AHP-based soil suitability analysis method, apparatus and device, and storage medium

Abstract

The embodiment of the invention provides a soil suitability analysis method, device and equipment based on AHP and a storage medium, and is applied to the technical field of big data. The method comprises the following steps: acquiring tiff image data of an area to be analyzed; inputting the tiff image data into a preset soil suitability analysis model, and outputting a crop soil suitability weight corresponding to the to-be-analyzed region; wherein the preset soil suitability analysis model is constructed based on an AHP algorithm according to influence factors, and the influence factors comprise the soil type, the soil moisture content and the soil water content; and on the basis of a GIS, according to the crop soil suitability weight and the tiff image data, generating crop soil suitability tiff image source data corresponding to the to-be-analyzed region. In this way, the accuracy and scientificity of soil suitability analysis can be improved, and powerful support is provided for agricultural production, land utilization planning and ecological environment protection.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of big data technology, and specifically to a soil suitability analysis method, device, equipment and storage medium based on AHP. Background Art

[0002] Land suitability evaluation refers to the process of assessing whether land is suitable for a certain purpose and the degree of suitability. For planting a certain crop, land suitability evaluation mainly focuses on the natural attributes and socio-economic attributes of the land, including natural conditions such as climate, topography, and soil, as well as socio-economic factors such as land use methods and economic benefits. Soil suitability evaluation is one of the important contents of land suitability evaluation, which is used to evaluate whether the land is suitable for planting crops. Soil suitability evaluation is a key link in land use planning and management, and is directly related to the rational allocation and sustainable use of land resources.

[0003] The soil suitability analysis process is a process of analyzing and evaluating complex soil influencing factors. Current soil suitability analysis methods often rely on expert experience and qualitative analysis, and have problems such as strong subjectivity and low accuracy.

[0004] Specifically, current soil suitability analysis methods often rely on the experience and subjective judgment of experts, which may lead to a certain degree of subjectivity in the evaluation results and make it difficult to ensure the objectivity and accuracy of the evaluation; soil suitability is affected by many factors, including soil type, topography, climatic conditions, hydrological conditions, vegetation cover, etc. Current soil suitability analysis methods may find it difficult to comprehensively and systematically consider these complex factors and their interrelationships, thus affecting the comprehensiveness and accuracy of the evaluation results; current soil suitability analysis methods may have difficulties in processing and analyzing large amounts of spatial data, and their evaluation results are often presented in the form of text descriptions or simple charts, lacking in intuitiveness and visualization, which is not conducive to decision makers' intuitive understanding of the spatial distribution characteristics and changing patterns of soil suitability. Summary of the invention

[0005] The present invention provides a soil suitability analysis method, device, equipment and storage medium based on AHP.

[0006] According to a first aspect of the present disclosure, a soil suitability analysis method based on AHP is provided. The method comprises: Get the tiff image data of the area to be analyzed; The tiff image data is input into a preset soil suitability analysis model, and the crop soil suitability weight corresponding to the area to be analyzed is output; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; Based on GIS, the crop soil suitability tiff image source data corresponding to the area to be analyzed is generated according to the crop soil suitability weight and the tiff image data.

[0007] According to the above aspects and any possible implementation, an implementation is further provided, wherein the preset soil suitability analysis model includes a target layer, a criterion layer and a decision layer, wherein the target layer includes crop planting suitability, the criterion layer includes soil type, soil moisture content and soil water content; and the decision layer includes very suitable, generally suitable and unsuitable; The construction of the preset soil suitability analysis model includes: Constructing a first pairwise comparison matrix of the criterion layer to the target layer according to first pairwise comparison values ​​determined by pairwise comparison between the soil type, the soil moisture content and the soil water content; Converting the first pairwise comparison matrix into a first standard matrix; Based on the first standard matrix, calculating a first weight vector of the criterion layer to the target layer; According to second pairwise comparison values ​​determined by pairwise comparison between the very suitable, the generally suitable and the unsuitable, construct a second pairwise comparison matrix of the decision layer to the criterion layer; Converting the second pairwise comparison matrix into a second standard matrix; Based on the second standard matrix, calculating a second weight vector of the decision layer to the criterion layer; The second weight vector is aggregated upward to the first weight vector to obtain the third weight vector of the decision layer to the target layer, and the crop soil suitability weight of the decision layer to the target layer is determined to complete the construction of the preset soil suitability analysis model.

[0008] According to the above aspects and any possible implementation, an implementation is further provided, wherein the preset soil suitability analysis model includes a target layer, a criterion layer and a decision layer, wherein the target layer includes crop planting suitability, the criterion layer includes soil type, soil moisture content and soil water content; and the decision layer includes very suitable, generally suitable and unsuitable; The construction of the preset soil suitability analysis model includes: Constructing a first pairwise comparison matrix of the criterion layer to the target layer according to first pairwise comparison values ​​determined by pairwise comparison between the soil type, the soil moisture content and the soil water content; Converting the first pairwise comparison matrix into a first standard matrix; Based on the first standard matrix, calculating a first weight vector of the criterion layer to the target layer; Based on a preset scoring mechanism, according to the very suitable, the generally suitable and the unsuitable, a second weight vector of the decision layer to the criterion layer is calculated; The second weight vector is aggregated upward to the first weight vector to obtain the third weight vector of the decision layer to the target layer, and the crop soil suitability weight of the decision layer to the target layer is determined to complete the construction of the preset soil suitability analysis model.

[0009] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes: Based on GIS, soil type tiff image source data, soil moisture tiff image source data and soil water content tiff image source data corresponding to the area to be analyzed are generated according to the weight corresponding to the second weight vector and the image data.

[0010] According to the above aspects and any possible implementation, an implementation is further provided, wherein generating the crop soil suitability tiff image source data corresponding to the area to be analyzed according to the crop soil suitability weight and the tiff image data comprises: The soil type tiff image source data, the soil moisture condition tiff image source data and the soil water content tiff image source data are fused; the fusion process includes balancing the low-resolution image to the high-resolution image and point matching; The crop soil suitability tiff image source data corresponding to the area to be analyzed is generated according to the crop soil suitability weight and the fused soil type tiff image source data, soil moisture tiff image source data and soil water content tiff image source data.

[0011] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the fusing processing of the soil type tiff image source data, the soil moisture condition tiff image source data, and the soil moisture content tiff image source data comprises: Based on the image resampling method, the soil type tiff image source data, the soil moisture condition tiff image source data and the soil water content tiff image source data are fused.

[0012] According to the above aspect and any possible implementation, an implementation is further provided, wherein the determination of the impact factor includes: Based on the Delphi method, expert opinions were obtained and an initial influencing factor judgment matrix was constructed. The influencing factor judgment matrix was normalized, and the corresponding eigenvalues ​​and weights of each initial influencing factor were calculated respectively, and soil type, soil moisture and soil water content were determined as influencing factors.

[0013] According to a second aspect of the present disclosure, a soil suitability analysis device based on AHP is provided. The device comprises: An acquisition module is used to acquire tiff image data of the area to be analyzed; A processing module, used for inputting the tiff image data into a preset soil suitability analysis model, and outputting the crop soil suitability weight corresponding to the area to be analyzed; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; A generation module is used to generate the crop soil suitability tiff image source data corresponding to the area to be analyzed based on GIS according to the crop soil suitability weight and the tiff image data.

[0014] According to a third aspect of the present disclosure, an electronic device is provided, which includes a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, the method described above is implemented.

[0015] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented.

[0016] The embodiment of the present application provides a soil suitability analysis method, device, equipment and storage medium based on AHP, which can obtain tiff image data of the area to be analyzed; then input the tiff image data into a preset soil suitability analysis model, and output the crop soil suitability weight corresponding to the area to be analyzed; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; then based on GIS, according to the crop soil suitability weight and tiff image data, the crop soil suitability tiff image source data corresponding to the area to be analyzed is generated; based on Therefore, a soil suitability analysis method integrating analytic hierarchy process (AHP) and geographic information system (GIS) technology is proposed. AHP can decompose the complex decision-making factors in soil suitability analysis into several levels and factors by constructing a hierarchical model, that is, it can decompose and quantify the complex soil influencing factors and calculate the relative weight of each factor. GIS technology can integrate, process and analyze spatial data, realize the comprehensive evaluation of soil suitability, provide strong technical support for soil suitability analysis, and thus improve the accuracy and scientificity of soil suitability analysis, and provide strong support for agricultural production, land use planning and ecological environmental protection.

[0017] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which: Figure 1 A flow chart of a soil suitability analysis method based on AHP according to an embodiment of the present disclosure is shown; Figure 2 A schematic diagram of a preset soil suitability analysis model according to an embodiment of the present disclosure is shown; Figure 3 A schematic diagram showing upward aggregation of weight results according to an embodiment of the present disclosure is shown; Figure 4 A schematic diagram showing soil moisture scoring according to an embodiment of the present disclosure is shown; Figure 5 A schematic diagram of a soil moisture tiff image according to an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of a soil moisture tiff image according to an embodiment of the present disclosure is shown; Figure 7 A schematic diagram of a soil type tiff image according to an embodiment of the present disclosure is shown; Figure 8 A schematic diagram of a crop soil suitability tiff image according to an embodiment of the present disclosure is shown; Fig. 9 A schematic diagram of resolution balancing according to an embodiment of the present disclosure is shown; Fig.10 A schematic diagram of point balancing according to an embodiment of the present disclosure is shown; Fig.11 A schematic diagram of another soil suitability analysis method based on AHP according to an embodiment of the present disclosure is shown; Fig.12 A block diagram of a soil suitability analysis device based on AHP according to an embodiment of the present disclosure is shown; Fig.13 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0020] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0021] In the present disclosure, a soil suitability analysis method which integrates the analytic hierarchy process (AHP) and geographic information system (GIS) technology is proposed. The AHP can decompose the complex decision-making factors in soil suitability analysis into several levels and factors by constructing a hierarchical model, that is, decompose and quantify the complex soil influencing factors, and calculate the relative weight of each factor. The GIS technology can integrate, process and analyze spatial data, realize the comprehensive evaluation of soil suitability, provide strong technical support for soil suitability analysis, and thus improve the accuracy and scientificity of soil suitability analysis, and provide strong support for agricultural production, land use planning and ecological environmental protection.

[0022] Figure 1 A flow chart of a soil suitability analysis method 100 based on AHP according to an embodiment of the present disclosure is shown.

[0023] In block 110 , tiff image data of the area to be analyzed is obtained.

[0024] In box 120, the tiff image data is input into a preset soil suitability analysis model, and the crop soil suitability weight corresponding to the area to be analyzed is output; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content.

[0025] In block 130 , based on GIS, crop soil suitability tiff image source data corresponding to the area to be analyzed is generated according to the crop soil suitability weight and the tiff image data.

[0026] In box 110, the tiff image data of the area to be analyzed can be the scene and its data information analyzed according to user needs, and the data is organized according to the three levels of target layer-criterion layer-decision layer in the preset soil suitability analysis model.

[0027] In box 120, the nature of complex problems, influencing factors and other internal relationships are hierarchically analyzed based on the Analytic Hierarchy Process (AHP), so as to use less quantitative information to mathematize the decision-making thinking process, thereby simplifying complex decision-making problems. Therefore, the AHP algorithm is used to construct a preset soil suitability analysis model based on the influencing factors.

[0028] In some embodiments, by calculating weights using the AHP method, the subjective influence of expert experience on the evaluation results can be reduced, thereby improving the objectivity and fairness of the evaluation.

[0029] like Figure 2 As shown, the preset soil suitability analysis model includes a target layer, a criterion layer and a decision layer. The target layer includes crop planting suitability, the criterion layer includes soil type, soil moisture and soil water content; the decision layer includes very suitable, generally suitable and unsuitable; The construction of the above preset soil suitability analysis model includes: According to the first pairwise comparison values ​​determined by pairwise comparison between soil type, soil moisture condition and soil water content, a first pairwise comparison matrix of the criterion layer to the target layer is constructed; Convert the first pairwise comparison matrix into the first standard matrix; Based on the first standard matrix, calculating a first weight vector of the criterion layer to the target layer; According to the second pairwise comparison values ​​determined by pairwise comparison between very suitable, generally suitable and unsuitable, a second pairwise comparison matrix of the decision layer for the criterion layer is constructed; Convert the second pairwise comparison matrix into a second standard matrix; Based on the second standard matrix, calculating a second weight vector of the decision layer to the criterion layer; The second weight vector is aggregated upward to the first weight vector to obtain the third weight vector of the decision layer to the target layer, and the crop soil suitability weight of the decision layer to the target layer is determined to complete the construction of the preset soil suitability analysis model.

[0030] In some embodiments, for ease of explanation, wheat will be selected as the analysis target, i.e., wheat planting suitability, to conduct an analysis of crop planting suitability.

[0031] In some embodiments, pairwise comparison of criteria is the core of hierarchical analysis. An importance value is obtained by comparing the importance of two criteria pairwise, such as the criterion comparison matrix example shown in Table 1. Each value represents the importance of the horizontal column compared with the vertical column, where the scale meaning is shown in Table 2; as shown in Table 1, the "*" mark indicates that the soil type criterion is slightly more important than the soil moisture criterion; a comparison matrix of the criterion layer to the target layer can be formed by pairwise comparison of values, and the comparison matrix will be used to evaluate the rationality of the matrix and calculate the weight information between the criteria.

[0032] Table 1: Example of a criteria comparison matrix Table 2: Scale meaning table In some embodiments, a comparison matrix is ​​constructed by comparing two by two between criterion layers, which is used to evaluate the rationality of the matrix and calculate the weight information between the criteria, so as to ensure that the importance of each factor in the evaluation process is reasonably reflected.

[0033] In some embodiments, the pairwise comparison matrix may be converted into a standard matrix through matrix operations.

[0034] In some embodiments, the pairwise comparison matrix a1, i.e., the first pairwise comparison matrix, can be converted into a standard matrix a2, i.e., the first standard matrix, according to formula ①, and the standard matrix is ​​a reference matrix for obtaining the criterion layer weight vector. Wherein, formula ① is as follows, the back side of the arrow is the matrix a1, and the front side of the arrow is the matrix a2: In some embodiments, the standard matrix can be used to calculate the weight vector of soil type, soil moisture, and soil water content according to formula ②, and the weights of the three correspond to the values ​​in vector w1 one by one; in formulas ① and ②, n is the number of criteria, i is the matrix row, and j is the matrix column. Among them, formula ② is as follows, the back side of the arrow is matrix a2, and the front side of the arrow is vector w1: like Figure 3 As shown above, the comparison matrix and weight vector formed by the decision layer for each criterion layer can be calculated, and finally a weight vector t1 of the criterion layer for the target layer and three weight vectors c1, c2, c3 of the decision layer for the criterion layer can be obtained. The weight vector of the criterion layer can be summarized to the target layer to obtain the proportion of the decision of each decision layer to the target layer. The higher the proportion, the more reasonable the decision; when summarizing upward, the weight results are summarized by formula (1) to obtain the weight value of the decision of the decision layer to the target. Among them, formula (1) is as follows: In some embodiments, the construction of the preset soil suitability analysis model further includes: According to the first pairwise comparison values ​​determined by pairwise comparison between soil type, soil moisture condition and soil water content, a first pairwise comparison matrix of the criterion layer to the target layer is constructed; Convert the first pairwise comparison matrix into the first standard matrix; Based on the first standard matrix, calculating a first weight vector of the criterion layer to the target layer; Based on the preset scoring mechanism, a second weight vector of the decision layer to the criterion layer is calculated according to very suitable, generally suitable and unsuitable; The second weight vector is aggregated upward to the first weight vector to obtain the third weight vector of the decision layer to the target layer, and the crop soil suitability weight of the decision layer to the target layer is determined to complete the construction of the preset soil suitability analysis model.

[0035] In some embodiments, when constructing the soil suitability analysis model, the soil suitability analysis model may also be optimized by introducing a scoring mechanism.

[0036] Specifically, a pairwise comparison matrix is ​​retained at the criterion layer to calculate the weight data between criteria, while the decision layer no longer uses matrix calculation for the criterion layer but converts it into a scoring mechanism, that is, a preset scoring mechanism is set.

[0037] In some embodiments, the preset scoring mechanism can be as follows: Figure 4 As shown: Taking soil moisture content as an example for the decision-making layer, the moisture content is divided into (66~100 points, 1-2 indicators) very suitable, (33~66 points, 3-4 indicators) generally suitable, (0~33 points, 5 indicators) unsuitable. The decision-making layer score is calculated based on the data, and finally the score of the criterion layer is summed up according to the weight to obtain a comprehensive score, and the decision is determined based on the comprehensive score range.

[0038] In some embodiments, the preset scoring mechanism may be set based on a scoring mechanism that divides specific intervals according to local natural environment conditions, which is then determined by voting by Delphi experts.

[0039] In some embodiments, in the evaluation of the criterion layer by the decision layer, a scoring mechanism is designed to divide the score data of each criterion into different levels and assign corresponding score intervals for quantitative evaluation. In addition, in the evaluation of the criterion layer by the decision layer, the scoring mechanism is used instead of the matrix calculation, which can also simplify the calculation process and improve the evaluation efficiency.

[0040] In some embodiments, the determination of the above impact factor includes: Based on the Delphi method, expert opinions were obtained and an initial influencing factor judgment matrix was constructed. The influencing factor judgment matrix was normalized, and the corresponding eigenvalues ​​and weights of each initial influencing factor were calculated respectively. Soil type, soil moisture and soil water content were determined as influencing factors.

[0041] In some embodiments, soil type, soil moisture, and soil water content may be determined as influencing factors through the Delphi Method, which is a method for reaching a consensus through the collection and analysis of expert opinions.

[0042] In some embodiments, the Delphi method is used to collect expert opinions and construct a judgment matrix, calculate the eigenvalues ​​and weights after normalization, rank the importance of each initial influencing factor, and select soil type, soil moisture and soil water content as influencing factors. Since soil type, soil moisture and soil water content are directly related to the suitable conditions for plant growth, they determine the production potential and water management of the soil, so the evaluation results obtained by using the above three points as influencing factors are more accurate.

[0043] In box 130 , a Geographic Information System (GIS) may be selected as a tool for visualizing crop soil suitability tiff images to perform data visualization.

[0044] In some embodiments, the spatial data required for soil suitability evaluation, including soil type, topography, climate conditions, hydrological conditions, vegetation coverage, etc., can be integrated, processed and analyzed based on GIS technology.

[0045] In some embodiments, AHP is combined with GIS technology to form a new soil suitability analysis method. This combination not only gives play to the advantages of AHP in decision analysis, but also makes full use of the capabilities of GIS in data processing and spatial analysis, thus achieving innovation in evaluation methods.

[0046] In some embodiments, the above method further comprises: Based on GIS, soil type tiff image source data, soil moisture tiff image source data and soil water content tiff image source data corresponding to the area to be analyzed are generated according to the weight corresponding to the second weight vector and the image data.

[0047] like Figure 5 , Figure 6 as well as Figure 7 As shown, in the soil moisture tiff image source data, the darker the color, the more suitable the moisture content; in the soil moisture tiff image source data, the darker the color, the more soil organic matter; in the soil type tiff image source data, each color represents a soil type.

[0048] like Figure 8 As shown in the figure, in the wheat soil suitability tiff image source data, each color represents the corresponding soil suitability.

[0049] In some embodiments, the above-mentioned generating the crop soil suitability tiff image source data corresponding to the area to be analyzed according to the crop soil suitability weight and the tiff image data includes: The soil type tiff image source data, soil moisture condition tiff image source data and soil water content tiff image source data are fused; the fusion process includes balancing the low-resolution image to high-resolution and point matching; The crop soil suitability tiff image source data corresponding to the area to be analyzed is generated according to the crop soil suitability weight and the fused soil type tiff image source data, soil moisture tiff image source data and soil water content tiff image source data.

[0050] In some embodiments, the fusion processing of soil type tiff image source data, soil moisture condition tiff image source data and soil water content tiff image source data includes: Based on the image resampling method, the soil type tiff image source data, soil moisture condition tiff image source data and soil water content tiff image source data are fused.

[0051] like Fig. 9 and Fig.10 As shown in the figure, the resolution of different images may be greatly different after processing. When the difference is too large, it is not conducive to point matching. At this time, the low-resolution image can be balanced to the high-resolution image, such as balancing the resolution of the soil type tiff image source data (762*582) to the soil moisture content tiff image source data (7076*5649); different images have size and position differences. The comprehensive evaluation of each point needs to be matched according to the algorithm to ensure data consistency. For example, based on the same points, the soil type tiff image source data and the soil moisture content tiff image source data are matched.

[0052] In some embodiments, the image resampling method can be used for balancing, wherein 'bilinear interpolation' is selected in the calculation strategy, the four nearest original pixels are selected around the new pixel position and the average value is taken as the balanced value, and the new pixel value is calculated according to the distance weighting. Balancing based on the above method can make the image data presented after GIS fusion more accurate, with better effect and higher smoothness.

[0053] In summary, a method involving geographic information system and multi-criteria decision analysis is proposed to simplify the soil suitability analysis process and greatly improve the analysis efficiency. Specifically, the selection of AHP for the construction of soil suitability analysis model can decompose the problem into clear levels, which is conducive to understanding the problem, and there is no complex and profound design. The problem is simply analyzed and divided into operations, which is conducive to operation. The algorithm only needs to quantitatively analyze each item at each level, without a large amount of systematic data; at the same time, through the integration and application of AHP and GIS technology, the quantitative evaluation and visualization of soil suitability are realized, the accuracy and reliability of the evaluation results are improved, and a scientific decision-making basis is provided for agricultural production, land use planning and ecological environment protection, which is conducive to the rational allocation and sustainable use of land resources. It also promotes the cross-integration and innovative development of GIS and multi-criteria decision analysis (MCDA) technology, and provides new ideas and methods for research and application in related fields.

[0054] According to the embodiments of the present disclosure, the following technical effects are achieved: It can obtain tiff image data of the area to be analyzed; then input the tiff image data into the preset soil suitability analysis model, and output the crop soil suitability weight corresponding to the area to be analyzed; wherein, the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; then based on GIS, according to the crop soil suitability weight and tiff image data, the crop soil suitability tiff image source data corresponding to the area to be analyzed is generated; based on this, a soil suitability analysis method integrating hierarchical analysis method and geographic information system technology is proposed. The hierarchical analysis method can decompose the complex decision factors in soil suitability analysis into several levels and factors by constructing a hierarchical structure model, that is, decomposing and quantifying the complex soil influencing factors, and calculating the relative weight of each factor, while the geographic information system technology can realize the integration, processing and analysis of spatial data, realize the comprehensive evaluation of soil suitability, provide strong technical support for soil suitability analysis, and thus improve the accuracy and scientificity of soil suitability analysis, and provide strong support for agricultural production, land use planning and ecological environment protection.

[0055] Fig.11 A schematic diagram of another soil suitability analysis method based on AHP according to an embodiment of the present disclosure is shown.

[0056] When the tiff image data of the area to be analyzed exist in single band and multi-band, in order to further refine the soil suitability analysis method based on AHP, another soil suitability analysis method based on AHP is proposed.

[0057] Specifically, according to the scene to be analyzed and its data information, the data is sorted according to the three levels of target layer-criterion layer-decision layer and the corresponding analysis model framework is built; according to the degree of influence of the criterion layer on the target layer, the pairwise comparison matrix is ​​constructed, and the scoring mechanism is set according to the suitability of each criterion layer for the decision layer to complete the construction of the analysis model; the tiff impact data file of each criterion layer is uploaded; the tiff image data is read, and different types of data are read according to the number of image channels, and the read data is quantified into score data according to the set scoring mechanism. If the number of tiff bands is equal to 1, the single channel value is read and scored, and if the number of tiff bands is equal to 3, the 3-channel value is read and scored; the tiff analysis data of each criterion layer is integrated into a comprehensive analysis result according to the weight value of the criterion layer, and the result is output as image data.

[0058] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0059] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0060] Fig.12 FIG. 1 shows a block diagram of a soil suitability analysis device 1200 based on AHP according to an embodiment of the present disclosure. Fig.12 As shown, the apparatus 1200 includes: An acquisition module 1210 is used to acquire tiff image data of the area to be analyzed; The processing module 1220 is used to input the tiff image data into a preset soil suitability analysis model, and output the crop soil suitability weight corresponding to the area to be analyzed; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; The generation module 1230 is used to generate the crop soil suitability tiff image source data corresponding to the area to be analyzed based on GIS according to the crop soil suitability weight and tiff image data.

[0061] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0062] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0063] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0064] Fig.13 A block diagram of an exemplary electronic device 1300 capable of implementing an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0065] The electronic device 1300 includes a computing unit 1301, which can perform various appropriate actions and processes according to a computer program stored in the ROM 1302 or a computer program loaded from the storage unit 1308 into the RAM 1303. In the RAM 1303, various programs and data required for the operation of the electronic device 1300 can also be stored. The computing unit 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An I / O interface 1305 is also connected to the bus 1304.

[0066] Multiple components in the electronic device 1300 are connected to the I / O interface 1305, including: an input unit 1306, such as a keyboard, a mouse, etc.; an output unit 1307, such as various types of displays, speakers, etc.; a storage unit 1308, such as a disk, an optical disk, etc.; and a communication unit 1309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1309 allows the electronic device 1300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0067] The computing unit 1301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1308.

[0068] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1300 via the ROM 1302 and / or the communication unit 1309. When the computer program is loaded into the RAM 1303 and executed by the computing unit 1301, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 1301 may be configured to execute the method 100 in any other appropriate manner (e.g., by means of firmware).

[0069] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0070] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0071] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0072] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0073] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0074] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0075] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0076] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A soil suitability analysis method based on AHP, characterized in that: include: Get the tiff image data of the area to be analyzed; The tiff image data is input into a preset soil suitability analysis model, and the crop soil suitability weight corresponding to the area to be analyzed is output; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; Based on GIS, the crop soil suitability tiff image source data corresponding to the area to be analyzed is generated according to the crop soil suitability weight and the tiff image data.

2. The method according to claim 1, characterized in that The preset soil suitability analysis model includes a target layer, a criterion layer and a decision layer, wherein the target layer includes crop planting suitability, the criterion layer includes soil type, soil moisture and soil water content; and the decision layer includes very suitable, generally suitable and unsuitable; The construction of the preset soil suitability analysis model includes: Constructing a first pairwise comparison matrix of the criterion layer to the target layer according to first pairwise comparison values ​​determined by pairwise comparison between the soil type, the soil moisture content and the soil water content; Converting the first pairwise comparison matrix into a first standard matrix; Based on the first standard matrix, calculating a first weight vector of the criterion layer to the target layer; According to second pairwise comparison values ​​determined by pairwise comparison between the very suitable, the generally suitable and the unsuitable, construct a second pairwise comparison matrix of the decision layer to the criterion layer; Converting the second pairwise comparison matrix into a second standard matrix; Based on the second standard matrix, calculating a second weight vector of the decision layer to the criterion layer; The second weight vector is aggregated upward to the first weight vector to obtain the third weight vector of the decision layer to the target layer, and the crop soil suitability weight of the decision layer to the target layer is determined to complete the construction of the preset soil suitability analysis model.

3. The method according to claim 1, characterized in that The preset soil suitability analysis model includes a target layer, a criterion layer and a decision layer, wherein the target layer includes crop planting suitability, the criterion layer includes soil type, soil moisture and soil water content; and the decision layer includes very suitable, generally suitable and unsuitable; The construction of the preset soil suitability analysis model includes: Constructing a first pairwise comparison matrix of the criterion layer to the target layer according to first pairwise comparison values ​​determined by pairwise comparison between the soil type, the soil moisture content and the soil water content; Converting the first pairwise comparison matrix into a first standard matrix; Based on the first standard matrix, calculating a first weight vector of the criterion layer to the target layer; Based on a preset scoring mechanism, according to the very suitable, the generally suitable and the unsuitable, a second weight vector of the decision layer to the criterion layer is calculated; The second weight vector is aggregated upward to the first weight vector to obtain the third weight vector of the decision layer to the target layer, and the crop soil suitability weight of the decision layer to the target layer is determined to complete the construction of the preset soil suitability analysis model.

4. The method according to claim 3, characterized in that The method further comprises: Based on GIS, soil type tiff image source data, soil moisture tiff image source data and soil water content tiff image source data corresponding to the area to be analyzed are generated according to the weight corresponding to the second weight vector and the image data.

5. The method according to claim 4, characterized in that The step of generating the crop soil suitability tiff image source data corresponding to the area to be analyzed according to the crop soil suitability weight and the tiff image data comprises: The soil type tiff image source data, the soil moisture condition tiff image source data and the soil water content tiff image source data are fused; the fusion process includes balancing the low-resolution image to the high-resolution image and point matching; The crop soil suitability tiff image source data corresponding to the area to be analyzed is generated according to the crop soil suitability weight and the fused soil type tiff image source data, soil moisture tiff image source data and soil water content tiff image source data.

6. The method according to claim 5, characterized in that The fusing processing of the soil type tiff image source data, the soil moisture condition tiff image source data and the soil water content tiff image source data comprises: Based on the image resampling method, the soil type tiff image source data, the soil moisture condition tiff image source data and the soil water content tiff image source data are fused.

7. The method according to any one of claims 1 to 6, characterized in that: The determination of the impact factor includes: Based on the Delphi method, expert opinions were obtained and an initial influencing factor judgment matrix was constructed. The influencing factor judgment matrix was normalized, and the corresponding eigenvalues ​​and weights of each initial influencing factor were calculated respectively, and soil type, soil moisture and soil water content were determined as influencing factors.

8. A soil suitability analysis device based on AHP, characterized in that: include: An acquisition module is used to acquire tiff image data of the area to be analyzed; A processing module, used for inputting the tiff image data into a preset soil suitability analysis model, and outputting the crop soil suitability weight corresponding to the area to be analyzed; wherein the preset soil suitability analysis model is constructed based on the AHP algorithm according to the influencing factors, and the influencing factors include soil type, soil moisture and soil water content; A generation module is used to generate the crop soil suitability tiff image source data corresponding to the area to be analyzed based on GIS according to the crop soil suitability weight and the tiff image data.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

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