Flood disaster emergency material dynamic demand prediction method

A technology for emergency materials and demand forecasting, applied in the field of network logistics analysis, which can solve the problems of difficult model construction and complex calculation process.

Inactive Publication Date: 2018-07-17
SHENYANG UNIV
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Problems solved by technology

However, the premise of regression analysis is that the sample data is required to be large enough, and the model construction is difficult and the calculation process is complicated when dealing with multiple independent variables.

Method used

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  • Flood disaster emergency material dynamic demand prediction method
  • Flood disaster emergency material dynamic demand prediction method
  • Flood disaster emergency material dynamic demand prediction method

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Experimental program
Comparison scheme
Effect test

Embodiment

[0054] Step 1: According to the statistics of the affected population released by the Hunan Flood Control and Drought Relief Network on March 28, 2016, the initial sequence of the affected population (one day is a time period, unit: ten thousand people) is obtained after sorting and summarizing as follows:

[0055] = (4.66, 7.9, 22.25, 24.33, 36.46, 57.33, 58.73, 66.48, 112.23, 122.55, 157.78, 185.8)

[0056] First select the first five data in the initial series for modeling, namely:

[0057] = (4.66, 7.9, 22.25, 24.33, 36.46)

[0058] Thus, its 1-AGO sequence can be obtained as

[0059] = (4.66,12.56, 34.81, 59.14, 95.6)

[0060] The immediate mean generating sequence of is

[0061] = (8.61,23.685,46.975,77.37)

[0062] therefore

[0063]

[0064] So you can get

[0065]

[0066] Substitute into formula (6) to get The time response equation for is:

[0067] (12)

[0068] Step 2: When When , the sequence of analog values ​​obtained by substituting...

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Abstract

The invention discloses a flood disaster emergency material dynamic demand prediction method, and relates to a network logistics analysis method. According to the method, emergency material demand prediction is researched from a dynamic angle, so that dynamics of emergency event development is sufficiently considered. A strong small sample data operability-based grey prediction model is designed,and emergency material demands after disasters are predicted by taking flood disasters as examples. On the basis of a grey system theory, an improved GM (1, 1) prediction model is constructed according to the characteristics of being small in sample quantity and less in history data so as to dynamically predict the numbers of affected in the flood disasters, so that the researches are closer to the practical conditions; and a safe inventory management method is combined to carry out demand prediction on multiple emergency materials required by the disasters, so that theoretical help is provided for the emergency demand prediction after the flood disasters.

Description

technical field [0001] The invention relates to a method for analyzing network logistics, in particular to a method for predicting the dynamic demand of emergency materials for flood disasters. Background technique [0002] Since the reliability of the disaster-affected information collected in a short period of time after the flood disaster cannot be determined, it is usually uncertain information with gray characteristics. The regression analysis method can be used to explore the law of data changes and study the relationship between variables. Predictive models make predictions about unknown data. However, the premise of regression analysis is that the sample data is required to be large enough, and the model construction is difficult and the calculation process is complicated when dealing with multiple independent variables. SUMMARY OF THE INVENTION [0003] The purpose of the present invention is to provide a method for predicting the dynamic demand of emergency mate...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/26
CPCG06Q10/04G06Q10/06312G06Q10/06315G06Q50/265Y02A10/40
Inventor 韩晓微谢英红杜英魁原忠虎
Owner SHENYANG UNIV
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