Abstract: A deep learning-based method for fusing multi-source urban energy data and a storage medium are provided to perform data fusion on multi-source urban energy data found in big data and perform multi-scale and multimodal information fusion by using a cross-modal transformer, thereby implementing cross-modal mutual fusion of multi-source heterogeneous types of data to obtain a fused feature for prediction of a quantity of energy that will be used in the future and a quantity of energy that needs to be produced. The present disclosure proposes a multi-scale cooperative multimodal transformer architecture to enhance an effect of representation learned from an unaligned multimodal sequence. Not only there is a higher degree of correlation in multi-source urban energy data fusion, but also a system becomes more lightweight.

Abstract: According to a transformer-architecture-based method and system for IoT for and smart control of urban integrated energy, urban integrated energy IoT information acquired by a terminal from different areas is output to a server after being standardized into a sequence signal, where energy demand prediction is performed through learning of a deep learning network configured on the server, a smart control network is configured and built by using the deep learning network, real-time prediction processing is performed on the urban integrated energy IoT information acquired in real time, and a result is uploaded to a workstation for reviewing. Based on the transformer architecture, the deep learning network is combined with a fast Fourier transform algorithm and inverse transform on a sequence, and an FFT-Attention mechanism is proposed. Compared with a conventional transformer architecture, frequency domain information of a sequence is given more emphasis.

Abstract: Implementations of this specification provide prediction methods and apparatuses for adjusting computing power. One method comprises receiving a prediction request, wherein the prediction request comprises a sample to be tested, determining a computing power coefficient allocated to the prediction request, wherein the computing power coefficient indicates a proportion of hardware computing power resources allocated to the prediction request to total hardware computing power resources needed for a neural network model to run on a computing platform, determining k sub-networks inn sub-networks of the neural network model to be used for a present time based on the computing power coefficient, where n>2, and inputting the sample to be tested to the k sub-networks to obtain a prediction result.