Automatic classification design model for surveying, mapping and geographic information results

Abstract

The invention discloses an automatic classification design model for surveying and mapping and geographic information achievements, and relates to the technical field of surveying and mapping and geographic information achievement classification. The automatic classification design model for surveying and mapping and geographic information achievements generates terrain classification results by comprehensively analyzing the surface and deep terrain features of the terrain area to be measured, and realizes visual display through model marking. The invention calculates the initial terrain assessment index by combining the slope, curvature, texture and undulation index of the elevation data through comprehensive analysis of the surface and deep terrain, and generates a terrain comprehensive assessment index by using the deep rock and soil density, field strength, energy characteristics and eco-hydrological data, which significantly improves the accuracy of classification and makes up for the defect that the traditional method does not pay enough attention to the deep features, so as to fully reflect the complex characteristics of the terrain area, provide a reliable basis for scientific planning, and ensure a high degree of matching between the terrain classification results and the actual application needs, especially in complex geological environments.

Description

Technical Field

[0001] The invention relates to the technical field of surveying and mapping and geographic information achievement classification, in particular to an automatic classification design model for surveying and mapping and geographic information achievement. Background Art

[0002] As an important application direction of surveying and mapping science and geographic information technology, terrain classification and geographic information processing widely serve key areas such as resource management, infrastructure construction, ecological protection and disaster prevention. In recent years, with the rapid development of science and technology, surveying and mapping and geographic information technology have made important progress in data collection, processing and application, promoting the widespread application of geographic information results in many fields.

[0003] With the increasing maturity of remote sensing technology, global navigation satellite systems and unmanned aerial vehicle surveying and mapping technology, the accuracy and coverage of terrain data collection have been greatly improved. Modern surveying and mapping technology can not only quickly and accurately obtain basic terrain data such as surface elevation, slope and curvature, but also deeply explore regional characteristics through field strength data, energy distribution data and hydrological and ecological data, providing comprehensive data support for terrain classification.

[0004] In the era of big data, traditional manual classification and empirical judgment can no longer meet the needs of efficient and accurate terrain classification. Automated classification models can quickly process complex multi-source data into intuitive classification results through algorithms and standardized processes, which not only improves work efficiency, but also creates conditions for the promotion and application of large-scale geographic information results. Therefore, classification methods based on automated models have gradually become a research hotspot. Such methods can extract deep features from surveying and mapping results and realize automated and precise classification.

[0005] Based on the above scheme, it is found that the limitations of the existing technology include at least the following problems. First, the traditional surveying and mapping and geographic information classification methods are mostly focused on single-dimensional data analysis, limited to elevation or simple terrain feature parameter evaluation, and fail to achieve a comprehensive and integrated analysis of surface and deep terrain, which can easily lead to one-sided classification results. Especially in complex terrain areas, it is easy to have terrain evaluation results that are inconsistent with the actual situation, thereby affecting the scientific nature of terrain results and the accuracy of application. Secondly, the existing technology mostly uses manual participation or single algorithm processing in classification methods, lacks automated and integrated classification models, especially in large-scale terrain area surveying and mapping, and has low efficiency. Due to the difficulty in quickly processing multi-dimensional and multi-source data, it is easy to extend the data analysis cycle, and thus it is difficult to meet the needs of efficient analysis and rapid decision-making. Finally, the classification results of the existing technology are mostly displayed in static reports or single results, lacking visual integration with specific terrain models, making it difficult to clearly and intuitively reflect the spatial distribution characteristics of the classification results, limiting users' global understanding of regional terrain characteristics, and not conducive to the scientific implementation of practical needs such as planning and design, regional development and ecological protection. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides an automatic classification design model for surveying and mapping and geographic information results, which solves the problem that traditional surveying and mapping and geographic information results classification methods are mostly focused on single-dimensional data analysis, limited to elevation or simple terrain feature parameter evaluation, and fail to achieve a comprehensive and integrated analysis of surface and deep terrain, which easily leads to one-sided classification results, especially in complex terrain areas, where the terrain evaluation results are likely to be inconsistent with the actual situation, thereby affecting the scientific nature of the terrain results and the accuracy of application. Secondly, the prior art mostly adopts manual participation or single algorithm processing in classification methods, lacks automated and integrated classification models, especially in large-scale terrain area surveying and mapping, and has low efficiency. Due to the difficulty in quickly processing multi-dimensional and multi-source data, it is easy to extend the data analysis cycle, and thus it is difficult to meet the needs of efficient analysis and rapid decision-making. Finally, the classification results of the prior art are mostly displayed in static reports or single results, lacking visual integration with specific terrain models, so that it is difficult to clearly and intuitively reflect the spatial distribution characteristics of the classification results, limiting the user's global understanding of regional terrain characteristics, and is not conducive to the scientific implementation of practical needs such as planning and design, regional development and ecological protection.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a model for automatic classification and design of surveying and mapping and geographic information results, including: a terrain establishment module, a surface terrain analysis module, a surface terrain classification module, a deep terrain analysis module, a deep terrain classification module, and a display mark module; the terrain establishment module is used to obtain the terrain elevation value of each measuring position point of each terrain area to be measured, and respectively establish a terrain model of each terrain area to be measured; the surface terrain analysis module is used to perform a comprehensive analysis on the terrain elevation value of each measuring position point of each terrain area to be measured, obtain the terrain slope index, terrain curvature index, terrain texture index, and terrain undulation index of each terrain area to be measured, and perform a comprehensive analysis to obtain the terrain initial evaluation index of each terrain area to be measured; the surface terrain classification module is used to judge and analyze the terrain initial evaluation index of each terrain area to be measured based on a preset surface terrain evaluation interval, and perform surface classification on each terrain area to be measured according to the judgment and analysis results; the deep terrain analysis module is used to The surface rock and soil density value, surface field strength data, surface energy data, and surface eco-hydrological data of each measuring position point of each terrain area to be measured are obtained respectively, and a comprehensive analysis is performed to obtain the terrain morphological index, surface field strength characteristic index, surface energy characteristic index, and surface eco-hydrological index of each terrain area to be measured, and a comprehensive analysis is performed to obtain the terrain comprehensive evaluation index of each terrain area to be measured, the surface field strength data includes gravity measurement values ​​and magnetic field strength measurement values, the surface energy data includes surface temperature values, surface reflectivity values, and surface thermal radiation values, and the surface eco-hydrological data includes near-infrared band reflectivity values, red light band reflectivity values, and surface moisture content values; the deep terrain classification module is used to judge and analyze the terrain comprehensive evaluation index of each terrain area to be measured based on a preset deep terrain evaluation interval, and perform deep classification on each terrain area to be measured according to the judgment and analysis results; the display marking module is used to mark and display the surface classification results and deep classification results of each terrain area to be measured in the corresponding terrain model.

[0008] Furthermore, the specific formula for calculating the initial terrain assessment index of each terrain area to be measured is as follows: ;in, For the The initial terrain assessment index of the terrain area to be measured, For the The terrain slope index of the terrain area to be measured, is the terrain slope coefficient stored in the database, For the The terrain curvature index of the terrain area to be measured, is the terrain curvature coefficient stored in the database, For the The terrain texture index of the terrain area to be measured, is the terrain texture coefficient stored in the database, For the The topographic relief index of the terrain area to be measured, is the terrain relief coefficient stored in the database, , is a natural constant, =1, 2, 3, …, , is the number of terrain areas to be measured.

[0009] Furthermore, the specific steps for obtaining the terrain slope index of each terrain area to be measured are as follows: for each terrain area to be measured, a number of measurement areas are randomly selected, and the regional diagonal distance value of each measurement area of ​​each terrain area to be measured is obtained respectively; the terrain elevation value of each measurement position point of each measurement area of ​​each terrain area to be measured is compared and analyzed respectively, and the maximum terrain elevation value and the minimum terrain elevation value of each measurement area of ​​each terrain area to be measured are obtained; the maximum terrain elevation value, the minimum terrain elevation value, and the regional diagonal distance value of each measurement area of ​​each terrain area to be measured are comprehensively analyzed respectively, and the terrain slope index of each terrain area to be measured is obtained; wherein, the specific formula for calculating the terrain slope index of each terrain area to be measured is as follows: ;in, For the The terrain slope index of the terrain area to be measured, For the The first The maximum terrain elevation of the measurement area, For the The first The minimum terrain elevation of the measurement area, For the The first The diagonal distance value of the measurement area, =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , is the number of measurement areas.

[0010] Furthermore, the specific steps for obtaining the terrain curvature index and terrain texture index of each terrain area to be measured are as follows: for each measurement position point of each terrain area to be measured, a number of adjacent measurement position points within a set neighborhood are read respectively; the terrain elevation value of each measurement position point of each terrain area to be measured is combined with the terrain elevation value of each adjacent measurement position point within the set neighborhood for comprehensive analysis to obtain the terrain curvature index of each terrain area to be measured; the terrain elevation value of each measurement position point of each terrain area to be measured is combined with the terrain elevation value of each adjacent measurement position point within the set neighborhood for elevation difference analysis to obtain the neighborhood elevation difference mean and neighborhood elevation difference standard deviation of each measurement position point of each terrain area to be measured, and a comprehensive analysis is performed to obtain the terrain texture index of each terrain area to be measured; wherein, the specific formulas for calculating the terrain curvature index of each terrain area to be measured, the neighborhood elevation difference mean and neighborhood elevation difference standard deviation of each measurement position point of each terrain area to be measured, and the terrain texture index of each terrain area to be measured are as follows: ;in, For the The terrain curvature index of the terrain area to be measured, For the The first The terrain elevation value of the measurement location point, For the The first The first measurement point within the set neighborhood The terrain elevation values ​​of adjacent measurement locations, For the The first The mean difference in elevation of the neighborhood of the measurement location point, For the The first The standard deviation of the neighborhood elevation difference of each measurement point, For the The terrain texture index of the terrain area to be measured, =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , is the number of measurement position points, =1, 2, 3, …, , The number of adjacent measurement points within the set neighborhood.

[0011] Furthermore, the specific steps for obtaining the terrain relief index of each terrain area to be measured are as follows: the terrain elevation values ​​of each measuring position point of each terrain area to be measured are comprehensively analyzed to obtain the terrain elevation mean of each terrain area to be measured; the terrain elevation values ​​of each measuring position point of each terrain area to be measured are combined with the terrain elevation mean of each terrain area to be measured to perform comprehensive analysis to obtain the terrain relief index of each terrain area to be measured; wherein, the specific formula for calculating the terrain elevation mean and the terrain relief index of each terrain area to be measured is as follows: ;in, For the The average terrain elevation of the terrain area to be measured, For the The first The terrain elevation value of the measurement location point, For the The topographic relief index of the terrain area to be measured, =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0012] Furthermore, the specific formula for calculating the terrain comprehensive evaluation index of each terrain area to be measured is as follows: ;in, For the The terrain comprehensive assessment index of the terrain area to be measured, For the The topographic morphological index of the terrain area to be measured, is the terrain morphological coefficient stored in the database, For the The surface field strength characteristic index of the terrain area to be measured, is the underground material characteristic coefficient stored in the database, For the The surface energy characteristic index of the terrain area to be measured, is the surface energy characteristic coefficient stored in the database, For the The surface ecohydrological index of the terrain area to be measured, is the surface eco-hydrological coefficient stored in the database, , is a natural constant, =1, 2, 3, …, , is the number of terrain areas to be measured.

[0013] Furthermore, the specific steps for obtaining the terrain morphological index of each terrain area to be measured are as follows: reading the surface rock and soil density value of each measuring position point of each terrain area to be measured, and performing comprehensive analysis in combination with the terrain elevation mean of each terrain area to be measured, to obtain the terrain undulation energy density index of each terrain area to be measured; performing comprehensive analysis on the terrain elevation mean and the terrain undulation energy density index of each terrain area to be measured, to obtain the terrain morphological index of each terrain area to be measured; wherein, the specific formula for calculating the terrain undulation energy density index and the terrain morphological index of each terrain area to be measured is as follows: ;in, For the The topographic energy density index of the terrain area to be measured, For the The first The surface rock and soil density value at each measurement location, is the value of gravity acceleration, For the The first The terrain elevation value of the measurement location point, For the The average terrain elevation of the terrain area to be measured, For the The topographic morphological index of the terrain area to be measured, is the terrain elevation coefficient stored in the database, is the fluctuation energy density coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0014] Furthermore, the specific steps for obtaining the surface field strength characteristic index of each terrain area to be measured are as follows: obtaining the gravity reference value and the magnetic field strength reference value of each terrain area to be measured; comprehensively analyzing the gravity measurement value and the magnetic field strength measurement value of each measurement position point of each terrain area to be measured in combination with the gravity reference value and the magnetic field strength reference value of each terrain area to be measured, and obtaining the gravity anomaly index and the magnetic field strength anomaly index of each terrain area to be measured; comprehensively analyzing the gravity anomaly index and the magnetic field strength anomaly index of each terrain area to be measured, and obtaining the surface field strength characteristic index of each terrain area to be measured; wherein, the specific formulas for calculating the gravity anomaly index, the magnetic field strength anomaly index and the surface field strength characteristic index of each terrain area to be measured are as follows: ;in, For the The gravity anomaly index of the terrain area to be measured, For the The first The gravity measurement value at each measurement location, For the The gravity reference value of the terrain area to be measured, For the The magnetic field intensity anomaly index of the terrain area to be measured, For the The first The magnetic field strength measurement value at each measurement location, For the The reference value of magnetic field strength in the terrain area to be measured, For the The surface field strength characteristic index of the terrain area to be measured, is the gravity coefficient stored in the database, is the magnetic field intensity coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0015] Furthermore, the specific steps for obtaining the surface energy characteristic index of each terrain area to be measured are as follows: reading the surface temperature value, surface reflectivity value, and surface thermal radiation value of each measuring position point of each terrain area to be measured, and performing comprehensive analysis respectively to obtain the surface temperature index, surface reflectivity index, and surface thermal radiation index of each terrain area to be measured; performing comprehensive analysis on the surface temperature index, surface reflectivity index, and surface thermal radiation index of each terrain area to be measured respectively to obtain the surface energy characteristic index of each terrain area to be measured; wherein, the specific formula for calculating the surface temperature index, surface reflectivity index, surface thermal radiation index, and surface energy characteristic index of each terrain area to be measured is as follows: ;in, For the The surface temperature index of the terrain area to be measured, For the The first The surface temperature value at each measuring point, For the The surface reflectivity index of the terrain area to be measured, For the The first The surface reflectance value of the measurement location point, For the The surface thermal radiation index of the terrain area to be measured, For the The first The surface thermal radiation value of the measurement location point, For the The surface energy characteristic index of the terrain area to be measured, is the surface temperature coefficient stored in the database, is the surface reflectance stored in the database, is the surface thermal radiation coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0016] Furthermore, the specific steps for obtaining the surface eco-hydrological index of each terrain area to be measured are as follows: read the near-infrared band reflectance value and the red light band reflectance value of each measuring position point of each terrain area to be measured, and perform comprehensive analysis respectively to obtain the vegetation index of each terrain area to be measured; read the surface moisture content value of each measuring position point of each terrain area to be measured and perform comprehensive analysis respectively to obtain the surface moisture content index of each terrain area to be measured; perform comprehensive analysis on the vegetation index and the surface moisture content index of each terrain area to be measured respectively to obtain the surface eco-hydrological index of each terrain area to be measured; wherein, the specific formulas for calculating the vegetation index, the surface moisture content index and the surface eco-hydrological index of each terrain area to be measured are as follows: ;in, For the The vegetation index of the terrain area to be measured, For the The first The near-infrared reflectance value of each measurement point, For the The first The red light band reflectance value of each measurement point, For the The surface moisture content index of the terrain area to be measured, For the The first The surface moisture content value at each measuring location, For the The surface ecohydrological index of the terrain area to be measured, is the vegetation coefficient stored in the database, is the surface moisture content coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0017] The present invention has the following beneficial effects:

[0018] (1) This model for automatic classification and design of surveying and mapping and geographic information results calculates the initial terrain assessment index through comprehensive analysis of surface and deep terrain, combined with the slope, curvature, texture and undulation index of elevation data. At the same time, it uses deep rock and soil density, field strength, energy characteristics and eco-hydrological data to generate a comprehensive terrain assessment index. Multi-dimensional data fusion significantly improves the accuracy of classification and makes up for the defect that traditional methods pay insufficient attention to deep characteristics. It can fully reflect the complex characteristics of the terrain area and provide a reliable basis for scientific planning, especially in complex geological environments, to ensure a high degree of match between terrain classification results and actual application needs.

[0019] (2) The automatic classification design model for surveying, mapping and geographic information results introduces a modular structure, covering terrain establishment, surface and deep layer analysis and classification, and result display. Each module operates independently and works together to process multidimensional data such as rock and soil density, field strength, temperature, reflectivity, and ecological characteristics through standardized algorithms to ensure efficient data analysis and uniform results. This design is not only suitable for different types of terrain areas, but also reduces human intervention and analysis bias, thereby improving overall work efficiency. At the same time, the modular structure is easy to expand and can adapt to new data sources and analysis methods as needed, providing flexible support for different application scenarios.

[0020] (3) The automatic classification design model of surveying and mapping and geographic information results uses a display marking module to intuitively mark the surface and deep classification results in the digital terrain model to form a visual result map. The visual presentation is clear and intuitive, allowing technicians to quickly grasp the classification information and comprehensive characteristics of the terrain area. This not only provides a scientific basis for project planning and design, but also reduces design deviations caused by incomplete terrain information during the construction phase. By improving the practicality and readability of the classification results, the model provides strong support for shortening the construction period and reducing construction costs, significantly improving the economic benefits and implementation efficiency of the project.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A model framework diagram is provided for the automatic classification design of surveying and mapping and geographic information results of the present invention.

[0023] Figure 2 The present invention is a flowchart of the specific steps for obtaining the terrain slope index of each terrain area to be measured in the automatic classification design model of surveying and mapping and geographic information achievements.

[0024] Figure 3 The present invention is a flowchart of the specific steps for obtaining the terrain morphological index of each terrain area to be measured in the automatic classification design model of surveying and mapping and geographic information achievements. DETAILED DESCRIPTION

[0025] The embodiment of the present application solves the problem that the traditional classification methods of surveying and mapping and geographic information achievements are mostly focused on single-dimensional data analysis, limited to elevation or simple terrain feature parameter evaluation, and fail to achieve a comprehensive and integrated analysis of surface and deep terrain, which easily leads to one-sided classification results, especially in complex terrain areas, where the terrain evaluation results are likely to be inconsistent with the actual situation, thereby affecting the scientific nature of the terrain achievements and the accuracy of application. Secondly, the existing technology mostly adopts manual participation or single algorithm processing in classification methods, lacks automated and integrated classification models, especially in large-scale terrain area surveying and mapping, and has low efficiency. Due to the difficulty in quickly processing multi-dimensional and multi-source data, it is easy to extend the data analysis cycle, and it is difficult to meet the needs of efficient analysis and rapid decision-making. Finally, the classification results of the existing technology are mostly displayed in static reports or single results, lacking visual integration with specific terrain models, so it is difficult to clearly and intuitively reflect the spatial distribution characteristics of the classification results, limiting the user's global understanding of regional terrain characteristics, and is not conducive to the scientific implementation of practical needs such as planning and design, regional development and ecological protection.

[0026] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0027] First, the terrain establishment module is used to collect data for each terrain area to be tested, including multi-dimensional features such as elevation value, rock and soil density, field strength data, energy data and eco-hydrological data, to ensure the comprehensiveness and accuracy of the data. At the same time, with the elevation value as the core, a digital terrain model is established to provide basic support for subsequent analysis and classification. The surface terrain analysis module is used to calculate the slope index, curvature index, texture index and undulation index, and these data are combined to generate the initial terrain assessment index. Subsequently, the surface terrain classification is completed based on the surface classification standard. At the same time, the deep terrain analysis module is used to integrate rock and soil density, field strength (such as gravity anomaly and magnetic field anomaly), energy characteristics (such as temperature, reflectivity and thermal radiation) and eco-hydrological data (such as vegetation index and moisture content) to generate a comprehensive terrain assessment index, and the deep terrain classification is completed based on the deep classification standard. Finally, the display mark module is used to mark the surface classification results and deep classification results on the digital terrain model to form a visual result. This process makes the classification results intuitively displayed, which helps technicians to fully understand the terrain characteristics and provide a scientific basis for engineering planning and design.

[0028] See also Figure 1The embodiment of the present invention provides a technical solution: a model for automatically classifying and designing surveying and mapping and geographic information achievements, including: a terrain establishment module, a surface terrain analysis module, a surface terrain classification module, a deep terrain analysis module, a deep terrain classification module, and a display mark module; the terrain establishment module is used to obtain the terrain elevation value of each measurement position point of each terrain area to be measured (which can be obtained by laser radar or global positioning system measurement), and respectively establish a terrain model of each terrain area to be measured; the surface terrain analysis module is used to perform a comprehensive analysis on the terrain elevation value of each measurement position point of each terrain area to be measured, and obtain each The terrain slope index, terrain curvature index, terrain texture index, and terrain undulation index of the terrain area to be measured are obtained by comprehensive analysis to obtain the terrain initial assessment index of each terrain area to be measured; the surface terrain classification module is used to judge and analyze the terrain initial assessment index of each terrain area to be measured based on the preset surface terrain assessment interval, and classify each terrain area to be measured on the surface according to the judgment and analysis results; the deep terrain analysis module is used to respectively obtain the surface rock and soil density value (which can be obtained by measuring with a density meter), surface field strength data, surface energy data, surface ecological water content data, and other data of each measuring position point of each terrain area to be measured. The surface field strength data includes gravity measurement value (which can be obtained by gravimeter measurement), magnetic field strength measurement value (which can be obtained by magnetometer measurement), surface energy data includes surface temperature value (which can be obtained by infrared temperature sensor measurement), surface reflectivity value (which can be obtained by hyperspectral imager measurement), surface thermal radiation value (which can be obtained by hyperspectral imager measurement) The surface eco-hydrological data include near-infrared band reflectance values ​​(which can be obtained by measuring with a hyperspectral sensor), red light band reflectance values ​​(which can be obtained by measuring with a hyperspectral sensor), and surface moisture content values ​​(which can be obtained by measuring with a soil moisture sensor); the deep terrain classification module is used to judge and analyze the terrain comprehensive evaluation index of each terrain area to be measured based on a preset deep terrain evaluation interval, and to perform deep classification on each terrain area to be measured according to the judgment and analysis results; the display marking module is used to mark and display the surface classification results and deep classification results of each terrain area to be measured in the corresponding terrain model.

[0029] The surface terrain assessment interval includes but is not limited to the following examples:

[0030] Stable terrain interval: indicates that the comprehensive assessment value falls within the low value interval, which usually corresponds to areas with flat terrain and small surface geometry changes. It is suitable for use in scenarios such as agriculture and urban construction that have high requirements for terrain stability.

[0031] Gentle slope transition interval: means that the comprehensive assessment value is in the medium-low range, which usually indicates that the area has a certain slope and undulation, but the overall geometric characteristics are still relatively flat, which is suitable for development, low-intensity utilization and other scenarios, while taking into account a certain adaptability to terrain changes.

[0032] Complex terrain range: indicates that the comprehensive assessment value is in the medium to high range, usually corresponding to areas with complex surface textures and undulations, and large changes in slope and curvature. It is suitable for scenarios with high requirements for protecting the natural state, such as ecological monitoring and low-disturbance development.

[0033] Steep slope high-risk interval: means that the comprehensive assessment value falls in the high value range, which usually corresponds to areas with steep slopes, drastic curvature changes, undulations and complex textures. It requires enhanced protection and monitoring of potential risks of geological disasters.

[0034] The deep terrain assessment interval includes but is not limited to the following examples:

[0035] Resource potential development range: indicates that the comprehensive assessment value falls within the low value range, which usually corresponds to areas with relatively stable terrain, excellent underground material properties, and balanced surface energy and ecological and hydrological conditions, and is suitable for economic activities such as mineral development, groundwater utilization and agricultural development.

[0036] Ecological function interval: means that the comprehensive assessment value is in the median interval, which usually indicates an area with moderate surface energy, relatively stable underground material conditions, and significant eco-hydrological characteristics. It is suitable for scenarios with high ecological function requirements such as ecological restoration, vegetation restoration or hydrological function protection.

[0037] Natural disaster sensitive range: indicates that the comprehensive assessment value is in the high value range, which usually corresponds to areas with more complex terrain, larger fluctuations in underground material properties, more active surface energy, and more fragile ecological and hydrological conditions. These areas need to be monitored or development restricted to prevent geological disasters such as landslides and mud-rock flows.

[0038] Key protection zone: indicates that the comprehensive assessment value falls in the highest range. Usually, the corresponding area exhibits unique terrain features (such as karst landforms) or abnormal underground material structures. At the same time, the ecological hydrological and energy characteristics deviate significantly from the normal range. It is suitable for scientific research, tourism development, or listed as a key protection area.

[0039] Specifically, the specific formula for calculating the initial terrain assessment index of each terrain area to be measured is as follows: ;in, For the The initial terrain assessment index of the terrain area to be measured, For the The terrain slope index of the terrain area to be measured, is the terrain slope coefficient stored in the database, For the The terrain curvature index of the terrain area to be measured, is the terrain curvature coefficient stored in the database, For the The terrain texture index of the terrain area to be measured, is the terrain texture coefficient stored in the database, For the The topographic relief index of the terrain area to be measured, is the terrain relief coefficient stored in the database, , is a natural constant, and in this embodiment, its value is 2.71. =1, 2, 3, …, , is the number of terrain areas to be measured.

[0040] The specific implementation example of calculating each terrain area to be measured is as follows. There are five terrain area data to be measured, including the following:

[0041] The terrain slope index of the first terrain area to be measured is approximately: 20.492.

[0042] The terrain curvature index of the first terrain area to be measured is approximately: 26.563.

[0043] The terrain texture index of the first terrain area to be tested is approximately: 19.544.

[0044] The topographic relief index of the first terrain area to be measured is approximately: 9.610.

[0045] The terrain slope index of the second terrain area to be measured is approximately: 37.283.

[0046] The terrain curvature index of the second terrain area to be measured is approximately: 17.793.

[0047] The terrain texture index of the second terrain area to be tested is approximately: 16.627.

[0048] The topographic relief index of the second terrain area to be measured is approximately: 5.442.

[0049] The terrain slope index of the third terrain area to be tested is approximately: 16.547.

[0050] The terrain curvature index of the third terrain area to be measured is approximately: 23.002.

[0051] The terrain texture index of the third terrain area to be tested is approximately: 19.092.

[0052] The topographic relief index of the third terrain area to be measured is approximately: 5.980.

[0053] The terrain slope index of the fourth terrain area to be tested is approximately: 55.920.

[0054] The terrain curvature index of the fourth terrain area to be measured is approximately: 23.668.

[0055] The terrain texture index of the fourth terrain area to be tested is approximately: 18.422.

[0056] The topographic relief index of the fourth terrain area to be measured is approximately: 5.226.

[0057] The terrain slope index of the fifth terrain area to be tested is approximately: 26.645.

[0058] The terrain curvature index of the fifth terrain area to be measured is approximately: 14.621.

[0059] The terrain texture index of the fifth terrain area to be tested is approximately: 13.968.

[0060] The topographic relief index of the fifth terrain area to be measured is approximately: 6.627.

[0061] The terrain slope coefficient stored in the database is: 0.22.

[0062] The terrain curvature coefficient stored in the database is: 0.24.

[0063] The terrain texture factor stored in the database is: 0.20.

[0064] The terrain relief coefficient stored in the database is: 0.34.

[0065] The value of the natural constant is 2.71.

[0066] And the implementation examples of the preset surface terrain assessment intervals are as follows:

[0067] Smooth terrain interval: [1.0, 1.3)

[0068] Gentle slope transition interval: [1.3, 1.8).

[0069] Complex terrain interval: [1.8, 2.5).

[0070] Steep slope high risk interval: [2.5, 3.0).

[0071] Substitute the above data into the specific formula for calculating the initial terrain assessment index of each terrain area to be measured, and obtain:

[0072] The initial terrain evaluation index of the first terrain area to be tested is ≈1.967, 1.967∈terrain complex interval: [1.8, 2.5), so the first terrain area to be tested is a terrain complex area.

[0073] The initial terrain evaluation index of the second terrain area to be tested is ≈1.944, 1.944∈terrain complex interval: [1.8, 2.5), so the second terrain area to be tested is a terrain complex area.

[0074] The terrain initial assessment index of the third terrain area to be tested is ≈1.783, 1.783∈gentle slope transition interval: [1.3, 1.8), so the third terrain area to be tested is a gentle slope transition area.

[0075] The initial terrain evaluation index of the fourth terrain area to be tested is ≈2.253, 2.253∈terrain complex interval: [1.8, 2.5), so the fourth terrain area to be tested is a terrain complex area.

[0076] The initial terrain evaluation index of the fifth terrain area to be tested is ≈1.744, 1.744∈gentle slope transition interval: [1.3, 1.8), so the fifth terrain area to be tested is a gentle slope transition area.

[0077] In this implementation scheme, the specific formula for calculating the initial terrain assessment index of each terrain area to be tested generates the initial terrain assessment index through weighted synthesis of multiple key terrain feature parameters, which can more accurately reflect the comprehensive characteristics of the terrain area. Among them, the terrain slope index is used to reflect the degree of terrain inclination, which directly affects the surface runoff, erosion and other characteristics. The terrain curvature index is used to describe the geometric characteristics of the terrain undulation, which is helpful for analyzing the geomorphic characteristics of the terrain. The terrain texture index is used to reveal the complexity of the terrain surface, which is closely related to land use and ecological characteristics. The terrain undulation index is used to reflect the overall characteristics of the terrain elevation fluctuation, which can reveal the terrain severity of the region. Through the integration of these indicators, the formula can evaluate the complexity and overall morphology of the terrain from multiple dimensions, which is suitable for the classification and research of different terrain environments, and the formula introduces the terrain slope coefficient , Terrain curvature coefficient , Terrain texture coefficient , terrain relief coefficient As weight coefficients, they control the contribution of slope, curvature, texture and undulation respectively. Users can adjust the weights according to specific application scenarios (such as hydrological analysis and land use planning) and flexibly adapt to various practical needs. At the same time, the weight values ​​can be calibrated through historical data to improve the applicability of the formula to specific areas. This formula avoids the limitations of single-dimensional evaluation. By integrating the four parameters of slope, curvature, texture and undulation, it not only considers the basic terrain features such as slope and curvature, but also reflects higher-order morphological complexity through texture and undulation. Compared with classification based on a single feature alone, the comprehensive index calculated by this formula can more accurately reflect the differences in terrain areas and provide higher accuracy for the classification model.

[0078] Specifically, Figure 2 As shown, the specific steps of obtaining the terrain slope index of each terrain area to be measured are as follows: for each terrain area to be measured, a number of measurement areas (square areas in this embodiment) are randomly selected, and the regional diagonal distance value of each measurement area of ​​each terrain area to be measured is obtained respectively; the terrain elevation value of each measurement position point of each measurement area of ​​each terrain area to be measured is compared and analyzed respectively to obtain the maximum terrain elevation value and the minimum terrain elevation value of each measurement area of ​​each terrain area to be measured; the maximum terrain elevation value, the minimum terrain elevation value, and the regional diagonal distance value of each measurement area of ​​each terrain area to be measured are comprehensively analyzed respectively to obtain the terrain slope index of each terrain area to be measured.

[0079] The specific formula for calculating the terrain slope index of each terrain area to be measured is as follows: ;in, For the The terrain slope index of the terrain area to be measured, For the The first The maximum terrain elevation of the measurement area, For the The first The minimum terrain elevation of the measurement area, For the The first The diagonal distance value of the measurement area, =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , is the number of measurement areas.

[0080] In this implementation scheme, the formula obtains the slope characteristics of each measuring area by calculating the maximum and minimum values ​​of the terrain elevation of each measuring area, as well as the regional diagonal distance value. By gradually accumulating these regional data, the comprehensive slope index of the terrain area to be measured is finally obtained. This method ensures that the evaluation of slope characteristics is more detailed and accurate, avoids the errors that may be caused by using only single-point elevation data, and makes the slope analysis results more consistent with the actual terrain conditions. This method randomly selects several measuring areas (such as square areas) and calculates the slope characteristics in each area. This regional processing method can not only reduce the impact of random errors, but also more comprehensively reflect the slope characteristics of the entire terrain area to be measured. The introduction of the regional diagonal distance value provides a benchmark length for slope calculation, combines the elevation difference with the actual spatial range, and enhances the physical meaning of the calculation results. At the same time, this formula is applicable to a variety of complex terrain environments, whether it is a flat area or a mountainous area with large undulations. Through comprehensive analysis The slope characteristics in different regions can generate accurate terrain slope index. The adaptability to different types of terrain makes this method more versatile and can be widely used in engineering planning, ecological assessment and other fields. The formula adopts a simple and clear calculation method to integrate the slope indexes of multiple regions in a weighted average manner, which simplifies the calculation process of complex terrain slope characteristics. This calculation method is suitable for automation and can quickly calculate slope characteristics in large-scale terrain areas, improving the efficiency and applicability of geographic information processing. The slope index is an important parameter in terrain characteristics and can directly affect the risk of soil erosion, the feasibility of project site selection, and vegetation distribution. Through this calculation method, the generated slope index provides a scientific and quantitative basis for road line selection, building foundation site selection, etc., avoiding the risks and waste of resources that may be caused by unclear slope information in the design stage. This standardized calculation method reduces the errors that may be caused by human intervention and enhances the reliability and objectivity of the slope index calculation results.

[0081] Specifically, the specific steps for obtaining the terrain curvature index and terrain texture index of each terrain area to be measured are as follows: for each measurement position point of each terrain area to be measured, a number of adjacent measurement position points within a set neighborhood are read respectively; the terrain elevation value of each measurement position point of each terrain area to be measured is combined with the terrain elevation value of each adjacent measurement position point within the set neighborhood for comprehensive analysis to obtain the terrain curvature index of each terrain area to be measured; the terrain elevation value of each measurement position point of each terrain area to be measured is combined with the terrain elevation value of each adjacent measurement position point within the set neighborhood for elevation difference analysis to obtain the neighborhood elevation difference mean and neighborhood elevation difference standard deviation of each measurement position point of each terrain area to be measured, and a comprehensive analysis is performed to obtain the terrain texture index of each terrain area to be measured.

[0082] The specific formulas for calculating the terrain curvature index of each terrain area to be measured, the mean value of the neighborhood elevation difference of each measurement location point in each terrain area to be measured, the standard deviation of the neighborhood elevation difference, and the terrain texture index of each terrain area to be measured are as follows: ;in, For the The terrain curvature index of the terrain area to be measured, For the The first The terrain elevation value of the measurement location point, For the The first The first measurement point within the set neighborhood The terrain elevation values ​​of adjacent measurement locations, For the The first The mean difference in elevation of the neighborhood of the measurement location point, For the The first The standard deviation of the neighborhood elevation difference of each measurement location point, For the The terrain texture index of the terrain area to be measured, =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , is the number of measurement position points, =1, 2, 3, …, , The number of adjacent measurement points within the set neighborhood.

[0083] In this implementation scheme, the terrain curvature index reveals the curvature of the terrain surface by comprehensively analyzing the elevation values ​​of the measurement point and the elevation values ​​of the adjacent points within the set neighborhood, reflecting the overall characteristics of the terrain surface geometry. The terrain texture index comprehensively reflects the complexity and undulation characteristics of the terrain surface by calculating the mean elevation difference and the standard deviation of the elevation difference in the neighborhood, and can describe the texture characteristics of the terrain. These two indexes comprehensively consider the macro and micro characteristics of the terrain, providing a scientific and accurate multi-dimensional evaluation basis for subsequent terrain classification. In this method, each measurement location point and its adjacent points within the set neighborhood are analyzed. Through the mutual influence of multi-point data in the region, the accidental errors that may be caused by single-point calculations are eliminated. This local analysis method can not only capture the local changes of the terrain more accurately, but also enhance the representativeness of the overall calculation results, making them more in line with the actual terrain characteristics. The introduction of the mean and standard deviation of the elevation difference provides important indicators for describing the local elevation fluctuations of the terrain. The mean elevation can reflect the overall change trend of the region, while the standard deviation can reveal the volatility and complexity of the local fluctuations. Through the combined effect of these parameters, the public The formula achieves a comprehensive characterization of terrain morphological characteristics, which helps to more accurately evaluate and classify terrain characteristics. The formula design structure is clear, and the method of combining point-by-point calculation with regional statistics provides convenient conditions for realizing the automation of the calculation process. This method is suitable for computer algorithm implementation, can quickly process large-scale terrain data, and significantly improves the efficiency and applicability of terrain feature analysis. Whether it is a flat terrain or a mountainous area with complex undulations, this method captures local characteristics through neighborhood analysis, making it more adaptable. In a complex terrain environment, the curvature and texture index can better reflect the true condition of the terrain, which is suitable for a variety of practical scenarios such as engineering planning and ecological assessment. The terrain curvature and texture index are important basic parameters for terrain classification. These indicators directly affect the functional evaluation and utilization of the terrain area. By accurately calculating these indices, reliable data support is provided for subsequent classification tasks and engineering designs (such as soil and water conservation and road site selection). This method can not only reflect local elevation differences by setting the analysis method of the neighborhood range, but also adjust the scale of analysis by setting different neighborhood ranges to adapt to terrain classification needs of different granularities.

[0084] Specifically, the specific steps for obtaining the terrain undulation index of each terrain area to be measured are as follows: the terrain elevation values ​​of each measuring position point of each terrain area to be measured are comprehensively analyzed to obtain the terrain elevation mean of each terrain area to be measured; the terrain elevation values ​​of each measuring position point of each terrain area to be measured are combined with the terrain elevation mean of each terrain area to be measured to obtain the terrain undulation index of each terrain area to be measured.

[0085] The specific formula for calculating the mean terrain elevation and terrain relief index of each terrain area to be measured is as follows: ;in, For the The average terrain elevation of the terrain area to be measured, For the The first The terrain elevation value of the measurement location point, For the The topographic relief index of the terrain area to be measured, =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0086] In this implementation plan, the average of the elevation values ​​of all the locations in the measured area is calculated to obtain the mean terrain elevation. This indicator can intuitively reflect the average elevation level of the entire area. The calculation of the mean elevation eliminates the influence of local extreme values, provides a scientific basis for evaluating the overall characteristics of the regional terrain (such as altitude trend, relative height difference), and helps to quickly understand the basic terrain conditions of the region. The calculation of the terrain undulation index is based on the difference between the mean elevation and the elevation value of the measuring point. The degree of elevation fluctuation in the region is quantified by statistically summing the squares and then taking the average. The undulation index can reveal the range of elevation changes within the region, especially in mountainous terrain with complex undulations. It can intuitively reflect the fluctuation amplitude of the terrain and provide important parameters for studying the complexity of the terrain. By combining the mean elevation with the undulation index, This calculation method can simultaneously reflect the macroscopic (overall level) and microscopic (local changes) characteristics of the terrain, forming a more comprehensive analysis of terrain characteristics. It is also applicable to various terrain environments. Whether it is a flat plain or a complex mountainous area, the terrain characteristics can be distinguished by the size of the undulation index, providing an accurate basis for classification and planning. The introduction of the undulation index can further refine the terrain classification. In areas with similar elevation levels, terrains of different complexities can be distinguished by the difference in undulation. For example, in plains and hilly areas with similar altitudes, this distinction is particularly important. By analyzing the mean elevation and the undulation index, it can provide higher accuracy for the terrain classification results and avoid the possible deviations caused by single parameter evaluation. The terrain undulation directly affects surface runoff, erosion risk and ecological suitability. For example, areas with high undulation may not be suitable for infrastructure construction, while areas with low undulation are more suitable for engineering development. The undulation index can also provide support for ecological assessment. For example, when analyzing vegetation distribution or habitat suitability, the undulation can reflect the impact of surface complexity on the ecosystem.

[0087] Specifically, the specific formula for calculating the terrain comprehensive evaluation index of each terrain area to be measured is as follows: ;in, For the The terrain comprehensive assessment index of the terrain area to be measured, For the The topographic morphological index of the terrain area to be measured, is the terrain morphological coefficient stored in the database, For the The surface field strength characteristic index of the terrain area to be measured, is the underground material characteristic coefficient stored in the database, For the The surface energy characteristic index of the terrain area to be measured, is the surface energy characteristic coefficient stored in the database, For the The surface ecohydrological index of the terrain area to be measured, is the surface eco-hydrological coefficient stored in the database, , is a natural constant, and in this embodiment, its value is 2.71. =1, 2, 3, …, , is the number of terrain areas to be measured.

[0088] It needs to be explained that when calculating the terrain morphological index, surface field strength characteristic index, surface energy characteristic index, and surface eco-hydrological index of each terrain area to be measured, their values ​​are all standardized, that is, they are removed from the unit. Therefore, they can be directly calculated when calculating the terrain comprehensive evaluation index of each terrain area to be measured.

[0089] The specific implementation example of calculating the terrain comprehensive evaluation index of each terrain area to be measured is as follows. There are currently five terrain area data to be measured, including the following:

[0090] The topographic morphological index of the first terrain area to be tested (complex terrain area) is approximately: 0.462.

[0091] The surface field strength characteristic index of the first terrain area to be tested (complex terrain area) is approximately: 1.373.

[0092] The surface energy characteristic index of the first terrain area to be tested (complex terrain area) is approximately: 1.071.

[0093] The surface eco-hydrological index of the first terrain area to be tested (complex terrain area) is approximately: 1.105.

[0094] The topographic morphological index of the second terrain area to be tested (complex terrain area) is approximately: 1.038.

[0095] The surface field strength characteristic index of the second terrain area to be tested (complex terrain area) is approximately: 0.868.

[0096] The surface energy characteristic index of the second terrain area to be tested (complex terrain area) is approximately: 0.602.

[0097] The surface eco-hydrological index of the second terrain area to be tested (complex terrain area) is approximately: 0.732.

[0098] The topographic morphological index of the third terrain area to be tested (gentle slope transition area) is approximately: 1.049.

[0099] The surface field strength characteristic index of the third terrain area to be tested (gentle slope transition area) is approximately: 0.655.

[0100] The surface energy characteristic index of the third terrain area to be tested (gentle slope transition area) is approximately: 0.533.

[0101] The surface eco-hydrological index of the third terrain area to be tested (gentle slope transition area) is approximately: 0.425.

[0102] The topographic morphological index of the fourth terrain area to be tested (complex terrain area) is approximately: 1.165.

[0103] The surface field strength characteristic index of the fourth terrain area to be tested (complex terrain area) is approximately: 1.175.

[0104] The surface energy characteristic index of the fourth terrain area to be tested (complex terrain area) is approximately: 1.751.

[0105] The surface eco-hydrological index of the fourth terrain area to be tested (complex terrain area) is approximately: 0.403.

[0106] The topographic morphological index of the fifth terrain area to be tested (gentle slope transition area) is approximately: 1.271.

[0107] The surface field strength characteristic index of the fifth terrain area to be tested (gentle slope transition area) is approximately: 0.598.

[0108] The surface energy characteristic index of the fifth terrain area to be tested (gentle slope transition area) is approximately: 1.587.

[0109] The surface eco-hydrological index of the fifth terrain area to be tested (gentle slope transition area) is approximately: 0.525.

[0110] The terrain morphology coefficient stored in the database is: 0.22.

[0111] The underground material characteristic coefficient stored in the database is: 0.28.

[0112] The surface energy characteristic coefficient stored in the database is: 0.30.

[0113] The surface eco-hydrological coefficient stored in the database is: 0.20.

[0114] The value of the natural constant is 2.71.

[0115] And the implementation examples of the preset deep terrain assessment intervals are as follows:

[0116] Resource potential development range: [1.5, 2.2).

[0117] Ecological function range: [2.2, 3.0).

[0118] Natural disaster sensitive range: [3.0, 3.8).

[0119] Key protection interval: [3.8, 5.0).

[0120] Substitute the above data into the specific formula for calculating the terrain comprehensive evaluation index of each terrain area to be measured, and obtain:

[0121] The terrain comprehensive assessment index of the first terrain area to be tested (complex terrain area) is ≈2.848, 2.848∈ecological function interval: [2.2, 3.0), so the first terrain area to be tested (complex terrain area) is an ecological function area.

[0122] The comprehensive terrain assessment index of the second terrain area to be tested (complex terrain area) is ≈2.214, 2.214∈ecological function range: [2.2, 3.0), so the second terrain area to be tested (complex terrain area) is an ecological function area.

[0123] The comprehensive terrain evaluation index of the third terrain area to be tested (gentle slope transition area) is ≈1.918, 1.918∈resource potential development interval: [1.5, 2.2), so the third terrain area to be tested (gentle slope transition area) is a resource potential development area.

[0124] The comprehensive terrain assessment index of the fourth terrain area to be tested (complex terrain area) is ≈3.292, 3.292∈natural disaster sensitive interval: [3.0, 3.8), so the fourth terrain area to be tested (complex terrain area) is a natural disaster sensitive area.

[0125] The comprehensive terrain assessment index of the fifth terrain area to be tested (gentle slope transition area) is ≈2.759, 2.759∈ecological function interval: [2.2, 3.0), so the fifth terrain area to be tested (gentle slope transition area) is an ecological function area.

[0126] In this implementation plan, four key terrain features are integrated, including the terrain morphological index used to reflect the geometric shape of the terrain and reveal the overall undulating characteristics, the surface field strength characteristic index used to reveal the characteristics of underground materials through parameters such as gravity and magnetic field and provide analysis of deep geology, the surface energy characteristic index that uses surface temperature, thermal radiation and other characteristics to reflect the regional energy status, and the surface eco-hydrological index that combines vegetation and moisture data to evaluate ecological and hydrological characteristics. Through the comprehensive evaluation of these characteristics, the formula can fully describe the comprehensive characteristics of the terrain area from multiple dimensions, making up for the shortcomings of single indicator analysis. The influence weight of each characteristic index on the comprehensive evaluation result is controlled by a coefficient, and these weights can be adjusted according to specific application needs. For example, in underground resource development, the weight of the surface field strength characteristics can be increased, and in ecological protection, the weight of the surface eco-hydrological index can be increased. The flexible adjustment of the weight makes the formula applicable to different scenarios, improving the practicality and adaptability of terrain evaluation. Each characteristic index is standardized Calculations are performed after processing to remove the influence of unit and dimension differences, ensuring comparability between different regions and data sources. This normalization method ensures the scientific nature and uniformity of the terrain comprehensive assessment index, providing a reliable basis for large-scale, cross-regional terrain assessments. The formula for calculating the terrain comprehensive assessment index for each terrain area to be measured is calculated in the form of an exponential function, so that the characteristic differences within a small range can be effectively amplified, thereby more clearly reflecting the differences in terrain characteristics between regions. This method significantly improves the accuracy and effect of the assessment for identifying the characteristics of complex terrain areas, especially for classification and grading analysis. The natural constants in the formula are commonly used cardinal numbers in the field of mathematics, which ensure the stability and universality of the calculation results in large-scale data analysis. This design allows the formula to be seamlessly applied to terrain data calculations of different scales, enhancing the applicability of the model. This efficient calculation method is particularly suitable for batch analysis of large-area, diversified terrain features, significantly improving the efficiency and accuracy of the assessment work.

[0127] Specifically, Figure 3 As shown, the specific steps for obtaining the terrain morphological index of each terrain area to be measured are as follows: read the surface rock and soil density value of each measuring position point of each terrain area to be measured, and perform comprehensive analysis in combination with the terrain elevation mean of each terrain area to be measured to obtain the terrain undulation energy density index of each terrain area to be measured; perform comprehensive analysis on the terrain elevation mean and terrain undulation energy density index of each terrain area to be measured to obtain the terrain morphological index of each terrain area to be measured.

[0128] The specific formulas for calculating the terrain undulation energy density index and terrain morphology index of each terrain area to be measured are as follows: ;in, For the The topographic energy density index of the terrain area to be measured, For the The first The surface rock and soil density value at each measurement location, is the gravity acceleration value, which is 9.81 m / s in this embodiment 2 , For the The first The terrain elevation value of the measurement location point, For the The average terrain elevation of the terrain area to be measured, For the The topographic morphological index of the terrain area to be measured, is the terrain elevation coefficient stored in the database, is the fluctuation energy density coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0129] It should be explained that when calculating the topographic morphological index of each terrain area to be measured, the data is standardized, that is, the unit is removed, so it can be calculated directly.

[0130] In this implementation plan, the formula for calculating the terrain fluctuation energy density index integrates the surface rock and soil density and elevation changes, and combines gravity acceleration to quantitatively describe the energy density characteristics of terrain elevation differences. This index can reflect the severity of terrain fluctuations and its internal gravity-related characteristics, and is particularly suitable for analysis of mountainous areas with complex geological conditions. The terrain morphological index combines the regional elevation mean and the fluctuation energy density index to generate a quantitative index of comprehensive morphological characteristics, further improving the ability to describe the overall characteristics of the terrain. This multi-level comprehensive analysis can fully reflect the morphological complexity of the terrain and provide more accurate data support for subsequent classification and planning. The formula comprehensively analyzes the intrinsic characteristics of the terrain through the combination of multi-dimensional parameters such as elevation, rock and soil density and gravity acceleration, including elevation characteristics (reflecting the overall trend and local changes of the terrain through the elevation mean and fluctuation changes), rock and soil characteristics (using rock and soil density to reveal the geological characteristics of the region and provide information related to underground materials), and gravity characteristics (introducing gravity Acceleration reflects the relationship between terrain and gravity effect, further enhancing the physical significance of the analysis). This multi-dimensional fusion method overcomes the limitations of traditional single elevation analysis, making terrain analysis more scientific and comprehensive. All data are standardized (unit-free) to ensure that data from different sources have a unified measurement standard, which provides a reliable basis for large-scale and multi-regional data comparison. Standardization also makes the calculation results more universal and adaptable to different types of terrain environments, making them more applicable. A weight coefficient is introduced into the terrain morphology index formula, allowing the influence of the mean elevation and the undulation energy density on the results to be adjusted according to actual needs. This flexible weight design enables the formula to adapt to a variety of scenarios and improves the adaptability and practicality of the model. The undulation energy density index amplifies the influence of local elevation differences through square calculation, making the regional characteristics of drastic elevation changes more prominent. This feature is particularly important for classification and grading analysis, and can accurately identify the characteristics of complex terrain areas and improve evaluation accuracy.

[0131] Specifically, the specific steps for obtaining the surface field strength characteristic index of each terrain area to be measured are as follows: obtain the gravity reference value and magnetic field strength reference value of each terrain area to be measured; comprehensively analyze the gravity measurement value and magnetic field strength measurement value of each measurement position point in each terrain area to be measured in combination with the gravity reference value and magnetic field strength reference value of each terrain area to be measured, and obtain the gravity anomaly index and magnetic field strength anomaly index of each terrain area to be measured; comprehensively analyze the gravity anomaly index and magnetic field strength anomaly index of each terrain area to be measured, and obtain the surface field strength characteristic index of each terrain area to be measured.

[0132] The specific formulas for calculating the gravity anomaly index, magnetic field intensity anomaly index, and surface field intensity characteristic index of each terrain area to be measured are as follows: ;in, For the The gravity anomaly index of the terrain area to be measured, For the The first The gravity measurement value at each measurement location, For the The gravity reference value of the terrain area to be measured, For the The magnetic field intensity anomaly index of the terrain area to be measured, For the The first The magnetic field strength measurement value at each measurement location, For the The reference value of magnetic field strength in the terrain area to be measured, For the The surface field strength characteristic index of the terrain area to be measured, is the gravity coefficient stored in the database, is the magnetic field intensity coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0133] It should be explained that when calculating the surface field strength characteristic index of each terrain area to be measured, the data is standardized, that is, the unit is removed, so it can be calculated directly.

[0134] In this implementation scheme, the gravity anomaly index calculates the gravity anomaly by comparing the gravity measurement value of the measurement location point with the reference value, which can reveal the distribution and characteristics of the underground material in the region and provide important information for geological structure analysis. The magnetic field intensity anomaly index calculates the magnetic field anomaly by comparing the magnetic field intensity measurement value of the measurement location point with the reference value, reflecting the distribution and strength of underground magnetic materials. Combining the gravity and magnetic field characteristics, the field strength characteristics of the terrain area can be fully described from a physical level, making up for the shortcomings of simple elevation or rock and soil density analysis. The calculation of the gravity and magnetic field anomaly index adopts the absolute difference method, which can amplify the local abnormal characteristics and is particularly sensitive to areas with significant changes in physical characteristics in the terrain area. This abnormal analysis method helps to discover special geological conditions in the region, such as underground faults, mineral-rich areas or abnormal geological structures. All data are standardized. , remove the unit influence and ensure the good comparability of the calculation results. Whether it is cross-regional analysis or multi-source data integration, the standardized processing provides a scientific basis for unified evaluation and is applicable to a variety of complex terrain environments. The surface field strength characteristic index controls the influence of gravity and magnetic field on the comprehensive results through the weight coefficient. The flexible adjustment of the weight enables the formula to adapt to a variety of application scenarios. For example, in underground resource development, the weight of magnetic field intensity anomalies can be increased. In geological disaster analysis, the weight of gravity anomalies can be enhanced. This flexibility in weight adjustment improves the versatility and practical application value of the model. The formula adopts point-by-point calculation and regional integration, with a clear structure and simple calculation steps, which is very suitable for automated processing. This efficient calculation method can quickly process large-scale terrain data and significantly improve the evaluation efficiency. It is particularly suitable for batch analysis of multi-regional field strength characteristics.

[0135] Specifically, the specific steps for obtaining the surface energy characteristic index of each terrain area to be measured are as follows: read the surface temperature value, surface reflectivity value, and surface thermal radiation value of each measuring position point in each terrain area to be measured, and perform comprehensive analysis respectively to obtain the surface temperature index, surface reflectivity index, and surface thermal radiation index of each terrain area to be measured; perform comprehensive analysis on the surface temperature index, surface reflectivity index, and surface thermal radiation index of each terrain area to be measured respectively to obtain the surface energy characteristic index of each terrain area to be measured.

[0136] The specific formulas for calculating the surface temperature index, surface reflectivity index, surface thermal radiation index, and surface energy characteristic index of each terrain area to be measured are as follows: ;in, For the The surface temperature index of the terrain area to be measured, For the The first The surface temperature value at each measuring point, For the The surface reflectivity index of the terrain area to be measured, For the The first The surface reflectance value of the measurement location point, For the The surface thermal radiation index of the terrain area to be measured, For the The first The surface thermal radiation value of the measurement location point, For the The surface energy characteristic index of the terrain area to be measured, is the surface temperature coefficient stored in the database, is the surface reflectance stored in the database, is the surface thermal radiation coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0137] It should be explained that when calculating the surface energy characteristic index of each terrain area to be measured, the data is standardized, that is, the unit is removed, so it can be calculated directly.

[0138] In this implementation plan, the surface temperature index is calculated based on the surface temperature value, reflects the overall level of surface energy absorption and heat dissipation, and is the core parameter of the surface energy characteristics. The surface reflectivity index quantifies the surface's ability to reflect solar energy by measuring the surface reflectivity, and can reveal the material properties of the regional surface and its energy reflection behavior. The surface thermal radiation index reveals the region's energy emission capacity by analyzing the surface thermal radiation characteristics, and is a key radiation parameter in the energy characteristics. The surface energy characteristic index forms a unified indicator through the weighted comprehensive calculation of the above parameters, which comprehensively reflects the surface energy characteristics of the region. This multi-dimensional comprehensive analysis method can scientifically evaluate the surface energy distribution from three core aspects: temperature, reflectivity, and radiation characteristics, and provides comprehensive support for the study of surface energy dynamics and regional characteristics. Weight coefficients are introduced in the formula to correspond to surface temperature, reflectivity, and thermal radiation characteristics, respectively. For example, in high-temperature areas, the weight of the surface temperature index can be increased. When studying the distribution of reflective materials, the weight of the surface reflectivity index can be increased. For energy balance research, the weight of the thermal radiation index can be enhanced. This flexible weight adjustment method enables the surface energy characteristic index to meet the needs of different application scenarios, such as ecological assessment, energy planning, or thermal environment research. Each energy characteristic parameter is standardized before calculation to remove the influence of units and dimensions, ensuring the consistency of results in different regions and data sources. This data normalization method is particularly suitable for cross-regional comparative analysis and can accurately reveal the differences in energy characteristics between different terrain regions. This efficient calculation method can quickly process large-scale and multi-regional surface energy data, significantly improving analysis efficiency, and is particularly suitable for large-region ecological assessment and energy characteristic research.

[0139] Specifically, the specific steps for obtaining the surface eco-hydrological index of each terrain area to be measured are as follows: read the near-infrared band reflectance value and the red light band reflectance value of each measuring position point of each terrain area to be measured, and perform comprehensive analysis respectively to obtain the vegetation index of each terrain area to be measured; read the surface moisture content value of each measuring position point of each terrain area to be measured and perform comprehensive analysis respectively to obtain the surface moisture content index of each terrain area to be measured; perform comprehensive analysis on the vegetation index and surface moisture content index of each terrain area to be measured respectively to obtain the surface eco-hydrological index of each terrain area to be measured.

[0140] The specific formulas for calculating the vegetation index, surface moisture content index, and surface eco-hydrological index of each terrain area to be measured are as follows: ;in, For the The vegetation index of the terrain area to be measured, For the The first The near-infrared reflectance value of each measurement point, For the The first The red light band reflectance value of each measurement point, For the The surface moisture content index of the terrain area to be measured, For the The first The surface moisture content value at each measuring location, For the The surface ecohydrological index of the terrain area to be measured, is the vegetation coefficient stored in the database, is the surface moisture content coefficient stored in the database, , =1, 2, 3, …, , is the number of terrain areas to be measured, =1, 2, 3, …, , The number of measurement position points.

[0141] It should be explained that when calculating the surface eco-hydrological index of each terrain area to be measured, the data is standardized, that is, the unit is removed, so it can be calculated directly.

[0142] In this implementation plan, the vegetation index can quantify the vegetation coverage status by analyzing the reflectance values ​​of the near-infrared band and the red light band, directly reflecting the ecological characteristics of the surface. The surface moisture content index is based on the surface moisture content value of the measuring point, quantifies the moisture status of the region, and is the core parameter of the surface hydrological characteristics. The surface eco-hydrological index generates a unified index through weighted comprehensive analysis of the vegetation index and the moisture content index, which comprehensively reflects the ecological and hydrological characteristics of the surface. This multi-dimensional comprehensive method can scientifically evaluate the surface ecology and hydrological characteristics from the two core aspects of vegetation coverage and moisture status, and provide support for regional ecological and water resources management. Vegetation coverage and moisture content are closely related. The vegetation index can reflect the regulatory effect of vegetation on surface moisture, while the moisture content The index reflects the impact of regional humidity on vegetation growth. Through the combination of these two parameters, the surface eco-hydrological index can more scientifically reflect the overall ecological and hydrological status of the surface system and is suitable for the assessment of complex ecological environments. Each ecological and hydrological characteristic parameter is standardized before calculation to remove unit influences and ensure the consistency of the calculation results in cross-regional and cross-data source analysis. This standardized processing method provides a scientific basis for large-scale, multi-regional comparative analysis and is suitable for the study of ecological and hydrological characteristics of different types of terrain. The weight coefficient can be flexibly adjusted to meet the needs of multiple scenarios. This efficient calculation method can quickly process a wide range of surface ecological and hydrological data and significantly improve analysis efficiency. It is particularly suitable for cross-regional ecological assessment and water resources management.

[0143] In summary, this application has at least the following effects:

[0144] Through comprehensive analysis of surface and deep terrain, combined with the slope, curvature, texture and undulation index of elevation data, the initial terrain assessment index is calculated. At the same time, the deep rock and soil density, field strength, energy characteristics and eco-hydrological data are used to generate a comprehensive terrain assessment index. Multi-dimensional data fusion significantly improves the accuracy of classification and makes up for the lack of attention paid to deep characteristics by traditional methods. It can fully reflect the complex characteristics of terrain areas and provide a reliable basis for scientific planning, especially in complex geological environments, to ensure a high degree of match between terrain classification results and actual application needs.

[0145] By introducing a modular structure, covering terrain establishment, surface and deep analysis and classification, and result display, each module operates independently and collaborates with each other, and processes multidimensional data such as rock and soil density, field strength, temperature, reflectivity, ecological characteristics, etc. through standardized algorithms to ensure the efficiency of data analysis and the uniformity of results. This design is not only suitable for different types of terrain areas, but also reduces human intervention and analysis bias, improving overall work efficiency. At the same time, the modular structure is easy to expand, and can adapt to new data sources and analysis methods according to needs, providing flexible support for different application scenarios.

[0146] The surface and deep classification results are intuitively marked in the digital terrain model through the display marking module to form a visual results map. The visualization presentation is clear and intuitive, allowing technicians to quickly grasp the classification information and comprehensive characteristics of the terrain area. This not only provides a scientific basis for project planning and design, but also reduces design deviations caused by incomplete terrain information during the construction phase. By improving the practicality and readability of the classification results, the model provides strong support for shortening the construction period and reducing construction costs, and significantly improves the economic benefits and implementation efficiency of the project.

[0147] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0148] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. Automatic classification and design device for surveying, mapping and geographic information results, characterized in that: include: Terrain establishment module, surface terrain analysis module, surface terrain classification module, deep terrain analysis module, deep terrain classification module, display marking module; The terrain establishment module is used to obtain the terrain elevation value of each measurement position point of each terrain area to be measured, and respectively establish a terrain model of each terrain area to be measured; The surface terrain analysis module is used to perform comprehensive analysis on the terrain elevation values ​​of each measuring position point of each terrain area to be measured, obtain the terrain slope index, terrain curvature index, terrain texture index, and terrain undulation index of each terrain area to be measured, and perform comprehensive analysis to obtain the terrain initial assessment index of each terrain area to be measured; The surface terrain classification module is used to judge and analyze the initial terrain evaluation index of each terrain area to be measured based on a preset surface terrain evaluation interval, and to classify the surface of each terrain area to be measured according to the judgment and analysis results; The deep terrain analysis module is used to respectively obtain the surface rock and soil density value, surface field strength data, surface energy data, and surface eco-hydrological data of each measurement position point of each terrain area to be measured, and conduct a comprehensive analysis to obtain the terrain morphological index, surface field strength characteristic index, surface energy characteristic index, and surface eco-hydrological index of each terrain area to be measured, and conduct a comprehensive analysis to obtain the terrain comprehensive evaluation index of each terrain area to be measured, the surface field strength data includes gravity measurement values ​​and magnetic field strength measurement values, the surface energy data includes surface temperature values, surface reflectivity values, and surface thermal radiation values, and the surface eco-hydrological data includes near-infrared band reflectivity values, red light band reflectivity values, and surface moisture content values; The deep terrain classification module is used to judge and analyze the terrain comprehensive evaluation index of each terrain area to be tested based on a preset deep terrain evaluation interval, and to perform deep classification on each terrain area to be tested according to the judgment and analysis results; The display marking module is used to mark and display the surface classification results and deep classification results of each terrain area to be measured in the corresponding terrain model; The specific formula for calculating the initial terrain assessment index for each terrain area to be measured is as follows: Among them, DcP u is the initial terrain assessment index of the u-th terrain area to be measured, PdZ u is the terrain slope index of the u-th terrain area to be measured, ξ1 is the terrain slope coefficient stored in the database, DqZ u is the terrain curvature index of the u-th terrain area to be measured, ξ2 is the terrain curvature coefficient stored in the database, DwZ u is the terrain texture index of the u-th terrain area to be measured, ξ3 is the terrain texture coefficient stored in the database, QfZ u is the terrain relief index of the u-th terrain area to be measured, ξ4 is the terrain relief coefficient stored in the database, ξ1+ξ 2i +ξ3+ξ4=1, e is a natural constant, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured; The specific steps for obtaining the terrain curvature index and terrain texture index of each terrain area to be measured are as follows: For each measurement position point in each terrain area to be measured, a number of adjacent measurement position points within a set neighborhood are read respectively; The terrain elevation value of each measuring position point of each terrain area to be measured is combined with the terrain elevation value of each adjacent measuring position point within the set neighborhood to perform a comprehensive analysis to obtain the terrain curvature index of each terrain area to be measured; The terrain elevation value of each measuring position point in each terrain area to be measured is combined with the terrain elevation value of each adjacent measuring position point in the set neighborhood range to perform elevation difference analysis, so as to obtain the neighborhood elevation difference mean value and neighborhood elevation difference standard deviation of each measuring position point in each terrain area to be measured, and a comprehensive analysis is performed to obtain the terrain texture index of each terrain area to be measured; The specific formulas for calculating the terrain curvature index of each terrain area to be measured, the mean value of the neighborhood elevation difference of each measurement location point in each terrain area to be measured, the standard deviation of the neighborhood elevation difference, and the terrain texture index of each terrain area to be measured are as follows: Among them, DqZ u is the terrain curvature index of the u-th terrain area to be measured, GcZ ui is the terrain elevation value of the ith measurement point in the uth terrain area to be measured, GcZ uir is the terrain elevation value of the rth adjacent measurement point within the set neighborhood of the ith measurement point in the uth terrain area to be measured, LgJ ui is the mean value of the neighborhood elevation difference of the ith measurement point in the uth terrain area to be measured, LgB ui is the standard deviation of the neighborhood elevation difference of the ith measurement point in the uth terrain area to be measured, DwZ u is the terrain texture index of the u-th terrain area to be measured, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured, i=1, 2, 3, …, i0, i0 is the number of measurement positions, r=1, 2, 3, …, r0, r0 is the number of adjacent measurement positions within the set neighborhood; The specific steps to obtain the terrain relief index of each terrain area to be measured are as follows: Comprehensively analyze the terrain elevation values ​​of each measuring position point in each terrain area to be measured, and obtain the average terrain elevation value of each terrain area to be measured; The terrain elevation value of each measuring position point of each terrain area to be measured is combined with the average terrain elevation of each terrain area to be measured to perform a comprehensive analysis to obtain the terrain relief index of each terrain area to be measured; The specific formula for calculating the mean terrain elevation and terrain relief index of each terrain area to be measured is as follows: Among them, GcJ u is the average terrain elevation of the u-th terrain area to be measured, GcZ ui is the terrain elevation value of the ith measurement point in the uth terrain area to be measured, QfZ u is the topographic relief index of the u-th terrain area to be measured, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured, i=1, 2, 3, …, i0, i0 is the number of measurement locations; The specific formula for calculating the terrain comprehensive evaluation index of each terrain area to be measured is as follows: Among them, DzP u is the comprehensive terrain evaluation index of the u-th terrain area to be tested, DxX u is the topographic morphological index of the u-th terrain area to be measured, θ1 is the topographic morphological coefficient stored in the database, DxW u is the surface field strength characteristic index of the u-th terrain area to be measured, θ2 is the underground material characteristic coefficient stored in the database, DbN u is the surface energy characteristic index of the u-th terrain area to be measured, θ3 is the surface energy characteristic coefficient stored in the database, DsS u is the surface eco-hydrological index of the u-th terrain area to be measured, θ4 is the surface eco-hydrological coefficient stored in the database, θ1+θ2+θ3+θ4=1, e is a natural constant, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured; The specific steps to obtain the topographic morphological index of each terrain area to be measured are as follows: Read the surface rock and soil density value of each measuring position point of each terrain area to be measured, and conduct a comprehensive analysis in combination with the average terrain elevation of each terrain area to be measured to obtain the terrain undulation energy density index of each terrain area to be measured; Comprehensively analyze the mean value of terrain elevation and terrain undulation energy density index of each terrain area to be measured, and obtain the terrain morphological index of each terrain area to be measured; The specific formulas for calculating the terrain undulation energy density index and terrain morphology index of each terrain area to be measured are as follows: Among them, QfM u is the topographic energy density index of the u-th terrain area to be measured, ρ ui is the surface rock and soil density value of the i-th measuring position point in the u-th terrain area to be measured, g is the gravitational acceleration value, GcZ ui is the terrain elevation value of the ith measurement point in the uth terrain area to be measured, GcJ u is the average terrain elevation of the u-th terrain area to be measured, DxX u is the terrain morphological index of the u-th terrain area to be measured, α1 is the terrain elevation coefficient stored in the database, α2 is the undulation energy density coefficient stored in the database, α1+α2=1, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured, i=1, 2, 3, …, i0, i0 is the number of measurement locations; The specific steps to obtain the surface field intensity characteristic index of each terrain area to be measured are as follows: Obtain the gravity reference value and magnetic field strength reference value of each terrain area to be measured; The gravity measurement value and magnetic field strength measurement value of each measuring position point in each terrain area to be measured are combined with the gravity reference value and magnetic field strength reference value of each terrain area to be measured to perform a comprehensive analysis to obtain the gravity anomaly index and magnetic field strength anomaly index of each terrain area to be measured; Comprehensively analyze the gravity anomaly index and magnetic field intensity anomaly index of each terrain area to be measured, and obtain the surface field intensity characteristic index of each terrain area to be measured; The specific formulas for calculating the gravity anomaly index, magnetic field intensity anomaly index, and surface field intensity characteristic index of each terrain area to be measured are as follows: Among them, ZyC u is the gravity anomaly index of the u-th terrain area to be measured, ZcL ui is the gravity measurement value of the ith measurement point in the uth terrain area to be measured, ZcC u is the gravity reference value of the u-th terrain area to be measured, CyC u is the magnetic field intensity anomaly index of the u-th terrain area to be measured, CqL ui is the magnetic field strength measurement value of the ith measurement point in the uth terrain area to be measured, CqC u is the reference value of magnetic field strength in the u-th terrain area to be measured, DxW u is the surface field intensity characteristic index of the u-th terrain area to be measured, χ1 is the gravity coefficient stored in the database, χ2 is the magnetic field intensity coefficient stored in the database, χ1+χ2=1, u=1, 2, 3, ..., u0, u0 is the number of terrain areas to be measured, i=1, 2, 3, ..., i0, i0 is the number of measurement positions; The specific steps to obtain the surface energy characteristic index of each terrain area to be measured are as follows: Read the surface temperature value, surface reflectivity value, and surface thermal radiation value of each measuring point in each terrain area to be measured, and conduct comprehensive analysis to obtain the surface temperature index, surface reflectivity index, and surface thermal radiation index of each terrain area to be measured; Comprehensively analyze the surface temperature index, surface reflectivity index, and surface thermal radiation index of each terrain area to be measured, and obtain the surface energy characteristic index of each terrain area to be measured; The specific formulas for calculating the surface temperature index, surface reflectivity index, surface thermal radiation index, and surface energy characteristic index of each terrain area to be measured are as follows: Among them, WdS u is the surface temperature index of the u-th terrain area to be measured, WdZ ui is the surface temperature value of the i-th measurement point in the u-th terrain area to be measured, FsS u is the surface reflectivity index of the u-th terrain area to be measured, FsL ui RfS is the surface reflectance value of the i-th measurement point in the u-th terrain area to be measured. u is the surface thermal radiation index of the u-th terrain area to be measured, RfZ ui is the surface thermal radiation value of the ith measurement point in the uth terrain area to be measured, DbN u is the surface energy characteristic index of the u-th terrain area to be measured, ω1 is the surface temperature coefficient stored in the database, ω2 is the surface reflection coefficient stored in the database, ω3 is the surface thermal radiation coefficient stored in the database, ω1+ω2+ω3=1, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured, i=1, 2, 3, …, i0, i0 is the number of measurement positions; The specific steps to obtain the surface eco-hydrological index of each terrain area to be measured are as follows: Read the near-infrared band reflectance value and the red light band reflectance value of each measuring point in each terrain area to be measured, and conduct comprehensive analysis to obtain the vegetation index of each terrain area to be measured; Read the surface moisture content value of each measuring position point of each terrain area to be measured and conduct comprehensive analysis respectively to obtain the surface moisture content index of each terrain area to be measured; Comprehensively analyze the vegetation index and surface water content index of each terrain area to be tested, and obtain the surface eco-hydrological index of each terrain area to be tested; The specific formulas for calculating the vegetation index, surface moisture content index, and surface eco-hydrological index of each terrain area to be measured are as follows: Among them, ZbZ u is the vegetation index of the u-th terrain area to be measured, JhW ui is the near-infrared band reflectivity value of the ith measurement point in the uth terrain area to be measured, HgB ui is the red light band reflectivity value of the ith measurement point in the uth terrain area to be measured, DhZ u is the surface moisture content index of the u-th terrain area to be measured, DhS ui DsS is the surface moisture content value of the i-th measurement point in the u-th terrain area to be measured, u is the surface eco-hydrological index of the u-th terrain area to be measured, η1 is the vegetation coefficient stored in the database, η2 is the surface moisture content coefficient stored in the database, η1+η2=1, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured, i=1, 2, 3, …, i0, i0 is the number of measurement locations.

2. The automatic classification and design device for surveying, mapping and geographic information achievements according to claim 1 is characterized in that: The specific steps to obtain the terrain slope index of each terrain area to be measured are as follows: For each terrain area to be measured, a number of measurement areas are randomly selected, and the regional diagonal distance value of each measurement area of ​​each terrain area to be measured is obtained respectively; Comparing and analyzing the terrain elevation values ​​of each measuring position point of each measuring area of ​​each terrain area to be measured, respectively, to obtain the maximum value and the minimum value of the terrain elevation of each measuring area of ​​each terrain area to be measured; Comprehensively analyze the maximum value of terrain elevation, the minimum value of terrain elevation, and the regional diagonal distance value of each measuring area of ​​each terrain area to be measured, and obtain the terrain slope index of each terrain area to be measured; The specific formula for calculating the terrain slope index of each terrain area to be measured is as follows: Among them, PdZ u is the terrain slope index of the u-th terrain area to be measured, is the maximum value of the terrain elevation of the tth measurement area in the uth terrain area to be measured, is the minimum value of the terrain elevation of the tth measurement area in the uth terrain area to be measured, DjL ut is the area diagonal distance value of the tth measuring area of ​​the uth terrain area to be measured, u=1, 2, 3, …, u0, u0 is the number of terrain areas to be measured, t=1, 2, 3, …, t0, t0 is the number of measuring areas.

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