Evolutionary Game Analysis of Co-Opetition Strategy in Energy Big Data Ecosystem under Government Intervention

This study discusses how to facilitate the barrier-free circulation of energy big data among multiple entities and how to balance the energy big data ecosystem under government supervision using dynamic game theory. First, we define the related concepts and summarize the recent studies and developments of energy big data. Second, evolutionary game theory is applied to examine the interaction mechanism of complex behaviors between power grid enterprises and third-party enterprises in the energy big data ecosystem, with and without the supervision of government. Finally, a sensitivity analysis is conducted on the main factors affecting co-opetition, such as the initial participation willingness, distribution of benefits, free-riding behavior, government funding, and punitive liquidated damages. The results show that both government supervision measures and the participants’ own will have an impact on the stable evolution of the energy big data ecosystem in the dynamic evolution process, and the effect of parameter changes on the evolution is more significant under the state of no government supervision. In addition, the effectiveness of the developed model in this work is verified by simulated analysis. The present model can provide an important reference for overall planning and efficient operation of the energy big data ecosystem.

Prevalence and risk factors for mental health symptoms in community epidemic prevention workers during the postpandemic era of COVID-19 in China.

Few people have paid attention to community epidemic prevention workers in the postpandemic era of COVID-19. This study aimed to explore the prevalence and risk factors for mental health symptoms in community epidemic prevention workers during the postpandemic era. Mental health status was evaluated by the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Chinese Perceived Stress Scale, Insomnia Severity Index, and Maslach Burnout Inventory-General Survey. The results showed that a considerable proportion of community epidemic prevention workers reported symptoms of depression (39.7%), anxiety (29.5%), high stress (51.1%), insomnia (30.8%), and burnout (53.3%). The prevalence of depression and anxiety in community epidemic prevention workers was higher than in community residents. Among community epidemic prevention workers, short sleep duration was a risk factor for depression, anxiety, high stress and insomnia. Concurrent engagement in work unrelated to epidemic prevention and current use of hypnotics were risk factors for depression, anxiety and insomnia. Our study suggests that during the postpandemic era, the mental health problems of community epidemic prevention workers are more serious than those of community residents. Several variables, such as short sleep duration and concurrent engagement in work unrelated to epidemic prevention, are associated with mental health among community epidemic prevention workers.