AI-Based Methods, Devices, and Storage Media for Processing National Territory Data

Abstract

The present invention relates to the technical field of data processing, and in particular to an AI-based method, device, and storage medium for processing national land data. The method includes: collecting the pH value, organic matter content, and heavy metal concentration of a target area to analyze the soil properties of the target area, collecting the surface fissure density and surface settlement rate of the target area to analyze the surface stability index, and constructing the terrain features of the target area based on the slope and surface stability index of the target area, collecting the annual average precipitation data and water source data of the target area to construct the hydrological features of the target area, and simultaneously updating the construction process of the terrain features of the target area based on the hydrological features of the target area, collecting the vegetation coverage rate of the target area to determine the hydrological adjustment weight, and classifying the target area based on the soil properties, terrain features, and hydrological features of the target area. The present invention effectively improves the processing efficiency of national land data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an AI-based method, device, and storage medium for processing land data. Background Art

[0002] Traditional land data processing methods mostly rely on single-index analysis or manual experience judgment, with limitations such as fragmented data dimensions and low processing efficiency. Existing technologies are difficult to achieve dynamic coupling analysis of multi-source data such as soil properties, terrain features, hydrological conditions, and vegetation coverage, resulting in one-sided and time-insufficient evaluation results. In addition, the manual sampling process is cumbersome, the professional analysis such as surface crack identification and heavy metal pollution calculation takes a long time, and the parameter adjustment lacks an adaptive mechanism and cannot cope with the dynamic changes of complex geographical environments. With the rapid increase in the amount of remote sensing and Internet of Things data, there is an urgent need for intelligent methods to integrate multi-dimensional information and improve the automation and decision-making accuracy of land data analysis. Summary of the Invention

[0003] The purpose of the present invention is to provide an AI-based method, device, and storage medium for processing land data to solve at least one of the problems existing in the prior art.

[0004] To achieve the above purpose, the present invention adopts the following technical solutions:

[0005] An AI-based method for processing land data includes:

[0006] Collecting the pH value, organic matter content, and heavy metal concentration of the target area to analyze the soil properties of the target area;

[0007] Collecting the surface crack density and surface settlement rate of the target area to analyze the surface stability index, and constructing the terrain features of the target area based on the slope and surface stability index of the target area;

[0008] Collecting the annual average precipitation data and water source data of the target area to construct the hydrological features of the target area, and simultaneously updating the construction process of the terrain features of the target area based on the hydrological features and hydrological adjustment weights of the target area;

[0009] Collecting the vegetation coverage rate of the target area to construct vegetation features, and determining the hydrological adjustment weight based on the vegetation features.

[0010] Optionally, constructing a heavy metal pollution index P according to the collected copper concentration C1, lead concentration C2, and zinc concentration C3 of the target area;

[0011] Construct a pH adjustment function based on the pH value of the collected target area, and standardize the organic matter content of the collected target area to obtain the standard organic matter content. Determine the soil properties of the target area based on the pH adjustment function, the standard organic matter content, and the heavy metal pollution index.

[0012] Optionally, construct a pH adjustment function f(pH) based on the pH value of the collected target area, and normalize the organic matter content m1 of the collected target area to obtain the standard organic matter content M.

[0013] Construct a soil quality index SQ based on the pH adjustment function f(pH), the standard organic matter content M, and the heavy metal pollution index P, and compare the soil quality index SQ with the soil quality discrimination factor to determine the soil properties of the target area. If the soil quality index SQ is less than or equal to the soil quality discrimination factor, determine that the soil properties of the target area are type I soil; otherwise, determine that the soil properties of the target area are type II soil.

[0014] Optionally, normalize the surface fissure density Dm of the collected target area to obtain the fissure density parameter Fd, normalize the surface subsidence rate Gs of the collected target area to obtain the subsidence rate parameter Fc, and fuse the fissure density parameter Fd and the subsidence rate parameter Fc to obtain the surface stability index SSI.

[0015] Optionally, perform data coupling on the surface stability index SSI and the slope θ of the target area to determine the terrain feature R of the target area. The expression of R is: R = (θ / θ1 × SSI × β) 0.5 ; where θ1 is the slope threshold and β is the stability adjustment factor.

[0016] Optionally, set the hydrological feature of the target area as HF. The expression of HF is HF = (js - jsmin) / (jsmax - jsmin) × g(SY) + γ × [1 - 1 / (1 + SY)], where js is the average value of the annual precipitation data, jsmin is the minimum value of the collected annual precipitation, jsmax is the maximum value of the collected annual precipitation, g(SY) is the water source weight function, and γ is the drought correction factor.

[0017] Compare the hydrological feature HF of the target area with the hydrological discrimination factor h0 to update the construction process of the terrain feature of the target area. When the hydrological feature HF of the target area is greater than or equal to the hydrological discrimination factor h0, no update is performed; otherwise, update the stability adjustment factor according to the hydrological adjustment weight.

[0018] Optionally, perform a ratio analysis on the vegetation coverage rate zf0 and the coverage rate threshold zf1 of the target area to obtain the vegetation feature ZT, and compare the vegetation feature ZT with the vegetation discrimination factor zt0 to determine the hydrological adjustment weight.

[0019] Optionally, it further includes: classifying the target area based on the soil properties, topographic features, and hydrological features of the target area; when the soil property of the target area is type I soil, dividing the target area into a low-potential area, and when the soil property of the target area is type II soil, if r1×SQ + r2×(1 - R) + r3×HF is less than or equal to the first category factor u1, dividing the target area into a low-potential area, if r1×SQ + r2×(1 - R) + r3×HF is greater than the first category factor u1 and less than or equal to the second category factor u2, dividing the target area into a medium-potential area, and if r1×SQ + r2×(1 - R) + r3×HF is greater than the second category factor u2, dividing the target area into a high-potential area;

[0020] Among them, r1 is the soil property weight, r2 is the topographic weight, and r3 is the hydrological weight.

[0021] According to another aspect of the present application, there is provided an AI-based national land data processing device, including:

[0022] An attribute determination unit for analyzing the soil properties of the target area based on the pH value, organic matter content, and heavy metal concentration of the collected target area;

[0023] A terrain determination unit for analyzing the surface stability index based on the surface fracture density and surface settlement rate of the collected target area, and constructing the topographic features of the target area based on the target area slope and surface stability index;

[0024] A hydrological determination unit for constructing the hydrological features of the target area based on the collected annual average precipitation data and water source data of the target area, and simultaneously updating the construction process of the topographic features of the target area based on the hydrological features and hydrological adjustment weight of the target area;

[0025] A vegetation analysis unit for constructing vegetation features based on the vegetation coverage rate of the collected target area, and determining the hydrological adjustment weight based on the vegetation features;

[0026] A classification unit for classifying the target area based on the soil properties, topographic features, and hydrological features of the target area.

[0027] According to another aspect of the present application, there is provided a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program is used to control an electronic device where the computer-readable storage medium is located to execute the above-mentioned AI-based national land data processing method when running.

[0028] The beneficial effects of the present invention are as follows: This method constructs a multi-dimensional national land data collaborative analysis framework based on AI technology, significantly improving data processing efficiency. Through the automatic collection and intelligent coupling of soil properties, terrain stability, hydrological characteristics, and vegetation coverage, the problems of isolated data and strong artificial dependence in traditional methods are solved; the AI model realizes the rapid calculation of complex indicators such as heavy metal pollution index and surface stability index, and supports dynamic parameter adjustment, enhancing the adaptability to different environmental conditions; through multi-feature weighted fusion, the reclamation potential level is accurately divided, providing efficient decision-making support for land improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1 It is a schematic flowchart of the AI-based national land data processing method for this embodiment.

[0031] Figure 2 It is a schematic flowchart of the analysis method of soil properties for this embodiment.

[0032] Figure 3 It is a schematic flowchart of the construction method of terrain features for this embodiment.

[0033] Figure 4 It is a schematic flowchart of the construction method of hydrological characteristics for this embodiment.

[0034] Figure 5 It is a schematic structural diagram of the AI-based national land data processing device for this embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to more clearly illustrate the present invention, the following further describes the present invention in conjunction with preferred embodiments and the drawings. Similar components in the drawings are represented by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the description, claims, and above-mentioned drawings of this application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances for the embodiments of this application described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0037] Specifically, this embodiment is applied to the processing of data of abandoned mining areas. Through the coupled analysis of multi-source data, dynamic grading of regional potential is realized, a four-dimensional coupling model of "soil-topography-hydrology-vegetation" is established, accurate assessment of regional quality potential, adaptive correction of parameters, and intelligent identification of reclamation grades are achieved, and finally scientific classification of low / medium / high potential areas is completed.

[0038] Please refer to Figure 1 As shown, it is a schematic flowchart of the AI-based national land data processing method in this embodiment, including:

[0039] Step S101, collect the pH value, organic matter content, and heavy metal concentration of the target area to analyze the soil properties of the target area.

[0040] Specifically, the target area is an abandoned mining area.

[0041] Exemplarily, in this embodiment, 10 sampling points are arranged in the target area, 5 sub-samples are mixed at each point, surface soil is collected, the pH value is measured by the laboratory potentiometric method, and the mean value is used as the representative value of the pH value. The organic matter content is measured by the wet oxidation method (potassium dichromate-sulfuric acid method), and the mean value is used as the representative value of the organic matter content. The concentrations of copper, lead, and zinc are measured by atomic absorption spectrometry, and the mean value is used as the representative value of the copper, lead, and zinc concentrations. In this embodiment, the collection methods of the pH value, organic matter content, and heavy metal concentration of the target area are not specifically limited, and those skilled in the art can freely set them according to needs.

[0042] Please refer to Figure 2 As shown, the analysis method of the soil properties includes:

[0043] Step S201, construct a heavy metal pollution index based on the copper, lead, and zinc concentrations of the collected target area.

[0044] Specifically, according to the copper concentration C1, lead concentration C2, and zinc concentration C3 of the collected target area, a heavy metal pollution index P is constructed, and the expression of P is:

[0045] where i = 1, 2, 3, S1 is the standard value of copper concentration, S2 is the standard value of lead concentration, S3 is the standard value of zinc concentration, and n = 3.

[0046] Specifically, by comprehensively averaging the squared multiples of exceedance of the three heavy metals copper, lead, and zinc, a non-linear pollution assessment model is constructed, which can identify the risk of hidden combined pollution and is superior to the traditional method of superimposing single pollution factors.

[0047] Please continue to refer to Figure 2 As shown, the analysis method of the soil properties further includes:

[0048] Step S202, construct a pH adjustment function based on the pH value of the target area collected, and standardize the organic matter content of the target area collected to obtain the standard organic matter content. Determine the soil properties of the target area based on the pH adjustment function, the standard organic matter content, and the heavy metal pollution index.

[0049] Specifically, construct a pH adjustment function f(pH) based on the pH value of the target area collected, and set:

[0050] where pH is the pH value of the currently collected target area;

[0051] Normalize the organic matter content m1 of the target area collected to obtain the standard organic matter content M. The expression of M is M = m1 / m2, where m2 is the threshold value of the organic matter content;

[0052] Construct a soil quality index SQ based on the pH adjustment function f(pH), the standard organic matter content M, and the heavy metal pollution index P. The expression of SQ is SQ = (f(pH) × M) / (1 + k × P), where k is an adjustment factor;

[0053] Compare the soil quality index SQ with the soil quality discrimination factor to determine the soil properties of the target area. If the soil quality index SQ is less than or equal to the soil quality discrimination factor, it is determined that the soil properties of the target area are class I soil; otherwise, it is determined that the soil properties of the target area are class II soil.

[0054] Exemplarily, in this embodiment, the standard values of copper concentration, lead concentration, and zinc concentration can be set with reference to the screening values corresponding to the Soil Environmental Quality - Agricultural Land Standards (GB 15618 - 2018). The m2 can be set to 0.08, the k can be set to 0.5, and the soil quality discrimination factor can be set to 0.5. In this embodiment, the settings of the above - mentioned data are not specifically limited, and those skilled in the art can freely set them according to requirements.

[0055] Specifically, the characterization ability of acidic or alkaline soil to the availability of organic matter is enhanced through a segmented pH adjustment function, scientifically reflecting the limiting effect of abnormal pH on fertility. At the same time, the dimensional difference is eliminated based on threshold normalization processing, highlighting the relative contribution of organic matter content in soil quality evaluation. The pollution index is incorporated into the quality model in the form of an inhibition factor to establish a dynamic offset relationship model between pollution risk and soil fertility, avoiding misjudgment that high organic matter masks pollution risk.

[0056] Please continue to refer to Figure 1 As shown, the AI-based national land data processing method further includes:

[0057] Step S102, collect the surface fissure density and surface settlement rate of the target area to analyze the surface stability index, and construct the terrain features of the target area based on the target area slope and surface stability index. Define the surface fissure density as the ratio of the total length of all surface fissures in the target area to the area of the target area.

[0058] Exemplarily, in this embodiment, through unmanned aerial vehicle (UAV) aerial photography, equipped with a visible light camera, flying at an altitude of 80 - 120 meters, images with a ground resolution ≤ 5 cm can be obtained (such as DJI Phantom 4 RTK), and the cracks can be interpreted and vectorized through ArcGIS Pro / QGIS + deep learning plug-ins (such as ENVI Deep Learning or third-party YOLOv5 models). The interpretation rule is that the actual ground width ≥ 0.2 m (image display ≥ 4 pixels) is included in the statistics, and the fracture interval ≤ 1 m is regarded as the same fissure segment. Import the fissure vector line layer into QGIS and calculate the length of each crack to obtain the fissure length, and divide the fissure length by the area of the target area to obtain the surface fissure density Lm; data can be collected through Sentinel-1, and the collected data is input into software tools such as GMTSAR (GMT+StaMPS) to obtain the surface settlement rate Cs of the target area. The slope of the target area can be obtained through software such as QGIS or Google Earth Engine. In this embodiment, no specific limitations are imposed on the collection methods of the surface fissure density, surface settlement rate, and slope of the target area, and those skilled in the art can freely set according to requirements.

[0059] Please refer to Figure 3 As shown, the construction method of the terrain features includes:

[0060] Step S301, normalize the collected surface fissure density and surface settlement rate of the target area to obtain a fissure density parameter and a settlement rate parameter, and analyze the surface stability index according to the fissure density parameter and the settlement rate parameter.

[0061] Specifically, the surface fissure density Dm of the collected target area is normalized in step S301 to obtain a fissure density parameter Fd. The expression of Fd is Fd = 1 / [1 + exp(-α×Dm / Dm0)], where α is a fissure limiting factor and Dm0 is a fissure density threshold;

[0062] The surface settlement rate Gs of the collected target area is normalized to obtain a settlement rate parameter Fc. The expression of Fc is Fc = min(1, Gs / Gs0), where Gs0 is a settlement rate threshold;

[0063] The fissure density parameter Fd and the settlement rate parameter Fc are fused to obtain a surface stability index SSI. The expression of SSI is SSI = w1×Fd + w2×Fc. In the formula, w1 is a fissure weight, w2 is a settlement weight, and w1 + w2 = 1.

[0064] Exemplarily, in this embodiment, the fissure limiting factor can be set to 0.8, the fissure density threshold can be set to 3 km / km², the settlement rate threshold can be set to 20 mm / year, w1 can be set to 0.6, and w2 can be set to 0.4. In this embodiment, no specific limitations are imposed on the values of each data, and those skilled in the art can freely set them according to requirements.

[0065] Specifically, normalization processing is adopted to balance the differences in low-density fissure areas and the saturation response in high-density areas, effectively suppressing the interference of extreme values. At the same time, the settlement rate parameter prevents evaluation distortion caused by local settlement anomalies through normalization, and differential weight allocation is implemented for the fissure and settlement factors, highlighting the typical characteristics of the influence of fissures on the stability of mining wastelands.

[0066] Please continue to refer to Figure 3 As shown, the method for constructing the terrain feature further includes:

[0067] Step S302, constructing the terrain feature of the target area based on the slope of the target area and the surface stability index.

[0068] Specifically, the surface stability index SSI and the slope θ of the target area are coupled with data to determine the terrain feature R of the target area. The expression of R is: R = (θ / θ1×SSI×β) 0.5 ; in the formula, θ1 is a slope threshold and β is a stability adjustment factor.

[0069] Exemplarily, in this embodiment, the slope threshold can be set to 45°, and the stability adjustment factor can be set to 0.6. In this embodiment, no specific limitations are imposed on the values of each data, and those skilled in the art can freely set them according to requirements.

[0070] Specifically, a coupling relationship is constructed by multiplying the slope threshold normalization by the stability index to quantify the synergistic effect between terrain steepness and geological safety. A stability adjustment factor is introduced to balance the over-sensitivity of the large slope area to the classification result, improving the rationality of the potential evaluation of slope reclamation.

[0071] Please continue to refer to Figure 1 As shown, the AI-based national land data processing method further includes:

[0072] Step S103, collect the annual average precipitation data and water source data of the target area to construct the hydrological characteristics of the target area. At the same time, based on the hydrological characteristics and hydrological adjustment weights of the target area, update the construction process of the terrain characteristics of the target area. The water source data is the Euclidean distance between the nearest river in the target area and the target area.

[0073] Exemplarily, in this embodiment, the collection of the annual average precipitation data of the target area should collect at least the annual average precipitation of the meteorological station for ten years, and the water source data can be collected interactively; in this embodiment, the collection methods of each data are not specifically limited, and those skilled in the art can freely set them according to needs.

[0074] Please refer to Figure 4 As shown, the construction method of the hydrological characteristics includes:

[0075] Step S401, construct the hydrological characteristics of the target area based on the collected annual average precipitation data and water source data of the target area.

[0076] Specifically, in step S401, the hydrological characteristics of the target area are set as HF, and the expression of HF is HF=(js-jsmin) / (jsmax-jsmin)×g(SY)+γ×[1-1 / (1+SY)], where js is the average value of the annual average precipitation data, jsmin is the minimum value of the collected annual average precipitation, jsmax is the maximum value of the collected annual average precipitation, g(SY) is the water source weight function, γ is the drought correction factor, and SY is the Euclidean distance between the nearest river in the currently collected target area and the target area;

[0077] The expression of the water source weight function g(SY) is:

[0078] .

[0079] Specifically, in this embodiment, the unit of SY is km. In the calculation of the construction of hydrological characteristics and the construction of the water source weight function, only its numerical value is used, and its physical meaning is not considered. The drought correction factor takes the value of 0.5 when js≤400mm, and takes the value of 0.2 when js>400mm.

[0080] Specifically, a hydrological network relationship is constructed by combining the annual average precipitation and the distance to the water source, which not only reflects the total regional water supply but also embodies the characteristics of water source accessibility, accurately identifies drought vulnerability, automatically adjusts the weight of the drought correction factor based on the precipitation threshold, and specifically enhances the sensitivity of the evaluation parameters in the arid area.

[0081] Please continue to refer to Figure 4 As shown, the construction method of the hydrological characteristics further includes:

[0082] Step S402, updating the construction process of the topographic characteristics of the target area based on the hydrological characteristics of the target area.

[0083] Specifically, in step S402, the hydrological characteristics HF of the target area are compared with the hydrological discrimination factor h0 to update the construction process of the topographic characteristics of the target area. When the hydrological characteristics HF of the target area are greater than or equal to the hydrological discrimination factor h0, no update is performed. Otherwise, the stable adjustment factor is updated to β1, and the expression of β1 is β1 = β × {1 + hydrological adjustment weight × lg[3 × (h0 - HF) + 1] / lg4}.

[0084] Exemplarily, in this embodiment, the hydrological discrimination factor h0 can be set to 0.6; in this embodiment, the setting of the hydrological discrimination factor is not specifically limited, and those skilled in the art can freely set it according to needs.

[0085] Specifically, the area to be corrected is screened through the hydrological discrimination factor, avoiding calculation redundancy caused by parameter adjustment in the entire area, and using the logarithmic function to achieve the amplification correction effect of low hydrological characteristic values, ensuring the gradualness and stability of topographic parameter correction.

[0086] Please continue to refer to Figure 1 As shown, the AI-based national land data processing method further includes:

[0087] Step S104, collecting the vegetation coverage rate of the target area to construct vegetation characteristics, and determining the hydrological adjustment weight based on the vegetation characteristics.

[0088] Exemplarily, in this embodiment, the vegetation coverage rate of the target area can be obtained through a satellite remote sensing data platform.

[0089] Specifically, in step S104, the ratio analysis of the vegetation coverage rate zf0 of the target area and the coverage rate threshold zf1 is performed to obtain the vegetation characteristics ZT, and the vegetation characteristics ZT are compared with the vegetation discrimination factor zt0 to determine the hydrological adjustment weight. When the vegetation characteristics ZT are greater than or equal to the vegetation discrimination factor zt0, the hydrological adjustment weight is set to T1, and it is set that T1 = η1. Otherwise, the hydrological adjustment weight is set to T2, and it is set that T2 = η1 × {1 + 0.2 × exp[3 × (zt0 - ZT) - 3]}, where η1 is the preset hydrological adjustment weight.

[0090] Exemplarily, in this embodiment, the coverage threshold can be set to 0.8, the vegetation discrimination factor can be set to 0.56, and the preset hydrological adjustment weight can be set to 0.3; in this embodiment, the values of each data are not specifically limited, and those skilled in the art can freely set them according to needs.

[0091] Specifically, use the vegetation coverage rate to invert the surface ecological restoration potential, dynamically adjust the influence of hydrological characteristics, enhance the model's discrimination ability for the feasibility of land reclamation in ecologically fragile areas, and at the same time, establish a vegetation-hydrology mutual feedback adjustment logic to adaptively optimize the weight parameters according to the ecological background conditions, so as to achieve precise adaptation to the microenvironment characteristics of the mining area.

[0092] Please continue to refer to Figure 1 As shown, the AI-based national land data processing method further includes:

[0093] Step S105, classify the target area based on the soil properties, topographic features, and hydrological features of the target area.

[0094] Specifically, when the soil property of the target area is type I soil, the target area is divided into a low-potential area; when the soil property of the target area is type II soil, if r1×SQ + r2×(1 - R) + r3×HF is less than or equal to the first category factor u1, the target area is divided into a low-potential area; if r1×SQ + r2×(1 - R) + r3×HF is greater than the first category factor u1 and less than or equal to the second category factor u2, the target area is divided into a medium-potential area; if r1×SQ + r2×(1 - R) + r3×HF is greater than the second category factor u2, the target area is divided into a high-potential area;

[0095] Wherein, r1 is the soil property weight, r2 is the topographic weight, r3 is the hydrological weight, and r1 + r2 + r3 = 1.

[0096] Specifically, the low-potential area is an area that needs to be reclaimed after long-term monitoring, the medium-potential area is an area that needs to be reclaimed after mild restoration, and the high-potential area is an area that can be directly reclaimed.

[0097] Exemplarily, in this embodiment, r1 can be set to 0.5, r2 can be set to 0.3, r3 can be set to 0.2, u1 can be set to 0.55, and u2 can be set to 0.75; in this embodiment, the values of each data are not specifically limited, and those skilled in the art can freely set them according to needs.

[0098] Specifically, a weighted combination model is used to balance the non-linear conflict relationship among soil, terrain, and hydrology, so as to improve the accuracy of target area division, thereby improving the accuracy of target area classification and the accuracy of land data processing.

[0099] Please refer to Figure 5 As shown, the AI-based land data processing device includes:

[0100] An attribute determination unit 501, configured to analyze the soil attributes of the target area according to the pH value, organic matter content, and heavy metal concentration of the collected target area;

[0101] A terrain determination unit 502, configured to analyze the surface stability index according to the surface fissure density and surface settlement rate of the collected target area, and construct the terrain features of the target area based on the target area slope and the surface stability index;

[0102] A hydrology determination unit 503, configured to construct the hydrological features of the target area according to the annual average precipitation data and water source data of the collected target area, and update the construction process of the terrain features of the target area based on the hydrological features and hydrological adjustment weights of the target area;

[0103] A vegetation analysis unit 504, configured to construct vegetation features according to the vegetation coverage rate of the collected target area, and determine the hydrological adjustment weight based on the vegetation features;

[0104] A classification unit 505, configured to classify the target area according to the soil attributes of the target area, the terrain features of the target area, and the hydrological features of the target area.

[0105] In this embodiment, the AI-based land data processing method can be implemented as a runnable computer program. When the computer program is loaded into the processor and when the computer program is loaded into the memory and processed by the processor through the bus, one or more steps of the AI-based land data processing method can be executed.

[0106] Those of ordinary skill in the art will understand that all or some of the steps and systems disclosed in the above methods can be implemented as software, firmware, hardware, and their appropriate combinations. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or can be implemented as hardware, or can be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable programs, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, a communication medium typically contains computer-readable programs, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

[0107] The above is a specific description of the preferred embodiments of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. An AI-based method for processing national land data, characterized in that, Including: Collecting the pH value, organic matter content, and heavy metal concentration of the target area to analyze the soil properties of the target area; Collecting the surface fissure density and surface settlement rate of the target area to analyze the surface stability index, and constructing the topographic features of the target area based on the slope of the target area and the surface stability index; Collecting the annual average precipitation data and water source data of the target area to construct the hydrological features of the target area, and updating the construction process of the topographic features of the target area based on the hydrological features and hydrological adjustment weights of the target area; Collecting the vegetation coverage rate of the target area to construct vegetation features, and determining the hydrological adjustment weight based on the vegetation features; Normalizing the collected surface fissure density Dm of the target area to obtain the fissure density parameter Fd. The expression of Fd is Fd = 1 / [1 + exp(-α × Dm / Dm0)], where α is the fissure limiting factor and Dm0 is the fissure density threshold; Normalizing the collected surface settlement rate Gs of the target area to obtain the settlement rate parameter Fc. The expression of Fc is Fc = min(1, Gs / Gs0), where Gs0 is the settlement rate threshold; Fusing the fissure density parameter Fd and the settlement rate parameter Fc to obtain the surface stability index SSI. The expression of SSI is SSI = w1 × Fd + w2 × Fc. In the formula, w1 is the fissure weight, w2 is the settlement weight, and w1 + w2 = 1; Couple the surface stability index SSI with the slope θ of the target area to determine the terrain feature R of the target area. The expression of R is: R = (θ / θ1 × SSI × β) 0.5 ; where θ1 is the slope threshold and β is the stability adjustment factor; Setting the hydrological features of the target area as HF. The expression of HF is HF = (js - jsmin) / (jsmax - jsmin) × g(SY) + γ × [1 - 1 / (1 + SY)]. In the formula, js is the average value of the annual average precipitation data, jsmin is the minimum value of the collected annual average precipitation, jsmax is the maximum value of the collected annual average precipitation, g(SY) is the water source weight function, γ is the drought correction factor, and SY is the Euclidean distance between the nearest river in the target area and the target area; Comparing the hydrological features HF of the target area with the hydrological discrimination factor h0 to update the construction process of the topographic features of the target area. When the hydrological features HF of the target area are greater than or equal to the hydrological discrimination factor h0, no update is performed. Otherwise, the stability adjustment factor is updated according to the hydrological adjustment weight; Performing a ratio analysis on the vegetation coverage rate zf0 and the coverage threshold zf1 of the target area to obtain the vegetation feature ZT, and comparing the vegetation feature ZT with the vegetation discrimination factor zt0 to determine the hydrological adjustment weight. When the vegetation feature ZT is greater than or equal to the vegetation discrimination factor zt0, the hydrological adjustment weight is set to T1, and it is set that T1 = η1. Otherwise, the hydrological adjustment weight is set to T2, and it is set that T2 = η1 × {1 + 0.2 × exp[3 × (zt0 - ZT) - 3]}, where η1 is the preset hydrological adjustment weight; 2. The AI-based national land data processing method according to claim 1, wherein Constructing a heavy metal pollution index P based on the collected copper concentration C1, lead concentration C2, and zinc concentration C3 of the target area; Construct a pH adjustment function based on the pH value of the collected target area, and standardize the organic matter content of the collected target area to obtain the standard organic matter content. Determine the soil properties of the target area based on the pH adjustment function, the standard organic matter content, and the heavy metal pollution index.

3. The AI-based national land data processing method according to claim 2, wherein Construct a pH adjustment function f(pH) based on the pH value of the collected target area, and set: ; where pH is the pH value of the target area currently collected; Normalize the organic matter content m1 of the collected target area to obtain the standard organic matter content M; Construct a soil quality index SQ based on the pH adjustment function f(pH), the standard organic matter content M, and the heavy metal pollution index P. The expression of SQ is SQ = (f(pH) × M) / (1 + k × P), where k is an adjustment factor; Compare the soil quality index SQ with the soil quality discrimination factor to determine the soil properties of the target area. If the soil quality index SQ is less than or equal to the soil quality discrimination factor, it is determined that the soil properties of the target area are type I soil. Otherwise, it is determined that the soil properties of the target area are type II soil.

4. The AI-based national land data processing method according to claim 3, wherein, It further includes: Classify the target area based on the soil properties of the target area, the topographic features of the target area, and the hydrological features of the target area; When the soil properties of the target area are type I soil, divide the target area into a low-potential area. When the soil properties of the target area are type II soil, if r1 × SQ + r2 × (1 - R) + r3 × HF is less than or equal to the first category factor u1, divide the target area into a low-potential area. If r1 × SQ + r2 × (1 - R) + r3 × HF is greater than the first category factor u1 and less than or equal to the second category factor u2, divide the target area into a medium-potential area. If r1 × SQ + r2 × (1 - R) + r3 × HF is greater than the second category factor u2, divide the target area into a high-potential area; Among them, r1 is the soil property weight, r2 is the topographic weight, and r3 is the hydrological weight.

5. An AI-based national land data processing device, applied to the AI-based national land data processing method as described in claim 1, characterized in that, It includes: An attribute determination unit for analyzing the soil properties of the target area according to the pH value, the organic matter content, and the heavy metal concentration of the collected target area; A topographic determination unit for analyzing the surface stability index according to the surface fracture density and the surface settlement rate of the collected target area, and constructing the topographic features of the target area based on the slope of the target area and the surface stability index; A hydrological determination unit for constructing the hydrological features of the target area according to the annual average precipitation data and the water source data of the collected target area, and updating the construction process of the topographic features of the target area based on the hydrological features of the target area and the hydrological adjustment weight; A vegetation analysis unit for constructing vegetation features according to the vegetation coverage rate of the collected target area, and determining the hydrological adjustment weight based on the vegetation features; A classification unit for classifying the target area according to the soil properties of the target area, the topographic features of the target area, and the hydrological features of the target area.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is used to control the electronic device where the computer-readable storage medium is located to execute the AI-based national land data processing method according to any one of claims 1-4 when running.

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