This paper firstly constructs a coupled evaluation index system based on three primary indicators, seven secondary indicators and 29 tertiary indicators for agriculture, culture and tourism. Then the entropy weight-TOPSIS method and the coupling coordination degree model are selected to measure the development of agriculture, culture and tourism industry and the coupling level in Yijun County respectively. Finally, 23 spatially related villages in Yijun county area are selected to reveal the reasons for their spatial differences with spatial measurement model, and analyze the factors affecting the coupled and coordinated development of agricultural, cultural and tourism industries in Yijun county area. From the comprehensive evaluation results, the trend of the development level of agriculture, culture and tourism in 2017-2023 was generally upward, in which the agriculture industry had the highest growth between 2022 and 2023, with an increase of 0.0445. After analyzing the factors influencing the development of the coupled agriculture, culture and tourism industries in the Yijun county region by applying the spatial Durbin model, it was found that the general budget expenditures, human capital, infrastructure construction, fixed asset investment and education investment in the region at a significant level of 0.01 correlation of 0.211, 0.03, 0.082 and 0.085, and education investment in the region at a significant level of 0.1 correlation of 0.211. These five factors significantly affect the Yijun County region agriculture, culture and tourism industry, and deepen the development of integration of tourism industry.

In recent years, intelligent control has realized rapid development in the field of electrical engineering, the article initially studied the principle of electrical intelligent control, accordingly built the electrical intelligent control system, and designed the system hardware, the system module is divided into the main control module, the expansion module, the digital input and output module and the mounting rail. Based on the working principle of fuzzy control, design the software of the electrical intelligent control system, and optimize the traditional fuzzy controller by using fuzzy adaptive hybrid genetic algorithm, so as to improve the fuzzy control accuracy of the electrical intelligent control system in this paper. The electrical control system of this paper is applied to greenhouse greenhouse temperature and humidity control, substation air conditioning energy consumption control and subway station illumination control, and the control effect of the electrical intelligent control system of this paper is known through three experimental data. The system of this paper can effectively deal with the dissimilar data in the greenhouse temperature control experiment. Under the steady state environment, the temperature deviation of this paper’s fuzzy control method and conventional single structure fuzzy control is within 0.1℃ and 1℃ respectively, and the humidity deviation is within 5%RH and 10%RH respectively. Obviously, the fuzzy control method in this paper has higher control accuracy. In the substation air conditioning energy consumption experiment, the annual power consumption of this paper’s electrical intelligent control system and the traditional ventilation and air conditioning system are 32,660 degrees and 45,620 degrees, respectively. The electrical intelligent control system in this paper can save 22,000 yuan per year. The output illuminance of the subway station of the fuzzy control system in this paper increases with the comfort of the light environment and the density of the crowd, which achieves the expected effect.

In today’s rapid development of information technology, the big data industry has ushered in explosive growth, and big data analysis has become an important research topic in the cross-cutting field of computing. This study constructs a big data prediction base model based on deep learning, and uses the improved butterfly optimization algorithm with OGRU model to realize feature selection and classification processing of big data. Then the Adam algorithm is used to optimize the parameters of the model, and finally the classification and prediction model of big data based on deep learning is constructed. Simulation and empirical analysis results show that the model proposed in this paper has excellent classification and prediction performance, and can meet the efficiency requirements of big data classification and prediction. The prediction errors of distribution network load data and smart charging pile operation data are lower than 9% and 16%, respectively, which have high practical application value. This study is of great significance to the research related to big data classification and prediction in different fields, and provides an effective method for data prediction in complex scenarios such as industrial as well as power grid scheduling.

Big data is an important foundation in social economy, science and technology, life and other fields, which also becomes a strategic emerging industry and has a crucial impact on the development of enterprises. As a new business model, its development is greatly limited due to the huge amount of data and difficult management. At present, there are many problems in power trading enterprises, such as backward management and low efficiency. The development of big data and blockchain technology would provide new management models for power trading enterprises and eliminate data inconsistency. It can improve data quality and help improve work efficiency, so as to reduce operating costs. Therefore, this paper introduced big data and blockchain based on fuzzy algorithm into the research of digital transformation of enterprise management. Blockchain technology provided technical support for enterprise data management. By starting from the concept of big data and blockchain, this paper would study and analyze how to promote the digital transformation of enterprise management. The research results showed that big data and blockchain based on fuzzy algorithm could promote the digital transformation of power enterprise management and improve the digital transformation process of power enterprises. This was about 11% higher than the digitalization process of traditional enterprises, and the satisfaction score was about 14.7% higher. Through data governance, the speed of digital transformation of power enterprise management was improved.

With the rapid expansion of high-speed railway network, the real-time monitoring of trackside equipment becomes particularly important. To detect trackside equipment information more accurately, a YOLO-R algorithm grounded on the improved You Only Look Once v3 (YOLOv3) algorithm is proposed, and the trackside equipment identification and detection model is constructed. By introducing feature pyramid network and adaptive Bessel curve network, the new model can effectively identify and locate different types of trackside equipment such as switch machine, derailer, and shaft counter. The experiment findings denote that the new model is superior to the existing technology in all aspects of on-orbit equipment recognition and detection, the computer resource occupancy rate is only 22%, the image recognition accuracy rate is more than 98%, and the processing speed is up to 200 images/second. This research not only raises the automation level of trackside equipment monitoring, but also provides a powerful technology for railway safety operation.

Generative artificial intelligence represented by ChatGPT has attracted wide attention in the field of education because of its powerful generative ability, both personalized learning, understanding the learner’s motivation, and providing personalized tutoring and feedback for education. With the advent of the Education 2.0 era, smart classroom has become a strategic choice for the construction of education modernization, and is widely used in higher education and vocational education. Generative AI enlightens students’ engineering thinking, computational thinking, design thinking and systems thinking, which not only helps students to master their professional courses, understand what they have learned, and improve their academic performance, but also assists teachers in updating their course content, keeping abreast of students’ learning trends, improving their teaching efficiency, and simplifying their work. However, generative AI is faced with expertise gaps and uncertainty about the existence of generated content in its application, as well as ethical issues, and this study proposes that the needs and values of education should be respected, with the aim of efficient and convenient services, and that data-driven and ethical ethics should be emphasized in future development. Smart classroom and enlightened thinking with the application of generative AI is a new way of thinking about educational change, which can help teachers and students to effectively carry out multiple interactions, enable teachers to better understand students, play the role of human beings in education, and truly allow technology to be used for teaching and promote classroom teaching reform.

Ultra-low energy buildings for building energy efficiency development, compared with traditional buildings have obvious advantages. This paper simulates ultra-low-energy residential buildings in severely cold regions through Software PHES, and calculates the energy-saving results of ultra-lowenergy residential buildings. The carbon emission factor method is analyzed, and the carbon emission factor is calculated at different stages in the life cycle of the building. Select ultra-low-energy residential buildings in cold regions for modeling, input meteorological parameters, indoor environmental parameters and internal disturbance settings, building envelope, and combine with heat recovery system to simulate the operation of ultra-low-energy residential buildings in cold regions. Analyze the indoor and outdoor temperature and humidity values of traditional houses and compare them with those of ultra-low-energy-consumption houses to verify the advantages of ultralow-energy-consumption residential buildings. Calculate the energy-saving efficiency of ultra-lowenergy residential buildings. Using the 9# residential building of Ruihu·Yunshanfu in Datong as a practical verification case, this ultra-low energy residential building has a total life-cycle carbon emission of 171.078 tCO₂/a, with a unit area carbon emission of 16.415 kgCO₂/m²·a. Compared to the energy-saving design standards implemented in 2016, the carbon emission intensity is reduced by 60.02%, fully confirming the carbon reduction benefits of ultra-low energy residential buildings in severe cold regions.

In recent years, the development of study activities is in full swing. In order to study the eco-education effect in national park study activities, this paper introduces Bayesian network and constructs an ecoeducation effect assessment model based on Bayesian inference. In the comparison of the absolute error of the assessment value with other assessment models, the assessment accuracy of the Bayesian inference assessment model in this paper is obtained. After constructing the ecological education effect assessment index system and completing the assignment, the level of ecological education that should be achieved in the national park study activities is obtained through Bayesian inference diagnosis. Finally, according to the results of education effect assessment, the probability of each indicator being in various states is obtained by simulation using Monte Carlo method. The mean absolute error of the Bayesian assessment model is 0.26 points, which is smaller than other comparative assessment models and has the highest assessment accuracy. The model’s ecosystem principles, anthropogenic intervention impacts, ecological disasters and ecological protection measures should be guaranteed to reach 75.6, 64.8, 67.9 and 69.4. The ecological operation rules (59.4→79.8), climate change (50.6→70.2), biodiversity reduction (52.2→69.8), and pollution prevention and control (56.4→78.3) have the highest accuracy for the ecosystem principle, anthropogenic intervention impacts, ecological disasters and ecological protection measures, respectively. , anthropogenic intervention effects, ecological disasters and ecological conservation measures, and ecological education effects had the greatest impact. The overall score of ecological education effect was 84.1, and the scores of ecosystem principle, human intervention impact, ecological disaster and ecological protection measures were 83.8, 85.2, 83.0 and 84.2.

For enterprises, development is ultimately reflected in the task completion performance of employees, and in order for employees to create higher task performance, it is necessary to consider not only their education and knowledge level, but also their emotional management ability. This study first collects data related to employees’ emotion management ability and task completion efficiency improvement through questionnaires, and then analyzes the statistical data by using the potential impact identification model designed based on Bayesian neural network model to obtain the potential impact probability of each dimension of emotion management ability on task completion efficiency improvement. The analysis of the forward and reverse inference probabilities of the Bayesian network model indicated that the most important potential influence factor leading to the improvement of task completion efficiency was the emotion expression ability, with a forward and reverse inference probability of 36.2% and 59.4%, respectively, followed by the emotion regulation ability and emotion acceptance ability. The results of this study reveal the important potential influence of emotion management ability on task completion efficiency enhancement, and the formulation of task completion efficiency enhancement strategies based on the perspective of emotion management ability can effectively enhance employee task performance, which in turn promotes the overall development and competitive advantage of enterprises.