## 4 Design of Multipoint Displacement Monitoring Algorithm for Bridge Structure
### The Method of Establishing the Space-Time Relationship of Image Sequence Data  
#### 4.2.1 Dataset Construction Based on Spatiotemporal Static Image Sequences  
To realize the holographic monitoring of the bridge structure with the uniaxial automatic cruise acquisition device, the key lies in setting up the global and local holographic data based on the dynamic and static image sequences, which were captured at different times from multiple angles and fields of view. The data in the static image sequences have four main features: Multi-time, multi-field of view, multi-angle, and a strong correlation between time and space. First, the holographic data collected in different fields of view differed in time history. Second, based on technical and economic considerations, the local details of the bridge structure were monitored with a few devices in different fields of view, yielding the local holographic data in each field of view. Third, the data were collected by the automatic cruise device at different watch positions, and the resulting angle difference should be adaptively equalized in the data processing. Fourth, the spatiotemporal features of the original data were determined by the random impact of the entire bridge at the current moment or period, and the structural response in local field of view reflects the overall state of the whole structure to different degrees. Meanwhile, the cameras responsible for six fields of view (overlap ratio: 20%-30%) each cruised seven times under each damage condition. For the stability of the collected data, seven sets of images were collected on the load in the same field of view under each damage condition. During data acquisition, the time and the space pointers were constructed based on the features of the intelligent NRS system and the image sequences. The former (time dimension) indicates the current damage condition and the field of view, and the latter (spatial dimension) reflects the position of the  
Figure 14: The workflow of denoising and marking of bridge contours. Step 1: Edge detection of static images; step 2: Acquisition of bridge contours through decontextualization of useless contours; step 3: Marking the original images based on the coordinates of boundary pixels. current local area relative to the global structure. The spatiotemporal features of the data sequences in the static images are presented in Figure 15 below. In view of the data features, temporal information and spatial information were added into the dataset as labels before deep learning. The temporal information indicates the variation in damage condition and the order of images in the same field of view, and the spatial information reflects the correlation between a local structure with the global structure in a field of view. On this basis, the temporal, spatial and angular data were constructed for the original data, and then integrated with the environmental data (i.e. temperature, humidity, and illumination). The labels can be expressed as:  
\[\textit{labels}[i,j](m,n)=\textit{np.arange}(\textit{Time\_label},\textit{ Space\_label},\textit{Angle\_label},\textit{Env\_label})\]  
where \(i\) is the serial number of damage conditions of the test bridge (\(i\) = 1-6); \(j\) is the label position under different damage conditions (1 for Time, 2 for Space, 3 for Angle,... ); \(m\) is the invocation parameter of the data on labels Time, Space, Angle and Environment in a local field of view; \(n\) is the serial number of measurements under the same damage condition, i.e. the time history of the same damage condition in the same field of view; \(\textit{Time\_label}\), \(\textit{Space\_label}\), \(\textit{Angle\_label}\) and \(\textit{Env\_label}\) are the matrices of labels Time, Space, Angle, and Environment, respectively. After tagging the photos in the spatiotemporal sequence through the above steps, the photos are connected in the spatial sequence by using the overlapping part between field of view \(n\) and field of view \(n\) + 1 (\(n\) = 1-5).