This paper examines the differences and convergence of regional real estate markets based on panel statistics of 28 provinces, autonomous regions and municipalities directly under the central government in China from 2010 to 2023. Relevant variables such as urban construction land area, population and economic growth are set and the data are processed. The data show that the degree of industrial convergence and circulation costs have a positive spatial correlation and an upward trend from 2015 to 2021. From the viewpoint of regional real estate market divergence, the proportion of the real estate industry in GDP has remained above 5% since 2015, and this proportion is larger in the eastern region, for example, it was 8.74% in Beijing in 2015, but it has slightly decreased in some provinces and cities. The proportion in central and western provinces and cities has been rising faster year by year. The extreme deviation and standard deviation coefficient of the eastern region are relatively large, with the extreme deviation of the eastern region being 4.35% and the standard deviation coefficient being 1.45529 in 2021, indicating that the internal development is not balanced. From the analysis of convergence, the rate of convergence in the absolute convergence test is 3.66%, and the rate of convergence in the conditional convergence test is 2.89%, with a half-life of about 23.8 years. It indicates that the regional real estate market differences are shrinking, showing a trend of convergence, but the convergence process is relatively slow, which provides an important basis for an in-depth understanding of the characteristics of the regional real estate market.

With the increasing complexity of the financial market, corporate financial fraud events occur frequently, posing a serious challenge to investors and market regulators. Aiming at the limitations of traditional financial fraud recognition methods, this paper constructs a financial fraud recognition model MCN based on the topological data analysis method. The model consists of two parts: the Mapper algorithm and one-dimensional convolutional neural network (1DCNN), which combines the global topology extracted by the Mapper algorithm with the local features of the IDCNN to realize the effective identification of financial fraud samples. In order to evaluate the recognition performance of the model, this paper controls the topological feature extraction method unchanged and the classifier unchanged respectively, and compares the performance of the MCN model with other financial fraud recognition models. The results show that the Acc and F1-score of the MCN-based financial fraud recognition model in this paper are 98.69% and 97.64%, respectively, which are better than other models in both perspectives, proving the superiority of the financial fraud recognition model based on topological data analysis constructed in this paper, and thus providing powerful technical support for the regulation of the financial market and the risk management of enterprises.

This paper applies smart technologies to urban rain garden design and analyzes the hydrological effects based on urban smart rain garden technologies. The SWMM model is used to simulate runoff water quantity and quality under the environment of long-term and continuous rainfall events in urban areas. Building area A is selected as the case study object of this paper, and its geographic location and precipitation data are analyzed to preliminarily explain the hydrological conditions of the case study area. Based on the SWMM model, the model pipe network generalization and other operations are carried out to establish the SWMM model of the study area. The SWMM model is calibrated in terms of the calibration of the model’s parameters and the feasibility of the structured network SWMM model to verify the validity of the SWMM model of the study area and its catchment delineation method. Based on the urban smart rain garden technology, the LID module is added to the SWMM model of the study area and the hydrological effects under different scenarios such as combined LID are analyzed. Each LID measure can have a certain reduction effect on the combined runoff coefficient and total runoff. The combined LID measures in this paper have the best reduction effect, with the reduction rate of the integrated runoff coefficient over 35% and the total runoff over 50% in the 2h rainfall event. The combined LID scheme has the best reduction effect on the flood peak, and the reduction rate can reach more than 40% in both 2h rainfall events.

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.