In this paper, a personalized scheme recommendation method for dance movements based on ontological similarity is proposed. An ontology model of trainers is established, and in order to explore the interactions between trainers’ attribute features and their influence on core parameters, SWRL rules are established using Jena inference engine for the inference of core parameters of training programs. The similarity degree is calculated according to the different types of user variables respectively, and the artificial neural network model is used to determine the degree of similarity between different trainers, in order to complete the recommendation of personalized training programs for dance movements. And then the requirements of the system are summarized to achieve the framework construction of the personalized dance movement training program recommendation system to achieve the health management in the training process. The recommendation effects presented by the similarity calculation method of this paper have reached the design goal of this paper, and the personalized recommendation system of this paper has also significantly improved the physical fitness level and the performance effect of dance skills of the experimental group of dance trainees, and the success rate of the kicking back leg movement has reached 91.67%. However, the system’s function of improving health knowledge and health awareness needs to be further upgraded.

In this paper, the autoregressive moving average model (ARMA) and LSTM deep neural network are first introduced, and the time series are decomposed into high volatility components and low volatility components by MA filtering method. Then the time series forecasting model ARMA and deep neural network LSTM are combined on the basis of MA filtering method to form ARMA-LSTM combination model based on MA filtering method, and the application effect of this model in financial market volatility forecasting and risk response is verified through empirical evidence. The results show that the ARMA-LSTM_t model will achieve relatively good results in predicting the GDP_IG of the current year using the data of the 12 months of the current year and the last month of the previous year, and the training and prediction sets of the ARMA-LSTM combination model proposed in this paper have the best results. In addition, there is a positive relationship between investment-related indicators and GDP_IG, and the addition of investment network search data improves the estimation accuracy of the model, obtains smaller prediction errors, and improves the prediction accuracy of the ARMA-LSTM model in the short and medium term.

In the context of urban elderly human resource development, differential evolutionary algorithms can be used to optimize the development strategy and improve the efficiency of resource utilization. The study constructs a multi-objective scheduling optimization model for human resources based on an improved differential evolutionary algorithm, which searches for the optimal development strategy by simulating the mutation, crossover, and selection operations in the process of biological evolution. In addition, the model combines a multi-objective feature selection algorithm to capture the data information of urban elderly resource development more accurately and ensure the scientific and practicality of the strategy. The pareto front of this paper’s algorithm on the optimal solution test function is more in line with the real frontier, and the GD value is between 0.00171 and 0.0325, which has better convergence. The execution time of this algorithm for elderly manpower resource scheduling is shortened compared to the comparison algorithm, and the convergence of different task sizes is accomplished when iterating to 110~150 rounds. The ADE-MOFS algorithm has the lowest running cost and the shortest completion period on elderly manpower resource scheduling. The research in this paper shows new ideas and methods for the rational development and utilization of urban elderly manpower resources, which has important theoretical and temporal significance.

In recent years, the green economy has been developing rapidly, and the environmentalization of industries has been widely popularized in various industries. This paper carries out an in-depth study on the relationship between agricultural carbon finance and carbon emission reduction, and after understanding the theory related to carbon finance and carbon emission, it adopts the method of system GMM estimation to construct a dynamic panel model for the study of agricultural carbon finance and carbon emission, and selects research variables. The development of agricultural carbon financial innovation and carbon emission in 30 provincial-level administrative regions in China from 2014 to 2024 is studied, and regression analysis is carried out using system GMM so as to obtain the relationship between the impact of agricultural carbon financial innovation on carbon emission reduction, and the robustness test is carried out. The maximum values of agricultural carbon financial scale, carbon financial efficiency, carbon financial structure, and per capita carbon emission are 16.942, 7.052, 1.926, and 128.945 respectively, while the minimum values are 0.965, 0.048, 0.079, and 0.145 respectively, and the maximum values are 17.56, 146.92, 100.33, and 889.28 times of the minimum values. There are large differences in the development of agricultural carbon financial innovation and carbon emission reduction effects among different provinces. Per capita carbon emissions are reduced by 22.5%, 20.5% and 24.5% for each unit increase in carbon financial scale, carbon financial efficiency and carbon financial structure, respectively. The parameter estimates of carbon financial scale, carbon financial efficiency, and carbon financial structure are significant at the levels of 10%, 1%, and 5%, respectively. It indicates that the innovative development of agricultural carbon finance can effectively promote carbon emission reduction.

Due to global warming and drought, land desertification has become more serious all over the world, and desertification control has become the focus of global attention. Salix sand barriers as a desert wind and sand area engineering sand control mechanical sand barriers have been more widely used. For this reason, this paper analyzes the demand for sand willow sand barriers, starting from the causes of desertification research, and for the sand willow sand barriers laying method for in-depth analysis. Taking the research on the governance of salix sand barriers as the theoretical basis, the self-propelled salix sand barriers horizontal laying machine insertion cutter head is designed, in order to improve the efficiency of salix sand barriers laying and the survival rate of salix spike insertion. Through the test data, the driving force, resistance, and tractability parameters of the laying machine when traveling were theoretically calculated. And according to the different slip rate when the drive seeks to come up with the slip rate when the whole machine is actually working, and finally determine whether the tractor meets the requirements of the passability. Through simulation calculations, it can be seen that the cutterhead laying machine designed in this paper can overcome the resistance generated by acceleration and climbing in the application of sand willow sand barrier laying, and has good versatility in the desert environment.

Bridges as the basic transportation facilities in the national daily life, with the development of road transportation, the number of urban viaducts and cross-sea bridges grows year by year, and the role of bridges in the transportation network becomes more and more significant. The article selects the Qingshuihe Bridge demolition project as the research object, and designs the Qingshuihe Bridge demolition implementation program based on the engineering characteristics. For the mechanical property changes and structural residual bearing capacity calculation during the demolition process of Qingshuihe Bridge, this paper constructs a finite element model of Qingshuihe Bridge based on ANSYS software, and analyzes the structural reliability of Qingshuihe Bridge after the demolition project by combining with structural reliability indexes. Under the same mid-span displacement condition, the cracking load error between the finite element simulation values and the experimental values is relatively small, and its fluctuation range is between 2.12%% and 6.58%. In the first 5 years after the demolition of the bridge section, the residual load capacity of the bridge structure increased from 5.23*103kN to 5.51*103kN. The reliability index value of the bridge over the Qingshui River changed relatively slowly in the early stage, and began to decline gradually in the later stage, and the greater the strength of the steel girders the higher time-varying reliability index of the bridge over the Qingshui River. In this paper, the design of the Qingshui River bridge demolition implementation program has a strong feasibility, in order to ensure that the Qingshui River bridge structural residual bearing capacity on the basis of the completion of the demolition of part of the bridge abutment, and can guarantee the safety of the bridge operation.

In recent years, it has become the frontier and hotspot of research in the field of intelligent robotics. In this study, a robot vision-guided unloading system is designed, and a robot grasping control method based on fuzzy mathematical method is proposed for the robot unloading problem under the uncertain information environment, and the particle swarm optimized fuzzy PID control algorithm is introduced into the grasping force control field. Comparison experiments of robot joint trajectory tracking, position and control inputs are carried out in the simulation environment, and the method in this paper can realize accurate tracking of motion trajectory and weaken the vibration phenomenon. The robot unloading experiments show that the success rate of single target and multi-target grasping and placing are both above 91% and 85% respectively, which verifies the effectiveness of the particle swarm fuzzy PID control algorithm of this paper in robotic grasping, and it has a certain value of engineering application for robotic unloading control.

With the in-depth promotion of ecological civilization education, the grassland natural ecology study course has become an important way to realize the comprehensive development of students. The article takes the design idea of grassland study course as the entry point, analyzes the design links of grassland ecology study course development, and proposes the design process of grassland ecology study course development based on this. Taking the second grade students of the first middle school in X city as the research object, the decision tree algorithm is used to optimize the relevant variables in the development of the grassland ecological study course, and the optimization model of the study path of the grassland ecological study course is established starting from the shortest distance and the least time. Based on the variables optimized by the decision tree algorithm, a DT-SVM model is built by combining the support vector mechanism to solve the learning path of the grassland ecological study course. Through the simulation and example data, it can be seen that the convergence accuracy of the DT-SVM algorithm shows the optimal accuracy under all experiments, and its mean value can be up to 3.652, and the average time consumed for obtaining the optimal learning path of the grassland ecology research course is only about 3.17min. And more than 65% of the students agreed with the teaching effect of the grassland ecology study course. The grassland ecology study course can significantly improve students’ core literacy in geography, enhance their independent living skills, and better realize the school’s teaching goal of promoting moral education.

The aim of this study is to construct a deep learning-based biomechanical model of musical instrument playing action that integrates skeletal pose estimation and action recognition techniques. PHRNet-based human pose estimation can extract the skeletal key points of a player from video data, and these key points provide basic data for instrumental performance action recognition and analysis. The human skeletal action recognition method based on diversity rewarded reinforcement learning framework (DDRL-GCN) classifies the extracted key point sequences into specific playing actions, and the musical instrument playing actions are successfully modeled. The biomechanical model of musical instrument playing action designed in this paper is applied to recognize the playing action of five different musical instruments, and the recognition accuracy can reach more than 90%. This paper is designed to distinguish between different musical instruments, the recognition effect is more satisfactory.

The planning of green logistics networks has gradually become the focus of attention in both academic and business circles, as it has been increasingly emphasized on environmental protection. This study aims to explore how to combine machine learning and carbon emission constraints to construct a more efficient and environmentally friendly green logistics network planning strategy. A machine learning-based logistics demand forecasting model is constructed by Support Vector Regression (SVR) machine, and the model parameters are optimized using genetic algorithm to improve the model accuracy. Analyze the sources of carbon emissions in the logistics network and establish a carbon emission calculation model. Construct a green logistics network planning model considering carbon emission constraints, and analyze the feasibility of the model through practical examples. The method of this paper can effectively measure the carbon emissions in the transportation and storage phases of the logistics network. Under the condition of considering carbon emission constraints, positioning the upper limit of carbon emission below 270,000 can realize a stable balance of economic and environmental benefits.