CN109102131A - A kind of optimization method of the Soybean production based on big data - Google Patents
A kind of optimization method of the Soybean production based on big data Download PDFInfo
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- 235000010469 Glycine max Nutrition 0.000 title claims abstract description 34
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 21
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- 239000003337 fertilizer Substances 0.000 claims abstract description 26
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- 239000000463 material Substances 0.000 claims abstract description 8
- 230000007115 recruitment Effects 0.000 claims description 12
- 239000000575 pesticide Substances 0.000 claims description 5
- 238000005553 drilling Methods 0.000 claims description 4
- 210000003608 fece Anatomy 0.000 claims description 3
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Abstract
The invention discloses a kind of optimization methods of Soybean production based on big data.This method includes obtaining the index for influencing soybean material investment, expense investment and chemical fertilizer investment；Multicollinearity index, the shortage of data index in the index are removed, screening index is obtained；Corresponding index is inquired according to the screening index；Every kind of index is taken by column and tires out the multiplied relative variation relatively downpayment to each year；It is related to the input amount of price, cost, expense, profit and income according to relative variation calculating；The index is screened using Lasso regression model, obtains cake compressibility；Soybean varieties are calculated in the investment per unit area yield in the whole nation according to the cake compressibility.The quantization situation for influencing all kinds of each influence factors of soybean per unit area yield is obtained using Lasso analysis of regression model, can be optimized production and is obtained more scientific and reasonable soybean and put into production mode.
Description
Technical field
The present invention relates to agricultural productions, more particularly to a kind of optimization method of Soybean production based on big data.
Background technique
With the rapid development of the information technologies such as Internet of Things, mobile interchange, geography information, agricultural shows information outburst
Trend, agricultural, which is just striding, marches toward the epoch of big data.The theory and technology of big data had both brought opportunity or had brought challenge.
China is the first in the world multi-form agriculture producing country, and the resource bearing sustainability of huge yield behind is difficult, excessively
The rough sexual development mode that ground relies on high material-consumption high investment is unsustainable.Chinese agriculture practitioner 2.7 hundred million, but labor productivity
Only the 64% of the world；0.63 hundred million hectares of effective irrigation area, but farmland irrigating water's effective utilization coefficients are only 0.52, are far below
The level of developed country 0.7-0.8 is sprayed insecticide 1,800,000 tons, but utilization rate is only 35%, 10 percentages lower than developed country
Point, 59,000,000 tons of the pure amount of chemical fertilizer application, but comprehensive utilization ratio is probably 30% or so；10.7 hundreds of millions watts of total Power of Agricultural Machinery, agriculture
Crop cultivation receives comprehensive mechanical level and reaches 60%, advance in agricultural science and technology contribution rate 55.6%.The agricultural output of China is promoted
Rely primarily on the increase of chemical fertilizer, pesticide, labour's input.
In agricultural production process, the investment factor of production can be divided into two major classes: 1, required purchase in agricultural production process
The production factors bought and rented, such as: fertilizer amount, pesticide dosage, agricultural film dosage；2, agricultural workforce puts into.
China's agricultural production results in pesticide and chemical fertilizer since pesticide and chemical fertilizer input amount are excessively high and investment output value rate is too low
The raising of input amount can not bring increase and our the main soybean producing region chemical fertilizer, agriculture of agricultural product yield per unit area
The main reason for medicine input and output elasticity is negative.And labour's output value rate in China's overwhelming majority area is high, labour's investment
Raising still is able to bring dramatically increasing for soybean yield per unit area, needs the further analysis to agriculture big data.
Be frequently encountered high-dimensional data in the analysis of agriculture big data, the variable space dimension of high dimensional data compared with
Height, but the dimension of data sample is not high, very few sample will lead to overfitting problem, and the scope of application of model is poor, especially
It is the underdetermined problem that will appear model solution when sample size is less than variable number.
Summary of the invention
The object of the present invention is to provide excessively quasi- caused by a kind of very few sample being able to solve during big data analysis
The optimization method of the Soybean production based on big data of conjunction problem.
To achieve the above object, the present invention provides following schemes:
A kind of optimization method of the Soybean production based on big data, the optimization method include:
Obtain the index for influencing soybean material investment, expense investment and chemical fertilizer investment；
Multicollinearity index, the shortage of data index in the index are removed, screening index is obtained；
Corresponding index is inquired according to the screening index；
Every kind of index is taken by column and tires out the multiplied relative variation relatively downpayment to each year；
It is related to the input amount of price, cost, expense, profit and income according to relative variation calculating；
The index is screened using Lasso regression model, obtains cake compressibility；
Soybean varieties are calculated in the investment per unit area yield in the whole nation according to the cake compressibility.
Optionally, the function of the Lasso regression model is
Wherein, w is the coefficient vector of regression equation, and X is the index, and YI is observation vector, and α is for independent variable
Number, m are the optimal index number, and N indicates the sum of the index, l1For regular terms.
Optionally, the investment per unit area yield according to cake compressibility calculating soybean varieties in the whole nation specifically includes:
The investment per unit area yield in the whole nation
Wherein,Cost-benefit situation factor vector,Expense and recruitment situation factor vector,To change
Learn fertilizers input factor vector, β1 YIFor the coefficient vector of the corresponding regression equation of cost-benefit situation factor, β2 YIFor expense and
The coefficient vector of the corresponding regression equation of recruitment situation factor, β3 YIIt is for what chemical fertilizer put into the corresponding regression equation of factor
Number vector.
Optionally, the screening index refers specifically to: major product yield per acre, and the output value is total per acre, per acre totle drilling cost, per acre
Net profit, per acre out-of-pocket cost, per acre cash earnings, every 50 kilograms of major products are averaged commercial value, and every 50 kilograms of major products are total
Cost, every 50 kilograms of major product net profits, every 50 kilograms of major product out-of-pocket costs, every 50 kilograms of major product cash earnings, per acre
Recruitment quantity, major product sells quantity per acre, and major product sells the output value, commodity rate per acre, and cost per mu is paid outside, seed,
Chemical fertilizer takes, and farmyard manure takes, and agricultural chemicals expense, agricultural film takes, and leases operation cost, and fuels and energy is taken, and technical service fee, tool materials take, and repair
Maintenance expense, indirect expense are managed, family's recruitment is converted into money, employment expense, per acre seed dosage, per acre agricultural film dosage, and chemical fertilizer is used per acre
It measures, per acre the chemical fertilizer amount of money.
Optionally, before the index using the screening of Lasso regression model further include: return the index
One change processing.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: the invention discloses one kind
The optimization method of Soybean production based on big data.Obtain the index for influencing soybean material investment, expense investment and chemical fertilizer investment；
Multicollinearity index, the shortage of data index in the index are removed, screening index is obtained；It is inquired according to the screening index
Corresponding index；Every kind of index is taken by column and tires out the multiplied relative variation relatively downpayment to each year；According to described
Relative variation calculating is related to the input amount of price, cost, expense, profit and income；Using described in the screening of Lasso regression model
Index obtains cake compressibility；Soybean varieties are calculated in the investment per unit area yield in the whole nation according to the cake compressibility.It is returned using Lasso
Model analysis obtains the quantization situation for influencing all kinds of each influence factors of soybean per unit area yield, and it is more scientific and reasonable can to optimize production acquisition
Soybean put into production mode.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the flow chart of the optimization method of the Soybean production provided by the invention based on big data.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The object of the present invention is to provide excessively quasi- caused by a kind of very few sample being able to solve during big data analysis
The optimization method of the Soybean production based on big data of conjunction problem.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
As shown in Figure 1, a kind of optimization method of the Soybean production based on big data, the optimization method include:
Step 100: obtaining the index for influencing soybean material investment, expense investment and chemical fertilizer investment；
Step 200: removing multicollinearity index, the shortage of data index in the index, obtain screening index；
Step 300: corresponding index is inquired according to the screening index；
Step 400: every kind of index being taken by column and tires out the multiplied relative variation relatively downpayment to each year；
Step 500: the input amount of price, cost, expense, profit and income is related to according to relative variation calculating；
Step 600: the index being screened using Lasso regression model, obtains cake compressibility；
Step 700: soybean varieties are calculated in the investment per unit area yield in the whole nation according to the cake compressibility.
The function of the Lasso regression model is
Wherein, w is the coefficient vector of regression equation, and X is the index, and YI is observation vector, and α is for independent variable
Number, m are the optimal index number, and N indicates the sum of the index, l1For regular terms.
Optionally, the investment per unit area yield according to cake compressibility calculating soybean varieties in the whole nation specifically includes:
The investment per unit area yield in the whole nation
Wherein,Cost-benefit situation factor vector,Expense and recruitment situation factor vector,To change
Learn fertilizers input factor vector, β1 YIFor the coefficient vector of the corresponding regression equation of cost-benefit situation factor, β2 YIFor expense and
The coefficient vector of the corresponding regression equation of recruitment situation factor, β3 YIIt is for what chemical fertilizer put into the corresponding regression equation of factor
Number vector.
Optionally, the screening index refers specifically to: major product yield per acre, and the output value is total per acre, per acre totle drilling cost, per acre
Net profit, per acre out-of-pocket cost, per acre cash earnings, every 50 kilograms of major products are averaged commercial value, and every 50 kilograms of major products are total
Cost, every 50 kilograms of major product net profits, every 50 kilograms of major product out-of-pocket costs, every 50 kilograms of major product cash earnings, per acre
Recruitment quantity, major product sells quantity per acre, and major product sells the output value, commodity rate per acre, and cost per mu is paid outside, seed,
Chemical fertilizer takes, and farmyard manure takes, and agricultural chemicals expense, agricultural film takes, and leases operation cost, and fuels and energy is taken, and technical service fee, tool materials take, and repair
Maintenance expense, indirect expense are managed, family's recruitment is converted into money, employment expense, per acre seed dosage, per acre agricultural film dosage, and chemical fertilizer is used per acre
It measures, per acre the chemical fertilizer amount of money.
Optionally, before the index using the screening of Lasso regression model further include: return the index
One change processing, the inconsistent bring of removal dimension influence.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (5)
1. a kind of optimization method of the Soybean production based on big data, which is characterized in that the optimization method includes:
Obtain the index for influencing soybean material investment, expense investment and chemical fertilizer investment；
Multicollinearity index, the shortage of data index in the index are removed, screening index is obtained；
Corresponding index is inquired according to the screening index；
Every kind of index is taken by column and tires out the multiplied relative variation relatively downpayment to each year；
It is related to the input amount of price, cost, expense, profit and income according to relative variation calculating；
The index is screened using Lasso regression model, obtains cake compressibility；
Soybean varieties are calculated in the investment per unit area yield in the whole nation according to the cake compressibility.
2. a kind of optimization method of Soybean production based on big data according to claim 1, which is characterized in that described
The function of Lasso regression model is
Wherein, w is the coefficient vector of regression equation, and X is the index, and YI is observation vector, and α is the coefficient of independent variable, and m is
The optimal index number, N indicate the sum of the index, l1For regular terms.
3. a kind of optimization method of Soybean production based on big data according to claim 1, which is characterized in that described
Investment per unit area yield of the soybean varieties in the whole nation is calculated according to the cake compressibility to specifically include:
The investment per unit area yield in the whole nation
Wherein,Cost-benefit situation factor vector,Expense and recruitment situation factor vector,For chemical fertilizer
Investment factor vector, β1 YIFor the coefficient vector of the corresponding regression equation of cost-benefit situation factor, β2 YIFor expense and recruitment feelings
The coefficient vector of the corresponding regression equation of condition factor, β3 YIThe coefficient vector of the corresponding regression equation of factor is put into for chemical fertilizer.
4. a kind of optimization method of Soybean production based on big data according to claim 1, which is characterized in that the sieve
Select index to refer specifically to: major product yield per acre, the output value is total per acre, per acre totle drilling cost, per acre net profit, per acre out-of-pocket cost,
Cash earnings per acre, every 50 kilograms of major products are averaged commercial value, every 50 kilograms of major product totle drilling costs, and every 50 kilograms of major products are net
Profit, every 50 kilograms of major product out-of-pocket costs, every 50 kilograms of major product cash earnings, recruitment quantity per acre, major product goes out per acre
Quantity is sold, major product sells the output value, commodity rate per acre, and cost per mu is paid outside, and seed, chemical fertilizer takes, and farmyard manure takes, pesticide
Take, agricultural film takes, and leases operation cost, and fuels and energy is taken, and technical service fee, tool materials take, and repairs maintenance expense, indirect expense, family
Front yard recruitment is converted into money, employment expense, per acre seed dosage, per acre agricultural film dosage, per acre fertilizer amount, per acre the chemical fertilizer amount of money.
5. a kind of optimization method of Soybean production based on big data according to claim 1, which is characterized in that described
Before the Lasso regression model screening index further include:
The index is normalized.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112580840A (en) * | 2019-09-27 | 2021-03-30 | 北京国双科技有限公司 | Data analysis method and device |
| CN113222294A (en) * | 2021-06-07 | 2021-08-06 | 中国农业科学院农业信息研究所 | Soybean input yield prediction method and system |
| CN114707283A (en) * | 2022-04-02 | 2022-07-05 | 中铁电气化铁路运营管理有限公司 | Grounding grid corrosion diagnosis method based on Lasso theory |
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| US20090234695A1 (en) * | 2007-10-16 | 2009-09-17 | Kapadi Mangesh D | System and method for harvesting scheduling, planting scheduling and capacity expansion |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580840A (en) * | 2019-09-27 | 2021-03-30 | 北京国双科技有限公司 | Data analysis method and device |
| CN113222294A (en) * | 2021-06-07 | 2021-08-06 | 中国农业科学院农业信息研究所 | Soybean input yield prediction method and system |
| CN113222294B (en) * | 2021-06-07 | 2023-09-26 | 中国农业科学院农业信息研究所 | Soybean input unit yield prediction method and system |
| CN114707283A (en) * | 2022-04-02 | 2022-07-05 | 中铁电气化铁路运营管理有限公司 | Grounding grid corrosion diagnosis method based on Lasso theory |
| CN114707283B (en) * | 2022-04-02 | 2022-10-21 | 中铁电气化铁路运营管理有限公司 | Grounding grid corrosion diagnosis method based on Lasso theory |
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