Industrial scene working condition clustering method, system and device based on Markov random field model and storage medium

A technology of random field model and clustering method, which is applied to computer parts, character and pattern recognition, instruments, etc., can solve the problems of unfavorable time series time consistency, limited use, and less data characteristics, so as to achieve high efficiency and economy, the effect of improving efficiency

Pending Publication Date: 2021-12-31
HARBIN ELECTRIC POWER GENERATION EQUIP NAT ENG RES CENT CO LTD
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  • Abstract
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  • Application Information

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Problems solved by technology

A subsequence that is too long may contain more than one hidden working condition, while a subsequence that is too short may contain fewer data features, which will have an adverse effect on the results of the clustering algorithm, and artificially segmenting the sequence is not conducive to retaining Time Consistency of the Original Time Series
The hierarchical clustering method based on data distribution intuitively determines the segmentation points for clustering according to the distribution characteristics of data in certain dimensions. It is a category feature with a very concentrated distribution. Unstable or volatile features cannot be used in this type of method, and the use is relatively limited.

Method used

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  • Industrial scene working condition clustering method, system and device based on Markov random field model and storage medium
  • Industrial scene working condition clustering method, system and device based on Markov random field model and storage medium
  • Industrial scene working condition clustering method, system and device based on Markov random field model and storage medium

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specific Embodiment approach 1

[0109]The purpose of this embodiment is to provide a method and system for clustering industrial scene working conditions based on a Markov random field model. The real-time operation data of the power plant obtained by arranging sensors at various positions in the power plant, through manual screening, selects as much as possible the characteristic data that can affect the change of boiler operating conditions, and processes the data through methods such as data cleaning, noise suppression, and feature dimension reduction. , and then cluster the data, and each category is regarded as a different working condition. This embodiment is mainly divided into a preliminary screening module; a noise suppression module; a feature dimensionality reduction module; a working condition clustering module;

[0110] Preliminary Screening Module

[0111] The role of this module is to preliminarily screen out the characteristics that can affect the changes in the operating conditions of the p...

specific Embodiment approach 2

[0129] In this embodiment, the real-time operation data of the power plant obtained by arranging sensors at various positions in the power plant is basically processed through data cleaning, feature dimensionality reduction and other methods, and then input into the working condition clustering module to cluster the boiler data. Each category is considered as a different working condition. Its process is as follows figure 1 and figure 2 shown, where:

[0130] 1. Preliminary Screening Module

[0131] The role of the preliminary screening module is to preliminarily screen out the characteristics that can affect the changes in the operating conditions of the power plant from the many characteristics of the power plant, and perform preliminary processing on these features.

[0132] In the coal-fired boiler power plant, all the measuring points have been collected for a certain period of time (for example, 5 seconds), and the total length of time is 4 months. Select the charact...

specific Embodiment approach 3

[0221] Those skilled in the art may use the systems and methods mentioned in the above embodiments, and this embodiment may be provided as a method, system, or computer program product. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware, and the modules may also be reorganized according to the computer logic structure. Furthermore, the embodiments may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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Abstract

The invention discloses an industrial scene working condition clustering method, system and device based on a Markov random field model and a storage medium, and belongs to the technical field of industrial data analysis. The method comprises the following steps: step S100, preliminarily screening out characteristics which can influence the change of the operating condition of the power plant, and performing preliminary treatment on the characteristics; step S200, performing noise reduction on the primarily processed characteristics affecting the operation condition change of the power plant; step S300, carrying out dimension reduction on the feature data influencing the change of the operating condition of the power plant after the noise reduction processing; and step S400, clustering the data after dimension reduction processing to obtain an operation condition. The system comprises a preliminary screening module, a noise suppression module, a feature dimension reduction module and a working condition clustering module. The efficiency of processing and analyzing the unit mass data is further improved, and high efficiency and economical efficiency which cannot be achieved when the unit mass data is manually processed are achieved.

Description

technical field [0001] The invention relates to a construction method, system, equipment and storage medium of working conditions in an industrial scene, and belongs to the technical field of industrial data analysis. Background technique [0002] In the field of industrial big data analysis, actual industrial signals often come from many production system sensors, and these data change over time. Working conditions are defined as several possible states that divide the high-dimensional time series signal. For example, the operating state of a thermal power plant can be roughly divided into start-up period, operation period, stop period, abnormal period and so on. Working condition mining and identification uses machine learning algorithms to mine hidden working condition information in high-dimensional data, which has certain guiding significance for actual production, and is also an important pre-processing method for many subsequent algorithms (prediction, classification...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23G06F18/2135G06F18/295
Inventor 翟俊鹏曲晓峰王达梦杨永明王克剑
Owner HARBIN ELECTRIC POWER GENERATION EQUIP NAT ENG RES CENT CO LTD
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