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Carrie rocket data prediction method and device, storage medium and electronic device

A launch vehicle and data prediction technology, applied in the field of data processing, can solve problems such as low accuracy, low efficiency of the least square method, and large amount of processing

Active Publication Date: 2019-01-15
PLA PEOPLES LIBERATION ARMY OF CHINA STRATEGIC SUPPORT FORCE AEROSPACE ENG UNIV
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AI Technical Summary

Problems solved by technology

The most widely used is simple weighted average, but this method cannot use the information that contains the prediction accuracy before, so the method of weighted average has the problem of low accuracy
Another simple method is to linearly mix the independent forecasts, that is to say, combine a part of the data from the two matrices of the past forecast value and the past observation value into a variety of matrices, and find the correlation in the various matrices. The most reliable matrix, and then determine the combination weight by the method of least squares (Ordinary Least Square, OLS). Due to the large number of matrices, the amount of processing required is large, resulting in the inefficiency of the least squares method
For the trend prediction of launch vehicle test data, few people use the method of time series prediction

Method used

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  • Carrie rocket data prediction method and device, storage medium and electronic device

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Embodiment Construction

[0025] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0026] figure 1 is a schematic flowchart of a method for predicting launch vehicle data according to the first embodiment of the present invention.

[0027] Such as figure 1 As shown, the method includes step S101-step S103.

[0028] Optionally, before step S101, the original launch vehicle test data is preprocessed. Remove data that obviously has errors. For example, most of the launch vehicle test data can show a c...

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Abstract

The invention discloses a method and a device for predicting data of a carrier rocket, a storage medium and an electronic device. The method comprises the following steps: step S101, determining a weighting coefficient vector K; 102, calculating a weighted prediction value set ZP_final based on that weight coefficient vector K; Step S103: based on the comparison result of the weighted prediction value set ZP_final and the actual value of the rocket test data corresponding thereto, all weighted prediction values in the weighted prediction value set ZP_final are outputted. The present application combines the AR model as a stochastic model with the regression analysis model as a deterministic model, and is applied to the prediction of rocket test data, so as to not only measure the influenceof a certain factor, but also predict the upcoming data according to the already tested data.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a data prediction method and device for a launch vehicle, a storage medium and electronic equipment. Background technique [0002] In the 1970s, G.E.P.Box and G.M.Jenkins developed a discrete parameter prediction recursive algorithm (Box-Jenkins model) that satisfies the finite parameter linear model, which made it possible to combine prediction theory with computers. With the development of computer technology, models such as AR, MA, ARMA and ARIMA have been widely used. [0003] At present, research on combined forecasting models is a hot spot. Many scholars have proposed combined time series forecasting methods, such as the time series forecasting method based on Hilbert-Huang transform and ARMA model, the time series forecasting method based on wavelet analysis and rolling time series forecasting algorithm. Optimization forecasting methods, forecasting methods based ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 解维奇蔡远文乐天姚静波程龙辛朝军李岩张宇
Owner PLA PEOPLES LIBERATION ARMY OF CHINA STRATEGIC SUPPORT FORCE AEROSPACE ENG UNIV
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