Urban planning construction land management and control system based on big data

Abstract

The invention relates to the technical field of urban planning, and particularly discloses an urban planning construction land management and control system based on big data, and the system comprises the steps: obtaining geological environment, landform, engineering construction and human activity data through multi-source data collection; multi-source data fusion analysis is adopted to construct a construction land comprehensive evaluation field including geological stability, engineering suitability and environmental bearing capacity; a development suitability partition is generated based on a density peak value spatial clustering method; updating the development suitability level by dynamically monitoring the verification process; according to the method, accurate analysis and early warning of the three-dimensional deformation field are realized, and scientificity and safety of urban planning in a complex geological environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of urban planning, and particularly relates to a city planning construction land management and control system based on big data. BACKGROUND

[0002] With the advancement of smart city construction, city planning construction land management and control gradually adopts digital means such as remote sensing monitoring and geographic information systems. The existing technology mainly establishes a static land suitability evaluation system by integrating basic geographic information, geological survey reports and planning indicators to support the preliminary screening of construction land. However, such methods rely heavily on historical data and periodic manual surveys, and the response to dynamic changes in the geological environment is lagging behind, especially when faced with complex topography, hidden geological disasters and the superimposed influence of human activities, it is difficult to achieve fine and adaptive spatial management and risk warning.

[0003] Traditional deformation monitoring technology can only obtain one-dimensional deformation projection along the radar line-of-sight direction when analyzing complex three-dimensional deformation fields such as mountain filling slopes and tectonically active areas, resulting in insufficient sensitivity to horizontal creep displacement perpendicular to the radar direction. Such horizontal displacement is often an early key signal of landslide disasters, and its missed detection will result in the continuous accumulation of progressive instability risks until the sudden collapse, posing an irreversible safety threat to major projects such as large transportation hubs and energy facilities. SUMMARY

[0004] The purpose of the present application is to provide a city planning construction land management and control system based on big data to solve the problems in the above background.

[0005] The purpose of the present application can be achieved by the following technical solutions: The city planning construction land management and control system based on big data comprises: A multi-source data acquisition module for acquiring multi-source spatio-temporal monitoring data in the city planning area, including geological environment data, topographic data, engineering construction data and human activity data; A multi-source data fusion analysis module for fusion analysis of multi-source spatio-temporal monitoring data to build a construction land comprehensive evaluation field containing geological stability, engineering suitability and environmental carrying capacity; A development suitability assessment module based on the construction land comprehensive evaluation field to generate a construction land development suitability zoning, which includes the engineering construction risk level of different plots; A dynamic monitoring and early warning module, which starts a dynamic monitoring and verification process for a high-risk area according to the engineering construction risk level in the construction land development suitability zoning, and updates the development suitability level by analyzing the matching degree of real-time multi-source spatio-temporal monitoring data and geological evolution rules; A management and control scheme generation module, which generates differentiated development intensity control indicators and engineering protection schemes based on the updated development suitability level and in combination with urban planning indicators, and forms a city construction land management and control scheme.

[0006] As a further scheme of the present application: the construction land comprehensive evaluation field specifically comprises: The obtained geological environment data, topographic and geomorphic data, engineering construction data and human activity data are standardized to form pretreatment data with unified dimensions and coordinate reference; Based on the pretreatment data, three core indicators of geological environment coordination degree, engineering activity adaptation degree and environment bearing balance degree are calculated, wherein the geological environment coordination degree is obtained by analyzing the spatial consistency of geological structure features and surface deformation trend, the engineering activity adaptation degree is obtained by evaluating the matching degree of existing engineering distribution and topographic and geomorphic features, and the environment bearing balance degree is obtained by calculating the spatial overlap degree of human activity intensity and ecological sensitive area; The three core indicators are subjected to spatial superposition analysis to construct a comprehensive evaluation system, and a construction land comprehensive evaluation field with spatial continuous distribution characteristics is generated.

[0007] As a further scheme of the present application: the generation of the construction land development suitability zoning specifically comprises: Three-dimensional feature space is constructed by extracting evaluation values of geological stability, engineering suitability and environment bearing capacity in three dimensions from the construction land comprehensive evaluation field; A spatial clustering method based on density peaks is adopted to perform clustering analysis on all grid cells in the three-dimensional feature space, and spatial agglomeration areas with similar features are identified; Risk transmission path analysis is performed on each spatial agglomeration area, the risk diffusion range and influence intensity of each zoning are determined by calculating the mutual influence degree of regional geological conditions; According to the clustering results, the planning area is divided into three levels of priority construction area, conditional construction area and restricted construction area, and each zoning is assigned a corresponding engineering construction risk level identifier to form the final construction land development suitability zoning.

[0008] As a further scheme of the present application: the spatial clustering method based on density peaks is adopted to perform clustering analysis on all grid cells in the three-dimensional feature space, and spatial agglomeration areas with similar features are identified, specifically comprising: calculating local density and relative distance of each grid cell in three-dimensional feature space, wherein the local density is obtained by counting the number of adjacent cells within a statistically set neighborhood radius, and the relative distance is obtained by calculating the minimum spatial distance from the corresponding grid cell to all higher density cells; constructing a decision graph according to the local density and the relative distance, and automatically identifying cluster centers by setting a double requirement of a density threshold and a distance threshold, wherein the density threshold is the upper quartile of the local density of all grid cells, and the distance threshold is the median of the relative distance of all grid cells; sequentially assigning the remaining grid cells to the nearest cluster center in descending order of local density, to form a spatial aggregation area.

[0009] As a further scheme of the present application: the risk transmission path analysis is performed on each spatial aggregation area, the risk diffusion range and influence intensity of each subarea are determined by calculating the mutual influence degree of the geological conditions between areas, and specifically includes: constructing a geological influence network based on the geological structure characteristics of the spatial aggregation area, wherein the potential influence path between the aggregation areas is determined by analyzing the spatial connectivity of the geological structure surface and the spatial variability of the geotechnical mechanics parameters; calculating the influence intensity index between adjacent aggregation areas; identifying the main risk transmission path according to the influence intensity index and the geological influence network, and determining the risk influence range of each subarea through iterative calculation, to finally form a complete risk transmission path analysis result.

[0010] As a further scheme of the present application: the calculation of the influence intensity index between adjacent aggregation areas specifically includes: extracting a geological feature vector between adjacent aggregation areas based on the topological structure of the geological influence network, wherein the geological feature vector is composed of three elements, i.e., geological structure surface inclination similarity, geotechnical body strength difference rate and topographic slope correlation degree; calculating the similarity degree of the geological feature vectors of adjacent aggregation areas by using the vector cosine method, wherein the geological structure surface inclination similarity is taken as the dominant factor, the geotechnical body strength difference rate is taken as the adjustment factor, and the topographic slope correlation degree is taken as the correction factor; performing distance attenuation processing on the vector cosine calculation result according to the spatial distance between adjacent aggregation areas, and converting the spatial distance into a distance influence factor by using an inverse proportional function; multiplying the vector cosine calculation result and the distance influence factor to obtain the final influence intensity index.

[0011] As a further scheme of the present application: the updating of the development suitability grade specifically includes: A multi-source spatio-temporal monitoring data cube is constructed for the high-risk area, and the multi-source spatio-temporal monitoring data cube is composed of a time dimension, a space dimension and a monitoring index dimension; The multi-source spatio-temporal monitoring data cube is analyzed for trend consistency with the geological evolution rule, the matching degree of the change trajectory of the multi-source spatio-temporal monitoring data and the trend line of the geological evolution is calculated, and the trend matching degree is obtained; According to the size of the trend matching degree, the update strategy of the development suitability level is determined, when the trend matching degree is lower than the set threshold, the grade update process is automatically triggered, and the development suitability level of the high-risk area is adjusted to a new level corresponding to the actual geological condition.

[0012] As a further scheme of the application, the trend matching degree is obtained, specifically including: The time series data of the key monitoring index is extracted from the multi-source spatio-temporal monitoring data cube to form a monitoring data change trajectory curve; The geological evolution trend line of the corresponding time period is generated based on the geological evolution rule; The dynamic time warping algorithm is used to calculate the shape similarity between the monitoring data change trajectory curve and the geological evolution trend line, and the shape similarity is taken as a quantitative index of the trend matching degree.

[0013] As a further scheme of the application, the urban construction land management and control scheme is formed, specifically including: The corresponding relationship between the development suitability level and the planning control parameter is established, and the volume rate interval, the building density limit value and the green space rate requirement are determined according to different levels; Based on the engineering geological characteristics and the risk level, a hierarchical protection scheme is formulated, and the foundation treatment depth, the support structure form and the drainage system standard are determined for different levels of areas respectively; The development intensity control index is combined and matched with the engineering protection scheme to form a plurality of selectable technical scheme packages; According to the functional positioning and spatial layout requirements in the urban planning index, the optimal combination is selected from the technical scheme package to generate the final urban construction land management and control scheme.

[0014] The application has the following beneficial effects: (1) The complementary nature of multi-platform observation geometry is used to accurately separate the vertical settlement and horizontal creep components, and the geological structure characteristics and rock mechanics parameters are combined to realize the spatial positioning and displacement trend analysis of the potential landslide hidden danger area. The accuracy and timeliness of the geological disaster warning of the construction land are improved, and reliable deformation data support is provided for the engineering layout and management and control decision of the high-risk area.

[0015] (2) By constructing a multi-source spatiotemporal monitoring data cube, multiple indicators such as surface deformation, groundwater level, and soil stress are fused over time, and morphological similarity is calculated with the geological evolution trend line. When the matching degree is lower than the set threshold, the system automatically triggers level adjustment and control scheme optimization, forming a closed-loop management of "monitoring-evaluation-feedback-control". This dynamic mechanism improves the adaptability and resilience of urban planning under uncertain geological conditions, and avoids engineering errors and resource waste caused by data lag or model deviation. Attached Figure Description

[0016] The invention will now be further described with reference to the accompanying drawings.

[0017] Figure 1 This is a flowchart of the system of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 As shown, this invention is a big data-based urban planning and construction land management system, comprising: A multi-source data acquisition module is used to acquire multi-source spatiotemporal monitoring data within the urban planning area, including geological environment data, topographic data, engineering construction data, and human activity data. A multi-source data fusion analysis module is used to fuse and analyze multi-source spatiotemporal monitoring data to construct a comprehensive evaluation field for construction land that includes geological stability, engineering suitability, and environmental carrying capacity. A suitability assessment module is developed, which generates development suitability zones for construction land based on a comprehensive evaluation field for construction land. The development suitability zones for construction land include the engineering construction risk levels of different plots. The dynamic monitoring and early warning module initiates a dynamic monitoring and verification process for high-risk areas based on the engineering construction risk level in the development suitability zoning of construction land. By analyzing the matching degree between real-time multi-source spatiotemporal monitoring data and geological evolution patterns, the development suitability level is updated. The management and control scheme generation module generates differentiated development intensity control indicators and engineering protection schemes based on the updated development suitability level and combined with urban planning indicators, thus forming an urban construction land management and control scheme.

[0020] In the multi-source data acquisition module, the acquisition of geological environment data adopts a star-ground cooperative observation method. Satellite remote sensing data mainly comes from synthetic aperture radar satellites and optical remote sensing satellites, which obtain surface deformation information through differential interferometric measurement technology and analyze lithology distribution characteristics using multi-spectral images. Ground monitoring data are collected through the laid geological monitoring network, including surface displacement monitoring using total station, deep soil displacement monitoring using inclinometer, and underground water level change monitoring using pore water pressure gauge. These data collectively constitute the geological environment monitoring dataset, providing a basis for evaluating geological stability.

[0021] Topography data are obtained through a combination of airborne laser radar scanning and ground three-dimensional laser scanning. Airborne laser radar scanning uses multi-echo technology to obtain digital elevation models and digital surface models, and ground three-dimensional laser scanning conducts detailed topographic mapping of key areas to obtain high-precision topographic feature data. Through fusion processing of the two types of data, a three-dimensional topographic model with a precision better than 0.1 meters is generated, fully reflecting the regional topographic feature and landform morphology.

[0022] Engineering construction data come from the construction project approval database of planning management departments and field survey data. The approval database contains basic information such as the location, scale, structure type, and foundation form of construction projects, and field surveys use mobile mapping systems to collect field data on built projects, recording information such as actual use conditions and structural integrity of buildings. Through spatial positioning technology, various engineering construction data are unified into the same coordinate system to form an engineering construction spatial database.

[0023] Human activity data are obtained through multi-source spatio-temporal data fusion analysis. Night light data are used to identify human activity intensity distribution, mobile phone signaling data are used to analyze population density and flow characteristics, traffic monitoring data are used to obtain vehicle flow and distribution rules, and social media geolocation data are used to analyze human activity hotspots. These data are spatially processed to generate a dataset reflecting the spatio-temporal characteristics of human activities.

[0024] In the multi-source data fusion analysis module, first, standardize the multi-source data. The surface deformation rate in the geological environment data is unified to millimeters per year, and the rock-soil strength parameters are unified to megapascals. The elevation in the topography data is converted to meters, and the slope is converted to degrees. The building density and volume rate in the engineering construction data are converted to percentages. The population density in the human activity data is unified to people per square kilometer, and the economic density is unified to ten thousand yuan per mu. The spatial coordinates of all data are converted to the national geodetic coordinate system, and the grid size is set to 10 meters x 10 meters, forming a preprocessed dataset with consistent spatial reference.

[0025] The spatial consistency analysis method was used to calculate the geological environment compatibility. Based on geological structural zoning data, the relationship between fault zone density and surface deformation characteristics within each grid cell was analyzed. For each 10m × 10m grid cell, the total length of fault zones within a 500m radius was calculated, and the standard deviation of the surface deformation sequence for the most recent 30 periods within this region was also calculated. The ratio of fault zone density to the standard deviation of the deformation sequence was calculated, with fault zone density expressed in meters per square kilometer and deformation sequence standard deviation expressed in millimeters per year. A ratio less than 0.1 was considered to indicate high compatibility, while a ratio greater than 0.5 was considered to indicate low compatibility. The compatibility values ​​were normalized to the 0-1 range through linear transformation.

[0026] The suitability of engineering activities was assessed using a terrain adaptability analysis method. Terrain slope data and existing engineering distribution data were extracted. For each grid cell, the difference between its slope value and the average slope value of existing projects within a 100-meter radius was calculated. The difference was calculated using the absolute value method, with the unit being degrees. Simultaneously, the correlation between engineering distribution density and terrain undulation was analyzed. Engineering distribution density was expressed as the number of projects per square kilometer, and terrain undulation was expressed as the standard deviation of elevation. In areas with a slope less than 5 degrees, a correlation coefficient greater than 0.8 between engineering density and terrain undulation was considered a high suitability, while a correlation coefficient less than 0.3 was considered a low suitability. By establishing a comprehensive function of slope difference and correlation coefficient, the suitability metric was standardized to a range of 0-1.

[0027] The calculation of environmental carrying capacity equilibrium adopts a spatial overlay analysis method. The scope of ecologically sensitive areas includes water source protection areas, basic farmland, and ecological public welfare forests. Human activity intensity data is spatially overlaid with ecologically sensitive area distribution data. The human activity intensity index is calculated by comprehensively considering three indicators: population density, economic density, and construction intensity. Ecological sensitivity levels are classified into 1-5 levels according to the protection zone level. For each grid cell, the product of its human activity intensity index and ecological sensitivity level index is calculated. When both human activity intensity and ecological sensitivity level are high, the equilibrium value is lower; when human activity intensity or ecological sensitivity level is low, the equilibrium value is higher. A piecewise function is established to normalize the equilibrium value to the 0-1 interval.

[0028] The construction of the comprehensive evaluation field for construction land adopts a hierarchical comprehensive approach. Three indicators—geological environment compatibility, engineering activity suitability, and environmental carrying capacity balance—are assigned weights of 0.4, 0.35, and 0.25, respectively. The weight allocation is determined based on expert surveys and the analytic hierarchy process (AHP). For each 10m × 10m grid cell, a weighted summation method is used to calculate the comprehensive evaluation value. Specifically, each indicator value is multiplied by its corresponding weight, and then the three weighted values ​​are summed to obtain the comprehensive evaluation value for that grid cell. The comprehensive evaluation values ​​of all grid cells together constitute a spatially continuous comprehensive evaluation field for construction land, which fully characterizes the comprehensive level of geological stability, engineering suitability, and environmental carrying capacity at various locations within the planning area.

[0029] In the suitability assessment module, evaluation values ​​for three dimensions—geological stability, engineering suitability, and environmental carrying capacity—are extracted from the comprehensive evaluation field of construction land to construct a three-dimensional feature space. The position of each grid cell in the three-dimensional feature space is determined by its corresponding three evaluation values: the geological stability evaluation value serves as the first dimension coordinate, the engineering suitability evaluation value as the second dimension coordinate, and the environmental carrying capacity balance evaluation value as the third dimension coordinate. In this way, each 10m × 10m grid cell is mapped to a data point in the three-dimensional feature space, forming a three-dimensional dataset with a clear spatial correspondence.

[0030] A density-peak-based spatial clustering method is employed to perform cluster analysis on all raster cells in a three-dimensional feature space. First, the local density of each raster cell in the three-dimensional feature space is calculated. Local density is defined as the number of other cells contained within a spherical neighborhood centered at that cell with a radius of 50 units. Simultaneously, the relative distance of each cell is calculated; the relative distance is the minimum Euclidean distance from that cell to all cells with a higher local density. For the cell with the highest local density, its relative distance is taken as the maximum Euclidean distance to all other cells.

[0031] A decision map is constructed based on local density and relative distance. Cluster centers are automatically identified by setting both density and distance thresholds. The density threshold is the upper quartile of the local density of all raster cells, meaning that 75% of the cells have a local density below this value. The distance threshold is the median of the relative distances of all raster cells. Cells that simultaneously satisfy both the density and distance thresholds are identified as cluster centers. The remaining raster cells are then assigned to the nearest cluster center in descending order of local density, forming spatially consistent clusters.

[0032] Risk transmission path analysis was conducted for each spatial cluster. A geological impact network was constructed based on the geological structural characteristics of the clusters. By analyzing the spatial connectivity of geological structural surfaces, it was determined whether continuous geological structural surfaces connected the clusters. Simultaneously, the spatial variability of geotechnical parameters was analyzed, and the gradient variation characteristics of soil and rock mass strength between the clusters were calculated. Based on the connectivity of geological structural surfaces and the coefficient of variation of soil and rock mass strength, potential impact paths between the clusters were determined.

[0033] The influence intensity index between adjacent clusters is calculated. First, geological feature vectors between adjacent clusters are extracted. These vectors consist of three elements: the similarity of geological structural surface dips is obtained by calculating the cosine of the dip angles of the main structural surfaces in the two clusters; the difference rate of soil and rock strength is calculated by the ratio of the average soil and rock strengths of the two clusters; and the correlation of topographic slope is determined by the difference in the average slopes of the two clusters. The similarity of the geological feature vectors between adjacent clusters is calculated using the cosine of the vector angle, where the similarity of geological structural surface dips is assigned a weight of 0.5, the difference rate of soil and rock strength is assigned a weight of 0.3, and the correlation of topographic slope is assigned a weight of 0.2.

[0034] The calculation result of the cosine of the vector angle between adjacent clusters is subjected to distance attenuation processing. An inverse proportional function is used to convert the spatial distance into a distance influence factor. When two clusters are adjacent, the distance influence factor is 1; for every 100 meters increase in distance, the distance influence factor decreases according to the inverse proportional function. The result of the vector angle cosine calculation is multiplied by the distance influence factor to obtain the final influence intensity index, which ranges from 0 to 1.

[0035] Based on the impact intensity index and geological impact network, the main risk transmission paths are identified. The risk impact range of each zone is determined through iterative calculations. Starting from the risk source area, risk propagation calculations are performed along the connecting paths in the geological impact network, in order of the magnitude of the impact intensity index. An impact intensity index greater than 0.7 is considered to indicate strong risk transmission, an index between 0.3 and 0.7 is considered to indicate moderate risk transmission, and an index less than 0.3 is considered to indicate negligible risk transmission. Through multiple iterative calculations, the risk impact range boundary of each zone is determined.

[0036] Based on clustering results and risk transmission characteristics, the planning area is divided into three levels. Priority construction areas correspond to spatial clusters with high geological stability, high engineering suitability, and high environmental carrying capacity, and have a small risk transmission impact range. Conditional construction areas correspond to spatial clusters with medium comprehensive evaluation values, requiring construction plans to be determined based on specific engineering conditions. Restricted construction areas correspond to spatial clusters with low geological stability, low engineering suitability, or low environmental carrying capacity, or areas located on high-risk transmission paths. Each zone is assigned a corresponding engineering construction risk level label: priority construction areas are labeled low risk, conditional construction areas are labeled medium risk, and restricted construction areas are labeled high risk, forming the final land development suitability zoning map.

[0037] In the dynamic monitoring and early warning module, a multi-source spatiotemporal monitoring data cube is constructed for high-risk areas. The data cube consists of three dimensions: the time dimension uses days as the basic unit, recording monitoring data for 30 consecutive days; the spatial dimension uses 10m × 10m grid units, covering the entire high-risk area; and the monitoring indicator dimension includes three core indicators: surface deformation rate, groundwater level change, and soil and rock stress state. Surface deformation data is acquired via synthetic aperture radar satellites, collected daily; groundwater level data is acquired through deployed automatic monitoring wells, recorded every 6 hours; and soil and rock stress data is acquired through embedded stress gauges, recorded every 12 hours. All monitoring data are organized according to a unified spatial benchmark and time series to form a complete monitoring data cube.

[0038] Time series data of key monitoring indicators were extracted from a multi-source spatiotemporal monitoring data cube. For each 10m × 10m grid cell, a 30-day sequence of surface deformation rate, groundwater level change, and soil and rock stress state was extracted. The three sequences were normalized to eliminate dimensional effects and then weighted and fused with weights of 0.4, 0.3, and 0.3 respectively to form the monitoring data change trajectory curve for that grid cell. The horizontal axis of the curve represents the time series, and the vertical axis represents the weighted and fused comprehensive monitoring value, reflecting the continuous change process of the geological environment at that location.

[0039] Based on the laws of geological evolution, geological evolution trend lines are generated for corresponding time periods. The basic laws of geological evolution are determined according to regional geological survey data, including the normal range of surface deformation, the seasonal fluctuation characteristics of groundwater levels, and the long-term trend of soil and rock stress. For each grid cell, a theoretical geological evolution trend line is generated, taking into account its specific geological conditions. The trend line reflects the natural evolution path of geological environmental parameters at that location under the condition of no strong external interference, serving as a benchmark reference for assessing whether actual monitoring data is abnormal.

[0040] A dynamic time warping algorithm is used to calculate the morphological similarity between the monitoring data change trajectory curve and the geological evolution trend line. First, the two curves are aligned in time series by stretching or compressing the time axis to achieve optimal matching of key feature points. Then, the cumulative distance between the two curves under optimal matching conditions is calculated; this distance reflects the degree of morphological difference between the two sequences. The cumulative distance is converted into a similarity value within the range of 0-1 using an exponential function; this value represents the trend matching degree. The trend matching degree is 1 when the two curves are completely identical in shape, and the greater the morphological difference, the closer the trend matching degree is to 0.

[0041] The update strategy for development suitability levels is determined based on the magnitude of the trend matching degree. A threshold of 0.85 is set for the trend matching degree. When the calculated trend matching degree is lower than this threshold, it indicates a significant deviation between the actual monitoring data and geological evolution patterns, automatically triggering the level update process. During the update, the specific value of the trend matching degree and the degree of anomaly of the three monitoring indicators are comprehensively considered to adjust the development suitability level to a new level consistent with the actual geological conditions. The new level label is updated on the development suitability zoning map of construction land, and the update time and reason for adjustment are recorded, forming a complete level update record.

[0042] In the management and control plan generation module, a correspondence is established between development suitability levels and planning control parameters. For priority development areas, the plot ratio is controlled between 2.0 and 3.5, the building density does not exceed 35%, and the green space ratio is not less than 30%. For conditional development areas, the plot ratio is controlled between 1.5 and 2.5, the building density does not exceed 30%, and the green space ratio is not less than 35%. For restricted development areas, the plot ratio is controlled below 1.0, the building density does not exceed 25%, and the green space ratio is not less than 40%. These parameters are determined based on site engineering geological conditions and regional development planning requirements, and reasonable ranges for each level are obtained through expert evaluation and case analysis.

[0043] A tiered protection plan was developed based on engineering geological characteristics and risk levels. In priority construction areas, the foundation treatment depth is 2 meters below the bearing stratum, the support structure adopts soil nailing walls, and the drainage system is designed for a 5-year return period rainstorm. In areas with suitable conditions for construction, the foundation treatment depth is 3 meters below the bearing stratum, the support structure adopts pile walls, and the drainage system is designed for a 10-year return period rainstorm. In restricted construction areas, the foundation treatment depth is 5 meters below the bearing stratum, the support structure adopts diaphragm walls, and the drainage system is designed for a 20-year return period rainstorm. The determination of the protection level comprehensively considers factors such as topographic slope, soil and rock characteristics, and groundwater conditions, and its rationality is verified through engineering experience and numerical simulation analysis.

[0044] The development intensity control indicators are combined and matched with engineering protection schemes. Based on different development suitability levels, the plot ratio range, building density limits, green space ratio requirements, and corresponding foundation treatment depth, support structure type, and drainage system standards are combined to form three basic technical solution packages. Each solution package contains complete technical parameters and implementation requirements, and calculates the corresponding engineering cost estimate and construction period prediction. The combination of solution packages follows the principles of technical feasibility and economic rationality, and the coordination and consistency of various technical indicators are ensured through multi-scheme comparison.

[0045] The optimal combination of plans is selected based on the functional positioning and spatial layout requirements in urban planning indicators. A scheme evaluation index system is established, comprehensively considering planning elements such as regional functional positioning, traffic organization requirements, municipal facility capacity, and environmental protection standards. An expert scoring method is used to comprehensively evaluate each technical scheme package, and the scheme with the highest comprehensive score is selected as the final implementation plan. During the scheme determination process, emphasis is placed on the overall coordination and sustainable development requirements of the planning area, ensuring that the final scheme meets current construction needs while leaving room for future development. The final urban construction land management plan includes detailed technical parameters, implementation standards, and safeguard measures, providing a direct basis for planning management.

[0046] The working principle of this invention is as follows: Geological environment, topography, engineering construction, and human activity data are acquired through multi-source data collection. Standardized processing and fusion analysis are then used to construct a comprehensive evaluation field for construction land, encompassing geological stability, engineering suitability, and environmental carrying capacity. Based on this, a spatial clustering method based on density peaks is used to identify spatially clustered areas with similar characteristics. Risk transmission path analysis is used to divide the area into priority construction zones, conditional construction zones, and restricted construction zones, and risk levels are identified. For high-risk areas, a spatiotemporal monitoring data cube is constructed. A dynamic time warping algorithm is used to analyze the matching degree between monitoring data and geological evolution patterns, enabling dynamic updates to the development suitability level. Finally, combined with urban planning indicators, differentiated development intensity control indicators and engineering protection schemes are generated, forming a scientifically sound urban construction land management plan.

[0047] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A big data-based urban planning and construction land management system, characterized in that: include: A multi-source data acquisition module is used to acquire multi-source spatiotemporal monitoring data within the urban planning area, including geological environment data, topographic data, engineering construction data, and human activity data. A multi-source data fusion analysis module is used to fuse and analyze multi-source spatiotemporal monitoring data to construct a comprehensive evaluation field for construction land that includes geological stability, engineering suitability, and environmental carrying capacity. A suitability assessment module is developed, which generates development suitability zones for construction land based on a comprehensive evaluation field for construction land. The development suitability zones for construction land include the engineering construction risk levels of different plots. The dynamic monitoring and early warning module initiates a dynamic monitoring and verification process for high-risk areas based on the engineering construction risk level in the development suitability zoning of construction land. By analyzing the matching degree between real-time multi-source spatiotemporal monitoring data and geological evolution patterns, the development suitability level is updated. The management and control scheme generation module generates differentiated development intensity control indicators and engineering protection schemes based on the updated development suitability level and combined with urban planning indicators, thus forming an urban construction land management and control scheme.

2. The urban planning and construction land management system based on big data according to claim 1, characterized in that, The comprehensive evaluation site for construction land specifically includes: The acquired geological environment data, topographic data, engineering construction data, and human activity data are standardized to form preprocessed data with unified dimensions and coordinate benchmarks; Based on the preprocessed data, three core indicators were calculated: geological environment coordination degree, engineering activity adaptability degree, and environmental carrying capacity balance degree. Geological environment coordination degree was obtained by analyzing the spatial consistency between geological structural features and surface deformation trends. Engineering activity adaptability degree was obtained by assessing the matching degree between the distribution of existing projects and topographic features. Environmental carrying capacity balance degree was obtained by calculating the spatial overlap between human activity intensity and ecologically sensitive areas. By spatially overlaying and analyzing the three core indicators, a comprehensive evaluation system is constructed, generating a comprehensive evaluation field for construction land with spatially continuous distribution characteristics.

3. The urban planning and construction land management system based on big data according to claim 1, characterized in that, The generation of suitable development zones for construction land specifically includes: Evaluation values ​​for three dimensions—geological stability, engineering suitability, and environmental carrying capacity—are extracted from the comprehensive evaluation field of construction land to construct a three-dimensional feature space. A spatial clustering method based on density peaks is used to perform cluster analysis on all grid cells in a three-dimensional feature space to identify spatial clusters with similar characteristics. Risk transmission path analysis is conducted for each spatial cluster area. By calculating the degree of mutual influence of geological conditions between regions, the risk diffusion range and impact intensity of each zone are determined. Based on the clustering results, the planning area is divided into three levels: priority construction area, conditional construction area, and restricted construction area. Each zone is assigned a corresponding engineering construction risk level label, forming the final construction land development suitability zone.

4. The urban planning and construction land management system based on big data according to claim 3, characterized in that, The method employs a density peak-based spatial clustering approach to perform cluster analysis on all grid cells in a three-dimensional feature space, identifying spatial clusters with similar characteristics. Specifically, this includes: Calculate the local density and relative distance of each grid cell in the three-dimensional feature space. The local density is obtained by statistically counting the number of neighboring cells within a set neighborhood radius, and the relative distance is obtained by calculating the minimum spatial distance from the corresponding grid cell to all higher density cells. A decision map is constructed based on local density and relative distance. Cluster centers are automatically identified by setting both density and distance thresholds. The density threshold is the upper quartile of the local density of all grid cells, and the distance threshold is the median of the relative distance of all grid cells. The remaining grid cells are distributed to the nearest cluster centers in descending order of local density, forming spatial clusters.

5. The urban planning and construction land management system based on big data according to claim 3, characterized in that, The risk transmission path analysis for each spatial cluster area, by calculating the degree of mutual influence of geological conditions between regions, determines the risk diffusion range and impact intensity of each zone, specifically including: Based on the geological structural characteristics of spatial clusters, a geological influence network is constructed. By analyzing the spatial connectivity of geological structural surfaces and the spatial variability of geotechnical parameters, potential influence paths between clusters are determined. Calculate the influence intensity index between adjacent clusters; Based on the impact intensity index and geological impact network, the main risk transmission paths are identified, and the risk impact range of each zone is determined through iterative calculation, ultimately forming a complete risk transmission path analysis result.

6. The urban planning and construction land management system based on big data according to claim 5, characterized in that, The calculation of the influence intensity index between adjacent clusters specifically includes: Based on the topology of the geological influence network, geological feature vectors between adjacent clusters are extracted. The geological feature vectors are composed of three elements: similarity of geological structural surface dip, difference rate of rock and soil strength, and correlation of topographic slope. The similarity of geological feature vectors between adjacent agglomeration areas is calculated using the vector angle cosine method, with the similarity of geological structural surface dip as the dominant factor, the difference rate of rock and soil strength as the moderating factor, and the correlation of topographic slope as the correction factor. Based on the spatial distance between adjacent clusters, the cosine of the vector angle is calculated and then attenuated. An inverse proportional function is used to convert the spatial distance into a distance influence factor. The final influence intensity index is obtained by multiplying the result of the cosine of the vector angle by the distance influence factor.

7. The urban planning and construction land management system based on big data according to claim 1, characterized in that, The updated development suitability level specifically includes: A multi-source spatiotemporal monitoring data cube is constructed for high-risk areas. The multi-source spatiotemporal monitoring data cube is composed of time dimension, spatial dimension and monitoring indicator dimension. A trend consistency analysis was conducted between the multi-source spatiotemporal monitoring data cube and the geological evolution law. The trend matching degree was obtained by calculating the degree of matching between the change trajectory of the multi-source spatiotemporal monitoring data and the geological evolution trend line. The development suitability level is updated based on the trend matching degree. When the trend matching degree is lower than the set threshold, the level update process is automatically triggered, and the development suitability level of high-risk areas is adjusted to a new level that matches the actual geological conditions.

8. The urban planning and construction land management system based on big data according to claim 7, characterized in that, The process of obtaining the trend matching degree specifically includes: Time series data of key monitoring indicators are extracted from multi-source spatiotemporal monitoring data cubes to form monitoring data change trajectory curves; Geological evolution trend lines for corresponding time periods are generated based on geological evolution patterns; The dynamic time warping algorithm is used to calculate the morphological similarity between the change trajectory curve of the monitoring data and the geological evolution trend line, and the morphological similarity is used as a quantitative indicator of the trend matching degree.

9. The urban planning and construction land management system based on big data according to claim 1, characterized in that, The aforementioned urban construction land management and control plan specifically includes: Establish a correspondence between development suitability levels and planning control parameters, and determine the corresponding plot ratio range, building density limit and green space ratio requirements according to different levels; Based on engineering geological characteristics and risk levels, a graded protection plan was developed, and the foundation treatment depth, support structure form and drainage system standards were determined for different levels of areas. By combining and matching development intensity control indicators with engineering protection schemes, multiple optional technical solution packages can be formed; Based on the functional positioning and spatial layout requirements in the urban planning indicators, the optimal combination is selected from the technical solution package to generate the final urban construction land management plan.

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