Research on the competitiveness of China’s leisure sports industry based on statistical method

Abstract

Leisure sport is a rising industry in recent decades. Its development speed is far more than that of other industries. Despite the rapid development of sports industry, the number of leisure sports enterprises in China is still scarce. Therefore, this paper put forward the research on the competitiveness of leisure sports, combined the data of leisure sports industry with computer technology, and solved the problem of lagging development of sports in China through statistical operation. In order to optimize the model, we added the association rule algorithm to further improve the accuracy of data, so as to further expand the leisure sports market. Through a comprehensive test of the function of the algorithm, the results show that the system is feasible in the use of the system.

Keywords

Statistics leisure sports association rules algorithm

1 Introduction

Influenced by the 2008 Olympic Games in Beijing, the sports undertakings of our country are increasing gradually, but the speed and scale of leisure sports still cannot keep up with the pace [1]. In particular, the level of competitiveness is low and the technology is backward [2]. The lack of management and operating mechanism leads to some restrictions and restrictions on leisure sports in China, which makes it impossible for leisure sports to compete in the international market [3]. Leisure sports is the integration of sports products and financial market, we need to deeply realize the importance of the nature of sports leisure industry and the basic attribute. It represents a national basic constitution and has its important role in promoting spiritual civilization, enhancing national physique, and riching social culture [4]. The Marx doctrine mentioned, whether it is the leisure industry or the leisure sports industry, its emergence and development must have two conditions: the rich social foundation and more leisure time; the leisure sports industry based on social development is necessary for human development and is a new way to stimulate economic development and consumption, adjust industrial structure chain and increase employment. It has an immeasurable role for meeting the needs of the masses, for a comprehensive well off society and for promoting the all-round development of the masses themselves [5]. It can help people improve their health quality, build spiritual civilization and cultivate their sentiment. Sports leisure includes many aspects, such as hiking, climbing, traveling, fishing and so on. It’s the main way to relieve stress and improve physical health in modern society.

Fig.1

Process diagram of model building.

2 State of the art

There are countless connections between the leisure sports industry and the colorful sports activities, engaging in leisure sports regularly is the main way for people to relieve stress and relax in modern society. It is the effective means to deal with many common problems of modern humans, to improve health, and also has a negligible effect against the disease and edifies sentiment [6]. The role of leisure sports industry of modern human has following several aspects: First, it can strengthen teamwork and promote the teamwork spirit of people through mutual help and good competition in leisure sports and leisure activities. It can also strengthen social cohesion, boycott backward and vulgar social atmosphere, and occupy a good point of social development [7]. Second, it can drive healthy economic consumption [8]. With the social and economic development in the country, people’s consumption idea becomes more and more advanced, leisure sports have become a trend in consumption, this is a kind of green recycling and is conducive to the healthy growth of people’s consumption, on the one hand to promote the tourism, catering, finance, transportation and development of all kinds of infrastructure construction, development on the other hand, the leisure sports is very important to the increase of our country to optimize the proportion of the first, second, third industry in the whole industrial structure in the. Leisure sports, which has the characteristics of double labor and capital, can attract more investment objects [9]. Finally, leisure sports played a decisive role in building a harmonious and harmonious society. With the ending of The 19th National Congress of CPC, China began to enter the new era. The Chinese nation is in an important turning stage. The development of leisure sports is the general trend [10].

Table 1
Comparison of statistical system of leisure sports industry

Effect feedback comparison operating system hardware environment data base Reducing the error rate

Traditional statistical system 77.1% 69.9% 70.1% Low

Statistical application system 91.6% 84.7% 84.9% High

Effect feedback comparison	operating system	hardware environment	data base	Reducing the error rate
Traditional statistical system	77.1%	69.9%	70.1%	Low
Statistical application system	91.6%	84.7%	84.9%	High

3 Methodology

3.1 Construction of competitiveness model of leisure sports industry based on statistics

China is moving fast on the way of social development, but the corresponding technology and resources are not synchronized. The economic level is not at a level, and many leisure sports equipments are relatively low. It shows that China contains a huge sports market. How to develop the hidden industry and enhance its competitiveness is precisely what we have studied in this paper. Through the establishment of the model of Leisure Sports Competitiveness Based on statistics, a new way for leisure sports in China is sought. Modern statistics is mainly derived from the theoretical statistics and development of social statistics and mathematical statistics. Social statistics mainly describes variables, while mathematics expresses random variables. Statistics is the way to find answers through mutual transformation between them. The leisure sports model based on statistics is a regular study of the related phenomena in leisure sports by using the principle of mathematical statistics. First, we examine and classify the data, integrate the related data, and identify the research objects according to the classification, and determine the relevant parameters and quantitative characteristics. Usually there is a test of random experiment before modeling, which determines the number of features, means and modes for the modeling and evaluation of use as a reference value. The relative numbers are determined by experiments to find things related to leisure sports and the relationship between them. The same kinds of indicators in different stages are arranged according to their order in order to form a set of dynamic numeric values. The weight of each number is the index of importance evaluation. A repeated step in the operation of the model will lead to random errors which are small and uncertain, including systematic errors, i.e., model errors. During the screening of sampling error, data error will appear artificially, through the related calculation and selecting the optimal data to reduce the error; there is the standard error in sample statistics and the equivalent parameters measured, sample selection has diversified, universal and large, with the increase of the number of samples when the independent variable and the equivalent parameters when the model is stable, and vice versa.

From the figure above, you can see, when the leisure sports industry is in the initial data to the statistical model based on the calculation, and then given a set of data, through a series of processing the confidence level of the target data to gradually achieve a balanced data integration for the filtered data by other test methods, to summarize the regularity the information given by evaluation, competitive sports leisure industry. Relying on big data, JSP and JDBC, the system works by means of computer algorithm. Its work is divided into three levels, namely, data level, logic level and expression level. Data analysis is used to find relevant points, and finally the results are expressed. The processing module includes intelligent information and statistics integration, intelligent screening, reliability evaluation, when given in the engine system can retrieve statistical information, more intelligent computing and leisure sports industry related industry competition scores, by comparing these industries and the leisure sports industry and help to judge the information constraints, the leisure sports industry development competence test the direction, finally find the best way for Industry. The modules in the system operate separately, and the data mining algorithms are interspersed to form an evaluation model which is complementary to each other. The specific system is compared to the following table.

Fig.2

Sequence schematic diagram of association rules.

3.2 Evaluation system of competitiveness of leisure sports industry based on association rules algorithm

The problem of the research on the origin of the algorithm is mainly the economic problem of society. Its development experienced a city-state political situation, political arithmetic and statistical analysis of three stages. Through continuous evolution and development, it has gradually become a comprehensive discipline. In this paper, we use association rules algorithm to search for new directions of leisure sports development through data collection and analysis of leisure industry. The leisure sports products have unique attributes of social value and economic value, which determines the industry to build a diversified sports service system, from the development of leisure sports industry competitiveness, the ultimate goal is to maximize the national health and physical and mental pleasure. To meet the needs of the masses and to improve the health of the people is the direction of the national development and the business opportunity of the leisure sports industry. We represent the support degree of the transaction D as, with a probability of the support degree contains the probability, and we define the support degree formula as the next.

The problem of the research on the origin of the algorithm is mainly the economic problem of society. Its development experienced a city-state political situation, political arithmetic and statistical analysis of three stages. Through continuous evolution and development, it has gradually become a comprehensive discipline. In this paper, we use association rules algorithm to search for new directions of leisure sports development through data collection and analysis of leisure industry. The leisure sports products have unique attributes of social value and economic value, which determines the industry to build a diversified sports service system, from the development of leisure sports industry competitiveness, the ultimate goal is to maximize the national health and physical and mental pleasure. To meet the needs of the masses and to improve the health of the people is the direction of the national development and the business opportunity of the leisure sports industry. We represent the support degree of the transaction D as Suppert (X), with a probability of P (X). The support degree contains the probability, and we define the support degree formula as the next $Suppert (X) = \frac{Sup (X)}{| D |}$ (1)

The upper Sup (X) is the support of the data set X. The support of the project set (X ∪ Y) is called the support of the X ⇒ Y, and we can understand it as the proportion of the (X ∪ Y) in the data, that is, the probability. The formula is expressed as follows $Suppert (X \Rightarrow Y) = \frac{Sup (X \cup Y)}{| D |}$ (2)

Confidence degree: the confidence level in association rules is the ratio of the number of transactions that contain X and Y at the same time and the number of transactions that contain X. That is the conditional probability P (Y|X) that we know well. The following forms are as follows $Confidence (X \Rightarrow Y) = \frac{Sup (X \cup Y)}{Sup (X)}$ (3)

Effective storage plays a significant role in improving the accuracy of computation. It can improve the anti-interference ability of our association rules algorithm, make our main research objects more prominent and reduce the computation time of association algorithm. We define the formula for the degree of interest as follows

$\begin{matrix} I & = & Interest (X \Rightarrow Y) \\ = & \frac{confidence (X \Rightarrow Y) - support (y)}{\max {confidence (X \Rightarrow Y), support (y)}} \end{matrix}$ (4)

The data cleaning algorithm calls the next layer of incoming data to select and analyze, exclude useless data, and then store information into the cloud computing database, which is used for association rules calculation. The standard deviation σ_p is used to calculate the data that does not need to be recorded into the storage $σ_{p} = \sqrt{\sum_{j = 1}^{m} \sum_{k = 1}^{m} A_{j} A_{K} σ_{jk}}$ (5)

Use covariance validation to verify the feasibility of a statistical dataset. The formula is as follows $σ_{jk} = r_{jk} \times σ_{j} \times σ_{k}$ (6)

We use related ways to sort the influencing factors, and divide the key factors into statistical errors, so as to identify the most critical factors and the ranking of the key factors. The construction of models mainly consists of collecting data, establishing evaluation criteria, establishing factor sets, establishing evaluation sets, establishing weighted average comprehensive evaluation models, and determining association orders. As shown in the picture

The specific research steps are as follows: first, the data collection is collected. In the construction of this model, we use the development of leisure sports industry in big cities as basic data. These basic data are all digital or evaluation, which cannot be directly used in models. The second is to establish evaluation criteria. The establishment of evaluation criteria is based on the theory of knowledge evaluation, we construct evaluation standards according to the relevant views of evaluation, in the construction process, to fully consider the actual situation of the scientific evaluation standard and related leisure industry, including economic income, the size of the actual situation to judge; third is to establish the factor set. We set up standards after using relevant knowledge of evaluation, and then set up relevant factor sets. These factors are related factors that our relevant scholars are doing after the study. The fourth is to set up evaluation set. In this part, we use statistics knowledge to build the evaluation set. The evaluation set is based on the evaluation criteria. We will leave behind the standard factors, and eliminate the factors that do not meet the evaluation criteria. The fifth is to establish the weight matrix. We use matrix form to identify the factors that affect the competitiveness of leisure sports industry and conduct consistency check. Finally, we analyze the reasons and test the feasibility of the model after being eliminated.

4 Methodology

After the model is completed, it is the next test. This test randomly selects the initial data node from the database as the test number. The performance of statistical computation has been evaluated before, which can be said to be ideal. Meanwhile, the accuracy of the overall statistical algorithm has been verified, and it has been compared with previous common screening algorithms. In order to make it can play its role for the competitiveness model of leisure sports industry, we will give an initial set of data to facilitate algorithm model start, so we used the data generated by the public survey and the test of the regression of the system for this model. According to the actual data to establish the evaluation system of modeling test, then calculate selection statistic to determine the significant level of a, the general value is 0.05 and the latter is 0.01, according to the a to draw the corresponding critical value, this value is reflected in statistics expectation value is close to the value that the more accurate statistics. On the premise of hypothesis test, the calculated statistic is compared with the expected value. If the former is larger than the latter, the difference is significant. On the other hand, the system is stable. The regression equation is used to analyze the quantitative relationship between variables. Through the prediction and control function of regression analysis, the calculated statistical value is controlled within the acceptable error scope, so as to ensure the feasibility of the leisure sports industry competitive model based on statistics.

Table 2
Comparison of coefficient fluctuation

Significant level Statistical error degree System fault tolerance rate Actual output value of algorithm

0.5 0.1 0.1 33

0.1 0.09 0.09 51

0.4 0.1 0.04 48

0.6 0.1 0.1 61

0.4 0.08 0.11 59

Significant level	Statistical error degree	System fault tolerance rate	Actual output value of algorithm
0.5	0.1	0.1	33
0.1	0.09	0.09	51
0.4	0.1	0.04	48
0.6	0.1	0.1	61
0.4	0.08	0.11	59

Secondly, we use the analysis of sports GDP increase value of production and production methods, the net sports product cost calculation, the consumption and investment amount of all the value of comprehensive analysis, and improve on the basis of it, to make it more fit with the leisure sports industry based on the statistical model and into them, and satisfied with the test after it has effect. The system was applied to statistical regression to forecast method, which mainly includes three elements: the mathematical model of the actual data, and the corresponding theoretical basis, these elements are to internal actions and decisions and produce benefits for the standard, the test is required to consider appropriate, cost model and accurate of the quantitative prediction of statistical data, the calculation results and compare it to summarize the program operation rules related to the large data processing by computer, then find the leisure sports industry compared to other industrial flash point does not carry forward fault. The results of the test are as follows.

Fig.3

Evaluation and test for association rule algorithm.

Table 3

whether the algorithm model can meet the practicability and reliability of the current large data environment

Data input value	Error rate	The algorithm obtains the adjustment value	The forecast value of the sports industry
100	60%	44	45
200	59%	45	49
300	58%	37	38
400	57%	39	41
500	65%	42	45

The above two tests form a positive and negative direction in two directions for synchronous testing and evaluation. They can timely and quickly respond to the problems that arise, so that some garbage data or models themselves can be effectively solved. After constructing this model, we will apply it to practice process in later stage, so we need to ensure the stability and correctness of the algorithm. After testing, we found that after using the technology of correlation algorithm, we could continuously find out hidden node elements from all levels of nodes and filter, analyze and record them, and then enter them into the final framework to make final calculation. So we will study and analyze the data, and then we will make the following predictions, and the results are shown in table three as follows.

From the above table, we can see that the maximum error of the output and expected value obtained by our algorithm model is 0.88%, which is compared with the actual situation. By analyzing the resulting solution set, a large part of the data can be applied to the model, and further improve and restructure the existing lagging leisure sports industry structure mode. It is only in the public place that it really likes the leisure sports factors, so that it can really choose the way people like or the industry. Therefore, we should consider the widespread adoption of the leisure sports industry model based on statistics, and prove that this model is feasible by modeling and testing a series of operations. In the test of data node management, we have found that the experimental group with statistical algorithms is more capable of data processing.

5 Conclusion

With the rapid development of the national economy in twenty-first Century, our statistics are becoming more and more widely used in all walks of life. Therefore, this paper puts forward the exploratory study of leisure sports based on statistics. We have added statistics and association rule algorithm, and utilized statistics, association rules algorithm’s universality, accuracy and optimization to realize the research and design for the optimization of leisure sports competitiveness. In order to test the feasibility of the model and the statistical algorithm of association rules, we first tested the relevant parameters in the algorithm, in the process of verification, the parameters of the statistical model accuracy is relatively stable on the whole, the statistical numerical value and real value related to the difference, and the fluctuation is small, the average parameter value is relatively high, which shows the statistics the parameters of the model stability is better. Secondly, in dealing with different problems, the computation time and processing method can fast clip into the main line and complete the calculation, but with the dynamic changes of the number of statistics, statistics of the generation algorithm overlap other auxiliary algorithm to the traditional model of the speed of the algorithm is more rapid, the overall efficiency is higher. On the basis of statistics management, we can achieve better management of data generated by leisure sports statistics, and make complex people’s orientation screening and screening more efficient and simplistic. To sum up, the algorithm proposed in this paper is feasible.

Footnotes

Acknowledgments

The study was supported by China Sports General Sports Culture Development Center Base Project: A comparative study of basketball culture between China and the United States in the perspective of Globalization (15B005, 2015–2017). Results of China Social Science Fund Project: Research on operation mechanism and mode of the government buying public service to the sports society (14CTY009, 2014–2017).

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