With the continuous expansion of power grid engineering and the rapid enhancement of information technology, the complexity of accident scenarios has increased significantly, leading to an explosive growth in monitoring data. This study aims to address the limitations of current power grid emergency plans in large-scale data querying and on-site guidance, and assist emergency decision-makers in quickly generating response plans, accurately allocating emergency resources, and promote the digitalization of emergency plans. Toward this goal, this study proposes an improved method for constructing an ontology model in power grid emergency planning.

First, the traditional seven-step ontology construction method is refined based on the Toronto virtual enterprise (TOVE) and skeletal methods. In the refinement process, an "application scenario analysis" phase is introduced in the initial step to enhance the relevance of the ontology construction. Additionally, after creating ontology instances, a "qualitative and quantitative analysis" phase is adopted to verify the scientific validity and feasibility of the ontology, thereby improving model quality. Subsequently, the improved method comprehensively implements the goal determination and construction processes of the ontology model. These processes include defining knowledge in the power grid emergency planning domain; evaluating the reuse of existing ontologies; clarifying key concepts from legislation, emergency scenarios, and enterprise planning systems; and establishing class hierarchies and attributes. Next, the Protégé tool is employed for model visualization. For the example of the emergency plan for typhoon disaster events from a provincial power company, a model was constructed comprising 39 ontology categories, 24 relationship categories, and 14 attribute categories, supplemented by 408 entities, 774 relationships, and 334 attributes. Finally, the ontology model is applied to study the semantic network of emergency plans, designing a schema for the knowledge graph of emergency plans for power enterprises based on the resource description framework schema and web ontology language frameworks. The ontology model in the field of power grid emergency planning is visualized using Protégé. The richness and structural integrity of the model are evaluated using a HermiT1.4.3.456 reasoning engine and the ontology quality analysis method.

The results indicate that the relationship richness of the model approaches 1, suggesting a rich relationship structure; the attribute richness value exceeds 1, indicating reasonable attribute settings; and the richness of major classes is 1, whereas that of minor classes is 0.9474, close to 1, demonstrating a high utilization rate of classes. Overall, the model exhibits good rationality and practicality.

Empirical results demonstrate that this ontology model effectively addresses impracticality issues, usability, and relevance often encountered in emergency plans. It significantly enhances the efficiency of emergency personnel in response and decision-making while also improving the expressiveness and digital construction of knowledge related to power grid emergency planning.

Over the years, earthquake accidents have emerged as significant geological disasters, causing substantial and persistent human casualties and property losses that are difficult to avoid. Despite the growing public awareness regarding the importance of acquiring earthquake self-rescue knowledge and skills, practical improvements are not yet evident. This is attributed to the inherent fear in humans in response to intense stimuli from earthquake disasters, leading to severe stress reactions that hinder the execution of self-rescue actions, making the effective application of earthquake self-rescue knowledge challenging. This study focuses on assessing and improving stress levels among the public while facilitating the smooth execution of self-rescue actions during real earthquake scenarios.

This study involved 16 male and 16 female participants who conducted simulated experiments at an earthquake experience center. The experimental process included baseline measurements, measurements during progressive seismic events of magnitudes 3-7, and measurements during seismic intermissions. Respiratory and electromyographic measurements were used to collect data on the neural and behavioral dimensions of stress during baseline and stress periods. Significance analysis was conducted using the paired samples Wilcoxon signed rank test to time-domain and frequency-domain indicators that exhibited differences. Individual seismic stress level reference values were calculated on the basis of fluctuations in individual data compared with the overall data. Using the K-means clustering method, the distribution of different stress response levels was determined. In addition, the paper reviewed previous research findings and constructed the "S-O-R-A" earthquake "stimulus-response" model, which encompasses earthquake risk information perception, risk information understanding, self-rescue decision-making, and self-rescue execution stages. The study identified 13 factors related to seismic stress levels, including basic qualities, emergency knowledge, skills, experience, awareness, personality, emotional stability, visual reactivity, auditory reactivity, attention, memory, thinking, and physical fitness. An experimental plan was established for measurement based on the Jinshuju platform, PsyLAB, and the BCS-400 digital backforce gauge. By correlating seismic stress level reference values with influencing factors using Kendall's Tau-b method and employing multiple linear regression analysis, we ranked the importance of each influencing factor and provided recommendations based on significant factors for improving earthquake stress responses.

Physiological measurements revealed that indicators such as minimum value, mean value, standard deviation, variance, and mean frequency in respiratory signals, as well as maximum value, minimum value, mean value, standard deviation, variance, root mean square, and mean absolute value in electromyographic signals, exhibited differences during the experimental process, indicating their effectiveness as indicators for calculating stress levels. Based on regression analysis results, among the influencing factors, emergency skills (67.9%), earthquake training (58.4%), emotional stability (44.8%), visual reaction power (39.8%), auditory reaction power (39.0%), and memory (30.5%) were the six most significant factors affecting seismic stress levels in the public. Furthermore, to help the public overcome stress responses, the study proposed 12 recommendations, including "establishing popular science channels, innovating educational works, enhancing training participation, promoting practical training, improving memory capacity, strengthening cognitive memory, emphasizing technological empowerment, optimizing visual training, conducting specialized courses, enhancing auditory reaction, prioritizing psychological counseling, and conducting psychological construction, " and presented a mind map.

This study provides researchers with experimental design and calculation methods for assessing seismic stress levels while identifying factors that effectively improve earthquake stress responses and offering recommendations to the public. By effectively improving stress responses, the public can utilize their acquired self-rescue knowledge and skills, thereby enhancing their response to earthquakes.