## 2 Methods and Steps
### Data Sample Processing Method
#### 2.3.1 Data Missing Processing Method Based on the RKNN Algorithm  
Affected by multiple factors such as the complexity of the transportation environment, signal weakness, and strong seismic activity of the vehicle body, it is easy to cause the loss of multiple variable values of truck running state data in the process of data acquisition by multiple sensors; that is, at least two or more indicators in multiple groups of data are missing. The data missing in the multivariable missing mode is shown in matrix \(X\). \[X= \begin{bmatrix}x_{11}&x_{12}&*&\cdots&x_{1n-1}&x_{1n}\\ x_{21}&*&x_{23}&\cdots&*&*\\ *&x_{32}&*&\cdots&x_{3n-1}&x_{3n}\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ *&x_{m2}&x_{m3}&\cdots&*&x_{mn}\end{bmatrix}  \tag{1}\]  
where * represents the missing item of the variable; am n represents the data variable index; n represents the variable type; and m represents the sample size of the variable. In a comprehensive consideration of the causes of missing data in mining truck running state prediction data, combined with the types of missing data information, this paper uses the RKNN algorithm to fill in the truck running state data, so as to increase the number of data samples and improve scientific support for subsequent data mining and analysis. The KNN (K-Nearest Neighbor) algorithm is one of the classical machine learning algorithms, and RKNN is a KNN [21; 22] algorithm with an improved reduced association coefficient, which has many advantages such as simple principles and convenient operation. The main idea of the data filling algorithm is to regard the samples with missing data as the samples to be tested, and then select the K data closest to the missing sample data from the data set. Finally, the missing data is analyzed and processed according to the mean or mode of the K data, and the missing data index is determined. The specific calculation formula can be expressed as:  
\[a_{ij}=\sqrt{\sum_{k=1}^{n}(x_{ik}-x_{ik})^{2}} \tag{2}\]  
where \(a_{ij}\) represents the Euclidean distance from sample \(i\) to sample \(j\) and \(x_{ik}\) represents the data of sample \(i\) in the k-dimension. The specific process of data filling by the RKNN algorithm can be divided into the following parts [23; 24]:Step 1: Firstly, the relevant data missing in the running state of the mining truck are pre-filled. If the missing data is continuous data, the average value is used to complete it. If the missing data is discrete data, the mode is used to complete it. Step 2: Normalize all sample data to eliminate the impact of the data structure caused by dimensional problems. Step 3: Formulas (3) and (4) were used to calculate the reduced correlation coefficient between the data to be filled and other samples. \[RRC(x_{iq},x_{jq})=\frac{\rho Max_{\
abla_{q}}\big{\{}\big{|}X_{iq}-\min_{q} \big{|},\big{|}X_{iq}-\max_{q}\big{|}\big{\}}}{\big{|}X_{iq}-X_{jq}\big{|}+\rho Max _{\
abla_{q}}\big{\{}\big{|}X_{iq}-\min_{q}\big{|},\big{|}X_{iq}-\max_{q}\big{|} \big{\}}} \tag{3}\]  
\[RRG(x_{i},x_{j})=\frac{1}{m}\sum_{q=1}^{m}RRC(x_{iq},x_{jq}) \tag{4}\]  
where \(RRC(x_{iq},x_{jq})\) represents the reduced correlation coefficient between sample \(i\) and sample \(j\) under index \(q\); \(x_{iq}\) and \(x_{jq}\) represent the value of index \(q\) in the \(i\)th sample and the value of index \(q\) in the \(j\) sample, respectively; and \(RRC(x_{i},x_{j})\) represents the reduced correlation coefficient between sample \(i\) and sample \(j\). Step 4: Order the determined reduced correlation coefficient from large to small and select K data samples that have been filled as reference data;  
Step 5: Determine the type of missing data. For discrete data, mode is used to fill in, while for continuous data, mean value is used to fill in. Specific expressions are shown in Formulas (5) and (6). \[\hat{y}_{ik}=\sum_{k=1}^{k}w_{ik}x_{ik} \tag{5}\]  
\[w_{ik}=RRG(x_{i},x_{k})/\sum_{k=1}^{k}RRG(x_{i},x_{k}) \tag{6}\]  
where \(w_{ik}\) represents the weight of the \(k\) adjacent sample with filling sample \(i\).