An Effective Credit Evaluation Mechanism with Softmax Regression and Blockchain in Power IoT

Da Li, Dong Wang, Wei Jiang, Qinglei Guo, Desheng Bai, Wei-Nan Shi, Linna Ruan
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引用次数: 1

Abstract

This paper is oriented to the credit investigation scenario of power grid supply chain enterprises and proposes a blockchain user credit assessment method based on improved Softmax regression in Power IoT. This method first designs a credit-rating mechanism that meets industry characteristics based on business needs. Second, it proposes a user credit evaluation model based on the blockchain architecture. Finally, the improved Softmax regression algorithm is used to train the proposed credit evaluation model, which effectively solves the credit rating. The multiclassification problem has achieved the goal of categorizing the credit rating of the enterprise. The simulation results show that the credit evaluation mechanism proposed in this paper can accurately evaluate the multisource credit data that lacks trust foundation and effectively realize the credit rating of power grid material supply chain enterprises. The credit evaluation mechanism proposed for Power IoT in this paper could have high potential for entity identity authentication and rating for securing mobile video communications.
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基于Softmax回归和区块链的电力物联网有效信用评估机制
本文针对电网供应链企业征信场景,提出了一种基于改进Softmax回归的电力物联网区块链用户信用评估方法。该方法首先根据业务需求设计符合行业特征的信用评级机制。其次,提出了基于区块链架构的用户信用评估模型。最后,利用改进的Softmax回归算法对所提出的信用评价模型进行训练,有效地解决了信用评级问题。多分类问题达到了对企业信用等级进行分类的目的。仿真结果表明,本文提出的信用评估机制能够准确评估缺乏信任基础的多源信用数据,有效实现电网物资供应链企业的信用评级。本文提出的电力物联网信用评估机制在保障移动视频通信安全的实体身份认证和评级方面具有很高的潜力。
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