Visual analysis method for rural land resources based on cloud database service

Abstract

The invention discloses a cloud database service-based rural land resource visual analysis method, which comprises the following steps of: obtaining a rural land resource data set in a cloud database, and generating a land region division index cluster; deviation fluctuation identification is carried out to obtain a deviation fluctuation factor; configuring a grid scale based on the deviation fluctuation factor, and performing integrated grid division on the divided land region set through the grid scale to obtain a grid region set; traversing the grid region set to carry out resource evaluation, and obtaining an evaluation factor set; performing region fusion identification on the grid region set to obtain a fusion region; and uploading the fusion identifier distribution map and the fusion region to a rural land resource visualization platform for visual display and management. The method has the advantages that land resource areas are accurately divided and deviation fluctuation is identified through the cloud database and rasterized analysis, refined data support is provided for land management, and intelligent management of rural land resources is realized through display of a visual platform.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a rural land resource visualization analysis method based on cloud database service. Background Art

[0002] The rational use and management of land resources has become a key factor in promoting agricultural modernization and promoting rural economic development. Due to the limitations of information collection, storage and analysis methods, traditional land resource management methods often face problems such as data dispersion, untimely updates, and insufficient decision support. Therefore, a rural land resource visualization analysis method based on cloud database services has emerged. As a new type of data storage and processing method, cloud database can provide efficient, secure and flexible storage solutions, and support real-time updates and efficient access to massive data. At the same time, through visualization technology, rural land resource data can be displayed in a graphical and map-based manner, making land use status, land type distribution, geographic information and other data more intuitive and easy to understand, providing accurate decision support for policy makers, agricultural managers and farmers. Through this cloud database-based visualization analysis method, the efficiency and scientificity of rural land resource management can be effectively improved, and the sustainable development and rational use of rural land can be promoted.

[0003] The current rural land resource visualization analysis methods on the market mainly rely on technologies such as geographic information systems, remote sensing technology and big data analysis. Geographic information system technology displays land use types, land quality and resource distribution in a mapped manner to support land planning and decision-making. Remote sensing technology uses satellite images and drone aerial photography to monitor land changes in real time and provide large-scale data support. Big data technology processes and stores large amounts of land data through cloud computing platforms to achieve data sharing and real-time analysis. The rural land resource visualization analysis methods on the market are limited by local storage and computing capabilities, making it difficult to process large-scale and real-time updated data, and data sharing and collaboration are relatively difficult. In contrast, methods based on cloud database services rely on the network environment and may face data transmission delays and security risks, but they have obvious advantages in processing big data and providing flexible expansion. Summary of the invention

[0004] In order to improve the existing rural land resource visualization analysis method, a rural land resource visualization analysis method based on cloud database service is provided. This method evaluates and integrates land resource information through data acquisition, regional division, deviation fluctuation identification and rasterization processing. Finally, the visualization platform is used to display the fusion area and evaluation results to support intelligent resource management and decision-making.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A rural land resource visualization analysis method based on cloud database service, characterized by comprising:

[0007] Acquire a rural land resource dataset in a cloud database, and generate a land area division indicator cluster, wherein each land area division indicator cluster includes a plurality of land area division indicator sets, and each land area division indicator set corresponds to a land area division;

[0008] Based on the benchmark indicator set corresponding to the land area set, the deviation fluctuation is identified and the deviation fluctuation factor is obtained;

[0009] Based on the deviation fluctuation factor, the grid scale is configured, and the land area set is integrated and grid-divided by the grid scale to obtain the grid area set;

[0010] Based on the preset indicator set, traverse the grid area set to perform resource evaluation and obtain the evaluation factor set;

[0011] Performing regional fusion identification on the raster region set based on the evaluation factor set to obtain a fusion region, wherein the fusion region includes a fusion identification distribution map;

[0012] Upload the integrated identification distribution map and integrated area to the rural land resources visualization platform for visual display and management.

[0013] Preferably, the rural land resource dataset in the cloud database is obtained, and a land area division indicator cluster is generated, wherein each land area division indicator cluster includes a plurality of land area division indicator sets, and each land area division indicator set corresponds to a land area division and specifically includes:

[0014] Obtain rural land resource data through cloud database;

[0015] Divide land areas based on rural land resource characteristics;

[0016] Based on the characteristic indicators of each land area, an indicator cluster is formed. Assuming that a region has k indicators I j , then the indicator cluster of this area is:

[0017] C(r i )={I 1 (r i ),I 2 (r i ),...,I k (r i )}

[0018] Among them, C(r i ) represents the region r i The indicator cluster.

[0019] Preferably, the step of identifying deviation fluctuations based on the benchmark indicator set corresponding to the land area set and obtaining the deviation fluctuation factor specifically includes:

[0020] Based on the benchmark indicator set B = {B 1 ,B 2 ,...,B k}, calculate the indicator deviation of the land area indicator cluster by absolute deviation, and obtain the land area indicator deviation cluster. The formula is:

[0021] D j (r i )=|I j (r i )-B j |

[0022] Among them, I j (r i ) is the region r i The value of the jth index in B j is the base value;

[0023] Performing weighted calculation on each indicator deviation in the land area division indicator deviation set in the land area division indicator deviation cluster to obtain a land area division indicator total deviation set, wherein each land area division indicator total deviation corresponds to one land area division;

[0024] Traversing the total deviation set of indicators of the divided land area to draw an indicator deviation distribution map, and obtaining a distribution map of the divided indicator deviation;

[0025] Based on the deviation distribution diagram of the divided indicators, the deviation fluctuation is identified and the deviation fluctuation factor is obtained.

[0026] Preferably, the step of identifying deviation fluctuation based on the distribution diagram of deviation of the divided indicators and obtaining the deviation fluctuation factor specifically includes:

[0027] Selecting a first partitioning indicator deviation distribution map in the partitioning indicator deviation distribution map, wherein the first partitioning indicator deviation distribution map includes a first upper quartile, a first lower quartile, a first median, a first maximum value, and a first minimum value;

[0028] Calculate the difference between the first upper quartile and the first lower quartile and the difference between the first maximum value and the first minimum value, and obtain a first dispersion coefficient based on the calculation results;

[0029] Calculate the sum of the first maximum value and the indicator deviation amount that is less than the first minimum value, and obtain the second dispersion coefficient based on the statistical result and the total amount of the indicator deviation amount in the first divided indicator deviation distribution diagram;

[0030] The deviation fluctuation factor σ(r i ).

[0031] Preferably, configuring the grid scale based on the deviation fluctuation factor, integrating the grid division of the land area set by the grid scale, and obtaining the grid area set specifically include:

[0032] Determine the initial grid scale based on land resource type 0 ;

[0033] Based on the deviation fluctuation factor σ(r i ), adjust the grid scale according to different land resource types, the formula is:

[0034] s=s 0 ·(1+σ(r i ))

[0035] Based on the adjusted grid scale s, the land area set is divided to obtain a new grid area set g = {g 1 ,g 2 ,,...g k}, where each grid region g k The size of is s×s, which realizes dynamic grid division based on fluctuation factor;

[0036] The land characteristics of the area are obtained based on the mean or weighted average of the attributes in the grid. The formula is:

[0037]

[0038] Among them, A(i) is the relevant index in grid i, |g k | is the grid area g k area;

[0039] Get the integrated grid area set G = {G 1 ,G 2 ,,...G k}.

[0040] Preferably, the step of traversing the grid area set to perform resource evaluation based on the preset indicator set and obtaining the evaluation factor set specifically includes:

[0041] Constructing land quality indicators, wherein the land quality indicators include soil type, soil organic matter content, and soil pH value;

[0042] constructing water resource indicators, wherein the water resource indicators include groundwater turbidity and groundwater volume;

[0043] Constructing environmental indicators, wherein the environmental indicators include vegetation coverage rate and land degradation coefficient;

[0044] The land quality index, water resource index and environmental index are used as the preset index set I = {I 1 ,I 2 ,...,I M};

[0045] Based on the integrated grid area set G, traverse and evaluate and calculate the evaluation factor based on the spatial data of each grid. The formula is:

[0046]

[0047] Among them, f k is the grid area G k The evaluation factor, ω m For indicator I m The weight of I m (G k ) is the grid area G k On I m The value of

[0048] Evaluation factor f based on all grid regions k , get the evaluation factor set F = {F 1 ,F 2 ,...,F K}.

[0049] Preferably, the grid area set is subjected to regional fusion identification based on the evaluation factor set to obtain the fusion area, wherein the fusion area includes a fusion identification distribution map specifically including:

[0050] Based on each grid area G k Slope simulation is performed, and the slope of the area is calculated by combining the influence of each evaluation factor through the weighted average method. The formula is:

[0051]

[0052] Among them, S i is the grid region grid region G k The slope value, ω j is the evaluation factor f j The weight, f j (G k ) is the grid area G k Medium factor f j The value of

[0053] The grid area with the smallest slope is used as the starting point for water injection. When the water level rises, if the current water level is close to the next slope area, a judgment is made. If the difference between the current slope and the next slope is within the threshold, water injection continues.

[0054] If the difference in water level rise exceeds the threshold, the first dividing ridge line is generated, and the position of the dividing ridge line is determined by the change between the slope contour lines;

[0055] The dividing ridge lines are obtained based on the rise of the water surface. When the water surface exceeds the maximum slope area, all the dividing ridge lines are connected to form a complete grid area set.

[0056] Different grid areas are marked with different colors or numbers, and the fusion area and fusion identification distribution map are obtained based on the simulated slope set.

[0057] Compared with the prior art, the advantages of the present invention are:

[0058] The rural land resource dataset based on cloud database can fully integrate and analyze the multi-dimensional information of land resources. By dividing land areas and constructing indicator clusters, the land use characteristics of different regions can be accurately identified, thereby providing refined data support for land management, resource allocation and environmental protection. The identification of deviation fluctuation factors helps to discover the imbalance between different regions, provide a more scientific basis for adjustment, and ensure the optimal allocation of land resources. The precise integration of land resources is achieved through the division of grid scales. Rasterization processing can ensure the high resolution of spatial data and conduct a detailed assessment for each grid area to ensure that the characteristics of each area are fully identified and processed. Resource assessment based on a preset indicator set can objectively reflect the resource potential and development space of the land, and provide effective guidance for land planning and rural revitalization. The analysis results are displayed through a visualization platform, making the management of land resources more transparent and intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the analysis method of the method proposed in the present invention;

[0060] Figure 2 This is a schematic diagram of obtaining the indicator cluster of the method proposed by the present invention;

[0061] Figure 3 This is a schematic diagram of deviation fluctuation identification of the method proposed in the present invention;

[0062] Figure 4 A schematic diagram of obtaining the deviation fluctuation factor of the method proposed in the present invention;

[0063] Figure 5 A schematic diagram of obtaining a grid area set according to the method proposed by the present invention;

[0064] Figure 6 A schematic diagram of obtaining a set of evaluation factors for the method proposed in the present invention;

[0065] Figure 7The figure is a schematic diagram of obtaining the fusion area of ​​the method proposed in the present invention. DETAILED DESCRIPTION

[0066] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0067] See also Figure 1 As shown, a rural land resource visualization analysis method based on cloud database service includes:

[0068] Step 1: Obtain a rural land resource dataset in a cloud database and generate a land area division indicator cluster, wherein each land area division indicator cluster includes a plurality of land area division indicator sets, and each land area division indicator set corresponds to a land area division;

[0069] Step 2: Based on the benchmark indicator set corresponding to the land area set, identify the deviation fluctuation and obtain the deviation fluctuation factor;

[0070] Step 3: Configure the grid scale based on the deviation fluctuation factor, integrate the land area set through the grid scale, and obtain the grid area set;

[0071] Step 4: Based on the preset indicator set, traverse the grid area set to perform resource evaluation and obtain the evaluation factor set;

[0072] Step 5: Based on the evaluation factor set, the raster region set is subjected to regional fusion identification to obtain a fusion region, wherein the fusion region includes a fusion identification distribution map;

[0073] Step 6: Upload the fusion identification distribution map and fusion area to the rural land resources visualization platform for visual display and management.

[0074] See also Figure 2 As shown, a rural land resource dataset in a cloud database is obtained, and a land area division indicator cluster is generated, wherein each land area division indicator cluster includes multiple land area division indicator sets, and each land area division indicator set corresponds to a land area division and specifically includes:

[0075] Obtain rural land resource data through cloud database;

[0076] Divide land areas based on rural land resource characteristics;

[0077] Based on the characteristic indicators of each land area, an indicator cluster is formed. Assuming that a region has k indicators I j , then the indicator cluster of this area is:

[0078] C(r i )={I 1 (r i ),I 2 (r i ),...,I k (r i )}

[0079] Among them, C(r i ) represents the region r i The indicator cluster.

[0080] Specifically, when dividing land, different land areas can be divided according to factors such as the geographical location of the land, soil properties, and climatic conditions. For example, land can be divided into different types of areas suitable for farming, forestry, construction, etc. according to agricultural planting conditions, land suitability, and other standards.

[0081] See also Figure 3 As shown, based on the benchmark indicator set corresponding to the land area set, the deviation fluctuation is identified, and the deviation fluctuation factor is obtained, which specifically includes:

[0082] Based on the benchmark indicator set B = {B 1 ,B 2 ,...,B k}, calculate the indicator deviation of the land area indicator cluster by absolute deviation, and obtain the land area indicator deviation cluster. The formula is:

[0083] D j (r i )=|I j (r i )-B j |

[0084] Among them, I j (r i ) is the region r i The value of the jth index in B j is the base value;

[0085] Performing weighted calculation on each indicator deviation in the land area division indicator deviation set in the land area division indicator deviation cluster to obtain a land area division indicator total deviation set, wherein each land area division indicator total deviation corresponds to one land area division;

[0086] Traversing the total deviation set of indicators of the divided land area to draw an indicator deviation distribution map, and obtaining a distribution map of the divided indicator deviation;

[0087] Based on the deviation distribution diagram of the divided indicators, the deviation fluctuation is identified and the deviation fluctuation factor is obtained.

[0088] It is understandable that when identifying deviation fluctuation factors, there may be situations where the fluctuation factors are small or not obvious, resulting in inaccurate identification of deviation fluctuations. The identification accuracy of fluctuation factors can be enhanced by increasing the data sample size, improving the data sophistication, or using techniques such as dynamic time warping, thereby more accurately identifying the key fluctuation factors that affect land resource utilization.

[0089] See also Figure 4 As shown, based on the distribution diagram of the deviation of the divided indicators, the deviation fluctuation identification is performed, and the deviation fluctuation factor is obtained, which specifically includes:

[0090] Selecting a first partitioning indicator deviation distribution map in the partitioning indicator deviation distribution map, wherein the first partitioning indicator deviation distribution map includes a first upper quartile, a first lower quartile, a first median, a first maximum value, and a first minimum value;

[0091] Calculate the difference between the first upper quartile and the first lower quartile and the difference between the first maximum value and the first minimum value, and obtain a first dispersion coefficient based on the calculation results;

[0092] Calculate the sum of the first maximum value and the indicator deviation amount that is less than the first minimum value, and obtain the second dispersion coefficient based on the statistical result and the total amount of the indicator deviation amount in the first divided indicator deviation distribution diagram;

[0093] The deviation fluctuation factor σ(r i ).

[0094] See also Figure 5 As shown in the figure, the grid scale is configured based on the deviation fluctuation factor, and the land area set is integrated and grid-divided by the grid scale. The grid area set is obtained specifically including:

[0095] Determine the initial grid scale based on land resource type 0 ;

[0096] Based on the deviation fluctuation factor σ(r i ), adjust the grid scale according to different land resource types, the formula is:

[0097] s=s 0 ·(1+σ(r i ))

[0098] Based on the adjusted grid scale s, the land area set is divided to obtain a new grid area set g = {g 1 ,g 2 ,,...g k}, where each grid region g k The size of is s×s, which realizes dynamic grid division based on fluctuation factor;

[0099] The land characteristics of the area are obtained based on the mean or weighted average of the attributes in the grid. The formula is:

[0100]

[0101] Among them, A(i) is the relevant index in grid i, |g k | is the grid area g k area;

[0102] Get the integrated grid area set G = {G 1 ,G 2 ,,...G k}.

[0103] Specifically, when dynamically adjusting the grid scale based on the fluctuation factor, the grid division may be inaccurate, especially at the boundary of the grid division, where discontinuous or overly fine regional division may occur, affecting the subsequent land feature extraction. Therefore, when dividing the grid, smoothing technology can be used, or adjacent small grid areas can be merged through clustering algorithms to avoid overly fine or overly rough divisions. In addition, a suitable grid boundary smoothing strategy can be designed to make the division boundary more natural and coherent.

[0104] See also Figure 6 As shown, based on the preset indicator set, the grid area set is traversed to perform resource evaluation, and the evaluation factor set obtained specifically includes:

[0105] Constructing land quality indicators, wherein the land quality indicators include soil type, soil organic matter content, and soil pH value;

[0106] constructing water resource indicators, wherein the water resource indicators include groundwater turbidity and groundwater volume;

[0107] Constructing environmental indicators, wherein the environmental indicators include vegetation coverage rate and land degradation coefficient;

[0108] The land quality index, water resource index and environmental index are used as the preset index set I = {I 1 ,I 2 ,...,I M};

[0109] Based on the integrated grid area set G, traverse and evaluate and calculate the evaluation factor based on the spatial data of each grid. The formula is:

[0110]

[0111] Among them, f k is the grid area G k The evaluation factor, ωm For indicator I m The weight of I m (G k ) is the grid area G k On I m The value of

[0112] Evaluation factor f based on all grid regions k , get the evaluation factor set F = {F 1 ,F 2 ,...,F K}.

[0113] It is understandable that there may be a high correlation between land quality indicators, water resource indicators and environmental indicators, especially some indicators may be redundant. For example, soil organic matter content and vegetation coverage may have a strong correlation in some areas, which will affect the accuracy and effect of the comprehensive assessment. Therefore, before the weight is determined, a correlation analysis is performed to select indicators with low correlation to reduce multicollinearity problems, thereby improving the stability and accuracy of the assessment model.

[0114] See also Figure 7 As shown, based on the evaluation factor set, the grid area set is regionally fused and identified to obtain the fused area, wherein the fused area includes a fusion identification distribution map specifically including:

[0115] Based on each grid area G k Slope simulation is performed, and the slope of the area is calculated by combining the influence of each evaluation factor through the weighted average method. The formula is:

[0116]

[0117] Among them, S i is the grid region grid region G k The slope value, ω j is the evaluation factor f j The weight, f j (G k ) is the grid area G k Medium factor f j The value of

[0118] The grid area with the smallest slope is used as the starting point for water injection. When the water level rises, if the current water level is close to the next slope area, a judgment is made. If the difference between the current slope and the next slope is within the threshold, water injection continues.

[0119] If the difference in water level rise exceeds the threshold, the first dividing ridge line is generated, and the position of the dividing ridge line is determined by the change between the slope contour lines;

[0120] The dividing ridge lines are obtained based on the rise of the water surface. When the water surface exceeds the maximum slope area, all the dividing ridge lines are connected to form a complete grid area set.

[0121] Different grid areas are marked with different colors or numbers, and the fusion area and fusion identification distribution map are obtained based on the simulated slope set.

[0122] It is understandable that when generating a dividing ridge line between slope areas, the position of the dividing ridge line may be inaccurate due to the lack of obvious changes in the slope contour lines or complex terrain. More sophisticated terrain analysis algorithms, such as contour interpolation methods and slope flow algorithms, can be used to ensure that the generation of the dividing ridge line is more accurate. In addition, the identification of the dividing ridge line can be combined with higher-precision terrain data such as digital elevation models.

[0123] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0125] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A rural land resource visualization analysis method based on cloud database service, characterized in that: include: Acquire a rural land resource dataset in a cloud database, and generate a land area division indicator cluster, wherein each land area division indicator cluster includes a plurality of land area division indicator sets, and each land area division indicator set corresponds to a land area division; Based on the benchmark indicator set corresponding to the land area set, the deviation fluctuation is identified and the deviation fluctuation factor is obtained; Based on the deviation fluctuation factor, the grid scale is configured, and the land area set is integrated and grid-divided by the grid scale to obtain the grid area set; Based on the preset indicator set, traverse the grid area set to perform resource evaluation and obtain the evaluation factor set; Performing regional fusion identification on the raster region set based on the evaluation factor set to obtain a fusion region, wherein the fusion region includes a fusion identification distribution map; Upload the integrated identification distribution map and integrated area to the rural land resources visualization platform for visual display and management.

2. The rural land resource visualization analysis method based on cloud database service according to claim 1 is characterized in that: The rural land resource dataset in the cloud database is obtained, and a land area division indicator cluster is generated, wherein each land area division indicator cluster includes a plurality of land area division indicator sets, and each land area division indicator set corresponds to a land area division and specifically includes: Obtain rural land resource data through cloud database; Divide land areas based on rural land resource characteristics; Based on the characteristic indicators of each divided land area, an indicator cluster is formed. Assuming that a region has k indicators I j , then the indicator cluster of this area is: C(r i )={I1(r i ),I2(r i ),...,I k (r i )} Among them, C(r i ) represents the region r i The indicator cluster.

3. The rural land resource visualization analysis method based on cloud database service according to claim 1 is characterized in that: The method of identifying deviation fluctuations based on the benchmark indicator set corresponding to the land area set and obtaining the deviation fluctuation factor specifically includes: Based on the benchmark indicator set B={B1,B2,...,B k }, calculate the indicator deviation of the land area indicator cluster by absolute deviation, and obtain the land area indicator deviation cluster. The formula is: D j (r i )=|I j (r i )-B j | Among them, I j (r i ) is the region r i The value of the jth index in B j is the base value; Performing weighted calculation on each indicator deviation in the land area division indicator deviation set in the land area division indicator deviation cluster to obtain a land area division indicator total deviation set, wherein each land area division indicator total deviation corresponds to one land area division; Traversing the total deviation set of indicators of the divided land area to draw an indicator deviation distribution map, and obtaining a distribution map of the divided indicator deviation; Based on the deviation distribution diagram of the divided indicators, the deviation fluctuation is identified and the deviation fluctuation factor is obtained.

4. The rural land resource visualization analysis method based on cloud database service according to claim 2 is characterized in that: The identification of deviation fluctuation based on the distribution diagram of the deviation of the divided indicators and obtaining the deviation fluctuation factor specifically include: Selecting a first partitioning indicator deviation distribution map in the partitioning indicator deviation distribution map, wherein the first partitioning indicator deviation distribution map includes a first upper quartile, a first lower quartile, a first median, a first maximum value, and a first minimum value; Calculate the difference between the first upper quartile and the first lower quartile and the difference between the first maximum value and the first minimum value, and obtain a first dispersion coefficient based on the calculation results; Calculate the sum of the first maximum value and the indicator deviation amount that is less than the first minimum value, and obtain the second dispersion coefficient based on the statistical result and the total amount of the indicator deviation amount in the first divided indicator deviation distribution diagram; The deviation fluctuation factor σ(r i ).

5. The rural land resource visualization analysis method based on cloud database service according to claim 1 is characterized in that: The configuration of the grid scale based on the deviation fluctuation factor, integrating the grid division of the land area set through the grid scale, and obtaining the grid area set specifically include: Determine the initial grid scale s0 based on the land resource type; Based on the deviation fluctuation factor σ(r i ), adjust the grid scale according to different land resource types, the formula is: s=s0·(1+σ(r i )) Based on the adjusted grid scale s, the land area set is divided to obtain a new grid area set g = {g1, g2,,...g k }, where each grid region g k The size of is s×s, which realizes dynamic grid division based on fluctuation factor; The land characteristics of the area are obtained based on the mean or weighted average of the attributes in the grid. The formula is: Among them, A(i) is the relevant index in grid i, |g k | is the grid area g k area; Get the integrated grid area set G = {G1, G2,,...G k }.

6. The rural land resource visualization analysis method based on cloud database service according to claim 1 is characterized in that: The method of traversing the grid area set to perform resource evaluation based on the preset indicator set and obtaining the evaluation factor set specifically includes: Constructing land quality indicators, wherein the land quality indicators include soil type, soil organic matter content, and soil pH value; constructing water resource indicators, wherein the water resource indicators include groundwater turbidity and groundwater volume; Constructing environmental indicators, wherein the environmental indicators include vegetation coverage rate and land degradation coefficient; The land quality index, water resource index and environmental index are used as the preset index set I = {I1, I2, ..., I M }; Based on the integrated grid area set G, traverse and evaluate and calculate the evaluation factor based on the spatial data of each grid. The formula is: Among them, f k is the grid area G k The evaluation factor, ω m For indicator I m The weight of I m (G k ) is the grid area G k On I m The value of Evaluation factor f based on all grid regions k , get the evaluation factor set F = {F1, F2, ..., F K }.

7. The rural land resource visualization analysis method based on cloud database service according to claim 1 is characterized in that: The step of performing regional fusion identification on the grid region set based on the evaluation factor set to obtain a fusion region, wherein the fusion region includes a fusion identification distribution map specifically including: Based on each grid area G k Slope simulation is performed, and the slope of the area is calculated by combining the influence of each evaluation factor through the weighted average method. The formula is: Among them, S i is the grid region grid region G k The slope value, ω j is the evaluation factor f j The weight, f j (G k ) is the grid area G k Medium factor f j The value of The grid area with the smallest slope is used as the starting point for water injection. When the water level rises, if the current water level is close to the next slope area, a judgment is made. If the difference between the current slope and the next slope is within the threshold, water injection continues. If the difference in water level rise exceeds the threshold, the first dividing ridge line is generated, and the position of the dividing ridge line is determined by the change between the slope contour lines; The dividing ridge lines are obtained based on the rise of the water surface. When the water surface exceeds the maximum slope area, all the dividing ridge lines are connected to form a complete grid area set. Different grid areas are marked with different colors or numbers, and the fusion area and fusion identification distribution map are obtained based on the simulated slope set.

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