## 1 Introduction  
In recent years, with the continuous development of the engineering and construction industry, the safety and management system is constantly improving through the use of the \"Internet of Things + construction\" mode combined with the traditional \"human operation\" mode optimised for \"modern information tools + system\" mode. An aided decision making and early warning mode has also been utilised [1]. This mode, by combining massive big data, relies on a mature, stable, low-power transmission network to aggregate [2] and, ultimately, to achieve analysis and early warning in the background. The engineering and construction industry is crucial to China's economic development [3; 4]. Unpredictable factors on the construction site are the main reason for the high accident rate and the difficulty in controlling the construction industry [5; 6; 7]. The Internet of Things and construction safety supervision methods and effects still need to continue to deepen and constantly optimise [8; 9; 10]. The application of retaining walls in engineering construction can be seen everywhere. In order to meet the \"carbon neutral, carbon peak\" new era of development, the engineering construction process for retaining wall structures not only has a functional ecological effect, but also has a certain landscape effect. The traditional retaining wall structure is not only handled in such a way that there are safety hazards [11], but is also unfavourable for the long-term development and use of engineering construction and is not in line with the ultimate goal of sustainable development [12]. Eco-retaining walls do not require the use of mortar masonry in the construction process, which not only meets the goal of sustainable development, but also ensures that the entire retaining wall structure has better drainage [13]. Gravity retaining walls are the most widely used type [14]. They are mostly made of concrete and slurry masonry, which is not in line with the strategic planning of resource conservation and sustainable development [15]. The new greening-type gravity retaining wall, also known as an ecological retaining wall, as shown in Figure 1, has a landscape function and can play a role in ecological environmental protection. It can achieve certain effects in preventing soil erosion, so it is widely promoted and applied in engineering construction. Therefore, some scholars examining ecological gravity retaining wall structure research, cement-modified loess, remoulded loess, and new greening class gravity retaining wall structure combinations have developed the best mixing ratio to apply to the new greening gravity retaining wall [16]. In terms of the safety of the greenable gravity retaining wall, [PERSON] [17] improved its safety and economy through the soil arch effect. [PERSON] et al. 's [18] retaining wall deformation monitoring system based on TI-CC2530 + GPRS technology monitors the retaining wall data in real time, transmits the data wirelessly, and processes the data through ZigBee. The coordinator + GPRS mode is used to interact with the cloud server, and the user can monitor the highway and data in real time. Users can monitor road deformation in real time. In order to solve the problem of the limited effectiveness of deep learning on real-world data, [PERSON] et al. [19] proposed a weakly supervised graph neural-network-assisted video anomaly detection framework called AD-Graph and represented 3D visual and motion features based on a language knowledge graph format, which greatly improved the effectiveness of the existing models. [PERSON] [20] also proposed a new anomaly recognition model based on a deep convolutional neural network (CNN). This model extracts deep features from surveillance video frames and uses the multi-head temporal attention mechanism to enable the model to obtain more key information about complex surveillance environments and to prove the effectiveness and superiority of the proposed framework.