In order to better let everyone choose the topic of this Yangtze River Delta competition, I will briefly analyze the topic of this competition. Digital-analog models are usually divided into three categories: optimization, prediction, and evaluation, and this math problem corresponds to A, B, and C respectively as optimization, prediction, and evaluation. The overall difficulty is not big, the main difficulty lies in the optimization of question A and the data collection of B and C. Later, I will collect some data for you to help you play better.
Difficulty evaluation of competition questions A>B>C
Estimated ratio of the number of people who choose the topic C>B>A
Question A express package packing optimization problem
Question A is based on the fast package. The topic of express delivery as optimization is the most common proposition background in the past two years. The question method of this question is somewhat similar to the heat dissipation problem of the submarine server in the 2021 Ma Cup, that is, to establish a reasonable construction layout of the optimization model, consumables, etc. The difficulty is medium, and it is not recommended for Xiaobai who is just starting the game. For this kind of question, the answer is basically fixed, and whether the award is good or bad has a lot to do with whether the result is correct or not. It is not recommended for beginners to try; if you want to win the prize, teams with many experience in digital simulation can try.
Let's take a brief look at question A.
Question 1, according to the two items of data in Appendix 1, put them in boxes or bags, take the minimum total volume of consumables as the objective function, set constraint variables according to the given data and actual conditions, and solve the problem.
Question 2: The number of types of consumables remains unchanged after optimization, but the size of consumables is changed, that is, the size change is added on the basis of Question 1, and the constraints on the number of boxes, total volume, and total volume of consumables are set according to the requirements of Question 2, and the Build an optimization model.
Question 3. When considering the stretching of consumables, the length, width and height should not exceed 5% of the original size. That is, new constraints are introduced again for solution.
The key core of the whole problem lies in the selection of decision variables. At present, initially, I think it is more appropriate to introduce 0-1 variables as decision variables.
Question B Research on the relationship between the development of new energy vehicles in the Yangtze River Delta and the relationship between carbon and carbon
The main problem of question B is prediction + data, with the background of the car, collecting data, building a prediction model, and analyzing the correlation. Regarding the data issue, I will help you to collect the data. The existing data is the data related to the third issue of carbon emissions, and some data of the automobile industry, which will continue to be collected later.
Question 1. According to the collected data, predict the market ownership of new energy vehicles in the Yangtze River Delta region in the next three years. For short-term forecasting, regression analysis and gray forecasting are best used. You can also choose other forecasting models, which are all possible. The main problem is the data. For the data collection problem here, we only need to grasp the general direction of data collection. We first collect the topic and give the data of various general directions. According to the collected data, the selection of the index of question 1 is completed. Remember, you can’t select the indicators first and then find the data, so it is easy to fall into the embarrassing situation of not being able to find the data. Many open databases for car data can be obtained directly, and we will collect and sort them out for you later .
Finally, there are small details of the data. We usually like this kind of questions with open results, the reason is that for this kind of questions, his answer must not be a fixed value , so as long as it is reasonable. If it is guaranteed to be reasonable, we need to read the literature roughly to understand the current situation. As long as the result is not outrageous, the judges cannot directly judge our paper wrong. Therefore, when we really can't find the data, or the data we find is not good, and the results of the code programming are not ideal, for this kind of open result topic, fabricate a data set, or fabricate a reasonable result It is understandable.
The second question is to study the market competition relationship between new energy vehicles and traditional fuel vehicles, and give the evolution law of the market ownership of new energy vehicles and traditional fuel vehicles in my country over time. Through the question stem, we can also see that this is a predictive model, which can be directly used again based on the model of question 1. For problem two, it is more appropriate to use regression analysis to represent the evolution law. Therefore, regression prediction can be used for problem one, and the model selection for problem two will have a better connection.
The third question is to predict the time of carbon neutrality based on the carbon emission data of self-owned mobile phones. Here is nothing more than the third prediction. For the selection of the model, you can continue to use the model of Question 1 and 2, or you can choose by yourself in the above figure, it is all feasible.
How Much Difficulty Do You Know About Question C ?
Question C currently seems to be a standard correlation analysis + comprehensive evaluation, the least difficult, as far as it is concerned. The difficulty of data collection is small, and the difficulty of competition questions is small.
Question 1 requires quantitative analysis. Quantification refers to analysis based on data. Therefore, the first question requires us to collect data by ourselves to find the main factors of the difficulty of the postgraduate entrance examination. The main factors here are mainly correlation analysis. For the selection of correlation analysis, you can refer to the table below.
Question 2 requires us to study the 10 most difficult schools in the 3-year postgraduate entrance examination, which can be regarded as a very classic comprehensive evaluation. That is, we can conduct a comprehensive evaluation based on the data we have collected.
Question 3, the prediction model is selected in the same way as Question B, both are short-term forecasts. There are many models to choose from.