Green Innovation Industrial Institute, School of Economics and Management, Chengdu Technological University, Chengdu 610000, China.
School of Business College, Changchun Guanghua University, Changchun 130000, China.
J Environ Public Health. 2022 Jun 28;2022:1103561. doi: 10.1155/2022/1103561. eCollection 2022.
The purpose is to improve Chinese enterprises' economic benefit evaluation system based on big data and promote sustainable enterprise production. This paper studies the power supply enterprises-oriented Evaluation Index System (EIS) under the big data environment. Firstly, it expounds on the construction theory of the enterprise economic benefit model. Secondly, the comprehensive Grey Model (GM) based on improved weight and the power consumption prediction model based on Least Mean Square (LMS) neural network (NN) algorithm are introduced. Finally, the comprehensive GM model based on improved weight is used to evaluate the economic benefits of power supply enterprises. The power consumption prediction model based on the LMS-NN algorithm is used to predict the sustainable development of power supply enterprises. The results show that the profitability and solvency of joint-stock power companies are about 90 and 100, respectively, and the social contribution of state-owned power supply enterprises is the strongest. Lastly, it is predicted that the region will have 134.8 billion kWh of electricity and about 137.2 billion kWh of power consumption in 2020. The growth model and trend are consistent, but there are some errors in the specific power consumption data. Therefore, the audit method based on big data has a good evaluation effect on the economic benefits of enterprises. For example, the profits of private and joint-stock power supply enterprises are relatively high. In contrast, state-owned power supply enterprises have outstanding social contribution ability. The big data method is used to predict the power consumption in some areas, and the predicted value is consistent with the actual value. This study provides a reference for the follow-up economic benefit evaluation and sustainable development of enterprises.
目的是基于大数据改进中国企业经济效益评价体系，促进企业生产的可持续性。本文研究了大数据环境下面向供电企业的评价指标体系（EIS）。首先，阐述了企业经济效益模型的构建理论。其次，引入了基于改进权重的综合灰色模型（GM）和基于最小均方（LMS）神经网络（NN）算法的电力消耗预测模型。最后，采用基于改进权重的综合 GM 模型对供电企业的经济效益进行评价，采用基于 LMS-NN 算法的电力消耗预测模型对供电企业的可持续发展进行预测。结果表明，股份制电力公司的盈利能力和偿债能力分别约为 90 和 100，国有供电企业的社会贡献能力最强。最后预测 2020 年该地区将有 1348 亿千瓦时电量和约 1372 亿千瓦时用电量，增长模式和趋势一致，但具体用电量数据存在一些误差。因此，基于大数据的审计方法对企业经济效益具有较好的评价效果。例如，私营和股份制供电企业的利润相对较高，相比之下，国有供电企业具有突出的社会贡献能力。该方法用于预测某些地区的用电量，预测值与实际值一致。本研究为企业后续经济效益评价和可持续发展提供了参考。
