Industrial data supplementation method

An industrial data and data technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as the inconsistency between the estimated data and the historical data, the inability to guarantee the accuracy of the data, and the lack of close correlation of the historical data, etc., to achieve The effect of shortening the estimation cycle, increasing calculation efficiency, and shortening the convergence cycle

Active Publication Date: 2013-04-03
STATE GRID CORP OF CHINA +1
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Problems solved by technology

[0003] When supplementing data, the following methods are commonly used: the expectation-maximization algorithm is simple and easy to use, but it is not closely related to the historical data, and the correlation between the previous and subsequent data is lost, resulting in the inconsistency between the estimated data and the historical data, and the convergence of the algorithm is relatively slow. Slow; gray cluster analysis enhances the relevance of the data, but the accuracy of the data cannot be guaranteed after repeated iterations

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[0020] The program flow chart is attached figure 1 shown. The technical solution includes the following steps:

[0021] Step A: Determine the reference sequence and deletion sequence. record sequence For the complete reference sequence, denote the sequence is the missing sequence, where .

[0022] Step B: Initialize the sequence. Since the number of elements in the complete reference sequence and the missing sequence are different, the next step of estimation cannot be performed, so the number of elements in the complete reference sequence is adjusted, and the data that is relatively missing in the missing sequence is deleted to form a temporary reference sequence , denoted as . sequence called sequence The zeroing image of the initial point of , denoted as .

[0023] Step C: Analyze the relationship between the missing sequence and the reference sequence, calculate the gray correlation degree, and divide the clusters. First calculate the area of ​​the seq...

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Abstract

The invention belongs to the technical field of data mining and provides a data supplementation technology which combines the advantages of an expectation maximization algorithm and grey clustering analysis and combines an expectation-maximization method and a clustering analysis method. The data supplementation method ensures the data relevance on the basis of repeated estimation and can be used for completely supplementing data to a missing sequence, thereby improving the calculation efficiency of missing data, shortening the convergence cycle and improving the estimation precision.

Description

technical field [0001] The invention belongs to the technical field of data mining, in particular to a data supplement method based on expectation maximization and cluster analysis. Background technique [0002] With the development of computer technology and the improvement of automation level, the speed of data access has been increasing, and a large number of data loss has followed. In recent years, data mining technology has been widely used in various industries, providing business intelligence with the ability to assist decision-making. However, in the investigation of the actual environment, it is found that the information system is incomplete, or there is a certain degree of incompleteness, which leads to the lack of industrial data. In the actual operation of industrial systems, there are a large number of missing data, and there are various reasons for incomplete data, which may be due to the failure of data acquisition equipment, failure of storage media, failur...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 吉琨
Owner STATE GRID CORP OF CHINA
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