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Prediction method of dredging energy consumption based on partial least squares method

A technology of partial least square method and forecasting method, which is applied in forecasting, data processing applications, instruments, etc., and can solve the problems of high energy consumption and low output in dredging operations

Active Publication Date: 2022-08-02
HOHAI UNIV CHANGZHOU
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Problems solved by technology

[0003] The purpose of the present invention is to provide a dredging energy consumption prediction method based on the partial least squares method to solve the technical problems of high energy consumption and low output in dredging operations in the prior art

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  • Prediction method of dredging energy consumption based on partial least squares method
  • Prediction method of dredging energy consumption based on partial least squares method
  • Prediction method of dredging energy consumption based on partial least squares method

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[0058] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.

[0059] like figure 1 As shown, the data of energy consumption and various process parameters in dredging operations are collected, and a sample matrix is ​​established; it is assumed that p independent variables and 1 dependent variable are observed for n times, and the independent variables and dependent variables are recorded as "sample point × variable" " type data matrix is ​​as follows:

[0060]

[0061] Among them, x represents the independent variable matrix, a ij Represents the i-th measurement value of the j-th independent variable, i=1,2,…,n, n represents the number of observations, that is, the number of measurements, j=1,2,…,p, p represents the number of independent ...

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Abstract

The invention discloses a dredging energy consumption prediction method based on the partial least squares method, aiming at solving the technical problems of high energy consumption and low output of dredging operations in the prior art, and a dredging energy consumption prediction method based on the partial least squares method , including the following steps: collecting data on energy consumption and process parameters in dredging operations, establishing a sample matrix; standardizing the collected data; extracting principal components according to the principle of partial least squares; establishing the relationship between energy consumption and process parameters mathematical model. The dredging energy consumption prediction method based on the partial least squares method of the invention can accurately predict the high energy consumption factors in the dredging operation process, thereby helping engineers to formulate effective energy saving and consumption reduction measures.

Description

technical field [0001] The invention belongs to the technical field of dredging engineering, and in particular relates to a method for predicting dredging energy consumption based on the partial least squares method. Background technique [0002] Dredging engineering is an important project of water conservancy and water transportation engineering, which plays a very important role in human social progress, environmental improvement and economic development. Modern dredging projects are mainly carried out by dredgers, and there are many process control parameters. In the current dredging operation, manual operation is the main method. Even for experienced operators, dredging operations have been in a state of high energy consumption and low output due to the relative difficulty of qualitative testing due to numerous influencing factors. Therefore, it is particularly important to explore the prediction method of energy consumption in dredging operations. Achieving high outp...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 高腾许焕敏
Owner HOHAI UNIV CHANGZHOU
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