Regional data processing method and device, storage medium and electronic equipment

Abstract

This disclosure relates to a data processing method, specifically a regional data processing method, apparatus, storage medium, and electronic device. The regional data processing method includes: responding to a user's input operation, acquiring regional data of a target region corresponding to the input operation; the regional data includes topographic data of the target region and soil data of sample plots within the target region; rasterizing the target region to obtain raster points, and determining factor data of the dominant micro-site features corresponding to the raster points based on the regional data; the dominant micro-site features include topographic factors and soil factors; determining the micro-site classification result corresponding to the raster points based on the factor data, and drawing a spatial distribution map of micro-site types in the target region based on the raster points and the micro-site classification result. The regional data processing method provided by this disclosure can conveniently, accurately, and intuitively obtain regional micro-site type classification results.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, specifically to a regional data processing method, apparatus, storage medium, and electronic device. Background Technology

[0002] While bringing clean energy, hydropower development projects have also caused a series of soil erosion and ecological problems. Site classification is the foundation for vegetation restoration. By classifying different site types, it is possible to improve the technical support for vegetation configuration by selecting plants according to local conditions.

[0003] Hydropower projects are mostly located in high mountain and canyon areas, where transportation is inconvenient, data acquisition is difficult, and traditional site division methods are inefficient and inaccurate.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide a regional data processing method, a regional data processing device, a storage medium, and an electronic device, which aim to solve the problem of obtaining regional micro-site type classification results conveniently, accurately, and intuitively.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a regional data processing method is provided, characterized by comprising:

[0008] In response to a user's input operation, regional data of the target area corresponding to the input operation is obtained; the regional data includes the topographic data of the target area and the soil data of the sample plots within the target area.

[0009] The target area is rasterized to obtain raster points, and the factor data of the micro-site dominant factors corresponding to the raster points are determined based on the area data; the micro-site dominant factors include topographic factors and soil factors;

[0010] Based on the factor data, the micro-site classification result corresponding to the grid point is determined, and a spatial distribution map of micro-site types in the target area is drawn based on the grid point and the micro-site classification result.

[0011] Optionally, when the dominant micro-site factor is a topographic factor, the step of determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes:

[0012] Spatial analysis is performed on the terrain data to obtain factor data corresponding to the terrain factors; wherein, the terrain factors include slope position, slope gradient, and slope aspect.

[0013] Optionally, when the dominant micro-site factor is a soil factor, the step of determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes:

[0014] Initial data for a soil factor is extracted based on the soil data; the soil factor includes soil layer thickness, soil water holding capacity, and soil nutrients.

[0015] Interpolation analysis is performed on the initial data to obtain the predicted values ​​of the soil factors at the grid points, which are used as the factor data of the soil factors.

[0016] Optionally, when the soil factor is soil nutrients, the initial data for extracting a soil factor based on the soil data includes:

[0017] Extract soil nutrient content data from the soil data;

[0018] Principal component analysis was used to comprehensively evaluate the soil nutrient content data to obtain a comprehensive value of soil nutrient content, which was used as the initial data for soil nutrients.

[0019] Optionally, determining the micro-site classification result corresponding to the grid point based on the factor data includes:

[0020] Pre-construct the classification thresholds corresponding to the dominant factors of the micro-site;

[0021] For a grid point, the primary classification result of the micro-site dominant factor is determined based on the factor data and classification threshold corresponding to the micro-site dominant factor at the grid point;

[0022] The primary classification results corresponding to each micro-site dominant factor are traversed to obtain the micro-site classification result corresponding to the grid point.

[0023] Optionally, the method further includes:

[0024] Obtain the micro-site classification system of the main region where the target region is located;

[0025] The micro-site classification results corresponding to the grid points are verified according to the micro-site classification system.

[0026] Optionally, the method further includes: constructing a micro-site classification system for the main region where the target region is located, wherein constructing the micro-site classification system for the main region where the target region is located includes:

[0027] Combining some of the aforementioned micro-site dominant factors to create micro-site type groups; and

[0028] Obtain regional data of multiple other regions within the main region where the target region is located, to obtain spatial distribution maps of micro-site types corresponding to each region;

[0029] Based on the micro-site classification results in the spatial distribution maps of each micro-site type, configure the micro-site types corresponding to each micro-site type group;

[0030] A micro-site classification system for the main area is created based on the micro-site types corresponding to the micro-site type groups.

[0031] According to a second aspect of this disclosure, a regional data processing apparatus is provided, comprising:

[0032] In response to a user's input operation, regional data of the target area corresponding to the input operation is obtained; the regional data includes the topographic data of the target area and the soil data of the sample plots within the target area.

[0033] The target area is rasterized to obtain raster points, and the factor data of the micro-site dominant factors corresponding to the raster points are determined based on the area data; the micro-site dominant factors include topographic factors and soil factors;

[0034] Based on the factor data, the micro-site classification result corresponding to the grid point is determined, and a spatial distribution map of micro-site types in the target area is drawn based on the grid point and the micro-site classification result.

[0035] According to a third aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the region data processing method as described in the above embodiments.

[0036] According to a fourth aspect of the present disclosure, an electronic device is provided, characterized in that it includes: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the region data processing method as described in the above embodiments.

[0037] The exemplary embodiments disclosed herein may have some or all of the following beneficial effects:

[0038] In the technical solutions provided by some embodiments of this disclosure, topographic data of the target area and soil data of sample plots within the target area are first acquired. Then, factor data of the dominant micro-site factors at the corresponding grid points in the target area are obtained based on these regional data. Finally, micro-site classification is performed based on the factor data, and a spatial distribution map of micro-site types in the target area is drawn. On the one hand, the micro-site classification not only integrates collectable topographic data but also soil data obtained through sample plot collection and experimental analysis, enabling the micro-site classification to shift from traditional extensive to intensive methods, resulting in more accurate classification results. It also saves on manpower survey costs and improves data acquisition efficiency. On the other hand, multivariate overlay analysis is used to overlay a comprehensive and detailed evaluation of multiple factors of topography and soil in the same area and at the same scale, outputting a micro-site type distribution map with intuitive visual representation.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0041] Figure 1 The illustration shows a flowchart of a regional data processing method according to an exemplary embodiment of the present disclosure.

[0042] Figure 2 The illustration schematically shows a flowchart of a method for determining micro-site classification results in an exemplary embodiment of the present disclosure;

[0043] Figure 3 This schematically illustrates a spatial distribution map of a micro-site type in an exemplary embodiment of the present disclosure;

[0044] Figure 4 This schematic diagram illustrates the composition of a regional data processing apparatus according to an exemplary embodiment of the present disclosure;

[0045] Figure 5 The schematic diagram illustrates the structure of a computer system of an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0046] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art.

[0047] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0048] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0049] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily need to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0050] The implementation details of the technical solutions of the embodiments of this disclosure are described in detail below.

[0051] Figure 1 This schematic diagram illustrates a flow chart of a regional data processing method according to an exemplary embodiment of this disclosure. Figure 1 As shown, the data processing method for this region includes steps S101 to S103:

[0052] Step S101: In response to the user's input operation, obtain the regional data of the target area corresponding to the input operation; the regional data includes the topographic data of the target area and the soil data of the sample plots within the target area;

[0053] Step S102: The target area is rasterized to obtain raster points, and the factor data of the micro-site dominant factors corresponding to the raster points are determined based on the area data; the micro-site dominant factors include topographic factors and soil factors;

[0054] Step S103: Determine the micro-site classification result corresponding to the grid point based on the factor data, and draw a spatial distribution map of micro-site types in the target area based on the grid point and the micro-site classification result.

[0055] In the technical solutions provided by some embodiments of this disclosure, topographic data of the target area and soil data of sample plots within the target area are first acquired. Then, factor data of the dominant micro-site factors at the corresponding grid points in the target area are obtained based on these regional data. Finally, micro-site classification is performed based on the factor data, and a spatial distribution map of micro-site types in the target area is drawn. On the one hand, the micro-site classification not only integrates collectable topographic data but also soil data obtained through sample plot collection and experimental analysis, enabling the micro-site classification to shift from traditional extensive to intensive methods, resulting in more accurate classification results. It also saves on manpower survey costs and improves data acquisition efficiency. On the other hand, multivariate overlay analysis is used to overlay a comprehensive and detailed evaluation of multiple factors of topography and soil in the same area and at the same scale, outputting a micro-site type distribution map with intuitive visual representation.

[0056] The steps of the regional data processing method in this exemplary embodiment will now be described in more detail with reference to the accompanying drawings and embodiments.

[0057] In step S101, in response to the user's input operation, the regional data of the target area corresponding to the input operation is obtained; the regional data includes the topographic data of the target area and the soil data of the sample plots within the target area.

[0058] Specifically, the regional data processing method provided in this disclosure can be used in map-making software on a Geographic Information System (GIS) platform to create spatial distribution maps of micro-site types. Map-making software can be, for example, ArcMap, an important component of the ArcGIS Desktop suite, primarily used for map creation, spatial data management, geographic analysis, and editing tasks. Other map-making software includes QGIS, SuperMap GIS, MapInfo, and other commercial GIS software. Therefore, users can input regional data for a target area using map-making software.

[0059] The target area is the region to be analyzed. The regional data of the target area mainly includes two aspects: the topographic data of the target area and the soil data of the sample plots within the target area.

[0060] Microsite is an important concept widely used in ecology, forestry, and vegetation restoration. It refers to a small plot of land or spatial unit with unique environmental conditions formed within a macro-site (such as a hillside or a forest) due to the heterogeneity of local environmental factors (such as soil, water, light, topography, etc.) at an extremely small spatial scale.

[0061] Specifically, terrain data can be a Digital Elevation Model (DEM). A DEM is a physical ground model that represents ground elevation in the form of a sequence of numerical arrays. It describes the spatial distribution of linear and nonlinear combinations of factors, including elevation, slope, aspect, and other factors. Terrain data can be acquired in various ways, such as through globally covered satellite radar, free and publicly available data sources like national or regional open data platforms, or through independent collection methods like drone aerial photography and lidar measurements.

[0062] Since it is difficult to obtain all soil data, this disclosure adopts the method of setting up quadrats within the target area to collect soil samples from the quadrats to reflect the overall soil conditions of the target area.

[0063] GPS was used to record data such as soil layer thickness and soil type in the quadrats. n soil samples were randomly collected from each quadrat for soil chemical analysis. Soil moisture content was determined by collecting soil samples from the 20-30 cm layer within each quadrat using an aluminum box. Soil moisture content was measured using the oven-drying method. Soil pH was determined using a potentiometric method. Total phosphorus was determined using the sulfuric acid-perchloric acid dissolution-molybdenum antimony colorimetric method. Soil organic matter was determined using the hydration heat potassium dichromate oxidation-colorimetric method. Available phosphorus was extracted using the NH4F-0.2 mol / L HCl extraction method. Soil ammonia nitrogen was determined using an automated continuous flow analyzer based on the experimental principle—the sample reacts with sodium salicylate and DCI to form a blue compound, which changes color at a wavelength of 660 nm. Soil nitrate nitrogen was also determined using an automated continuous flow analyzer. Total nitrogen was determined using the Kjeldahl method.

[0064] In step S102, the target area is rasterized to obtain raster points, and the factor data of the micro-site dominant factors corresponding to the raster points are determined based on the area data; the micro-site dominant factors include topographic factors and soil factors.

[0065] In one embodiment of this disclosure, the method further includes: determining the micro-site dominant factor. Determining the micro-site dominant factor includes: constructing habitat characterization indicators for exposed surfaces in a sampling area, and collecting regional data of the sampling area; calculating the coefficient of variation corresponding to each habitat characterization indicator based on the regional data; and comparing the coefficient of variation with a preset coefficient of variation threshold to screen out the micro-site dominant factor from the habitat characterization indicators.

[0066] Specifically, in hydropower projects, exposed surfaces refer to surface areas that were originally covered by vegetation or soil, exposed due to human interventions such as construction activities, terrain excavation, and infrastructure development. These areas, having lost the natural protection of their original vegetation layer and topsoil, face severe risks of soil erosion and ecosystem degradation. Soil erosion not only leads to a sharp decline in soil fertility but may also increase the probability of geological disasters, such as landslides and debris flows, thus threatening project safety and the surrounding environment. Furthermore, ecological degradation affects the maintenance of biodiversity, further weakening the regional ecosystem's service functions and resilience. Therefore, when implementing ecological restoration strategies for exposed surfaces in high-altitude hydropower project areas, it is essential to thoroughly consider their unique habitat characteristics.

[0067] Habitat characterization indicators are key parameters for assessing the health status of ecosystems, biodiversity distribution, and restoration potential. By reviewing relevant data and analyzing key influencing factors of soil erosion risk in the region, multiple habitat characterization indicators can be constructed, including but not limited to humidity, wind speed, altitude, slope aspect, slope gradient, soil thickness, soil type, soil water holding capacity and soil nutrients, pH, etc.

[0068] Subsequently, sampling can be conducted in the target area or the main project area where the target area is located. The sampling results can be analyzed using the coefficient of variation method to screen habitat characterization indicators. For example, the sampling results show that the coefficient of variation for humidity among meteorological factors is 12%, and the coefficient of variation for wind speed is 19.34%, with not very significant differences; the elevation range within the area is 3721~3888m, with a coefficient of variation of only about 1%, indicating that the elevations of small areas of mountains are similar and the terrain is relatively uniform; the average slope is 34.58°, which is relatively large, with a coefficient of variation of 37.97%, belonging to the level of moderate variability; the coefficient of variation for soil thickness is 31.46%, also belonging to the level of moderate variability; the soil water holding capacity shows significant differences among sample plots, with a coefficient of variation close to 50%, also belonging to the level of moderate variability; the soil nutrient fluctuation range is large, with a coefficient of variation of 94.47%, which may be related to human factors.

[0069] Different coefficient of variation (COP) thresholds can be set for each habitat characteristic indicator, or the same COP threshold can be set for all indicators. These COP thresholds can then be used to screen habitat characteristic indicators. Based on the above analysis results, humidity, wind speed, and altitude can be eliminated, and slope, aspect, slope position, soil thickness, soil water holding capacity, and soil nutrient retention can be considered as the dominant micro-site factors. Specifically, slope, aspect, and slope position are classified as topographic factors, while soil thickness, soil water holding capacity, and soil nutrients are classified as soil factors.

[0070] Map creation software includes a spatial analysis module, which can be used specifically for performing spatial analysis and modeling based on raster data. Therefore, the target area can be rasterized before image rendering.

[0071] In one embodiment of this disclosure, when the dominant micro-site factor is a topographic factor, determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes: performing spatial analysis on the topographic data to obtain the factor data corresponding to the topographic factor; wherein the topographic factor includes slope position, slope gradient, and slope aspect.

[0072] Specifically, three topographic factors can be set: slope position, slope gradient, and slope aspect. Based on the topographic data of the target area, topographic features such as slope position, slope gradient, and slope aspect can all be derived from the topographic data.

[0073] In one embodiment of this disclosure, when the dominant micro-site factor is a soil factor, the step of determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes: extracting initial data of a soil factor based on the soil data; the soil factor includes soil layer thickness, soil water holding capacity, and soil nutrients; performing interpolation analysis on the initial data to obtain the predicted value of the soil factor at the grid point, which is used as the factor data of the soil factor.

[0074] Generally, map-making software is a platform for terrain factor analysis. Therefore, it can directly obtain the corresponding factor data for terrain factors from terrain data collected via methods such as UAV remote sensing. However, it cannot directly obtain thematic maps of soil factors such as soil layer thickness, soil moisture, and soil nutrients for a small area from UAV remote sensing data. However, the interpolation module of map-making software can predict raster data values ​​using a limited sample of data points, thus obtaining the predicted value for each raster point. Therefore, when acquiring soil factor data, interpolation methods can be used to predict soil factors for raster points in the target area.

[0075] Specifically, three soil factors can be set: soil layer thickness, soil water holding capacity, and soil nutrients. First, initial data for these soil factors are extracted. Initial data for soil layer thickness and soil water holding capacity can be directly extracted from soil data. However, soil nutrients, as a qualitative indicator, are not easily obtained directly. Therefore, principal component analysis can be used to comprehensively evaluate soil nutrients and quantify their content to obtain initial data.

[0076] Therefore, in one embodiment of this disclosure, when the soil factor is soil nutrients, the step of extracting initial data of a soil factor based on the soil data includes: extracting soil nutrient content data from the soil data; and using principal component analysis to comprehensively evaluate the soil nutrient content data to obtain a comprehensive value of soil nutrient content, which is used as the initial data of the soil nutrients.

[0077] Specifically, Principal Component Analysis (PCA) is a data analysis method that uses orthogonal transformations to combine a group of correlated indicators or variables into a new, independent composite index. Soil data can be used to obtain soil nutrient content data for variables such as total nitrogen, soil organic matter, total phosphorus, available phosphorus, ammonia nitrogen, and nitrate nitrogen. PCA groups soil nutrient variables to ensure high correlation between variables within the same group and low correlation between variables in different groups. The results are then analyzed using a linear function of a small number of common factors and the sum of specific factors (F0). 总 This is used to express the original six soil nutrient index variables, in order to reasonably explain the correlation between the original six soil indicators and simplify the variable dimension.

[0078] After obtaining the initial data, interpolation analysis is performed. Interpolation methods can include inverse distance weighted interpolation and ordinary kriging. Taking ArcGIS as an example, the Multi-Value Extract to Point function in Spatial Analyst in ArcGIS 10.2 can extract one or more raster cell values ​​and export them to the attribute table of a point feature class. This function can be used to extract soil thickness, soil water holding capacity, and F... 总 The specific values ​​of the interpolated raster thematic map are extracted into the attribute table.

[0079] In step S103, the micro-site classification result corresponding to the grid point is determined based on the factor data, and a spatial distribution map of micro-site types in the target area is drawn based on the grid point and the micro-site classification result.

[0080] Figure 2 This schematically illustrates a flowchart of a method for determining micro-site classification results in an exemplary embodiment of this disclosure. Figure 2 As shown, the steps for determining the micro-site classification results are as follows:

[0081] Step S201: Pre-construct the classification thresholds corresponding to the dominant micro-site factors;

[0082] Step S202: For a grid point, determine the primary classification result of the micro-site dominant factor based on the factor data and classification threshold corresponding to the micro-site dominant factor at the grid point;

[0083] Step S203: Traverse each of the micro-site dominant factors to fuse the primary classification results corresponding to each of the micro-site dominant factors to obtain the micro-site classification result corresponding to the grid point.

[0084] Specifically, for each grid point, there is factor data corresponding to each micro-site dominant factor. When classifying micro-sites, it is necessary to merge the classification results corresponding to each micro-site dominant factor to obtain the final micro-site classification result. Therefore, it is necessary to predetermine the classification rules for each micro-site dominant factor and the classification thresholds corresponding to each level of classification.

[0085] Regarding slope aspect, drawing on the slope aspect classification method widely used in production practice, semi-sunny slopes are classified as sunny slopes and semi-shady slopes are classified as shady slopes. Based on the actual conditions of the study area, the slope aspect classification standard is determined as follows: sunny slopes (SE112.5°~NW292.5°) and shady slopes (0°~SE112.5°, NW292.5°~360°).

[0086] Based on the actual conditions of the study area, and referring to the "GB-T 15772-2008 Comprehensive Water and Soil Conservation Management Planning Guidelines", slope and slope position are classified as follows: Slope: gentle slope (≤15°), steep slope (15°<slope≤35°), dangerous slope (<35°); Slope position: the slope position is divided into three levels: uphill, middle and downhill.

[0087] Regarding soil layer thickness, it can be determined based on the soil layer thickness of the surveyed sample plots. According to the aforementioned sampling results, the soil layer thickness is mainly concentrated between 20 and 60 cm. Therefore, in combination with relevant grading standards and actual conditions, the soil layer thickness can be divided into two levels: medium soil (20 cm ≤ soil layer thickness < 40 cm) and thick soil (≥ 40 cm).

[0088] Regarding soil water holding capacity and soil nutrients, referring to GB-T 7833-1987 "Determination of Moisture Content in Forest Soils", soil water holding capacity is classified and the classification standards for soil water holding capacity are defined as: medium water holding capacity (5% < moisture content ≤ 10%) and strong water holding capacity (moisture content ≥ 10%); F 总 They are divided into four levels based on their scores: Low Nutrients (-1.0) <F 总 ≤0), medium nutrients (0) <F 总 ≤1.0) and high nutrient (F 总>1.0).

[0089] Table 1 summarizes the classification rules for the dominant factors of each microsite and the corresponding classification thresholds for each level of classification.

[0090] Table 1 Classification criteria for dominant factors of micro-site

[0091]

[0092] Based on this, the factor data corresponding to each grid point are reclassified according to the micro-site dominant factor classification standard. Then, the micro-site classification results of each grid point are obtained according to the naming standard of slope position + slope degree + slope aspect + soil layer thickness + soil water holding capacity + soil nutrients.

[0093] Finally, a remote sensing image processing platform can be selected to perform overlay analysis of six key micro-site dominant factors. This platform enables spatial synthesis of the classification results corresponding to these factor data, thereby obtaining a micro-site type distribution map of the entire target area.

[0094] Based on the above method, microsite classification not only integrates collectable topographic data, but also uses soil data obtained from sample collection and experimental analysis, enabling microsite classification to shift from the traditional extensive to intensive approach. The classification results are more accurate, while also saving manpower survey costs and improving data acquisition efficiency.

[0095] In addition, by using spatial analysis techniques of multivariate overlay analysis to draw micro-site type maps, a variety of geospatial information is integrated to achieve a comprehensive and detailed evaluation of multiple factors of topography and soil in the same region at the same scale, and output a micro-site type distribution map with intuitive display of the effect.

[0096] In one embodiment of this disclosure, the method further includes: obtaining a micro-site classification system for the main region where the target region is located; and verifying the micro-site classification results corresponding to the grid points according to the micro-site classification system.

[0097] Specifically, the micro-site classification results can also be verified. In one embodiment of this disclosure, the method further includes: constructing a micro-site classification system for the main region where the target region is located, wherein constructing the micro-site classification system for the main region where the target region is located includes:

[0098] The microsite dominant factors are combined to create multi-level site classification factors;

[0099] Acquire data from other regions that have a temporal or spatial correspondence with the target region, as well as the micro-site classification results corresponding to the other region data;

[0100] Based on the micro-site classification results corresponding to the data from other regions, configure the mapping relationship between the multi-level site classification factors to create a micro-site classification system for the main region.

[0101] This involves combining and stratifying the dominant micro-site factors. For example, micro-topographic factors such as slope position, slope gradient, and slope aspect are used as primary site classification factors. Following the naming rule of slope position + slope gradient + slope aspect, different primary micro-site type groups can be obtained, such as steep sunny slope on upper slope, dangerous sunny slope on upper slope, and steep sunny slope on middle slope. Then, soil factors such as soil thickness, soil water holding capacity, and soil nutrients are used as secondary site classification factors and named in sequence, resulting in thick soil with medium water holding capacity and low nutrients, thick soil with strong water holding capacity and high nutrients, and middle soil with strong water holding capacity and high nutrients.

[0102] It should be noted that the combination of micro-site dominant factors in this disclosure is only an illustrative example. In actual use, it can be divided differently, and this disclosure does not make specific limitations here.

[0103] Then, a mapping relationship between primary and secondary site classification factors can be established based on the known microsite classification results. This mapping relationship can be created based on the spatial distribution results of microsite types from other regional data that have a temporal or spatial correspondence with the target region.

[0104] Specifically, the micro-site classification results corresponding to other regional data can be based on field survey data or other validated classification results. Other regional data that are temporally related to the target area include spatial distribution maps of micro-site types in the target area at different past periods; data that spatially correspond to the target area can be spatial distribution maps of micro-site types in multiple other areas of the main region where the target area is located. The resulting micro-site classification system is shown in Table 2.

[0105] Table 2 Microsite classification system

[0106]

[0107] After obtaining the micro-site classification system, the micro-site classification results in the spatial distribution map of micro-site types in the target area can be verified. If a mapping relationship is not found, anomaly handling can be performed in a timely manner. This can further improve the accuracy of the micro-site classification results.

[0108] Figure 4 This schematic diagram illustrates the composition of a regional data processing apparatus according to an exemplary embodiment of the present disclosure, such as... Figure 4 As shown, the area data processing device 400 may include a response module 401, a determination module 402, and a classification module 403. Wherein:

[0109] The response module is used to respond to user input operations and obtain regional data of the target area corresponding to the input operation; the regional data includes topographic data of the target area and soil data of sample plots within the target area;

[0110] The determination module is used to perform rasterization processing on the target area to obtain raster points, and determine the factor data of the micro-site dominant factors corresponding to the raster points based on the area data; the micro-site dominant factors include topographic factors and soil factors;

[0111] The classification module is used to determine the micro-site classification result corresponding to the grid point based on the factor data, and to draw a spatial distribution map of micro-site types in the target area based on the grid point and the micro-site classification result.

[0112] According to an exemplary embodiment of this disclosure, when the dominant micro-site factor is a topographic factor, the determining module is further configured to perform spatial analysis on the topographic data to obtain factor data corresponding to the topographic factor; wherein, the topographic factor includes slope position, slope gradient, and slope aspect.

[0113] According to an exemplary embodiment of this disclosure, when the dominant micro-site factor is a soil factor, the determining module is further configured to extract initial data of a soil factor based on the soil data; the soil factor includes soil layer thickness, soil water holding capacity, and soil nutrients; and to perform interpolation analysis on the initial data to obtain the predicted value of the soil factor at the grid point, which is used as the factor data of the soil factor.

[0114] According to an exemplary embodiment of this disclosure, when the soil factor is soil nutrients, the determining module is further configured to extract soil nutrient content data from the soil data; and to use principal component analysis to comprehensively evaluate the soil nutrient content data to obtain a comprehensive value of soil nutrient content, which is used as the initial data of the soil nutrients.

[0115] According to an exemplary embodiment of this disclosure, the classification module is further configured to pre-construct a classification threshold corresponding to the micro-site dominant factor; for a grid point, determine the primary classification result of the micro-site dominant factor based on the factor data corresponding to the micro-site dominant factor at the grid point and the classification threshold; traverse each of the micro-site dominant factors to fuse the primary classification results corresponding to each of the micro-site dominant factors to obtain the micro-site classification result corresponding to the grid point.

[0116] According to an exemplary embodiment of this disclosure, the regional data processing device further includes a verification module, configured to obtain a micro-site classification system for the main region where the target region is located; and to verify the micro-site classification results corresponding to the grid points according to the micro-site classification system.

[0117] According to an exemplary embodiment of this disclosure, the verification module is further configured to combine the micro-site dominant factors to create multi-level site classification factors; acquire data of other regions that have a temporal or spatial correspondence with the target region, and the micro-site classification results corresponding to the other region data; configure the mapping relationship between the multi-level site classification factors based on the micro-site classification results corresponding to the other region data, so as to create a micro-site classification system for the main region.

[0118] The specific details of each module in the aforementioned regional data processing device 400 have been described in detail in the corresponding regional data processing method, so they will not be repeated here.

[0119] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0120] In exemplary embodiments of this disclosure, a storage medium capable of implementing the above-described methods is also provided. It may be a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a terminal device, such as a mobile phone. However, the program product of this disclosure is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0121] In an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided. Figure 5 The schematic diagram illustrates the structure of a computer system of an electronic device according to an exemplary embodiment of the present disclosure.

[0122] It should be noted that, Figure 5 The computer system 500 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0123] like Figure 5As shown, the computer system 500 includes a Central Processing Unit (CPU) 501, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 502 or programs loaded from storage section 508 into Random Access Memory (RAM) 503. The RAM 503 also stores various programs and data required for system operation. The CPU 501, ROM 502, and RAM 503 are interconnected via a bus 504. An Input / Output (I / O) interface 505 is also connected to the bus 504.

[0124] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. Removable media 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 510 as needed so that computer programs read from them can be installed into storage section 508 as needed.

[0125] In particular, according to embodiments of this disclosure, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by central processing unit (CPU) 501, it performs various functions defined in the system of this disclosure.

[0126] It should be noted that the computer-readable medium shown in the embodiments of this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0128] The units described in the embodiments of this disclosure can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the unit itself.

[0129] In another aspect, this disclosure also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0130] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0131] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0132] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0133] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for processing regional data, characterized in that, include: In response to a user's input operation, obtain the region data of the target region corresponding to the input operation; The target area is the exposed surface of the high-altitude hydropower project area, and the regional data includes the topographic data of the target area and the soil data of the sample plots within the target area. The target area is rasterized to obtain raster points, and the factor data of the micro-site dominant factor corresponding to the raster points is determined based on the area data. The dominant micro-site factors include topographic factors and soil factors; When the dominant micro-site factor is a soil factor, the step of determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes: extracting initial data of a soil factor based on the soil data; the soil factor includes soil layer thickness, soil water holding capacity, and soil nutrients; performing interpolation analysis on the initial data to obtain the predicted value of the soil factor at the grid point, which is used as the factor data of the soil factor. Based on the factor data, the micro-site classification result corresponding to the grid point is determined, and a spatial distribution map of micro-site types in the target area is drawn based on the grid point and the micro-site classification result; The method further includes: determining the dominant microsite factor, including: constructing habitat characterization indicators for exposed surfaces in the sampling area, and collecting regional data of the sampling area; calculating the coefficient of variation corresponding to each habitat characterization indicator based on the regional data; and comparing the coefficient of variation with a preset coefficient of variation threshold to screen out the dominant microsite factor from the habitat characterization indicators.

2. The regional data processing method according to claim 1, characterized in that, When the dominant micro-site factor is a topographic factor, the step of determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes: Spatial analysis is performed on the terrain data to obtain factor data corresponding to the terrain factors; wherein, the terrain factors include slope position, slope gradient, and slope aspect.

3. The regional data processing method according to claim 1, characterized in that, When the soil factor is soil nutrients, the initial data for extracting a soil factor based on the soil data includes: Extract soil nutrient content data from the soil data; Principal component analysis was used to comprehensively evaluate the soil nutrient content data to obtain a comprehensive value of soil nutrient content, which was used as the initial data for soil nutrients.

4. The regional data processing method according to claim 1, characterized in that, The determination of the micro-site classification result corresponding to the grid point based on the factor data includes: Pre-construct the classification thresholds corresponding to the dominant factors of the micro-site; For a grid point, the primary classification result of the micro-site dominant factor is determined based on the factor data and classification threshold corresponding to the micro-site dominant factor at the grid point; The primary classification results corresponding to each micro-site dominant factor are traversed to obtain the micro-site classification result corresponding to the grid point.

5. The regional data processing method according to claim 1, characterized in that, The method further includes: Obtain the micro-site classification system of the main region where the target region is located; The micro-site classification results corresponding to the grid points are verified according to the micro-site classification system.

6. The regional data processing method according to claim 5, characterized in that, The method further includes: constructing a micro-site classification system for the main region where the target region is located, wherein constructing the micro-site classification system for the main region where the target region is located includes: The microsite dominant factors are combined to create multi-level site classification factors; Acquire data from other regions that have a temporal or spatial correspondence with the target region, as well as the micro-site classification results corresponding to the other region data; Based on the micro-site classification results corresponding to the data from other regions, configure the mapping relationship between the multi-level site classification factors to create a micro-site classification system for the main region.

7. A regional data processing device, characterized in that, include: The response module is used to respond to the user's input operation and obtain the region data of the target area corresponding to the input operation; The target area is the exposed surface of the high-altitude hydropower project area, and the regional data includes the topographic data of the target area and the soil data of the sample plots within the target area. The determination module is used to perform rasterization processing on the target area to obtain raster points, and determine the factor data of the micro-site dominant factor corresponding to the raster points based on the area data; The dominant micro-site factors include topographic factors and soil factors; When the dominant micro-site factor is a soil factor, the step of determining the factor data of the dominant micro-site factor corresponding to the grid point based on the regional data includes: extracting initial data of a soil factor based on the soil data; the soil factor includes soil layer thickness, soil water holding capacity, and soil nutrients; performing interpolation analysis on the initial data to obtain the predicted value of the soil factor at the grid point, which is used as the factor data of the soil factor; and further determining the dominant micro-site factor includes: constructing habitat characterization indicators for the exposed surface of the sampling area, and collecting regional data of the sampling area; calculating the coefficient of variation corresponding to each habitat characterization indicator based on the regional data; and comparing the coefficient of variation with a preset coefficient of variation threshold to screen out the dominant micro-site factor from the habitat characterization indicators. The classification module is used to determine the micro-site classification result corresponding to the grid point based on the factor data, and to draw a spatial distribution map of micro-site types in the target area based on the grid point and the micro-site classification result.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the regional data processing method as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the area data processing method as described in any one of claims 1 to 6.

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