Method and system for collecting regional spatial planning data based on multi-point monitoring

Abstract

This invention discloses a method and system for acquiring regional spatial planning data based on multi-point monitoring, relating to the field of spatial planning technology. The method includes: before regional spatial planning, continuously sampling the environmental status of each unit area after the target area is divided into grids to obtain environmental status data information; analyzing the degree of concentrated abrupt changes in topographic disturbances in each unit area during the sampling period, and screening out topographic abrupt change unit areas; analyzing the degree of concentrated abrupt changes in microclimate in each topographic abrupt change unit area during the sampling period, and analyzing the richness of ecological structure in each topographic abrupt change unit area by scanning the apparent images of each topographic abrupt change unit area; based on the degree of concentrated abrupt changes in microclimate and the richness of ecological structure in each topographic abrupt change unit area, analyzing the comprehensive degradation risk level of each topographic abrupt change unit area, and generating a regional spatial planning report of the corresponding level. This invention provides real-time and accurate decision-making basis for regional spatial planning.

Description

Technical Field

[0001] This invention relates to the field of spatial planning technology, specifically to a method and system for acquiring regional spatial planning data based on multi-point monitoring. Background Technology

[0002] As ecological civilization construction and the national spatial governance system increasingly become national strategic priorities, the demand for more scientific, refined, and dynamic regional spatial planning is constantly rising. Traditional static planning models are no longer adequate to meet the diverse needs of ecological security, land resource protection, and sustainable urban development. Against this backdrop, data acquisition technology based on multi-point monitoring, with its high timeliness, high resolution, and multi-dimensional data fusion capabilities, is gradually becoming a key supporting means for promoting the transformation and upgrading of spatial planning technology. Especially in emerging application scenarios such as intelligent land sensing and dynamic ecological assessment, this technology has good adaptability and expansion potential, and can provide real-time, accurate, and systematic decision-making basis for future regional spatial governance. It is foreseeable that building a dynamic data acquisition system based on multi-point monitoring will become the core direction of the development of the next-generation spatial planning platform, with broad industrial application prospects and social benefits.

[0003] In current regional spatial planning practices, most existing data collection methods still rely primarily on manual ground patrols. This results in long information update cycles and insensitivity to small-scale topographic or ecological changes. Furthermore, it struggles to capture rapidly occurring micro-changes within the region, particularly in areas of topographic disturbance, microclimate shifts, and dynamic adjustments in ecological structure. Traditional data collection methods often rely on single environmental factors, lacking cross-integration analysis across the "topography-climate-ecology" dimensions. This directly leads to low accuracy in identifying risk areas within the planning region. Moreover, due to the lack of time-series data, spatial planning reports often lag behind environmental changes, making it difficult to provide early warnings of regional degradation risks. This directly results in insufficient identification of regional degradation risks and delayed response mechanisms during the planning process, thus affecting the scientific validity and feasibility of regional planning schemes. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for acquiring regional spatial planning data based on multi-point monitoring, thus solving the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a regional spatial planning data acquisition method based on multi-point monitoring, comprising the following steps: S1. Before regional spatial planning, continuously sample the environmental status of each unit area after the target area is divided into grids to obtain environmental status data information. S2. Based on environmental status data, analyze the degree of concentrated abrupt changes in terrain disturbance in each unit area during the sampling period, and screen out the unit areas with abrupt terrain changes. S3. Based on the selected topographic abrupt change unit regions, analyze the degree of concentrated abrupt change in microclimate in each topographic abrupt change unit region during the sampling period, and analyze the richness of ecological structure in each topographic abrupt change unit region by scanning the apparent images of each topographic abrupt change unit region. S4. Based on the degree of concentrated microclimate abrupt change and the richness of ecological structure in each topographic abrupt change unit region, analyze the comprehensive degradation risk of each topographic abrupt change unit region, generate a regional spatial planning report of the corresponding level, and then perform corresponding regional spatial planning operations on the target region.

[0006] Preferably, step S1 specifically includes: S11. Before regional spatial planning, the target area is divided into grids to obtain several unit areas. The center of each unit area in the target area is used as the monitoring point to obtain the static monitoring point of the corresponding unit area in the target area. Based on the multiple sets of environmental monitoring equipment deployed at the static monitoring points in each unit area, the environmental status of each unit area is continuously sampled within the set sampling period to obtain environmental status data information. The environmental monitoring equipment includes multiple sets of lidar ranging sensors, infrared thermal radiation sensors, temperature sensors, and resistive soil moisture sensors. The environmental status data includes the elevation, surface temperature, and surface soil moisture values ​​of each unit area at each sampling time point.

[0007] Preferably, step S2 specifically includes: S21. Analyze the trend of terrain elevation disturbance rate change in each unit area during the sampling period, and obtain the terrain disturbance trend value of each unit area during the sampling period, specifically including: S211. Perform feature recognition on the environmental status data information, take the elevation value corresponding to the first sampling time point of each unit area as the initial elevation value of each unit area in the sampling period, take the elevation value corresponding to the last sampling time point of each unit area as the final elevation value of each unit area in the sampling period, calculate the difference between the initial elevation value and the final elevation value of each unit area in the sampling period to obtain the elevation change difference of each unit area in the sampling period; S212. Calculate the ratio of the elevation change difference of each unit area within the sampling period to the sampling period duration. After dimensionless processing, analyze the trend of the terrain elevation disturbance rate change of each unit area within the sampling period and obtain the terrain disturbance trend value of each unit area within the sampling period. S22. Taking each unit area as the central unit area, select the eight neighboring grid units surrounding each central unit area as the local comparison area of ​​the corresponding central unit area to obtain the corresponding local comparison area of ​​each central unit area. Record the end elevation value of each neighboring grid unit area in the corresponding local comparison area, and calculate the local difference component with the end elevation value of the corresponding central unit area. Analyze the disturbance difference between the terrain height of each unit area and the terrain height of the surrounding neighboring grid units during the sampling period, and determine the terrain disturbance difference value of each unit area during the sampling period. S23. Correlate the topographic disturbance trend value and topographic disturbance difference value of each unit area within the sampling period. After dimensionless processing, analyze the degree of concentrated abrupt change in topographic disturbance in each unit area within the sampling period, and determine the concentrated abrupt change value of topographic disturbance in each unit area within the sampling period, specifically: In the formula, This represents the concentrated abrupt change value of the terrain in the i-th unit region during the sampling period. This represents the terrain disturbance trend value of the i-th cell region within the sampling period. This represents the difference in terrain disturbance value of the i-th unit region within the sampling period.

[0008] Preferably, step S2 further includes: S24. Based on the concentrated abrupt change values ​​of terrain in each unit area within the target area during the sampling period, and combined with the statistical mean-averaging algorithm, obtain the mean value of concentrated abrupt change in terrain. S25. Compare and analyze the concentrated abrupt change value of the terrain in each unit area with the average concentrated abrupt change value of the terrain in the sampling period. If the concentrated abrupt change value of the terrain in the corresponding unit area exceeds the average concentrated abrupt change value of the terrain in the sampling period, mark the corresponding unit area as a terrain abrupt change unit area and record the concentrated abrupt change value of the terrain in the corresponding terrain abrupt change unit area. Otherwise, mark the corresponding unit area as a normal terrain unit area.

[0009] Preferably, step S3 specifically includes: S31. Analyze the trend of surface temperature fluctuation rate in each abrupt change unit region within the sampling period, and determine the temperature fluctuation trend value of each abrupt change unit region, specifically including: S311. Based on the marked terrain abrupt change unit regions, perform feature recognition on the environmental state data information, extract the highest and lowest surface temperature values ​​of each terrain abrupt change unit region within the sampling period, sum the highest and lowest surface temperature values ​​of each terrain abrupt change unit region within the sampling period, and obtain the surface temperature range value of each terrain abrupt change unit region within the sampling period. S312. Calculate the ratio of the surface temperature range of each topographic abrupt change unit region within the sampling period to the sampling period duration, and after dimensionless processing, analyze the trend of surface temperature fluctuation rate of each topographic abrupt change unit region within the sampling period to obtain the temperature fluctuation trend value of each topographic abrupt change unit region within the sampling period.

[0010] Preferably, step S3 further includes: S32. Compare and analyze the surface soil moisture values ​​at each sampling time point in each topographic abrupt change unit area to determine the differences in surface moisture values ​​among different topographic abrupt change unit areas, specifically including: S321. Perform feature identification on environmental status data information, extract the surface soil moisture value of each topographic abrupt change unit area at each sampling time point, compare the surface soil moisture value of the corresponding topographic abrupt change unit area at each sampling time point with the set reference surface soil moisture range, if the surface soil moisture value of the corresponding sampling time point is greater than the set reference surface soil moisture range, then the corresponding sampling time point is recorded as a high humidity sampling time point, if the surface soil moisture value of the corresponding sampling time point is less than the set reference surface soil moisture range, then the corresponding sampling time point is recorded as a low humidity sampling time point. S322. Count the total number of low-humidity sampling time points and high-humidity sampling time points in each topographic change unit area. Take the ratio of the total number of low-humidity sampling time points and high-humidity sampling time points in each topographic change unit area to the total number of sampling time points in the corresponding topographic change unit area within the sampling period as the surface humidity difference value of each topographic change unit area within the sampling period.

[0011] Preferably, step S3 further includes: S33. Correlate the temperature fluctuation trend values ​​and surface humidity differences of each topographic abrupt change unit region within the sampling period. After dimensionless processing, analyze the degree of concentrated abrupt changes in microclimate within each topographic abrupt change unit region within the sampling period, and determine the microclimate abrupt change values ​​of each topographic abrupt change unit region within the sampling period. Specifically: In the formula, This represents the microclimate abrupt change value of the j-th topographic abrupt change unit region within the sampling period. and These represent the temperature fluctuation trend and surface humidity difference values ​​of the j-th terrain abrupt change unit region during the sampling period, respectively.

[0012] Preferably, step S3 further includes: S34. By scanning the apparent images of each topographic abrupt change unit region with an optical camera, analyze the ecological structure richness of each topographic abrupt change unit region, and determine the ecological richness coefficient of each topographic abrupt change unit region, specifically including: S341. Based on the marked terrain change unit areas, and in conjunction with the UAV cruise image system, use the optical camera in the UAV cruise image system to scan the apparent images of each terrain change unit area to obtain the apparent images of each terrain change unit area. S342. Match the appearance images of each terrain mutation unit region with the appearance images corresponding to each preset appearance type to obtain the appearance category of each terrain mutation unit region. The appearance type includes vegetation cover ground category and bare ground category. S343. Classify the corresponding topographic abrupt change unit regions into blocks according to their apparent categories, obtain each vegetation-covered ground block and each bare ground block in each topographic abrupt change unit region, and extract the area of ​​each vegetation-covered ground block and the area of ​​each bare ground block from the apparent images of each topographic abrupt change unit region. Add up the areas of each vegetation-covered ground block and the area of ​​each bare ground block in each topographic abrupt change unit region to obtain the total area of ​​vegetation-covered ground blocks and the total area of ​​bare ground blocks in each topographic abrupt change unit region. S344. Based on the vegetation-covered ground blocks and bare ground blocks obtained in each terrain mutation unit area, count the number of vegetation-covered ground blocks and the number of bare ground blocks in each terrain mutation unit area. S345. Correlate the total area of ​​vegetation-covered ground blocks, the total area of ​​bare ground blocks, the number of vegetation-covered ground blocks, and the number of bare ground blocks in each topographic abrupt change unit region. After dimensionless processing, analyze the ecological richness of each topographic abrupt change unit region and determine the ecological richness coefficient of each topographic abrupt change unit region, specifically: ; This represents the ecological richness coefficient of the j-th abrupt topographic change unit. and Let represent the number of vegetation-covered ground patches and the number of bare ground patches in the j-th terrain abrupt change unit region, respectively. and Let represent the total area of ​​vegetation-covered ground blocks and the total area of ​​bare ground blocks in the j-th topographic abrupt change unit region, respectively.

[0013] Preferably, step S4 specifically includes: S41. Correlate the concentrated topographic abrupt change values ​​and microclimate abrupt change values ​​of each topographic abrupt change unit region, and combine them with the ecological richness coefficient of the corresponding topographic abrupt change unit region. After dimensionless processing, analyze the comprehensive degradation risk degree of each topographic abrupt change unit region, and determine the degradation risk degree index of each topographic abrupt change unit region, specifically: In the formula, This represents an index indicating the degree of degradation risk in the j-th abrupt topographic change unit region. , and These represent the topographic abrupt change value, microclimate abrupt change value, and ecological richness coefficient of the j-th topographic abrupt change unit region, respectively. S42. Compare and analyze the degradation risk index of each terrain mutation unit region with the preset risk threshold, screen out the corresponding degradation risk unit regions and mark them, specifically including: If the degradation risk index of the corresponding topographic abrupt change unit area exceeds the risk threshold, it indicates that the corresponding topographic abrupt change unit area is in a degradation risk state, and the corresponding topographic abrupt change unit area is marked as a degradation risk unit area. If the degradation risk index of the corresponding topographic abrupt change unit area does not exceed the risk threshold, it means that the corresponding topographic abrupt change unit area is not in a degradation risk state. In this case, the corresponding topographic abrupt change unit area will not be marked separately. S43. Count the marked degradation risk unit areas to obtain the total number of degradation risk unit areas. If the total number of degradation risk unit areas exceeds 5% of the total number of unit areas in the target area, generate a first-level regional spatial planning report with the content: the current target area needs to perform regional spatial planning operations; if the total number of degradation risk unit areas does not exceed 5% of the total number of unit areas in the target area, generate a second-level regional spatial planning report with the content: the current target area does not need to perform regional spatial planning operations.

[0014] A regional spatial planning data acquisition system based on multi-point monitoring includes a division module, a terrain screening module, a regional analysis module, and a planning determination module. The partitioning module is used to continuously sample the environmental status of each unit area after the target area is divided into grids before regional spatial planning, and to obtain environmental status data information. The terrain screening module analyzes the degree of concentrated abrupt changes in terrain disturbances in each unit area within the sampling period based on environmental status data, and screens out the unit areas with abrupt terrain changes. The regional analysis module analyzes the degree of concentrated microclimate changes in each selected topographic abrupt change unit region during the sampling period, and analyzes the richness of the ecological structure of each topographic abrupt change unit region by scanning the apparent images of each region. The planning and judgment module analyzes the comprehensive degradation risk of each topographic abrupt change unit region based on the degree of concentrated microclimate change and the richness of its ecological structure, generates a regional spatial planning report of the corresponding level, and then performs the corresponding regional spatial planning operation on the target region.

[0015] This invention provides a method and system for acquiring regional spatial planning data based on multi-point monitoring, which has the following beneficial effects: (1) By constructing a multi-point monitoring data collection and analysis system that links the three indicators of “topographic disturbance, microclimate change, and ecological structure”, the fundamental transformation of regional spatial planning from the static and single-dimensional traditional model to the dynamic, multi-dimensional, and refined direction has been realized. It can not only collect environmental status information of the target area in real time and continuously, but also conduct in-depth fusion analysis of topographic change, climate fluctuation and ecological structure evolution through a series of quantitative algorithms. It can also achieve accurate identification and planning response of risk unit areas based on the comprehensive degradation risk index, which significantly improves the ability to perceive natural environmental changes and risk warning. Compared with the traditional method of relying on manual inspection, this invention has the advantages of high timeliness, high resolution and high integration, effectively avoiding the problems of lagging spatiotemporal response, single information dimension and large judgment error of existing methods, thereby enhancing the scientificity, systematicness and execution of regional spatial planning, and has broad application and promotion prospects and policy support potential.

[0016] (2) In terms of terrain disturbance identification, a dual judgment mechanism based on the initial and final elevation difference and the comparison of neighboring grids is introduced. After the target area is divided into grids, each unit area is continuously sampled. The elevation information of the initial and final sampling time points is extracted using high-frequency lidar data. Combined with the trend of disturbance rate change and the calculation of neighboring grid differences, a set of terrain concentrated abrupt change identification model based on trend value and difference value is established. It can not only effectively capture subtle local landform changes, but also quantify the degree of abrupt change in terrain disturbance of each unit area through spatial comparison, breaking through the bottleneck that traditional manual inspection is difficult to cover micro-scale disturbances. In particular, through the dimensionless standardized index, the terrain changes between different time periods and different areas can be compared horizontally, laying an accurate spatial foundation for subsequent microclimate and ecological structure assessment. This method has good adaptability and responsiveness in areas with slope development and soil erosion.

[0017] (3) In terms of microclimate change and ecological structure identification, a variety of non-traditional parameters are introduced, including temperature fluctuation trend value, humidity difference ratio and ecological richness coefficient. Through the fusion processing of multi-source sensing data, the limitations of traditional methods that rely only on a single meteorological or vegetation factor are broken through. Specifically, the temperature fluctuation trend is quantified by calculating the ratio of surface temperature range to sampling period length. The soil humidity difference value is formed by statistically analyzing the proportion of high and low humidity time points. Then, the area and quantity of surface vegetation and bare ground classification data are accumulated by combining UAV image system to form ecological richness coefficient. Finally, the microclimate change value, topographic concentrated change value and ecological richness coefficient are comprehensively correlated to calculate the degradation risk index and compare it with the set threshold to form a dynamic risk identification model. Compared with the existing technical path that mainly uses single parameter division, this multi-dimensional fusion assessment method has higher accuracy and foresight in identifying regional degradation risk areas. It not only avoids the problem of misjudgment caused by the abnormality of a certain parameter, but also provides comprehensive early warning for ecologically vulnerable points under the cross influence of multiple factors. It significantly improves the scientificity and controllability of regional spatial planning and helps the intelligent transformation of ecological civilization construction and land space governance. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the block diagram of the regional spatial planning data acquisition method and system based on multi-point monitoring of the present invention; Figure 2 This is a schematic diagram of the block diagram of the regional spatial planning data acquisition method and system based on multi-point monitoring of the present invention; Figure 3 This is a graph showing the topographic disturbance trend value, topographic disturbance difference value, and topographic concentrated abrupt change value of each unit area in S23 during the sampling period. Detailed Implementation

[0019] 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.

[0020] Example 1: Please see Figure 1 This invention provides a method for acquiring regional spatial planning data based on multi-point monitoring, comprising the following steps: S1. Before regional spatial planning, continuously sample the environmental status of each unit area after the target area is divided into grids to obtain environmental status data information. S2. Based on environmental status data, analyze the degree of concentrated abrupt changes in terrain disturbance in each unit area during the sampling period, and screen out the unit areas with abrupt terrain changes. S3. Based on the selected topographic abrupt change unit regions, analyze the degree of concentrated abrupt change in microclimate in each topographic abrupt change unit region during the sampling period, and analyze the richness of ecological structure in each topographic abrupt change unit region by scanning the apparent images of each topographic abrupt change unit region. S4. Based on the degree of concentrated microclimate abrupt change and the richness of ecological structure in each topographic abrupt change unit region, analyze the comprehensive degradation risk of each topographic abrupt change unit region, generate a regional spatial planning report of the corresponding level, and then perform corresponding regional spatial planning operations on the target region.

[0021] In this embodiment, by constructing a technical process centered on continuous sampling, trend analysis, and multi-dimensional fusion assessment, the problem of data update lag, dependence on single environmental factors, and insensitivity to micro-changes in existing technologies is effectively solved. First, this method uses high-frequency and multi-dimensional environmental state sampling to acquire real-time key data on elevation, temperature, and humidity of each unit within the region, breaking the limitations of traditional manual inspections that rely on static observation. Then, by combining terrain disturbance trend analysis and neighborhood difference quantification, dynamic identification of terrain abrupt change units is achieved, effectively improving the ability to perceive local terrain anomalies. Furthermore, through a comprehensive analysis model of microclimate and ecological structure, the technical bottleneck of assessing degradation risk using only single factors is overcome, achieving a fusion judgment of the "topography-climate-ecology" three dimensions. Finally, by calculating a comprehensive degradation risk index and generating a planning level report, regional spatial planning can possess dynamic early warning and forward-looking decision-making capabilities, thereby truly realizing the refinement and intelligence of planning methods, significantly improving the scientific nature, timeliness, and feasibility of land spatial governance, and meeting the urgent need for high-quality development of spatial planning under the current background of ecological civilization construction.

[0022] Example 2: Please refer to Figure 1 Specifically: S1 includes the following steps: S11. Before regional spatial planning, the target area is divided into grids to obtain several unit areas. The center of each unit area in the target area is used as the monitoring point to obtain the static monitoring point of the corresponding unit area in the target area. Based on the multiple sets of environmental monitoring equipment deployed at the static monitoring points in each unit area, the environmental status of each unit area is continuously sampled within the set sampling period to obtain environmental status data information. The environmental monitoring equipment includes multiple sets of lidar ranging sensors, infrared thermal radiation sensors, temperature sensors, and resistive soil moisture sensors. The environmental status data includes the elevation, surface temperature, and surface soil moisture values ​​of each unit area at each sampling time point.

[0023] In this embodiment, before regional spatial planning, the target area is systematically divided into grids, and multiple sets of environmental monitoring equipment are deployed at the center of each unit to construct a high-density and high-frequency static monitoring point network. This effectively solves the key problems of traditional manual inspection methods, such as long information acquisition cycles, limited spatial coverage, and untimely response to minor changes. Through the combined deployment of lidar ranging sensors, infrared thermal radiation sensors, temperature sensors, and resistive soil moisture sensors, continuous and dynamic data collection of key ecological indicators such as elevation, surface temperature, and soil moisture within the set sampling period can be achieved, ensuring the integrity and timeliness of the sampling information. This method based on multi-point distributed sensing and multi-dimensional data collection significantly enhances the real-time perception capability of environmental state changes compared to traditional single data source or point-in-time observation methods. In particular, it exhibits higher sensitivity and accuracy in quickly capturing topographic disturbances, microclimate changes, and ecological humidity fluctuations within the region, improving the accuracy and representativeness of the initial data for spatial planning. This provides solid data support for subsequent dynamic assessment and risk identification, effectively overcoming the shortcomings of outdated data collection methods and delayed early warning in existing regional spatial planning models.

[0024] Example 3: Please refer to Figure 1 and Figure 3 Specifically: The specific steps of S2 include: S21. Analyze the trend of terrain elevation disturbance rate change in each unit area during the sampling period, and obtain the terrain disturbance trend value of each unit area during the sampling period, specifically including: S211. Perform feature recognition on the environmental status data information, take the elevation value corresponding to the first sampling time point of each unit area as the initial elevation value of each unit area in the sampling period, take the elevation value corresponding to the last sampling time point of each unit area as the final elevation value of each unit area in the sampling period, calculate the difference between the initial elevation value and the final elevation value of each unit area in the sampling period to obtain the elevation change difference of each unit area in the sampling period; S212. Calculate the ratio of the elevation change difference of each unit area within the sampling period to the sampling period duration. After dimensionless processing, analyze the trend of the terrain elevation disturbance rate change of each unit area within the sampling period and obtain the terrain disturbance trend value of each unit area within the sampling period. S22. Taking each unit area as the central unit area, select the eight neighboring grid units surrounding each central unit area as the local comparison area of ​​the corresponding central unit area to obtain the corresponding local comparison area of ​​each central unit area. Record the end elevation value of each neighboring grid unit area in the corresponding local comparison area, and calculate the local difference component with the end elevation value of the corresponding central unit area. Analyze the disturbance difference between the terrain height of each unit area and the terrain height of the surrounding neighboring grid units during the sampling period, and determine the terrain disturbance difference value of each unit area during the sampling period. S23. Correlate the topographic disturbance trend value and topographic disturbance difference value of each unit area within the sampling period. After dimensionless processing, analyze the degree of concentrated abrupt change in topographic disturbance in each unit area within the sampling period, and determine the concentrated abrupt change value of topographic disturbance in each unit area within the sampling period. Specifically: In the formula, This represents the concentrated abrupt change value of the terrain in the i-th unit region during the sampling period. This represents the terrain disturbance trend value of the i-th cell region within the sampling period. This represents the difference in terrain disturbance value of the i-th unit region within the sampling period.

[0025] It should be noted that the formula in S23 is a mathematical model used to quantify the degree of concentrated abrupt changes in terrain disturbance in each unit area. It originates from an in-depth analysis of the coupling relationship between temporal variation trends and spatial disturbance differences during terrain evolution. The derivation of this formula is based on two core dimensions: first, the terrain disturbance trend value characterizes the overall trend of elevation change in the unit area within the sampling period, reflecting the intensity of disturbance in the temporal dimension; second, the terrain disturbance difference value characterizes the degree of fluctuation in elevation difference between the area and its neighboring grids within the same period, reflecting the abrupt changes in the spatial dimension. The formula uses the product of these two dimensions, reflecting that only when... A concentrated abrupt change characteristic is constituted only when a region itself exhibits a strong trend of elevation change and shows significant differences compared to its neighboring regions. This numerically amplifies the identification effect of sudden disturbances, ensuring the accurate identification of abrupt change regions. In this invention, this formula is used in S23 as a key indicator for identifying terrain abrupt change unit regions, serving the entire process of subsequent microclimate assessment and degradation risk identification. It effectively improves the sensitivity to microscale geomorphic changes, solves the problem of misjudging the disturbance level due to neglecting neighboring comparisons in traditional technologies, and provides strong data support and decision-making basis for constructing an accurate and efficient dynamic spatial planning system.

[0026] Specifically, the S2 steps also include: S24. Based on the concentrated abrupt change values ​​of terrain in each unit area within the target area during the sampling period, and combined with the statistical mean-averaging algorithm, obtain the mean value of concentrated abrupt change in terrain. S25. Compare and analyze the concentrated abrupt change value of the terrain in each unit area with the average concentrated abrupt change value of the terrain in the sampling period. If the concentrated abrupt change value of the terrain in the corresponding unit area exceeds the average concentrated abrupt change value of the terrain in the sampling period, mark the corresponding unit area as a terrain abrupt change unit area and record the concentrated abrupt change value of the terrain in the corresponding terrain abrupt change unit area. Otherwise, mark the corresponding unit area as a normal terrain unit area.

[0027] In this embodiment, a high-precision data analysis mechanism is proposed for terrain disturbance identification and abrupt change region screening. By performing difference calculation and rate analysis on the continuously sampled elevation data, it not only effectively overcomes the limitations of traditional manual inspection methods in terrain change identification, such as slow response, coarse scale, and strong subjectivity, but also achieves quantitative judgment of terrain dynamics and accurate identification of significant abrupt change units within the region. Specifically, by extracting the initial and final elevation values ​​of each unit region within the sampling period, calculating the difference in elevation changes, and constructing a disturbance rate index based on the sampling period duration, it can intuitively reflect the time-series variation trend of regional terrain elevation. Furthermore, a neighbor grid comparison mechanism is introduced, selecting the eight neighboring grids of each unit as local comparison areas, and performing local difference calculation on the final elevation value to capture the spatial abrupt change characteristics of terrain disturbance from a spatial dimension. This disturbance modeling method, which integrates time and space, effectively improves... This method improves the sensitivity and accuracy of responding to microscale topographic changes within the region. Especially in the early stages of severe topographic disturbances, it constructs a topographic abrupt change value model by combining topographic disturbance trend values ​​and topographic disturbance difference values. Furthermore, it utilizes a statistical mean algorithm to construct a dynamic threshold judgment mechanism, enabling intelligent screening and automatic labeling of abrupt change areas. Compared to traditional judgment methods that rely primarily on subjective experience or manual observation, this method not only possesses advantages such as strong objectivity, high automation, and good repeatability, but also significantly enhances the early warning capability and recognition efficiency of local geomorphological anomalies, providing spatial foundational support for subsequent joint assessments of microclimate and ecological structure. This method is particularly suitable for mountainous areas and water conservation areas with complex geomorphological dynamics, providing more forward-looking and scientific topographic disturbance identification results for spatial planning. It effectively solves the problem of the hidden propagation of degradation risks caused by untimely or insufficient topographic disturbance identification in existing technologies.

[0028] Example 4: Please refer to Figure 1 Specifically: The specific steps of S3 include: S31. Analyze the trend of surface temperature fluctuation rate in each abrupt change unit region within the sampling period, and determine the temperature fluctuation trend value of each abrupt change unit region, specifically including: S311. Based on the marked terrain abrupt change unit regions, perform feature recognition on the environmental state data information, extract the highest and lowest surface temperature values ​​of each terrain abrupt change unit region within the sampling period, sum the highest and lowest surface temperature values ​​of each terrain abrupt change unit region within the sampling period, and obtain the surface temperature range value of each terrain abrupt change unit region within the sampling period. S312. Calculate the ratio of the surface temperature range of each topographic abrupt change unit region within the sampling period to the sampling period duration, and after dimensionless processing, analyze the trend of surface temperature fluctuation rate of each topographic abrupt change unit region within the sampling period to obtain the temperature fluctuation trend value of each topographic abrupt change unit region within the sampling period.

[0029] Specifically, the S3 steps also include: S32. Compare and analyze the surface soil moisture values ​​at each sampling time point in each topographic abrupt change unit area to determine the differences in surface moisture values ​​among different topographic abrupt change unit areas, specifically including: S321. Perform feature identification on environmental status data information, extract the surface soil moisture value of each topographic abrupt change unit area at each sampling time point, compare the surface soil moisture value of the corresponding topographic abrupt change unit area at each sampling time point with the set reference surface soil moisture range, if the surface soil moisture value of the corresponding sampling time point is greater than the set reference surface soil moisture range, then the corresponding sampling time point is recorded as a high humidity sampling time point, if the surface soil moisture value of the corresponding sampling time point is less than the set reference surface soil moisture range, then the corresponding sampling time point is recorded as a low humidity sampling time point. S322. Count the total number of low-humidity sampling time points and high-humidity sampling time points in each topographic change unit area. Take the ratio of the total number of low-humidity sampling time points and high-humidity sampling time points in each topographic change unit area to the total number of sampling time points in the corresponding topographic change unit area within the sampling period as the surface humidity difference value of each topographic change unit area within the sampling period.

[0030] Specifically, the S3 steps also include: S33. Correlate the temperature fluctuation trend values ​​and surface humidity differences of each topographic abrupt change unit region within the sampling period. After dimensionless processing, analyze the degree of concentrated abrupt changes in microclimate within each topographic abrupt change unit region within the sampling period, and determine the microclimate abrupt change values ​​of each topographic abrupt change unit region within the sampling period. Specifically: In the formula, This represents the microclimate abrupt change value of the j-th topographic abrupt change unit region within the sampling period. and These represent the temperature fluctuation trend and surface humidity difference values ​​of the j-th terrain abrupt change unit region during the sampling period, respectively.

[0031] It should be noted that the formula in S33 is the core expression for quantitative modeling of the degree of microclimate abrupt change. It originates from the comprehensive analysis of the coupling relationship between temperature fluctuation trends and soil moisture differences. This formula multiplies the temperature fluctuation trend value with the surface moisture difference value and performs dimensionless processing to construct a microclimate abrupt change value that can simultaneously reflect the coupling strength of thermal energy anomalies and moisture anomalies. This value is used to identify sensitive areas with significant microclimate disturbances. The derivation logic is that although individual temperature or humidity changes can reflect local climate anomalies, their spatial scale impact is often limited. However, the enhanced coupling between the two often indicates potential damage to ecosystem stability. Therefore, forming a joint index through multiplication can significantly amplify the ability to identify dual anomalies. In this invention, this formula is applied to S33 as a quantitative tool for assessing the degree of climate dynamic fluctuations in areas with abrupt topographic changes, and is ultimately used for the subsequent construction of a degradation risk index. This method can effectively capture ecologically vulnerable zones in areas with extreme precipitation events and high temperature and drought transitions, significantly improving the comprehensive identification ability and prediction accuracy of microclimate anomalies. It solves the shortcomings of traditional technologies in analyzing temperature and humidity changes in a coordinated manner, and enhances the foresight of climate response dimensions and ecological intervention.

[0032] Specifically, the S3 steps also include: S34. By scanning the apparent images of each topographic abrupt change unit region with an optical camera, analyze the ecological structure richness of each topographic abrupt change unit region, and determine the ecological richness coefficient of each topographic abrupt change unit region, specifically including: S341. Based on the marked terrain change unit areas, and in conjunction with the UAV cruise image system, use the optical camera in the UAV cruise image system to scan the apparent images of each terrain change unit area to obtain the apparent images of each terrain change unit area. S342. Match the appearance images of each terrain mutation unit region with the appearance images corresponding to each preset appearance type to obtain the appearance category of each terrain mutation unit region. The appearance type includes vegetation cover ground category and bare ground category. S343. Classify the corresponding topographic abrupt change unit regions into blocks according to their apparent categories, obtain each vegetation-covered ground block and each bare ground block in each topographic abrupt change unit region, and extract the area of ​​each vegetation-covered ground block and the area of ​​each bare ground block from the apparent images of each topographic abrupt change unit region. Add up the areas of each vegetation-covered ground block and the area of ​​each bare ground block in each topographic abrupt change unit region to obtain the total area of ​​vegetation-covered ground blocks and the total area of ​​bare ground blocks in each topographic abrupt change unit region. S344. Based on the vegetation-covered ground blocks and bare ground blocks obtained in each terrain mutation unit area, count the number of vegetation-covered ground blocks and the number of bare ground blocks in each terrain mutation unit area. S345. Correlate the total area of ​​vegetation-covered ground blocks, the total area of ​​bare ground blocks, the number of vegetation-covered ground blocks, and the number of bare ground blocks in each topographic abrupt change unit region. After dimensionless processing, analyze the ecological richness of each topographic abrupt change unit region and determine the ecological richness coefficient of each topographic abrupt change unit region, specifically: ; This represents the ecological richness coefficient of the j-th abrupt topographic change unit. and Let represent the number of vegetation-covered ground patches and the number of bare ground patches in the j-th terrain abrupt change unit region, respectively. and Let represent the total area of ​​vegetation-covered ground blocks and the total area of ​​bare ground blocks in the j-th topographic abrupt change unit region, respectively.

[0033] It should be noted that the formula in S345 is an ecological richness coefficient calculation model used to measure the richness of ecological structure in abrupt topographic unit areas. It originates from the dual measurement logic of spatial heterogeneity and vegetation cover ratio in ecodiversity assessment. The formula calculates the proportion of vegetation-covered ground patches by number and area, respectively, and then multiplies them to construct a comprehensive index that reflects both spatial distribution density and cover intensity, used to comprehensively assess the ecological complexity of a region. The derivation process is based on the fact that the health of an ecosystem depends not only on the total vegetation cover area, but also on the degree of fragmentation and diversity of its spatial distribution. The method is related to the number and area of ​​vegetation blocks in a region; if a region has a large number of vegetation blocks and a large area ratio, it usually indicates that its ecological structure is richer and its ecosystem is more stable. This formula uses UAV optical images to acquire data on vegetation and bare ground blocks, extracts quantity and area parameters, and then quantifies the ecological status. Its implementation results show that this method can accurately identify areas with sparse vegetation, ecological degradation, or severe human interference, making up for the technical shortcomings of traditional ecological assessment that relies on qualitative analysis and lacks spatial quantitative indicators. It enhances the ability to quantitatively express ecological integrity and vulnerability in spatial planning and provides solid data support for degradation risk assessment and ecological restoration priority classification.

[0034] In this embodiment, an innovative data analysis process is proposed for identifying microclimate fluctuations and ecological structures, significantly solving the key bottleneck problems of existing technologies, such as the single ecological evaluation dimension and insufficient response to climate anomalies and ecological changes. By introducing two unconventional parameters—temperature fluctuation trend value and soil moisture difference value—the heat changes and moisture dynamics in areas of abrupt topographic changes are effectively captured during the sampling period, constructing a multi-source and multi-time period microclimate change assessment model with strong dynamic response capability and environmental adaptability. In particular, regarding temperature fluctuations, the ratio of the range index constructed based on the maximum and minimum temperature values ​​to the sampling period is standardized, which can keenly reflect the imbalance of surface heat balance and its evolution trend. Regarding humidity, by comparing with the reference humidity range and calculating the time-series proportion, not only is the degree of humidity fluctuation quantified, but the ability to identify periodic drought or wet anomalies is also enhanced. This dual-indicator fusion method greatly improves the identification accuracy of microclimate abrupt change areas, and through normalized fusion, a microclimate abrupt change value is further constructed, providing a basic support for subsequent risk level judgment. At the same time, this invention combines an unmanned aerial vehicle (UAV) cruise image system to analyze the terrain. This study identifies the apparent ecological structure of anomalous unit regions and innovatively introduces the ecorich coefficient as an indicator to measure the complexity and vulnerability of regional ecosystems. Through image matching and regional segmentation, it classifies and statistically analyzes vegetation cover and bare ground, accurately acquiring spatial distribution and quantity information of various surface structures, achieving cross-modal analysis transformation from scanned images to ecological structure indicators. This process fully leverages the advantages of fusing high-altitude remote sensing and ground data, overcoming the shortcomings of traditional methods in low ecological identification accuracy and poor quantitative capabilities. Ultimately, the ecorich coefficient, through joint modeling of the number and area of ​​blocks, accurately reflects the level of regional ecological diversity and integrity, effectively identifying areas with monoculture and high potential degradation risks, providing crucial support for constructing a three-dimensional collaborative analysis system of "topography-climate-ecology." Overall, this approach not only enhances the perception and response capabilities of spatial planning in the climate and ecological dimensions but also establishes a dynamic ecological diagnostic mechanism with both temporal continuity and spatial accuracy, effectively improving the monitoring capabilities and early warning levels of regional ecological security, highly aligning with the core needs of national ecological civilization construction for dynamic governance and precise planning.

[0035] Example 5: Please refer to Figure 1 Specifically: The specific steps of S4 include: S41. Correlate the concentrated topographic abrupt change values ​​and microclimate abrupt change values ​​of each topographic abrupt change unit region, and combine them with the ecological richness coefficient of the corresponding topographic abrupt change unit region. After dimensionless processing, analyze the comprehensive degradation risk degree of each topographic abrupt change unit region, and determine the degradation risk degree index of each topographic abrupt change unit region, specifically: In the formula, This represents an index indicating the degree of degradation risk in the j-th abrupt topographic change unit region. , and These represent the topographic abrupt change value, microclimate abrupt change value, and ecological richness coefficient of the j-th topographic abrupt change unit region, respectively. It should be noted that the formula in S41 is the core assessment model used to calculate the degradation risk index of topographic abrupt change units. It originates from the multi-factor comprehensive risk assessment theory, integrating three key indicators: topographic disturbance, microclimate abrupt change, and ecological structure richness. The derivation logic of the formula lies in the fact that the degradation risk of a regional environment is often driven by dual abrupt changes in topography and climate, while the complexity of the ecological structure determines its resistance and recovery capacity to external disturbances. Therefore, this calculation model reflects the intensity of external pressure by multiplying the topographic disturbance value by the microclimate abrupt change value, and then introduces the ecological resilience factor as the denominator, forming... A degradation risk index that weighs the "impact-carrying capacity" relationship; in this invention, this formula is applied in S4 for degradation risk identification and classification, and by quantifying the comprehensive environmental stability of each topographic abrupt change unit, the risk unit area is accurately screened; in practical application, this method can effectively distinguish high-risk degradation areas composed of high disturbance and low ecological resilience, making up for the one-sidedness of traditional methods that judge the degree of risk based on only local indicators, improving the scientificity, systematicness and early warning foreseeability of degradation risk assessment, and providing high-value quantitative support for accurately delineating priority areas for ecological restoration and dynamically adjusting spatial planning strategies.

[0036] S42. Compare and analyze the degradation risk index of each terrain mutation unit region with the preset risk threshold, screen out the corresponding degradation risk unit regions and mark them, specifically including: If the degradation risk index of the corresponding topographic abrupt change unit area exceeds the risk threshold, it indicates that the corresponding topographic abrupt change unit area is in a degradation risk state, and the corresponding topographic abrupt change unit area is marked as a degradation risk unit area. If the degradation risk index of the corresponding topographic abrupt change unit area does not exceed the risk threshold, it means that the corresponding topographic abrupt change unit area is not in a degradation risk state. In this case, the corresponding topographic abrupt change unit area will not be marked separately. The risk level threshold is dynamically determined by setting quantiles or standard deviation intervals, based on the statistical distribution characteristics of historical monitoring data and the empirical values ​​of actual risk indices for typical degraded areas.

[0037] S43. Count the marked degradation risk unit areas to obtain the total number of degradation risk unit areas. If the total number of degradation risk unit areas exceeds 5% of the total number of unit areas in the target area, generate a first-level regional spatial planning report with the content: the current target area needs to perform regional spatial planning operations; if the total number of degradation risk unit areas does not exceed 5% of the total number of unit areas in the target area, generate a second-level regional spatial planning report with the content: the current target area does not need to perform regional spatial planning operations.

[0038] In this embodiment, a degradation risk index assessment mechanism integrating topographic abrupt change values, microclimate abrupt change values, and ecological richness coefficients is constructed. This achieves quantitative analysis and intelligent identification of regional environmental degradation risks, overcoming the technical bottlenecks of traditional spatial planning that rely on single-factor judgments, lack comprehensive assessment, and suffer from delayed response. By uniformly analyzing the multidimensional monitoring data of each topographic abrupt change unit region after dimensionless processing, not only is the overall perception of regional environmental dynamic changes improved, but misjudgment due to individual parameter anomalies is also effectively avoided, significantly enhancing the stability and accuracy of risk identification. Furthermore, by comparing the data with preset thresholds... By comparing data, the system automatically identifies and marks areas at risk of degradation, and further uses their proportion as a criterion to generate tiered spatial planning reports, thus establishing a rapid closed-loop process from risk identification to planning response. Compared with existing technologies that rely on static data or subjective experience for delayed judgment, this system not only enables early warning of potential degradation risks but also allows for dynamic adjustment of planning strategies based on real-time data. This improves the timeliness, scientific rigor, and precision of spatial governance, making it particularly suitable for efficient governance in ecologically sensitive areas or areas with frequent development activities. It effectively meets the urgent need for forward-looking and precise regional planning decisions in current ecological civilization construction.

[0039] Example 6: Please refer to Figure 1 and Figure 2 Specifically: a regional spatial planning data acquisition system based on multi-point monitoring, including a division module, a terrain screening module, a regional analysis module, and a planning determination module; The partitioning module is used to continuously sample the environmental status of each unit area after the target area is divided into grids before regional spatial planning, and to obtain environmental status data information. The terrain screening module analyzes the degree of concentrated abrupt changes in terrain disturbances in each unit area within the sampling period based on environmental status data, and screens out the unit areas with abrupt terrain changes. The regional analysis module analyzes the degree of concentrated microclimate changes in each selected topographic abrupt change unit region during the sampling period, and analyzes the richness of the ecological structure of each topographic abrupt change unit region by scanning the apparent images of each region. The planning and judgment module analyzes the comprehensive degradation risk of each topographic abrupt change unit region based on the degree of concentrated microclimate change and the richness of its ecological structure, generates a regional spatial planning report of the corresponding level, and then performs the corresponding regional spatial planning operation on the target region.

[0040] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for collecting regional spatial planning data based on multi-point monitoring, characterized in that: Includes the following steps: S1. Before regional spatial planning, continuously sample the environmental status of each unit area after the target area is divided into grids to obtain environmental status data information. S2. Based on environmental status data, analyze the degree of concentrated abrupt changes in terrain disturbance in each unit area during the sampling period, and screen out the unit areas with abrupt terrain changes. S3. Based on the selected topographic abrupt change unit regions, analyze the degree of concentrated abrupt change in microclimate in each topographic abrupt change unit region during the sampling period, and analyze the richness of ecological structure in each topographic abrupt change unit region by scanning the apparent images of each topographic abrupt change unit region. S4. Based on the degree of concentrated microclimate abrupt change and the richness of ecological structure in each topographic abrupt change unit region, analyze the comprehensive degradation risk of each topographic abrupt change unit region, generate a regional spatial planning report of the corresponding level, and then perform corresponding regional spatial planning operations on the target region. The specific steps in S2 include: S21. Analyze the trend of terrain elevation disturbance rate change in each unit area during the sampling period, and obtain the terrain disturbance trend value of each unit area during the sampling period, specifically including: S211. Perform feature recognition on the environmental status data information, take the elevation value corresponding to the first sampling time point of each unit area as the initial elevation value of each unit area in the sampling period, take the elevation value corresponding to the last sampling time point of each unit area as the final elevation value of each unit area in the sampling period, calculate the difference between the initial elevation value and the final elevation value of each unit area in the sampling period to obtain the elevation change difference of each unit area in the sampling period; S212. Calculate the ratio of the elevation change difference of each unit area within the sampling period to the sampling period duration. After dimensionless processing, analyze the trend of the terrain elevation disturbance rate change of each unit area within the sampling period and obtain the terrain disturbance trend value of each unit area within the sampling period. S22. Taking each unit area as the central unit area, select the eight neighboring grid units surrounding each central unit area as the local comparison area of ​​the corresponding central unit area to obtain the corresponding local comparison area of ​​each central unit area. Record the end elevation value of each neighboring grid unit area in the corresponding local comparison area, and calculate the local difference component with the end elevation value of the corresponding central unit area. Analyze the disturbance difference between the terrain height of each unit area and the terrain height of the surrounding neighboring grid units during the sampling period, and determine the terrain disturbance difference value of each unit area during the sampling period. S23. Correlate the topographic disturbance trend value and topographic disturbance difference value of each unit area within the sampling period. After dimensionless processing, analyze the degree of concentrated abrupt change in topographic disturbance in each unit area within the sampling period, and determine the concentrated abrupt change value of topographic disturbance in each unit area within the sampling period. Specifically: In the formula, This represents the concentrated abrupt change value of the terrain in the i-th unit region during the sampling period. This represents the terrain disturbance trend value of the i-th cell region within the sampling period. This represents the difference in terrain disturbance value of the i-th unit region within the sampling period.

2. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 1, characterized in that: The specific steps in S1 include: S11. Before regional spatial planning, the target area is divided into grids to obtain several unit areas. The center of each unit area in the target area is used as the monitoring point to obtain the static monitoring point of the corresponding unit area in the target area. Based on the multiple sets of environmental monitoring equipment deployed at the static monitoring points in each unit area, the environmental status of each unit area is continuously sampled within the set sampling period to obtain environmental status data information. The environmental monitoring equipment includes multiple sets of lidar ranging sensors, infrared thermal radiation sensors, temperature sensors, and resistive soil moisture sensors. The environmental status data includes the elevation, surface temperature, and surface soil moisture values ​​of each unit area at each sampling time point.

3. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 1, characterized in that: The specific steps in S2 also include: S24. Based on the concentrated abrupt change values ​​of terrain in each unit area within the target area during the sampling period, and combined with the statistical mean-averaging algorithm, obtain the mean value of concentrated abrupt change in terrain. S25. Compare and analyze the concentrated abrupt change value of the terrain in each unit area with the average concentrated abrupt change value of the terrain in the sampling period. If the concentrated abrupt change value of the terrain in the corresponding unit area exceeds the average concentrated abrupt change value of the terrain in the sampling period, mark the corresponding unit area as a terrain abrupt change unit area and record the concentrated abrupt change value of the terrain in the corresponding terrain abrupt change unit area. Otherwise, mark the corresponding unit area as a normal terrain unit area.

4. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 3, characterized in that: The specific steps of S3 include: S31. Analyze the trend of surface temperature fluctuation rate in each abrupt change unit region within the sampling period, and determine the temperature fluctuation trend value of each abrupt change unit region, specifically including: S311. Based on the marked terrain abrupt change unit regions, perform feature recognition on the environmental state data information, extract the highest and lowest surface temperature values ​​of each terrain abrupt change unit region within the sampling period, sum the highest and lowest surface temperature values ​​of each terrain abrupt change unit region within the sampling period, and obtain the surface temperature range value of each terrain abrupt change unit region within the sampling period. S312. Calculate the ratio of the surface temperature range of each topographic abrupt change unit region within the sampling period to the sampling period duration, and after dimensionless processing, analyze the trend of surface temperature fluctuation rate of each topographic abrupt change unit region within the sampling period to obtain the temperature fluctuation trend value of each topographic abrupt change unit region within the sampling period.

5. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 4, characterized in that: The specific steps in S3 also include: S32. Compare and analyze the surface soil moisture values ​​at each sampling time point in each topographic abrupt change unit area to determine the differences in surface moisture values ​​among different topographic abrupt change unit areas, specifically including: S321. Perform feature identification on environmental status data information, extract the surface soil moisture value of each topographic abrupt change unit area at each sampling time point, compare the surface soil moisture value of the corresponding topographic abrupt change unit area at each sampling time point with the set reference surface soil moisture range, if the surface soil moisture value of the corresponding sampling time point is greater than the set reference surface soil moisture range, then the corresponding sampling time point is recorded as a high humidity sampling time point, if the surface soil moisture value of the corresponding sampling time point is less than the set reference surface soil moisture range, then the corresponding sampling time point is recorded as a low humidity sampling time point. S322. Count the total number of low-humidity sampling time points and high-humidity sampling time points in each topographic change unit area. Take the ratio of the total number of low-humidity sampling time points and high-humidity sampling time points in each topographic change unit area to the total number of sampling time points in the corresponding topographic change unit area within the sampling period as the surface humidity difference value of each topographic change unit area within the sampling period.

6. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 5, characterized in that: The specific steps in S3 also include: S33. Correlate the temperature fluctuation trend values ​​and surface humidity differences of each topographic abrupt change unit region within the sampling period. After dimensionless processing, analyze the degree of concentrated abrupt changes in microclimate within each topographic abrupt change unit region within the sampling period, and determine the microclimate abrupt change values ​​of each topographic abrupt change unit region within the sampling period. Specifically: In the formula, This represents the microclimate abrupt change value of the j-th topographic abrupt change unit region within the sampling period. and These represent the temperature fluctuation trend and surface humidity difference values ​​of the j-th terrain abrupt change unit region during the sampling period, respectively.

7. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 6, characterized in that: The specific steps in S3 also include: S34. By scanning the apparent images of each topographic abrupt change unit region with an optical camera, analyze the ecological structure richness of each topographic abrupt change unit region, and determine the ecological richness coefficient of each topographic abrupt change unit region, specifically including: S341. Based on the marked terrain change unit areas, and in conjunction with the UAV cruise image system, use the optical camera in the UAV cruise image system to scan the apparent images of each terrain change unit area to obtain the apparent images of each terrain change unit area. S342. Match the appearance images of each terrain mutation unit region with the appearance images corresponding to each preset appearance type to obtain the appearance category of each terrain mutation unit region. The appearance type includes vegetation cover ground category and bare ground category. S343. Classify the corresponding topographic abrupt change unit regions into blocks according to their apparent categories, obtain each vegetation-covered ground block and each bare ground block in each topographic abrupt change unit region, and extract the area of ​​each vegetation-covered ground block and the area of ​​each bare ground block from the apparent images of each topographic abrupt change unit region. Add up the areas of each vegetation-covered ground block and the area of ​​each bare ground block in each topographic abrupt change unit region to obtain the total area of ​​vegetation-covered ground blocks and the total area of ​​bare ground blocks in each topographic abrupt change unit region. S344. Based on the vegetation-covered ground blocks and bare ground blocks obtained in each terrain mutation unit area, count the number of vegetation-covered ground blocks and the number of bare ground blocks in each terrain mutation unit area. S345. Correlate the total area of ​​vegetation-covered ground blocks, the total area of ​​bare ground blocks, the number of vegetation-covered ground blocks, and the number of bare ground blocks in each topographic abrupt change unit region. After dimensionless processing, analyze the ecological richness of each topographic abrupt change unit region and determine the ecological richness coefficient of each topographic abrupt change unit region, specifically: ; This represents the ecological richness coefficient of the j-th abrupt topographic change unit. and Let represent the number of vegetation-covered ground patches and the number of bare ground patches in the j-th terrain abrupt change unit region, respectively. and Let represent the total area of ​​vegetation-covered ground blocks and the total area of ​​bare ground blocks in the j-th topographic abrupt change unit region, respectively.

8. The regional spatial planning data acquisition method based on multi-point monitoring according to claim 7, characterized in that: The specific steps of S4 include: S41. Correlate the concentrated topographic abrupt change values ​​and microclimate abrupt change values ​​of each topographic abrupt change unit region, and combine them with the ecological richness coefficient of the corresponding topographic abrupt change unit region. After dimensionless processing, analyze the comprehensive degradation risk degree of each topographic abrupt change unit region, and determine the degradation risk degree index of each topographic abrupt change unit region, specifically: In the formula, This represents an index indicating the degree of degradation risk in the j-th abrupt topographic change unit region. , and These represent the topographic abrupt change value, microclimate abrupt change value, and ecological richness coefficient of the j-th topographic abrupt change unit region, respectively. S42. Compare and analyze the degradation risk index of each terrain mutation unit region with the preset risk threshold, screen out the corresponding degradation risk unit regions and mark them, specifically including: If the degradation risk index of the corresponding topographic abrupt change unit area exceeds the risk threshold, it indicates that the corresponding topographic abrupt change unit area is in a degradation risk state, and the corresponding topographic abrupt change unit area is marked as a degradation risk unit area. If the degradation risk index of the corresponding topographic abrupt change unit area does not exceed the risk threshold, it means that the corresponding topographic abrupt change unit area is not in a degradation risk state. In this case, the corresponding topographic abrupt change unit area will not be marked separately. S43. Count the marked degradation risk unit areas to obtain the total number of degradation risk unit areas. If the total number of degradation risk unit areas exceeds 5% of the total number of unit areas in the target area, generate a first-level regional spatial planning report with the content: the current target area needs to perform regional spatial planning operations; if the total number of degradation risk unit areas does not exceed 5% of the total number of unit areas in the target area, generate a second-level regional spatial planning report with the content: the current target area does not need to perform regional spatial planning operations.

Images