Based on the complex structure of electric hybrid car and uncertainty in driving force structure of electric hybrid car, different strategies have been presented for optimal management of energy based on smart methods. In this study by the decision making nature of fuzz logic, a movement map for Parallel Hybrid Electric Vehicle (PHEV) is made based on the required path. In a parallel hybrid car, recharging control of battery and auxiliary torque of electric engine are used as the key points of movement. Based on the disadvantages of pure electric car, to increase the life of battery and its easy use, we need a movement strategy balancing the battery charge for a movement path. If the battery is charged at no load by the combustion engine, NOx emission is increased and the battery charge is not good and adequate for HEV performance under no-load condition by the energy retrieval power and combustion engine. For a movement structure, it is hard to define the conversion point between the motor performance and generator performance exactly. By a drive strategy based on crisp methods, the battery charge is sensitive to the moving samples of driver, path condition and load conditions. Using fuzzy control strategy to control varied non-linear systems is very suitable and it is robust against the changes of components of sub-systems and inexact measurements. New York City Cycle (NYCC) is considered to perform simulation. As shown in paper, the fuzzy control strategy can keep the charge stage of batteries at good range

Point cloud data reconstruction is the basis of point cloud data processing. The reconstruction effect has a great impact on application. For the problems of low precision, large error, and high time consumption of the current scattered point cloud data reconstruction algorithm, a new algorithm of scattered point cloud data reconstruction based on local convexity is proposed in this paper. Firstly, according to surface variation based on local outlier factor (SVLOF), the noise points of point cloud data are divided into near outlier and far outlier, and filtered for point cloud data preprocessing. Based on this, the algorithm based on local convexity is improved. The method of constructing local connection point set is used to replace triangulation to analyze the relationship of neighbor points. The connection part identification method is used for data reconstruction. Experimental results show that, the proposed method can reconstruct the scattered point cloud data accurately, with high precision, small error and low time consumption.

To solve the shortage problem of the computing power provided by the single machine or the small cluster system in scientific research, we offer a collaborative computing system for users. This system has massive operation ability. It introduced a scalable mixed collaborative computing model. Through the internet and the heterogeneous computing equipment, the system uses the task decomposition model. This system can solve the research and development problem because of the shortage of capacity. To test the model, a subtask decomposition example is used. The results of the example analysis show that the computing work can obtain the shortest computation time when the number of calculation nodes is more than the number of subtasks; Maximum calculation efficiency can be achieved when the number of the calculating nodes closes to the number of subtasks. Through joint collaborative computing, the extensible mixed collaborative computing mode can effectively solve the mass computing problem for the system with heterogeneous hardware and software. This paper provides the reference for the system, which provides large scale computing power through the Internet and the research problem of due to the lack of computing ability.

In vehicular ad hoc networks, the current method does not consider the delay of data transmission, resulting in slower vehicle data transmission speed. A vehicle data transmission method based on backbone network is proposed in this paper. Firstly, the characteristics of vehicle ad hoc network are analyzed. Based on the statistics of the road, the vehicle cluster is composed of the vehicles parking on the roadside and no roadside according to the different directions of the vehicle driving. The backbone network is constructed on the basis of the cluster of vehicles, and the data transmission between the vehicles is implemented by the data transmission method of overlay network. This method can overcome the disadvantages of traditional data transmission methods, improve the efficiency of on-board data transmission, and complete the research on fast data transmission method in wireless vehicle ad hoc network. The experimental results show that the proposed method can achieve higher data transmission success rate with lower data transmission overhead and smaller transmission delay.

The traditional UAV aerial photography system has the disadvantages of unclear imaging, low system efficiency and poor flight control effect. Thus, a digital aerial photography system based on wireless sensor network is proposed. Firstly, the principle of aerial photography system is analyzed, and the wireless sensor network is set up. A large number of wireless sensor nodes are deployed in the interval, and functions such as wireless communication and calculation are completed by nodes; the SN-RN data acquisition layer, the RN-UAV relay transmission layer and the UAV-DC mobile aggregation layer are designed to form a wireless sensor network architecture, and the UAV digital aerial photography technology is combined to form the wireless sensor network. Experiments show that the medium error, maximum error and medium error limit of the digital aerial photography of the UAV are low, the total working time of the system is short, and the accuracy of the flight execution of the UAV is maintained between 93%-95%, and always stable. Therefore, the overall imaging effect of this method is better, the system work efficiency is higher, the system control effect is better, and it is more practical and advantageous.

The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.