A Method of Mining Association Rules in Time Series Data Stream

Abstract

The invention discloses a boiler control method and device based on association rule mining. The method includes the steps that state data of a boiler are collected; preprocessing is performed on the data, piecewise linearization approximation is performed on the data, the data are converted into vectors in a two-dimensional space and then subjected to time series fitting, clustering is performed on all time series, and then time series flows are converted into transaction flows; a global SWFI-tree and a local SWFI-tree are established, information of the local SWFI-tree is added to the global SWFI-tree, and the global SWFI-tree is pruned; frequent item sets are generated according to an FP-growth algorithm; by the utilization of preset confidence coefficients, the association rules are generated through the frequent item sets; the association rules are used for predicating change states and trends of all parameters of the boiler after appointed time; according to predication results, the parameters are modified in advance, and the boiler is controlled to operate. Uniform mining is performed on the state data of the boiler, and the mined association rules are used for modifying the parameters of the boiler, so that the purpose of intelligently controlling the boiler to operate is achieved.

Description

technical field [0001] The invention relates to the technical field of industrial control, in particular to a boiler control method and device based on association rule mining. Background technique [0002] In the field of coal-fired boiler control, using the association rules of data flow to adjust parameters to control equipment can effectively improve production efficiency. Existing techniques for mining association rules of time series data streams can generally be divided into two steps: first, frequent pattern mining is performed, and then association rules that meet the requirements are generated according to the frequent patterns. Mining frequent patterns in time series data streams, most of them are counted based on the Apriori algorithm, traversing the database multiple times, some have been improved for the Apriori technology, the most classic is the FP-growth technology, which can traverse the database in a limited number of times Then get frequent itemsets. Af...

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Problems solved by technology

[0009] In order to solve the technical problem of lack of effective association rule mining for boiler state data in the field of coal-fired boiler control in the prior art, the present invention provides a boiler control method and device based on association rule mining to realize boiler state data mining Unified mining, using the mined association rules to modify boiler parameters, so as to achieve the purpose of intelligently controlling boiler operation

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