In order to improve the efficiency of dam safety operation and maintenance, the dam defect object detection models can help to assist inspectors in defect detection. There is variability of the geometric shapes of dam defects, and the Single Shot MultiBox Detector (SSD) model using traditional convolution methods for feature extraction cannot adapt to the geometric transformation of defects. Focusing on the above problem, a DeFormable convolution Single Shot multi-box Detector (DFSSD) was proposed. Firstly, in the backbone network of the original SSD:Visual Geometry Group (VGG16), the standard convolution was replaced by the deformable convolution, which was used to deal with the geometric transformation of defects, and the model's spatial information modeling ability was increased by learning the convolution offset. Secondly, according to the sizes of different features, the ratio of the prior bounding box was improved to prompt the detection accuracy of the model to the bar feature and the model's generalization ability. Finally, in order to solve the problem of unbalanced positive and negative samples in the training set, an improved Non-Maximum Suppression (NMS) algorithm was adopted to optimize the learning effect. Experimental results show that the average detection accuracy of DFSSD is improved by 5.98% compared to the benchmark model SSD on dam defect images. By comparing with Faster Region-based Convolutional Neural Network (Faster R-CNN) and SSD models, it can be seen that DFSSD model has a better effect in improving the detection accuracy of dam defect objects.

To realize reasonable allocation and scheduling of mobile user task requests under cloud and fog collaboration, a task assignment algorithm based on cloud-fog collaboration model, named IGA (Improved Genetic Algorithm), was proposed. Firstly, individuals were coded in the way of mixed coding, and initial population was generated randomly. Secondly, the objective function was set as the cost of service providers. Then select, cross, and mutate were used to produce new qualified individuals. Finally, the request type in a chromosome was assigned to the corresponding resource node and iteration counter was updated until the iteration was completed. The simulation results show that compared with traditional cloud model, cloud-frog collaboration model reduces the time delay by nearly 30 seconds, reduces Service Level Objective (SLO) violation rate by nearly 10%, and reduces the cost of service providers.

The real-time time-series data of multiple water parameters are acquired via the water sensor networks deployed in the water supply network. The accurate and efficient detection and warning of pollution events to prevent pollution from spreading is one of the most important issues when the pollution occurs. In order to comprehensively evaluate the abnormal event detection to reduce the detection deviation, a Temproal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, it could analyze the time-series data of multiple parameters with BP (Back Propagation) model to determine the possible outliers, respectively. M-TAEDA algorithm could detect the potential pollution events through Bayesian sequential analysis to estimate the probability of an abnormal event. Finally, it can make decision through the multiple event probability fusion in the water supply systems. The experimental results indicate that the proposed M-TAEDA algorithm can get the 90% accuracy with BP model and improve the rate of detection about 40% and reduce the false alarm rate about 45% compared with the temporal abnormal event detection of Single-Variate Temproal Abnormal Event Detection Algorithm (S-TAEDA).

Focused on the issues of low accuracy, high energy consumption of target tracking network and short life cycle of network in Wireless Sensor Network (WSN), the moving target tracking technology based on dynamic clustering was proposed. Firstly, a Two-Ring Dynamic Clustering (TRDC) structure and the corresponding TRDC updating methods were proposed; secondly, based on centroid localization, considering energy of node, the Centroid Localization based on Power-Level (CLPL) algorithm was proposed; finally, in order to further reduce the energy consumption of the network, the CLPL algorithm was improved, and the random localization algorithm was proposed. The simulation results indicate that compared with static cluster, the life cycle of network increased by 22.73%; compared with acyclic cluster, the loss rate decreased by 40.79%; there was a little difference from Received Signal Strength Indicator (RSSI) algorithm in accuracy. The proposed technology can effectively ensure tracking accuracy and reduce energy consumption and loss rate.