OMO teaching mode based on artificial intelligence big model is one of the important future research directions and application landing forms in the future education field. The learning path recommendation algorithm based on big language model is constructed by integrating Transformer architecture, neural network architecture and self-attention mechanism. Combining it with the course knowledge graph, it links the learners with the knowledge system and visualizes the results of the intelligently planned learning path. The study shows that compared with several other algorithms, the personalized learning path recommendation algorithm based on AI big model has better convergence speed and stability. The optimal solution for learning path planning is found after only about 90 iterations. Taking “Chemical Process and Control Simulation” as the target course, the method in this paper gives the learning path and course. Through the questionnaire survey, the mean value of the four dimensions of pre-class pre-study, classroom exploration, post-class enhancement, and learning satisfaction is more than 3 points, which indicates that the OMO model and the teaching model of the artificial intelligence big model have a better experience.

Accurate distribution system topology is of great significance for distribution network planning operation and analysis. This project constructs a distribution system network model, applies graph convolutional network and graph attention network in graph neural network, and designs the topology identification method of distribution system. On this basis, a reconfiguration model of the distribution system is given, and the network structure after topology identification is used for trend calculation, and the model reconfiguration is realized by using the extensive learning quantum evolutionary algorithm. Through experimental analysis of several test systems, it is found that the topology identification F1 values of this paper’s method are all above 0.9, which are 5.64% to 29.64% higher than other methods, confirming the good accuracy and robustness of the GNN topology identification model. In addition, the CLQIEA method can give the correct distribution system reconfiguration optimization scheme, which reduces the network loss to a larger extent and improves most of the node voltage values, and the network loss decreases by 31.91% and 56.11%, and the voltage values are improved by an average of 1.95% and 1.23% in the two test systems, which makes the power supply of the distribution system of a higher quality, and the operation of the power supply system is more economical, which is important for the distribution automation and the power supply department’s optimal scheduling is of great significance.

On the basis of ensuring the balance between supply and demand of the power grid, fully realizing the automatic control of the air conditioning system can make the energy consumption of the air conditioning operation reduce significantly, thus realizing the purpose of energy saving. This paper combines a variety of technologies to establish an intelligent air conditioning measurement and control system, realizes terminal communication through the CoAP protocol, and designs the corresponding system hardware as well as the real-time data acquisition method for air conditioning equipment. Based on the PID principle, the temperature and humidity control strategy of air conditioning equipment based on expert PID is proposed. In order to better ensure the energy-saving control efficiency of air-conditioning equipment, this paper fully considers human thermal comfort and the interaction between supply and demand of the power grid, establishes a comprehensive optimization control model with the objectives of user power consumption and human comfort, and passes through the PSO algorithm in order to obtain the optimal control results. Simulation found that when the initial temperature is lower than the set value, the expert PID control strategy will adaptively realize the air conditioning temperature and humidity adaptive regulation to ensure that the indoor temperature is within a reasonable range. The total power consumption of the grid is reduced by 90.18kW compared with that before optimization, and the maximum value of human comfort evaluation is improved by 11.39%. Relying on the intelligent air conditioning control system, the adaptive control of temperature and humidity can be effectively realized and the indoor air quality can be better ensured, and a reliable control strategy can also be provided to ensure the balance between supply and demand of the power grid.

This paper follows the active reactive power cooperative control strategy of station voltage autonomy, combines the operation scenarios of the autonomous control strategy within the group, and establishes the reactive power optimization objective function of the low-voltage distribution network to improve the voltage quality and reduce the active loss, which takes into account the installation location of reactive power compensation device, and the constraints include the system power balance constraints and voltage quality constraints. In order to solve the reactive power optimization model of low-voltage distribution network containing distributed photovoltaic, the uniformity of the population distribution of the MPA algorithm is initialized using Bernoulli mapping, the inertia weight function and elite strategy of nonlinear attenuation are introduced to enhance the optimization capability of the MPA algorithm in the iterative process, and the eddy-current and fish aggregation effects are applied to widen the scope of optimization search. The network loss and voltage amplitude of the proposed strategy are analyzed to compare the changes of node voltage, voltage offset, objective function value and branch circuit active loss before and after the voltage autonomous reactive power control of low voltage stations. After adopting the optimization strategy of voltage autonomous reactive power control for LV stations, the branch circuit active loss of LV distribution network decreases with the increase of the proportion of distributed PV, and the branch circuit active loss of LV distribution network can be reduced by up to 60%.

Bitumen is a high-quality raw material for the preparation of carbon materials due to its high carbon and low ash characteristics, and its use in the preparation of supercapacitor electrode materials plays a significant role in the enhancement of the economic benefits of the entire coal chemical process. In this paper, the raw materials and experimental equipment required for this study were selected to prepare porous carbon samples under the guidance of the raw material pretreatment process. After completing the preparation of porous carbon samples, the finite element analysis software ANSYS was used to investigate the effect of bitumen pretreatment on the structure and electrochemical properties of porous carbon. With the rising air oxidation time, the peak ratio of porous carbon showed a trend of decreasing and then increasing, with specific values of 2.627, 1.958, 2.083, and 2.486, which was the same trend as that of the XRD test results, suggesting that the asphalt pretreatment has a moderating effect on the structure of porous carbon. The study in this paper further recognizes the effect of asphalt pretreatment on the structure and electrochemical properties of porous carbon, which provides a reference for research and development and innovation in materials chemistry.

Asphalt mixture is a multiphase composite material composed of aggregates, asphalt, fillers and other materials of different properties, in which the coarse aggregate forms the main bearing structure, and the fine aggregate fills the voids formed by the coarse aggregate to improve the structural stability. In this paper, computerized tomography is used to obtain the preliminary tomographic images of asphalt mixture specimens, and the image is effectively segmented through the grayscale thresholding method, and the scanning results are refined. Using voxel-based three-dimensional reconstruction method, the three-dimensional finite element model of asphalt mixture is reconstructed, and the corresponding fine structural characterization index is proposed to prepare asphalt mixture specimens and study the fine structural characteristics of asphalt mixture. The distribution characteristics of the contact connectivity tree of the asphalt mixture are analyzed, with 61.54% of the primary and middle order trees in gradation 1, 92.31% in gradation 2, 83.33% in gradation 3 and 58.82% in gradation 4. It shows that the higher the percentage of second-order connectivity tree, the worse the skeleton contact connectivity, which is not conducive to the improvement of asphalt mixture shear strength. For the four different gradation specimen slice images, the generated areas of each order tree were statistically analyzed. Most of the primary order tree areas were distributed between 100 mm²-300 mm², the intermediate order tree areas were basically distributed between 200 mm²-400 mm², and the high order trees were distributed between 400 mm²-500 mm². The area distribution of high-order tree of grade 4 is more uniform and concentrated, which has better load transfer chain and rutting resistance.

Traditional digital signatures are often publicly verifiable, and in certain applications with privacy preservation requirements, the signer does not want the sensitive information it signed to be redelivered by a dishonest verifier. Aiming at the problem that traditional chameleon signatures (CS) cannot resist quantum computer attacks, this paper proposes a lattice-based authentication CS scheme. Based on the analysis of the lattice difficulty problem and the security vulnerability of the CS scheme, it is pointed out that it does not satisfy the third-party unforgeability and the signer rejectability, and a new lattice-based identity CS scheme is established, which is verified under the stochastic predicate machine model, and the storage and transmission efficiency of the scheme is analyzed. The results show that the newly designed identity-based CS scheme on the lattice can effectively resist quantum computer attacks, can sign messages of arbitrary length, and possesses more lightweight storage and transmission efficiency. The optimized chameleon signature scheme has better security and also provides a new solution for digital signatures to resist quantum computer attacks.

Appropriate use of emotions as a means to intervene in students’ sports behaviors in physical education can promote individuals to form correct concepts of sports and physical exercise. In this paper, in order to construct an emotion intervention model, a cross-temporal adaptive graph convolution network (CST-AGCN) model for whole-body limb emotion recognition is proposed by using the method of spatio-temporal graph convolution. The model was applied to the first stage of negative emotion intervention, after which the appropriate intervention strategy was selected from the intervention strategy library. Then the system was used to assist the teacher in completing some of the intervention initiatives. Finally, based on the empirical study and the system, the learners’ classroom status after the intervention was analyzed again. In addition the study also designed strategies related to enhancement of students’ mental health to further promote students’ physical and mental health. After applying the emotional intervention model and mental health enhancement strategies to the second year (1) class of Secondary School S, this group of students showed significant differences in subjective experience, emotional vitality, body value, interpersonal perception, and dilemma coping, and their mental health was significantly improved. Physical education scores were 7.96 points higher compared to the traditional teaching class, and anxiety decreased significantly. It indicates that the intervention model and mental health enhancement strategies in this study can reduce students’ anxiety behavior and have a more significant relief of students’ negative emotional symptoms such as anxiety and depression, thus promoting the quality of physical education teaching.

This paper constructs a scientific and systematic model for evaluating the quality of Civic and Political teaching in physical education courses with the core concept of establishing morality and combining the intrinsic requirements of collaborative parenting between physical education courses and Civic and Political education. The evaluation indicators use the hierarchical analysis method to assign weights to the established indicators, and at the same time, the consistency test is carried out to ensure that the weights are assigned reliably. The evaluation model is applied to a sports college, scored by questionnaire survey, and combined with the fuzzy comprehensive evaluation method to realize the rating division of the teaching quality of the college. At the beginning of the study, the first-level indicator “Chinese sportsmanship” was rated by experts as low, with a mean value of 2.4, so it was revised to “professionalism”. The importance of the indicator “ideal belief” compared with other level 1 indicators in the evaluation model ranges from 2.24 to 2.65, with the highest weight of 0.308. A university implemented the evaluation model in this paper, and the quality of the university’s comprehensive sports ideology teaching was rated at 4.36 points, which is a good rating. Among them, most students rated the secondary index under “ideal belief” as excellent. The results of the study can be used as a theoretical basis and a practical tool to promote the design and evaluation of the Civics teaching in college sports courses.

The recent frequent occurrence of students’ psychological crisis events has drawn widespread attention to mental health education in colleges and universities. Based on students’ behavioral data, we use big data and data mining technology to model and analyze students’ daily behaviors, complete the construction of students’ social intimacy features based on Dijkstra’s algorithm, use the C4.5 decision tree improvement algorithm based on variable-precision rough set to realize the identification of students’ psychological problems, and analyze the intervention paths of students’ psychological problems and the evaluation of the results of the intervention. The proposed method can recognize students’ psychological problems more accurately, and the recognition accuracy of different levels of psychological problems reaches more than 72%, which is significantly higher than other classification methods. Learning anxiety, loneliness tendency and terror tendency of students in the intervention group were significantly reduced after the psychological intervention (P < 0.05), and the overall factor scores decreased by 9.85%, and the level of mental health was answered to be improved, which reflected the effectiveness of the proposed mental health intervention. The experiment proves that the model in this paper can effectively identify students with psychological abnormalities, and the proposed intervention path for students' psychological problems has a positive impact on the development of students' mental health.