State Grid Corporation of China

Beijing University of Posts and Telecommunications

China Agricultural University

State Grid Beijing Electric Power Company

Yifeng Ding

Lin Ma

Zhao Wang

Xianglong Li

Zhanyu Ma

Zongsheng Li

Anqi Liang

Mingquan Qiu

Dongliang Chang

Ping Chen

Qijing Xing

Jiyang Xie

Dapeng Duan

Shuang Zeng

Huanna Niu

Zhongwei Si

Xuwu Ge

A transaction harmony degree-based method and system for transaction matching between a power grid and building energy. The method includes: obtaining a transaction harmony degree of a historical transaction cycle; sending, by using a blockchain, the transaction harmony degree to a corresponding building energy user, and sending a maximum compensated electricity price and an expected compensated electricity price in peak-valley regulation to all building energy users; determining, by each building energy user, a transaction-based compensated electricity price in the peak-valley regulation based on the received transaction harmony degree and the received maximum compensated electricity price and expected compensated electricity price in the peak-valley regulation, and feeding back the transaction-based compensated electricity price in the peak-valley regulation to a power grid; and determining, by the power grid, a building energy user that successfully performs transaction matching with the power grid.

Transaction harmony degree-based method and system for transaction matching between power grid and building energy

The present disclosure provides a fine-grained image recognition method, an electronic device and a computer readable storage medium. The method comprises the steps of feature extraction, calculation of feature discriminant loss function, calculation of feature diversity loss function and calculation of model optimization loss function. The present disclosure comprehensively considers influences of factors such as a large intra-class difference, a small inter-class difference, and a great influence of background noise of the fine-grained image, and makes constrains such that the feature maps belonging to each class are discriminative and have the features of corresponding class, thus reducing the intra-class difference, decreasing the learning difficulty and learning better discriminative features. The constraints make the feature maps belonging to each class have a diversity, which increases the inter-class difference, achieves a good result, and is easy for practical deployment, thereby obviously improving the effect of multiple fine-grained image classification tasks.

Fine-grained image recognition method, electronic device and storage medium

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Beijing, China

Yifeng Ding