Long-Term Care Insurance Policy Intensity and the Policy Matching Degree of Pilot Cities in China

Since China launched long-term care insurance (LTCI) pilot in 15 cities in 2016, the LTCI policy schemes have been developed gradually. Based on the 61 LTCI policy documents, this study evaluated the LTCI policies intensity in each pilot area by building a PMC index model with 10 primary variables and 44 two-level variables. Using the coupled coordination model, the coordination development indexes were calculated to evaluate the level of matching between LTCI policies and economy development and population structure. The results showed that the PMC index valued between 0.646 and 0.922. The three cities having the highest level of policy strength (PMC>0.8) were as follows: Qingdao, Nantong, and Jingmen. The three cities having the lowest level (PMC<0.7) were Ningbo, Qiqihar and Chongqing. The indexes of the coordination degree varied from 0.263 to 0.594. Shanghai, Qingdao and Guangzhou had the highest level of coordination degree, while Anqing, Chende and Qiqihaer are the lowest .The difference of financing mechanism of pilot cities was one of the main determinants of policy intensity. The matching degree was relatively low. Qingdao was a unique city having both high policy intensity and matching degree. It was suggested that the intensity and matching degree of LTCI policies should be improved to develop a national LTCI system in China.

Recognition Algorithm of Popular Elements of Ethnic Minority Traditional Clothing Based on PCA

Image recognition of ethnic minority costumes is helpful for people to understand, carry forward, and inherit national culture. Taking the minority clothing image as the research object, the image enhancement and threshold segmentation are completed; the principal component features of the minority clothing image are extracted by PCA method; and the image matching degree is obtained according to the principle of minimizing the Euclidean distance. Finally, the calculation process of the PCA method is optimized by a wavelet transform algorithm to realize the recognition of popular elements of minority traditional clothing. The comparative experimental results show that the PCA + BP neural network algorithm is better than the other two recognition algorithms in recognition rate and recognition time.

Oracle Recognition of Oracle Network Based on Ant Colony Algorithm

Recent studies have shown that compared with traditional social networks, networks in which users socialize through interest recommendation have obvious homogeneity characteristics. Recommending topics of interest to users has become one of the main objectives of recommendation systems in such social networks, and the widespread data sparsity in such social networks has become the main problem faced by such recommendation systems. Particularly, in the oracle interest network, this problem is more difficult to solve because there are very few people who read and understand the Oracle. To address this problem, we propose an ant colony algorithm based recognition algorithm that can greatly expand the data in the oracle interest network and thus improve the efficiency of oracle interest network recommendation in this paper. Using the one-to-one correspondence between characters and translation in Oracle rubbings, the Oracle recognition problem is transformed into character matching problem, which can skip manual feature engineering experts, so as to realize efficient Oracle recognition. First, the coordinates of each character in the oracle bones are extracted. Then, the matching degree value of each oracle character corresponding to the translation of the oracle rubbings is assigned according to the coordinates. Finally, the maximum matching degree value of each character is searched using the improved ant colony algorithm, and the search result is the Chinese character corresponding to the oracle rubbings. In this paper, through experimental simulation, it is proved that this method is very effective when applied to the field of oracle recognition, and the recognition rate can approach 100% in some special oracle rubbings.

A Matching Degree Management Model of Human Body Shape and Fashion Design Based on Big Data Analysis

The existing clothing design model lacks the screening link of the human body part index, and the output clothing data are affected by the high correlation coefficient, resulting in large matching errors. Therefore, based on the analysis of human body shape, a management model of matching degree of human body shape and clothing design based on big data is constructed. After processing with big data methods, human body characteristic data used signals as the input layer of a neural network model and the matching degree management model of human body shape and fashion design. The simulation results show that the built-up model has a matching error of less than 5%, which can effectively improve the matching of human body shape and clothing design.

Topology identification method of distribution network based on branch active power

Because of low measurement redundancy and frequent switch changes, it is difficult to identify the correct topology structure. In this paper, a topology recognition method of distribution network based on branch active power is proposed. Firstly, branch active power residual algorithm is used to identify the topological structure. The topology obtained by this method has the highest matching degree with the real-time measured data. Then genetic algorithm is used to optimize the inverse recognition of power grid topology. The numerical example shows that the method is reasonable, effective, rapid and simple. It also has good adaptability with a large number of measurement errors.

A Method of Recommending Physical Education Network Course Resources Based on Machine Learning Algorithms

Aiming at the problem of difficult selection of physical education online course resources, a method of recommending online course resources based on machine learning algorithms is proposed. The information recommendation model is established through the expression of a collaborative filtering algorithm and resource feedback matrix. According to the feedback score of any user on the same data resource in the project set, the interest matching degree is established by comparative analysis, and the matching degree is substituted into the cosine similarity function to calculate the similarity threshold between each item and so on, calculate the similarity threshold number of all items, select the project resource that best matches the user according to the threshold number, and complete the recommendation. The experimental results show that the recommended method of physical education network curriculum resources based on machine learning algorithm is relatively excellent in recommendation accuracy and efficiency; this method can realize the innovation of higher physical education network curriculum teaching mode.

A Method for Recommending Sports Network Course Resources Based on Collaborative Filtering Technology

In the process of online course resource recommendation, the output of recommendation results is often unstable. Therefore, a physical education online course resource recommendation method based on collaborative filtering technology is proposed. Firstly, the learning preference of e-learners is calculated, the frequency index of the word frequency-inverse document is defined, the correlation between courses is reflected, and the specific needs of students for PE online course resource recommendation are understood. Then, the collaborative filtering recommendation algorithm is used to generate the similarity matrix and correlation matrix, update the edge characteristics of sports online curriculum resources, collect and refine the hidden index of sports online curriculum resources, optimize the prediction rules of the neighborhood of the most similar teaching unit, and complete the recommendation of sports online curriculum resources. Experimental results show that, for 1000 keywords, the method has the highest single average matching degree, the recommendation process is stable, and the F1 value is more than 0.9, and the practical application is ensured.

DWCA-YOLOv5: An Improve Single Shot Detector for Safety Helmet Detection

Aiming at solving the problem that the detection methods used in the existing helmet detection research has low detection efficiency and the cumulative error influences accuracy, a new algorithm for improving YOLOv5 helmet wearing detection is proposed. First of all, we use the K -means++ algorithm to improve the size matching degree of the a priori anchor box; secondly, integrate the Depthwise Coordinate Attention (DWCA) mechanism in the backbone network, so that the network can learn the weight of each channel independently and enhance the information dissemination between features, thereby strengthening the network’s ability to distinguish foreground and background. The experimental results show as follows: in the self-made safety helmet wearing detection dataset, the average accuracy rate reached 95.9%, the average accuracy of the helmet detection reached 96.5%, and the average accuracy of the worker’s head detection reached 95.2%. Making a comparison with the YOLOv5 algorithm, our model has a 3% increase in the average accuracy of helmet detection, which is in line with the accuracy requirements of helmet wearing detection in complex construction scenarios.

Research on Restoration Algorithm of Tomb Murals Based on Sequential Similarity Detection

Considering the problems of fuzzy repair and low pixel similarity matching in the repair of existing tomb murals, we propose a novel tomb mural repair algorithm based on sequential similarity detection in this paper. First, we determine the gradient value of tomb mural through second-order Gaussian Laplace operator in LOG edge detection and then reduce the noise in the edge of tomb mural to process a smooth edge of tomb mural. Further, we set the mathematical model to obtain the edge features of tomb murals. To calculate the average gray level of foreground and background under a specific threshold, we use the maximum interclass variance method, which considers the influence of small cracks on the edge of tomb murals and separates the cracks through a connected domain labelling algorithm and open and close operations to complete the edge threshold segmentation. In addition, we use the priority calculation function to determine the damaged tomb mural area, calculate the gradient factor of edge information, obtain the information entropy of different angles, determine the priority of tomb mural image repair, detect the similarity of tomb mural repair pixels with the help of sequential similarity, and complete the tomb mural repair. Experimental results show that our model can effectively repair the edges of the tomb murals and can achieve a high pixel similarity matching degree.