Information data processing method based on farmland construction quality supervision

Abstract

The invention discloses an information data processing method based on supervision of farmland construction quality, and the method comprises the steps: selecting a target region for constructing a farmland, and dividing the target region into sub-farmland blocks, based on the soil condition parameters on the sub-farmland blocks and the ideal soil condition parameters of the planted crops, constructing a constraint function of matching between the crop types and the sub-farmland blocks; matching a sub-farmland block suitable for planting for each crop according to the constraint function, trimming and constructing the sub-farmland blocks, and planting the matched crops after the trimming and construction of the sub-farmland blocks are completed; in the crop growth process, growth data representing the crop growth quality are collected, the crop growth quality coefficient is calculated, and the construction quality of the sub-farmland blocks is evaluated based on the standard value of the quality coefficient after the crops grow maturely. According to the method, fine management and scientific decision-making of farmland construction quality are facilitated, and spatial distribution difference characteristics and change trends of large-area farmland construction can be fully utilized.

Description

Technical Field

[0001] The present invention relates to the field of farmland construction quality assessment, and in particular to an information data processing method based on supervising farmland construction quality. Background Art

[0002] High-quality farmland construction is a high-standard farmland with complete land planning, concentrated and continuous areas, complete facilities, fertile soil, good ecology, strong disaster resistance, drought and flood-resistant and high-yield and stable farmland that is compatible with modern agricultural production and management methods. It is the essence of cultivated land. In the process of high-quality farmland construction, making full use of the characteristics of adapting to local conditions and the different adaptability of crop soil conditions to plan the planting area will help to better improve the quality of farmland construction and ensure the yield of crops from the source. However, how to match different types of farmland with suitable crops is a major problem. The existing technology is based on experience or a small amount of test data for matching, with poor matching accuracy and low efficiency, and is not suitable for large-scale farmland construction. At the same time, during the growth of crops, traditional manual inspections and simple data analysis have been difficult to meet the needs of high-quality supervision, and it is impossible to accurately evaluate and adjust the planting condition parameters of farmland according to the needs of crops, resulting in low crop yields. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides an information data processing method based on supervising the quality of farmland construction, aiming to achieve high-quality farmland construction and supervision through data analysis and processing.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: Provided is an information data processing method based on supervising farmland construction quality, comprising: Step S1: Select a target area for farmland construction, divide the target area into sub-farmland blocks, and construct a constraint function matching the crop types with the sub-farmland blocks based on the soil condition parameters on the sub-farmland blocks and the ideal soil condition parameters for growing crops; Step S2: matching suitable sub-farm plots for planting for each crop according to the constraint function, and repairing and constructing the sub-farm plots. After the repairing and construction of the sub-farm plots are completed, the matching crops are planted; Step S3: During the growth of crops, growth data characterizing the growth quality of crops are collected, and the quality coefficient of crop growth is calculated. Based on the standard value of the quality coefficient after the crops grow to maturity, the construction quality of the sub-farm plots is evaluated.

[0005] Further, step S1 includes: Step S11: Select the target area for farmland construction, divide the target area into different sub-farmland blocks according to the crop planting types and corresponding planting areas, collect soil condition parameters for planting crops in each sub-farmland block, and form a soil condition data set , n is the number of soil condition parameters, For the n Soil condition parameters, N The number of the sub-farm plot; Step S12: Establishing a data set of ideal soil conditions that determine the yield of different types of crops , m is the number of ideal soil condition parameters, b m For the m Parameters of ideal soil conditions, M For the types of crops; Step S13: Establish a matching function between crop types and sub-farm plots: ; in, is the mapping function between the soil condition parameters of the sub-farm plot and the ideal soil condition parameters of the crop, x is the mapping coefficient of the mapping function, d is the correction term, is the matching function; Step S14: Obtaining the mapping coefficient matrix between crop types and sub-farm plots X ; ; in, Soil condition parameters Parameters of ideal soil conditions b m The corresponding mapping coefficients; Step S15: Filter out the mapping coefficient matrix X The minimum value of each row of mapping coefficients is taken as the optimal matching coefficient to obtain the optimal mapping coefficient matrix ; ; in, For the m Optimal mapping coefficient values; Step S16: Calculate the comprehensive mapping coefficient F ; ; in, is the optimal mapping coefficient matrix Middle iOptimal mapping coefficients; Step S17: After obtaining the comprehensive mapping coefficients for each crop and sub-farm plot, a comprehensive mapping coefficient data set is obtained. , set the maximum threshold of the comprehensive mapping coefficient , construct a constraint function for matching crop types with sub-farm plots.

[0006] Furthermore, the constraint function is: ; in, Represents the comprehensive mapping coefficient data set When the minimum value is taken, the type of crop and the corresponding sub-farm plot number are output; is the comprehensive mapping coefficient data set Middle I The comprehensive mapping coefficients, I is the number of the comprehensive mapping coefficient.

[0007] Further, step S3 includes: Step S31: During the growth of crops, growth data characterizing the growth quality of crops are collected, and the quality coefficient of crop growth is evaluated based on the growth data. Z ; Step S32: Setting the standard value of the quality coefficient of crops after they grow and mature Z 标准 , and calculate the crops in different growth cycles T The standard value of the quality coefficient when Z T标准 ; ; in, t The growth cycle of crops matures. is the growth coefficient of different growth cycles; Step S33: Comparing quality coefficients Z With standard value , evaluate the quality of crop growth within sub-farm plots; when , then the crops grow well in the sub-farm plots and the sub-farm plot construction effect is good; when , the crops in the sub-farm plots grow slowly and the construction effect of the sub-farm plots is poor. The sub-farm plots need to be repaired again to ensure the growth quality of crops in the sub-farm plots.

[0008] Furthermore, the quality factor Z The calculation method is: ; in,L n For crops in different growth cycles T The first n Length of blade, n The number of the leaves on the main stem, U The number of leaves on the main stem, L 0 The ideal length for leaves growing on the main stem. The length of the leaves on the main stem after they are formed. G T For the growth cycle T The effective accumulated temperature of the blade is is the effective accumulated temperature when the leaves emerge, L 2 is the effective accumulated temperature coefficient, L 1 is the correction factor for calculating the ideal length of leaf growth, G 0 is the effective accumulated temperature required for crops from sowing to germination. G u The effective accumulated temperature required by different leaves of crops. W For crops during the growth cycle T The total amount of leaves u Number the leaves.

[0009] The beneficial effects of the present invention are as follows: the present invention ensures that suitable crop types and good farmland suitable for crop planting are screened out by matching farmland soil conditions with soil conditions required for crop planting through big data, reduces the cost of farmland improvement and repair, and can greatly increase crop yields, which is suitable for the concept of crop planting that is adapted to local conditions. The quality of crop growth in farmland is used to evaluate the quality of farmland construction, which is conducive to timely improving planting conditions during crop growth, reducing the impact on crop yields, and finely managing and scientifically deciding the quality of farmland construction, and can make full use of the spatial distribution difference characteristics and change trends of large-scale farmland construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 The flowchart of the information data processing method based on supervising farmland construction quality. DETAILED DESCRIPTION

[0011] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0012] like Figure 1As shown, an information data processing method based on supervising farmland construction quality includes: Step S1: Select a target area for farmland construction, divide sub-farmland blocks in the target area, and match sub-farmland blocks for crops based on soil condition parameters on the sub-farmland blocks and ideal soil condition parameters for growing crops; Step S1 specifically includes: Step S11: Select the target area for farmland construction, divide the target area into different sub-farmland blocks according to the crop planting types and corresponding planting areas, collect soil condition parameters for planting crops in each sub-farmland block, and form a soil condition data set , n is the number of soil condition parameters, For the n Soil condition parameters, N The number of the sub-farm plot.

[0013] For example, if it is planned to plant 10 kinds of crops in the target area, and the planting area of ​​the target area is 100 mu, the target area can be divided into 10 sub-farm plots, each with a planting area of ​​10 mu, and one sub-farm plot is used to plant one crop.

[0014] Under different terrain conditions in the target area, the split sub-farm plots are suitable for planting different types of crops. For example, the terminal farm plots located on the slope are more suitable for planting crops that like sunshine, are drought-resistant, and are conducive to resisting soil and water loss (such as wheat, sorghum, potatoes and other crops) because they have faster soil and water loss and sufficient sunlight compared to the sub-farm plots at the bottom of the slope. The sub-farm plots at the bottom of the slope have sufficient water and relatively less sunlight (more suitable for planting crops such as corn and soybeans).

[0015] In the process of high-quality farmland construction, soil is an important ecological foundation for the growth and development of crops. Crop growth requires soil to provide nutrients, retain water, provide support and stabilize the root system. Therefore, obtaining soil condition parameters is crucial for matching crop planting. Different crops have different requirements for soil types and nutrients; for example, acidic soil is suitable for growing spinach and strawberries, while alkaline soil is suitable for growing cauliflower and broccoli. Some crops prefer fertile soil, while some crops have lower requirements for fertility.

[0016] In the construction of high-quality farmland, matching the types of crops planted with soil condition parameters can help increase crop yields and greatly increase the scientific nature of high-quality farmland construction.

[0017] Step S12: Establishing a data set of ideal soil conditions that determine the yield of different types of crops , m is the number of ideal soil condition parameters, bm For the m Parameters of ideal soil conditions, M For the type of crop, soil condition parameters include pH, moisture, soil temperature, etc.; Step S13: Establish a matching function between crop types and sub-farm plots: ; in, is the mapping function between the soil condition parameters of the sub-farm plot and the ideal soil condition parameters of the crop, x is the mapping coefficient of the mapping function, d is the correction term, is a matching function; the data in the ideal soil condition data set is matched with the data in the soil condition data set through the matching function, and the output mapping coefficient represents the matching degree between the data. The larger the mapping coefficient, the smaller the matching degree, and vice versa; Step S14: Obtaining the mapping coefficient matrix between crop types and sub-farm plots X ; ; in, Soil condition parameters Parameters of ideal soil conditions b m The corresponding mapping coefficients; Step S15: Filter out the mapping coefficient matrix X The minimum value of each row of mapping coefficients is taken as the optimal matching coefficient to obtain the optimal mapping coefficient matrix ; ; in, For the m Optimal mapping coefficient values; Step S16: Calculate the comprehensive mapping coefficient F ; ; in, is the optimal mapping coefficient matrix Middle i Optimal mapping coefficients; Step S17: After obtaining the comprehensive mapping coefficients for each crop and sub-farm plot, a comprehensive mapping coefficient data set is obtained. , set the maximum threshold of the comprehensive mapping coefficient , construct the constraint function for matching crop types with sub-farm plots; ; in, Represents the comprehensive mapping coefficient data set When the minimum value is taken, the type of crop and the corresponding sub-farm plot number are output; is the comprehensive mapping coefficient data set Middle I The comprehensive mapping coefficients, I is the number of the comprehensive mapping coefficient.

[0018] Step S2: matching suitable sub-farm plots for planting for each crop according to the constraint function, and repairing and constructing the sub-farm plots. After the repairing and construction of the sub-farm plots are completed, the matching crops are planted; Step S3: During the growth of crops, growth data characterizing the growth quality of crops are collected, and the quality coefficient of crop growth is calculated. Based on the standard value of the quality coefficient after the crops grow and mature, the construction quality of the sub-farm plot is evaluated. Step S3 specifically includes: Step S31: During the growth of crops, growth data characterizing the growth quality of crops are collected, and the quality coefficient of crop growth is evaluated based on the growth data. Z ; Step S32: Setting the standard value of the quality coefficient of crops after they grow and mature Z 标准 , and calculate the crop growth rate in different growth cycles T The standard value of the quality coefficient when Z T标准 ; ; in, t The growth cycle of crops matures. is the growth coefficient of different growth cycles; Step S33: Comparing quality coefficients Z With standard value , evaluate the quality of crop growth within sub-farm plots; when , indicating that the growth state of crops is good, then crops grow well in the sub-farm plots, and the construction effect of the sub-farm plots is good; when , indicating that the growth state of crops is poor, the crops grow slowly in the sub-farm plots, and the construction effect of the sub-farm plots is poor. The sub-farm plots need to be repaired again to ensure the growth quality of crops in the sub-farm plots.

[0019] The present invention matches farmland soil conditions with soil conditions required for crop planting through big data, ensures that suitable crop types and good farmland suitable for crop planting are screened out, reduces the cost of farmland improvement and repair, and can greatly increase crop yields, which is suitable for the concept of crop planting that adapts to local conditions. The quality of crop growth in farmland is used to evaluate the quality of farmland construction, which is conducive to timely improving planting conditions during crop growth, reducing the impact on crop yields, and fine management and scientific decision-making of farmland construction quality, which can make full use of the spatial distribution difference characteristics and change trends of large-scale farmland construction.

Claims

1. An information data processing method based on supervising farmland construction quality, characterized in that: include: Step S1: Select a target area for farmland construction, divide the target area into sub-farmland blocks, and construct a constraint function matching the crop types with the sub-farmland blocks based on the soil condition parameters on the sub-farmland blocks and the ideal soil condition parameters for growing crops; Step S2: matching suitable sub-farm plots for planting for each crop according to the constraint function, and repairing and constructing the sub-farm plots. After the repairing and construction of the sub-farm plots are completed, the matching crops are planted; Step S3: During the growth of crops, growth data characterizing the growth quality of crops are collected, and the quality coefficient of crop growth is calculated. Based on the standard value of the quality coefficient after the crops grow to maturity, the construction quality of the sub-farm plots is evaluated.

2. The information data processing method based on supervising farmland construction quality according to claim 1 is characterized in that: The step S1 comprises: Step S11: Select the target area for farmland construction, divide the target area into different sub-farmland blocks according to the crop planting types and corresponding planting areas, collect soil condition parameters for planting crops in each sub-farmland block, and form a soil condition data set , n is the number of soil condition parameters, For the n Soil condition parameters, N The number of the sub-farm plot; Step S12: Establishing a data set of ideal soil conditions that determine the yield of different types of crops , m is the number of ideal soil condition parameters, b m For the m Parameters of ideal soil conditions, M For the types of crops; Step S13: Establish a matching function between crop types and sub-farm plots: ； in, is the mapping function between the soil condition parameters of the sub-farm plot and the ideal soil condition parameters of the crop, x is the mapping coefficient of the mapping function, d is the correction term, is the matching function; Step S14: Obtaining the mapping coefficient matrix between crop types and sub-farm plots X ; ； in, Soil condition parameters Parameters of ideal soil conditions b m The corresponding mapping coefficients; Step S15: Filter out the mapping coefficient matrix X The minimum value of each row of mapping coefficients is taken as the optimal matching coefficient to obtain the optimal mapping coefficient matrix ; ； in, For the m Optimal mapping coefficient values; Step S16: Calculate the comprehensive mapping coefficient F ; ； in, is the optimal mapping coefficient matrix Middle i Optimal mapping coefficients; Step S17: After obtaining the comprehensive mapping coefficients for each crop and sub-farm plot, a comprehensive mapping coefficient data set is obtained. , set the maximum threshold of the comprehensive mapping coefficient , construct a constraint function for matching crop types with sub-farm plots.

3. The information data processing method based on supervising farmland construction quality according to claim 2 is characterized in that: The constraint function is: ； in, Represents the comprehensive mapping coefficient data set When the minimum value is taken, the type of crop and the corresponding sub-farm plot number are output; is the comprehensive mapping coefficient data set Middle I The comprehensive mapping coefficients, I is the number of the comprehensive mapping coefficient.

4. The information data processing method based on supervising farmland construction quality according to claim 1 is characterized in that: The step S3 comprises: Step S31: During the growth of crops, growth data characterizing the growth quality of crops are collected, and the quality coefficient of crop growth is evaluated based on the growth data. Z ; Step S32: Setting the standard value of the quality coefficient of crops after they grow and mature Z 标准 , and calculate the crops in different growth cycles T The standard value of the quality coefficient when Z T标准 ; ； in, t The growth cycle of crops matures. is the growth coefficient of different growth cycles; Step S33: Comparing quality coefficients Z With standard value , evaluate the quality of crop growth within sub-farm plots; when , then the crops grow well in the sub-farm plots and the sub-farm plot construction effect is good; when , the crops in the sub-farm plots grow slowly and the construction effect of the sub-farm plots is poor. The sub-farm plots need to be repaired again to ensure the growth quality of crops in the sub-farm plots.

5. The information data processing method based on supervising farmland construction quality according to claim 4 is characterized in that: The quality factor Z The calculation method is: ； in, L n For crops in different growth cycles T The first n Length of blade, n The number of the leaves on the main stem, U The number of leaves on the main stem, L 0 The ideal length for leaves growing on the main stem. The length of the leaves on the main stem after they are formed. G T For the growth cycle T The effective accumulated temperature of the blade is is the effective accumulated temperature when the leaves emerge, L 2 is the effective accumulated temperature coefficient, L 1 is the correction factor for calculating the ideal length of leaf growth, G 0 is the effective accumulated temperature required for crops from sowing to germination. G u The effective accumulated temperature required by different leaves of crops. W For crops during the growth cycle T The total amount of leaves u Number the leaves.

Images