The Nature Of Absolute Grey Degree Of Reverse Accumulation Generating And Its Application In Sudden Disaster Events

The grey prediction model has always been a research hotspot of grey system,and its application scope has been continuously expanded during the development of grey system for more than 40 years.However,as the actual problem system is becoming more and more complex,the sequence with approximate exponential growth characteristics in practical problems is only a few after all,especially in some practical problems,the data collection itself is a difficult point,such as sudden natural disasters,geological exploration,soil environment,etc.These data are not only difficult to obtain,but also have a variety of unique characteristics of their own systems.To solve these problems,it is necessary to “targeted treatment”.Considering the characteristics of its data,the traditional grey model is not effective in the face of complex systems that need to be processed,so the grey model still has much room for improvement and perfection.This paper mainly uses the absolute grey degree to study the gray exponential law characteristics of the data sequence,discusses the properties of the absolute grey degree of the sequence under the reverse accumulation transformation,and explores the grey modeling prediction method of the fusion of absolute grey degree and reverse accumulation,and the application of the method in the grey modeling prediction of sudden disaster events.The main contents are as follows :The first is to discuss the change rule of the class ratio of the original sequence under the action of the reverse accumulation generating operator and the average weakening buffer operator.Then,Based on the relationship between class ratio and absolute grey degree the exact relationship expression and related properties of the absolute grey degree of the original sequence under the action of reverse accumulation generation operator and average weakening buffer operator are discussed.Secondly,considering the characteristics of sudden disaster data,such as “the more new information,the more important it is,the poor information,the precious data,and the large fluctuation of data”,absolute grey degree is used as the quantitative index of the significance degree of sequence exponential law,and several reverse cumulative GOM(1,1)models based on reverse cumulative generation are discussed.The modeling effects of different original sequences of absolute grey degree are compared.The final experimental results also verify the correctness of the conclusion of the first part absolute grey degree.Thirdly,according to the characteristics of the emergency material system in sudden disasters,a reverse cumulative T＿Reverhulst model with linear time-varying parameters is constructed.compared with the traditional reverse cumulative verhulst model,the T＿Reverhulst model overcomes the structural defects of the traditional verhulst model,introduces the linear time-varying term,and optimizes the initial value selection and parameter source.The new model has good adaptability and high precision in the face of saturated “S” data and common monotone growth data.finally,according to the prediction results of T＿Reverhulst for sudden disasters,an emergency material inventory management model in line with the characteristics of sudden disasters is constructed.