Deep reinforcement learning, as an advanced machine learning method, is capable of automatically learning optimal decision-making strategies in complex environments. The core objective of this paper is to apply deep reinforcement learning algorithms to SolidLab’s microcontroller programming in order to realize the intelligent control of the linear one-stage inverted pendulum system. The study takes the linear one-stage inverted pendulum produced by A Technology Company as the control object, and adopts the model-free control structure of the deep reinforcement learning algorithm to build the controller and conduct virtual simulation experiments. Comparing the experimental effects of LQR and DQN algorithms, the LQR algorithm is better than the DQN algorithm in stabilizing pendulum control of inverted pendulum. Accordingly, a balance controller based on the offline Q learning algorithm is further designed to realize the inverted pendulum stabilization in kind. After optimizing the design strategy, the inverted pendulum system can be rapidly stabilized within 0.9s when it is perturbed by a small angle of about 12°. It shows that the method in this paper can realize the intelligent control of the inverted pendulum system at the linear level.

The steady development of economy makes the number of high-rise buildings increasing, and the water supply and drainage system becomes an important part of high-rise building construction. The purpose of this paper is to explore the drainage efficiency and fire fighting efficiency of BIM and green new energy in water supply and drainage system. Revit Mep software is mainly used to design the fire fighting module in the drainage system of high-rise buildings by combining BIM technology and new energy. A simulation mathematical model of water supply and drainage efficiency (including fluid control equations, etc.) was designed, and simulation experiments were conducted on the fire treatment efficiency of green new energy fire protection technology. The investigators’ ratings of the evaluation indexes of the water supply and drainage system design of BIM synergistic green new energy fire protection technology ranged from 4.06 to 4.54, indicating the feasibility of the approach. When the building floor is 25 floors, the water supply and drainage efficiency of the system in this paper is 80.45% and 84.52%, respectively. The fire fighting simulation experiment shows that the green new energy fire fighting technology integrating BIM can reduce the temperature of the room to 200~500℃ in 5 minutes. In the experiment, the method can extinguish the fire quickly and the residual concentration of smoke after extinguishing can be reduced to the normal range.

The purpose of this paper is to explore the effectiveness of integrated energy electric energy substitution in agriculture in the environment of energy saving and emission reduction. The fuzzy clustering algorithm is used to divide different family clusters according to the energy saving and emission reduction ability based on the use of comprehensive energy in agricultural enterprises. Based on the exponential smoothing method and equivalent calorific value method, a prediction model of electric energy substitution efficiency was constructed. Combined with the ANP method, the evaluation indexes of agricultural electric energy substitution efficiency were weighted and graded. In this paper, 100 agricultural enterprises are divided into 5 clusters according to their energy saving and emission reduction ability, which are optimization level, managed level, development level, pressure-based level, and initial level. The model of this paper predicts that the consumption of terminal coal, oil, and electric energy of agricultural projects in 2027 is between 31663.68 and 7447.991.67 million tons of standard coal, and that the electric energy substitution in the same year can be up to 693,755.69 million tons of standard coal. The comprehensive scores of the first-level indicators of economic benefit, environmental benefit, and social benefit are 91.06, 91.26, and 92.01 in order, and the comprehensive efficiency grade is “Excellence”. To summarize the results, this study suggests increasing the investment of electric energy substitution equipment in agricultural production to promote the synergistic development of integrated energy system and electric energy substitution strategy.

This paper describes the teaching problems and methods that need to be solved for the innovation of education mode from the needs of teaching conditions, teacher strength, school-enterprise cooperation, etc., and puts forward the “three lines and four passes” education mode based on situational teaching workshop for geology majors. The evaluation index system is constructed for the quality of “three lines and four passes” education model, and various colleges and universities in a certain province are selected for empirical analysis to analyze the quality of education for students in the province from three aspects. In order to determine the contribution size of the 10 secondary indicators in the education quality evaluation index system, the method of determining the weights of the indicators by using principal component analysis was used to calculate the education quality indexes of the public higher vocational colleges and universities and private higher vocational colleges and universities. The K-means cluster analysis method was performed on the basis of the hierarchical cluster analysis method, and seven tiers were divided. The analysis results show that professional education, facilities and equipment, teacher-student cooperation, group competitions, practical exercises, talent cultivation and vocational training have a greater weight of 5% in the evaluation index system of public and private institutions. In conclusion, it is concluded that the “three lines and four passes” education model based on master craftsmen workshop proposed in this paper has a better teaching effect on the quality of education.

Robotic process automation (RPA) technology along with the rapid development of information technology is increasingly widely used in various industries. This paper mainly explores its application in the field of electric power, and utilizes RPA technology to improve the quality and efficiency of power marketing audit. In order to solve the data anomaly problem in the process of power marketing audit, this paper adopts K-means algorithm to cluster the anomalous data, and combines with the correlation calculation to realize the identification and monitoring of the anomalous data of power marketing audit. Applying RPA technology to intelligent power marketing audit, we learn the normal pattern of data by training the self-encoder network, and correct or reject the abnormal data monitored. Reinforcement learning is used to optimize the audit strategy of RPA technology, and the efficiency of the audit is improved by maximizing the cumulative rewards. The application of RPA technology significantly improves the efficiency and accuracy of the overdue prediction and the work order generation and dispatching in the electric power marketing audit, in which the average working time of the overdue prediction work is reduced by 94.92% after the application of RPA technology, the average accuracy is improved by 21.80%, and the average working time of the work order generation and dispatching process is reduced by 21.80%, and the average working time of the work order generation and dispatching process is improved by 21.80%. The average working time of work order generation and dispatching process is reduced by 97.99% and the average accuracy rate is increased by 14.54%. The application of RPA technology effectively improves the efficiency and quality of power marketing audit.

In this paper, the entire chord progression is added to the generation process through a bidirectional LSTM model, and the Skip-connection method is used to accelerate the convergence speed in all recursive layers except the first one. Different musical emotions are classified based on the Hevner emotion model, and features such as pitch, duration, and tempo of musical emotions are parameterized. The forward neural network is used to construct the music emotion classification model, and the gradient descent learning algorithm is used to algorithmically control the forward neural network model. At the same time, this paper explains the significant enhancement of college students’ self-identity by music aesthetic education based on neural network model from two perspectives: theoretical research and empirical analysis. The results show that the music generation and music emotion classification models constructed based on the neural network algorithm in this paper show good performance in the experiments. After applying the neural network model containing music generation and music emotion classification to music aesthetic education and counseling college students on self-identity, the mean score of self-identity scale of students in the experimental group increased from 50.83 to 88.56, with an improvement of 75.78%, and the results were significant at the 1% level. The effectiveness of this paper’s method in enhancing college students’ self-identity is fully demonstrated.

VTOL uavs combine the advantages of VTOL capability of multi-rotor uavs and efficient fixed-wing cruise, but they also face challenges in performance, including weak wind resistance when hovering, low flight mode conversion efficiency and high-altitude fluctuation during conversion. In view of this, this paper introduces a new type of composite wing UAV, namely lifting wing quadrotor. Compared with quadrotor UAV, it is unique in that it is equipped with a lifting wing installed at a special Angle, which effectively improves the range and load, and solves the problem of weak wind resistance in the hovering stage of tail-seat UAV, and can realize efficient transition flight. A longitudinal position controller based on TECS total energy control algorithm is designed according to the flight characteristics of the transition stage. The effectiveness of the dynamic model and controller design is verified by experiments. The results show that the control algorithm can effectively improve the flight stability of the lift-wing quadrotor during the transition stage.

The article evaluates and predicts the effectiveness of medical English teaching through stepwise regression analysis in multivariate analysis method. It constructs an analytical prediction model of medical English teaching evaluation based on multivariate regression analysis. After initially establishing the evaluation index system of medical English teaching, effective evaluation indexes are screened out through stepwise regression, an effective evaluation index system of medical English teaching is constructed, and multiple regression equations for the quality of medical English teaching (students’ performance in medical English) are established. The prediction model of medical English teaching quality is constructed by eliminating the influential factors and abnormal data that do not have significance through multiple linear regression analysis. Teaching quality prediction equations were constructed by choosing teaching content, teaching method, teaching organization, teaching expression, teaching attitude, and overall effect of teaching. Among them, teaching content and teaching expression were significant, and the final prediction model of medical English teaching quality was Y=0.1958+0.1142*teaching content+0.7232* teaching expression. The 79.26% of students’ medical English performance can be explained by the multivariate linear regression analysis model.

Dance is interpreted through human body movements, dance movements can express the thoughts and emotions of dancers, and whether the dance movements are standardized or not in the creation of dance drama determines the quality of the creation of dance drama. In this paper, with the support of artificial intelligence and information technology, based on image recognition technology, we carry out the optimization research of dance movement recognition for dance drama creation. In this study, the principle and process of image recognition technology are first studied in depth, and then the motion detection method for dance movements is analyzed considering the static state of the background of the dance drama. On this basis, the recognition optimization of dance movements is completed based on the deep convolutional embedding attention mechanism, and the evaluation method based on recognition optimization is proposed for the creation of dance drama. The embedding method in this paper improves 12% over the baseline method, with an OA of 98.65%, while the amount of participation and FLOPs increase slightly. And the score1 and score2 of this paper’s method are the highest, which indicates that this method obtains a high model accuracy while sacrificing less number of model parameters and computational complexity. In addition, the network model structure of this paper is more efficient compared to other network model results. In the recognition effect analysis, the correct recognition rate of six standard dance movements such as center of gravity transition, time step, square step, lock step, fixed step and others are above 80%, with high recognition accuracy and excellent model performance.

Dance drama performance is an important part of human civilization, which runs through the long history of human development. With the continuous improvement of people’s spiritual and cultural needs, the pursuit of dance theater performance has become more and more intense. In this paper, we start from the basic knowledge of fractal geometry, such as the theory, concept, dimension, and generation mechanism of fractal, to launch the research on the optimization of the spatial layout of dance drama performance. The main work of this paper involves the following aspects: (1) It focuses on the definition and characteristics of fractal geometry, and deeply studies the application of fractal geometry in the optimization of the spatial layout of dance drama performances. (2) The demand for dance drama performance space optimization is analyzed in terms of developmental changes and audience demand. It can be seen that the footprint of the dance performance continues to increase, but the use of space is gradually decreasing, and the audience has higher expectations for the stage performance, the demand for the development of the dance performance and the audience demand to promote the process of optimization of the space layout of the dance performance. (3) This paper adopts the left-right symmetrical presentation, fully considers the visual effect of the viewers, and constructs the optimized layout of dance drama performance space based on fractal geometry. (4) The optimized layout method of this paper is applied to the actual evaluation of the effect, in which the two groups A and B, located in the main audience area, have higher overall ratings, respectively 5.78 and 6.04, and the main audience area has a better perspective experience, so the overall experience is relatively better. The score of the viewer experience after optimization of the dance drama performance space layout in this paper is 5.715 points, which indicates that the viewers are satisfied with the effect of optimization of the layout in this paper, and the better results of the optimization of the dance drama performance space layout in this paper provide some reference for the dance drama performance space layout.