Manufacturing industry information quantitative analysis method and device based on analytic hierarchy process

Abstract

The invention discloses a manufacturing industry information quantitative analysis method and device based on an analytic hierarchy process, and the method comprises the steps: constructing an analysis index database for evaluating manufacturing industry information and a plurality of analysis index levels, wherein each analysis index level comprises a plurality of analysis indexes; acquiring a demand of a demand side for manufacturing industry information to construct an evaluation model, and matching the demand with the analysis indexes in the analysis index database step by step; comparinganalysis index importance measures in pairs step by step; constructing a judgment matrix step by step, and judging whether the judgment matrix meets an acceptable consistency condition or not; obtaining scoring rules and scores of the analysis indexes, and performing weighting to obtain effect scores; adding the effect scores to obtain final evaluation scores; and sorting the final evaluation scores according to a sorting rule. According to the invention, the analysis index database is constructed in advance, and the corresponding evaluation model is selected according to the specific situation of the demander for quick and accurate judgment, so that the accuracy and speed of manufacturing industry information evaluation are improved.

Description

technical field [0001] The present application relates to the technical field of computer analysis, in particular to a quantitative analysis device for manufacturing information based on Analytic Hierarchy Process. Background technique [0002] In the field of supply chain, when the demand side puts forward the demand for parts processing, it is often necessary to evaluate the manufacturing capabilities of the parts processing manufacturers. The usual method is to quantitatively evaluate the manufacturing capabilities of the processing manufacturers from a single dimension; or from Several dimensions are simply integrated to evaluate its value. For example, for a certain non-standard component, evaluate the manufacturing capabilities of each processing factory. [0003] The existing schemes include the split method and the simple comprehensive weighting method. The split method separates several analysis indicators for evaluation and comparison. For example, evaluate proc...

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Problems solved by technology

[0004] However, these two schemes have the following disadvantages: 1). Dimensionless: the scores of the existing manufacturing capacity impact factors are calculated based on absolute or relative quantities to obtain their quantitative values, while the number of different manufacturing capacity impact factors, the unit There are differences in quantification (that is, the order of magnitude is different, different data levels are not comparable, and the calculation scale is inconsistent)

2). Insufficient accuracy: Different manufacturing capacity influencing factors show different effects on each index. If you simply assign a weight value to each index and score after weighting, or take a certain index for comparison, it is not accurate enough and cannot be effective. Evaluate the optimal manufacturing capacity influencing factors and promote them

3). The reuse rate is not high, the evaluation structure is arbitrary, separate evaluations are carried out for different demand parties, and the visualization utilization rate of evaluation data is not high

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