Aiming at the problem that there are many deficiencies in the existing dressing recommendation service, a big data dressing recommendation service system based on the optimization algorithm of BP neural network combined with the ant colony is designed. First, based on the weather data of the city where the recommended service object is located, the weather data set is constructed, and then, the dress recommendation model is constructed, based on the current weather data set, the dressing index is calculated, and finally, the push set is generated according to the dressing index recommendation table to recommend the dressing to the user. Based on the real data collected by the smart light pole, the local weather data set is constructed to achieve the recommendation results, and the recommendation results show that the recommendation algorithm improves the recommendation accuracy. Addresses issues such as inaccurate recommendation results.
Based on the current characteristics of street lighting, the sensor data is extracted from the existing smart lighting devices, and the lighting failure prediction model of pigeon optimal BP network is built. The algorithm introduces magnetic navigation and landmark navigation in pigeon-inspired optimization into BP network, which solves the drawbacks of slow convergence and easy falling into local optimum of traditional network. The simulation results show that the algorithm can be applied to the prediction and diagnosis of urban street light illumination faults.
KEYWORDS: Data modeling, Error analysis, Differential equations, Systems modeling, Statistical modeling, Process modeling, Matrices, Data centers, Probability theory, Light sources and illumination
The Grey model GM(1,1) is always used in dynamic prediction of grey system. But actually, it is difficult for transmission and distribution loss to grow exactly as GM(1,1), which makes loss of prediction accuracy and limits its application in reality. By combining GM(1,1) with the Markov model, we predict the transmission and distribution loss. The result indicates: the Grey-Markov model can improve the prediction accuracy of transmission and distribution loss.
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