KEYWORDS: Data modeling, Data conversion, Education and training, Performance modeling, Deep learning, Convolution, Roads, Statistical modeling, Data storage, Data backup
In order to efficiently predict and analyze massive traffic big data and improve the intelligent level of road traffic rate and urban traffic, a high-precision parallel convolutional neural network traffic flow big data prediction model based on deep learning is proposed The model first preprocesses the data to obtain an effective data set, converts one-dimensional time series samples and images with regular time intervals into two-dimensional pixel grids with one-dimensional time and one-dimensional location, constructs a parallel convolutional neural network model to predict the traffic flow through a road section, and uses prediction factors to model the traffic flow data The experimental results show that, compared with other models, the model proposed in this paper is superior to the comparison method in terms of average absolute error, average relative error and root mean square error.
Aiming at the complex optimization problem of power electronic distribution network planning, a multi-objective genetic algorithm based on Multi Strategy improvement (genetic algorithm for short) is proposed. Combining genetic algorithm with distribution network planning effectively, the chromosome group coding mode of genetic algorithm in planning scheme is studied; The multi strategy improvement of genetic algorithm involves the improvement of population selection, crossover and mutation operators and adaptive genetic operators; Through the heuristic algorithm, the ability of searching the optimal solution of the population can be improved. The performance of the improved genetic algorithm based on multi strategy is tested by Schaffer function and Griewank function, and its composition, search characteristics and search optimization process are analyzed and discussed respectively. The results show that the improved genetic algorithm based on multi strategy has great advantages in search accuracy and calculation efficiency, and is of great value for distribution network planning and optimization.
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