Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm.

Rowing competition in colleges and universities is an international competition, and it is also a favorite competition for college students. However, in the course of rowing competition, the stability of athletes' injuries often occurs, which is difficult to solve effectively. Aiming at the problem that the loss of athletes in rowing competition in colleges and universities cannot be accurately prevented, this paper puts forward a multiple regression prevention effect model and makes a comprehensive analysis combined with complex reasons. Through the integration of multiple regression and residual analysis, we can better find out the influencing factors, aiming at finding out the causes of athletes' injuries and putting forward corresponding countermeasures. First of all, analyze the causes of loss, establish a framework of injury prevention for college rowers, and the overall diagnosis framework is reasonable. Then, according to the "University Rowing Prevention and Control Standards" divided into various prevention measures, through the comprehensive prevention and control measure mechanism to get the cause of injury, finally, the optimal combination of various control measures forms a control system. The results of MATLAB show that the combination of multiple regression and residual analysis can improve the accuracy of athletes' injury prevention and treatment, make the accuracy more than 90%, shorten the diagnosis time less than 10 minutes, and meet the requirements of athletes' injury diagnosis under normal rowing competition.

1. Introduction

With the continuous improvement of rowing intensity and the publication of “Rowing Standards in Colleges and Universities” and “Guidelines for Diagnosing Athletes' Injuries,” the prevention and treatment of rowers' injuries are becoming increasingly severe and showing a rapid development trend [1]; so, it is particularly important to diagnose athletes' injuries reasonably. Athletes' injury diagnosis is a comprehensive judgment of competition level, competition requirements, and competition evaluation. Multiple regression analysis can not only judge the diagnostic characteristics of athletes' injuries but also solve the problems of massive injuries and complex injuries, which is the main method of comprehensive judgment at present. Literature research shows that multiple regression analysis can classify athletes' injuries, preliminarily judge the relationship between different scenes, and improve the overall diagnosis complexity of intelligent teachers. The specific results are shown in Figure 1.

Figure 1

The prevention and treatment investigation of athletes' injuries (data source: CNKI, Economic Statistics Yearbook).

It can be seen from Figure 1 that the support rate of athletes' injury prevention and control in 2022 is increasing year by year, which is significantly better than that in 2019, indicating that multiple regression has become the trend of future research. However, in the process of multiple regression diagnosis of athletes' injuries, the complexity of massive injury causes [2], the initial prevention and treatment of injury causes, and the selection of related parameters will have an impact on the diagnosis results of injury causes. At the same time, the data in Figure 1 shows that the analysis of athletes' injury causes increased year by year in 2019, 2020, and 2021, with the largest increase in 2022, and it has become a hot research direction. In terms of the proportion of support and opposition, the proportion will be 50% in 2020 and will gradually increase in 2021 and 2022. Therefore, the research on the causes of athletes' injuries is hotter in 2022, which also suggests that the prevention and treatment of athletes' injuries have been paid attention to by scholars at home and abroad. Some scholars put forward the method of prevention and control effect and determined the proportion of subjectivity and complexity by analyzing the normality of residual error, so as to judge the feasibility of multiple regression. Multivariate regression analysis method can be used for comprehensive analysis of multiple factors and get the influence of multiple factors on the results. At the same time, the process of multiple regression analysis is a progressive process, which can constantly adjust the calculation index, find out many factors of sports loss, and find out the main influencing factors. At present, there are many reasons for athletes' losses, and the treatment mechanism is complex; so, a single calculation method cannot accurately identify them. In order to prevent and control sports loss better, it is necessary to carry out the intelligent comprehensive analysis method to improve the accuracy of analysis results. The multivariate regression analysis method has the problem of large transformation error in local and global analysis; so, it needs to be combined with the residual algorithm to realize the transformation between different data analysis. The multivariate regression analysis method can be used for comprehensive analysis of multiple factors, but there are some errors in the transformation of multivariate analysis and multivariate regression, which affect the analysis results of causes. The residual algorithm determines the error results of different factors and the degree of error by analyzing the residual of data and eliminates the factors that have great influence on the results. In addition, the residual algorithm can find out the factors with large errors, analyze the weights and thresholds of the factors, and finally sort the influencing factors to get more accurate comprehensive results. Therefore, the combination of multiple regression analysis and residual analysis can not only improve the accuracy of calculation but also improve the credibility of results, which is suitable for analyzing the causes of athletes' injuries. At the same time, residual analysis can integrate subjective judgment factors to avoid the misunderstanding of overcalculation. At present, rower injury is a hot research issue at home and abroad, but the reasons and methods of its research have been controversial, and no effective evaluation method has been found. Therefore, looking for an effective analysis method is an urgent problem to be solved at present, and finding a scientific and reasonable analysis method is the trend of future research. However, the feasibility of the control effect method is controversial and lacks corresponding evidence [3]. In order to verify the accuracy of the above analysis, this paper combines multiple regression analysis with the residue optimization model to optimize the cause of injury in rowing competition.

2. Related Concepts

2.1. Multiple Regression Analysis

Multiple regression is a comprehensive analysis method, which mainly finds out the main factors from complex factors and eliminates the secondary factors. At the same time, multiple regression realizes the adjustment and analysis of relevant indicators through progressive regression. In the process of regression analysis, the weights of different reasons are determined, and the corresponding regression equations are constructed. Multivariate regression is an analysis method based on unitary regression, which can analyze more comprehensively and improve the objectivity of analysis results. Compared with ant colony algorithm and domino analysis method, the multiple regression method is simpler and more effective in multifactor analysis, which is suitable for simple speech processing of complex problems. Multivariate regression method is widely used in machinery, construction, economy, and other fields but less applied in sports injuries. However, multiple regression analysis can comprehensively judge the causes of injury, infer the influence of different causes on injury, provide theoretical support for injury prevention and control, and make prevention and control measures more reasonable. Therefore, the application of multiple regression analysis in sports injuries is more reasonable. Multiple regression analysis is a comprehensive analysis method, which analyzes the structure of rowing competition by using characteristic injury causes, integrates the injury causes, and realizes the prevention and control system [4]. Multivariate regression is widely used in electric power, computer, and other fields, which can accurately calculate the relationship between rowing races, judge the logic between diagnostic structures, and finally improve the accuracy of calculation results. In order to further explain the diagnosis of rowing competition, the following hypotheses are put forward.

Hypothesis 1 .

Preventive measures of rowing competition g = x, the relationship r between injury causes, the requirements q of prevention and control of injury causes, and the influencing factors b of rowing competition. The diagnostic function before the analysis of sports injury causes is K(d, q, b), and the diagnostic function after optimization is L(x, y, z) [5]. If the value of S is larger, the optimization effect is better, as shown in formula (1).

Among them, w is the design weight before optimization and the standard after optimization λ. At that time S = 1, the diagnostic effect of prevention and treatment measures for sports injuries was the best, and the difference between the results before and after optimization was the largest [6]; At that time S = 0, the optimization results were the worst, and the results before and after were the smallest.

Theorem 1 .

The prevention and treatment effect coefficient ξ represent the influencing factors of sports injuries, which shows that the prevention and treatment measures of sports injuries are more influential than external factors. The smallest value represents the smallest influence degree; otherwise, it is the largest, as shown in Formula (2).

Among them, α · ξ is subjective residual, and β · ξ is objective residual.

Theorem 2 .

If the athlete is injured K(d, q, b) = φ(d)′ · φ(q)′ · φ(b)′, then formula (1) can be expressed as formula (3).

where φ(d)′, φ(q)′, φ(b)′ are derivatives of φ(d), φ(q), φ(b).

Equation Formula (3) realizes the projection of damage cause diagnosis, as shown in Figure 2.

Figure 2

Projection of injury causes in rowing competition.

Figure 2 shows that the damage cause in formula (1) is projected onto the plane by formula (3), which further simplifies the related damage cause and achieves the preliminary purpose of preventing and treating the damage cause. The projection of points a, b, and c in two-dimensional space in figure 2 represents the standardized processing of data. In addition, the data in Figure 2 is discrete data, and the projected data is directional and belongs to vector data. However, the projected data is only the corresponding value, and the attribute of the data itself is proposed, which belongs to the data cleaning process.

2.2. Mathematical Description of Athletes' Injury Causes

The prevention and control effect mainly analyzed the key indicators, including competition reasons [5], prevention and control methods, training time, and injury location [6]. In the control system, the number of control measures is the same in rowing competition [7], and different optimization degrees represent the best measures. Firstly, the injury causes and prevention measures of rowing competition are randomly obtained, the optimization results of injury causes are optimized among the measures with better matching degree, and the final optimization results are determined by screening one by one. Then, multiple regression adopts the combination form of probability calculation to select the best measure and give the best weight [8]. Finally, the optimization methods that do not meet the standards are eliminated.

Assuming that the initial number of prevention measures is n in rowing competition, the prevention measure function L = (x, y, z) of rowing competition x, y represents plane coordinates, z represents influencing factors, and the overall prevention measures of rowing competition are shown in Formula (4).

Among them, x, y, and z are arbitrary measures, and rand(0, 1) is the random control measure selection function. Randomly select the causes of sports injure, carry out crossanalysis [9], and update the analysis degree. Under the constraint of matching degree, the optimal measure conforming to the weight is obtained through the combination form, and the calculation process is shown in Formula (5).

Among them, the change process of formula (5) is shown in Figure 3.

Figure 3

Crossrelationship of related damage causes.

Multivariate regression is to realize the optimum of rowing competition in the form of probability p and combination and carry out neighborhood analysis on the optimal measures to obtain the final measures that meet the standards [9]. The calculation process is shown in Formula (6).

Among them, p is the occurrence probability of different measures.

If the rowing has not been analyzed for many times, the relevant preventive measures will be eliminated. Multivariate regression will continue to analyze other prevention measures [10], and according to formulas (1)~(4), a new cause analysis of sports injuries will be carried out [11].

The dynamic adjustment of analysis accuracy is as follows. When analyzing the causes of sports injuries, multiple regression cannot guarantee the overall analysis, which may lead to local analysis and reduce the overall performance of rowing competition [12]. Therefore, in the process of analyzing the causes of sports injuries, we should try our best to expand the scope of analysis, make in-depth analysis of each prevention and control measure in the optimal measures, and constantly adjust the accuracy. Assuming that the accuracy of the analysis is spiral, in order to reduce randomness and locality, a three-dimensional analysis of the accuracy should be carried out, and a dynamic adjustment factor should be introduced for analysis. The process is shown in formula (7).

Among them, k is the I-order adjustment factor, and line(x, y, z) is a linear relationship function. The control system method of residual error and multiple regression is shown in Formula (8).

In the analysis of sports injury causes, the value ξ is relatively small, and the value k is relatively large, so as to expand the analysis range [13] and keep the diversity of injury causes. In the later stage of analysis, the value ξ is relatively large, and k is relatively small, which deepens the accuracy of analysis, improves the mining ability of analysis measures, and enhances the performance of multiple regression and control effect. The whole process is shown in Figure 4.

Figure 4

Dynamic adjustment process of accuracy.

It can be seen from Figure 4 that multiple regression and control effect belong to a spiral adjustment process, which is a dynamic adjustment of accuracy and obtains a global extreme value. The analysis measures of injury causes in rowing competition are in multiple regression model, and the overall fusion effect is better. Therefore, the dynamic adjustment of analysis accuracy can make athletes' injury causes meet the requirements and improve the accuracy of calculation results.

Introduce coordination factors for analysis. Rowing competition is collected many times, and the collection of injury causes will reach the limit. Multivariate regression needs continuous injury causes. Therefore, it is necessary to find the blank reasons for injury in rowing competition and complete the analysis measures for the reasons of injury in rowing competition [14]. Because of the control effect, randomness, antiuncertain factors, and local extremum are eliminated. Therefore, it is necessary to increase the coordination factor, realize the joint calculation of damage cause collection, and realize the integrity of damage cause calculation. The specific calculation is shown in Formula (9).

When l(x, y, z) is 1, it shows that the calculation results of the whole function are relatively complete, and there is no abnormality in the process of collecting damage causes. When l(x, y, z) is 0, it shows that the damage cause has reached the limit in the whole collection process, and the coordination of the damage cause is better [15]. When l(x, y, z) is 0.5, it shows that the coordination of damage causes and the collection process are better, and the whole damage causes are in the best. In order to fill in the blank of the cause of injury, the cause of injury should be filled, as shown in Formula (10).

Among them, it is the proportion of random injury causes.

2.3. Mathematical Model of Multiple Regression

Use the combined model of multiple regression and control effects to analyze the reasonable selection of multiple regression mechanisms and control effects. Competition content, competition process, competition feedback, and other prevention and control systems [16] can not only balance the relationship between global analysis and local analysis but also improve the arrangement ability of classroom injury causes. From formula (10), we can see that when analyzing the causes of sports injuries, we should pay attention to the global analysis ability, and when analyzing later, we should pay attention to the local analysis; so, multiple regression should realize the combination selection according to the prevention and control effect. The specific combination is as follows.

The damage cause analysis measures of information characteristics, as shown in Formula (11).

(2) The damage cause analysis measures of content characteristics, as shown in Formula (12).

(3) The damage cause analysis measures of content characteristics, as shown in Formula (13).

In this paper, there are two main improvements in the prevention and treatment system: at the same time, the process of prevention and control system should be continuously standardized to improve the overall analysis ability [17]. On the other hand, the coordination factor is used to realize the balance between global analysis and local analysis, and through the matching function, the athletes' injury causes and prevention measures are analyzed to improve the accuracy of the final results of injury causes. The results are shown in Figure 5.

Figure 5

Stage analysis process of athletes' injury causes.

The synergistic combination of athletes' injury causes is as follows: collaboration is the main way to realize dynamic analysis in rowing competition. The algorithm in this paper is based on the prevention effect of multiple regression and diagnoses the dynamic distributed cooperative combination form [18]. Different submultiple regressions use different cooperative combination forms, complex parameters, and operations. Multiple regression was randomly divided into five characteristics, each of which represented a series of injury causes. In each analysis process, features will randomly select collaborative measures. After each athlete's injury cause is collected, compare the matching degree between different characteristics and injury cause analysis, analyze the complexity, and record the global optimal measures; Other sub-multifeatures use optimal measures to compare the analysis degree of different measures, and the results are shown in Table 1.

Table 1

Collaborative combination table of rowing competition.

Multifeature	Matching comparison	Combination	Parameter optimization	Residual optimization
Causes of injury	{completely, incomplete, unknown}	{1, 2, 3, 5, 4}	{optimization, not optimized}	{subjectivity, objectivity, semisubjective}
	{completely, incomplete, unknown}	{1, 2, 3, 5, 4}	{optimization, not optimized}	{subjectivity, objectivity, semisubjective}
Training time	{completely, incomplete, unknown}	{1, 2, 3, 5, 4}	{optimization, not optimized}	{subjectivity, objectivity, semisubjective}
	{completely, incomplete, unknown}	{1, 2, 3, 5, 4}	{optimization, not optimized}	{subjectivity, objectivity, semisubjective}
Result characteristics	{completely, incomplete, unknown}	{1, 3, 5, 2, 4}	{optimization, not optimized}	{subjectivity, objectivity, semisubjective}
	{completely, incomplete, unknown}	{1, 1, 3, 2, 4}	{optimization, not optimized}	{subjectivity, objectivity, semisubjective}

2.4. Multiple Regression and Prevention and Control Effect on Prevention and Control Measures and Steps of Athletes' Injury Causes

The basic idea of multiple regression and prevention effect is to use different synergy and combination forms to realize damage cause analysis [18], obtain global optimal measures, and improve the accuracy of measure formulation. The implementation steps of this algorithm are shown in Figure 6.

Figure 6

Implementation process of multiple regression and control effect.

Step 1 .

Determine the structure and complexity of damage cause analysis. According to the characteristics of damage causes, the analysis measures are determined.

Step 2 .

In the prevention and control system, determine the relevant parameters, analyze that the prevention and control measures are consistent with the number of damage causes, and set up functions.

Step 3 .

Set the match function. By means of multiple regression and prevention effect, this paper analyzes the causes of athletes' injuries. Determine the weight w and standard λ for later comparison. At the same time, the matching measures of rowing competition are compared to determine the local optimal matching measures.

Step 4 .

Calculate the global and local optimal measures. Multivariate regression is divided into five features, the matching degree is obtained, and the optimal global measures and local optimal measures are compared.

Step 5 .

Comparison of analysis degree is as follows. According to the change of rowing competition, the five characteristics dynamically adjust the analysis factors and randomly select the combination forms to realize the stability of local analysis.

Step 6 .

Dynamic coevolution of each Dortmund is as follows. After the control system measures are determined, the optimal global measures are selected, different measures are compared, and irrelevant measures are eliminated.

Step 7 .

Judge whether the injury cause of rowing competition reaches the maximum. If it does not reach the maximum, repeat steps 1-5; otherwise, stop analysis and return to the global best measure.

3. Empirical Analysis of Prevention and Control System

3.1. Analysis Results of Injury Causes in Rowing Competition in Colleges and Universities

The analysis results of injury causes in rowing competition in colleges and universities are as follows:

In analysis of parameters, the results are shown in formula (14).

Among them, k is analysis parameters, ξ is prevention and control effects, and the analysis process is shown in Figure 7.

Figure 7

Parameter analysis process.

As can be seen from Figure 7, the relationship between the analysis times and the analysis degree of parameters is basically linear, which shows that the parameters of injury causes in rowing competition are in line with positivity and can be analyzed later.

(2) In multiple regression analysis, the result is as shown in formula (15).

Among them, m2 is the characteristic index, M is the characteristic index of local optimal measures, and the results are shown in Figure 8.

Figure 8

Multiple regression of injury causes.

As can be seen from Figure 8, in the process of damage cause analysis, the fusion trend of multiple features is basically the same, showing ups and downs. Among them, negative change is reverse fusion, and positive change is positive fusion. However, on the whole, the radian of multiple regression is consistent, which shows that the effect of multiple regression is better [19].

(3) In the control effect, the result is shown in formula (16).

Among them, it is a linear function and a residual adjustment, and the result is shown in Figure 9.

Figure 9

Residual analysis results.

As can be seen from Figure 9, the overall analysis result of residual error is good. The results of subjective and objective analysis are the best, followed by other nature, comprehensive, and semisubjective analysis. All the control effects showed ups and downs, and there was no biased result. The results are as shown in Table 2 in the process of 50 analysis of damage causes.

Table 2

Result test of injury cause.

Damage content	Algorithm	Prevention and control measures	Classification of control	Control time	Proportion of control	Global optimal measure	Theoretical optimal measure
Training is not in place	Multiple regression and residual error algorithm	12	6	4	89	3	3
	Multiple regression algorithm	7	5	2	56	1	3
Poor recovery activities	Multiple regression and residual error algorithm	11	6	6	92	3	3
	Multiple regression algorithm	4	2	1	70	2	3
The training plan is not rigorous	Multiple regression and residual error algorithm	16	8	8	90	3	3
	Multiple regression algorithm	6	3	3	67	2	3
Unreasonable policy management	Multiple regression and residual error algorithm	10	10	10	98	3	3
	Multiple regression algorithm	3	5	2	82	1	3

It can be seen from Table 2 that the multiple regression and residual model are superior to the control effect, and its global optimal measures are closer to the theoretical optimal measures. Moreover, the range of measures and average measures and calculation errors of multiple regression and residual models are less than the control effect.

3.2. Prevention and Treatment of Athletes' Injuries in Rowing in Colleges and Universities

In this paper, 12 colleges and universities were selected as the research objects. After preliminary arrangement of injury causes, 6 injury causes and 4 types of athletes' injury causes were obtained. According to the diagnostic criteria of athletes' injuries, the judgment results are shown in Table 3.

Table 3

Accuracy of collecting damage cause judgment.

Classification	Number of injury causes (number)	Accuracy (%)
The preparation activities before training are not in place	43	98.05
The recovery activities after training are not done well	31	99.90
Life is not self-disciplined, which leads to physical fatigue	23	97.79
The training plan is not rigorous	98	98.26

3.3. Accuracy of Different Damage Analyses

According to the experiment, Xi'an JiaoTong University, Tianjin University of Science and Technology, Henan University of Finance and Economics, Tongji University, Shanghai JiaoTong University, North China University of Water Resources and Hydropower, Nanchang University, Jiangxi Yichun University, Jimei University, Tianjin Normal University, Shanghai Ocean University, Shanghai Lixin School of Accounting and Finance, Zhejiang University, and Peking University are selected as the research objects. The analysis is shown in Table 4.

Table 4

Contents of different research projects.

Research project	Content
Cause of injury	The preparation activities before training are not in place
	The recovery activities after training are not done well
	Life is not self-disciplined, which leads to physical fatigue (staying up late, fatigue training, irregular diet)
	The training plan is not rigorous
	The policy management is unreasonable. Colleges and universities have no special policies on study, work, and rest for sports teams, which leads to insufficient training time
	The level of coaches needs to be improved
	Diet and nutrition cannot keep up after a lot of exercise
Control method	Precompetition preparation activities
	Postmatch recovery, stretching, and rehabilitation exercises
	Strict work and rest time and diet
	Hire high-level coaches
	Hire high-level coaches
Injury ratio of college rowers	The injury rate after three years of practice is 75%
	The injury rate of rowing practice for two years is 60%
	The injury rate of rowing practice for two years is 30%
Classification of injuries and injuries of college rowers	Lumbar injuries, including sprain, strain, and lumbar disc herniation, are 80%
	Knee injuries account for 10%

In order to further prove the effectiveness of the model proposed in this paper, other comparative models are introduced for comparative analysis: (1) multivariate regression, (2) multivariate regression combined residual, (3) residual algorithm, (4) multivariate regression, and residual model in this paper [20]. Methods 2 and 4 are not an algorithm. Method 4 is based on method 2 and makes corresponding improvement and perfection. The accuracy of different algorithms is analyzed from the aspect of diagnosing the types of athletes' injury causes from complex athletes' injury causes, and the results are shown in Table 5.

Table 5

Accuracy of judging athletes' injury prevention and treatment by different methods.

Prevention and control measures	Multivariate regression and residual model	Multiple regression	Residual error
Precompetition preparation activities	99.94	98.93	97.92
Postmatch recovery, stretching, and rehabilitation exercises	99.97	98.92	98.97
Strict work and rest time and diet	99.91	95.94	95.93
Hire high-level coaches	99.94	97.92	94.93
The policy of colleges and universities is inclined, leaving enough training time	98.21	97.23	96.36

It can be seen from the above table that the prevention and treatment accuracy of multiple regression and residual model is higher [21], and the accuracy does not change with the change of athletes' injury cause types. The main reason is that the dynamic analysis of residual factor on the prevention measures of rowing competition makes its continuous calculation time shorter and can change the types of athletes' injury causes more flexibly [22]. Therefore, the residual factor can not only reduce the impact of complexity on the results but also quickly realize the accurate analysis of different athletes' injury causes.

4. Conclusion

Regarding rowing athletes in the course of the process of injury, how to effectively prevent injury is an urgent problem to be solved. However, there are many reasons for rowers' injuries [23], and it is difficult to obtain results through simple calculation. In this paper, based on multiple regression, combined with multiple regression method, a multiple regression model is proposed. By setting standards, weights, and dynamic cooperative combination forms, the causes of athletes' injuries are classified, and finally, the regression results are obtained [24]. At the same time, in order to avoid the excessive error of multiple regression, the residual analysis method is integrated to find out the causes of athletes' injuries and put forward reasonable prevention measures [25]. MATLAB simulation results show that the accuracy of the proposed diagnosis algorithm is more than 90%, the convergence time is less than 10 minutes, and the overall convergence is good, converging at 10 iterations, which is significantly better than other algorithms. The algorithm proposed in this paper adapts to different types of athletes' injury causes and has high global analysis ability. However, the research on the internal relationship between injury causes is insufficient, and future research will pay attention to the analysis of internal causes in order to improve the algorithm in this paper.