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The widespread scale effect always generates significant changes in the properties or characteristics of management objects with different observation scales. Thus, this paper studies the scale transformation mechanism problem of management objects. The observation scale hierarchy (management scale) with clear management objectives could automatically be recognized through changing the observation scales, in order to improve the practical management efficiency. Firstly, an intelligent computing framework based on the scale transformation is established, which reduces the over-dependency of human involvement in traditional scale transformation methods. Then, the scale characteristic reasoning inference is put forward to improve the knowledge acquisition mechanism of scale transformation. Finally, a knowledge acquisition algorithm based on the variable-scale clustering (KAVSC) is proposed. Experiments selected the multiple products inventory data of a manufacturing enterprise from 1 January 2015 to 31 December 2017. The experiment results illustrate that the proposed algorithm KAVSC is able to accurately recognize different management scale levels and scale characteristics of each product, which could effectively support managers making differentiated inventory management plans.

Intelligent Computing: Knowledge Acquisition Method Based on the Management Scale Transformation