Power grid enterprise key data analysis method

Abstract

The invention discloses a power grid enterprise key data analysis method. For the problem of frequent database scanning of an Apriori algorithm, scholars at home and abroad make a lot of research workin the aspect, but relatively numerous problems still exist. The method comprises the steps of establishing a power grid enterprise operation performance index detection system; setting dynamic threshold adjustment based on a Monte Carlo simulation method; and quantizing a correlation relationship among data sets by adopting an improved Apriori algorithm, wherein the process for quantizing the correlation relationship among the data sets by adopting the improved Apriori algorithm comprises the operation of mining frequent items by the improved Apriori algorithm, namely, mining the frequent items in candidate sets through the improved Apriori algorithm. Evaluation index data of enterprise operation can be effectively analyzed through basic change data, so that the calculation efficiency iseffectively improved.

Description

technical field [0001] The invention relates to the field of data mining, in particular to an analysis method for key data of power grid enterprises based on improved Apriori algorithm and Monte Carlo simulation. Background technique [0002] With the development of informatization in the electric power industry, the data on the operation of power grid enterprises has also grown rapidly. Research on electric power big data is of great significance for the optimal allocation of power resources, the improvement of energy efficiency levels, and the improvement of operating profit margins of power grid enterprises. The value of electric power big data in power grid operation lies in mining the relationship and rules between operating data to meet the needs of enterprise power production and operation management; constructing core indicators that can reflect the operating status of enterprises and correlation indicators that can support the improvement of core indicators . As a ...

AI Technical Summary

Problems solved by technology

The depth-first search algorithm is restricted not only by the structural complexity of the constructed FP-tree, but also by the physical storage consumption of the recording nodes.

[0004] In recent years, many scholars at home and abroad have done a lot of research work in this area. Aiming at the problem of frequently scanning the database with the Apriori algorithm, Park et al. proposed the Direct Hashing and Pruning algorithm. ) method to generate candidate sets, but when the number of item sets in the database is large, the generation efficiency of the hash table will drop significantly due to the amount of calculation

Toivonen generates candidate sets through sampling. Although sampling can effectively extract frequent items, due to the randomness of the sampling process, it is easy to cause imperfect association rule mining of data.

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