# A Multidimensional Data Collection and Edge Computing Analysis Method
## 3 Overall System Architecture and Improved Target Detection Model  
In this section, we will focus on the construction of data processing modules and functions of urban public space utilization. The overall structure of data processing and function construction is shown in Figure 2. The overall system structure is divided into four parts: data collection module, target detection and sensing data calibration module, utilization calculation module, and data visualization module. The data collection module is to collect image data and other sensor data, and this part has been explained in detail in Section 2. The target detection and sensing data calibration module first performs some filtering process on the collected data for wrong data, and then performs target detection on the image data, and this target detection is the improved target detection model proposed in this system. At the same time, the data from the temperature sensor and GPS sensor are effectively separated, where the data acquired by GPS sensor will be used as a calibration method to improve the accuracy of target detection results by acquiring target position information in the improved target detection model. And the temperature data is used as for image comparison of different temperature conditions during image acquisition to study the common space. The two sensors are effectively integrated into the target detection of the images.  
The results of the detection are used to extract the parameters used to study the public space. The utilization rate calculation module is mainly used to fit an effective utilization function for the public space from three parameters: the change of temperature, the change of number of people, and the change of age obtained from the space. These functions are used to illustrate the research value of the public space. The data visualization module  
\begin{table}
\begin{tabular}{l l l} \hline
**Sensor** & **Data Range** & **Sampling Rate** \\ \hline Age (\(A\)t) & 1–2 & Image recognition \\ Location (\(L\)t) & Latitude and longitude & every 5 min \\ Temperature (\(T\)t) & \(-\)10\(\sim\)80 \({}^{\circ}\)C & very 5 min \\ High (\(H\)t) & 80\(\sim\)200 cm & every 10 min \\ Coordinate (Ct) & 0 \(\times\) 0 \(\sim\) 1920 \(\times\)1080 pixels & every 10 min \\ Distance (\(D\)t) & None & every 10 min \\ Number of People (Nt) & None & every 10 min \\ \hline \end{tabular}
\end{table}
Table 1: Sensor data specification and sampling rate.  
mainly transforms the three-dimensional form of the public space into two-dimensional for effective display, and through the two-dimensional display we can also better study the distribution of various targets in the space and the value of space utilization.  
Through the overall structure of the system, we characterize the data obtained as a dataset consisting of \(\text{Ft}\) = [Lt, \(\text{Tt}\), \(\text{Ht}\), \(\text{Ct}\), \(\text{Dt}\), \(\text{Nt}\), \(\text{At}\), \(\text{Wt}\)] as perception sensor, while [\(\text{At}\), \(\text{Wt}\), \(\text{Nt}\)] is a key factor in monitoring urban public space utilization. The four parts of the system are described in detail below.