In this paper, the image parameters are preprocessed by the gray scale histogram statistical image parameters, which reflect the gray scale distribution information of the plant images, using the zero-mean normalization formula. According to different lighting conditions, the plant image is segmented, and the texture feature information in the plant image is extracted by using the improved grayscale covariance matrix. The hyperspectral linear mixing model is constructed, and the MVSA algorithm meta-decomposes the mixing model to solve the solution optimization problem. Using the natural gravity embossing method, produce plant embossed flowers and analyze the features and spectral curves of different parts of the embossed flowers to evaluate the comprehensive use of the embossing method proposed in this paper. The ROI images of 1200 embossed pattern petals were calculated to obtain the sample spectral matrix of embossed petals, in which the reflectance of the central petal was the highest among the three parts at a wavelength of 450 nm, with a reflectance of 0.46487, and then decreased, and then gradually increased to one place after the wavelength was equal to 694, with a reflectance of 0.8. The reflectance of the Shaanxi Weixiang (Weixia), the single side-embossed Yuanbaosi (Yuanbao maple), the hammered elm (fruits), and the pachypodium (Green) obtained a full score of 35 in the comprehensive evaluation after drying, which is a perfect embossed plant material, and all the plant materials embossed using the method proposed in this paper averaged above 30, and the comprehensive effect of plant embossing was good.

Since the financial crisis, the economies of all countries have been affected by the recession triggered by global events, and the uncertainty brought by the changes in economic policies has also become a risky shock, and the uncertainty of economic policies has been climbing worldwide. This paper firstly briefly analyzes the mechanism of economic policy and financial market, in order to comprehensively study the changes of market economic liquidity, this paper starts from the return of the market economy, and adopts the symbolic time series analysis method to analyze the prediction of the financial market by taking the stock market as an example. Then construct the regression model, and then study the impact of economic policy uncertainty on market liquidity. The regression coefficient of economic policy uncertainty is 0.064, which is significant at 1% level. Secondly, when GDP growth rate and inflation level are added as control variables, the regression coefficient of economic policy uncertainty obtained is 0.108, which is still significant at 1% level, implying that a rise in economic policy uncertainty brings about a decline in market liquidity. This study provides an effective analytical tool for the impact of economic policies on market liquidity. It also provides a basis for the government to improve market liquidity and enhance market vitality.

The study adopts a detection followed by tracking paradigm. In the detection stage, the BiFormer dynamic sparse attention module is embedded in the YOLOv8 network model, while the original nearest neighbor interpolation upsampling is improved by replacing it with the lightweight upsampling operator CARAFE. In the target tracking stage, a multi-vehicle steering trajectory tracking algorithm based on particle filtering is proposed, and the particle filtering algorithm is improved by combining the target motion direction weighted resampling algorithm. The two improved algorithms are combined for multi-vehicle detection and tracking in tunnel scenarios, and the average tracking accuracy can reach 97.3%. Compared with the traditional YOLOv8 combined with particle filtering algorithm for tracking, the method in this paper is more advantageous.

Curriculum Civics refers to the integration of Civics elements into the teaching of professional courses, so that courses other than Civics courses can also play the role of Civics teaching. In this paper, we study a knowledge mapping-based content generation technology for teaching course Civics and Politics, so that the knowledge of Civics and Politics courses can be integrated and visualized. The knowledge points, concepts, definitions and other information of the course Civics and Politics are extracted in the form of Civics and Politics knowledge triples. Through the extraction of the knowledge entity of curriculum Civics and politics, the relationship between semi-structured data and unstructured data is extracted to realize the integration of knowledge and content generation. After achieving content generation, the generated content is personalized through a deep reinforcement learning recommendation algorithm based on diversity optimization. Taking the two courses of Engineering Cost Management and Engineering Economics in the engineering management specialty as an example, it is found that the proposed knowledge graph construction method has an accuracy rate of 96.2%, which is able to effectively establish the knowledge association between the civic elements and the elements of professional knowledge, and realize the mining and generation of the civic elements. Meanwhile, the DDRL-Base recommendation algorithm achieves the optimum in accuracy, recall and F1 value indexes, and optimizes the problems such as cold start and sparse data in resource matrix, which improves the effect of recommending the Civics and Politics teaching content of the course.

The technical analysis of conventional tennis sports basically focuses on individual studies, with less research on the basic theory of tennis, and the theoretical analysis of tennis trajectory is even rarer. In this study, based on the calculation equations of the main forces during tennis movement, the dynamics analysis of tennis serve movement is carried out, and the three-dimensional trajectory equations of tennis serve are established. Then, based on the ODE dynamics engine technology, the simulation platform of tennis serve is built to realize the simulation and visualization analysis of tennis trajectory. Since the simulation system beat frequency is 1000Hz, the time difference between tennis simulation and actual movement is the smallest, so the frequency of 1000Hz is chosen for the simulation study of tennis serve trajectory. The simulation results show that under the same hitting height and ball angle, the larger the initial velocity of the tennis ball is, the farther the X-axis landing point is from the center line. In addition, under the consideration of air resistance and Malnus force, the difference between the Y-axis landing point of tennis ball when the initial serve angle is 30° and 60° is 1.81098 m. The present study provides a certain reference for the in-depth study of the serving strategy of tennis ball, and at the same time, it also provides a certain theoretical basis for the improvement of the tennis players’ training method and technical playing style.

Flipped classroom teaching puts forward new requirements on the enthusiasm of students’ independent learning, however, the traditional independent learning lacks scientific aids and cannot meet the individual needs of students in the process of self-study. Therefore, this paper exploits the neural network technology in intelligent computing technology to extract the deep implicit semantic representation, combines the implicit semantic indexing (LSI) to improve the traditional collaborative filtering algorithm, and explores an optimized implementation path of the flipped classroom teaching mode. The improved ICF algorithm outperforms the comparison algorithm in terms of recommendation accuracy, average recall, and average coverage in the three datasets. The computational time consumed is reduced by 44.85%, 57.34%, and 73.68%, respectively, compared with UCF. Incorporating the learning resource recommendation model constructed in this paper in a traditional flipped classroom, it is found that the post-test scores of the experimental class in Moral Education are significantly higher than those of the control class (p<0.01), and its post-test scores are significantly higher than its pre-test scores (p<0.01). The collaborative filtering algorithm optimized by intelligent computing technology facilitates students' personalized independent learning, innovates the general flipped classroom teaching mode, and receives the expected results.

The study applies machine vision technology to the production and operation process of energy enterprises, and constructs a fire detection model based on improved YOLOv4 from the real-time monitoring of fire emergency safety scenarios. Based on the original YOLOv4 algorithm, the model lightens the feature extraction and feature fusion networks, and introduces CA attention mechanism in the bottom layer of the feature extraction network to improve the accuracy of target detection. An intelligent fire alarm system is built on this basis as a response method for emergency security scenarios. Comparison with the basic YOLOv4 algorithm reveals that the improved YOLOv4 algorithm reduces the parameter amount by 45.97%, improves the FPS by 27.75, and improves the mAP value by 14.10%, which achieves a better detection accuracy on the basis of greatly reducing the amount of computation and parameter count, and also achieves a better Loss value and mAP in the comparison with other detection methods. Intelligent Fire Alarm The system integrates intelligent detection, intelligent alarm, intelligent alarm receiving and intelligent alarm dispatching, and can complete the fire alarm process within 6s. In summary, it shows that the method proposed in this paper can be used in real-time monitoring of emergency security scenarios and can provide timely warning at the early stage of security hazards.

In recent years, study travel has become a popular way to expand teaching outside the classroom. Based on the trajectory of the development of study travel, the article conducts an in-depth study of the current development of study travel in the context of the new era, and explores the 4.0 model of regional study travel development. Introducing big data and new technologies into study travel and designing a digital platform for study travel. Construct the evaluation index system of study travel, and evaluate the study travel 4.0 mode through questionnaires. Detect the study effect of the study travel 4.0 mode by comparing the impact of the study travel 4.0 mode and the traditional study travel mode on students’ disciplinary literacy. The comprehensive score of the evaluation of the study trip was 4.17, and the study trip 4.0 mode achieved excellent evaluation results. The experimental group and the control group did not show significant differences before the experiment, and significant differences were produced after the experiment. The experimental group’s scores on each dimension of geographic literacy increased by 6.35, 5.56, 7.57, 5.01, 7.89, 5.75, and 38.13 points after the experiment, showing significant differences (p<0.05), while none of the control group's scores increased by more than 1.5 points, with p-values of greater than 0.05. The research and study trip 4.0 model has a significant positive effect on improving students' disciplinary literacy. At the same time, under the background of regional study tours, the cultural innovation strategy is put forward.

The rapid development of the economy in recent years has brought convenience to enterprises, but also made the competition between enterprises more intense, enterprises want to stand firm in the fierce competition not only to improve financial performance, but also from a multi-dimensional integrated perspective. For this reason, this paper launched a multidimensional financial comprehensive evaluation research for enterprises. Based on the Harvard analytical framework, the study firstly emphasizes the financial performance of enterprises and at the same time combines the social responsibility perspective to screen the indicators. Then the quantitative evaluation method of this paper is proposed, i.e. the entropy weight method and gray correlation method are combined to analyze the development status of multidimensional financial performance from an objective point of view. Then the entropy weight method and gray correlation method model are introduced respectively, and the modeling method of combining the two applied in this paper is explained. Finally, by analyzing and evaluating the results of the sample company M, it can be obtained that (1) the results of the correlation degree of company M from 2017 to 2022 are 0.722, 0.473, 0.398, 0.389, 0.426, and 0.496 respectively, and the results of the multidimensional financial synthesis evaluation of company M during these six years are optimal in 2017. (2) The overall performance of the financial capital status of Company M from 2017 to 2022 is gradually deteriorating. (3) Overall, the performance of Company M’s responsibility to its employees is evolving from 2017 to 2022. (4) The company’s performance of responsibility to consumers and government during the six years from 2017 to 2022 is good, but ecological responsibility is at a medium level and has some room for development. This paper provides a multidimensional and comprehensive evaluation of the financial indicators of the company from a scientific point of view, which provides some reference for investors and business managers.

The article is based on Cite Space software for bibliometric analysis of the impact of artificial intelligence on economic development. Literature information comes from CNKI Knowledge Network database, identifying the hotspots and characteristics of the research related to artificial intelligence and economic development from the perspective of the number of articles issued, core authors, keywords, etc., and comprehensively analyzing 3,340 pieces of literature during the period from 2013 to 2023. The study shows that the number of published articles on the research on the impact of artificial intelligence on economic development increases year by year, and by 2021, the number of published articles is more than 600. Most authors publish related articles in the range of 3-7 articles, and there are fewer collaborations between authors. There are 16 keywords that appear more than 30 times in the field of the impact of AI on the economy between 2013~2023, which is statistically accounted for the total of 15.41%. The keyword clustering is divided into 7 cluster classes, and the clustering module Q=0.781, S=0.877, which has a high feasibility degree. The keyword with the highest intensity of emergence (3.91) in the field of research on the impact of artificial intelligence on economic development after 2018 is “research and development applications”.