This paper deeply analyzes the innovative application and intelligent upgrading steps of Artificial Intelligence Generated Content (AIGC) in Civic and Political Education. Based on metadata, we construct an automated generation model of Civics education resources, divide the meta-properties of education knowledge resources, set up a knowledge tracking model DT-BKT to obtain students’ mastery of Civics knowledge, adopt personalized recommendation model to realize the high adaptability of education resources based on students’ Civics learning, and combine the functions of each model to build a Civics education content intelligent generation and adaptability system. Knowledge tracking experiments show that the AUC and R2 indexes of the DT-BKT model in this paper are better than those of other comparative models, and it can better simulate the response of learners on the dataset. Facing different groups of learners is able to recommend Civics courses that meet the learners’ abilities. For active learners and potential learners, the average difficulty of the recommended client layer is higher by 0.08~0.15 and 0.06~0.085 respectively, while the overall difficulty difference for inactive learners is between -0.01~0.015, and the recommended difficulty is in line with the characteristics of the learner groups.

In this paper, a K⁃Means clustering algorithm based on improved differential evolution (AGDE⁃KM) is proposed to design the adaptive operation operator, design the multi-variation strategy and introduce the weight coefficients in the variation stage to regulate the searching ability of the algorithm and accelerate its convergence speed. The Gaussian perturbation crossover operation based on the best individual of the current population is introduced, and the optimal solution output from the improved differential evolution algorithm is used as the clustering center to realize the cluster analysis of students’ sports performance data. Afterwards, the hierarchical recognition algorithm and support vector machine are used to recognize students’ sports patterns, and the wavelet transform algorithm is used to extract and select the students’ sports feature quantities, so as to improve the accuracy of students’ sports pattern recognition in sports teaching. In the process of physical education teaching, AGDE ⁃ KM algorithm is more pertinent to the clustering effect of students’ sports performance, and its explanatory degrees of Calinski-harabasz metrics, profile coefficients, and Dunn metrics are 860.0276, 0.3928, and 0.0486, which are 19.0382, 0.0435, and 0.0099. In addition, the AGDE⁃KM algorithm achieves 95.7625%, 99.75%, and 99.85% of the mean value of step recognition accuracy for different testers in the 50m, 800m, and 1000m events, respectively, which is a good recognition effect.

Music conductors rely on the visual impact of gestures and emotions for the interpretation and expression of musical works. In this paper, we utilize spatio-temporal two-stream convolutional neural network and replace the original VGG-16 network with ResNet-34 network with deeper network structure to construct a conductor recognition model for improving music conductor level. The Dropou optimization is applied in the fully connected layer to reduce the overfitting phenomenon, and the network structure is designed to fuse the temporal and spatial networks in advance with the feature maps, in view of the defects that the network structure of dual-stream convolutional neural network is shallow and the temporal and spatial networks do not learn the temporal and spatial information correlation. After the construction is completed, the model is applied in the teaching of a music college. The spatio-temporal information fusion convolutional neural network proposed in this paper is compared with other existing methods, and it is found that the optimized design helps the convolutional neural network to learn better, and better emotion and action effects can be obtained. It has better recognition accuracy on the dataset and obtained the highest accuracy of 74.3% on the CoST dataset. The results of the dimensions of music perception ability of the conductor students in the experimental class are better than the reference class, and the dimensions of pitch and intensity are more than 20% ahead of the control class, which proves that the model in this paper is more powerful to promote the development of music perception of the conductor students.

In the context of rapid research and development of unmanned equipment products, how can we better design an environment sensing system suitable for unmanned equipment combat missions and combat tasks from the perspective of actual combat has become an important research topic. This paper explores the optimization scheme of unmanned equipment environment sensing system based on blockchain technology, proposes PBFT (DTPBFT) consensus algorithm based on C4.5 decision tree optimization, and combines with the full homomorphic encryption algorithm to put forward the shared data encryption scheme of unmanned equipment environment sensing system. The experimental results show that the classification accuracy of C4.5 decision tree is as high as 94.37%, which is better than other classification algorithms, indicating that the use of C4.5 decision tree can effectively improve the accuracy of the classification of the consensus nodes and the security of the PBFT algorithm. In the case of the same number of nodes, the throughput size of the DTPBRT algorithm proposed in this paper is always higher than that of the PBFT algorithm, and the consensus latency is higher than that of the PBFT algorithm only when there are Byzantine nodes inside the system, but the DTPBRT algorithm is able to effectively remove the Byzantine nodes inside the system, which verifies the superiority of this paper’s algorithm. Comprehensive encryption and decryption time-consuming and throughput data, this paper’s scheme in general can realize high data sharing efficiency and ensure the security of data sharing, which can provide technical support for the data security of unmanned equipment environment sensing system.

Teaching optimization algorithm is a new type of group intelligence algorithm, which simulates the teaching process of teachers, and this paper improves the algorithm to realize the improvement of music teachers’ teaching ability. Aiming at the shortcomings of the teaching optimization algorithm which is easy to mature prematurely, has low solution accuracy and converges to the local optimum, this paper proposes a teaching optimization algorithm which integrates the improved Tennessee whisker search. The algorithm combines Tent mapping and inverse learning strategy to initialize the population and improve the quality of the initial population. Tennessee whisker search is performed on teachers to improve their teaching ability. Incorporating the hybrid variation operator into the individual student variation formula allows the algorithm to quickly jump out of the local optimum dilemma. The experimental results show that the hybrid teaching optimization algorithm based on BASTLBO proposed in this paper has good solution accuracy and robustness in finding the optimum on different types of optimization problems. The algorithm in this paper can achieve better teaching ability results than the unimproved TLBO algorithm and the teaching optimization algorithm incorporating the hippocampus strategy, and the objective function on two different indexes is reduced by 8.75% and 7% compared with that of the TLBO algorithm, respectively, and the hybrid teaching multi-objective optimization model designed in this paper has stronger practicality.

Accurately capturing the behavioral factors of different types of customer groups and adopting targeted service strategies is the key to business competition in the hotel industry. In this paper, we combine the variance Boston matrix and PSO-based K-means algorithm to achieve hotel customer attribute segmentation based on customer behavior, customer value and word-of-mouth reliability, and then use deep learning algorithms to construct a hotel customer behavior prediction model. The feature fusion layer and SENet are incorporated into the residual network in order to utilize the feature expression ability of different layers and the spatial coding ability between different channels to enhance the hotel customer behavior predictive ability. Downloading the public dataset from the online wine travel platform for example analysis, it is found that the classification of this paper’s algorithm before customer segmentation has a correct rate of 83.75%, which is higher than the rest of the baseline models. After customer segmentation this paper’s algorithm achieves the highest recall rate in all customer categories, and the recall rate is as high as 84% on category 1 customer groups, and the superiority of the designed algorithm is verified. This study facilitates hotel management to target customer service and retention according to different customer groups.

In order to improve the consistency of on-chain-off-chain interaction of private data supported by blockchain and reduce the redundancy of data storage performance, this paper applies an efficient data interaction method of prefix hashing with improved red-black tree index to store public indexes and improve the efficiency of retrieval and interaction of blockchain data. Under the idea of generalization, anonymous region (AR) is used to hide the real location of participating nodes and protect the privacy of realized nodes. To reduce the computational overhead of the selection process, a cooperative sensing location privacy preserving optimization mechanism, LPPOM, is proposed. The scheme in this paper has a slow growth of data size on the chain with higher storage efficiency, larger throughput, and shorter query time (0.1899ms). The time cost consumed when the number of privacy chains is 15, 30, and 60 only increases by 0.2309-0.4855ms compared to the single chain system, indicating that the scheme scales well. When the file size is within 200 and the number of encrypted attributes is less than 4, its total encryption time meets the user’s privacy data encryption needs (between 66.1765-236.7081ms). The IPFS read/write module is able to satisfy the people’s daily use needs under the public network conditions, and its read/write speed is between 0.1568 and 0.2639MB/ms (file <100M).

This paper firstly starts from the thermodynamic theory, based on the classical heat transfer theory, and adopts the finite difference dichotomy method for mathematical modeling, and uses the secondorder center difference format to discretize the space, and solves the non-Fourier heat conduction equation. After completing the algorithmic solution of thermodynamic theory and finite difference method, the two are combined to deeply analyze and discuss the thermodynamic behavior of highspeed mechanical devices represented by high-speed rotating bearings. When the bearings operate at high speed, with the increase of stiffness, the pressure change in the middle and rear part of the bearings gradually flattens out, the temperature gradually rises, and the relative bearing capacity of the bearings decreases. The increase in the number of bearings also brings about an increase in the pressure at the centerline of the bearings, and the temperature of the air film corresponds to the increase in the average pressure, and there is a risk of over-temperature. In the thermodynamic characterization, the work done by the air film under compression and the heat generation due to viscous shear will lead to an increase in the temperature of the air film, which will lead to the temperature rise of the bearings, and will have a very great impact on the bearing performance.

Water resource is a high degree of unity between quantity and quality, once the water body suffers from pollution will make the water resources more scarce, and karst groundwater resource is one of the main water resources in the seismic area. In this paper, we chose Baiquanquan area in the low-mountain hilly area at the eastern foot of the south section of Taihang Mountains in H province as the research object, set 25 sampling points and collected 20 groups of karst groundwater samples and 5 groups of surface water samples, and carried out the reliability test by the ion balance method to control the error within ±5%. Based on the karst groundwater samples, the general characteristics of its hydrochemistry were analysed, and its hydrochemical characteristics were explored by cluster analysis. The causes of hydrochemical ions in karst groundwater were investigated by Gibbs plot, chlor-alkali index and saturation index, and the related factors affecting the hydrochemical characteristics of karst groundwater were investigated by factor analysis. The hydrochemical cations and anions in karst groundwater were mainly composed of Ca²⁺ and HCO₃^–, and the average concentrations of the two were 132.15 mg/L and 193.66 mg/L, respectively. The cast points of karst groundwater all fell between the dolomite and calcite areas, and their Mg²⁺/Ca²⁺ values ranged from 0.11 to 0.75, and the contribution of the F1 factor composed of Ca²⁺, Mg²⁺, SO₄^2-, TDS, HCO₃^– was the maximum of 38.91%. Karst groundwater in the seismic area will be affected by rock weathering, human activities, etc., which will affect the flow path of karst groundwater, and then have an impact on the hydrochemical composition of karst water.

In the context of building a new type of energy system, pumped storage projects have been widely adopted as a form of energy storage with the most mature technology and the most economical investment. In this paper, a hybrid pumped storage project online monitoring system based on multisensors is proposed, and an online monitoring database is designed and constructed. Based on the data in the online monitoring database, the soil erosion of the hybrid pumped storage project is calculated and analyzed by combining the CSLE model. Then the attention mechanism is combined with BiLSTM model to construct the landslide risk prediction model of hybrid pumped storage project. The soil erosion during the construction of the hybrid pumped storage project is mainly distributed on the construction land, garden land, grassland and cropland, among which the construction land has the largest area of soil erosion (132.19 km²), followed by the area of soil erosion of cropland (29.24 km²). The MAPE is between 0.002% and 0.005% when predicting landslide risk deformation of hybrid pumped storage project using CNN-BiLSTM-ATT model. And using the model in this paper can minimize the error of rainfall on the prediction of landslide risk deformation and realize the safe and stable construction and operation of hybrid pumped storage projects.