This paper analyzes the development trend of Adobe After Effects-based movie special effects technology, including accelerated GPU research, integration and enhancement of 3D special effects production functions, cross-platform and cross-software collaboration, as well as the impact on the way special effects artists create. In terms of GPU research, the acceleration performance of different GPUs in movie simulation in China and abroad is compared, and the 3D effects production function involves the creation of 3D models, movie settings, rendering standardization, as well as the rendering output and the addition of special effects. Cross-platform and cross-software collaboration focuses on the cross-platform nature of AE, designing a tagged text file format and a movie playback engine based on the Cocos2d-JS game engine, and dividing the file system module. The analysis shows that the waiting time for movie rendering under this paper’s model is 1s and 2s, and the end-of-task ratio is 0.02, which are the lowest in both sets of experiments. The highest mean values for the 10 simulations of GPU utilization are 72.42% and 72.83%, respectively. It can be seen that the CPU acceleration model based on Adobe After Effect in this paper can effectively reduce the waiting time for movie rendering and improve the processing speed and stability of movie special effects.

Sustainable agricultural development is a key component of the rural revitalization strategy, and strengthening the guidance and support for sustainable agricultural development is an inevitable choice for improving agricultural production capacity and realizing rural revitalization. The study constructs an evaluation system of sustainable agricultural development based on four dimensions: economic opportunity, social well-being, environmental quality, and climate action, selects relevant index data of each province from 2004 to 2022, and adopts a multi-level factor analysis method to comprehensively evaluate the sustainable agricultural development as well as the dynamic distribution of 31 provinces in China. The results show that Henan leads other provinces in economic opportunities with a score of 1.21, and Hebei ranks first in social well-being with abundant human resources and policy support. In the level of regional sustainable agricultural development, there is an uneven distribution pattern of “North > Central > South”. From the dynamic distribution of agricultural sustainable development in 31 provinces from 2004 to 2022, it is concluded that the development trend of agricultural sustainable development in China is better, and the gap between provinces has been narrowed. Finally, policy recommendations are put forward based on the situation of agricultural sustainable development to provide reference for the subsequent work on agricultural sustainable development.

The family, as a socially balanced system, possesses the functions of social information communication and social resource conversion. From the perspective of the family system as a provider of educational resources, the input of various forms of resources into the family environment either helps parents to teach their children, helps them to learn, or is detrimental to the healthy growth of their children. In this paper, based on the nonlinear model of the static resource-opportunity allocation problem study, the objective constraints are added to establish a linear programming model. The column enumeration method is used to solve the linear programming, while the sensitivity of the linear programming is analyzed by pairwise test. On the basis of the random initial solution, a multilayer transportation algorithm is designed as the initial solution to further reduce the time of enumerating columns and complete the construction of the solution framework for the resource-opportunity allocation problem. The model is used to solve the problem of the distribution of socio-economic resources to educational opportunities between “two-child” and “one-child” families. The results show that the socio-economic resources of different families have different opportunities for children’s education, and there are significant differences between different types of “two-child” families in the three aspects of parent-child relationship satisfaction, feelings of parenting, and interpersonal evaluation of the child, with the F-values of 5.265, 4.859, and 5.136, respectively, with a p<0.01. The “last-child advantage” in children's education is related to the number of years of education of the fathers. In the 1949-1969 generation, the average number of years of education of the fathers was only 6.763, while in the 1970-1990 generation, the average number of years of education of the fathers increased to 8.685, and the cultural level of the family as a whole improved significantly, and the mechanism of resource constraints on the cultural level of the family began to take effect. The resource constraint mechanism at the cultural level is beginning to take effect.

The temperature gradient formed by cooling and heat dissipation after shutdown of aero-engine will lead to thermal bending of rotor, which is also the main reason for bending vibration of rotor after engine secondary start-up. In this paper, the rotor system model of a GTF motor considering the gearbox structure is established, and the thermal bending deformation of the fan-gearbox-low pressure rotor caused by temperature gradient is analyzed. The critical speed of the rotor system considering the temperature field is calculated and the vibration characteristics of the engine after the second start are analyzed, which provides a reference for the design of the rotor system of GTF engine. The results show that the rotor mainly appears hot bending deformation in the direction of vertical axis, especially in the joint of disc axis. The large bearing stiffness of the gearbox has obvious inhibition on the hot bending deformation of the rotor, and the effect is obvious when the bearing stiffness is above 1E6. The vibration characteristics of rotor are greatly affected by temperature field. The amplitude of rotor system is larger and the sensitivity of gearbox structure is higher under the influence of temperature field. The amplitude is also the largest when the thermal bending amount is maximum about 10min, and the amplitude decreases by 50% after 40min. The bearing stiffness of the gearbox has a great influence on the vibration characteristics of the rotor system with hot bending deformation, and the vibration suppression effect is best when the bearing stiffness is between 1E6 and 1E8, and the peak point above 1E8 is close to the operating speed of the fan, which is bad for the safe operation of the engine.

Wind energy is a widespread natural phenomenon, which receives more and more attention because of its renewable and non-polluting nature, but the unpredictable and unstable wind speed makes the wind power control technology become a hot spot of concern. Firstly, the working principle of the wind turbine system is introduced, and the wind turbine speed model of the wind turbine drive system is established according to the system stability characteristics. Then on the basis of the traditional PID control algorithm, a wind turbine rotation speed regulation optimization algorithm based on PID optimization control is proposed-PID neural network control. The algorithm designs a three-layer forward PID neural network, and through the PID variable structure control, the low-speed axis of the fan connects the rotor axis with the gear box, which excites the operation of the aerodynamic gate for speed regulation, and compared with the traditional PID control, the method can regulate the airflow of the coal mine fan more quickly, and the overshooting amount is reduced by about 22%. Then, the BP neural network control is used to predict the air demand, and the deviation of the predicted air demand from the current air demand and its chemical rate are input into the controller. Finally, through the comparison of the control system and simulation experiments, it is proved that the BP neural network control has stronger robustness and adaptability, and can achieve better control effect.

The study uses a multilevel nonlinear optimization algorithm to optimize the low-carbon development path of agriculture with the dual constraints of government regulation and agricultural insurance. The algorithm solves the development path optimally through convergence analysis, parameter setting and constraint problem modeling. In addition, the study establishes an index system for evaluating agricultural low-carbon development, and assesses the effectiveness of low-carbon development through field application. The algorithmic path optimization in this paper has better performance in terms of solution quality, iteration number and solution time. At iterations 17, 43, 62 and 82, the algorithm of this paper found feasible solutions for path optimization. By 2023, the annual increase in pollutant emissions from agricultural production, total carbon emissions, carbon emission intensity of 10,000 yuan output value, and comprehensive energy consumption of 10,000 yuan output value are projected to be reduced to 42696.39(tons), 21141.5(10,000 tons), 1017.9(tons), and 6422.6(tons), respectively. The evaluation indicators Agricultural activity average carbon intensity, Reduction of carbon intensity and other indicators have relatively high weights, which is the main reason for the differences in low carbon development.The correlation between the effectiveness of agricultural low carbon development and the optimal sequence in 2023 is 0.9981, which demonstrates that the role of government regulation and agricultural insurance in promoting agricultural low carbon development.

In order to improve the efficiency of rail bolt automation operation, this study proposes a non-dominated sorting genetic algorithm II (NSGA-II) based on the improvement of elite strategy for the multi-robot task allocation problem. First, a multi-objective optimization model is established by combining the actual demands of rail bolt operations. Then, the classical NSGA-II algorithm is improved by introducing an elite strategy to enhance its global search capability and convergence performance. Finally, the effectiveness and superiority of the improved algorithm in task assignment are verified by simulation experiments. The experimental results show that the improved NSGA-II algorithm has significant advantages in optimizing the efficiency of rail bolting operations and task balancing, which provides a strong support for task allocation in multi-robot systems.

The integration of Civics elements into the EFL classroom is an organic supplement and deepening of the teaching content and materials, while EFL Civics classroom teaching is a powerful means to strengthen the deep and longitudinal development of students’ critical thinking. This paper discusses the relationship between EFL Civics classroom teaching and the development of critical thinking ability from the theoretical and practical levels respectively. On the basis of existing research, the evaluation index of students’ critical thinking ability is proposed. The CVM coefficient of variation method is improved, and the ICVM and BP neural network algorithm are combined to constitute the evaluation model of students’ critical thinking ability based on ICVM and BP neural network. According to the evaluation process, the level of students’ critical thinking ability after EFL-based Civics classroom teaching is derived. It also integrates teachers’ and students’ evaluation of the effect of English Civics elements integrated into the EFL classroom, and finally obtains the practical teaching effect of the EFL Civics classroom. The overall mean value in the teacher’s side is greater than 3.5 points, which indicates that teachers are basically positive about the effect of integrating Civics in EFL courses, and basically agree with the positive impact of English Civics elements on EFL classroom teaching. Based on the evaluation results of ICVM and BP model, the evaluation scores of students’ critical thinking skills and critical thinking monitoring are higher than the evaluation scores of critical thinking tendency, i.e., the elements of English Thinking can be effectively integrated into the EFL classroom and promote the development of students’ critical thinking skills.

Digital teaching strategies can significantly stimulate students’ interest in learning and provide personalized learning pathways. This paper proposes a multimodal action recognition method that integrates the word vector method, and designs a teaching decision optimization strategy based on this idea. Firstly, we compare the information of different modalities, complete the construction of multimodal action recognition network through the processing of image information and optical flow information, and combine the word vector method to guide the semantic learning of students’ actions. Then the design and realization process of the teaching decision aid system is introduced. Based on the above proposed action recognition method to collect students’ classroom behavior data for model training to be used in the system, the system consists of four modules: model training, classroom data collection, behavior recognition and data presentation. After the data collection, the action recognition of student behavior is carried out to provide teachers with feedback on student behavior information and assist them in making teaching decisions. In this paper, the above algorithms and systems have been verified by relevant experiments. After comparison with other algorithms, it is verified that the multimodal action recognition method designed in this paper, which incorporates the word vector method, has a high accuracy rate. In the comparison of the overall quality of instructional design decisions, the average value of the instructional decision aid system in this paper is 17.35, which is higher than the average score of excellent human teachers in the overall quality of instructional design decisions, indicating that the instructional decision aid system designed in this paper achieves the optimization of instructional decisions and reaches the level of excellent decisions.

This paper proposes a user electricity data mining method based on deep learning and improved locust optimization algorithm, and at the same time adopts the Pearson correlation coefficient method to reduce its dimension to improve the data mining effect of linear weighted KFCM algorithm. In order to deal with the electricity demand of massive electricity customers, the user electricity demand forecasting model is constructed based on the Extreme Learning Machine ELM algorithm by combining the relationship between short-term loads and factors of electricity customers. Construct the service optimization model with the maximization of benefit index as the objective function, and use the BAS algorithm to solve the optimal solution in order to achieve the effect of user service optimization. Determine the experimental platform and model parameters, and carry out an example analysis of demand forecasting and service optimization for electricity users.C class users have a small electricity load except for breakfast and dinner, and the maximum time period of the electricity load is from 18:00 to 20:00 hours. Combined with MAPE, the ELM model improves 4.57% than SVR, 21.9% than LSTM, and 34.37% than ARIMA, which indicates that the ELM model is more effective and higher in demand forecasting for electricity users. In addition, the optimal solution of the effect of the BAS algorithm is 69 yuan, 102 yuan and 49 yuan higher than that of the GA algorithm in terms of dividend transmission benefit, energy saving and emission reduction benefit, and electricity right trading benefit, respectively, and the optimal solution based on the BAS algorithm is closer to the actual benefit value, which fully proves the effectiveness of the service optimization model based on the BAS algorithm.