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In the power system, power marketing is a very important work. How to evaluate whether the currently used power marketing strategy is efficient and useful has always been the most important concern of the power system. This paper proposes an efficient electricity marketing strategy evaluation method based on multi-dimensional clustering algorithm, aiming to evaluate whether a new policy has an incentive effect on enterprises through the means of big data. First, we use big data to group motivated users, and then analyze the effect of personalized marketing and key marketing on target customer groups. Based on the existing marketing methods, the influence of different target groups is analyzed, and finally the feasibility of the proposed method is verified through two experiments. In the A and B companies selected in Experiment 1, the average errors of the predicted value and the actual value are 1.4% and 1.2%, respectively. For the 12 industries in the C area selected in experiment 2, the average error between the predicted value and the actual value is 0.55%. Both experiments show that the method proposed in this paper has certain applicability. The method in this paper can be applied to the marketing process of the power supply company, and the corresponding promotion strategy is put forward, which is helpful to improve the management level of the power supply enterprise and increase the economic income of the power supply enterprise.

Background: Females with novel coronavirus disease 2019 (COVID-19) state-ordered home isolation were associated with higher anxiety and reduced sleep quality than males. Sex differences in psychobehavioral changes during the COVID-19 stay-at home orders among healthcare workers remained unclear. The purpose of this study was to explore the sex differences in psychological burden and health behaviors among these persons. Methods: This was a cross-sectional study using online data available in the open Interuniversity Consortium for Political and Social Research (OPENICPSR). Healthcare workers including females and males who transitioned to working from home during the COVID-19 stay-at-home orders were included. Sex differences were compared using the chi-square test and Student's t-test. We performed logistic and linear regression analyses to determine the association of females with psychological burden and health behaviors. Results: A total of 537 respondents (425 females and 112 males) were enrolled in our study. Sex differences in age (42.1 ± 12.3 years vs. 46.6 ± 15.7 years, t = −2.821, p = 0.005), occupation (χ(2) = 41.037, p < 0.001), mood change (n = 297, 69.9% vs. n = 61, 54.5%, χ(2) = 9.482, p = 0.002), bedtime schedule (χ(2) = 6.254, p = 0.044) and news consumption (n = 344, 80.9% vs. n = 76, 67.9%, χ(2) = 8.905, p = 0.003) were statistically significant. Logistic regression showed that females was negatively associated with better mood status (OR = 0.586, 95% CI 0.153–2.247, p = 0.436). In addition, linear regression showed that females were not correlated with total sleep time after adjusting for sio-demographics, mental health outcomes and health behaviors (B = 0.038, 95% CI −0.313–0.388, p = 0.833). Conclusion: No sex differences in psychological burden and health behaviors of healthcare workers were found during the COVID-19 stay-at-home orders. The COVID-19 state-ordered home isolation may be a potential way to reduce disproportionate effects of COVID-19 pandemic on females and help to ...