Large-scale, complex, and high-risk industrial systems such as nuclear power plants have developed detailed operating procedures. Under the expected conditions, the operators operate the system according to the operating procedures to avoid human error. However, under complex and unfamiliar conditions, once the guidance of operating procedures is lost, serious consequences may be caused. This paper proposes a No-Procedure guided Operation Supervision Technology (NoP-OST). The key idea is to identify the success paths by a forward-searching approach from source to sink functions. On this basis, the success paths are combined to generate various operation schemes to achieve the main objectives of the system. The paper illustrates the application of NoP-OST through the case of loss of the Residual Heat Removal System (RHRS) in a Pressurized Water Reactor (PWR) nuclear power plant. The research results show that the method proposed in this paper can provide a valuable operational reference for complex systems by making use of the available functions of the system.

Human error is still one of the main factors threatening the safe operation of nuclear power plants. In view of the deviation of operators’ actions from operating procedures and the impact of implementation of operating procedures on safety functions, this paper proposes the Operating Procedures Supervision System (OPSS). The OPSS monitors operators’ performance deviating from operating procedures, determines the operation state of a nuclear power plant according to realization of the three key safety functions of the plant (reactivity control, radioactivity containment, and residual heat removal), and evaluates the impact of the implementation of operating procedures on the safety state. Although the OPSS supports both off-line and online applications, this paper presents only an example of off-line evaluation to illustrate the method. On this basis, the paper proposes a risk matrix to comprehensively weigh operator action deviation and the impact of operator actions on plant safety functions, which can more finely perceive the changes of system safety status and of operator performance.

The introduction of digital technologies into the main control room of a nuclear power plant also introduces new human errors. The operator log records the control information of operators on systems and equipment, and provides an important data source for the retrospective investigation of operating events in a nuclear power plant. A traditional operator log review is conducted manually, which has some major problems, such as being time-consuming and inefficient. This paper proposes an automatic detection method for operator logs, which models an operating procedure at three levels, including procedure, step and action. Such a model clarifies the overall logic and basic attributes of the operating procedure, and can be used as a standardized template of a control action sequence to compare with the actual operation actions in the operator log, so as to identify possible human performance deviations. This paper explains the method, and discusses the advantages and limitations of the proposed method.

Under the control of the epidemic, the main way of working for knowledge workers is to work from home, and these groups of people often do not pay enough attention to their physical and mental health. In order to understand the impact of online physical exercise behavior on the physical and mental health of knowledge workers who work from home, and put forward the physical exercise methods of knowledge workers who work from home under the background of epidemic lockdown and control. We will use the questionnaire survey and obtain the factors that affect the exercise of knowledge working from home, and use mathematical statistics to analyze the data collected. Through the analysis, we find the relationship between online physical exercise and the physical and mental health of knowledge workers working from home. The results of mathematical statistics show that online physical exercise has a positive effect on improving the physical and mental health of knowledge workers working from home, but it takes time. Finally, at the significance level of ɑ=0.05, the respondents generally believe that online physical exercise can effectively improve the physical and mental health of knowledge workers working from home. Therefore, if the policy of epidemic control cannot be changed, we should consider popularizing exercise behavior to knowledge workers working from home.