CN104778328B - No-tillage seeding machine earth pack mode selecting method - Google Patents
No-tillage seeding machine earth pack mode selecting method Download PDFInfo
- Publication number
- CN104778328B CN104778328B CN201510197056.5A CN201510197056A CN104778328B CN 104778328 B CN104778328 B CN 104778328B CN 201510197056 A CN201510197056 A CN 201510197056A CN 104778328 B CN104778328 B CN 104778328B
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- msup
- determined
- soil
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000003971 tillage Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000010899 nucleation Methods 0.000 title abstract description 14
- 239000002689 soil Substances 0.000 claims abstract description 101
- 238000011156 evaluation Methods 0.000 claims abstract description 47
- 230000001629 suppression Effects 0.000 claims abstract description 39
- 238000003825 pressing Methods 0.000 claims abstract description 9
- 238000005056 compaction Methods 0.000 claims description 10
- 239000002131 composite material Substances 0.000 claims description 7
- 238000005315 distribution function Methods 0.000 claims description 7
- 238000002360 preparation method Methods 0.000 claims description 7
- 238000010187 selection method Methods 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 6
- 239000002245 particle Substances 0.000 claims description 6
- 230000005477 standard model Effects 0.000 claims description 6
- 239000013598 vector Substances 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 abstract description 5
- 238000013459 approach Methods 0.000 abstract description 2
- 238000003745 diagnosis Methods 0.000 abstract 1
- 238000009313 farming Methods 0.000 description 4
- 241000209140 Triticum Species 0.000 description 3
- 235000021307 Triticum Nutrition 0.000 description 3
- 238000005094 computer simulation Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 241000209149 Zea Species 0.000 description 2
- 235000005824 Zea mays ssp. parviglumis Nutrition 0.000 description 2
- 235000002017 Zea mays subsp mays Nutrition 0.000 description 2
- 235000005822 corn Nutrition 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003909 pattern recognition Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 235000008331 Pinus X rigitaeda Nutrition 0.000 description 1
- 235000011613 Pinus brutia Nutrition 0.000 description 1
- 241000018646 Pinus brutia Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 230000004720 fertilization Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000000643 oven drying Methods 0.000 description 1
- 230000007226 seed germination Effects 0.000 description 1
- 239000002688 soil aggregate Substances 0.000 description 1
- 238000009331 sowing Methods 0.000 description 1
- 230000003746 surface roughness Effects 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Pretreatment Of Seeds And Plants (AREA)
Abstract
A kind of no-tillage seeding machine earth pack mode selecting method, methods described include step：A, collect no-tillage seeding machine device for pressing soil information, have determined that suppression schematically block message and ground to be determined block message；B, according to no-tillage seeding machine device for pressing soil information determine device for pressing soil pattern prepare, according to have determined that suppression schematically block message determine evaluation index；C, the weights of each evaluation index are determined；D, suppression schematically block message and plot information architecture master pattern storehouse to be determined and sample pattern are had determined that using collection, it is determined that having determined that the earth pack pattern corresponding to suppression pattern plot with the ground to be determined block message approach degree highest；E, earth pack pattern of the earth pack pattern for selecting to determine in step D as the plot to be determined.Using the no-tillage seeding machine earth pack mode selecting method of the present invention, can the comprehensive evaluation model based on fuzzy diagnosis, select appropriate suppression pattern for plot to be determined.
Description
Technical Field
The invention relates to the field of agricultural automation, in particular to a soil compacting technology in an automatic seeding process.
Background
A no-tillage planter is commonly used in the large-scale crop planting process, and the no-tillage planter is a planting machine which replaces the original steps of manual sowing, fertilization, farming and the like, is completed in one step, and is particularly applied to wheat, corn and other economic farming suitable for large-scale planting. The no-tillage planter mainly comprises machines such as a wheat planter and a corn planter, and mainly aims to solve the problems of insufficient labor and labor cost in rural areas and enlarge the agricultural planting area and the mechanization level.
Soil suppression is the important link of crops no-tillage seeder field seeding process after the crops are sowed, and suitable suppression device can improve soil pine bits degree and earth's surface roughness, finely tunes the degree of depth difference of crops seed in soil, improves the seeding quality, reduces big space in the soil, reduces the evaporation of water, creates good condition for crops seed germination and emergence. At present, mainly design to suppression device structure itself among the prior art, like the patent: a composite press device (ZL201220035593.1) for a ridge-free combined wheat cultivator, a swinging self-cleaning press device (ZL201310404712) and the like. Although the number of compacting devices in the prior art is large, there is a lack of a method for selecting a suitable compacting mode in combination with soil conditions.
Disclosure of Invention
In view of the above, the present invention is directed to overcome the disadvantages of the prior art, and provide a method for constructing a soil compacting mode selection model of a crop no-tillage planter by determining soil conditions, and selecting a suitable compacting mode for the crop no-tillage planter by using the model based on the soil conditions of the area where the compacting mode is to be determined.
In order to achieve the purpose, the invention adopts the following technical scheme.
A soil suppression mode selection method of a no-tillage planter comprises the following steps:
A. collecting information of a soil compacting device of the no-tillage planter, land parcel information with a determined compacting mode and land parcel information to be determined;
B. determining mode preparation of a soil compacting device according to the information of the soil compacting device of the no-tillage planter, and determining an evaluation index according to the land parcel information of the determined compacting mode;
C. determining the weight of each evaluation index, comprising the following steps:
c1, standardizing each index data: there are m kinds of modes A1，A2…AmN evaluation indexes A* 1，A* 2，…，A* nWherein
A* j＝{A1j,A2j,…Amjwhere j is 1,2, …, n
The normalized value of each index data is C* 1，C* 2，…C* nWherein
wherein i is 1,2, …, m; j is 1,2, …, n
C2, determining the information entropy of each evaluation index as follows:
wherein,
c3, determining the weight of each evaluation index according to the information entropy of each evaluation index as follows:
wherein j is 1,2, …, n;
D. constructing a standard model library and a sample model by using the collected information of the land parcel with the determined compacting mode and the information of the land parcel to be determined, and determining a soil compacting mode corresponding to the land parcel with the determined compacting mode and the highest closeness of the information of the land parcel to be determined; the method comprises the following steps:
d1, determination of AijWherein A isijA fuzzy subset of jth evaluation indicators representing ith defined ballasting mode plots in sample A;
d2, in which B is ═ B1,B2,…Bj,…,Bn) For the land compacting mode and evaluation index to be determined, wherein BjA fuzzy subset for sample B with respect to the jth evaluation index;
Aijthe membership function of (a) is a symmetric normal fuzzy distribution function:
and BjThe membership function of (a) is a symmetric normal fuzzy distribution function:
d3 calculating a weighted closeness ofWherein,
the closeness of the generalized fuzzy vector is:
d4, according to closing to the principle, confirm with treat that confirm that the soil suppression mode that the most height is close to the piece information corresponds to the suppression mode piece is:
E. and D, selecting the soil compacting mode determined in the step D as the soil compacting mode of the land parcel to be determined.
Further, no-tillage seeder soil suppression device information is including the shape, the structure of suppression wheel and the pressure size when soil suppression. Further, the shape of the press wheel includes a cylindrical press wheel and a conical composite press wheel. Further, the evaluation index is derived from the soil type, soil compactness, soil mass size, soil particle distribution and moisture in the determined soil block with the compacting mode, which can be measured and counted and is related to the soil compacting mode selection.
By adopting the soil compacting mode selection method of the no-tillage planter, the following technical effects can be realized.
1. The method utilizes the soil compaction mode which is determined and the compaction mode of the crop plot with objective yield as a standard template to construct a model library of the soil compaction mode, selects a proper compaction mode for the soil plot to be determined, does not need a large amount of field tests, and can be realized through mathematical modeling and computer simulation in the whole process.
2. The method comprises the steps of determining decision-making land parcel information according to experimental data of land parcels with determined suppression modes, calculating evaluation index weights by using an entropy weight method, constructing a mode recognition comprehensive evaluation model based on fuzzy recognition, evaluating land parcels to be determined in suppression modes by observing and comparing approach values of the land parcels to be determined and the land parcels with the determined suppression modes, and determining soil suppression modes of the land parcels. The fuzzy pattern recognition comprehensive evaluation model can select a proper soil suppression mode for plots with different soil conditions.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for selecting a soil compacting mode of a no-tillage planter according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating the determination of each evaluation index weight value by the method for selecting the soil compacting mode of the no-tillage planter according to the embodiment of the present invention.
Fig. 3 is a flowchart illustrating a procedure of determining closeness of a soil compacting mode selection method of a no-tillage planter according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
Detailed exemplary embodiments are disclosed below. However, specific structural and functional details disclosed herein are merely for purposes of describing example embodiments.
It should be understood, however, that the intention is not to limit the invention to the particular exemplary embodiments disclosed, but to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure. Like reference numerals refer to like elements throughout the description of the figures.
It will also be understood that the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. It will be further understood that when an element or unit is referred to as being "connected" or "coupled" to another element or unit, it can be directly connected or coupled to the other element or unit or intervening elements or units may also be present. Moreover, other words used to describe the relationship between components or elements should be understood in the same manner (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.).
As shown in fig. 1, the embodiment of the present invention discloses a soil compacting mode selection method for a no-tillage planter, comprising the steps of:
A. collecting information of a soil compacting device of the no-tillage planter, land parcel information with a determined compacting mode and land parcel information to be determined;
B. determining mode preparation of a soil compacting device according to the information of the soil compacting device of the no-tillage planter, and determining an evaluation index according to the land parcel information of the determined compacting mode;
C. determining the weight of each evaluation index;
D. constructing a standard model library and a sample model by using the collected information of the land parcel with the determined compacting mode and the information of the land parcel to be determined, and determining a soil compacting mode corresponding to the land parcel with the determined compacting mode and the highest closeness of the information of the land parcel to be determined;
E. and D, selecting the soil compacting mode determined in the step D as the soil compacting mode of the land parcel to be determined.
Therefore, by using the method for selecting the soil suppression mode of the no-tillage planter, the standard model library of the soil suppression mode can be constructed by using the determined soil suppression mode and the suppression mode of the crop plot with objective yield, a proper suppression mode is selected for the plot to be determined, a large amount of field tests are not needed, and the whole process can be realized through mathematical modeling and computer simulation.
In the above embodiments, the no-tillage planter soil press device information for determining mode preparation of the soil press device may include various attributes of the press device, for example, in a more specific embodiment, the no-tillage planter soil press device information includes the shape, structure, and soil press pressure magnitude of the press wheel.
In particular, the shape of the press wheel includes a cylindrical press wheel and a conical composite press wheel. Of course, the invention is also applicable in the case of press wheels of other shapes.
In the above embodiments, the evaluation index is derived from various performance indexes that are measurable statistically in the land parcel with the determined compacting mode and are related to the soil compacting mode selection, for example, in one embodiment, the evaluation index includes soil type, soil compactness, soil size, soil particle distribution, moisture, and the like.
In a specific embodiment, the method for determining the weight of each evaluation indicator is an entropy weight method, and includes the following steps:
c1, standardizing each index data: there are m kinds of modes A1，A2…AmN evaluation indexes A* 1，A* 2，…，A* nWherein
A*j＝{A1j,A2j,…Amj}(j＝1,2,…,n)，
the normalized value of each index data is C* 1，C* 2，…C* nWherein
c2, determining the information entropy of each evaluation index as follows:
wherein,
c3, determining the weight of each evaluation index according to the information entropy of each evaluation index as follows:
in addition, the step of constructing the standard model library and the sample model comprises the following steps:
d1, determination of AijWherein A isijFuzzy of j-th evaluation index of ith type of land blocks with determined compacting mode in sample AA subset;
d2, in which B is ═ B1,B2,…Bj,…,Bn) For the land compacting mode and evaluation index of the farming mode to be determined, wherein BjA fuzzy subset of sample B with respect to the jth evaluation index.
For fuzzy sets, the membership functions may be different distribution functions, e.g., in one embodiment, AijThe membership function of (a) is a symmetric normal fuzzy distribution function:
and BjThe membership function of (a) is a symmetric normal fuzzy distribution function:
correspondingly, the step of determining the soil compaction mode corresponding to the determined compaction mode land parcel with the highest proximity of the information of the land parcel to be determined comprises the following steps:
d3 calculating a weighted closeness ofWherein,
the closeness between the generalized blur vectors is:
wherein a isij,bij,σij,aj,bj,σjAnd C, obtaining the weight value by a statistical method, and determining the weight value according to the step C.
D4, according to closing to the principle, confirm with treat that confirm that the most soil suppression mode that corresponds of the parcel of parcel information closeness degree has already confirmed the suppression mode parcel:
the technical effects of the present invention will be described below with reference to a specific example.
Firstly, according to the information of the soil pressing device of the no-tillage planter, determining the mode preparation of the soil pressing device, specifically comprising:
① cylindrical press wheel, no-tillage seeding +0.2kg/cm2Compacting;
② cylindrical press wheel, no-tillage seeding +0.3kg/cm2Compacting;
③ cylindrical press wheel, no-tillage seeding +0.4kg/cm2Compacting;
④ conical composite press wheel for no-tillage seeding and 0.2kg/cm2Compacting;
⑤ conical composite press wheel for no-tillage seeding and 0.3kg/cm2Compacting;
○ 6 conical composite press wheel for no-tillage seeding and 0.4kg/cm2And (7) compacting.
The principle of the method is that the similarity between the land parcel with the suppression mode to be determined and the land parcels with the 6 determined suppression modes is respectively calculated, so that a reasonable suppression mode is selected. Soil information data of the land parcel with the determined suppression mode and the land parcel to be determined, including soil type, soil compactness, soil parcel size, soil particle distribution, moisture and the like, are determined before mathematical modeling and computer simulation. The land parcel with the determined suppression mode can be determined through measured experimental data, and soil data of the land parcel to be measured are measured by the following method: the soil type is determined by an observation and analysis method, the soil compactness is measured by a soil compactness meter, the size of a soil block is measured by a measurement and statistics method, the distribution of soil particles is measured by a constant-temperature soil aggregate analyzer, and the water content is measured by a constant-temperature oven drying method.
The model preparation is derived from the model class of the land mass for which the ballast model has been determined, and the evaluation index is derived from soil information of measurable statistics related to decision making. In one embodiment of the present invention, the number of mode preparations is six, that is, six land parcels (m ═ 6) with the determined compacting mode are provided. The number of the evaluation indexes is five (n is 5), and the five evaluation indexes are respectively soil type, soil compactness, soil block size, soil particle distribution and water content.
Next, the weight of each evaluation index is determined according to the entropy weight method.
The index data is normalized, and the index data before normalization is shown in the following table:
wherein there are 6 modes prepared A1，A2…A65 evaluation indexes A* 1，A* 2，…，A* 5。
The normalized value of each index data is C* 1，C* 2，…C* 5Wherein
therefore, the normalized index data is shown in the following table:
next, the information entropy of each evaluation index was calculated as shown in the following table:
wherein
And is
Thus, the weight of each evaluation index is determined as follows:
wherein
In step D1, a library of standard models is constructed as:
wherein A isijAnd showing fuzzy subsets corresponding to j-th evaluation indexes of the ith type of land block compacting mode with the determined compacting mode.
In addition, the sample model was constructed as follows: by changing B to (B)1,B2,…Bj,…,B5) For the land compacting mode and evaluation index of the farming mode to be determined, wherein BjA fuzzy subset of sample B with respect to the jth evaluation index.
Thus, a closeness table can be listed as follows:
wherein
Therefore, the selection problem of the soil compacting device of the crop no-tillage planter is reduced to the problem of the closeness of the generalized fuzzy matrix.
Are respectively according to AijMembership function of (a):
and BjMembership function of (a):
to determine closeness as:
and substituting the weight determined according to the step C to determine the weighted closeness as follows:
in the above calculation, the skilled person can obtain the parameter a by statistical methodsij,bij,σij,aj,bj,σjThe detailed values of (a) are not described in detail in this specification.
Thus according to the principle of selection:
thereby selecting the soil compacting mode A closest to the land mass B to be determinedk。
Therefore, the method determines decision-making land parcel information according to experimental data of the land parcels with the determined suppression mode, calculates evaluation index weight by using an entropy weight method, constructs a mode recognition comprehensive evaluation model based on fuzzy recognition, evaluates the land parcels with the determined suppression mode and determines the soil suppression mode of the land parcels by observing and comparing the close values of the land parcels to be determined and the land parcels with the determined suppression mode. The fuzzy pattern recognition comprehensive evaluation model can select a proper soil suppression mode for plots with different soil conditions.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the scope of the present invention, and any minor changes and modifications to the present invention are within the scope of the present invention without departing from the spirit of the present invention.
Claims (4)
1. A soil suppression mode selection method of a no-tillage planter comprises the following steps:
A. collecting information of a soil compacting device of the no-tillage planter, land parcel information with a determined compacting mode and land parcel information to be determined;
B. determining mode preparation of a soil compacting device according to the information of the soil compacting device of the no-tillage planter, and determining an evaluation index according to the land parcel information of the determined compacting mode;
C. determining the weight of each evaluation index, comprising the following steps:
c1 for each index numberAccording to the standardization: there are m kinds of modes A1，A2…AmN evaluation indexes A* 1，A* 2，…，A* nWherein
A* j＝{A1j,A2j,…Amjwhere j is 1,2, …, n
The normalized value of each index data is C* 1，C* 2，…C* nWherein
wherein i is 1,2, …, m; j is 1,2, …, n
C2, determining the information entropy of each evaluation index as follows:
<mrow> <msub> <mi>E</mi> <mi>j</mi> </msub> <mo>=</mo> <mo>-</mo> <mi>l</mi> <mi>n</mi> <msup> <mrow> <mo>(</mo> <mi>m</mi> <mo>)</mo> </mrow> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&CenterDot;</mo> <mi>ln</mi> <mi> </mi> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>,</mo> </mrow>
wherein,
<mrow> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>/</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>C</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>;</mo> </mrow>
c3, determining the weight of each evaluation index according to the information entropy of each evaluation index as follows:
wherein j is 1,2, …, n;
D. constructing a standard model library and a sample model by using the collected information of the land parcel with the determined compacting mode and the information of the land parcel to be determined, and determining a soil compacting mode corresponding to the land parcel with the determined compacting mode and the highest closeness of the information of the land parcel to be determined; the method comprises the following steps:
d1, determination of AijWherein A isijA fuzzy subset of jth evaluation indicators representing ith defined ballasting mode plots in sample A;
d2, in which B is ═ B1,B2,…Bj,…,Bn) For the land compacting mode and evaluation index to be determined, wherein BjA fuzzy subset for sample B with respect to the jth evaluation index;
Aijthe membership function of (a) is a symmetric normal fuzzy distribution function:
<mrow> <msub> <mi>A</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = "}"> <mtable> <mtr> <mtd> <mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> <msub> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msup> <mo>,</mo> <mi>x</mi> <mo><</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&le;</mo> <mi>x</mi> <mo>&le;</mo> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> <msub> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msup> <mo>,</mo> <mi>x</mi> <mo>></mo> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
and BjThe membership function of (a) is a symmetric normal fuzzy distribution function:
<mrow> <msub> <mi>B</mi> <mi>j</mi> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = "}"> <mtable> <mtr> <mtd> <mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> <msub> <mi>&sigma;</mi> <mi>j</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msup> <mo>,</mo> <mi>x</mi> <mo><</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> <mo>;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> <mo>&le;</mo> <mi>x</mi> <mo>&le;</mo> <msub> <mi>b</mi> <mi>j</mi> </msub> <mo>;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>b</mi> <mi>j</mi> </msub> </mrow> <msub> <mi>&sigma;</mi> <mi>j</mi> </msub> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msup> <mo>,</mo> <mi>x</mi> <mo>></mo> <msub> <mi>b</mi> <mi>j</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
d3 calculating a weighted closeness ofWherein,
the closeness of the generalized fuzzy vector is:
<mrow> <mi>&sigma;</mi> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>,</mo> <msub> <mi>B</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>&lsqb;</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> <mrow> <msub> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&sigma;</mi> <mi>j</mi> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msup> <mo>+</mo> <mn>1</mn> <mo>&rsqb;</mo> <mo>,</mo> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mo><</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <msub> <mi>a</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>&le;</mo> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mo>&le;</mo> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mn>2</mn> </mfrac> <mo>&lsqb;</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msup> <mrow> <mo>(</mo> <mfrac> <mrow> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>b</mi> <mi>j</mi> </msub> </mrow> <mrow> <msub> <mi>&sigma;</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>&sigma;</mi> <mi>j</mi> </msub> </mrow> </mfrac> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msup> <mo>+</mo> <mn>1</mn> <mo>&rsqb;</mo> <mo>,</mo> <mover> <mi>x</mi> <mo>&OverBar;</mo> </mover> <mo>></mo> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow>
d4, according to closing to the principle, confirm with treat that confirm that the soil suppression mode that the most height is close to the piece information corresponds to the suppression mode piece is:
E. and D, selecting the soil compacting mode determined in the step D as the soil compacting mode of the land parcel to be determined.
2. The method for selecting a soil pressing mode of a no-tillage planter according to claim 1, wherein the no-tillage planter soil pressing device information includes a shape and a structure of the pressing wheel and a magnitude of a pressure at the time of soil pressing.
3. The no-till planter soil press mode selection method as in claim 2, wherein the shape of the press wheels comprises cylindrical press wheels and conical composite press wheels.
4. The method of selecting a soil compaction mode for a no-till planter according to claim 1 wherein the evaluation criteria is derived from soil type, soil compaction, soil mass size, soil particle distribution, moisture, measurable statistics in the determined compaction mode plot and associated with soil compaction mode selection.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510197056.5A CN104778328B (en) | 2015-04-23 | 2015-04-23 | No-tillage seeding machine earth pack mode selecting method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510197056.5A CN104778328B (en) | 2015-04-23 | 2015-04-23 | No-tillage seeding machine earth pack mode selecting method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104778328A CN104778328A (en) | 2015-07-15 |
| CN104778328B true CN104778328B (en) | 2018-02-16 |
Family
ID=53619789
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510197056.5A Expired - Fee Related CN104778328B (en) | 2015-04-23 | 2015-04-23 | No-tillage seeding machine earth pack mode selecting method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104778328B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110214498B (en) * | 2019-07-26 | 2024-04-05 | 山东农业大学 | Compacting device and method for corn planter |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201197266Y (en) * | 2008-04-23 | 2009-02-25 | 郝德有 | Device for pressing soil |
| CN101430735B (en) * | 2008-11-13 | 2011-09-21 | 中国农业大学 | Protective farming mode selection method |
| CN102073869A (en) * | 2010-12-27 | 2011-05-25 | 中国农业大学 | Point location data and remote sensing image data-based regional farmland quality monitoring method |
-
2015
- 2015-04-23 CN CN201510197056.5A patent/CN104778328B/en not_active Expired - Fee Related
Also Published As
| Publication number | Publication date |
|---|---|
| CN104778328A (en) | 2015-07-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gili et al. | Comparison of three methods for delineating management zones for site-specific crop management | |
| Aggarwal | Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications | |
| Liu et al. | Numerical simulation of Fertilizer Shunt-Plate with uniformity based on EDEM software | |
| CN107256451A (en) | Agriculture suitability evaluation analysis method of the agriculture big data based on GIS | |
| CN105740759A (en) | Middle-season rice information decision tree classification method based on multi-temporal data feature extraction | |
| CN106547948A (en) | The integrated evaluating method of the afforestation dungarunga growth adaptability for introducing a fine variety | |
| Fan et al. | Agro-ecological suitability assessment of Chinese Medicinal Yam under future climate change | |
| Zhang et al. | Simulating soybean productivity under rainfed conditions for major soil types using APEX model in East Central Mississippi | |
| CN116485040B (en) | Seed vitality prediction method, system, electronic equipment and storage medium | |
| Sharififar | Assessment of different methods of soil suitability classification for wheat cultivation. | |
| CN111340347A (en) | Method for evaluating comprehensive quality index of rice variety | |
| Kuang et al. | Simulation of soil moisture dynamics, evapotranspiration, and water drainage of summer maize in response to different depths of subsoiling with RZWQM2 | |
| CN101427655A (en) | Multi-purpose evaluating method for sugarcane family | |
| CN111257509A (en) | Method for drought tolerance identification of corn in germination period | |
| CN116681169A (en) | Method for evaluating influence of extreme climate on crop yield | |
| Wu et al. | Adopting different cotton cropping systems may regulate the spatiotemporal variation in soil moisture and affect the growth, WUE and yield of cotton | |
| Zhang et al. | Quality evaluation of the soil-root composites layer of Leymus chinensis grassland based on different degradation degrees | |
| Roham et al. | Spatial distribution of nutsedge (Cyperus spp. L.) seed bank in rice growth cycle using geostatistics | |
| CN104778328B (en) | No-tillage seeding machine earth pack mode selecting method | |
| CN101430735B (en) | Protective farming mode selection method | |
| Saha et al. | Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India | |
| Wang et al. | Simulating potential yields of Chinese super hybrid rice in Bangladesh, India and Myanmar with EPIC model | |
| Singh | Crop growth simulation models | |
| Gao et al. | Temporal-spatial variability and fractal characteristics of soil nitrogen and phosphorus in Xinji District, Hebei Province, China | |
| Peake et al. | A rapid method for estimating the plant available water capacity of Vertosols |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180216 Termination date: 20190423 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |