithms) are used to identify garbage in-and-out behaviors. By analyzing the in-and-out records, the patterns and rules of garbage in-and-out behaviors are identified, such as peak hours, common vehicle types, etc. The identified garbage in-and-out behaviors are recorded as garbage in-and-out behavior data, including: the number of vehicles in and out in each time period, the amount of garbage in and out each time, the type of vehicle and its frequency of use. Scheduling data related to transfer vehicles is collected, including vehicle type, capacity, working hours, and current tasks. Scheduling algorithms in operations research (such as the shortest path algorithm, genetic algorithm, or linear programming) are used to analyze the scheduling of transfer vehicles, extract transfer vehicle paths, and generate optimal path data for transfer vehicles based on the functional areas of the transfer station and the direction of garbage flow. Factors considered include: path length, congestion, task priority, and garbage in and out. Behavioral data, analyze the flow of garbage inside the transfer station, identify the flow of garbage reception and processing in different areas, use traffic analysis tools (such as fluid dynamics models or data visualization tools) to perform evolutionary analysis on the flow inside the transfer station, identify flow patterns and changing trends, and generate garbage flow data inside the transfer station, including: the amount of garbage inflow and outflow in different time periods, and the garbage flow relationship between functional areas. Use data mining technology (such as association rule learning and pattern recognition) to conduct in-depth mining of the garbage flow data inside the transfer station, analyze the transfer logic, and identify the garbage transfer logic between different functional areas, for example: the flow pattern of garbage from the receiving area to the sorting area, and the flow path of garbage between internal processing equipment. Based on the analysis results, generate a dynamic transfer logic model to describe the overall process and optimization suggestions for garbage treatment in the transfer station, and record the generated dynamic transfer logic in the report for subsequent optimization and decision support.

[0160] In this embodiment, the specific steps of step S6 are:

[0161] Step S61: Perform dynamic transfer behavior simulation on the transfer station address twin model based on dynamic transfer logic to generate transfer station transfer behavior simulation data;

[0162] Step S62: simulating available transfer paths for the transfer station transfer behavior simulation data, thereby generating multiple transfer path adjustment plans;

[0163] Step S63: Calculating the transfer operation efficiency of each of the multiple transfer route adjustment plans, thereby generating the operation efficiency of each route plan;

[0164] Step S64: evaluating the optimal path plan according to the operation efficiency of the path plan, and extracting the optimal path plan;

[0165] Step S65: Optimizing the functional area layout based on the optimal path solution to generate functional area layout optimization data;

[0166] Step S66: performing dynamic structural planning optimization on the three-dimensional structural model of the transfer station according to the functional area layout optimization data, thereby obtaining a dynamic structural planning three-dimensional model;

[0167] Step S67: Perform three-dimensional planning visualization on the dynamic structure planning three-dimensional model to construct a three-dimensional planning visualization model.

[0168] In this embodiment, a twin model of the transfer station address is used to ensure that it contains dynamic transfer logic and key parameters. Appropriate simulation software (such as AnyLogic, SIMUL8 or Arena) is selected to handle the simulation of dynamic systems. Based on the dynamic transfer logic, simulation parameters (such as vehicle arrival frequency, garbage processing time, transfer delay, etc.) are set. The simulation model is run to generate dynamic transfer behavior data of the transfer station, including vehicle entry and exit records, processing time, flow, etc. The generated transfer behavior simulation data is saved to a database or file for subsequent analysis and comparison. The simulation data is used to analyze the available transfer paths in the transfer station. A path network is constructed using graph theory methods. Based on the simulation results, multiple feasible transfer paths are identified and simulated, including the connection methods between different functional areas. The generated multiple transfer path adjustment schemes are recorded in a data structure for subsequent evaluation and comparison. The calculation criteria for operating efficiency are defined, such as: the transfer time of each path, the garbage processing volume, and the vehicle utilization rate. The operating efficiency of each transfer path adjustment scheme is calculated one by one. Calculate, use simulation data to analyze the operation efficiency of the path, determine the indicators for evaluating the optimal path plan, such as total operation efficiency, transit time, cost, etc., conduct a comprehensive evaluation of multiple path plans based on the recorded operation efficiency data, use weighted scoring method or multi-criteria decision analysis (MCDA) method, extract the best performing path plan in the evaluation results, record its key parameters and efficiency values, set the goal of optimizing the functional area layout according to the results of the optimal path plan, such as improving transportation efficiency, reducing transit time, etc., use spatial optimization algorithms (such as genetic algorithms, simulated annealing) to re-layout the functional areas, optimize the connections between the areas, generate functional area layout optimization data, record the new position of each area and their mutual relationship, obtain the three-dimensional structural model of the transfer station, and integrate the functional area layout optimization data, apply building information modeling (BIM) software (such as Revit Or ArchiCAD) for dynamic structural planning, adjust the 3D model to reflect the optimized regional layout, update the position of each functional area in the 3D model to ensure the logic and functionality of the entire structure, save the obtained dynamic structural planning 3D model for future visualization and actual construction, select suitable visualization software (such as Unity, Blender or SketchUp) for 3D model visualization, import the dynamic structural planning 3D model, set lighting, material and environmental effects to improve the visualization effect of the model, add interactive functions to enable users to navigate in the 3D model, view details of different areas, etc., generate the final 3D planning visualization model, and prepare for display and review.

[0169] In this embodiment, a construction planning visualization system for a garbage transfer station is provided, which is used to execute the construction planning visualization method for the garbage transfer station as described above, including:

[0170] The road distribution module obtains the satellite positioning map of the area to be developed and the design drawings of the garbage transfer station; reconstructs the image details of the satellite positioning map, performs road spatial distribution fitting, and constructs a regional road distribution network;

[0171] The optimal location module calculates the location of surrounding buildings based on the detailed optimization and reconstruction of satellite images based on the regional road distribution network, makes optimal location decisions, and extracts the optimal address area image;

[0172] The 3D structure module identifies the key feature points of the garbage transfer station based on the design drawings, and then performs 3D point cloud modeling to construct a 3D structure model of the transfer station.

[0173] The spatial positioning and mapping module accurately matches the three-dimensional structure model of the transfer station with the corresponding foundation according to the image of the optimal address area, and performs spatial positioning and mapping to build a twin model of the transfer station address;

[0174] The transfer behavior module conducts dynamic transfer behavior analysis based on the design drawings of the garbage transfer station and conducts in-depth mining of the garbage transfer logic to generate dynamic transfer logic;

[0175] The three-dimensional planning visualization module simulates the dynamic transfer behavior of the transfer station address twin model based on dynamic transfer logic, and performs dynamic structural planning optimization to construct a three-dimensional planning visualization model.

[0176] The present invention reconstructs satellite positioning maps and road spatial distribution fitting to construct a regional road distribution network, which helps to determine the optimal transfer station location. Such analysis improves the accessibility and traffic efficiency of the site, and provides a basis for subsequent planning. Based on the regional road distribution network and surrounding building positioning calculations, the optimal site location decision is made, and the optimal address area image is extracted to ensure the rationality and convenience of the transfer station location. This step helps to optimize the surrounding environment and traffic convenience of the site. By identifying the key feature points of the transfer station design drawings and performing three-dimensional point cloud modeling, a three-dimensional structural model of the transfer station is accurately constructed, providing a visualization basis for subsequent planning. The three-dimensional structural model helps planners better understand Understand the spatial layout and characteristics of the site, build a transfer station address twin model by matching and spatially mapping the optimal address area image and the three-dimensional structural model of the transfer station, achieve precise correspondence between the site location and the design, ensure the accuracy and practicality of site planning, and generate dynamic transfer logic through dynamic transfer behavior analysis and logic mining to help optimize the site transportation process and efficiency. Such analysis helps to improve the operational efficiency of the site and reduce potential problems. Based on dynamic transfer logic and structural planning optimization, build a three-dimensional planning visualization model to more intuitively display the design and operation of the transfer station. This visualization model helps communication and decision-making, and improves the transparency and comprehensibility of planning.

[0177] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0178] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for visualizing architectural planning of a garbage transfer station, characterized in that: The following steps are involved: Step S1: Obtaining a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station; Reconstructing image details of the satellite positioning map, fitting road spatial distribution, and constructing a regional road distribution network; Step S2: Based on the regional road distribution network, the satellite image is optimized and reconstructed to calculate the location of surrounding buildings, and the optimal address decision is made to extract the optimal address area image; the specific steps of step S2 are: Step S21: performing surrounding building positioning calculation on the detail optimized and reconstructed satellite image to obtain the surrounding building positioning coordinates; Step S22: performing regional spacing topological correlation analysis on the surrounding building positioning coordinates based on the regional road distribution network, and extracting surrounding building-road distribution topological correlation data; Step S23: performing environmental terrain undulation recognition on the detail optimized and reconstructed satellite image to obtain environmental terrain undulation information; Step S24: Analyze the topographic features of the environmental terrain undulation information to extract the environmental terrain features of the area to be developed; Step S25: Optimizing the site location by optimizing and reconstructing the satellite image according to the environmental topographic features of the area to be developed and the surrounding building-road distribution topological correlation data, and extracting the optimal address area image; Step S3: Identify key feature points of the transfer station based on the design drawings of the garbage transfer station, perform three-dimensional point cloud modeling, and construct a three-dimensional structural model of the transfer station; Step S4: accurately match the three-dimensional structure model of the transfer station with the corresponding foundation according to the optimal address area image, and perform spatial positioning mapping to construct a transfer station address twin model; Step S5: Perform dynamic transfer behavior analysis based on the design drawings of the garbage transfer station, and conduct in-depth mining of the garbage transfer logic to generate dynamic transfer logic; the specific steps of step S5 are: Step S51: Perform dynamic transfer behavior analysis based on the garbage transfer station design drawings to generate dynamic transfer behavior characteristic data; Step S52: identifying the garbage in and out of the transfer station based on the dynamic transfer behavior characteristic data to obtain the garbage in and out behavior data; Step S53: performing transfer vehicle scheduling analysis based on multiple different transfer station functional areas and extracting transfer vehicle routes; Step S54: Evolving the internal flow direction of the transfer station for the garbage in-and-out behavior data, thereby generating internal garbage flow direction data of the transfer station; Step S55: Based on the internal garbage flow data of the transfer station, the garbage transfer logic is deeply mined on the transfer vehicle path, thereby generating a dynamic transfer logic; Step S6: Based on the dynamic transfer logic, the dynamic transfer behavior of the transfer station address twin model is simulated, and dynamic structure planning optimization is performed to construct a three-dimensional planning visualization model.

2. The method for visualizing the construction planning of a garbage transfer station according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Obtaining a satellite positioning map of the area to be developed and a design drawing of a garbage transfer station; Step S12: reconstructing image details of the satellite positioning map to construct a detailed optimized reconstructed satellite image; Step S13: Performing regional road visual recognition on the detail optimized and reconstructed satellite image to extract regional road contour lines; Step S14: performing road distribution analysis on the regional road contour lines to generate road distribution data; Step S15: Perform road spatial distribution fitting on the road distribution data to construct a regional road distribution network.

3. The method for visualizing the construction planning of a garbage transfer station according to claim 2, characterized in that: The specific steps of step S12 are: Performing grayscale histogram calculation on the satellite positioning map to obtain grayscale histogram parameters; Perform image grayscale value distribution analysis based on grayscale histogram parameters to generate image grayscale value distribution data; Perform image brightness distribution recognition according to image gray value distribution data to extract image brightness distribution data; Performing global pixel brightness enhancement processing on the satellite positioning map based on the image brightness distribution data, thereby obtaining a brightness enhanced positioning map; Perform image edge feature recognition on the brightness enhancement positioning map and mark the image edge feature information; Perform sharpening filter optimization on the image edge feature information to generate filter-optimized edge features; The image details of the brightness enhanced positioning map are reconstructed based on the filtered optimized edge features, and the detail optimized reconstructed satellite image is constructed.

4. The method for visualizing the construction planning of a garbage transfer station according to claim 3, characterized in that: The specific steps of step S25 are: Calculate the area of ​​the transfer station based on the design drawings of the garbage transfer station and extract the area of ​​the transfer station; Mining spatial layout features based on the area of ​​the transfer station to generate spatial layout features of the transfer station; Based on the surrounding building-road distribution topological correlation data and the environmental topographic features of the area to be developed, the optimal location decision is made for the transfer station spatial layout characteristics, thereby obtaining the optimal transfer station building address; According to the optimal transfer station building address, the satellite image is reconstructed through detailed optimization to segment the building address area and extract the optimal address area image.

5. The method for visualizing the construction planning of a garbage transfer station according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: Identify key feature points of the garbage transfer station on the design drawing of the transfer station and mark multiple key feature points; Step S32: calculating the distances between the multiple key feature points, and extracting the distances between the multiple feature points; Step S33: performing geometric analysis of the transfer station according to the distance between the feature points to generate geometric features of the transfer station; Step S34: performing functional area analysis on the garbage transfer station design drawings to generate a plurality of different transfer station functional areas; Step S35: performing topological structure association analysis on a plurality of different transfer station functional areas, thereby generating functional area topological structure association features; Step S36: Based on the associated features of the functional area topological structure, three-dimensional point cloud modeling is performed on the geometric features of the transfer station to construct a three-dimensional structural model of the transfer station.

6. The method for visualizing the construction planning of a garbage transfer station according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Analyze the bearing points of the building structure of the transfer station on the three-dimensional structural model of the transfer station, and mark the bearing points of the building structure; Step S42: Calculate the spatial position of the bearing point of the building structure to obtain the spatial position coordinates of the bearing point; Step S43: Performing ground point visual analysis on the optimal address area image to extract the area image ground points; Step S44: accurately matching the regional image foundation points with the corresponding foundations according to the spatial position coordinates of the anchoring points, thereby obtaining foundation point matching data; Step S45: Based on the foundation point matching data, the three-dimensional structure model of the transfer station is used to spatially locate and map the optimal address area image to construct a transfer station address twin model.

7. The method for visualizing the construction planning of a garbage transfer station according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Perform dynamic transfer behavior simulation on the transfer station address twin model based on dynamic transfer logic to generate transfer station transfer behavior simulation data; Step S62: simulating available transfer paths for the transfer station transfer behavior simulation data, thereby generating multiple transfer path adjustment plans; Step S63: Calculating the transfer operation efficiency of each of the multiple transfer route adjustment plans, thereby generating the operation efficiency of each route plan; Step S64: evaluating the optimal path plan according to the operation efficiency of the path plan, and extracting the optimal path plan; Step S65: Optimizing the functional area layout based on the optimal path solution to generate functional area layout optimization data; Step S66: performing dynamic structural planning optimization on the three-dimensional structural model of the transfer station according to the functional area layout optimization data, thereby obtaining a dynamic structural planning three-dimensional model; Step S67: Perform three-dimensional planning visualization on the dynamic structure planning three-dimensional model to construct a three-dimensional planning visualization model.

8. A building planning visualization system for a garbage transfer station, characterized in that: The method for implementing the architectural planning visualization method of a garbage transfer station as claimed in claim 1 comprises: The road distribution module obtains the satellite positioning map of the area to be developed and the design drawings of the garbage transfer station; reconstructs the image details of the satellite positioning map, performs road spatial distribution fitting, and constructs a regional road distribution network; The optimal site location module calculates the location of surrounding buildings based on the detailed optimization and reconstruction of satellite images based on the regional road distribution network, makes optimal site location decisions, and extracts the optimal address area image; The 3D structure module identifies the key feature points of the garbage transfer station based on the design drawings, and then performs 3D point cloud modeling to construct a 3D structure model of the transfer station. The spatial positioning and mapping module accurately matches the three-dimensional structure model of the transfer station with the corresponding foundation according to the image of the optimal address area, and performs spatial positioning and mapping to build a twin model of the transfer station address; The transfer behavior module conducts dynamic transfer behavior analysis based on the design drawings of the garbage transfer station and conducts in-depth mining of the garbage transfer logic to generate dynamic transfer logic; The three-dimensional planning visualization module simulates the dynamic transfer behavior of the transfer station address twin model based on dynamic transfer logic, and performs dynamic structural planning optimization to construct a three-dimensional planning visualization model.

Images