A planning scheme generation method, system and storage medium for old village renovation

Abstract

Embodiments of the present invention provide a method, system, and storage medium for generating a planning scheme for old village renovation. The method includes: obtaining multi-source data for an old village to be renovated; constructing a pre-renovation GIS model of the old village to be renovated based on the multi-source data, so that the pre-renovation GIS model can visually display the multi-source data on a map; analyzing the multi-source data based on the pre-renovation GIS model to obtain a target renovation area for the old village to be renovated; generating a first multi-dimensional renovation scheme for the target renovation area based on renovation requirements; superimposing the first multi-dimensional renovation scheme onto a real-life map corresponding to the target renovation area, simulating the first multi-dimensional renovation scheme to obtain a simulation result; and revising the first multi-dimensional renovation scheme based on the simulation result to obtain a second multi-dimensional renovation scheme. Automatically generating renovation schemes based on multi-source data improves planning efficiency; and revising the renovation schemes through simulation ensures the accuracy and completeness of the renovation schemes.

Description

Technical Field

[0001] The present invention relates to the field of, but is not limited to, data processing technology, and in particular to a method, system, and storage medium for generating a planning scheme for old village renovation. Background Art

[0002] The renovation of old villages is a comprehensive deployment to study the future development of villages, the rational layout of villages and the arrangement of various rural construction projects. It is a blueprint for rural development in a certain period of time, an important part of rural management, and the basis for rural construction and management.

[0003] The existing planning schemes for old village renovation are obtained through manual analysis, which has low planning efficiency. In addition, due to the complexity of the data and the single and complicated working process, it is easy to lead to incomplete planning. Summary of the Invention

[0004] The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.

[0005] The main purpose of the embodiments of the present invention is to propose a method, system and storage medium for generating a planning scheme for old village renovation, which can improve the planning efficiency and completeness of the old village planning scheme.

[0006] In a first aspect, an embodiment of the present invention provides a method for generating a planning scheme for old village renovation, comprising:

[0007] Obtain multi-source data on the old village to be renovated, including building distribution data, road network data, vegetation and water system data, infrastructure data, and historical and cultural data;

[0008] Constructing a pre-renovation GIS model of the old village to be renovated based on the multi-source data, so that the pre-renovation GIS model can visually display the multi-source data on a map;

[0009] Analyzing the multi-source data based on the pre-renovation GIS model to obtain the target renovation area of ​​the old village to be renovated;

[0010] generating a first multi-dimensional renovation plan for the target renovation area according to the renovation requirements;

[0011] After superimposing the first multi-dimensional transformation plan on the real-life map corresponding to the target transformation area, simulating the first multi-dimensional transformation plan to obtain a simulation result;

[0012] The first multi-dimensional transformation plan is modified according to the simulation results to obtain a second multi-dimensional transformation plan, and the second multi-dimensional transformation plan is transmitted to the operation and maintenance GIS platform.

[0013] In some optional embodiments, constructing the pre-renovation GIS model of the old village to be renovated based on the multi-source data includes:

[0014] Acquiring topographic data of the old village to be renovated;

[0015] Constructing a terrain model of the old village to be renovated according to the terrain data;

[0016] generating a building model on the terrain model according to the building distribution data, and displaying the building location, building shape, building equipment and building aging degree through the building model;

[0017] generating a road model on the terrain model according to the road network data, and displaying road grade, road alignment, road capacity and road signs through the road network;

[0018] generating a vegetation and water system model on the terrain model according to the vegetation and water system data, and displaying vegetation type, vegetation shape, vegetation coverage area and water system through the vegetation and water system model;

[0019] generating an infrastructure model on the terrain model according to the infrastructure data, and displaying the infrastructure type, infrastructure location and infrastructure aging degree through the infrastructure model;

[0020] After marking corresponding historical and cultural information on the building model, the road model, the vegetation and water system model, and the infrastructure model according to the historical and cultural data, the GIS model before renovation of the old village to be renovated is obtained.

[0021] In some optional embodiments, before generating a building model on the terrain model according to the building distribution data, the method further includes:

[0022] After removing noise data, duplicate data and outliers from the multi-source data using a spatial data quality control algorithm, first intermediate data is obtained;

[0023] Performing spatial matching on the first intermediate data using a feature matching algorithm so that the first intermediate data has spatial coordinates to obtain second intermediate data;

[0024] A data prediction model is constructed based on historical data, where the historical data represents the second intermediate data of each period;

[0025] The third intermediate data is obtained by predicting and supplementing the missing data in the second intermediate data through the data prediction model. The building distribution data used to generate the building model, the road network data used to generate the road model, the vegetation and water system data used to generate the vegetation and water system model, and the historical and cultural data used to mark the historical and cultural information all belong to the third intermediate data.

[0026] In some optional embodiments, obtaining the target renovation area of ​​the old village to be renovated after analyzing the multi-source data based on the pre-renovation GIS model includes:

[0027] Determine a fire protection coefficient and a traffic coefficient for each area based on the road network data and the building distribution data corresponding to each area on the pre-transformation GIS model, wherein the fire protection coefficient represents the fire protection capability of each area, and the traffic coefficient represents the traffic capability of each area;

[0028] generating a geological hazard risk distribution map according to the geological characteristic data corresponding to each area on the GIS model before the transformation, and determining a geological hazard risk coefficient for each area on the GIS model before the transformation based on the geological hazard risk distribution map;

[0029] Determine the building quality coefficient of each area based on the building distribution data corresponding to each area on the GIS model before the transformation;

[0030] Determining the infrastructure improvement coefficient of each area based on the infrastructure data corresponding to each area on the GIS model before the transformation;

[0031] Determine the greening coefficient of each area based on the vegetation and water system data corresponding to each area on the GIS model before the transformation;

[0032] Determine the historical and cultural value coefficient of each area based on the historical and cultural data corresponding to each area on the GIS model before the transformation;

[0033] Determine the transformation coefficient of each area on the GIS model before transformation according to the fire protection coefficient, the traffic coefficient, the geological disaster risk coefficient, the building quality coefficient, the infrastructure improvement coefficient, the greening coefficient, and the historical and cultural value coefficient of each area on the GIS model before transformation;

[0034] The area where the transformation coefficient is greater than or equal to the preset coefficient is configured as the target transformation area.

[0035] In some optional embodiments, after configuring the region having the transformation coefficient greater than or equal to the preset coefficient as the target transformation region, the method further includes:

[0036] Determining a first priority ranking of each target sub-region according to the transformation coefficient corresponding to each target sub-region in the target transformation region;

[0037] The benefit index of each target sub-region is obtained by evaluating the transformation benefit of each target sub-region through a preset economic benefit model;

[0038] Modifying the first priority ranking according to the benefit index to obtain a second priority ranking;

[0039] Determine the influence coefficients between the target sub-regions by using a network analysis method;

[0040] The second priority ranking is modified according to the influence coefficient to obtain a third priority ranking, so that each of the target sub-areas is transformed in turn according to the third priority ranking.

[0041] In some optional embodiments, generating a first multi-dimensional transformation plan for the target transformation area according to the transformation requirements includes:

[0042] Determining a first demand level for each demand indicator in the transformation demand based on a preset demand level and a regional transformation target;

[0043] obtaining a second demand level by modifying the first demand level according to public demand data, wherein the public demand data is collected through a public demand collection system;

[0044] Comparing the indicator difference maps of the demand indicator and the target indicator in sequence according to the first demand level, the target indicator representing the indicator corresponding to the demand indicator in the target transformation area;

[0045] The first multi-dimensional renovation plan is generated based on the indicator difference map, including the optimal building layout, optimal road network, optimal greening layout, optimal infrastructure and optimal historical and cultural protection measures.

[0046] In some optional embodiments, generating the first multi-dimensional renovation plan for optimal building layout, optimal road network, optimal greening layout, optimal infrastructure, and optimal historical and cultural protection measures based on the indicator difference map includes:

[0047] Determining a building renovation target based on building index differences in the index difference map;

[0048] determining the optimal building layout of the target renovation area according to the building renovation goal and the building differentiated renovation strategy, wherein the building differentiated renovation strategy represents a retention, repair, demolition or new construction strategy formulated according to building quality and the renovation needs;

[0049] Determining a road reconstruction target based on the road index difference in the index difference map;

[0050] The optimal road network is obtained by calculation according to the road reconstruction target, the shortest path algorithm and the minimum spanning tree algorithm;

[0051] Determining a greening transformation target based on the greening index difference in the index difference map;

[0052] Calculating the optimal greening layout according to the greening transformation goal and the greening layout algorithm;

[0053] Determining infrastructure transformation targets based on infrastructure indicator differences in the indicator difference map;

[0054] Calculating the optimal infrastructure according to the infrastructure transformation goals, the pipe network system planning algorithm, and the sponge city planning algorithm;

[0055] Determining historical and cultural transformation targets based on historical and cultural indicator differences in the indicator difference map;

[0056] The optimal historical and cultural protection measures are determined based on the historical and cultural transformation goals and differentiated buildings.

[0057] In some optional embodiments, after superimposing the first multi-dimensional transformation plan onto the real-life map corresponding to the target transformation area, simulating the first multi-dimensional transformation plan to obtain a simulation result includes:

[0058] Overlaying the optimal building layout, the optimal road network, the optimal greening layout, the optimal infrastructure, and the optimal historical and cultural protection measures of the first multi-dimensional renovation plan with the real-life map one by one, so that the real-life map accurately matches the first multi-dimensional renovation plan;

[0059] Reconstructing the pre-transformation GIS model in sequence according to the optimal building layout, the optimal road network, the optimal greening layout, the optimal infrastructure, the optimal historical and cultural protection measures, and the third priority ranking to obtain a post-transformation GIS model;

[0060] After performing spatial performance simulation, environmental performance simulation, traffic performance simulation, economic performance evaluation and historical and cultural value evaluation on the transformed GIS model, a simulation evaluation report and a transformation score are obtained. The simulation results include the simulation evaluation report and the transformation score.

[0061] In a second aspect, an embodiment of the present invention provides a controller comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for generating a planning scheme for old village renovation as described in the first aspect is implemented.

[0062] In a third aspect, an embodiment of the present invention provides a system for generating a planning scheme for old village renovation, including the controller involved in the second aspect above.

[0063] In a fourth aspect, a computer storage medium stores computer executable instructions, wherein the computer executable instructions are used to execute the method for generating a planning scheme for old village renovation described in the first aspect.

[0064] The beneficial effects of the present invention include: obtaining multi-source data of an old village to be renovated, the multi-source data including building distribution data, road network data, vegetation and water system data, infrastructure data and historical and cultural data; constructing a pre-renovation GIS model of the old village to be renovated based on the multi-source data, so that the pre-renovation GIS model can visually display the multi-source data on a map; obtaining a target renovation area of ​​the old village to be renovated after analyzing the multi-source data based on the pre-renovation GIS model; generating a first multi-dimensional renovation plan for the target renovation area according to renovation requirements; superimposing the first multi-dimensional renovation plan on a real-life map corresponding to the target renovation area, simulating the first multi-dimensional renovation plan to obtain a simulation result; correcting the first multi-dimensional renovation plan based on the simulation result to obtain a second multi-dimensional renovation plan, and transmitting the second multi-dimensional renovation plan to an operation and maintenance GIS platform. The system automatically generates a pre-renovation GIS model based on the multi-source data of the old village to be renovated, and obtains the specific target renovation area after corresponding data analysis, thereby generating a corresponding first multi-dimensional renovation plan based on the specific renovation needs, and the planning efficiency of the renovation plan is high; the first multi-dimensional renovation plan is corrected by superimposing it on the real-life map for simulation, thereby ensuring the accuracy and completeness of the renovation plan.

[0065] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a flowchart of the steps of a method for generating a planning scheme for old village renovation provided by an embodiment of the present invention;

[0067] Figure 2 Schematic diagram of a controller provided by one embodiment of the present invention.

[0068] Reference numerals: controller 1000 , processor 1100 , memory 1200 . DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0070] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and the like in the specification, claims, or accompanying drawings are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0071] In the distribution network, submerged batteries are widely used in various grid equipment, such as distribution transformers and reactive power compensation devices. They can provide stable voltage support, improve the operating efficiency and reliability of equipment, reduce power outages, and enhance user experience.

[0072] Existing backup power sources, such as submerged batteries, experience a large inrush current during startup due to the sudden start-up of the device, causing a sudden drop in battery voltage. In some special application scenarios, such as outdoor locations with low ambient temperatures, the battery's temperature is relatively low, and its internal resistance increases accordingly. In these cases, the voltage differential during startup can become even greater, easily triggering the battery's low-voltage protection mechanism. Once the low-voltage protection mechanism is triggered, the battery will experience a power failure, affecting the normal operation of the entire backup power system. It will not be able to perform its backup function at critical moments, posing a risk to the continued operation of related equipment.

[0073] In order to solve the above-mentioned problems, the present application provides a method, system and storage medium for generating a planning scheme for old village renovation.

[0074] In this application, a method, system and storage medium for generating a planning scheme for old village renovation are provided, which are described in detail one by one in the following embodiments.

[0075] like Figure 1 As shown, an embodiment of the present invention provides a method for generating a planning scheme for old village renovation, comprising:

[0076] S100: Acquire multi-source data of the old village to be renovated, wherein the multi-source data includes building distribution data, road network data, vegetation and water system data, infrastructure data, and historical and cultural data;

[0077] S200: constructing a pre-renovation GIS model of the old village to be renovated based on the multi-source data, so that the pre-renovation GIS model can visually display the multi-source data on a map;

[0078] S300, analyzing the multi-source data based on the pre-renovation GIS model to obtain a target renovation area for the old village to be renovated;

[0079] S400: generating a first multi-dimensional renovation plan for the target renovation area according to renovation requirements;

[0080] S500: After superimposing the first multi-dimensional transformation plan onto the real-life map corresponding to the target transformation area, the first multi-dimensional transformation plan is simulated to obtain a simulation result;

[0081] S600: Modify the first multi-dimensional transformation plan according to the simulation result to obtain a second multi-dimensional transformation plan, and transmit the second multi-dimensional transformation plan to the operation and maintenance GIS platform.

[0082] Specifically, the data types of the multi-source data of this application include satellite data, 3D scanning data, photo data, video data, text description data, audio data, and professional surveying and mapping data, etc. The specific data types and data content are not limited here. For example, using drone oblique photography technology, three-dimensional stereo images of the buildings in the old village to be renovated are obtained, and the location, height, outline, number of floors and other spatial information of the buildings are accurately recorded; at the same time, combining field survey data with pre-existing building information in the system, attribute information such as the use function, construction age, and structural type of the building is obtained. If the building is a residential building, the number of residents and the type of residents will also be obtained, so as to facilitate the determination of the corresponding renovation plan for the residential building based on the number and type of residents.

[0083] The acquisition of road network data includes: extracting the centerline and boundary information of the roads in the old villages to be renovated through satellite remote sensing image data; collecting detailed data such as road width, material, slope, traffic signs, etc. through a vehicle-mounted mobile measurement system; and referencing road planning information from the transportation department to ensure the integrity and accuracy of the data.

[0084] The acquisition of vegetation and water system data specifically includes: obtaining high-resolution satellite images of the old villages to be renovated based on high-resolution satellites and obtaining drone orthophotos of the old villages to be renovated based on drones, and then identifying the vegetation type, distribution range and coverage area in the high-resolution satellite images and drone orthophotos through remote sensing image classification; at the same time, through spatial analysis, extracting the location, flow direction, length, width and other information of the water system in the high-resolution satellite images and drone orthophotos, and combining with the hydrological data of the water conservancy department to supplement dynamic information such as water level and flow.

[0085] The acquisition of infrastructure data includes: accessing the systems of power, communications, water supply and drainage departments to obtain underground pipeline layout drawings and related data, including pipeline type, diameter, burial depth, connection method, etc.; at the same time, through field detection data, correcting and improving data to ensure data accuracy; and collecting the location and distribution data of ground infrastructure, such as street lights, trash cans, fire hydrants, etc.

[0086] The acquisition of historical and cultural data includes: obtaining relevant information on historical buildings, cultural relics, and traditional streets and alleys, including names, ages, historical evolution, and cultural value, through local historical records and archival materials; and recording their current status and preservation status through professional surveying and mapping data obtained through field surveys and mapping by professionals.

[0087] The acquired multi-source data is processed through format conversion, coordinate system integration, and data cleaning to remove noise and erroneous data and ensure data consistency and accuracy. The processed multi-source data is imported into the geographic information system software. Based on the data type and characteristics, the appropriate data model, such as vector model or raster model, is selected to organize and store the spatial data. Topological relationships are then constructed to ensure the logical consistency of the spatial data.

[0088] Spatial data such as buildings, roads, vegetation, and infrastructure are linked to corresponding attribute data, enabling bidirectional query and update of spatial and attribute data through unique identifiers. An attribute database is established to store and manage detailed attribute information. Using a geographic information system (GIS), the constructed data model and attribute database are 3D modeled, rendered, and symbolized to create a pre-renovation GIS (Geographic Information System) model. By setting different colors, line types, and symbols, various data of the old village can be intuitively displayed. Multiple viewing angles and browsing methods, such as 2D maps, 3D scenes, and bird's-eye views, are provided to facilitate observation and analysis from different dimensions.

[0089] By applying geographic information system spatial analysis tools such as buffer analysis, overlay analysis, and network analysis, a comprehensive analysis of multi-source data in the pre-renovation GIS model was conducted. For example, buffer analysis determined the service coverage of firefighting facilities and identified areas with insufficient coverage. Overlay analysis identified areas with poor building quality, inadequate infrastructure, and potential for renovation. Based on the goals and needs of old village renovation, a corresponding evaluation index system was established, encompassing aspects such as building quality, infrastructure support, transportation accessibility, ecological environment, and historical and cultural preservation. Methods such as the Analytic Hierarchy Process and the Delphi method were used to determine the weights of each indicator and highlight key areas for renovation. Combining the spatial analysis results with the evaluation index system, a comprehensive evaluation and grading of each area of ​​the old village was conducted. Based on the evaluation scores, the areas were divided into key renovation areas, general renovation areas, and preservation and protection areas, thereby identifying target areas for renovation.

[0090] For the target transformation area, multiple different transformation plans can be formulated to meet the transformation needs from different angles and levels; use the multi-criteria decision analysis method to comprehensively evaluate and compare the various plans, considering factors such as economic benefits, social benefits and environmental benefits; based on the evaluation results, further optimize and improve the preferred plan to determine the final first multi-dimensional transformation plan.

[0091] Using technologies such as drone oblique photography and ground-based 3D scanning, a high-precision real-world map of the target renovation area is obtained. Data processing, including point cloud filtering, texture mapping, and model optimization, is performed on the real-world map to improve its quality and visualization. Various data from the first multi-dimensional renovation plan, such as building models, road designs, and landscaping layouts, are overlaid on the real-world map. Spatial registration and data fusion are performed to ensure accurate alignment and seamless integration between the plan and the real-world map. Using the simulation and analysis capabilities of the geographic information system, a multi-dimensional simulation is performed on the overlaid plan, including spatial layout simulation, traffic flow simulation, sunlight analysis, wind environment simulation, and ecological impact simulation. The simulations are used to evaluate the plan's effectiveness in actual application and potential problems. Based on the simulation results, the first multi-dimensional renovation plan is evaluated and analyzed to identify any problems and deficiencies. This is then revised and improved to form a second multi-dimensional renovation plan.

[0092] The second multi-dimensional transformation plan is delivered to the operation and maintenance GIS platform to achieve the connection and sharing of plan data with the operation and maintenance GIS platform; on the operation and maintenance GIS platform, the plan is stored, managed and displayed to facilitate subsequent construction, operation and maintenance, and management decision-making.

[0093] In some optional embodiments, the revised second multi-dimensional transformation plan is submitted to relevant departments and stakeholders for review, and based on feedback, the second multi-dimensional transformation plan is further improved to ensure that the second multi-dimensional transformation plan complies with policy requirements and meets actual needs.

[0094] In some optional embodiments, constructing the pre-renovation GIS model of the old village to be renovated based on the multi-source data includes:

[0095] S210, obtaining topographic data of the old village to be renovated;

[0096] S220, constructing a terrain model of the old village to be renovated based on the terrain data;

[0097] S230: Generate a building model on the terrain model according to the building distribution data, and display the building location, building shape, building equipment and building aging degree through the building model;

[0098] S240: Generate a road model on the terrain model based on the road network data, and display road grade, road alignment, road capacity, and road signs through the road network;

[0099] S250: generating a vegetation and water system model on the terrain model according to the vegetation and water system data, and displaying vegetation type, vegetation shape, vegetation coverage area, and water system through the vegetation and water system model;

[0100] S260: Generate an infrastructure model on the terrain model based on the infrastructure data, and display the infrastructure type, infrastructure location, and infrastructure aging degree through the infrastructure model;

[0101] S270. After marking corresponding historical and cultural information on the building model, the road model, the vegetation and water system model, and the infrastructure model based on the historical and cultural data, obtain the GIS model of the old village before renovation.

[0102] It should be noted that this application acquires high-precision topographic data through satellite remote sensing, drone aerial surveys, and ground-based 3D laser scanning. Satellite remote sensing can obtain a large-scale topographic overview of the old village to be renovated; drone aerial surveys can collect high-resolution data of the old village's topography using flexible flight paths; and ground-based 3D laser scanning can capture topographic point cloud data with centimeter or even millimeter-level accuracy in key areas. Furthermore, existing topographic surveying and mapping results from surveying and mapping departments, such as contour maps and digital elevation models (DEMs), can be combined to supplement and improve the data. This application does not limit the specific methods for obtaining topographic data.

[0103] The collected terrain data was imported into GIS software. Using the software's terrain modeling capabilities, a digital terrain model (DTM) was constructed based on contour lines, elevation points, and other data. Interpolation algorithms, such as inverse distance weighted (IDW) and kriging, were used to process the discrete elevation data to generate a continuous terrain surface model, recreating the village's topographical characteristics, including undulations, slope, and aspect.

[0104] Specifically, after generating the terrain model, the coordinate information in the building distribution data is used to precisely determine the location of each building on the terrain model. For building shapes, the building's outline data can be used to convert the 2D outline into a 3D building model through operations such as stretching and lofting in 3D modeling software, visually displaying the building's exterior form. For buildings with regular shapes, parametric modeling methods can be used to quickly generate models by setting parameters such as the building's length, width, and height. For buildings with complex shapes, reverse modeling can be performed using point cloud data obtained through laser scanning to ensure model accuracy. In combination with the building's internal facility layout data, corresponding facility models such as doors, windows, staircases, elevators, and pipelines can be added to the building model. For large public buildings, internal functional zoning can be further refined, such as the different business areas in a shopping mall or the functions of each floor. Layered display or color coded methods can be used to clearly display the distribution of building facilities.

[0105] The building model can be assigned different colors, textures, or symbols to indicate its age. For example, the building's age can be categorized as good, fair, or poor. Good buildings can have vibrant, intact textures; fair buildings appear slightly worn; and poor buildings can have damaged, peeling textures. Textual information about the age level can also be annotated on the model to provide a visual understanding of the building's current state. Alternatively, the age level of different building parts can be displayed using corresponding age coefficients. The specific age level displayed is not limited here.

[0106] Based on the road grade information in the road network data, the road alignment and shape are planned on the terrain model. For high-grade roads, such as main roads, a wider road model with multiple lanes is used. For lower-grade roads, such as alleys and side roads, a narrower road model is used. The specific width is determined by the modeling scale and is not limited here. Road alignment is determined by drawing curves or straight segments in 3D space based on design drawings or actual measurement data, ensuring that the slope, curve radius, and other factors are consistent with the actual road. Road capacity is represented on the road model by setting different colors, widths, or lane marking styles. For example, roads with high capacity are represented by wider lane markings and bright colors, while roads with low capacity are represented by narrower lane markings and dim colors. Road sign models, such as traffic lights, road signs, and road signs, are also added to accurately display traffic information and guidance.

[0107] Generate a corresponding vegetation model on the terrain model based on the vegetation type and distribution information in the vegetation and water system data. For large areas of green space and forests, batch generation can be used to quickly generate vegetation communities by setting parameters such as vegetation density and height. For individual trees or special vegetation, detailed modeling can be used to simulate their true shape and appearance. Different vegetation types are distinguished by different colors and shapes, such as green grass, dark green woods, and colorful flowers, and the boundaries of vegetation-covered areas are marked. Based on the water system data, the shape and location of the water system are drawn on the terrain model. For larger water bodies such as rivers and lakes, three-dimensional water models can be generated through operations such as stretching and lofting, and properties such as the water body's color and transparency can be set to simulate the effect of a realistic water surface. For smaller water systems such as streams and ditches, lines or tubular models can be used to represent them, and the flow direction and name of the water system can be marked.

[0108] Based on the information contained in the infrastructure data, various infrastructure types, such as power facilities (substations, utility poles), communications facilities (base stations, fiber optic cables), and water supply and drainage facilities (water pipes, sewers), are precisely located on the terrain model. 3D modeling software is used to create corresponding infrastructure models and adjust them based on their actual size and shape to ensure consistency between the models and the actual facilities, thereby improving the accuracy of subsequent renovation plans. Similar to the method used to represent the degree of building aging, the degree of aging is indicated by assigning different appearance characteristics to the infrastructure models. For example, aged water pipes can be given textures such as rust and cracks; old utility poles can be tilted or damaged. The aging of the infrastructure is also annotated to facilitate understanding of the facility's operational status.

[0109] Historical and cultural data is sorted and classified to clarify historical and cultural information related to buildings, roads, vegetation, water systems, and infrastructure, such as the name, age, and cultural value of historical buildings, the historical evolution of traditional roads, legends about culturally significant vegetation or water systems, and historical events related to infrastructure. The corresponding historical and cultural information is annotated on the constructed building models, road models, vegetation, water system models, and infrastructure models by adding text annotations, icons, hyperlinks, and other methods. For example, the name and introduction of the historical building model are annotated on the model, and a hyperlink is added so that users can click on it to view detailed historical information and pictures; icons and text are placed next to traditional roads to explain their historical background; and relevant legends are annotated on the models of culturally valuable vegetation or water systems. This ensures that the GIS model before the transformation not only displays the current state of the old village, but also inherits and displays its historical and cultural connotations.

[0110] In some embodiments, before generating a building model on the terrain model according to the building distribution data, the method further includes:

[0111] S221. Using a spatial data quality control algorithm to remove noise data, duplicate data, and outliers from the multi-source data to obtain first intermediate data.

[0112] S222: Perform spatial matching on the first intermediate data using a feature matching algorithm to obtain second intermediate data after the first intermediate data has spatial coordinates;

[0113] S223: Construct a data prediction model based on historical data, where the historical data represents the second intermediate data of each period;

[0114] S224. The third intermediate data is obtained by predicting and supplementing the missing data in the second intermediate data through the data prediction model. The building distribution data used to generate the building model, the road network data used to generate the road model, the vegetation and water system data used to generate the vegetation and water system model, and the historical and cultural data used to mark the historical and cultural information all belong to the third intermediate data.

[0115] Specifically, the spatial data quality control algorithm of this application utilizes a spatial filtering algorithm to process multi-source data. This algorithm can effectively identify and eliminate noise points caused by factors such as measurement error and sensor interference. For discrete noise points in building distribution data, a distance threshold can be set. Isolated points that deviate from the main building area (i.e., points whose distance from the main building area is greater than the distance threshold) are considered noise and removed. Duplicate data is identified using a combination of spatial location matching and attribute feature comparison. For records with similar spatial locations and highly similar attribute information, such as two completely overlapping road segments or multiple identical facilities labeled at the same location, one complete record is retained and the remaining duplicates are deleted to prevent data redundancy from interfering with subsequent analysis. Outliers are detected based on statistical analysis and spatial clustering algorithms. For example, the Z-score (standard score) method is used to identify outliers in building height data. If a building height exceeds three standard deviations from the mean, field research or historical data can be used to determine its rationality, and confirmed erroneous data can be corrected or removed. For abnormal points whose spatial positions deviate significantly from the normal distribution, such as buildings located in water bodies, they can be corrected through spatial topological relationship analysis. After removing noise points, deduplication, and abnormal points, the first intermediate data is obtained.

[0116] Feature point matching algorithms, such as SIFT (Scale-Invariant Feature Transform) or SURF (Speeded Robust Features), are used to extract and match features from data from different sources. Stable feature points are found in image data of different resolutions and perspectives, and spatial matching of data is achieved by calculating the spatial relationship between feature points. A unified coordinate system is established and all data is converted to this coordinate system. Data obtained from different surveying and mapping units is prone to inconsistent coordinate systems. Therefore, coordinate conversion parameters are used to convert the first intermediate data into the same coordinate system, allowing different data points in the first intermediate data to be matched to different spatial locations.

[0117] By collecting and organizing secondary intermediate data from various time periods, we analyze the data's time series characteristics and spatial evolution patterns. For example, we can study the changing trends in building distribution, building aging curves, road network expansion processes, and infrastructure installation. Key features and patterns can be extracted from these data to provide a data basis for building prediction models. Based on the data characteristics and prediction requirements, we select an appropriate prediction model, such as a time series analysis model (such as ARIMA and LSTM), a spatial interpolation model (such as Kriging and Inverse Distance Weighted Interpolation), or a machine learning model (such as Random Forest and Support Vector Machine). For example, if building distribution data is missing in some areas, we can use spatial interpolation models to predict building distribution in the missing areas based on the building characteristics and historical development trends of the surrounding areas. Alternatively, if building aging information is missing in some areas, we can construct an aging curve for the missing areas based on historical aging information (this is the prediction curve in the data prediction model). This aging curve can then be used to predict building aging information in the missing areas.

[0118] After inputting the second intermediate data into the trained data prediction model, the data prediction model is used to predict and supplement missing data. Once the predictions are complete, they are compared with known data to evaluate the accuracy and reliability of the predictions. For areas with large prediction errors, manual corrections can be made using expert knowledge and field research to ensure the integrity and accuracy of the third intermediate data. It's easy to see that building distribution data used to generate building models, road network data used to generate road models, vegetation and water system data used to generate vegetation and water system models, and historical and cultural data annotated with historical and cultural information all constitute third intermediate data.

[0119] In some embodiments, the analyzing the multi-source data based on the pre-renovation GIS model to obtain the target renovation area of ​​the old village to be renovated includes:

[0120] S310, determining a fire protection coefficient and a traffic coefficient for each area based on the road network data and the building distribution data corresponding to each area on the pre-renovation GIS model, wherein the fire protection coefficient represents the fire protection capability of each area, and the traffic coefficient represents the traffic capability of each area;

[0121] S320, generating a geological hazard risk distribution map according to the geological characteristic data corresponding to each region on the pre-transformation GIS model, and determining a geological hazard risk coefficient for each region on the pre-transformation GIS model based on the geological hazard risk distribution map;

[0122] S330, determining a building quality coefficient for each area based on the building distribution data corresponding to each area on the GIS model before transformation;

[0123] S340, determining the infrastructure improvement coefficient of each area based on the infrastructure data corresponding to each area on the GIS model before transformation;

[0124] S350, determining the greening coefficient of each area based on the vegetation and water system data corresponding to each area on the GIS model before transformation;

[0125] S360, determining the historical and cultural value coefficient of each area based on the historical and cultural data corresponding to each area on the GIS model before transformation;

[0126] S370, determining a renovation coefficient for each area on the pre-renovation GIS model based on the fire protection coefficient, the traffic coefficient, the geological disaster risk coefficient, the building quality coefficient, the infrastructure improvement coefficient, the greening coefficient, and the historical and cultural value coefficient of each area on the pre-renovation GIS model;

[0127] S380: Configure the area where the transformation coefficient is greater than or equal to the preset coefficient as the target transformation area.

[0128] Specifically, due to the narrow roads and old buildings in the old village, the firefighting capacity and road traffic capacity are difficult to meet the firefighting and traffic needs. Based on the road network data (road width, turning radius, clearance height) and building distribution data (building spacing, height, density), GIS network analysis is used to calculate the shortest path from the nearest fire station to each building. If the path width is less than 4m or the turning radius is less than 9m, points will be deducted; through buffer analysis, check whether the building spacing meets the corresponding requirements (for example, if the spacing between multi-story buildings is less than 6m, points will be deducted); based on the fire hydrant distribution data, a 50m service buffer is generated to calculate the water source coverage rate of the buildings in the area. The specific fire protection coefficient is calculated as follows: .

[0129] Road capacity calculation: Determine the corresponding main road capacity based on the road grade; combine historical traffic data and traffic demand generated by buildings to simulate peak congestion in the morning and evening; evaluate the width of sidewalks, crossing facilities, and the completeness of barrier-free passages. The calculation formula for the traffic coefficient is: .

[0130] The geological characteristic data used in this application includes terrain slope, rock and soil layer type, groundwater level, and the distribution of historical disaster sites (landslides, collapses, and debris flows), with no specific restrictions. Weights were assigned using the Analytic Hierarchy Process (AHP): for example, slope (0.3), rock and soil layer stability (0.25), groundwater level (0.2), and historical disasters (0.25). Different factor scores were determined based on different values ​​of slope, rock and soil layer stability, groundwater level, and historical disasters. The factor scores and weights for different regions were superimposed and calculated to produce the corresponding risk distribution map. The risk distribution map clearly displays the geological hazard risk coefficient for each region on the pre-modification GIS model.

[0131] For the calculation of the building quality coefficient, a preliminary quality value can be assigned based on the age of the building; a secondary value can be assigned based on the structural type of the building; and a tertiary value can be assigned based on the degree of damage to the building, thereby determining the corresponding building quality coefficient based on the three assignments.

[0132] The calculation of the infrastructure improvement coefficient can be based on a comprehensive consideration of infrastructure aging and layout. For example, for water supply and drainage networks (pipe diameters <100mm are deducted 0.3 points, aging and corrosion are deducted 0.2 points, with a maximum score of 1); power facilities (insufficient transformer capacity is deducted 0.3 points, disordered wiring is deducted 0.2 points, with a maximum score of 1); and communications networks (fiber optic coverage <50% is deducted 0.4 points, with a maximum score of 1). Specific infrastructure and scoring items are set based on actual needs and are not limited here. The corresponding improvement coefficient can be calculated based on the deduction points.

[0133] The greening coefficient can be calculated by scoring the greening ratio (current greening ratio divided by the planned standard greening ratio; if it exceeds 1.2, a value of 1 is assigned; if it is between 0.8 and 1.2, a proportional value is assigned; if it is less than 0.8, a linear deduction is made); the tree cover ratio (projected tree crown area divided by the total area of ​​the region; if it is greater than 30%, a value of 0.8 is assigned; if it is between 10% and 30%, a proportional value is assigned; if it is less than 10%, a value of 0.2 is assigned); and the integrity of the water system (the proportion of the length of the natural water system encroached upon; a deduction of 0.1 is made for every 10% encroached upon). The greening coefficient is then determined based on the total score.

[0134] The historical and cultural value coefficient is calculated by comprehensively scoring the cultural relics protection units (1.0 for national level, 0.8 for provincial level, 0.6 for municipal level), the integrity of traditional architectural features (0.7 for features >80% integrity, 0.4 for 50%-80%, 0.1 for <50% integrity), and the degree of intangible cultural heritage relevance (0.5 for intangible cultural heritage items at or above the provincial level, 0.3 for municipal level). The historical and cultural value coefficient is then determined based on the specific scores.

[0135] The transformation coefficient is calculated by weighting by assigning different total weights to different coefficients, such as fire protection coefficient (0.2), traffic coefficient (0.15), geological disaster risk coefficient (0.15), building quality coefficient (0.15), infrastructure improvement coefficient (0.1), greening coefficient (0.1), and historical and cultural value coefficient (0.15).

[0136] Through the GIS symbolization function, the areas with a transformation coefficient greater than 0.6 are marked red, those between 0.4 and 0.6 are marked yellow, and those less than 0.4 are marked green. Finally, the red areas are extracted as the target transformation areas.

[0137] In some optional embodiments, after configuring the region having the transformation coefficient greater than or equal to the preset coefficient as the target transformation region, the method further includes:

[0138] S381, determining a first priority ranking of each target sub-region in the target transformation area according to the transformation coefficient corresponding to each target sub-region;

[0139] S382. Evaluate the transformation benefit of each target sub-region using a preset economic benefit model to obtain a benefit index for each target sub-region;

[0140] S383. Modify the first priority ranking according to the benefit index to obtain a second priority ranking;

[0141] S384, determining the influence coefficients between the target sub-regions by using a network analysis method;

[0142] S385. Modify the second priority ranking according to the influence coefficient to obtain a third priority ranking, so that each of the target sub-areas is transformed in turn according to the third priority ranking.

[0143] Specifically, the renovation coefficients of each target sub-area within the target renovation area reflect the urgency of renovation in various aspects, such as fire protection, accessibility, and geological disaster risk. Higher values ​​indicate more urgent renovation needs. This is used to perform a preliminary ranking, resulting in a first-priority ranking and clarifying the initial order of renovation. Specifically, the renovation coefficients of each target sub-area are extracted and sorted in descending order, assigning each sub-area a corresponding ranking number. For example, the sub-area with the highest renovation coefficient is ranked 1, and so on, forming a first-priority ranking list.

[0144] The preset economic benefit model evaluates the transformation benefits of each target sub-area by quantitatively analyzing the transformation input and output, and derives a benefit index. It measures the feasibility and value of the transformation from an economic perspective and provides an economic basis for priority adjustment. The economic benefit model comprehensively considers indicators such as construction cost, expected benefits, and investment payback period. Construction costs cover land acquisition, building demolition, material procurement, labor costs, etc.; expected benefits include land appreciation, property appreciation, increased commercial income, and economic benefits from the transformation of social benefits brought about by improvements in public service facilities. The benefit index is calculated using a preset formula, such as benefit index = present value of expected benefits / present value of construction cost, to obtain the benefit index of each target sub-area. The first priority ranking is revised in combination with the benefit index, so that the transformation order not only considers the urgency of regional transformation, but also takes into account economic rationality, avoids waste of resources due to blind transformation, and ensures that the economic benefits of the transformation project are maximized. The specific method for determining the second priority ranking is: comprehensively analyze the benefit index and the transformation coefficient in the first priority ranking, and use a weighted fusion method to assign a weight to the transformation coefficient. α， Benefit index weight β ( α+β= 1), New Priority =α ×Renovation coefficient ranking score +β ×Benefit index ranking score, recalculate the priority score of each target sub-area, adjust the ranking according to the score, and get the second priority ranking 。

[0145] Determining the impact coefficient involves using the Analytical Network Process (ANP) to consider the mutual influence between target sub-regions. By analyzing factors such as inter-regional resource flows, functional complementarity, and transportation connections, the impact coefficient is determined to reflect the driving or restrictive impact of regional transformation on other regions. Specifically, an ANP model is constructed, comprising a control layer (transformation objectives and principles, etc.) and a network layer (target sub-regions and their interrelationships). Based on pre-set scoring rules, the impact levels between sub-regions are compared and scored pairwise to construct a judgment matrix. Matrix operations are then used to determine the impact coefficients between target sub-regions, forming an impact coefficient matrix. Based on the impact coefficients, the second priority ranking is further revised, prioritizing transformation of sub-regions with a strong driving effect on surrounding areas (e.g., sub-regions on both sides of a road, or prioritizing the outermost sub-regions and then proceeding sequentially along the road, etc.). This promotes coordinated regional development, enhances overall transformation effectiveness, and achieves optimal resource allocation.

[0146] The specific method for determining the third priority ranking is to incorporate the impact coefficient into the second priority ranking score calculation. Based on the original calculation, increase the impact coefficient weight γ ( α+β+γ = 1), with the final priority = α × renovation coefficient ranking score + β × benefit index ranking score + γ × impact coefficient ranking score. The priority scores of each target sub-area are recalculated, and the ranking is adjusted based on the new scores to obtain the third priority ranking. Renovation work is carried out in each target sub-area in sequence according to the third priority ranking, ensuring that the renovation work is carried out in a scientific, orderly, and efficient manner.

[0147] In some embodiments, generating a first multi-dimensional transformation plan for the target transformation area according to the transformation requirements includes:

[0148] S410: Determine a first demand level for each demand indicator in the transformation demand based on a preset demand level and a regional transformation target;

[0149] S420: Modify the first demand level according to public demand data to obtain a second demand level, wherein the public demand data is collected through a public demand collection system;

[0150] S430: comparing the indicator difference maps of the demand indicator and the target indicator in sequence according to the first demand level, where the target indicator represents the indicator corresponding to the demand indicator in the target transformation area;

[0151] S440. Generate the first multi-dimensional renovation plan for optimal building layout, optimal road network, optimal greening layout, optimal infrastructure, and optimal historical and cultural protection measures based on the indicator difference map.

[0152] Specifically, demand indicators can be divided into mandatory demands (such as fire protection regulations and safety standards), guiding demands (such as green coverage rate and public service facility configuration) and flexible demands (such as characteristic landscape shaping and industrial development). At the same time, based on the corresponding construction specifications and combined with local policy documents, the baseline value and ideal value of each indicator can be determined to obtain the preset demand level.

[0153] The priority of each indicator is determined based on the regional transformation goals. For example, if the transformation goal is "ecological livability", the priority of ecological and environmental protection indicators (such as green space ratio and sponge city construction) will be increased; if the transformation goal is "historical and cultural heritage", the weight of indicators such as historical building protection and traditional street patterns will be increased.

[0154] The Analytic Hierarchy Process (AHP) is used to construct a demand index hierarchy and score each index to determine its weight. For example:

[0155] Safety requirements (0.3): fire escape width (0.15), building spacing (0.15);

[0156] Functional requirements (0.25): public service facility coverage (0.12), transportation accessibility (0.13);

[0157] Ecological needs (0.2): green space ratio (0.1), carbon emission control (0.1);

[0158] Cultural needs (0.15): historical building preservation rate (0.08), traditional style integrity (0.07);

[0159] Economic needs (0.1): return on investment (0.06), land use efficiency (0.04).

[0160] The collection and revision of public demand data includes: collecting suggestions through corresponding mobile questionnaire systems and opinion collection systems to determine public needs; and / or collecting suggestions through offline surveys to determine public needs; and / or obtaining corresponding public needs through big data analysis of public comments and requests.

[0161] Public opinions are classified and quantified, and frequently mentioned demands are incorporated into the index system, so as to obtain the second demand level after revising the first demand level based on the public demand data.

[0162] The target renovation area is divided into multiple grid cells, ensuring that each cell contains a complete set of analysis elements. Spatial topological relationships are constructed for elements such as buildings, roads, and green spaces to ensure data consistency. Quantitative indicators (such as green space ratio and volume ratio) are dimensionless and standardized using the Z-score. Qualitative indicators (such as historical integrity) are quantified using a grading system, with scores ranging from "complete" = 4, "relatively complete" = 3, "average" = 2, and "poor" = 1. The ideal and current values ​​of the demand indicators are spatially superimposed, and the difference between the indicators is calculated for each grid cell. A heat map visualization (i.e., a map of the indicator differences) is generated to visually demonstrate the spatial distribution of the demand gap.

[0163] Differentiated renovation plans are generated based on the differences in building-related target indicators and building quality assessment (intact, repair, demolition, and new construction). Genetic algorithms are used to optimize building layout, with space utilization, sunlight spacing, and ventilation conditions as objective functions, to generate the optimal building layout.

[0164] Based on the differences in road-related target indicators, land use functions, and population size, the traffic generation volume after the transformation is predicted. At the same time, traffic models are used to simulate traffic flows, and graph theory algorithms are used to analyze road network connectivity, identify bottleneck sections, and construct a multi-level road system (main roads, secondary roads, branches, and lanes) to ensure full coverage of fire passages, thereby obtaining the optimal road network.

[0165] Based on the differences in greening-related target indicators and an ecological sensitivity analysis, ecological source areas and corridors were identified. The minimum cumulative resistance (MCR) model was applied to plan the green space system. The SWMM (Storm Water Management Model) was used to simulate stormwater runoff and optimize the layout of facilities such as sunken green spaces and rain gardens, thereby generating an optimal greening layout.

[0166] Based on the differences in infrastructure-related target indicators, terrain analysis, and building layout, the directions of water supply, drainage, power, communications, and other pipelines are optimized; hydraulic models are used to calculate pipe diameters and slopes to ensure that drainage capacity meets heavy rain standards, thereby generating optimal infrastructure.

[0167] According to the differences in historical and cultural target indicators, and based on historical building assessments and spatial syntax analysis, core protection areas, construction control zones and style coordination areas are delineated, and a digital archive of historical buildings is established to record building information and repair suggestions, thereby obtaining the optimal historical and cultural protection measures.

[0168] In some embodiments, generating the first multi-dimensional renovation plan for optimal building layout, optimal road network, optimal greening layout, optimal infrastructure, and optimal historical and cultural protection measures based on the indicator difference map includes:

[0169] S441, determining a building renovation target according to the building index difference in the index difference map;

[0170] S442. Determine the optimal building layout of the target renovation area based on the building renovation goal and a differentiated building renovation strategy, where the differentiated building renovation strategy represents a retention, repair, demolition, or new construction strategy formulated based on building quality and the renovation requirements;

[0171] S443, determining a road reconstruction target according to the road index difference in the index difference map;

[0172] S444. Obtain the optimal road network by calculating according to the road reconstruction target, the shortest path algorithm, and the minimum spanning tree algorithm;

[0173] S445, determining a greening transformation target based on the greening index difference in the index difference map;

[0174] Calculating the optimal greening layout according to the greening transformation goal and the greening layout algorithm;

[0175] S446. Determine infrastructure transformation targets based on infrastructure indicator differences in the indicator difference map;

[0176] S447. Calculate the optimal infrastructure based on the infrastructure transformation goal, the pipe network system planning algorithm, and the sponge city planning algorithm;

[0177] S448, determining a historical and cultural transformation target based on the historical and cultural indicator differences in the indicator difference map;

[0178] S449. Determine the optimal historical and cultural protection measures based on the historical and cultural transformation goals and differentiated buildings.

[0179] Specifically, the difference between the current and target values ​​for indicators such as building density, volume ratio, sunlight spacing, and building height is extracted from the indicator difference map. For example, if the current building density is 60% and the target value is 45%, "reducing building density" is determined as the building renovation goal. Through GIS spatial overlay, specific areas with excessive building density are identified. Combined with building quality assessments (such as structural safety and service life), a "high-density + low-quality" priority renovation map is generated. This map then incorporates differentiated building renovation strategies to generate the optimal building layout: For buildings of good quality and in compliance with planning, the main structure is retained, with only facade repairs and functional optimization. For buildings of average quality, measures such as foundation reinforcement and replacement of damaged components are adopted, while also improving the seismic fortification rating. For buildings of poor quality or that violate planning regulations, a demolition schedule is established, along with temporary resettlement plans. In demolished areas or idle land, parametric design is used to generate new building models that meet sunlight and spacing requirements. For example, in controlling the floor area ratio, genetic algorithms are used to optimize the building area while meeting sunlight standards. In terms of apartment type allocation, based on demographic data (such as the aging rate), priority is given to allocating elderly-friendly units.

[0180] Generating an optimal road network involves first extracting the differences in indicators such as road width, traffic capacity, and fire lane coverage to determine road reconstruction targets. For example, if the existing main road width is less than 12 meters (target value 15 meters) and the fire lane coverage rate is only 60% (target value 100%), the road reconstruction target is determined to be "widening the main road and increasing the number of fire lanes."

[0181] Then, starting from the fire station, the shortest path to each building is calculated, bottleneck sections with a width less than 4 meters are identified, and a list of mandatory widenings is generated. Incorporating traffic flow forecasts (such as morning and evening peak traffic volumes), parallel alternative routes are planned for congested sections (travel time greater than 20 minutes). Existing roads are treated as edges in the graph, and construction costs (land acquisition costs + construction costs) are used as weights to generate a minimum-cost road network connecting all clusters. High-grade, well-maintained sections of existing roads are prioritized to minimize renovation costs, resulting in an optimal road network.

[0182] Generating the optimal greening layout involves extracting differences in indicators such as green space ratio (e.g., current 20% to target 35%), tree coverage (e.g., current 15% to target 25%), and ecological corridor integrity (e.g., current 5 breakpoints to target 0) to determine greening transformation targets. Based on topographic and water system data, the minimum cumulative resistance (MCR) model is used to identify ecological sources (e.g., wooded areas) and potential corridors, prioritizing the planning of green belts ≥10m wide at breakpoints. A Voronoi diagram is then used to delineate the green space service area, ensuring that coverage within a 500m service radius (for example, not limited to this example) increases from the current 60% to 85% (for example, not limited to this example), thereby generating the optimal greening layout.

[0183] Generating optimal infrastructure involves determining infrastructure improvement targets by extracting differences in drainage pipe diameter (e.g., current pipe diameter 300mm → target pipe diameter 500mm), sewage treatment rate (e.g., current 40% → target 90%), and renewable energy share (e.g., current 5% → target 20%). Then, based on terrain elevation data, a hydraulic model is used to simulate stormwater runoff, increasing drainage capacity from a one-year to a three-year flood. A topological analysis of the existing pipe network identifies sections with insufficient diameter or inverse slope, and corresponding network improvement plans are generated. Once all infrastructure improvement plans have been finalized, the optimal infrastructure is achieved.

[0184] The optimal historical and cultural preservation measures were generated by extracting differences in indicators such as the historical building retention rate (e.g., current 30% → target 80%), the integrity of traditional streets and alleys (e.g., current 40% → target 70%), and the proportion of cultural exhibition space (e.g., current 5% → target 15%) to determine corresponding historical and cultural transformation targets. An "authentic restoration" strategy (specific strategies are not specified) was then adopted, using traditional materials (e.g., bricks and wood) and craftsmanship, while prohibiting alterations to the building form. Internal functional transformations (e.g., residential to cultural exhibition) were permitted for historical buildings, but the facades and structural systems must remain unchanged. Traditional-style buildings could be adjusted in height (≤2 stories), but their characteristic "whitewashed walls and black tiles" style must be retained. Subsequently, space syntax analysis was used to identify "highly integrated" nodes within traditional streets and alleys (e.g., historical squares and bridgeheads), prioritizing their transformation into cultural exhibition spaces. This ultimately led to the generation of the optimal historical and cultural preservation measures.

[0185] In some embodiments, after superimposing the first multi-dimensional transformation plan onto the real-life map corresponding to the target transformation area, simulating the first multi-dimensional transformation plan to obtain a simulation result includes:

[0186] S510: Overlaying the optimal building layout, the optimal road network, the optimal greening layout, the optimal infrastructure, and the optimal historical and cultural preservation measures of the first multi-dimensional renovation plan with the real-life map one by one, so that the real-life map accurately matches the first multi-dimensional renovation plan;

[0187] S520, reconstructing the pre-renovation GIS model in sequence according to the optimal building layout, the optimal road network, the optimal greening layout, the optimal infrastructure, the optimal historical and cultural protection measures, and the third priority ranking to obtain a post-renovation GIS model;

[0188] S530. After performing spatial performance simulation, environmental performance simulation, traffic performance simulation, economic performance evaluation, and historical and cultural value evaluation on the transformed GIS model, a simulation evaluation report and a transformation score are obtained. The simulation results include the simulation evaluation report and the transformation score.

[0189] Specifically, the spatial coordinates corresponding to the optimal building layout, optimal road network, optimal green space layout, optimal infrastructure, and optimal historical and cultural preservation measures of the first multi-dimensional renovation plan are overlaid one by one onto the real-life map, thereby displaying the first multi-dimensional renovation plan on the specific real-life map. This ensures a precise match between the real-life map and the first multi-dimensional renovation plan and provides a more intuitive display of the specific renovation plan. The pre-renovation GIS model is updated based on the optimal building layout, optimal road network, optimal green space layout, optimal infrastructure, and optimal historical and cultural preservation measures of the first multi-dimensional renovation plan, thereby generating a post-renovation GIS model. It is readily apparent that the post-renovation GIS model represents the GIS model of the old village after the first multi-dimensional renovation plan. Overlaying the first multi-dimensional renovation plan onto the real-life map maps the post-renovation GIS model to the specific real-life space. This allows for spatial positional adjustments based on the compatibility between the real-life space and the first multi-dimensional renovation plan (e.g., if Area A of the first multi-dimensional renovation plan matches Area B of the real-life space, Area A of the first multi-dimensional renovation plan and Area B of the first multi-dimensional renovation plan are swapped).

[0190] The spatial performance simulation of the renovated GIS model included: space syntax analysis, calculating the integration degree based on the axis model to evaluate the changes in spatial accessibility after the renovation; sunlight and ventilation simulation, simulating the full window sunlight time on the winter solstice and performing fluid simulation to determine the ventilation performance between buildings; and finally, obtaining the spatial performance simulation results.

[0191] Traffic performance simulation of the remodeled GIS model includes dynamic traffic flow simulation and parking demand prediction, thereby obtaining corresponding traffic performance simulation results. Environmental performance simulation includes calculating carbon emissions through a carbon footprint calculation model and determining the corresponding stormwater drainage function through stormwater management simulation, thereby generating environmental performance simulation results. Economic performance evaluation includes cost-benefit analysis and analysis of related economic factors to obtain economic performance evaluation results. Historical and cultural value evaluation includes landscape integrity index evaluation and cultural space vitality evaluation, thereby generating historical and cultural value evaluation results. The traffic performance simulation results, environmental performance simulation results, economic performance evaluation results, and historical and cultural value evaluation results are integrated to generate a corresponding simulation evaluation report and renovation score. Based on the simulation evaluation report and renovation score, the corresponding correction items and correction values ​​are determined, and then the second multi-dimensional renovation plan is corrected.

[0192] In some optional embodiments, the differences between the old village to be renovated before and after the renovation are dynamically displayed through the post-renovation GIS model. Specifically, the dynamic simulation of the renovation process can be displayed according to demand, and the corresponding sub-area can be selected for dynamic simulation of the renovation, or multiple sub-areas can be selected for dynamic simulation of the renovation at different simulation speeds.

[0193] The beneficial effects of the present invention include: obtaining multi-source data of an old village to be renovated, the multi-source data including building distribution data, road network data, vegetation and water system data, infrastructure data and historical and cultural data; constructing a pre-renovation GIS model of the old village to be renovated based on the multi-source data, so that the pre-renovation GIS model can visually display the multi-source data on a map; obtaining a target renovation area of ​​the old village to be renovated after analyzing the multi-source data based on the pre-renovation GIS model; generating a first multi-dimensional renovation plan for the target renovation area according to renovation requirements; superimposing the first multi-dimensional renovation plan on a real-life map corresponding to the target renovation area, simulating the first multi-dimensional renovation plan to obtain a simulation result; correcting the first multi-dimensional renovation plan based on the simulation result to obtain a second multi-dimensional renovation plan, and transmitting the second multi-dimensional renovation plan to an operation and maintenance GIS platform. The system automatically generates a pre-renovation GIS model based on the multi-source data of the old village to be renovated, and obtains the specific target renovation area after corresponding data analysis, thereby generating a corresponding first multi-dimensional renovation plan based on the specific renovation needs, and the planning efficiency of the renovation plan is high; the first multi-dimensional renovation plan is corrected by superimposing it on the real-life map for simulation, thereby ensuring the accuracy and completeness of the renovation plan.

[0194] like Figure 2 As shown, Figure 21 shows a block diagram of a controller 1000 according to an embodiment of the present application. The components of the controller 1000 include, but are not limited to, a memory 1200 and a processor 1100. The processor 1100 and the memory 1200 are connected via a bus, and the memory 1200 is used to store data.

[0195] The controller 1000 also includes an access device that enables the controller 1000 to communicate via one or more networks. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 340 may include one or more of any type of network interface (e.g., a network interface card (NIC)) that may be wired or wireless, such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a World Wide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a universal serial bus (USB) interface, a cellular network interface, a Bluetooth interface, a near field communication (NFC) interface, and the like.

[0196] The controller 1000 may be any type of stationary or mobile electronic device, including a mobile computer or mobile electronic device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook computer, etc.), a mobile phone (e.g., a smartphone), a wearable electronic device (e.g., a smartwatch, smart glasses, etc.), or other types of mobile devices, or a stationary electronic device such as a desktop computer or PC. The controller 1000 may also be a mobile or stationary server.

[0197] Among them, the processor 1100 is used to execute computer executable instructions of the method for generating a planning scheme for old village renovation.

[0198] The above is a schematic diagram of a controller of this embodiment. It should be noted that the technical solution of this controller is based on the same concept as the technical solution of the method for generating a planning scheme for old village renovation described above. For details not described in detail in the technical solution of the controller, please refer to the description of the technical solution of the method for generating a planning scheme for old village renovation described above.

[0199] According to one embodiment of the present application, a planning scheme generation system for old village renovation is also provided. The planning scheme generation system for old village renovation includes a hospital bed, in which a controller 1000 is installed, or the hospital bed and the controller 1000 are connected via communication, so that the hospital bed can be adjusted by the controller 1000. It should be noted that the technical solution of the planning scheme generation system for old village renovation and the technical solution of the planning scheme generation method for old village renovation mentioned above are of the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the planning scheme generation method for old village renovation mentioned above.

[0200] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method for generating a planning scheme for old village renovation.

[0201] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may include a memory remotely located relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0202] Those skilled in the art will appreciate that all or some of the steps and systems described above can be implemented as software, firmware, hardware, or any combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, communication media typically includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0203] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above implementation mode. Technical personnel familiar with the art can also make various equivalent modifications or substitutions under the shared conditions that do not violate the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A method for generating a planning scheme for old village reconstruction, characterized in that: include: Obtain multi-source data on the old village to be renovated, including building distribution data, road network data, vegetation and water system data, infrastructure data, and historical and cultural data; Constructing a pre-renovation GIS model of the old village to be renovated based on the multi-source data, so that the pre-renovation GIS model can visually display the multi-source data on a map; Analyzing the multi-source data based on the pre-renovation GIS model to obtain the target renovation area of ​​the old village to be renovated; Specifically, it includes: determining the fire protection coefficient and traffic coefficient of each area according to the road network data and the building distribution data corresponding to each area on the GIS model before the transformation, wherein the fire protection coefficient represents the fire protection capability of each area, and the traffic coefficient represents the traffic capability of each area; generating a geological disaster risk distribution map according to the geological characteristic data corresponding to each area on the GIS model before the transformation, and determining the geological disaster risk coefficient of each area on the GIS model before the transformation based on the geological disaster risk distribution map; determining the building quality coefficient of each area according to the building distribution data corresponding to each area on the GIS model before the transformation; and determining the geological disaster risk coefficient of each area according to the geological characteristic data corresponding to each area on the GIS model before the transformation. The infrastructure improvement coefficient of each area is determined based on the infrastructure data; the greening coefficient of each area is determined based on the vegetation and water system data corresponding to each area on the GIS model before the transformation; the historical and cultural value coefficient of each area is determined based on the historical and cultural data corresponding to each area on the GIS model before the transformation; the transformation coefficient of each area on the GIS model before the transformation is determined based on the fire protection coefficient, the traffic coefficient, the geological disaster risk coefficient, the building quality coefficient, the infrastructure improvement coefficient, the greening coefficient and the historical and cultural value coefficient of each area on the GIS model before the transformation; the area whose transformation coefficient is greater than or equal to the preset coefficient is configured as the target transformation area; Determining a first priority ranking of each target sub-region according to the transformation coefficient corresponding to each target sub-region in the target transformation region; The benefit index of each target sub-region is obtained by evaluating the transformation benefit of each target sub-region through a preset economic benefit model; Modifying the first priority ranking according to the benefit index to obtain a second priority ranking; Determine the influence coefficients between the target sub-regions by using a network analysis method; A third priority ranking is obtained by modifying the second priority ranking according to the influence coefficient, so that each of the target sub-areas is transformed in turn according to the third priority ranking; generating a first multi-dimensional renovation plan for the target renovation area according to the renovation requirements; After superimposing the first multi-dimensional transformation plan on the real-life map corresponding to the target transformation area, simulating the first multi-dimensional transformation plan to obtain a simulation result; The first multi-dimensional transformation plan is modified according to the simulation results to obtain a second multi-dimensional transformation plan, and the second multi-dimensional transformation plan is transmitted to the operation and maintenance GIS platform.

2. The method for generating a planning scheme for old village renovation according to claim 1 is characterized in that: The step of constructing a pre-transformation GIS model of the old village to be transformed based on the multi-source data includes: Acquiring topographic data of the old village to be renovated; Constructing a terrain model of the old village to be renovated according to the terrain data; generating a building model on the terrain model according to the building distribution data, and displaying the building location, building shape, building equipment and building aging degree through the building model; generating a road model on the terrain model according to the road network data, and displaying road grade, road alignment, road capacity and road signs through the road network; generating a vegetation and water system model on the terrain model according to the vegetation and water system data, and displaying vegetation type, vegetation shape, vegetation coverage area and water system through the vegetation and water system model; generating an infrastructure model on the terrain model according to the infrastructure data, and displaying the infrastructure type, infrastructure location and infrastructure aging degree through the infrastructure model; After marking corresponding historical and cultural information on the building model, the road model, the vegetation and water system model, and the infrastructure model according to the historical and cultural data, the GIS model before renovation of the old village to be renovated is obtained.

3. The method for generating a planning scheme for old village renovation according to claim 2, characterized in that: Before generating the building model on the terrain model according to the building distribution data, the method further includes: After removing noise data, duplicate data and outliers from the multi-source data using a spatial data quality control algorithm, first intermediate data is obtained; Performing spatial matching on the first intermediate data using a feature matching algorithm so that the first intermediate data has spatial coordinates to obtain second intermediate data; A data prediction model is constructed based on historical data, where the historical data represents the second intermediate data of each period; The third intermediate data is obtained by predicting and supplementing the missing data in the second intermediate data through the data prediction model. The building distribution data used to generate the building model, the road network data used to generate the road model, the vegetation and water system data used to generate the vegetation and water system model, and the historical and cultural data used to mark the historical and cultural information all belong to the third intermediate data.

4. The method for generating a planning scheme for old village renovation according to claim 1, characterized in that: Generating a first multi-dimensional transformation plan for the target transformation area according to the transformation requirements includes: Determining a first demand level for each demand indicator in the transformation demand based on a preset demand level and a regional transformation target; obtaining a second demand level by modifying the first demand level according to public demand data, wherein the public demand data is collected through a public demand collection system; comparing the indicator difference maps of the demand indicator and the target indicator in sequence according to the second demand level, the target indicator representing the indicator corresponding to the demand indicator in the target transformation area; The first multi-dimensional renovation plan is generated based on the indicator difference map, including the optimal building layout, optimal road network, optimal greening layout, optimal infrastructure and optimal historical and cultural protection measures.

5. The method for generating a planning scheme for old village renovation according to claim 4 is characterized in that: The first multi-dimensional renovation plan for generating the optimal building layout, optimal road network, optimal greening layout, optimal infrastructure, and optimal historical and cultural protection measures based on the indicator difference map includes: Determining a building renovation target based on building index differences in the index difference map; determining the optimal building layout of the target renovation area according to the building renovation goal and the building differentiated renovation strategy, wherein the building differentiated renovation strategy represents a retention, repair, demolition or new construction strategy formulated according to building quality and the renovation needs; Determining a road reconstruction target based on the road index difference in the index difference map; The optimal road network is obtained by calculation according to the road reconstruction target, the shortest path algorithm and the minimum spanning tree algorithm; Determining a greening transformation target based on the greening index difference in the index difference map; Calculating the optimal greening layout according to the greening transformation goal and the greening layout algorithm; Determining infrastructure transformation targets based on infrastructure indicator differences in the indicator difference map; Calculating the optimal infrastructure according to the infrastructure transformation goals, the pipe network system planning algorithm, and the sponge city planning algorithm; Determining historical and cultural transformation targets based on historical and cultural indicator differences in the indicator difference map; The optimal historical and cultural protection measures are determined based on the historical and cultural transformation goals and differentiated buildings.

6. The method for generating a planning scheme for old village renovation according to claim 4 is characterized in that: After superimposing the first multi-dimensional transformation plan on the real-life map corresponding to the target transformation area, simulating the first multi-dimensional transformation plan to obtain a simulation result includes: Overlaying the optimal building layout, the optimal road network, the optimal greening layout, the optimal infrastructure, and the optimal historical and cultural protection measures of the first multi-dimensional renovation plan with the real-life map one by one, so that the real-life map accurately matches the first multi-dimensional renovation plan; Reconstructing the pre-transformation GIS model in sequence according to the optimal building layout, the optimal road network, the optimal greening layout, the optimal infrastructure, the optimal historical and cultural protection measures, and the third priority ranking to obtain a post-transformation GIS model; After performing spatial performance simulation, environmental performance simulation, traffic performance simulation, economic performance evaluation and historical and cultural value evaluation on the transformed GIS model, a simulation evaluation report and a transformation score are obtained. The simulation results include the simulation evaluation report and the transformation score.

7. A planning scheme generation system for old village reconstruction, characterized in that: The method comprises a controller, wherein the controller comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for generating a planning scheme for old village renovation as described in any one of claims 1 to 6 is implemented.

8. A computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, and the computer-executable instructions are used to execute the method for generating a planning scheme for old village renovation as described in any one of claims 1-6.

Images