Farmland soil maturation effect analysis system and method based on big data analysis

Abstract

The present invention discloses a farmland soil maturation effect analysis system and method based on big data analysis, which belongs to the field of electronic digital data processing. The present invention obtains sample image information of soil samples and imports it into a soil property analysis strategy to perform soil property matching analysis, obtains nutrient distribution matching analysis results and soil property matching analysis results and imports them into a maturation matching analysis model to perform maturation matching analysis, selects the most matching mature farmland for crop planting according to the maturation matching analysis results, conducts a three-dimensional comprehensive analysis of the nutrient distribution and soil properties of the mature soil, comprehensively evaluates and judges the three-dimensional analysis results and the properties of the soil required for the crops to be planted, selects the most matching mature farmland for crop planting, improves the matching degree between crop planting and soil, and further improves the yield of crop planting.

Description

Technical Field

[0001] The present invention belongs to the field of electronic digital data processing, and specifically relates to a farmland soil maturation effect analysis system and method based on big data analysis. Background Art

[0002] Farmland soil maturation is a process that uses a series of agricultural technical measures to improve soil tillage and soil fertility, thereby facilitating crop growth and development. This process involves improving the physical, chemical and biological properties of the soil, and the farmland soil maturation process requires time and continuous efforts. Through scientific farming management, reasonable fertilization, effective irrigation and drainage and other measures, the soil maturation process can be accelerated, the soil fertility can be improved, and a good soil environment can be provided for the growth and development of crops. At the same time, soil maturation is also an important link in agricultural production and is of great significance for improving the yield and quality of crops. When analyzing the maturation effect, the existing technology is unable to conduct a three-dimensional comprehensive analysis of the nutrient distribution and soil properties of the matured soil, and is unable to comprehensively match the three-dimensional analysis results with the properties of the soil required for the crops to be planted, resulting in the inability to select the most suitable mature farmland for the crops. Most of the existing technologies have the above problems.

[0003] For example, a Chinese patent application with the publication number CN110489716A discloses a method and system for evaluating the maturation of raw land, which belongs to the field of soil evaluation and detection and analysis technology. The method includes the following steps: (1) Establishing a raw land maturation evaluation index system; (2) Defining raw land, mature land, and semi-mature land; (3) Standardizing the numerical values ​​of evaluation indicators; (4) Calculating the raw land index; (5) Calculating the degree of maturity; (6) Evaluating the degree of maturation of raw land: Based on the calculation results of steps (4) and (5), the degree of maturation of newly reclaimed land at different locations is evaluated. This invention evaluates the degree of maturation of newly reclaimed soil on a large scale and in different regions by establishing an evaluation method and an evaluation index system for raw land, semi-mature land, and mature land, and classifies newly reclaimed land according to the degree of soil maturity.

[0004] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a farmland soil maturation effect analysis system and method based on big data analysis. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention proposes a system and method for analyzing the effect of farmland soil maturation based on big data analysis.

[0006] To achieve the above object, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for analyzing the effect of farmland soil maturation based on big data analysis, which comprises the following specific steps:

[0007] Step 1: obtaining microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process;

[0008] Step 2: Obtain microbial composition information and nutrient information and import them into the nutrient analysis strategy to perform nutrient distribution matching analysis;

[0009] Step 3: Obtain sample image information of soil samples and import it into the soil property analysis strategy to perform soil property matching analysis;

[0010] Step 4: import the obtained nutrient distribution matching analysis results and soil property matching analysis results into the maturation matching analysis model for maturation matching analysis;

[0011] Step 5: Select the most matching mature farmland for crop planting based on the results of the maturation matching analysis.

[0012] As a preferred technical solution of the method for analyzing the effect of farmland soil maturation based on big data analysis, the specific contents of obtaining the microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process are as follows:

[0013] S11. Obtaining microbial composition information at each depth of farmland soil samples from mature farmland, wherein the microbial composition information is microbial community density and community type information per unit area;

[0014] It should be noted here that the number and species of microbial communities at different depths in the farmland are collected here because the degree and species of microorganisms required by plants corresponding to different root and stem parts are different;

[0015] S12, obtaining nutrient information of each depth of the farmland soil sample, wherein the nutrient information includes the content of each nutrient element, wherein the nutrient element is an element related to plant growth, including nitrogen, phosphorus, potassium and other elements;

[0016] It should be noted that the nutrient information of each depth of farmland soil samples is collected here because different types of plants have different degrees and types of nutrient information requirements corresponding to different root depths;

[0017] S13, obtaining image data of farmland soil samples, and importing the images into image processing software to obtain surface pore distribution data of the soil samples.

[0018] As a preferred technical solution for the method for analyzing the effect of farmland soil maturation based on big data analysis, the method of obtaining microbial composition information and nutrient information and importing them into the nutrient analysis strategy for nutrient distribution matching analysis includes the following specific steps:

[0019] S21, obtaining the microbial composition information and nutrient information required for each corresponding depth position of the corresponding planted crops, and simultaneously obtaining the microbial composition information affecting the growth of the corresponding planted crops, and simultaneously obtaining the microbial composition information and nutrient information of each depth of the farmland soil sample;

[0020] S22, substituting the acquired microbial composition information required for each corresponding depth position and the microbial composition information of each depth of the farmland soil sample into the microbial beneficial effect matching value calculation formula to calculate the microbial beneficial effect matching value, wherein the microbial beneficial effect matching value calculation formula is: , where m is the number of depth layers, Li is the average depth of the i-th depth layer from top to bottom, Ni is the required microbial species of the i-th depth layer, aij is the proportion coefficient of the j-th required microbial species of the i-th depth layer, xij is the community density of the j-th required microbial species of the i-th depth layer, and xijm is the median of the safe range of the community density of the j-th required microbial species of the i-th depth layer;

[0021] S23, substituting the obtained harmful microbial composition information at each corresponding depth position and the microbial composition information at each depth of the farmland soil sample into the microbial harmful impact matching value calculation formula to calculate the microbial harmful impact matching value, wherein the microbial harmful impact matching value calculation formula is: , where Pi is the type of harmful microorganisms in the i-th depth layer, bic is the proportion coefficient of the j-th harmful microorganism in the i-th depth layer, kic is the density of the j-th harmful microbial community in the i-th depth layer, and kicm is the safety value of the density of the j-th harmful microbial community in the i-th depth layer;

[0022] S24, importing the obtained microbial beneficial effect matching value and microbial harmful effect matching value into the microbial matching value calculation formula to calculate the microbial matching value, wherein the microbial matching value calculation formula is: ,in, is the harmful weight coefficient.

[0023] This step allows for a comprehensive analysis of the microbial impacts of soil layers at different depths.

[0024] As a preferred technical solution for the method for analyzing the effect of farmland soil maturation based on big data analysis, the method of obtaining microbial composition information and nutrient information and importing them into the nutrient analysis strategy for nutrient distribution matching analysis also includes the following specific steps:

[0025] S25, obtaining nutrient information of each depth of the farmland soil sample, and importing the obtained nutrient information of each depth of the farmland soil sample into a nutrient matching value calculation formula to calculate the nutrient matching value, wherein the nutrient matching value calculation formula is: , where Si is the nutrient type of the i-th depth layer, wis is the proportion coefficient of the s-th nutrient in the i-th depth layer, tis is the content of the s-th nutrient in the i-th depth layer, and tism is the content standard of the s-th nutrient in the i-th depth layer;

[0026] S26. Obtain the calculated microbial matching value and nutrient matching value and substitute them into the nutrient distribution matching calculation formula to calculate the nutrient distribution matching value, wherein the nutrient distribution matching calculation formula is: ,in, is the weight of microorganism proportion.

[0027] As a preferred technical solution of the method for analyzing the maturation effect of farmland soil based on big data analysis, the method of obtaining sample image information of soil samples and importing it into the soil property analysis strategy for soil property matching analysis includes the following specific contents:

[0028] The surface pore distribution data of the soil sample is obtained, and the surface pore distribution data of the soil sample is imported into the soil property analysis value calculation formula to calculate the soil property analysis value, wherein the soil property analysis value calculation formula is: , where G is the stomatal distribution density of the collected samples, Gm is the standard stomatal density required for plant cultivation, X is the number of stomata, cv is the size of the vth stoma, and cm is the standard size of the stoma.

[0029] As a preferred technical solution of the method for analyzing the maturation effect of farmland soil based on big data analysis, the obtained nutrient distribution matching analysis results and soil property matching analysis results are imported into the maturation matching analysis model for maturation matching analysis, which includes the following specific contents:

[0030] Obtain the calculated nutrient distribution matching value and soil property analysis value and substitute them into the ripening matching value calculation formula to calculate the ripening matching value, wherein the ripening matching value calculation formula is: ,in, is the weight of nutrient distribution.

[0031] As a preferred technical solution for the farmland soil maturation effect analysis method based on big data analysis, the method of selecting the most matching mature farmland for crop planting based on the maturation matching analysis results includes the following specific contents: obtaining the calculated maturation matching value of the mature farmland, and selecting the mature farmland corresponding to the largest maturation matching value for crop planting.

[0032] In a second aspect, the present invention provides a farmland soil maturation effect analysis system based on big data analysis, which is implemented based on the farmland soil maturation effect analysis method based on big data analysis, and specifically includes a sample data acquisition module, a soil property matching analysis module, a maturation matching analysis module, a planting selection analysis module, a nutrient distribution matching analysis module and a control module;

[0033] The sample data acquisition module is used to acquire microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process;

[0034] The nutrient distribution matching analysis module is used to obtain microbial composition information and nutrient information and import them into the nutrient analysis strategy to perform nutrient distribution matching analysis;

[0035] The soil property matching analysis module is used to obtain sample image information of soil samples and import it into the soil property analysis strategy to perform soil property matching analysis;

[0036] The maturation matching analysis module is used to obtain the nutrient distribution matching analysis results and the soil property matching analysis results and import them into the maturation matching analysis model to perform maturation matching analysis;

[0037] The planting selection analysis module is used to select the most matching mature farmland for planting crops according to the maturation matching analysis results;

[0038] The control module is used for controlling the operation of the sample data acquisition module, the soil property matching analysis module, the maturation matching analysis module, the planting selection analysis module and the nutrient distribution matching analysis module.

[0039] In a third aspect, the present invention provides an electronic device, comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0040] The processor executes the above-mentioned farmland soil maturation effect analysis method based on big data analysis by calling the computer program stored in the memory.

[0041] In a fourth aspect, the present invention provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the above-mentioned method for analyzing the effect of farmland soil maturation based on big data analysis.

[0042] Compared with the prior art, the beneficial effects of this application are:

[0043] The present application obtains microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process, imports the microbial composition information and nutrient information into the nutrient analysis strategy for nutrient distribution matching analysis, obtains sample image information of soil samples and imports it into the soil property analysis strategy for soil property matching analysis, imports the obtained nutrient distribution matching analysis results and soil property matching analysis results into the maturation matching analysis model for maturation matching analysis, selects the most matching mature farmland for crop planting according to the maturation matching analysis results, conducts a three-dimensional comprehensive analysis of the nutrient distribution and soil properties of the mature soil, comprehensively evaluates and judges the three-dimensional analysis results and the properties of the soil required for the crops to be planted, selects the most matching mature farmland for crop planting, thereby improving the matching degree between crop planting and soil, and further improving the yield of crop planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the overall process of the method for analyzing the effect of farmland soil maturation based on big data analysis of the present invention;

[0045] Figure 2 This is a schematic diagram of step 2 of the method for analyzing farmland soil maturation effect based on big data analysis of the present invention;

[0046] Figure 3 It is a schematic diagram of stomatal data analysis of the method for analyzing the effect of farmland soil maturation based on big data analysis of the present invention;

[0047] Figure 4 It is a schematic diagram of the overall framework of the farmland soil maturation effect analysis system based on big data analysis of the present invention;

[0048] Figure 5 This is a schematic diagram of information transmission of the present invention;

[0049] Figure 6 Schematic diagram of soil collection point distribution in the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0051] Example 1

[0052] The application scenarios of this embodiment are: Figure 5As shown, the crop information collection terminal collects crop information and transmits it to the server, and the soil information collection terminal collects soil information and transmits it to the server. The server matches the crop information and soil information and transmits the matching information to the management terminal;

[0053] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1 - Figure 2 As shown, the method for analyzing the effect of farmland soil maturation based on big data analysis includes the following specific steps:

[0054] Step 1: obtaining microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process;

[0055] In a specific embodiment, the specific contents of obtaining the microbial composition information, nutrient information and sample image information of the farmland soil sample during the farmland maturation process are:

[0056] S11. Obtaining microbial composition information at each depth of farmland soil samples from mature farmland, wherein the microbial composition information is microbial community density and community type information per unit area;

[0057] It should be noted that in this embodiment, the number and type of microbial communities at different depths in the farmland are collected here because different root and stem parts of the plants have different requirements for the degree and type of microorganisms;

[0058] It should be noted that in this embodiment, in a preferred embodiment, the distribution of soil sample collection points is as follows: Figure 6 As shown, sampling points are set at the center and four corners of the farmland, and sampling points are set at the middle of the line connecting the center and the corners, and then the data obtained from the sampling points are averaged to obtain the data;

[0059] S12, obtaining nutrient information of each depth of the farmland soil sample, wherein the nutrient information includes the content of each nutrient element, wherein the nutrient element is an element related to plant growth, including nitrogen, phosphorus, potassium and other elements;

[0060] It should be noted that in this embodiment, the nutrient information of each depth of the farmland soil sample is collected here because different types of plants have different degrees and types of nutrient information requirements corresponding to different root depths;

[0061] S13, obtaining image data of farmland soil samples, and importing the images into image processing software to obtain surface pore distribution data of the soil samples;

[0062] It should be noted that in this embodiment, Figure 4 As shown, the specific steps of S13 are:

[0063] S131. Collecting soil sample images: Select representative soil samples in the farmland, use a high-resolution camera or scanner to photograph the surface of the soil samples, and obtain clear soil surface images;

[0064] S132, select image processing software: according to the needs, select the appropriate image processing software, such as AdobePhotoshop, GIMP, MATLAB, etc.;

[0065] S133, import image data: import the collected soil sample surface image into the selected image processing software;

[0066] S134, identifying pore distribution: using edge detection, threshold segmentation and other techniques in image processing software to identify pore distribution on the soil surface;

[0067] S135, obtaining pore distribution data: extracting parameters such as the number, size, and shape of the pores according to the identified pore distribution, and performing statistics and analysis;

[0068] Step 2: Obtain microbial composition information and nutrient information and import them into the nutrient analysis strategy to perform nutrient distribution matching analysis;

[0069] In a specific embodiment, Figure 2 As shown, obtaining microbial composition information and nutrient information and importing them into the nutrient analysis strategy for nutrient distribution matching analysis includes the following specific steps:

[0070] S21, obtaining the microbial composition information and nutrient information required for each corresponding depth position of the corresponding planted crops, and simultaneously obtaining the microbial composition information affecting the growth of the corresponding planted crops, and simultaneously obtaining the microbial composition information and nutrient information of each depth of the farmland soil sample;

[0071] S22, substituting the acquired microbial composition information required for each corresponding depth position and the microbial composition information of each depth of the farmland soil sample into the microbial beneficial effect matching value calculation formula to calculate the microbial beneficial effect matching value, wherein the microbial beneficial effect matching value calculation formula is: , where m is the number of depth layers, Li is the average depth of the i-th depth layer from top to bottom, Ni is the required microbial species of the i-th depth layer, aij is the proportion coefficient of the j-th required microbial species of the i-th depth layer, xij is the community density of the j-th required microbial species of the i-th depth layer, and xijm is the median of the safe range of the community density of the j-th required microbial species of the i-th depth layer;

[0072] It should be noted in this embodiment that in the above formula, It is the difference between the community density of the jth required microbial species in the i-th depth layer and the median of the safe range of the community density of the jth required microbial species in the i-th depth layer. The superimposed symbol is the difference between the actual community density and the safe range of community density. Since different required microbial species have different effects on plants, weights are assigned through aij. Since the deeper the depth, the closer it is to the root system of the plant, the impact of communities at different depths is weighted by depth here;

[0073] S23, substituting the obtained harmful microbial composition information at each corresponding depth position and the microbial composition information at each depth of the farmland soil sample into the microbial harmful impact matching value calculation formula to calculate the microbial harmful impact matching value, wherein the microbial harmful impact matching value calculation formula is: , where Pi is the type of harmful microorganisms in the i-th depth layer, bic is the proportion coefficient of the j-th harmful microorganism in the i-th depth layer, kic is the density of the j-th harmful microbial community in the i-th depth layer, and kicm is the safety value of the density of the j-th harmful microbial community in the i-th depth layer;

[0074] Among them, microorganisms in the soil that are harmful to plant growth mainly include pathogens and other microorganisms that can produce and accumulate toxic substances. The presence of these microorganisms may have a negative impact on the growth and development of plants. Pathogens are microorganisms that can cause plant diseases. They can invade plant cells and tissues, destroy the normal physiological functions of plants, and cause various disease symptoms in plants, such as yellowing leaves and root rot. Common plant pathogens include bacteria, fungi, viruses, etc.

[0075] S24, importing the obtained microbial beneficial effect matching value and microbial harmful effect matching value into the microbial matching value calculation formula to calculate the microbial matching value, wherein the microbial matching value calculation formula is: ,in, is the harmful weight coefficient;

[0076] This step provides a comprehensive analysis of the microbial impacts of different soil depth layers;

[0077] S25, obtaining nutrient information of each depth of the farmland soil sample, and importing the obtained nutrient information of each depth of the farmland soil sample into a nutrient matching value calculation formula to calculate the nutrient matching value, wherein the nutrient matching value calculation formula is: , where Si is the nutrient type of the i-th depth layer, wis is the proportion coefficient of the s-th nutrient in the i-th depth layer, tis is the content of the s-th nutrient in the i-th depth layer, and tism is the content standard of the s-th nutrient in the i-th depth layer;

[0078] S26. Obtain the calculated microbial matching value and nutrient matching value and substitute them into the nutrient distribution matching calculation formula to calculate the nutrient distribution matching value, wherein the nutrient distribution matching calculation formula is: ,in, is the weight of microorganism proportion;

[0079] Step 3: Obtain sample image information of soil samples and import it into the soil property analysis strategy to perform soil property matching analysis;

[0080] In a specific embodiment, obtaining sample image information of a soil sample and importing it into a soil property analysis strategy for soil property matching analysis includes the following specific contents:

[0081] The surface pore distribution data of the soil sample is obtained, and the surface pore distribution data of the soil sample is imported into the soil property analysis value calculation formula to calculate the soil property analysis value, wherein the soil property analysis value calculation formula is: , where G is the stomatal distribution density of the collected sample, Gm is the standard stomatal density required for plant cultivation, X is the number of stomata, cv is the size of the vth stoma, and cm is the standard size of the stoma;

[0082] Step 4: import the obtained nutrient distribution matching analysis results and soil property matching analysis results into the maturation matching analysis model for maturation matching analysis;

[0083] In a specific embodiment, the obtained nutrient distribution matching analysis results and soil property matching analysis results are imported into the maturation matching analysis model for maturation matching analysis, which includes the following specific contents:

[0084] Obtain the calculated nutrient distribution matching value and soil property analysis value and substitute them into the ripening matching value calculation formula to calculate the ripening matching value, wherein the ripening matching value calculation formula is: ,in, is the nutrient distribution weight;

[0085] Step 5: Select the most suitable mature farmland for crop planting based on the results of the mature matching analysis

[0086] In a specific embodiment, selecting the most matching mature farmland for planting crops according to the maturation matching analysis result includes the following specific contents: obtaining the calculated maturation matching value of the mature farmland, and selecting the mature farmland corresponding to the maximum maturation matching value for planting crops.

[0087] It should be noted in this embodiment that the value determination method of the proportion system of the jth required microbial species in the i-th depth layer, the proportion coefficient of the jth harmful microorganism in the i-th depth layer, the harmful weight coefficient, the microbial proportion weight and the nutrient distribution proportion weight is as follows: the microbial composition information, nutrient information and sample image information of 100 groups of farmland soil samples in the farmland maturation process are obtained, and the information is substituted into the maturation matching value calculation formula to calculate the maturation matching value, and the crop yield under each maturation data is obtained. The calculated maturation matching value and the calculated highest yield crop are imported into the fitting software, and the value of the proportion system of the jth required microbial species in the i-th depth layer, the proportion coefficient of the jth harmful microorganism in the i-th depth layer, the harmful weight coefficient, the microbial proportion weight and the nutrient distribution proportion weight that meet the maximum crop growth judgment accuracy is output.

[0088] The advantages of this embodiment over the prior art are as follows: by obtaining the microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process, the microbial composition information and nutrient information are imported into the nutrient analysis strategy for nutrient distribution matching analysis, the sample image information of the soil sample is imported into the soil property analysis strategy for soil property matching analysis, the nutrient distribution matching analysis results and the soil property matching analysis results are imported into the maturation matching analysis model for maturation matching analysis, and the most matching mature farmland is selected for crop planting based on the maturation matching analysis results. By performing a three-dimensional comprehensive analysis on the nutrient distribution and soil properties of the mature soil, the three-dimensional analysis results and the properties of the soil required for the crops to be planted are comprehensively evaluated and judged, and the most matching mature farmland is selected for crop planting, thereby improving the matching degree between crop planting and soil, and further improving the yield of crop planting.

[0089] Example 2

[0090] like Figure 4As shown, a farmland soil maturation effect analysis system based on big data analysis is implemented based on the above-mentioned farmland soil maturation effect analysis method based on big data analysis, and specifically includes a sample data acquisition module, a soil property matching analysis module, a maturation matching analysis module, a planting selection analysis module, a nutrient distribution matching analysis module and a control module; the sample data acquisition module is used to obtain the microbial composition information, nutrient information and sample image information of the farmland soil sample during the farmland maturation process; the nutrient distribution matching analysis module is used to obtain the microbial composition information and nutrient information and import them into the nutrient analysis strategy for nutrient distribution matching analysis; the soil property matching analysis module is used to obtain the sample image information of the soil sample and import it into the soil property analysis strategy for soil property matching analysis; the maturation matching analysis module is used to obtain the obtained nutrient distribution matching analysis results and the soil property matching analysis results and import them into the maturation matching analysis model for maturation matching analysis; the planting selection analysis module is used to select the most matching mature farmland for crop planting according to the maturation matching analysis results; the control module is used to control the operation of the sample data acquisition module, the soil property matching analysis module, the maturation matching analysis module, the planting selection analysis module and the nutrient distribution matching analysis module.

[0091] Example 3

[0092] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0093] The processor executes the above-mentioned farmland soil maturation effect analysis method based on big data analysis by calling the computer program stored in the memory.

[0094] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the farmland soil maturation effect analysis method based on big data analysis provided by the above method embodiment. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as a wired or wireless network interface and an input and output interface to input and output data. This embodiment will not be described in detail here.

[0095] Example 4

[0096] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0097] When the computer program runs on a computer device, the computer device executes the above-mentioned farmland soil maturation effect analysis method based on big data analysis.

[0098] For example, the computer readable storage medium can be a read-only memory, a random access memory, a read-only CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0099] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

Claims

1. A method for analyzing farmland soil maturation effect based on big data analysis, characterized in that: It includes the following specific steps: Obtaining microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process; Obtain microbial composition information and nutrient information and import them into nutrient analysis strategy for nutrient distribution matching analysis; Obtain sample image information of soil samples and import it into the soil property analysis strategy to perform soil property matching analysis; The obtained nutrient distribution matching analysis results and soil property matching analysis results are imported into the maturation matching analysis model for maturation matching analysis; According to the results of the maturation matching analysis, the most matching mature farmland is selected for crop planting; The step of obtaining microbial composition information and nutrient information and importing them into the nutrient analysis strategy to perform nutrient distribution matching analysis includes the following specific steps: Obtain the microbial composition information and nutrient information required for each corresponding depth position of the corresponding planted crops, and at the same time obtain the microbial composition information that affects the growth of the corresponding planted crops, and at the same time obtain the microbial composition information and nutrient information of each depth of the farmland soil samples; Substitute the acquired microbial composition information required for each corresponding depth position and the microbial composition information of each depth of the farmland soil sample into the microbial beneficial effect matching value calculation formula to calculate the microbial beneficial effect matching value, wherein the microbial beneficial effect matching value calculation formula is: , where m is the number of depth layers, Li is the average depth of the i-th depth layer from top to bottom, Ni is the required microbial species of the i-th depth layer, aij is the proportion coefficient of the j-th required microbial species of the i-th depth layer, xij is the community density of the j-th required microbial species of the i-th depth layer, and xijm is the median of the safe range of the community density of the j-th required microbial species of the i-th depth layer; Substitute the obtained harmful microbial composition information at each corresponding depth position and the microbial composition information at each depth of the farmland soil sample into the microbial harmful impact matching value calculation formula to calculate the microbial harmful impact matching value, wherein the microbial harmful impact matching value calculation formula is: , where Pi is the type of harmful microorganisms in the i-th depth layer, bic is the proportion coefficient of the j-th harmful microorganism in the i-th depth layer, kic is the density of the j-th harmful microbial community in the i-th depth layer, and kicm is the safety value of the density of the j-th harmful microbial community in the i-th depth layer; The obtained microbial beneficial effect matching value and microbial harmful effect matching value are introduced into the microbial matching value calculation formula to calculate the microbial matching value, wherein the microbial matching value calculation formula is: ,in, is the harmful weight coefficient; The step of obtaining microbial composition information and nutrient information and importing them into the nutrient analysis strategy for nutrient distribution matching analysis also includes the following specific steps: The nutrient information of each depth of the farmland soil sample is obtained, and the nutrient information of each depth of the farmland soil sample obtained is imported into the nutrient matching value calculation formula to calculate the nutrient matching value, wherein the nutrient matching value calculation formula is: , where Si is the nutrient type of the i-th depth layer, wis is the proportion coefficient of the s-th nutrient in the i-th depth layer, tis is the content of the s-th nutrient in the i-th depth layer, and tism is the content standard of the s-th nutrient in the i-th depth layer; Obtain the calculated microbial matching value and nutrient matching value and substitute them into the nutrient distribution matching calculation formula to calculate the nutrient distribution matching value, wherein the nutrient distribution matching calculation formula is: ,in, is the weight of microorganism proportion; The method of obtaining sample image information of soil samples and importing it into the soil property analysis strategy for soil property matching analysis includes the following specific contents: The surface pore distribution data of the soil sample is obtained, and the surface pore distribution data of the soil sample is imported into the soil property analysis value calculation formula to calculate the soil property analysis value, wherein the soil property analysis value calculation formula is: , where G is the stomatal distribution density of the collected samples, Gm is the standard stomatal density required for plant cultivation, X is the number of stomata, cv is the size of the vth stoma, and cm is the standard size of the stoma.

2. The method for analyzing farmland soil maturation effect based on big data analysis according to claim 1, characterized in that: The obtained nutrient distribution matching analysis results and soil property matching analysis results are introduced into the maturation matching analysis model for maturation matching analysis, which includes the following specific contents: Obtain the calculated nutrient distribution matching value and soil property analysis value and substitute them into the ripening matching value calculation formula to calculate the ripening matching value, wherein the ripening matching value calculation formula is: ,in, is the weight of nutrient distribution.

3. The method for analyzing farmland soil maturation effect based on big data analysis according to claim 2, characterized in that: The method of selecting the most matching mature farmland for planting crops according to the results of the maturation matching analysis includes the following specific contents: obtaining the calculated maturation matching value of the mature farmland, and selecting the mature farmland corresponding to the maximum maturation matching value for planting crops.

4. The method for analyzing farmland soil maturation effect based on big data analysis according to claim 3, characterized in that: The specific contents of obtaining the microbial composition information, nutrient information and sample image information of the farmland soil sample during the farmland maturation process are as follows: Obtaining microbial composition information at each depth of farmland soil samples from mature farmland, wherein the microbial composition information is the density and species of microbial communities per unit area; Obtaining nutrient information at each depth of farmland soil samples, wherein the nutrient information includes the content of each nutrient element; The image data of the farmland soil samples were obtained, and the images were imported into the image processing software to obtain the surface pore distribution data of the soil samples.

5. A farmland soil maturation effect analysis system based on big data analysis, which is implemented based on the farmland soil maturation effect analysis method based on big data analysis as claimed in any one of claims 1 to 4, characterized in that: It specifically includes a sample data acquisition module, a soil property matching analysis module, a maturation matching analysis module, a planting selection analysis module, a nutrient distribution matching analysis module and a control module; The sample data acquisition module is used to acquire microbial composition information, nutrient information and sample image information of farmland soil samples during the farmland maturation process; The nutrient distribution matching analysis module is used to obtain microbial composition information and nutrient information and import them into the nutrient analysis strategy to perform nutrient distribution matching analysis; The soil property matching analysis module is used to obtain sample image information of soil samples and import it into the soil property analysis strategy to perform soil property matching analysis; The maturation matching analysis module is used to obtain the nutrient distribution matching analysis results and the soil property matching analysis results and import them into the maturation matching analysis model to perform maturation matching analysis; The planting selection analysis module is used to select the most matching mature farmland for planting crops according to the maturation matching analysis results; The control module is used for controlling the operation of the sample data acquisition module, the soil property matching analysis module, the maturation matching analysis module, the planting selection analysis module and the nutrient distribution matching analysis module.

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