## 5 Discussion  
### Optimizations of Data Processing  
In order to quickly process a large amount of base station data in the aforementioned raw data processing, we simply organize the time part of the data in an interval of 0.25 h  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Source** & **Reach Rate** & **Material** & **Delivery Result** & **Times** \\ \hline Through 5G & A: 50\%, B: 50\% & Random & 50\% & Max: 4 \\  & A: 80\%, B: 20\% & A & 80\% & (80\%/20\%) \\  & A: 20\%, B: 80\% & B & 80\% & \\ Through GPS & A: 50\%, B: 50\% & Random & 50\% & Avg: 1.6 \\  & A: 80\%, B: 20\% & A & 80\% & (80\%/50\%) \\  & A: 80\%, B: 20\% & B & 20\% & \\  & A: 20\%, B: 80\% & A & 20\% & Min: 1 \\  & A: 20\%, B: 80\% & B & 80\% & (50\%/50\%) \\ \hline \end{tabular}
\end{table}
Table 20: Comparison of delivery effectiveness on upcoming passers nearby outdoor media.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Source** & **Reach Rate** & **Accuracy** & **Delivery Result** & **Times** \\ \hline Through 5G & 100\% & 100\% & 100\% & 100 or \(\infty\) \\ Through GPS & 100\% & 10\% & 10\% & (the times is calculated \\  & 100\% & 1\% & 0\% (too small) & as \(\infty\) when a result is \\  & 25\% & 4\% & 1\% & too small) \\ \hline \end{tabular}
\end{table}
Table 21: Comparison of delivery effectiveness on commuters traveling between specific two places.  
and use it for calculation. However, the size of the selected time interval may significantly affect the calculation performance and accuracy. With different application scenarios and requirements, the time interval size can be appropriately adjusted to obtain better calculation results.  
In addition, when processing various types of time and location-based data statistics, various frequently used data should be pre-calculated as much as possible, and various pre-calculated results should be updated regularly in a near-real-time manner according to the probability of use. The discussion of the process is more complex, and it is not within the scope of this study.  
In the data processing of user attributes, in order to simplify the calculation, we convert part of the categorical data into numerical data and normalize some of the numerical data into consistent numerical types or intervals. If the aforementioned processing is implemented using a deep neural network, the above numerical interval unity may be omitted.  
However, in practical applications, it is often impossible to perform real-time calculations at the moment of launch. In order to improve the modeling speed and match actual applications, the data processing methods and selected algorithms need to be changed according to the existing system hardware resources.  
More attention should focus on the processing of categorical data. The processing of categorical data into numerical data often has a crucial impact on the accuracy of model predictions. Many algorithms and statistical conversion techniques can assist in converting categorical data, such as statistical dummy variables skills. If the best conversion value can be directly determined manually, the prediction accuracy will, of course, significantly surpass the results of other direct-use model training.  
In order to quickly obtain similar advertising audiences in the practical application of real-time delivery, this study selected collaborative analysis and cooperated with similar algorithms to achieve this goal. However, if the system's computing resources are fast enough, or in an application scenario that does not require real-time delivery, other deep learning algorithms can be tried, and it may help to improve prediction accuracy.