Microbial ecological configuration method based on soil nutrient analysis

Abstract

The invention relates to a microbial ecological configuration method based on soil nutrient analysis, and belongs to the technical field of microbial ecological analysis. Abnormal nutrient soil areas are classified to obtain a first-class abnormal nutrient soil area and a second-class abnormal nutrient soil area; carrying out initial configuration on microorganism types according to the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area, obtaining an initial microorganism configuration scheme, and finally obtaining environment characteristic data in the target area. And carrying out secondary configuration according to the environmental characteristic data in the target area and the initialized microorganism configuration scheme to obtain a final microorganism configuration scheme, and carrying out microorganism configuration according to the final microorganism configuration scheme. According to the method, the nutrient data in the soil are analyzed, so that the related microorganism types are configured according to the nutrient data conditions, the increase or decomposition of the nutrients in the soil is improved, and better growth conditions are provided for crops.

Description

Technical Field

[0001] The present invention relates to the technical field of microbial analysis, and particularly to a microbial ecological configuration method based on soil nutrient analysis. Background Art

[0002] Soil microorganisms are the general term for all tiny organisms that are invisible or unclear to the naked eye in the soil. Strictly speaking, they should include bacteria, archaea, fungi, viruses, protozoa, and microalgae. They are tiny in size, generally calculated in micrometers or nanometers. Usually, there are hundreds of millions to tens of billions in 1 gram of soil, and their types and quantities vary with the different soil-forming environments and soil layer depths. They carry out processes such as oxidation, nitrification, ammonification, nitrogen fixation, and sulfidation in the soil, promoting the decomposition of soil organic matter and the transformation of nutrients. Since the soil provides various conditions such as nutrients, water, air, acidity and alkalinity, osmotic pressure, and temperature required for the growth and development of various microorganisms, it has become a good environment for microorganisms to live. It can be said that the soil is the "natural medium" for microorganisms and their "headquarters", and for humans, it is the richest germplasm resource library. However, due to the fact that the current soil has been repaired after pollution, most of the beneficial microorganisms have been killed, which will slow down the processes such as oxidation, nitrification, ammonification, nitrogen fixation, and sulfidation of the nutrient components in the soil, and is not conducive to the timely supply of nutrients for crops. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a microbial ecological method based on soil nutrient analysis.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of the present invention provides a microbial ecological configuration method based on soil nutrient analysis, including the following steps:

[0006] Obtain the multi-source characteristic data information of the soil in each target area, and obtain the nutrient characteristic data of the soil in each target area by performing feature extraction on the multi-source characteristic data information of the soil in each target area;

[0007] Identify the abnormal nutrient soil areas based on the nutrient characteristic data of the soil in each target area, and classify the abnormal nutrient soil areas to obtain the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas;

[0008] Perform initial configuration of the microbial types according to the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas to obtain an initial microbial configuration plan;

[0009] Obtain the environmental feature data in the target area, and perform secondary configuration according to the environmental feature data in the target area and the initialized microbial configuration scheme to obtain the final microbial configuration scheme, and perform microbial configuration according to the final microbial configuration scheme.

[0010] Further, in this method, obtain the multi-source feature data information of the soil in each target area, and obtain the nutrient feature data of the soil in each target area by performing feature extraction on the multi-source feature data information of the soil in each target area, specifically including:

[0011] Obtain the multi-source feature data information of the soil in each target area and the crop type information in the target area, and construct a retrieval tag according to the crop type information in the target area;

[0012] Retrieve through big data based on the retrieval tag to obtain the nutrient type data corresponding to the crop type information in the target area;

[0013] Calculate the Euclidean distance value between the nutrient type data corresponding to the crop type information in the target area and each type of data in the multi-source feature data information of the soil in the target area;

[0014] Obtain the type of data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value, and count the type of data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value and the associated concentration data to construct the nutrient feature data of the soil in each target area.

[0015] Further, in this method, identify the abnormal nutrient soil area based on the nutrient feature data of the soil in each target area, specifically:

[0016] Set a nutrient feature data threshold, and judge whether the nutrient feature data of the soil in each target area is greater than the nutrient feature data threshold;

[0017] When the nutrient feature data of the soil in the target area is not greater than the nutrient feature data threshold, the area where the nutrient feature data is not greater than the nutrient feature data threshold is used as the abnormal nutrient soil area;

[0018] When the nutrient feature data of the soil in the target area is greater than the nutrient feature data threshold, the area where the nutrient feature data is greater than the nutrient feature data threshold is used as the normal nutrient soil area.

[0019] Further, in this method, classify the abnormal nutrient soil area to obtain a class of abnormal nutrient soil areas and a class two of abnormal nutrient soil areas, specifically:

[0020] There is nutrient type data in the abnormal nutrient soil area. Set classification evaluation indicators, and classify the nutrient type data existing in the abnormal nutrient soil area based on the classification evaluation indicators;

[0021] Through classification, obtain the flowable nutrient type data and the non-flowable nutrient type data;

[0022] Take the area where only flowable nutrient data exists as one type of abnormal nutrient soil area, and take the area where non-flowable nutrient data and flowable nutrient data exist, and the area where only non-flowable nutrient data exists as the second type of abnormal nutrient soil area;

[0023] Output the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area as the output result.

[0024] Further, in this method, perform an initial configuration of the microbial types according to the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area, and obtain an initial microbial configuration plan, specifically:

[0025] Obtain the ability characteristic data information of each microbial type through big data, introduce a Bayesian network, and obtain the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area;

[0026] Take the ability characteristic data information of the microbial type as the first independent event, and take the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area as the second independent event;

[0027] Take the first independent event and the second independent event as a group, calculate the joint probability value of the group through the Bayesian network, obtain the group with the joint probability value greater than the preset joint probability value, and obtain the microbial types associated with the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area based on the group with the joint probability value greater than the preset joint probability value;

[0028] Generate an initial microbial configuration plan according to the microbial types associated with the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area, and output the initial microbial configuration plan.

[0029] Further, in this method, obtain the environmental characteristic data in the target area, and perform a secondary configuration according to the environmental characteristic data in the target area and the initial microbial configuration plan to obtain the final microbial configuration plan, specifically including:

[0030] Obtain the environmental characteristic data in the target area and the types of microorganisms in the microorganism configuration plan, and obtain the reproduction and growth data information of microorganisms under each environmental characteristic data through big data;

[0031] Based on the reproduction and growth data information of microorganisms under each environmental characteristic data, the environmental characteristic data in the target area, and the types of microorganisms in the microorganism configuration plan, obtain the reproduction and growth data information of the types of microorganisms in the microorganism configuration plan under the environmental characteristic data in the target area;

[0032] Set a reproduction and growth data threshold, and determine whether the reproduction and growth data information of the types of microorganisms in the microorganism configuration plan under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold;

[0033] When the reproduction and growth data information of the types of microorganisms in the microorganism configuration plan under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold, then use the corresponding type of microorganism as the final microorganism configuration plan.

[0034] The second aspect of the present invention provides a microbial ecological configuration system based on soil nutrient analysis. The configuration system includes a memory and a processor. The memory includes a microbial ecological method program based on soil nutrient analysis. When the microbial ecological method program based on soil nutrient analysis is executed by the processor, the steps of any one of the microbial ecological configuration methods based on soil nutrient analysis are implemented.

[0035] The third aspect of the present invention provides a computer-readable storage medium, including a microbial ecological method program based on soil nutrient analysis. When the microbial ecological method program based on soil nutrient analysis is executed by the processor, the steps of any one of the microbial ecological configuration methods based on soil nutrient analysis are implemented.

[0036] The present invention solves the defects existing in the background technology, and the present invention has the following beneficial effects:

[0037] The present invention obtains multi-source characteristic data information of soil in each target area, extracts characteristics from the multi-source characteristic data information of soil in each target area to obtain nutrient characteristic data of soil in each target area, then identifies abnormal nutrient soil areas based on the nutrient characteristic data of soil in each target area, classifies the abnormal nutrient soil areas to obtain Class I abnormal nutrient soil areas and Class II abnormal nutrient soil areas, thereby initializing the configuration of microorganism types according to the Class I abnormal nutrient soil areas and Class II abnormal nutrient soil areas to obtain an initial microorganism configuration plan, finally obtains environmental characteristic data in the target area, and performs secondary configuration according to the environmental characteristic data in the target area and the initial microorganism configuration plan to obtain a final microorganism configuration plan, and performs microorganism configuration according to the final microorganism configuration plan. The present invention analyzes the nutrient data in the soil, and thus configures relevant microorganism types according to the nutrient data situation to increase or decompose the nutrients in the soil and provide better growth conditions for crops. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and for those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0039] Figure 1 Shows the overall flowchart of the microorganism ecological configuration method based on soil nutrient analysis;

[0040] Figure 2 Shows a partial method flowchart of the microorganism ecological configuration method based on soil nutrient analysis;

[0041] Figure 3 Shows the system block diagram of the microorganism ecological configuration system based on soil nutrient analysis. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to be able to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0043] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0044] As Figure 1 shown, the first aspect of the present invention provides a microbial ecological configuration method based on soil nutrient analysis, including the following steps:

[0045] S102: Obtain the multi-source characteristic data information of the soil in each target area, and through feature extraction of the multi-source characteristic data information of the soil in each target area, obtain the nutrient characteristic data of the soil in each target area;

[0046] S104: Identify abnormal nutrient soil areas based on the nutrient characteristic data of the soil in each target area, and classify the abnormal nutrient soil areas to obtain a first-class abnormal nutrient soil area and a second-class abnormal nutrient soil area;

[0047] S106: Initialize the configuration of microbial types according to the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area to obtain an initial microbial configuration plan;

[0048] S108: Obtain the environmental characteristic data in the target area, and perform secondary configuration according to the environmental characteristic data in the target area and the initial microbial configuration plan to obtain a final microbial configuration plan, and perform microbial configuration according to the final microbial configuration plan.

[0049] It should be noted that the present invention analyzes the nutrient data in the soil, and then configures relevant microbial types according to the nutrient data situation, so as to increase or decompose the nutrients in the soil and provide better growth conditions for crops.

[0050] Further, in this method, obtaining the multi-source characteristic data information of the soil in each target area, and through feature extraction of the multi-source characteristic data information of the soil in each target area, obtaining the nutrient characteristic data of the soil in each target area specifically includes:

[0051] Obtain the multi-source characteristic data information of the soil in each target area and the crop type information in the target area, and construct a retrieval label according to the crop type information in the target area;

[0052] Exemplarily, the multi-source characteristic data information includes data such as the type of soil, the type information of each chemical ion in the soil, the type information of microorganisms in the soil, the humidity information of the soil, the component information of each compound in the soil, and the component concentration information of each compound in the soil.

[0053] Retrieve through big data based on the retrieval label to obtain the nutrient type data corresponding to the crop type information in the target area;

[0054] Calculate the Euclidean distance value between the nutrient type data corresponding to the crop type information in the target area and each type of data in the multi-source feature data of the soil in the target area;

[0055] Obtain the type of data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value, and count the type of data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value and the associated concentration data, and construct the nutrient characteristic data of the soil in each target area.

[0056] It should be noted that different crops require different nutrients. The closer the Euclidean distance value between the nutrient type data corresponding to the crop type information in the target area and each type of data in the multi-source feature data of the soil in the target area, the more it indicates that the two are corresponding type of data. Among them, the concentration data can be the proportion of chemical components per unit soil area, the concentration proportion of chemical components per unit soil volume, etc.

[0057] Furthermore, in this method, based on the nutrient characteristic data of the soil in each target area, the abnormal nutrient soil area is identified, specifically:

[0058] Set the nutrient characteristic data threshold, and judge whether the nutrient characteristic data of the soil in each target area is greater than the nutrient characteristic data threshold;

[0059] When the nutrient characteristic data of the soil in the target area is not greater than the nutrient characteristic data threshold, the area where the nutrient characteristic data is not greater than the nutrient characteristic data threshold is used as the abnormal nutrient soil area;

[0060] When the nutrient characteristic data of the soil in the target area is greater than the nutrient characteristic data threshold, the area where the nutrient characteristic data is greater than the nutrient characteristic data threshold is used as the normal nutrient soil area.

[0061] Furthermore, in this method, the abnormal nutrient soil area is classified to obtain the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area, specifically:

[0062] Obtain the nutrient type data existing in the abnormal nutrient soil area, set the classification evaluation index, and classify the nutrient type data existing in the abnormal nutrient soil area based on the classification evaluation index;

[0063] Through classification, obtain the flowable nutrient type data and the non-flowable nutrient type data;

[0064] The area where only the flowable nutrient data exists is used as the first-class abnormal nutrient soil area, and the area where the non-flowable nutrient data and the flowable nutrient data exist and the area where only the non-flowable nutrient data exists are used as the second-class abnormal nutrient soil area;

[0065] Output a first-class abnormal nutrient soil area and a second-class abnormal nutrient soil area as output results.

[0066] It should be noted that the flowable nutrient data is the nutrient that can flow with water (soluble in water), and the non-flowable nutrient data is the nutrient that cannot flow with water (insoluble in water), such as solid chemical nutrients (for example, compound precipitates are insoluble in water).

[0067] Such as Figure 2 As shown, further, in this method, the microbial types are initialized and configured according to the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area to obtain an initial microbial configuration plan, specifically:

[0068] S202: Obtain the ability characteristic data information of each microbial type through big data, introduce a Bayesian network, and obtain the nutrient characteristic data information of the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area;

[0069] S204: Take the ability characteristic data information of the microbial type as the first independent event, and take the nutrient characteristic data information of the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area as the second independent event;

[0070] S206: Take the first independent event and the second independent event as a group, calculate the joint probability value of the group through the Bayesian network, obtain the group with the joint probability value greater than the preset joint probability value, and obtain the microbial type associated with the nutrient characteristic data information of the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area based on the group with the joint probability value greater than the preset joint probability value;

[0071] S208: Generate an initial microbial configuration plan according to the microbial type associated with the nutrient characteristic data information of the first-class abnormal nutrient soil area and the second-class abnormal nutrient soil area, and output the initial microbial configuration plan.

[0072] It should be noted that different microbial types have different ability characteristic data information. For example, some microorganisms have decomposition ability, and some microorganisms have the ability to synthesize compounds. By calculating the joint probability value of the group through the Bayesian network, when the joint probability value is greater than the preset joint probability value, it indicates that the first independent event is related to the second independent event, that is, the ability characteristic data of soil microorganisms is related to the nutrient characteristic data. For example, soil microorganisms have the ability to synthesize or decompose certain nutrients, and the correlation between the two is high. Through this method, an initial microbial configuration plan can be obtained.

[0073] Further, in this method, environmental characteristic data in the target area is obtained, and secondary configuration is performed according to the environmental characteristic data in the target area and the initial microbial configuration scheme to obtain the final microbial configuration scheme, which specifically includes:

[0074] Obtain the environmental characteristic data in the target area and the microbial types in the microbial configuration scheme, and obtain the reproduction and growth data information of microorganisms under each environmental characteristic data through big data;

[0075] Based on the reproduction and growth data information of microorganisms under each environmental characteristic data, the environmental characteristic data in the target area, and the microbial types in the microbial configuration scheme, obtain the reproduction and growth data information of the microbial types in the microbial configuration scheme under the environmental characteristic data in the target area;

[0076] Set a reproduction and growth data threshold, and determine whether the reproduction and growth data information of the microbial types in the microbial configuration scheme under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold;

[0077] When the reproduction and growth data information of the microbial types in the microbial configuration scheme under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold, then use the corresponding microbial types as the final microbial configuration scheme.

[0078] It should be noted that different temperature and humidity environments result in different microorganisms having different reproduction and growth data information (including reproduction and growth data, such as the change in the number of reproductions and growths within a time period, the growth rate of the number of reproductions within a time period, etc.). Through this method, the microbial configuration scheme can be further optimized.

[0079] In addition, this method includes:

[0080] Obtain the microbial population data information in the target area and the microbial type information of the microbial configuration scheme, construct a retrieval keyword based on the microbial type information of the microbial configuration scheme, and perform a retrieval through big data based on the retrieval keyword;

[0081] Through the retrieval, obtain the natural enemy microbial population types corresponding to the microbial type information of the microbial configuration scheme, and obtain the concentration ratio of the natural enemy microbial population types corresponding to the microbial type information of the microbial configuration scheme according to the microbial population data information in the target area;

[0082] Set a concentration ratio threshold for the natural enemy microbial population types, and determine whether the concentration ratio of the natural enemy microbial population types corresponding to the microbial type information of the microbial configuration scheme is greater than the concentration ratio threshold of the natural enemy microbial population types;

[0083] When the concentration ratio of the natural enemy microorganism population type corresponding to the microorganism type information of the microorganism configuration scheme is greater than the concentration ratio threshold of the natural enemy microorganism population type, the microorganism configuration scheme corresponding to the concentration ratio greater than the concentration ratio threshold of the natural enemy microorganism population type is deleted.

[0084] It should be noted that there will also be a certain predatory relationship between microorganisms. Through this method, the rationality of the microorganism configuration scheme can be improved and the microorganism configuration scheme can be optimized.

[0085] As Figure 3 shown, the second aspect of the present invention provides a microorganism ecological configuration system 4 based on soil nutrient analysis. The configuration system 4 includes a memory 41 and a processor 42. The memory 41 includes a microorganism ecological method program based on soil nutrient analysis. When the microorganism ecological method program based on soil nutrient analysis is executed by the processor 42, the following steps are implemented:

[0086] Obtain the multi-source characteristic data information of the soil in each target area, and obtain the nutrient characteristic data of the soil in each target area by performing feature extraction on the multi-source characteristic data information of the soil in each target area;

[0087] Identify the abnormal nutrient soil areas based on the nutrient characteristic data of the soil in each target area, and classify the abnormal nutrient soil areas to obtain the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas;

[0088] Perform initial configuration of the microorganism type according to the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas to obtain an initial microorganism configuration scheme;

[0089] Obtain the environmental characteristic data in the target area, and perform secondary configuration according to the environmental characteristic data in the target area and the initial microorganism configuration scheme to obtain the final microorganism configuration scheme, and perform microorganism configuration according to the final microorganism configuration scheme.

[0090] Further, in this system, obtaining the multi-source characteristic data information of the soil in each target area, and obtaining the nutrient characteristic data of the soil in each target area by performing feature extraction on the multi-source characteristic data information of the soil in each target area specifically includes:

[0091] Obtain the multi-source characteristic data information of the soil in each target area and the crop type information in the target area, and construct a retrieval label according to the crop type information in the target area;

[0092] Perform retrieval through big data based on the retrieval label to obtain the nutrient type data corresponding to the crop type information in the target area;

[0093] Calculate the Euclidean distance value between the nutrient type data corresponding to the crop type information in the target area and each type of data in the multi-source feature data of the soil in the target area;

[0094] Obtain the type data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value, and count the type data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value and the associated concentration data, and construct the nutrient characteristic data of the soil in each target area.

[0095] Further, in this system, identify the abnormal nutrient soil areas based on the nutrient characteristic data of the soil in each target area, specifically:

[0096] Set a nutrient characteristic data threshold, and determine whether the nutrient characteristic data of the soil in each target area is greater than the nutrient characteristic data threshold;

[0097] When the nutrient characteristic data of the soil in the target area is not greater than the nutrient characteristic data threshold, the area where the nutrient characteristic data is not greater than the nutrient characteristic data threshold is used as the abnormal nutrient soil area;

[0098] When the nutrient characteristic data of the soil in the target area is greater than the nutrient characteristic data threshold, the area where the nutrient characteristic data is greater than the nutrient characteristic data threshold is used as the normal nutrient soil area.

[0099] Further, in this system, classify the abnormal nutrient soil areas to obtain the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas, specifically:

[0100] Obtain the nutrient type data existing in the abnormal nutrient soil area, set classification evaluation indicators, and classify the nutrient type data existing in the abnormal nutrient soil area based on the classification evaluation indicators;

[0101] Through classification, obtain the flowable nutrient type data and the non-flowable nutrient type data;

[0102] The area where only the flowable nutrient data exists is used as the first-class abnormal nutrient soil area, and the area where the non-flowable nutrient data and the flowable nutrient data exist and the area where only the non-flowable nutrient data exists are used as the second-class abnormal nutrient soil areas;

[0103] Output the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas as the output results.

[0104] Further, in this system, perform initial configuration on the microbial types according to the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas to obtain the initial microbial configuration plan, specifically:

[0105] Obtain the ability characteristic data information of each microorganism type through big data, introduce the Bayesian network, and obtain the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area;

[0106] Take the ability characteristic data information of the microorganism type as the first independent event, and take the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area as the second independent event;

[0107] Take the first independent event and the second independent event as a group, calculate the joint probability value of the group through the Bayesian network, obtain the group with the joint probability value greater than the preset joint probability value, and obtain the microorganism type associated with the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area based on the group with the joint probability value greater than the preset joint probability value;

[0108] Generate an initial microorganism configuration plan according to the microorganism type associated with the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area, and output the initial microorganism configuration plan.

[0109] Furthermore, in this system, obtain the environmental characteristic data in the target area, and perform secondary configuration according to the environmental characteristic data in the target area and the initial microorganism configuration plan to obtain the final microorganism configuration plan, specifically including:

[0110] Obtain the environmental characteristic data in the target area and the microorganism types in the microorganism configuration plan, and obtain the reproduction and growth data information of the microorganisms under each environmental characteristic data through big data;

[0111] Obtain the reproduction and growth data information of the microorganism types in the microorganism configuration plan under the environmental characteristic data in the target area based on the reproduction and growth data information of the microorganisms under each environmental characteristic data, the environmental characteristic data in the target area, and the microorganism types in the microorganism configuration plan;

[0112] Set a reproduction and growth data threshold, and judge whether the reproduction and growth data information of the microorganism types in the microorganism configuration plan under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold;

[0113] When the reproduction and growth data information of the microorganism types in the microorganism configuration plan under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold, then take the corresponding microorganism type as the final microorganism configuration plan.

[0114] A third aspect of the present invention provides a computer-readable storage medium, including a microbial ecological method program based on soil nutrient analysis. When the microbial ecological method program based on soil nutrient analysis is executed by a processor, the steps of any one of the microbial ecological configuration methods based on soil nutrient analysis are implemented.

[0115] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0116] The units described above as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0117] In addition, in each embodiment of the present invention, each functional unit can be all integrated in one processing unit, or each unit can be separately used as one unit, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0118] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments. The foregoing storage medium includes various media that can store program codes, such as removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs.

[0119] Alternatively, if the above integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention essentially or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as removable storage devices, ROM, RAM, magnetic disks, or optical discs.

[0120] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for microbial ecological configuration based on soil nutrient analysis, characterized in that, Including the following steps: Obtain the multi-source characteristic data information of the soil in each target area, and extract the characteristics of the multi-source characteristic data information of the soil in each target area to obtain the nutrient characteristic data of the soil in each target area; Based on the nutrient characteristic data of the soil in each target area, identify the abnormal nutrient soil areas, and classify the abnormal nutrient soil areas to obtain the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas; According to the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas, perform initial configuration on the microbial types to obtain the initial microbial configuration plan; Obtain the environmental characteristic data in the target area, and perform secondary configuration according to the environmental characteristic data in the target area and the initial microbial configuration plan to obtain the final microbial configuration plan, and perform microbial configuration according to the final microbial configuration plan.

2. The microbial ecological configuration method based on soil nutrient analysis according to claim 1, wherein Obtain the multi-source characteristic data information of the soil in each target area, and extract the characteristics of the multi-source characteristic data information of the soil in each target area to obtain the nutrient characteristic data of the soil in each target area, specifically including: Obtain the multi-source characteristic data information of the soil in each target area and the crop type information in the target area, and construct a retrieval label according to the crop type information in the target area; Based on the retrieval label, perform a search through big data to obtain the nutrient type data corresponding to the crop type information in the target area; Calculate the Euclidean distance value between the nutrient type data corresponding to the crop type information in the target area and each type of data in the multi-source characteristic data information of the soil in the target area; Obtain the type data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value, and count the type data corresponding to the Euclidean distance value not greater than the preset Euclidean distance value and the associated concentration data to construct the nutrient characteristic data of the soil in each target area.

3. The method for microbial ecological configuration based on soil nutrient analysis according to claim 1, wherein Based on the nutrient characteristic data of the soil in each target area, identify the abnormal nutrient soil areas, specifically: Set a nutrient characteristic data threshold, and judge whether the nutrient characteristic data of the soil in each target area is greater than the nutrient characteristic data threshold; When the nutrient characteristic data of the soil in the target area is not greater than the nutrient characteristic data threshold, the area where the nutrient characteristic data is not greater than the nutrient characteristic data threshold is used as the abnormal nutrient soil area; When the nutrient characteristic data of the soil in the target area is greater than the nutrient characteristic data threshold, the area where the nutrient characteristic data is greater than the nutrient characteristic data threshold is used as the normal nutrient soil area.

4. The method for microbial ecological configuration based on soil nutrient analysis according to claim 1, characterized in that, Classify the abnormal nutrient soil areas to obtain the first-class abnormal nutrient soil areas and the second-class abnormal nutrient soil areas, specifically: Obtain the nutrient type data existing in the abnormal nutrient soil area, set a classification evaluation index, and classify the nutrient type data existing in the abnormal nutrient soil area based on the classification evaluation index; Through classification, obtain the flowable nutrient type data and the non-flowable nutrient type data; Take the area where only the mobile nutrient data exists as a first type of abnormal nutrient soil area, and take the area where both immobile and mobile nutrient data exist and the area where only immobile nutrient data exists as a second type of abnormal nutrient soil area; Output the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area as the output result.

5. The method for microbial ecological configuration based on soil nutrient analysis according to claim 1, wherein Perform initial configuration of the microbial types based on the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area to obtain an initial microbial configuration plan, specifically: Obtain the ability characteristic data information of each microbial type through big data, introduce a Bayesian network, and obtain the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area; Take the ability characteristic data information of the microbial type as the first independent event, and take the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area as the second independent event; Take the first independent event and the second independent event as a group, calculate the joint probability value of the group through the Bayesian network, obtain the group with the joint probability value greater than the preset joint probability value, and based on the group with the joint probability value greater than the preset joint probability value, obtain the microbial types associated with the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area; Generate an initial microbial configuration plan according to the microbial types associated with the nutrient characteristic data information of the first type of abnormal nutrient soil area and the second type of abnormal nutrient soil area, and output the initial microbial configuration plan.

6. The microbial ecological configuration method based on soil nutrient analysis according to claim 1, characterized in that, Obtain the environmental characteristic data in the target area, and perform secondary configuration according to the environmental characteristic data in the target area and the initial microbial configuration plan to obtain the final microbial configuration plan, specifically including: Obtain the environmental characteristic data in the target area and the microbial types in the microbial configuration plan, and obtain the reproduction and growth data information of the microorganisms under each environmental characteristic data through big data; Based on the reproduction and growth data information of the microorganisms under each environmental characteristic data, the environmental characteristic data in the target area, and the microbial types in the microbial configuration plan, obtain the reproduction and growth data information of the microbial types in the microbial configuration plan under the environmental characteristic data in the target area; Set a reproduction and growth data threshold, and determine whether the reproduction and growth data information of the microbial types in the microbial configuration plan under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold; When the reproduction and growth data information of the microbial types in the microbial configuration plan under the environmental characteristic data in the target area is greater than the reproduction and growth data threshold, then take the corresponding microbial types as the final microbial configuration plan.

7. A microbial ecological configuration system based on soil nutrient analysis, characterized in that, The configuration system includes a memory and a processor. The memory includes a microbial ecological method program based on soil nutrient analysis. When the microbial ecological method program based on soil nutrient analysis is executed by the processor, the steps of the microbial ecological configuration method based on soil nutrient analysis according to any one of claims 1-6 are implemented.

8. A computer-readable storage medium, characterized in that, Including a microbial ecological method program based on soil nutrient analysis, when the microbial ecological method program based on soil nutrient analysis is executed by a processor, the steps of the microbial ecological configuration method based on soil nutrient analysis according to any one of claims 1-6 are implemented.

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