Rank learning-based quality evaluation method for deep scarification operation of farm machine

A quality evaluation, sorting and learning technology, applied in computer parts, data processing applications, instruments, etc., can solve the problem of not adopting an objective evaluation system for the quality of agricultural machinery subsoil operations.

Active Publication Date: 2017-09-01
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In order to solve the problem that the existing evaluation of the quality of agricultural machinery subsoiling operation does not adopt an objective evaluation system, the present invention proposes a method for evaluating the quality of agricultural machinery subsoiling operation based on ranking learning

Method used

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  • Rank learning-based quality evaluation method for deep scarification operation of farm machine
  • Rank learning-based quality evaluation method for deep scarification operation of farm machine
  • Rank learning-based quality evaluation method for deep scarification operation of farm machine

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Experimental program
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Embodiment 1

[0031] Embodiment one: combine below figure 1 This embodiment will be described in detail.

[0032] The method for evaluating the quality of subsoiling operations of agricultural machinery based on ranking learning described in this embodiment includes:

[0033] Step 1. Collect the operation data of multiple agricultural machines in one subsoiling operation;

[0034] The operation data of an agricultural machine includes multiple time nodes and the longitude, latitude, triaxial acceleration, triaxial angular velocity and cultivated land depth data of the deep plow of the agricultural machinery at each time node;

[0035] The time interval between two adjacent time nodes contained in the operation data of an agricultural machine is the same;

[0036] The time interval between two adjacent time nodes contained in the operation data of each agricultural machine is the same;

[0037] The three axes are X-axis, Y-axis and Z-axis respectively, the forward direction of the deep pl...

Embodiment 2

[0053] Embodiment 2: This embodiment further limits the method for evaluating the quality of subsoiling operations of agricultural machinery based on ranking learning described in Embodiment 1.

[0054] In the method for evaluating the quality of subsoiling operations of agricultural machinery based on ranking learning described in this embodiment, in step 2, the Gauss-Krüger projection method is used to convert the longitude and latitude coordinates under the geodetic coordinates into coordinates under the plane Cartesian coordinate system.

Embodiment 3

[0055]Embodiment 3: This embodiment further limits the method for evaluating the quality of subsoiling operations of agricultural machinery based on ranking learning described in Embodiment 1.

[0056] The method for evaluating the quality of subsoiling operations of agricultural machinery based on sorting learning described in this embodiment uses the dynamic time warping method to obtain the trajectory regularity of multiple plot trajectories of each agricultural machine. The specific process is: the plot trajectory and the plot The corresponding standard trajectory is normalized, and the dynamic time warping distance between the normalized plot trajectory and the normalized standard trajectory is taken as the trajectory regularity of the plot trajectory.

[0057] For trajectory A={a 1 ,a 2 ,...,a m} and trajectory B={b 1 ,b 2 ,...,b n}, in order to use the dynamic time warping method to nonlinearly align the two time series, it is necessary to construct an m×n cost mat...

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Abstract

The invention provides a rank learning-based quality evaluation method for the deep scarification operation of a farm machine. The method solves the problem in the prior art that the existing evaluation for the deep scarification operation quality of farm machines does not adopt any objective evaluation system. The method comprises the steps of collecting the operation data of multiple farm machines during one deep scarification operation, wherein the operation data are composed of a plurality of equispaced time points, and the longitude, the latitude, the triaxial acceleration, the triaxial angular velocity and the ploughing depth data of the deep digging plough of each farm machine at each time point; pre-processing the operation data of the farm machines; extracting operation feature values out of the pre-processed operation data of the farm machines, and adopting the feature values and corresponding tag values as a training sample set, wherein the feature values comprise the plot track regularity, the bad operation behavior number of the farm machines within a unit mileage, and the ploughing depth stable value; adopting the rank learning method to train the training sample set, and obtaining an optimal farm machine deep scarification operation quality evaluation model; by adopting the obtained model, evaluating the deep scarification operation quality of farm machines.

Description

technical field [0001] The invention relates to a method for evaluating the quality of subsoiling operations of agricultural machinery, and belongs to the field of evaluating the quality of subsoiling operations of agricultural machinery. Background technique [0002] Due to the large-scale popularization and use of traditional plowing and land preparation operations such as shallow plowing, the soil plow layer in my country has become shallower year by year, resulting in a hard plow bottom layer under the soil plow layer. The appearance of the bottom of the plow makes it difficult for the roots of the crops to pierce, which not only reduces the yield but also easily induces water and soil erosion. Subsoiling operation is an effective way to solve this series of problems. Subsoiling can loosen the soil, break the bottom layer of the plow, improve the structure of the plow layer, and enhance the ability of the soil to store water and moisture, resist drought and drain waterl...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06K9/62
CPCG06Q10/06395G06F18/2411
Inventor 吴芝路安普强尹振东马波杨柱天
Owner HARBIN INST OF TECH
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