Electric power material warehousing quantity prediction method

Abstract

In order to guide an electric power enterprise to reserve and optimize a storage space difficultly when facing electric power materials with uncertainty, the invention provides an electric power material warehousing quantity prediction method, and associative prediction is performed on potential materials which are not warehoused yet. The electric power material warehousing quantity prediction method comprises the following steps: firstly, quantifying randomness and warehousing time of the warehousing quantity of the electric power materials; deducing an expression of the expected warehousingquantity of the electric power materials; finding out the number ratio mean value and standard deviation between different electric power materials through historical data analysis, quantifying through a likelihood function, and deriving an update and correction formula of the expected warehousing number of the electric power materials which are not warehoused through a Bayesian analysis method; and carrying out weighted integration on a plurality of formulas to obtain a result which is the result of the invention. The electric power material warehousing quantity prediction method provided bythe invention can effectively improve the precision of electric power material warehousing quantity prediction, reduces the uncertainty of prediction, and guides an electric power enterprise to reasonably configure limited warehouse storage location resources.

Description

【Technical field】 [0001] The invention relates to the field of electric power technology, in particular to a method for predicting the quantity of electric power materials put into storage. 【Background technique】 [0002] Due to the strong professionalism, variety, miscellaneous specifications, and large quantities of power material products, the storage data of power materials is quite large and complex, and it is difficult to find the rules simply by checking the inbound and outbound documents with the naked eye. Therefore, it is proposed to use big data technology to automatically collect and process massive inbound and outbound and in-store data, and to conduct big data processing and analysis, so as to dig out whether some valuable information is hidden in these data, and to optimize the power supply. Warehouse management provides a scientific basis. [0003] From the perspective of the material department of the power enterprise, the optimized management of material r...

AI Technical Summary

Problems solved by technology

[0002] Due to the strong professionalism, variety, miscellaneous specifications, and large quantities of power material products, the storage data of power materials is quite large and complex, and it is difficult to find the rules simply by checking the inbound and outbound documents with the naked eye.

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