Land surveying and mapping data intelligent management system

Abstract

The invention relates to the technical field of geographic information management, in particular to a land surveying and mapping data intelligent management system, which comprises an information acquisition module for acquiring image information, environment parameter information and soil parameters of a surveying and mapping area, an edge analysis module for analyzing an edge curve of a planting land, an area division module and a data processing module, the system comprises an area division module used for carrying out area division on a planting land to obtain a plurality of fluctuation analysis areas, a terrain fluctuation analysis module used for analyzing the terrain fluctuation state of the planting land and adjusting the analysis process of the fluctuation state of the planting land, and a quality analysis module used for analyzing the land fertility state of the planting land and analyzing the quality of the planting land. And the data storage module is used for storing a land quality grading result and an edge curve analysis result of the planting land. The accuracy of land quality analysis is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of geographic information management technology, and in particular to an intelligent management system for land surveying and mapping data. Background Art

[0002] With the rapid development of social economy and the acceleration of urbanization, the rational planning and efficient use of land resources have become an urgent need. The traditional land surveying and mapping data management method has problems such as data dispersion, low processing efficiency, and weak analysis ability, which can hardly meet the current needs for rapid response and in-depth mining of land information. Therefore, it is particularly important to develop an intelligent management system that can integrate data collection, storage, processing, analysis and display functions.

[0003] China Patent Publication No.: CN116070887B discloses a land surveying and mapping data intelligent analysis and management system. The invention selects each abnormal area according to the remote sensing image model and the artificial measured model corresponding to the target agricultural land, and obtains the corresponding target model according to the coincidence ratio index of the remote sensing image model and the artificial measured model corresponding to each abnormal area, and then obtains the integrated model of the target agricultural land, thereby realizing multi-faceted and multi-level surveying and mapping management of the target agricultural land, improving the accuracy of the agricultural land model, and then increasing the reliability and accuracy of the agricultural land surveying and mapping data, laying a good foundation for the later agricultural land management. At the same time, combined with the land allocation level and allocation priority coefficient of each household to be allocated, the land allocation management is carried out for each household to be allocated, thereby improving the rationality of agricultural land allocation and allocation satisfaction, which is beneficial to the effectiveness and efficiency of land management of agricultural land management departments; it can be seen that the invention does not analyze the quality level of the cultivated land, cannot realize the accurate mapping of the land boundary and the accurate judgment of the land quality, and has the problem of low accuracy of land quality analysis. Summary of the invention

[0004] To this end, the present invention provides a land surveying and mapping data intelligent management system to overcome the problem of low accuracy of land quality analysis in the prior art.

[0005] To achieve the above-mentioned purpose, the present invention provides a land surveying and mapping data intelligent management system, comprising:

[0006] Information acquisition module, used to obtain image information, environmental parameter information and soil parameters of the surveying area;

[0007] An edge analysis module is used to analyze the edge curve of the planting land based on the regional image;

[0008] A region division module is used to divide the planting land into regions according to the edge curve analysis results of the planting land to obtain multiple fluctuation analysis regions;

[0009] A terrain fluctuation analysis module is used to calculate the vertical height difference of each fluctuation analysis area according to the terrain image, and to analyze the terrain fluctuation state of the planted land according to the calculation results of the vertical height difference of each fluctuation analysis area, and to adjust the analysis process of the fluctuation state of the planted land according to the environmental parameter information;

[0010] The quality analysis module is used to analyze the soil fertility status of the planting land according to the soil quality parameters, and to classify the land quality level of the planting land according to the analysis results of the soil fertility status of the planting land and the analysis results of the fluctuation status of the planting land;

[0011] The data storage module is used to store the land quality grade classification results and edge curve analysis results of the cultivated land.

[0012] Furthermore, the edge analysis module is provided with an image processing unit, and the image processing unit clusters the image pixels according to the image gray value G(i) to obtain various types of clusters F(j);

[0013] Wherein, G(i) represents the gray value of the i-th pixel in the regional image, i=1,2...I, I is the number of pixels in the regional image, F(j) represents the j-th cluster, j=1,2...J, J is the number of clusters obtained by clustering analysis of the regional image;

[0014] The image processing unit compares the number of pixels Fn(j) of each cluster with the minimum attribute number N1, and performs error judgment on each cluster according to the comparison result, wherein:

[0015] When Fn(j)<N1, the image processing unit lists the cluster as an error cluster;

[0016] When Fn(j)≥N1, the image processing unit lists the cluster as an actual cluster;

[0017] The image processing unit deletes the pixel points of the error cluster from the regional image.

[0018] Furthermore, the edge analysis module is further provided with an edge curve analysis unit, which performs mean calculation on the grayscale values ​​of the pixels of each actual clustering cluster to obtain the sample characteristic grayscale value Gray(x) of each actual clustering cluster, where Gray(x) represents the sample characteristic grayscale value of the xth actual clustering cluster;

[0019] The edge curve analysis unit analyzes the boundary pixel points of the regional image according to the sample characteristic grayscale value Gray(x) of each actual cluster and the pixel point coordinates of each actual cluster, wherein:

[0020] When GY1 ≤ Gray(x) < GY2, the edge curve analysis unit determines that the actual clustering cluster is a boundary clustering cluster. At this time, if min{{[A(a1) - A(a2)] 2 + [B(a1) - B(a2)] 2} 1 / 2} < DT, the edge curve analysis unit determines that this clustering cluster is a boundary pixel point. If min{{[A(a1) - A(a2)] 2 + [B(a1) - B(a2)] 2} 1 / 2} ≥ DT, the edge curve analysis unit determines that this clustering cluster is a non-boundary pixel point;

[0021] When Gray(x) < GY1 or Gray(x) ≥ GY2, the edge curve analysis unit determines that the actual clustering cluster is a non-boundary clustering cluster, and the pixels therein are non-boundary pixel points;

[0022] Among them, GY1 is the first preset pixel point gray value, GY2 is the second preset pixel point gray value, GY1 < GY2, A(a1) is the horizontal coordinate of the a1-th pixel point in the x-th actual clustering cluster, A(a2) is the horizontal coordinate of the a2-th pixel point in the x-th actual clustering cluster, B(a2) is the vertical coordinate of the a1-th pixel point in the x-th actual clustering cluster, B(a2) is the vertical coordinate of the a2-th pixel point in the x-th actual clustering cluster, and DT is the boundary continuous pixel point distance threshold;

[0023] The edge curve analysis unit connects the lines between each boundary pixel point to form the edge curve of the cultivated land.

[0024] Furthermore, the area division module divides the surveying area according to the analysis result of the edge curve of the cultivated land;

[0025] The area division module calculates the area S of the cultivated land within the edge curve according to the scale μ of the area image, and sets S = n3 × μ, where n3 is the number of pixel points included within the edge curve;

[0026] The area division module compares the area S of the cultivated land within the edge curve with each preset area, and analyzes the divided area of the cultivated land according to the comparison result, where:

[0027] When S < s1, the area division module determines that the cultivated land is a small-area cultivated land, and sets the divided area of this cultivated land to HS1, and sets HS1 = HS × exp[(S - s1) / s1];

[0028] When s1 ≤ S < s2, the area division module determines that the cultivated land is medium-sized cultivated land, and sets the divided area of the cultivated land to HS2, where HS2 = HS;

[0029] When S ≥ s2, the area division module determines that the cultivated land is large-sized cultivated land, and sets the divided area of the cultivated land to HS3, where HS3 = HS × {1 + arctan[(S - s2) / s1]};

[0030] Where s1 is the first preset area, s2 is the second preset area, and HS is the preset fluctuation analysis area division area;

[0031] The area division module divides the cultivated land into each fluctuation analysis area M(k) according to the divided area HSv of the cultivated land, where k = 1, 2... N3, N3 = S / HSv, and v = 1, 2, 3.

[0032] Further, the terrain fluctuation analysis module is provided with an area height analysis unit, and the area height analysis unit calculates the vertical height difference d(k) of each fluctuation analysis area M(k) according to the terrain image. The calculation formula of the vertical height difference d(k) of each fluctuation analysis area is as follows:

[0033] d(k) = max{[K(n1) - K(n2)] / (4 × π × λ × cos(θ 入 )}

[0034] Where λ is the wavelength of the radar signal, K(n1) is the phase of the n1th pixel point in the kth fluctuation analysis area of the terrain image, K(n2) is the phase of the n2th pixel point in the kth fluctuation analysis area of the terrain image, and θ 入 is the incident angle of the radar wave.

[0035] Further, the terrain fluctuation analysis module is also provided with an extreme value adjustment unit. The extreme value adjustment unit calculates the average height R(k) of the pixel points in each fluctuation analysis area according to the terrain image of each fluctuation analysis area, and sets

[0036] Where K(n) is the phase of the nth pixel point in the kth fluctuation analysis area, and nk is the number of pixel points in the kth fluctuation analysis area;

[0037] The extreme value adjustment unit adjusts the calculation process of the vertical height difference of each fluctuation analysis area according to the average height R(k) and the vertical height difference d(k) of each fluctuation analysis area, where:

[0038] When [d(k)-R(k)] / R(k)<W, the extreme value adjustment unit determines that the extreme value of the fluctuation analysis area is normal and does not make any adjustment;

[0039] When [d(k)-R(k)] / R(k)≥W, the extreme value adjustment unit determines that the extreme value of the fluctuation analysis area is large, and adjusts the vertical height difference of the fluctuation analysis area to d'(k), setting d'(k)=d(k)×ln{e-{[d(k)+R(k)] / R(k)-W} / W};

[0040] Where W is the extreme value ratio threshold.

[0041] Furthermore, the terrain fluctuation analysis module is also provided with a meteorological optimization unit, which optimizes the adjustment process of calculating the vertical height difference of each fluctuation analysis area according to the environmental parameter information;

[0042] The meteorological optimization unit calculates the meteorological influence factor β according to the environmental parameter information, and sets β=exp{ln(1+T / t)+sin(P / atm)+arctan(sd)};

[0043] Where T is the temperature gradient, t is the standard Celsius temperature gradient, P is the atmospheric pressure in the survey area, atm is the standard atmospheric pressure, and sd is the absolute ambient humidity in the survey area;

[0044] The meteorological optimization unit compares the meteorological influence factor β with the preset influence index B, and optimizes the adjustment process of calculating the vertical height difference of each fluctuation analysis area according to the comparison result, wherein:

[0045] When β<B, the meteorological optimization unit determines that the meteorological state of the surveying and mapping area is normal and does not perform optimization;

[0046] When β≥B, the meteorological optimization unit determines that the meteorological state of the surveying area is abnormal, and optimizes the extreme value ratio threshold to W', setting W'=W×[1+cos(β)].

[0047] Furthermore, the terrain fluctuation analysis module is further provided with a terrain fluctuation analysis unit, which analyzes the terrain fluctuation state of the planting land according to the vertical height difference d(k) of each fluctuation analysis area;

[0048] The terrain fluctuation analysis unit calculates the fluctuation variation coefficient α according to the vertical height difference d(k) of each fluctuation analysis area. The calculation formula of the fluctuation variation coefficient α is as follows:

[0049] α=σ / E×100%;

[0050] σ={{[d(1)-E] 2+[d(2)-E] 2 +...+[d(N2)-E] 2} / N2} 1 / 2 ;

[0051] E=[d(1)+d(2)+...+d(N2)] / N2;

[0052] Where, d(1) is the vertical height difference of the first fluctuation analysis area, d(2) is the vertical height difference of the second fluctuation analysis area, d(N2) is the vertical height difference of the N2th fluctuation analysis area, N2 is the number of divisions of the fluctuation analysis area, σ is the standard deviation of the vertical height difference data, and E is the mean of the vertical height difference of each fluctuation analysis area;

[0053] The terrain fluctuation analysis unit compares the fluctuation coefficient with each preset variation coefficient, and analyzes the terrain fluctuation state of the planting land according to the comparison result, wherein:

[0054] When α<C1, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is flat;

[0055] When C1≤α<C2, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is normal fluctuation;

[0056] When α≥C2, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is severe.

[0057] Furthermore, the quality analysis module is provided with a soil quality analysis unit, which analyzes the soil fertility status of the planting land according to the land parameters of the surveying area;

[0058] The soil quality analysis unit calculates the soil fertility index γ of the planting land according to the land parameters of the surveying area, and sets γ = {b1×[ρ(K)-BK] / BK+b2×[ρ(NH4)-BN] / BN} / ρ(salt);

[0059] Where b1 is the potassium fertilizer weight, b2 is the nitrogen fertilizer weight, b2>b1 and b2+b1=1, ρ(K) is the soil potassium ion concentration, ρ(NH4) is the soil ammonium salt concentration, BK is the standard soil potassium fertilizer concentration, BN is the standard soil nitrogen fertilizer concentration, and ρ(salt) is the inorganic salt concentration;

[0060] The soil quality analysis unit compares the soil fertility index γ with each preset fertility index, and analyzes the soil fertility status of the planting land according to the comparison result, wherein:

[0061] When γ<Y1, the soil quality analysis unit determines that the soil fertility state of the planting land is level 3 fertility;

[0062] When Y1≤γ<Y2, the soil quality analysis unit determines that the soil fertility state of the planting land is secondary fertility;

[0063] When γ≥Y2, the soil quality analysis unit determines that the soil fertility state of the planting land is first-level fertility;

[0064] Among them, Y1 is the first preset fertility index, Y2 is the second preset fertility index, and Y1<Y2.

[0065] Furthermore, the quality analysis module is further provided with a quality analysis unit, which classifies the land quality level according to the land fertility state analysis result and the fluctuation state analysis result of the planting land, wherein:

[0066] When the terrain fluctuation state of the planting land is flat, if the land fertility state is level one fertility, the quality analysis unit determines that the quality level of the planting land is excellent; if the land fertility state is level two fertility, the quality analysis unit determines that the quality level of the planting land is good; if the land fertility state is level three fertility, the quality analysis unit determines that the quality level of the planting land is unqualified;

[0067] When the terrain fluctuation state of the planting land is normal undulation, if the land fertility state is level one fertility, the quality analysis unit determines that the quality level of the planting land is good; if the land fertility state is level two fertility, the quality analysis unit determines that the quality level of the planting land is qualified; if the land fertility state is level three fertility, the quality analysis unit determines that the quality level of the planting land is unqualified;

[0068] When the terrain fluctuation state of the planting land is severe, if the soil fertility state is level one fertility, the quality analysis unit determines that the quality grade of the planting land is qualified; if the soil fertility state is level two fertility, the quality analysis unit determines that the quality grade of the planting land is unqualified; if the soil fertility state is level three fertility, the quality analysis unit determines that the quality grade of the planting land is unqualified.

[0069] Compared with the prior art, the beneficial effects of the present invention lie in that the accuracy and completeness of information acquisition are improved by acquiring the information required by the present invention through the information acquisition module, the accuracy of edge pixel point analysis is improved by analyzing the edge curve through the edge analysis module, the accuracy of land fluctuation analysis area division is improved by performing regional division on the cultivated land through the regional division module, the accuracy of terrain analysis of the cultivated land is improved by analyzing the terrain fluctuation state through the terrain fluctuation analysis module, the accuracy of land quality analysis of the cultivated land is improved by dividing the land quality grade of the cultivated land through the quality analysis module, the accuracy of quality analysis of the cultivated land is improved, the land quality grade division results and edge curve analysis results of the cultivated land are stored through the data storage module, the security of data storage is improved, and ultimately the accuracy of land quality analysis is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a schematic diagram of the structure of the land surveying and mapping data intelligent management system of this embodiment;

[0071] Figure 2 Schematic diagram of the structure of the edge analysis module of this embodiment;

[0072] Figure 3 This is a schematic diagram of the structure of the terrain fluctuation analysis module of this embodiment;

[0073] Figure 4 Schematic diagram of the structure of the quality analysis module of this embodiment. DETAILED DESCRIPTION

[0074] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0075] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0076] It should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0077] See also Figure 1As shown, it is a structural diagram of the land surveying and mapping data intelligent management system of this embodiment, including:

[0078] The information acquisition module is used to obtain image information, environmental parameter information and soil parameters of the surveying and mapping area; the image information includes regional images and terrain images, the regional image is a grayscale image taken by a drone at high altitude, and the terrain image is a raw format image taken by a remote sensing satellite through a synthetic aperture radar; the regional image information in this embodiment is an elevation synthetic aperture radar image taken by a remote sensing satellite, and the image data format is raw; the image information in this embodiment only captures a complete piece of cultivated land; the environmental parameter information includes the temperature gradient, the atmospheric pressure of the surveying and mapping area, and the absolute environmental humidity of the surveying and mapping area. degree; the soil parameters include soil potassium ion concentration, soil ammonium salt concentration, standard soil potassium fertilizer concentration, standard soil nitrogen fertilizer concentration and inorganic salt concentration; in this embodiment, no specific limitation is made on the acquisition method of image information, environmental parameter information and soil parameters, and those skilled in the art can freely set them, as long as the acquisition requirements of image information, environmental parameter information and soil parameters are met, such as obtaining environmental parameter information through meteorological websites, obtaining standard soil potassium fertilizer concentration and standard soil nitrogen fertilizer concentration through land quality detection websites, and obtaining soil potassium ion concentration, soil ammonium salt concentration and inorganic salt concentration through soil detection instruments;

[0079] An edge analysis module, used to analyze the edge curve of the planting land according to the regional image, and the edge analysis module is connected to the information acquisition module;

[0080] A region division module, used for dividing the planting land into regions according to the edge curve analysis result of the planting land, to obtain a plurality of fluctuation analysis regions, the region division module being connected to the edge analysis module;

[0081] A terrain fluctuation analysis module, used to calculate the vertical height difference of each fluctuation analysis area according to the terrain image, and to analyze the terrain fluctuation state of the planted land according to the calculation results of the vertical height difference of each fluctuation analysis area, and also used to adjust the analysis process of the fluctuation state of the planted land according to the environmental parameter information, and the terrain fluctuation analysis module is connected to the regional analysis module;

[0082] A quality analysis module, used to analyze the soil fertility status of the planting land according to the soil quality parameters, and to analyze the quality status of the planting land according to the soil fertility status analysis results and the fluctuation status analysis results of the planting land, the quality analysis module is connected to the terrain fluctuation analysis module;

[0083] The data storage module is used to store the land quality grade classification results and edge curve analysis results of the cultivated land, and the data storage module is connected to the quality analysis module.

[0084] See also Figure 2 As shown, it is a schematic diagram of the structure of the edge analysis module of this embodiment, including:

[0085] An image processing unit, used for preprocessing the regional image;

[0086] The edge curve analysis unit is used to analyze the boundary pixel points of the regional image according to the regional image preprocessing result, and to construct the edge curve of the planted land according to the boundary pixel points. The edge curve analysis unit is connected to the image processing unit.

[0087] See also Figure 3 As shown, it is a schematic diagram of the structure of the terrain fluctuation analysis module of this embodiment, including:

[0088] A regional height analysis unit, used to calculate the vertical height difference of each fluctuation analysis area M(k) according to the terrain image;

[0089] An extreme value adjustment unit, used to adjust the calculation process of the vertical height difference of each fluctuation analysis area according to the terrain image, and the extreme value adjustment unit is connected to the area height analysis unit;

[0090] A meteorological optimization unit, used for optimizing the adjustment process of calculating the vertical height difference of each fluctuation analysis area according to the environmental parameter information, the meteorological optimization unit being connected to the extreme value adjustment unit;

[0091] The terrain fluctuation analysis unit is used to analyze the terrain fluctuation state of the cultivated land according to the vertical height difference of each fluctuation analysis area, and the terrain fluctuation analysis unit is connected to the area height analysis unit.

[0092] See also Figure 4 As shown, it is a schematic diagram of the structure of the quality analysis module of this embodiment, including:

[0093] Soil analysis unit, used to analyze the soil fertility status of the cultivated land according to the land parameters of the surveyed area;

[0094] The quality analysis unit is used to classify the land quality level according to the soil fertility state analysis result and the fluctuation state analysis result of the cultivated land, and the quality analysis unit is connected to the soil quality analysis unit.

[0095] Specifically, the land surveying and mapping data intelligent management system described in this embodiment is applied to the intelligent management of land surveying and mapping data in mountainous areas; in this embodiment, the mountainous land is specifically cultivated land with cultivated land isolation ridges at its edges; the present invention analyzes and judges the edge curves of the cultivated land, and then divides the cultivated land into fluctuation analysis areas, and analyzes the fluctuation state of the cultivated land according to the division results and images, thereby judging the quality of the cultivated land according to the land fertility and fluctuation state analysis results, thereby improving the accuracy of land quality analysis.

[0096] Specifically, the edge analysis module is provided with an image processing unit, and the image processing unit analyzes edge image points according to regional image information;

[0097] The image processing unit clusters the image pixels according to the image grayscale value G(i) to obtain various types of clusters F(j);

[0098] Wherein, G(i) represents the gray value of the i-th pixel in the regional image, i=1,2...I, I is the number of pixels in the regional image, F(j) represents the j-th cluster, j=1,2...J, J is the number of clusters obtained by clustering analysis of the regional image;

[0099] The image processing unit compares the number of pixels Fn(j) of each cluster with the minimum attribute number N1, and performs error judgment on each cluster according to the comparison result, wherein:

[0100] When Fn(j)<N1, the image processing unit lists the cluster as an error cluster;

[0101] When Fn(j)≥N1, the image processing unit lists the cluster as an actual cluster;

[0102] Among them, Fn(j) is the number of pixels in the jth cluster;

[0103] The image processing unit deletes the pixel points of the error cluster from the regional image.

[0104] Specifically, the image processing unit improves the accuracy of image processing by clustering the regional image and screening the cluster clusters according to the clustering results, thereby improving the accuracy of edge pixel analysis, thereby improving the accuracy of judgment on the volatility and quality grade of cultivated land, and ultimately improving the accuracy of land quality analysis; in this embodiment, there is no specific limitation on the value of the minimum attribute number N1, and technical personnel in this field can set it freely, as long as the value requirement of the minimum attribute number N1 is met, such as the minimum attribute number N1 can be set to I×0.05.

[0105] It is worth noting that the error clustering clusters of the regional image in this embodiment specifically refer to the larger stones in the mountain farmland, which will cause errors in the process of analyzing the edge curve of the farmland in this embodiment. Therefore, preliminary processing is required; at the same time, the specific effect of the regional image obtained after deleting the pixel points of the error clustering clusters in this embodiment is the same as the specific effect of the regional image after the regional image is cleaned and denoised.

[0106] Specifically, the edge curve analysis unit calculates the mean grayscale value of the pixel points of each actual cluster to obtain the sample characteristic grayscale value Gray(x) of each actual cluster, where Gray(x) represents the sample characteristic grayscale value of the xth actual cluster;

[0107] The edge curve analysis unit analyzes the boundary pixel points of the regional image according to the sample characteristic grayscale value Gray(x) of each actual cluster and the pixel point coordinates of each actual cluster, wherein:

[0108] When GY1≤Gray(x)<GY2, the edge curve analysis unit determines that the actual cluster is a boundary cluster. At this time, if min{{[A(a1)-A(a2)] 2 +[B(a1)-B(a2)] 2} 1 / 2}<DT, the edge curve analysis unit determines that the cluster is a boundary pixel point, if min{{[A(a1)-A(a2)] 2 +[B(a1)-B(a2)] 2} 1 / 2}≥DT, the edge curve analysis unit determines that the cluster is a non-boundary pixel point;

[0109] When Gray(x)<GY1 or Gray(x)≥GY2, the edge curve analysis unit determines that the actual cluster is a non-boundary cluster and the pixels inside it are non-boundary pixels;

[0110] Wherein, GY1 is the gray value of the first preset pixel, GY2 is the gray value of the second preset pixel, GY1<GY2, A(a1) is the horizontal coordinate of the a1th pixel in the xth actual cluster, A(a2) is the horizontal coordinate of the a2th pixel in the xth actual cluster, B(a2) is the vertical coordinate of the a1th pixel in the xth actual cluster, B(a2) is the vertical coordinate of the a2th pixel in the xth actual cluster, and DT is the distance threshold of continuous pixels at the boundary;

[0111] The edge curve analysis unit connects the boundary pixel points to form an edge curve of the planted land.

[0112] Specifically, the edge curve analysis unit analyzes each clustering cluster after image clustering processing, and analyzes and judges the boundary pixels of the cultivated land according to the analysis results, thereby improving the accuracy of edge pixel judgment, and further improving the accuracy of fluctuation analysis area division, thereby improving the accuracy of judgment on the fluctuation and quality grade of cultivated land, and ultimately improving the accuracy of land quality analysis; it can be understood that in this embodiment, no specific limitation is made on the value of the first preset pixel grayscale value GY1 and the second preset pixel grayscale value GY2, and those skilled in the art can set them freely, as long as the value requirements of the first preset pixel grayscale value GY1 and the second preset pixel grayscale value GY2 are met, such as the first preset pixel grayscale value GY1 can be set to 100, and the second preset pixel grayscale value GY2 can be set to 120. It can be understood that the values ​​of the first preset pixel grayscale value GY1 and the second preset pixel grayscale value GY2 are specifically the optimal values ​​for application in the scenario of this embodiment.

[0113] It is worth noting that the edge curve obtained by performing edge curve analysis on the regional image after image processing in this embodiment is specifically an irregular closed curve, within which the closed curve is cultivated land, and outside the closed curve is non-cultivated land; at the same time, since the cultivated land in mountainous areas is mostly divided by rocks and higher ridged soil layers, and there is a difference in the grayscale value of the rocks, higher ridged soil layers and the soil after cultivation in the cultivated land, the edge pixel points obtained by analysis are connected to obtain the edge curve of the cultivated land.

[0114] Specifically, the region division module divides the surveying and mapping area according to the edge curve analysis result of the planting land;

[0115] The region division module calculates the area S of the planted land within the edge curve according to the scale μ of the region image, and sets S=n3×μ, where n3 is the number of pixels contained in the edge curve;

[0116] The area division module compares the area S of the planted land within the edge curve with each preset area, and analyzes the divided area of ​​the planted land according to the comparison result, wherein:

[0117] When S<s1, the area division module determines that the planting land is a small-area planting land, and sets the divided area of ​​the planting land to HS1, setting HS1=HS×exp[(S-s1) / s1];

[0118] When s1≤S<s2, the area division module determines that the planting land is medium-sized planting land, and sets the divided area of ​​the planting land to HS2, setting HS2=HS;

[0119] When S≥s2, the area division module determines that the planting land is a large area of ​​planting land, and sets the divided area of ​​the planting land to HS3, setting HS3=HS×{1+arctan[(S-s2) / s1]};

[0120] Wherein, s1 is the first preset area, s2 is the second preset area, and HS is the preset fluctuation analysis area division area;

[0121] The area division module divides the cultivated land into various fluctuation analysis areas M(k) according to the divided area HSv of the cultivated land, sets k=1, 2...N3, N3 is the number of fluctuation analysis areas and N3=S / HSv, and sets v=1, 2, 3.

[0122] Specifically, the area division module improves the accuracy of the fluctuation analysis area division by performing fluctuation analysis area division on the planting area within the edge curve, thereby improving the accuracy of the judgment of the volatility and quality grade of the planting land, and ultimately improving the accuracy of the land quality analysis; in this embodiment, no specific limitation is made on the values ​​of the first preset area s1 and the second preset area s2, and technical personnel in this field can set them freely, as long as the value requirements of the first preset area s1 and the second preset area s2 are met. For example, the first preset area s1 can be set to 1334 square meters, and the second preset area s2 can be set to 2000 square meters. At the same time, the optimal value of the preset fluctuation analysis area division area HS in this embodiment is 2 square meters.

[0123] It is worth noting that the fluctuation analysis areas divided in this embodiment are specifically square areas of the same area, and the largest inscribed rectangle of the irregular boundary area is selected as the fluctuation analysis area.

[0124] Specifically, the regional height analysis unit calculates the vertical height difference d(k) of each fluctuation analysis region M(k) according to the synthetic aperture radar image. The calculation formula of the vertical height difference d(k) of each fluctuation analysis region is as follows:

[0125] d(k)=max{[K(n1)-K(n2)] / 4×π×λ×cos(θ 入 )}

[0126] Where λ is the wavelength of the radar signal, K(n1) is the phase of the n1th pixel in the kth wave analysis area in the terrain image, K(n2) is the phase of the n2th pixel in the kth wave analysis area in the terrain image, and θ 入 is the angle of incidence of the radar wave.

[0127] Specifically, the regional height analysis unit improves the accuracy of the fluctuation state analysis of the cultivated land by calculating the vertical height difference of each fluctuation analysis area, thereby improving the accuracy of the cultivation land quality grade classification, and ultimately improving the accuracy of the land quality analysis.

[0128] Specifically, the extreme value adjustment unit calculates the average height R(k) of the pixels in each fluctuation analysis area according to the terrain image of each fluctuation analysis area, and sets

[0129] Where K(n) is the phase of the nth pixel in the kth fluctuation analysis area, and nk is the number of pixels in the kth fluctuation analysis area;

[0130] The extreme value adjustment unit adjusts the calculation process of the vertical height difference of each fluctuation analysis area according to the average height R(k) and the vertical height difference d(k) of each fluctuation analysis area, wherein:

[0131] When [d(k)-R(k)] / R(k)<W, the extreme value adjustment unit determines that the extreme value of the fluctuation analysis area is normal and does not make any adjustment;

[0132] When [d(k)-R(k)] / R(k)≥W, the extreme value adjustment unit determines that the extreme value of the fluctuation analysis area is large, and adjusts the vertical height difference of the fluctuation analysis area to d'(k), setting d'(k)=d(k)×ln{e-{[d(k)+R(k)] / R(k)-W} / W};

[0133] Where W is the extreme value ratio threshold.

[0134] Specifically, the extreme value adjustment unit calculates the average height of the pixel points, and adjusts the calculation process of the vertical height difference of each fluctuation analysis area according to the calculation result and the calculation result of the vertical height difference, thereby improving the accuracy of the fluctuation state analysis of the cultivated land, and then improving the accuracy of the classification of the quality grade of the cultivated land, and ultimately improving the accuracy of the land quality analysis; it can be understood that in this embodiment, the value of the extreme value ratio threshold W is not specifically limited, and technical personnel in this field can set it freely, as long as the value requirement of the extreme value ratio threshold W is met, such as the extreme value ratio threshold W can be set to 0.3.

[0135] Specifically, the meteorological optimization unit optimizes the calculation process of the vertical height difference of each fluctuation analysis area according to the environmental parameter information;

[0136] The meteorological optimization unit calculates the meteorological influence factor β according to the environmental parameter information, and sets β=exp{ln(1+T / t)+sin(P / atm)+arctan(sd)};

[0137] Where T is the temperature gradient, t is the standard Celsius temperature gradient, P is the atmospheric pressure in the survey area, atm is the standard atmospheric pressure, and sd is the absolute ambient humidity in the survey area;

[0138] The meteorological optimization unit compares the meteorological influence factor β with the preset influence index B, and optimizes the calculation process of the vertical height difference of each fluctuation analysis area according to the comparison result, wherein:

[0139] When β<B, the meteorological optimization unit determines that the meteorological state of the surveying and mapping area is normal and does not perform optimization;

[0140] When β≥B, the meteorological optimization unit determines that the meteorological state of the surveying area is abnormal, and optimizes the extreme value ratio threshold to W', setting W'=W×[1+cos(β)].

[0141] Specifically, the meteorological optimization unit analyzes the environmental parameter information and sets the meteorological influence factor to analyze the influence of meteorological conditions on the shooting accuracy of synthetic aperture radar images, and optimizes the extreme value adjustment process according to the analysis results, thereby improving the accuracy of the fluctuation state analysis of the cultivated land, thereby improving the accuracy of the classification of the cultivated land quality level, and finally improving the accuracy of the land quality analysis; in this embodiment, no specific limitation is made to the value of the preset influence index B, and technical personnel in this field can set it freely, as long as the value requirement of the preset influence index B is met, such as the preset influence index B can be set to 2; it can be understood that the temperature gradient size T in this embodiment is specifically the temperature gradient vector Modulus, temperature gradient vector The calculation process is T, and are the partial derivatives of temperature in the x, y, and z directions in three-dimensional space, respectively. In this embodiment, the function of the standard Celsius temperature gradient t is to eliminate the dimension of the temperature gradient magnitude T. Its value is 1 and its unit is the same as the temperature gradient magnitude T.

[0142] Specifically, the terrain fluctuation analysis unit analyzes the terrain fluctuation state of the planting land according to the vertical height difference d(k) of each fluctuation analysis area;

[0143] The terrain fluctuation analysis unit calculates the fluctuation variation coefficient α according to the vertical height difference d(k) of each fluctuation analysis area. The calculation formula of the fluctuation variation coefficient α is as follows:

[0144] α=σ / E×100%

[0145] σ={{[d(1)-E] 2 +[d(2)-E]2 +...+[d(N2)-E] 2} / N2} 1 / 2 ;

[0146] E=[d(1)+d(2)+...+d(N2)] / N2;

[0147] Where, d(1) is the vertical height difference of the first fluctuation analysis area, d(2) is the vertical height difference of the second fluctuation analysis area, d(N2) is the vertical height difference of the N2th fluctuation analysis area, N2 is the number of divisions of the fluctuation analysis area, σ is the standard deviation of the vertical height difference data, and E is the mean of the vertical height difference of each fluctuation analysis area;

[0148] The terrain fluctuation analysis unit compares the fluctuation coefficient with each preset variation coefficient, and analyzes the terrain fluctuation state of the planting land according to the comparison result, wherein:

[0149] When α<C1, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is flat;

[0150] When C1≤α<C2, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is normal fluctuation;

[0151] When α≥C2, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is severe fluctuation;

[0152] Wherein, C1 is the first preset coefficient of variation, C2 is the second preset coefficient of variation, and C1<C2.

[0153] Specifically, the terrain fluctuation analysis unit analyzes the terrain fluctuation state of the cultivated land by setting the fluctuation variation coefficient, thereby improving the accuracy of the fluctuation state analysis of the cultivated land, thereby improving the accuracy of the cultivation land quality grade classification, and ultimately improving the accuracy of the land quality analysis; in this embodiment, no specific limitation is made on the values ​​of the first preset coefficient of variation C1 and the second preset coefficient of variation C2, and technical personnel in this field can set them freely, as long as the value requirements of the first preset coefficient of variation C1 and the second preset coefficient of variation C2 are met, such as the first preset coefficient of variation C1 can be set to 10%, and the second preset coefficient of variation C2 can be set to 30%.

[0154] Specifically, the quality analysis module is provided with a soil quality analysis unit, which analyzes the soil fertility status of the planting land according to the land parameters of the surveying area;

[0155] The soil quality analysis unit calculates the soil fertility index γ of the planting land according to the land parameters of the surveying area, and sets γ = {b1×[ρ(K)-BK] / BK+b2×[ρ(NH4)-BN] / BN} / ρ(salt);

[0156] Where b1 is the potassium fertilizer weight, b2 is the nitrogen fertilizer weight, b2>b1 and b2+b1=1, ρ(K) is the soil potassium fertilizer concentration, ρ(NH4) is the soil nitrogen fertilizer concentration, ρ(P) is the soil phosphorus fertilizer concentration, BK is the standard soil potassium fertilizer concentration, BN is the standard soil nitrogen fertilizer concentration, and ρ(salt) is the inorganic salt concentration;

[0157] The soil quality analysis unit compares the soil fertility index γ with each preset fertility index, and analyzes the soil fertility status of the planting land according to the comparison result, wherein:

[0158] When γ<Y1, the soil quality analysis unit determines that the soil fertility state of the planting land is level 3 fertility;

[0159] When Y1≤γ<Y2, the soil quality analysis unit determines that the soil fertility state of the planting land is secondary fertility;

[0160] When γ≥Y2, the soil quality analysis unit determines that the soil fertility state of the planting land is first-level fertility;

[0161] Among them, Y1 is the first preset fertility index, Y2 is the second preset fertility index, and Y1<Y2.

[0162] Specifically, the soil analysis unit calculates the soil fertility index according to the soil parameters, and analyzes the land fertility status of the cultivated land according to the calculation result of the soil fertility index, thereby improving the accuracy of the land fertility status analysis of the cultivated land, thereby improving the accuracy of the land quality grade classification, and ultimately improving the accuracy of the land quality analysis; in this embodiment, no specific limitation is made on the values ​​of the first preset fertility index Y1 and the second preset fertility index Y2. Those skilled in the art can freely set them as long as the value requirements of the first preset fertility index Y1 and the second preset fertility index Y2 are met, such as the first preset fertility index Y1 can be set to 0.3, and the second preset fertility index Y2 can be set to 0.7; the inorganic salt concentration ρ (salt) described in this embodiment is the salt concentration that causes soil salinization, and the salts described in this embodiment are specifically sodium, calcium, magnesium and other salts.

[0163] Specifically, the quality analysis unit classifies the land quality level of the planting land according to the land fertility state analysis result and the fluctuation state analysis result of the planting land, wherein:

[0164] When the terrain fluctuation state of the planting land is flat, if the land fertility state is level one fertility, the quality analysis unit determines that the quality level of the planting land is excellent; if the land fertility state is level two fertility, the quality analysis unit determines that the quality level of the planting land is good; if the land fertility state is level three fertility, the quality analysis unit determines that the quality level of the planting land is unqualified;

[0165] When the terrain fluctuation state of the planting land is normal undulation, if the land fertility state is level one fertility, the quality analysis unit determines that the quality level of the planting land is good; if the land fertility state is level two fertility, the quality analysis unit determines that the quality level of the planting land is qualified; if the land fertility state is level three fertility, the quality analysis unit determines that the quality level of the planting land is unqualified;

[0166] When the terrain fluctuation state of the planting land is severe, if the soil fertility state is level one fertility, the quality analysis unit determines that the quality grade of the planting land is qualified; if the soil fertility state is level two fertility, the quality analysis unit determines that the quality grade of the planting land is unqualified; if the soil fertility state is level three fertility, the quality analysis unit determines that the quality grade of the planting land is unqualified.

[0167] Specifically, the quality analysis unit improves the accuracy of land quality grade classification by dividing the land quality grade of the planting land according to the land fertility status analysis results and the fluctuation status analysis results of the planting land, thereby improving the accuracy of land quality analysis; in this embodiment, the quality grade of excellent means that the land quality state is high, and the land can be used to plant high-economic crops that rely on mechanized farming, such as yam, seasonings, etc.; the quality grade is good, which means that food crops and low-economic crops that do not rely on land fertility and flatness can be planted, such as soybeans, wheat, cotton, etc.; the quality grade is qualified, which means that crops with high adaptability that are harmless to the soil can be planted; the quality grade is unqualified, which means that it is not suitable for crop planting.

[0168] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. An intelligent management system for land surveying and mapping data, characterized in that: include, Information acquisition module, used to obtain image information, environmental parameter information and soil parameters of the surveying area; An edge analysis module is used to analyze the edge curve of the planting land based on the regional image; A region division module is used to divide the planting land into regions according to the edge curve analysis results of the planting land to obtain multiple fluctuation analysis regions; A terrain fluctuation analysis module is used to calculate the vertical height difference of each fluctuation analysis area according to the terrain image, and to analyze the terrain fluctuation state of the planted land according to the calculation results of the vertical height difference of each fluctuation analysis area, and to adjust the analysis process of the fluctuation state of the planted land according to the environmental parameter information; The quality analysis module is used to analyze the soil fertility status of the planting land according to the soil quality parameters, and to classify the land quality level of the planting land according to the analysis results of the soil fertility status of the planting land and the analysis results of the fluctuation status of the planting land; The data storage module is used to store the land quality grade classification results and edge curve analysis results of the cultivated land.

2. The land surveying and mapping data intelligent management system according to claim 1, characterized in that: The edge analysis module is provided with an image processing unit, and the image processing unit is used to cluster the image pixels according to the image gray value G(i) to obtain various types of clusters F(j); Wherein, G(i) represents the gray value of the i-th pixel in the regional image, i=1,2...I, I is the number of pixels in the regional image, F(j) represents the j-th cluster, j=1,2...J, J is the number of clusters obtained by clustering analysis of the regional image; The image processing unit compares the number of pixels Fn(j) of each cluster with the minimum attribute number N1, and performs error judgment on each cluster according to the comparison result, wherein: When Fn(j)<N1, the image processing unit lists the cluster as an error cluster; When Fn(j)≥N1, the image processing unit lists the cluster as an actual cluster; The image processing unit deletes the pixel points of the error cluster from the regional image.

3. The land surveying and mapping data intelligent management system according to claim 2 is characterized in that: The edge analysis module is further provided with an edge curve analysis unit, which is used to calculate the mean of the grayscale values ​​of the pixels of each actual clustering cluster to obtain the sample characteristic grayscale value Gray(x) of each actual clustering cluster, where Gray(x) represents the sample characteristic grayscale value of the xth actual clustering cluster; The edge curve analysis unit analyzes the boundary pixel points of the regional image according to the sample characteristic grayscale value Gray(x) of each actual cluster and the pixel point coordinates of each actual cluster, wherein: When GY1≤Gray(x)<GY2, the edge curve analysis unit determines that the actual cluster is a boundary cluster. At this time, if min{{[A(a1)-A(a2)] 2 +[B(a1)-B(a2)] 2 } 1 / 2 }<DT, the edge curve analysis unit determines that the cluster is a boundary pixel point, if min{{[A(a1)-A(a2)] 2 +[B(a1)-B(a2)] 2 } 1 / 2 }≥DT, the edge curve analysis unit determines that the cluster is a non-boundary pixel point; When Gray(x)<GY1 or Gray(x)≥GY2, the edge curve analysis unit determines that the actual cluster is a non-boundary cluster and the pixels inside it are non-boundary pixels; Wherein, GY1 is the gray value of the first preset pixel, GY2 is the gray value of the second preset pixel, GY1<GY2, A(a1) is the horizontal coordinate of the a1th pixel in the xth actual cluster, A(a2) is the horizontal coordinate of the a2th pixel in the xth actual cluster, B(a2) is the vertical coordinate of the a1th pixel in the xth actual cluster, B(a2) is the vertical coordinate of the a2th pixel in the xth actual cluster, and DT is the distance threshold of continuous pixels at the boundary; The edge curve analysis unit connects the boundary pixel points to form an edge curve of the planted land.

4. The land surveying and mapping data intelligent management system according to claim 3 is characterized in that: The area division module divides the surveying area into regions according to the edge curve analysis result of the planting land; The region division module calculates the area S of the planted land within the edge curve according to the scale μ of the region image, and sets S=n3×μ, where n3 is the number of pixels contained in the edge curve; The area division module compares the area S of the planted land within the edge curve with each preset area, and analyzes the divided area of ​​the planted land according to the comparison result, wherein: When S<s1, the area division module determines that the planting land is a small-area planting land, and sets the divided area of ​​the planting land to HS1, setting HS1=HS×exp[(S-s1) / s1]; When s1≤S<s2, the area division module determines that the planting land is a medium-sized planting land, and sets the divided area of ​​the planting land to HS2, setting HS2=HS; When S≥s2, the area division module determines that the planting land is a large area of ​​planting land, and sets the divided area of ​​the planting land to HS3, setting HS3=HS×{1+arctan[(S-s2) / s1]}; Wherein, s1 is the first preset area, s2 is the second preset area, and HS is the preset fluctuation analysis area division area; The area division module divides the cultivated land into various fluctuation analysis areas M(k) according to the divided area HSv of the cultivated land, sets k=1,2...N3, N3=S / HSv, and sets v=1,2,3.

5. The land surveying and mapping data intelligent management system according to claim 4 is characterized in that: The terrain fluctuation analysis module is provided with a regional height analysis unit, which is used to calculate the vertical height difference d(k) of each fluctuation analysis area M(k) according to the terrain image. The calculation formula of the vertical height difference d(k) of each fluctuation analysis area is as follows: d(k)=max{[K(n1)-K(n2)] / 4×π×λ×cos(θ 入 )} Where λ is the wavelength of the radar signal, K(n1) is the phase of the n1th pixel in the kth wave analysis area in the terrain image, K(n2) is the phase of the n2th pixel in the kth wave analysis area in the terrain image, and θ 入 is the angle of incidence of the radar wave.

6. The land surveying and mapping data intelligent management system according to claim 5, characterized in that: The terrain fluctuation analysis module is also provided with an extreme value adjustment unit, which is used to calculate the average height R(k) of the pixel points in each fluctuation analysis area according to the terrain image of each fluctuation analysis area, and set Where K(n) is the phase of the nth pixel in the kth fluctuation analysis area, and nk is the number of pixels in the kth fluctuation analysis area; The extreme value adjustment unit adjusts the calculation process of the vertical height difference of each fluctuation analysis area according to the average height R(k) and the vertical height difference d(k) of each fluctuation analysis area, wherein: When [d(k)-R(k)] / R(k)<W, the extreme value adjustment unit determines that the extreme value of the fluctuation analysis area is normal and does not make any adjustment; When [d(k)-R(k)] / R(k)≥W, the extreme value adjustment unit determines that the extreme value of the fluctuation analysis area is large, and adjusts the vertical height difference of the fluctuation analysis area to d'(k), setting d'(k)=d(k)×ln{e-{[d(k)+R(k)] / R(k)-W} / W}; Where W is the extreme value ratio threshold.

7. The land surveying and mapping data intelligent management system according to claim 6, characterized in that: The terrain fluctuation analysis module is also provided with a meteorological optimization unit, and the meteorological optimization unit is used to optimize the adjustment process of calculating the vertical height difference of each fluctuation analysis area according to the environmental parameter information; The meteorological optimization unit calculates the meteorological influence factor β according to the environmental parameter information, and sets β=exp{ln(1+T / t)+sin(P / atm)+arctan(sd)}; Where T is the temperature gradient, t is the standard Celsius temperature gradient, P is the atmospheric pressure in the survey area, atm is the standard atmospheric pressure, and sd is the absolute ambient humidity in the survey area; The meteorological optimization unit compares the meteorological influence factor β with the preset influence index B, and optimizes the adjustment process of calculating the vertical height difference of each fluctuation analysis area according to the comparison result, wherein: When β<B, the meteorological optimization unit determines that the meteorological state of the surveying and mapping area is normal and does not perform optimization; When β≥B, the meteorological optimization unit determines that the meteorological state of the surveying area is abnormal, and optimizes the extreme value ratio threshold to W', setting W'=W×[1+cos(β)].

8. The land surveying and mapping data intelligent management system according to claim 7, characterized in that: The terrain fluctuation analysis module is further provided with a terrain fluctuation analysis unit, and the terrain fluctuation analysis unit is used to analyze the terrain fluctuation state of the planting land according to the vertical height difference d(k) of each fluctuation analysis area; The terrain fluctuation analysis unit calculates the fluctuation variation coefficient α according to the vertical height difference d(k) of each fluctuation analysis area. The calculation formula of the fluctuation variation coefficient α is as follows: α=σ / E×100%; σ={{[d(1)-E] 2 +[d(2)-E] 2 +...+[d(N2)-E] 2 } / N2} 1 / 2 ; E=[d(1)+d(2)+...+d(N2)] / N2; Where, d(1) is the vertical height difference of the first fluctuation analysis area, d(2) is the vertical height difference of the second fluctuation analysis area, d(N2) is the vertical height difference of the N2th fluctuation analysis area, N2 is the number of divisions of the fluctuation analysis area, σ is the standard deviation of the vertical height difference data, and E is the mean of the vertical height difference of each fluctuation analysis area; The terrain fluctuation analysis unit compares the fluctuation coefficient with each preset variation coefficient, and analyzes the terrain fluctuation state of the planting land according to the comparison result, wherein: When α<C1, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is flat; When C1≤α<C2, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is normal fluctuation; When α≥C2, the terrain fluctuation analysis unit determines that the terrain fluctuation state of the planting land is severe.

9. The land surveying and mapping data intelligent management system according to claim 1, characterized in that: The quality analysis module is provided with a soil quality analysis unit, and the soil quality analysis unit is used to analyze the soil fertility status of the planting land according to the land parameters of the surveying area; The soil quality analysis unit calculates the soil fertility index γ of the planting land according to the land parameters of the surveying area, and sets γ = {b1×[ρ(K)-BK] / BK+b2×[ρ(NH4)-BN] / BN} / ρ(salt); Where b1 is the potassium fertilizer weight, b2 is the nitrogen fertilizer weight, b2>b1 and b2+b1=1, ρ(K) is the soil potassium ion concentration, ρ(NH4) is the soil ammonium salt concentration, BK is the standard soil potassium fertilizer concentration, BN is the standard soil nitrogen fertilizer concentration, and ρ(salt) is the inorganic salt concentration; The soil quality analysis unit compares the soil fertility index γ with each preset fertility index, and analyzes the soil fertility status of the planting land according to the comparison result, wherein: When γ<Y1, the soil quality analysis unit determines that the soil fertility state of the planting land is level 3 fertility; When Y1≤γ<Y2, the soil quality analysis unit determines that the soil fertility state of the planting land is secondary fertility; When γ≥Y2, the soil quality analysis unit determines that the soil fertility state of the planting land is first-level fertility; Among them, Y1 is the first preset fertility index, Y2 is the second preset fertility index, and Y1<Y2.

10. The land surveying and mapping data intelligent management system according to claim 9, characterized in that: The quality analysis module is further provided with a quality analysis unit, which is used to classify the land quality level according to the soil fertility state analysis result and the fluctuation state analysis result of the cultivated land, wherein: When the terrain fluctuation state of the planting land is flat, if the land fertility state is level one fertility, the quality analysis unit determines that the quality level of the planting land is excellent; if the land fertility state is level two fertility, the quality analysis unit determines that the quality level of the planting land is good; if the land fertility state is level three fertility, the quality analysis unit determines that the quality level of the planting land is unqualified; When the terrain fluctuation state of the planting land is normal undulation, if the land fertility state is level one fertility, the quality analysis unit determines that the quality level of the planting land is good; if the land fertility state is level two fertility, the quality analysis unit determines that the quality level of the planting land is qualified; if the land fertility state is level three fertility, the quality analysis unit determines that the quality level of the planting land is unqualified; When the terrain fluctuation state of the planting land is severe, if the soil fertility state is level one fertility, the quality analysis unit determines that the quality grade of the planting land is qualified; if the soil fertility state is level two fertility, the quality analysis unit determines that the quality grade of the planting land is unqualified; if the soil fertility state is level three fertility, the quality analysis unit determines that the quality grade of the planting land is unqualified.

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