基于Grey-Markov的生鲜配送需求量预测系统

对生鲜农产品配送需求量进行预测,实现按需配送,可以降低甚至实现生鲜农产品的零库存,减少生鲜农产品变质、腐烂等损耗。为了高效预测生鲜农产品配送需求量,提出一种基于Grey-Markov的生鲜配送需求量预测系统。系统以Grey-Markov为预测模型,对生鲜农产品下一日的配送需求量进行预测,与指数回归等预测模型相比,Grey-Markov模型具有更强的稳定性和更好的准确性。系统设计采用C/S架构,实现以按需配送为目标的原始数据导入、原始数据管理、历史销量分析、需求量预测等功能。详细介绍了系统的架构设计、功能设计、数据库设计和程序设计。测试与应用结果表明,系统实现了预期的功能,应用效果良好,可有效减少生鲜农产品的损耗。与使用系统前相比,日剩余未售出生鲜农产品总量可降低20百分点以上。

基于改进Grey-Markov模型的耕地数量预测

耕地资源的变化事关国家粮食安全与经济社会的可持续发展,对其做科学合理的预测具有重要的意义。文章借鉴新陈代谢和移动平滑的思想和统计分组方法对Grey-Markov模型进行改进,以江苏省南京市为例预测了耕地资源规模变动,并根据模型预测精确度分析改进模型的应用适应性。结果表明,与直接采用GM(1,1)预测模型相比,改进的Grey-Markov模型模拟和预测精度更高,与传统Grey-Markov预测模型对比,改进的Grey-Markov模型更适合对波动较大数据预测,能够为科学掌握耕地资源变化状况提供可靠依据。

Grey-Markov Model for Road Accidents Forecasting

In order to improve the forecasting precision of road accidents, by introducing Markov chains forecasting method, a grey-Markov model for forecasting road accidents is established based on grey forecasting method. The model combines the advantages of both grey forecasting method and Markov chains forecasting method, overcomes the influence of random fluctuation data on forecasting precision and widens the application scope of the grey forecasting. An application example is conducted to evaluate the grey-Markov model, which shows that the precision of the grey-Markov model is better than that of grey model in forecasting road accidents.

Partial Improvement of Traditional Grey-Markov Model and Its Application on Fault Prediction

Modeling experiences of traditional grey-Markov show that the prediction results are not accurate when analyzed data are rare and fluctuated.So it is necessary to revise or improve the original modeling procedure of the grey-Markov(GM)model.Therefore,a new idea is brought forward that the Markov theory is used twice,where the first time is to extend the original data and the second to calculate and estimate the residual errors.Then by comparing the original data sequence from a fault prediction case with the simulation sequence produced by the use of GM(1,1) and the new GM method,results are conforming to the original data.Finally,an assumption of GM model is put forward as the future work.

城乡客运一体化需求预测的Grey-Markov二次修正模型

为准确地预测城乡客运一体化需求,本文在结合灰色预测和马尔科夫预测理论基础上对Grey-Markov模型提出了二次修正,即利用灰色波动多项式替代GM(1,1)传统模型中的指数型曲线,采用滑动转移概率矩阵修正Markov传统链,然后通过线性加权求和的方法获取最终的预测结果。最后,利用西部某地区城乡旅客运输总量的调研数据进行实例研究,综合分析最大相对误差、平均相对误差、相对残差平方和等因素,以及对比指数平滑法、回归分析法预测误差情况,结果表明Grey-Markov二次修正模型能够更好地预测城乡客运一体化需求状况。

中国大豆2021—2025年消费量和生产量预测分析

中国是世界上大豆消费量和进口量最大的国家,为了较为准确地预测我国大豆未来的消费量和生产量,维护我国大豆供给安全,本研究提出Grey-Markov大豆消费量预测模型,采用GM(1, 1)模型和Grey-Markov模型对2017—2020年我国大豆消费量进行拟合,并利用其中预测精度较高的模型预测我国2021—2025年的大豆消费量和生产量。结果表明:Grey-Markov模型预测精度较高,利用该模型的进一步预测结果表明,2017—2020年我国大豆供给量与需求量之间的缺口分别为10.37,10.87,11.40,11.95和12.53亿t, 2021—2025年我国大豆生产量和消费量年增长率将分别为6.06%和5.01%。最后,根据预测数据提出建议:我国应力争实现大豆进口多元化、大力发展国内大豆产业,以提高粮食安全性。

A Prediction Model for Railway Transportation Sofety and Its Application

This paper discusses the characterization of railway transportation safety, andapplies the Grey-Markov forecasting model to predict the occurrences of thedriving accident on railway according to their speciality. It will offer a reliableargument for taking measures to prevent driving accidents.