2021年美赛M奖,圆我两年建模梦

本文记录了一位参赛者从2019年到2021年,历时两年的数学建模比赛经历。从最初的中青杯国三,到国赛省三、省二,最终在美赛中获得M奖的惊喜。作者与队友一起经历了五场比赛,不断成长,尽管有时感到煎熬,但收获的快乐和团队合作的经验无比珍贵。

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        美赛官方公布会在4月24号公布成绩,但没说是几点!!!根据往年数据,因为时差,成绩会在4月23号九点左右相继公布,然后我就在寝室从七点躺到了九点,还没出。不过感觉十二点之前一定会出,但是这样干等时间过得太慢了,于是开了把游戏,直到凌晨过三十分钟,队友在群里说成绩查到了(我们之前有商量,如果成绩出来了一定要先提醒,不能着急说,让大家做好心理准备)。在此之前,我们三个人都指望能拿个H奖就心满意足了,因为我个人感觉自己用的模型还有些不足,能拿个H奖也就知足了,毕竟是年前的几天,不能白费了。回到凌晨三十分,看到我队友发消息,虽然他没有表露出心情,但我就感觉字里行间中就很欢快,做好准备之后,队友发了证书,我满怀期待的打开,wc!wc!M奖!
数学建模这条路就算圆满的画上句号了。感谢陪伴了我两年的建模队友,让我成为了一名专业的划水运动员。
        我们这只队伍是大一下组建的,选队友的时候没有考虑什么,只是在学校老乡群里发了个招建模队友的消息,和另外两个队友一拍即合就组队了。整个建模历程中,一共比了五场比赛,建模这条路不好走,但好在走到终点了。
        第一场:2019年的数学建模中青杯:国三。这个比赛含金量不高,只要有前百分之五十就能有国三。虽然说是国家级,但我感觉应该没什么外校的参加,哦对了,这是我们本校组织的比赛!在没有任何建模经验的前提下,我们决定参加第一次建模比赛(在这之前,对建模比赛可以说是一无所知,只知道比赛时间三四天,可能要熬夜),记得当时是一个高铁列车上下乘客的题目,具体忘了,到处找论文,也不是说学习,确实是看不懂,拼拼凑凑的完成了论文。我们的初衷也只是想熟悉一下流程,那时的我们是没有做什么赛前准备的,流程熟悉了就行。
        第二场:2019年的数学建模国赛前的校赛:校三。因为学校报名国赛的队伍较多,学校组织了校赛以筛选掉一部分队伍,校三以上的有资格参加国赛。记得那个暑假还留校培训了,原来的七百多支队伍留下了三百只。我们很幸运的拿到了打国赛的资格。建模题目想不起来了,印象中挺难的。
        第三场:2019年的数学建模国赛:省三。看到成绩的时候心里还是有些失落的(虽然准备的不多),反正就是拿了个最次的奖就让人很不舒服,题目是机场出租车司机的载客去留问题。记得比赛结束的时候,我们在出租上也问过司机相应的问题,司机的回答也正好符合我们论文里的答案,应该是在那时,我们对这一次的建模比赛信心就比较大吧。
        第四场:2020年的数学建模国赛:省二。差四名省一!有点可惜。这一次比赛时这么多场比赛中个人感觉写的最好的,以前的几篇都会参考别人发布的论文,这一次从头到尾都是我们自己原创的,写完还是很有成就感的。题目是穿越沙漠的问题,当时写完后我的感觉是,保省二冲省一,也写了篇博客记录这次比赛。虽然最后的结果没有达到预期,但也还算是有所进步了。
        第五场:2021年的数学建模美赛:M奖。这个结果真的是出乎我们意料的,即使是给个H奖我们也会很开心,一样能画上句号,哈哈哈哈!题目是美国黄蜂的路径预测分析问题,题目里有很多子题目,分给我的那部分我个人觉得做的不是很好,但其它部分做的还是很棒的。
不管怎么说,两年下来的五场比赛,比赛过程很煎熬,拿奖的日子很快乐!

Bad climate change may greatly increase the fragility of the country. How to evaluate the impact of climate change and mitigate the impact of climate change has become an urgent problem. With regard to task one, a data envelopment analysis (DEA) model is established to get the country's fragility. First of all, we selected 4 climate factors as input indicators and 5 output indicators. Then, we use the entropy method to determine the weight and then the national vulnerability is divided. At the same time, we get the conclusion that temperature affects GDP and the times of armed conflict directly and affects the fragility indirectly. In view of task two, we choose Somalia as an object of study. First, all the indexes are divided into 5 levels by the method of cluster analysis. Second, we select 10 countries including Somalia, to solve the decision unit matrix. Then, using the model of the problem one, it is found that the increase in temperature and rainfall will cause the national vulnerability to rise and decrease, respectively. Finally, we assign 4 climate indicators to 0 of the decision units, and draw the conclusion that national vulnerability will be reduced without the impact of climate factors. When it comes to task three, we use the rough set theory to reduce the output index to the number of armed conflicts. Then, we use the BP neural network model to predict the conclusion: There is a significant increase in fragility in cases of much more armed conflict and abnormal temperature. When the average annual armed conflict is certain, the national vulnerability index will face an increasing turning point at the temperature of 10.01 and the rainfall of 1823mm. As to task four, three policies on energy reduction and emission reduction issued by the government have been selected, and a model of carbon cycle is established. Taking China as an example, we calculate the extent of the change of the average temperature by reducing the carbon dioxide emissions from the state, and calculate the change of the national vulnerability through the change of temperature. We conclude that when the temperature drops 1.9 degrees, the national vulnerability decreases by 0.1593 and the cost is 20.3 billion $. Last but not least, due to the relative accuracy of the DEA model, the urban fragile performance is accurately predicted while the continent is not. In this paper, the TOPSIS model of distance entropy of three parameter interval number is used to modify the decision matrix of the DEA model. By increasing the upper and lower bounds of the interval, the value of the decision unit is more accurate, and then the weight of the index is modified based on the schedule. When we use the North American continent for test, the error was about 2.9%。 主要解决国家脆弱性的问题,欢迎下载。
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