The ship scheduling affected by severe weather is a very complex optimization problem, and is also one of the key issues needed to pay attention in liner companies. Therefore, based on the premise of obtaining the latest weather forecasting information in the designed multi-stage rescheduling mechanism period and the real-time positions of all the ships in service in one liner company on a shipping network, the restriction of liner shipping schedule was focused on, and the realistic constraints such as the change of ship's speed between different ports and the ship capacity, a nonlinear mathematical model was built to minimize the total shipping cost of all the ships during the fixed planning period. And an improved genetic algorithm embedded with gene repair operator was designed to solve the built model. Then the optimal multi-stage rescheduling scheme, which was integrated by the solution strategies of charting for direct-transport, dispatching ship across different routes, adjusting port reaching order and goods transfer, was given. Experimental results of examples with large, medium, and small scales show that, compared with the traditional waiting method, multi-stage rescheduling saves more than 15% of the total shipping cost, verifying the effectiveness of the proposed model and scheme; compared with Cplex, the improved genetic algorithm has the calculation efficiency greatly improved and all the deviation values within 5%; and compared with Ant Colony Optimization (ACO) algorithm, Tabu Search (TS) algorithm, Quantum Differential Evolution (QDE) algorithm, the improved genetic algorithm has the cost reduced by about 10% in the effective time, proving that the algorithm is scientific. It can be seen that the proposed method can provide the reference for actual ship scheduling of liner companies.

For the monitoring dead angles of fixed surveillance cameras installed in large quantities and low hardware performance of mobile devices, an urban management case target recognition algorithm that can run on IOS mobile devices with low performance was proposed. Firstly, the number of channels of input and output images and the number of feature maps generated by each channel were optimized by adding new hyperparameters to MobileNet. Secondly, a new recognition algorithm was formed by combining the improved MobileNet with the SSD recognition framework and was transplanted to the IOS mobile devices. Finally, the accurate detection of the common 8 specific urban management case targets was achieved by the proposed algorithm, in which the camera provided by the mobile device was used to capture the scene video. The mean Average Precision (mAP) of the proposed algorithm was 15.5 percentage points and 10.4 percentage points higher than that of the prototype YOLO and the prototype SSD, respectively. Experimental results show that the proposed algorithm can run smoothly on low-performance IOS mobile devices, reduce the dead angles of monitoring, and provide technical support for urban management team to speed up the classification and processing of cases.

Two improvement methods were presented to avoid ineffective circulation and invalid waiting state problems when running algorithm Randomized Switching for Maximizing Lifetime (RaSMaLai) and then a new random switching algorithm New Randomized Switching for Maximizing Lifetime (NRaSMaLai) was put forward: the first improvement was to conduct the initialized inspection in the process of traversing tree nodes in order to prevent the tree from entering into invalid waiting state; the second improvement was to do the state inspection for the maximum load node and all its descendant nodes in the operation process of updating the tree to avoid ineffective circulation. The tree balance was achieved by increasing the load of the minimum node and its descendant nodes with NRaSMaLai. The simulation experiment shows that these two methods can make the tree achieve the balance state or at least get closer to the presupposed state. When the sink node was located in the regional center, the iterative steps which make the tree balanced can be reduced to 1/5 of the original by NRaSMaLai and also it appears little oscillation. This is significant for the data collection tree's rapid convergence and the extension of the network's lifetime.