Teachers and students will form a variety of dependent behaviors and interactions centered on teaching activities in the teaching process, thus, the teaching process can be regarded as a typical game process. This paper invokes game theory, takes teacher-student behavioral interaction as the research object, constructs a game model of teacher-student behavior in the process of English teaching, and proposes a teaching optimization strategy for English flipped classroom. At the same time, numerical simulation of the teacher-student game model is carried out to explore the dynamic game equilibrium under the cooperative behavior of teachers and students. The simulation results show that in the teacher-student game network, the strategy choices of teachers and students change over time, and different benefit-loss parameter μ, additional gain parameter β₀, and cost-saving parameter ψ have a greater impact on the replication of the strategy choice behaviors of the game parties. In addition, the increase of the parameters of the gain PT obtained by the instructor’s conscientious instruction, the gain PS obtained by the student’s conscientious learning, and the loss KS of the punishment that the student receives for not learning conscientiously are conducive to the promotion of the instructor and the student’s strategy evolution towards cooperation (conscientious instruction, conscientious learning), while the increase of the instructional cost CT of the instructor’s conscientious instruction and the learning cost CT paid by the student’s conscientious learning are not conducive to the promotion of the two parties’ cooperation. And when the proportion of instructors and students initially choosing cooperation is larger, the likelihood of both parties evolving toward cooperation is greater. This paper provides theoretical support for the optimization of English teaching process.

College students’ physical fitness is an important part of national health, and analyzing physical fitness data in college physical education teaching helps to dig out the factors affecting students’ physical fitness and adjust the teaching plan in time. The article reviews some basic regression tools and selects variables such as BMI dietary habits for logistic regression analysis to analyze the factors affecting students’ physical fitness. The similarity, uncertainty and dissimilarity between students and their friends are calculated by Top-N recommendation set algorithm, and the physical education teaching program is dynamically adjusted with the new SFD recommendation algorithm. Finally, values were assigned to different movement banks and risk factors, and the experts’ agreement with the new adjusted program was examined. The intensity of physical activity had the greatest relationship with passing or failing physical fitness among all factors (regression coefficient = 0.927, p70%), reflecting the rationality and feasibility of this study.

In order to optimize the pattern design method in lacquerware decoration design, this paper first analyzes the discrete and continuous situation of the pattern in time and frequency by Fourier transform method, and explains the mapping principle of Fourier variation. After that, the original image is processed such as sharpening and smoothing under the Fourier transform algorithm, and the lacquer decorative pattern after automatic deformation is obtained through interaction on the basis of 2D affine transformation technology. Finally, the geometric deformation of the lacquer decoration design from 2D to 3D is simulated and verified. The results show that in this paper, the threshold value, brightness and contrast of the lacquer decorative design patterns can be obtained by the geodesic distance deformation algorithm under the Fourier transform in MATLAB software to get the geometric patterns of the lacquer decorative design with the main color of the appropriate filler blocks. The corresponding blue values of the four patterns are 418, 38, 104 and 256; the optimal values of green are 256, 100, 87 and 405; and the optimal values of red are 256, 57, 63 and 117. 3-D imaging simulation experiments show that the average absolute error, root mean square error and maximum absolute error of the depth of the geometric patterns of the 3-D imaging method and the geometric patterns proposed in this paper are all significantly reduced, and the depth of the geometric patterns in the 20- mm depth range are reduced significantly. and the advantages of this paper’s method are more obvious in the depth variation range of 20mm. It can be seen that the algorithm of this paper can improve the deformation effect of geometric patterns in lacquer decorative design.

In this paper, the problem of piano practice time allocation is categorized as an integer planning problem, and focuses on 0-1 integer planning in integer planning. Based on the advantageous information in the 0-1 integer programming problem, the value of feasible solutions and the index set corresponding to the feasible solutions are proposed to realize the piano practice time allocation based on integer programming. For the evaluation of piano playing effect, a piano playing effect evaluation method based on the extraction of musical melody features is proposed, which adopts the base note cycle extraction algorithm based on the short-time autocorrelation method to extract the base note of the musical melody, and improves the linear scaling algorithm to solve the problem of uneven playing speeds and so on. In the piano practice practice allocation experiment, the average allocation time of player A applying the time allocation method of this paper is 2516s, which is higher than that of player B with the traditional allocation time, and the average concentration time accounts for 98.53% of the average time, which is better than that of player B’s 95.43% share. Compared with the traditional manual evaluation method, the evaluation results of this paper’s piano playing effect evaluation method in different test times sum up to 1, and the evaluation effect is better.

This paper constructs the evaluation index system of city image IP brand communication efficacy, and utilizes hierarchical analysis and fuzzy comprehensive evaluation to construct a comparison matrix to assign and quantify them. Then, it constructs a regression model to analyze the influencing factors of city brand image communication efficacy with city brand image communication management power, communication power and relationship power as independent variables and city brand image perception as dependent variable. With empirical factor analysis, the chi-square degrees of freedom ratio CMIN/DF is 1.034, and the root mean square of approximation error RMSEA is 0.017, the assessment model has a good fit, which verifies the scientificity of the communication effectiveness assessment framework system. The communication effect of a city’s brand image is assessed and found to have a comprehensive score of 86.16. The city brand image communication management power, communication power and relationship power all have a positive influence on the city brand image communication effectiveness.

Bridge construction is an important link in the construction of transportation infrastructure, which plays a key role in ensuring the smoothness and safety of road traffic. This paper systematically organizes the process of laser point cloud technology in bridge quality monitoring, and proposes an improved adaptive hyperparametric RANSAC point cloud segmentation algorithm to realize the bridge quality monitoring. Firstly, the basic process of RANSAC algorithm is sorted out, and the mean downsampling operation is adopted to replace the center of gravity downsampling method, which improves the point average degree of downsampling. Next, the FPS algorithm is combined with the method of selecting seed points to expand the range of selected values of seed points under the premise of meeting the relevant requirements. After splitting multiple fitting surfaces, the split fitting surfaces are combined to optimize the unfitted points and improve the fitting rate of the algorithm. The detection accuracy of the bearing flatness of bridge number 3 under the method of this paper is improved by 78.26%, and the maximum deviation of the detected bridge constitutive point offset is only 0.623m, which is within the acceptable range of bridge error monitoring. The feasibility of laser point cloud technology for bridge quality monitoring is verified.

Wireless sensor networks, which integrate a variety of technologies such as sensors, microelectromechanical systems, wireless communications, and distributed information processing, have become a cutting-edge field for studying the behavior of intelligent autonomous self-governing systems in groups. This paper explores distributed sensor networks in intelligent buildings, uses QoS routing algorithm based on ant colony optimization to implement the strategy of energy efficiency regulation of distributed sensor networks, and conducts experimental analysis on the performance of the algorithm as well as distributed sensor networks. Compared with the PCCAA algorithm, the node degree variance and channel percentage variance of this paper’s algorithm are smaller, the network link distribution and channel allocation are more balanced, and the topology is better. Meanwhile, the average power of this paper’s algorithm is slightly larger than that of the PCCAA algorithm, which is able to increase the robustness of the network while reducing the energy consumption and BER to ensure the network performance. In addition, the variance of the node energy consumption of this paper’s algorithm in different networks is smaller than that of the PCCAA algorithm, which indicates that this paper’s algorithm can make the node energy consumption of the whole network more balanced, and then improve the energy efficiency of the whole network. Simulation experiments prove that the algorithm in this paper effectively allocates node bandwidth through the quantization mechanism, thus reducing the amount of inter-node communication, while the corresponding sampling interval extension strategy can save the overall energy consumption of the network. The algorithm proposed in this paper has important practical value for energy efficiency regulation of sensor networks in intelligent buildings.

With the arrival of the big data era, a huge amount of text data of college language is generated, and how to manage these text data efficiently and mine useful information has become the focus of many scholars. The study first preprocesses and represents the university language text data, proposes a feature screening method based on Shannon entropy and JS-scatter, and then combines the principal component analysis algorithm with the dimensionality reduction of the extracted features on this basis. Subsequently, a pre-trained high-dimensional word vector spatial mapping model is introduced to generate richer semantic representations, and a pre-trained high-dimensional word vector spatial mapping model based on the pre-trained high-dimensional word vector spatial mapping model is designed. Finally, the method proposed in this paper is tested experimentally. Under different feature dimensions, the macro-averages of this paper’s method are 72%, 44.2%, 67.1%, and 3.3% higher than those of IG, PMI, ANOVA, and JS methods. At the feature dimension k=350, the macro-mean of this paper’s method is 0.853, when the classification effect reaches the optimization. In the spatial mapping relationship of word vectors, the accuracy of the mapping of this paper’s method also reaches 11.2% for the words with word frequency sorted from the first 5000 to the first 6000. This proves the effectiveness and feasibility of this paper’s method.

Knowledge mapping technology can effectively integrate and manage knowledge, and fully show the relationship between knowledge. Based on this, knowledge mapping is applied to the construction of the resource base of the ideology and politics course to explore its association with the teaching content. After sorting out the relevant concepts and construction methods of knowledge mapping, this paper proposes the design method of course ideology based on knowledge mapping. The web crawler tool is utilized to crawl the text data of the Civics material and preprocess the data. The seven-step method and Protégé, an important tool for ontology modeling, were used to complete the construction of the ontology model of the curriculum Civics and Politics domain. Finally, BERT, GGAT, CRF, and graph pooling techniques are combined to construct the general architecture of the Civics knowledge extraction model to realize the extraction of Civics knowledge. The method of Civics knowledge relation extraction in this paper performs well in the comparison experiment, and the AUC value of the method reaches 41.59%. More than 90% of the students express their liking and agreement with the teaching model based on knowledge graph, which verifies that the teaching model based on knowledge graph proposed in this paper has a positive and active effect on the learning aspect of students’ Civics knowledge.

Frequent lightning activity has the potential to cause damage to man-made facilities, cause forest fires and other hazards, and the prediction of lightning activity can help to avoid the occurrence of these disasters. In this paper, based on the lightning activity data of a region, the distribution pattern of lightning activity is identified at different elevations and latitudes and longitudes. Then geodetic distance and contributing nearest-neighbor similarity are introduced, and a GS-DBSCAN clustering algorithm is proposed to realize the spatial prediction of lightning activity by using the method of leastsquares fitting of prediction equations. The lightning activity directions after data clustering show topographic correlation, and the overlap between lightning activity directions and topography is about 35%. Combined with the prediction images, it is found that the lightning activity prediction results of this paper’s method are closer to the real value than other algorithms, with an average offset error of less than 1.1km, an accuracy rate of >85%, and a false alarm rate of <35%, which reflects a good prediction performance.