【作者】 吴卫兵；

【导师】 虞丽娟；

【作者基本信息】 上海体育学院 ， 运动人体科学， 2009， 博士

【摘要】 通过对运动训练机能监控来检查和评定运动员承受训练负荷的状态,是运动员竞技能力诊断与监测的一种重要手段,也是当今科学化训练的一个重要环节。其中,运用生理生化测试指标对运动员机能监控是一种常用的方法,现已在运动队中得到广泛应用。但是,在目前的运动训练机能监控中,大部分还是依靠传统的经验和模式来进行,缺乏科学的评价诊断方法和标准。同时,对运动训练的数据处理效率低、分析不系统、信息反馈慢,限制了对训练效果和运动员机能状态的准确诊断与评价。本研究以国家羽毛球队运动员48人为研究对象,采样测试2006年2月9日至2008年8月4日运动训练期间的一些机能指标,共计测试运动员6000余人次。探讨了优秀羽毛球运动员机能监控评价方法和指标数值变化范围,及对我国优秀羽毛球运动员运动训练机能监控进行个性化分析研究。同时,运用时间序列分析方法,对羽毛球运动员运动训练的机能变化进行监控及预测;应用数据库技术和系统动力学模型,研究并构建了优秀羽毛球运动员运动训练机能监控决策支持系统。论文采用的研究方法包括:文献资料法、访问调查法、数理统计法、个案分析法、系统分析法和软件工程法。论文研究主要成果如下:(1)对我国优秀羽毛球男子、女子运动员运动训练监控期间身体机能变化进行了分析,得出了我国优秀羽毛球运动员运动训练机能监控某些生化指标数值变化的警戒低限和高限及等级评价。①通过对林×和谢×运动训练机能变化进行个性化监控分析,得出林×机能指标WBC、HGB、T、T/C监控低限分别为5.67х109、142g/L、721ng/dl、0.052,CK、BUN、C监控高限分别为706IU/L、8.79mmol/L、19.55ug/dl,相对个人的基础值,WBC、HGB、T、T/C下降百分比分别为17.83%、6.58%、15.38%、23.53%,CK、BUN、C升高的百分比分别是:112.01%、19.59%、28.20%;谢×机能指标WBC、HGB、T、T/C监控低限分别为4.40х109、116.7g/L、31.51ng/dl、0.0022,CK、BUN、C监控高限分别为230.1IU/L、5.96mmol/L、18.88ug/dl,相对个人的基础值,WBC、HGB、T、T/C下降百分比分别为16.98%、6.27%、30.62%、40.54%,CK、BUN、C升高的百分比分别是:83.35%、27.35%、20.41%。②通过对羽毛球男子和女子运动员训练机能变化监控分析,得出了我国优秀羽毛球男子、女子运动员机能指标变化监控的低限和高限。相对个人的基础值,男子羽毛球运动员WBC、HGB、T、T/C下降百分比分别为19.04%、5.27%、16.45%、25.00%,CK、BUN、C升高的百分比分别是:105.50%、21.98%、22.73%;女子羽毛球运动员WBC、HGB、T、T/C下降百分比分别为17.01%、5.84%、27.35%、33.22%,CK、BUN、C升高的百分比分别是:84.05%、25.76%、24.79%。(2)运用时间序列分析方法,对我国羽毛球运动员运动训练机能变化进行监控和预测。通过对林×运动训练机能指标数据时间序列进行实证分析,建立了能够比较精确地反映运动训练机能监测数据时间序列中所包含动态依存关系的数学模型。通过比较AR(1),ARMA(1,1),ARMA(1,2)三个模型,根据决定系数、AIC准则、SC准则,并综合考虑,HGB时间序列模型选定ARMA(1,1)。即:Xt=151.2178+0.5276Xt-1 +εt + 0.1928εt-1(3)通过对数据库、模型库的分析与设计,构建了羽毛球运动员运动训练机能监控决策支持系统。①在掌握决策支持系统的基本特征、模式、组成和结构的基础上,结合羽毛球运动训练机能监控的需求,从系统数据库设计、模型设计和知识库设计考虑,分析了羽毛球运动员运动训练机能监控决策支持系统。②运用数据库等技术,设计和实现了羽毛球运动训练机能监控数据库管理系统。通过对该系统的使用,可以有效地组织管理运动训练机能测试数据并进行机能评价分析,为羽毛球运动训练机能监控提供辅助决策。③运用系统动力学模型,通过羽毛球运动训练机能监控系统的流位变量(BFL、TLQ、BRQ),流率变量(BFLI、BFLD、TLQI、BRQI),辅助变量(TT、TI、RT、RM),外生变量(RYN),以及各变量要素之间的因果关系,构建了羽毛球运动训练机能监控系统动力学模型,并分别以不同调控参量方案对羽毛球运动员训练监控机能变化进行仿真。更多还原

【Abstract】 Checking-up and assessing athletes’training state by BFM (body function monitoring,BFM), it is an important measure of diagnosing competitive capability, and it is also a key part of scientific training nowadays. It is a regular way of BFM by testing physiologic and biochemical indexes, which has been abroad used in sports team. But, current way in BFM has been short of scientific estimate and reference, and training data is processed poor efficiency and information is feed back slowly, which affects training purpose and exactly diagnosing physical performance.The study objects are athletes of Chinese Badminton team (n=48). Body function indexes are tested in time from Feb 9,2006 to Aug 4,2008. The paper has discussed the evaluating means and bound of elite athletes’BFM. By the analysis of time series, body function of badminton athletes’Training is monitored and forecasted. By the model of system dynamics and database technology and so on, the decision support system of BFM for elite badminton athletes’training is studied and constructed.The methods of study are included: document summary, interview survey, mathematics statistics, case analysis, systems analysis and software engineering.The research findings are as follows:(1) The data during BFM for Chinese elite badminton athletes’Training has been analyzed, and the paper has established the measuring scale of grade evaluation for men’s and woman’s badminton athletes by deviation method and method of percentiles.①The paper has analyzed the changes of body function for Lin and Xie during training. It discovers that Lin’s low limits of WBC, HGB, T and T/C are 5.67×109, 142g/L, 721ng/dl and 0.052, and Lin’s high limits of CK, BUN and C are 706IU/L, 8.79mmol/L and 19.55ug/dl. Comparing with individual basic value, the descending per cent of WBC, HGB, T and T/C are 17.83%、6.58%、15.38% and 23.53%, and the rising per cent of CK, BUN and C are 112.01%、19.59% and 28.20%. It discovers that Xie’s low limits of WBC, HGB, T and T/C are 4.40×109, 116.7g/L, 31.51ng/dl and 0.0022, and Xie’s high limits of CK, BUN and C are 230.1IU/L, 5.96mmol/L and 18.88ug/dl. Comparing with individual basic value, the descending per cent of WBC, HGB, T and T/C are 16.98%、6.27%、30.62% and 40.54%, and the rising per cent of CK, BUN and C are 83.35%、27.35% and 20.41%.②The paper has analyzed the changes of body function for men’s and woman’s badminton athletes during training. The low limits and high limits of BFM for Chinese elite men’s and woman’s badminton athletes have been discovered. Comparing with individual basic value, the descending per cent of WBC, HGB, T and T/C are 19.04%, 5.27%, 16.45% and 25.00%, and the rising per cent of CK, BUN and C are 105.50%, 21.98% and 22.73% in men’s athletes. The descending per cent of WBC, HGB, T and T/C are 17.01%, 5.84%, 27.35% and 33.22%, and the rising per cent of CK, BUN and C are 84.05%, 25.76% and 24.79% in woman’s athletes.(2) Using the method of time series, the paper has demonstrated analyses to the time series data of Lin’s body function. The model of BFM for badminton athletes’training has been established.By compared the models of AR(1), ARMA(1,1) and ARMA(1,2), and based on adjusted R-squared, akaike info criterion and schwarz criterion, the model of HGB time series is selected ARMA(1,1): Xt=151.2178+0.5276Xt-1 +εt + 0.1928εt-1.(3) By analyzed and designed of database and model, the decision support system of BFM for elite badminton athletes’training is constructed.①Basing on the understandability of DSS’s character, mode, constitution and structure and following the requirement analysis of BFM, the DSS of BFM for badminton athletes is constructed by designing of database, model and knowledge base.②Applied database technique, the paper has analyzed and constructed the DBMS for body function monitoring of badminton athletes’training. By used the system, the testing data can be effectively managed, evaluated and analyzed, and assistant decision-making can be provided for the BFM of badminton athletes’training.③Applied system dynamics model, the paper has constructed system dynamics model of BFM for badminton athletes’training by the causal relationships of stock variables(BFL、TLQ、BRQ),flow variables(BFLI、BFLD、TLQI、BRQI) and convertor variables(TT、TI、RT、RM), and has simulated the changes of body function for badminton athletes’training by different projects of adjustment parameter.更多还原