We bring a question c of the May 1st competition to analyze this purpose, which is to help everyone choose a better topic, simply look at these few topics. Then the folder given by our topic includes three competition questions, as well as the paper specification templates for each of our competition questions. These three will only be used when we write our thesis. The main thing is to look at our three competition questions, let's expand and take a look. Let me give you a simple analysis,
Analysis of the problem
Question A: The problem of fixed-point delivery of drones
The search for relationships requires data, and it is best to use data to establish quantitative analysis (data?)
or
Physical Model Building Differential Equations
Background knowledge, related terms, etc.
The most difficult. Very professional
Question B: Express delivery demand analysis problem
Give data data preprocessing (outliers, default values)
Many questions, semi-open results
Evaluation + Prediction + Optimization
Question C: Research on Low-Carbon Buildings under the "Double Carbon" Goal
environment
data collection topics
Reviews + Forecasts + Non-Technical Articles
difficulty
A>B>C
topic
B>C>A
B=C>A
Question A: The problem of fixed-point delivery of drones
If you read it through, you will find that we need to build a relational model. You can simply read it again. First, as a background, consider a jet drone. In fact, this also limits the scope of our consideration of the problem. When we can only collect data, what we need to consider is jet drones. The first question is only in the case of horizontal flight. Let's determine this relationship model, determine the flight altitude and flight speed of our drone, and the relationship model between them. For our non-deterministic relationship, we can only use relationship coefficients such as pearson coefficient to express. We want to determine the relationship, we can build their function expression. If we can collect data. To use data for quantitative analysis, then based on the collected data, establish a related function expression such as information regression regression analysis, and use this function expression to analyze their relationship.
This is a bad suggestion, because we found that he had no data for question a, and he didn’t give us the data first, or let us collect the data. In fact, his idea for question a was to let us build a physical model, physical differential Model. Such a model is a ceiling for our numerical simulation problems. Basically, every event has a physical model. Unless you have a good knowledge of physics, such a model. You have a good understanding of differential equations, and you are very familiar with your background, so you can build a better differential equation model, otherwise, it will be very difficult.
The second question is that not only can materials be delivered, but we also need to conduct fixed-point blasting. Two conditions need to be considered, and then let us consider its launch distance. The relationship between the flight altitude, flight speed and dive of the drone of the aircraft is still a relationship, and we need to consider this relationship. Association analysis between univariate between multiple variables,
It is nothing more than adding a condition on the basis of question one. If we want to collect data for quantitative analysis, then we also need to collect some data on fixed-point blasting. These data should be available, and there are related papers, but. It should be that it is difficult to have a complete data set in the paper. If not, we can only build a physical model, a physical differential model.
The third question is that it says that stability has a lot to do with it. Under certain circumstances, the more stable, the higher the high-pressure accuracy. So we introduce some other variables. In the case of our comprehensive consideration, we also need to quantify the stability of our UAV after the success of our aircraft speed. We need to investigate the stability, and then build a numerical simulation. I haven't seen many problems in numerical simulation in undergraduate numerical simulation problems.
Question 12 is a physical equation model, and then it is a numerical simulation problem for our question 3. If you prefer to major in physics or major in such related background knowledge, you can try this topic, which is also very professional.
Question B: Express delivery demand analysis problem
The problem of express delivery demand, this topic allows us to give three sets of data, but the express data between cities of a certain express company, we use this data to do some modeling, so the first step comes. According to what we told you when we were teaching, given the data, what is the first step? Preprocess the data. Outliers, missing values, some data preprocessing.
Question 1, perform a comprehensive sort, and then put the sorted results on top. This question is about the several models in the comprehensive evaluation that I told you about. Objective evaluation, principal components, rank sum ratio, ideal solution, etc. are used. Although this question is said to be an open-ended comprehensive evaluation, it allows We fill in this form specifically. If you fill in the form, the top five may have a range, so for this question, its comprehensive evaluation is a semi-open result. Not completely open ended. There are many problems. The result is semi-open ended. Just choose a comprehensive evaluation model and substitute it into the code package.
Question 2 is to predict the quantity of a courier, so question 2 is a forecasting model. Like the 2005 national competition, the evaluation and prediction of the water quality of the Yangtze River, evaluation + prediction, the model is easy and simple. It is enough to use the four or five basic models in the evaluation, and there are also predictions. Forecasts such as time series prediction can be used directly. , and then everything depends on our data, so the data processing of this question must be done, and it must be done well. How to do specific data? For this question, we still need to show you the detailed ideas later.
The third question also requires us to make predictions. Due to emergencies, some cities cannot be transported. Use Annex 2 to build a mathematical model or a prediction model. Let us predict that there will be a result and a range of accuracy. We are not saying that we are blind One is suitable. We can also go to the Internet to check, and some public ones can be used. For this question, the accuracy of its results is within a certain range, for example, it may be 900-1500.
these constraints. Then set a minimum shipping cost. This optimization should belong to the problem of open-pit mining in our national competition in 2003. For the variant tsp problem, if the decision variable is set, it should be Xij, I am in the i-th city, what is the currency to the j-th city. , The result may be difficult for us novices to realize. We just need to find a more suitable cost and put it in. There is no need to say that we calculate it accurately, because our result is not 100% accurate, it is within a certain range, or you can just go to the Internet to see it. . You can also calculate it yourself.
It should be a pre-evaluation prediction plus optimization, using all three categories of our digital model. But it is still easier than question A. Although it is said to involve a lot, each question has a large volume, but each question is not difficult. We can use the code package or SPSSpro to solve the first three questions. The fourth question is definitely a bit difficult, so we can get a result. Don’t leave it empty, we can’t give you points.
Question C: Research on Low-Carbon Buildings under the "Double Carbon" Goal
There are a lot of numerical problems that are based on the background of double exploration. For example, the carbon neutrality in 2011 and the carbon sequestration in the Asia-Pacific Saihanba Forest Farm are all related to carbon. This is why we need to consider low-carbon buildings. . Carbon is just a big background, we need to consider the carbon emissions of low-carbon buildings.
Question 1, given us so many, let us try to use the conditions given in this question to calculate carbon emissions. Another one is our carbon emission formula, what exactly is it? This thing requires everyone to check it online. There should be a clear formula for carbon emissions. It should be possible to calculate.
The second question, such as the design requirements of buildings, the differences in climate and transportation operations, etc., guides us to find data, which is a general direction for us to find data. Find indicators that are highly correlated with the above factors and are easy to quantify. What is quantification ? It is based on the analysis of the data, and we found that we did not give the data to the question. This is the biggest problem of our C question. Find data, if we have data, then the problem will be solved. The data collection topic, in fact, after the data collection is completed, the whole problem is not difficult. What about data collection? Haha, I can’t guarantee that 100% of the data for this problem can be collected for everyone, only a part of it can be collected.
In the afternoon, try to share it with everyone, but it may not be very useful, because this data is too biased. The model is still very simple for comprehensive evaluation. What is used is objective evaluation, principal components, rank sum ratio, ideal solution, etc. It is not a difficult model.
Second, why does Jiangsu Province belong to Jiangsu Province? Everyone recalls May Day. It is actually organized by the China University of Mining and Technology, which is located in Jiangsu Province, so the background of this question is that of other schools. Some of his more questions may be biased in terms of regional bias. . Everyone needs to understand. Comprehensive evaluation of residential carbon emissions in 13 prefecture-level cities, what data do we need to collect for this place? A carbon emission of these 13 prefecture-level cities. Data, this data should exist, I feel that I heard that such data has been collected in it, and I can share it with you.
Including the data, even if I haven't collected it, I just find a set of data and name it, saying that this is the data I found to transport these climates. The judges can't say what we are, and the judges can't see it only through some of our data corners. This is our greatest benefit. data collection topics. Purely open, we can do whatever we want, as long as our model is reasonable and the result is reasonable. The premise is that the data we compiled must be reasonable and not too outrageous. This is also an evaluation model that needs to be established to test its effectiveness and establish an evaluation model for evaluation.
Question four, build a predictive model, based on our whole building, right? We must have the data of Jiangsu Province. However, there is no way to reduce carbon emissions from buildings in Jiangsu Province. A good suggestion for everyone is to do some proportional scaling. Then make predictions. For predictions, you can choose some basic prediction models, such as interpolation fitting, regression prediction, time series, and so on.