本文選題：高考志愿填報(bào) + 院校投檔線預(yù)測(cè)�。� 參考：《江蘇大學(xué)》2017年碩士論文

【摘要】：高考志愿填報(bào)關(guān)乎考生一生的命運(yùn),然而每年都會(huì)有許多考生因?yàn)橹驹柑顖?bào)失當(dāng)導(dǎo)致“高分低就”甚至“高分不就”。高考志愿填報(bào)是一項(xiàng)龐大又復(fù)雜的系統(tǒng)工程。因?yàn)闀r(shí)間緊、信息多而導(dǎo)致家長(zhǎng)及考生無(wú)法填出合理的志愿,所以開(kāi)發(fā)一套志愿填報(bào)指導(dǎo)系統(tǒng)具有非常大的實(shí)際意義。對(duì)院校投檔分?jǐn)?shù)線的準(zhǔn)確預(yù)測(cè)是志愿填報(bào)最為關(guān)鍵的問(wèn)題。目前常用的預(yù)測(cè)方法主要有三種:兩線差法、錄取難度系數(shù)法以及分?jǐn)?shù)排序定位法。這三種方法計(jì)算比較簡(jiǎn)單,但都是使用線性函數(shù)進(jìn)行逼近,其預(yù)測(cè)結(jié)果與真實(shí)院校投檔分?jǐn)?shù)線存在較大誤差�；诖�,本文提出了基于神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)方法,并在此基礎(chǔ)之上設(shè)計(jì)實(shí)現(xiàn)了一套高考志愿填報(bào)指導(dǎo)系統(tǒng)。本文在數(shù)據(jù)預(yù)測(cè)方面的工作主要包括兩個(gè)方面:(1)基于神經(jīng)網(wǎng)絡(luò)的院校投檔分?jǐn)?shù)線的預(yù)測(cè)。針對(duì)往年錄取數(shù)據(jù)與當(dāng)年錄取結(jié)果具有高度非線性的特點(diǎn),本文提出一種基于神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)院校投檔分?jǐn)?shù)線預(yù)測(cè)的方法。該方法首先使用粒子群算法對(duì)神經(jīng)網(wǎng)絡(luò)模型進(jìn)行優(yōu)化,通過(guò)粒子群算法在迭代過(guò)程中對(duì)粒子的自身歷史能力認(rèn)知和全局環(huán)境認(rèn)知進(jìn)行動(dòng)態(tài)分配,解決了神經(jīng)網(wǎng)絡(luò)算法容易陷入局部極小值的問(wèn)題。通過(guò)對(duì)江蘇省2011-2015年的數(shù)據(jù)進(jìn)行實(shí)驗(yàn),實(shí)驗(yàn)結(jié)果表明與線差法相比,直接采用神經(jīng)網(wǎng)絡(luò)將本一、本二院校投檔分預(yù)測(cè)誤差不超過(guò)1分、2分的準(zhǔn)確率分別提高了12%、14%,將本三、高職專(zhuān)科預(yù)測(cè)誤差不超過(guò)3分的準(zhǔn)確率提高了43%,這說(shuō)明采用神經(jīng)網(wǎng)絡(luò)進(jìn)行預(yù)測(cè)準(zhǔn)確率更高。與直接使用神經(jīng)網(wǎng)絡(luò)進(jìn)行相比,優(yōu)化后的網(wǎng)絡(luò)模型進(jìn)一步將本一、本二院校投檔分預(yù)測(cè)誤差不超過(guò)1分、2分的準(zhǔn)確率提高了4%、8%,將本三、高職專(zhuān)科預(yù)測(cè)誤差不超過(guò)3分的準(zhǔn)確率提高了4%,說(shuō)明優(yōu)化后的模型具有更高的預(yù)測(cè)準(zhǔn)確率。(2)基于C4.5的六檔專(zhuān)業(yè)推薦法。江蘇省高考志愿填報(bào)規(guī)則規(guī)定每位考生同批次同科類(lèi)填報(bào)8所院校,每所院校選擇6個(gè)專(zhuān)業(yè)。針對(duì)現(xiàn)有基于線差法進(jìn)行六檔專(zhuān)業(yè)推薦的不足,首先提出了基于C4.5算法實(shí)現(xiàn)的六檔專(zhuān)業(yè)推薦法,該方法在準(zhǔn)確率方面比基于線差法專(zhuān)業(yè)推薦方法有了大幅提升。在此基礎(chǔ)之上又采用了基于等價(jià)無(wú)窮小理論對(duì)C4.5選擇分裂屬性的進(jìn)行優(yōu)化,使得計(jì)算過(guò)程與直接采用原始C4.5算法相比所花費(fèi)時(shí)間大大縮短。實(shí)驗(yàn)結(jié)果表明基于C4.5與基于線差法的六檔專(zhuān)業(yè)推薦法相比,對(duì)本一、本二、本三以及高職專(zhuān)業(yè)批次在誤差0檔范圍內(nèi)分別提高了11%、17%、20%以及26%;而基于優(yōu)化C4.5的六檔專(zhuān)業(yè)分類(lèi)法與基于C4.5六檔專(zhuān)業(yè)推薦法相比,在保持精度不變的情況下,其時(shí)間復(fù)雜度由O(9)27)2)2n)變?yōu)镺(9)2),效率方面得到明顯提升。本文在系統(tǒng)實(shí)現(xiàn)方面的工作包括兩個(gè)方面:(1)為了滿足系統(tǒng)的業(yè)務(wù)需求以及后期的業(yè)務(wù)擴(kuò)展,本文在數(shù)據(jù)庫(kù)設(shè)計(jì)方面針對(duì)數(shù)據(jù)項(xiàng)太多的問(wèn)題,提出數(shù)據(jù)表分表的方法;針對(duì)數(shù)據(jù)變動(dòng)的問(wèn)題,提出根據(jù)數(shù)據(jù)變動(dòng)頻率進(jìn)行分表的方法;針對(duì)同一院校在同批次同科類(lèi)下有多個(gè)招生代碼的問(wèn)題,提出不同招生代碼對(duì)應(yīng)院校做子父級(jí)關(guān)聯(lián)的方法;針對(duì)高考政策經(jīng)常性改革的問(wèn)題,提出高考政策及志愿填報(bào)規(guī)則配置化管理方案。(2)為了提高系統(tǒng)對(duì)于Web請(qǐng)求的訪問(wèn)效率,本文提出一種基于Apache Shiro的Web高效訪問(wèn)控制方案,該方案采用對(duì)Web應(yīng)用中的訪問(wèn)控制模塊進(jìn)行封裝,并且形成濾器鏈的形式,允許鏈中任意相連模塊進(jìn)行通信,從而提升了系統(tǒng)的訪問(wèn)效率。實(shí)驗(yàn)結(jié)果表明,當(dāng)系統(tǒng)在并發(fā)線程數(shù)達(dá)到5000時(shí),采用該方案將吞吐量從24rps提高到41rps,提高了70%,將平均響應(yīng)時(shí)間從1700ms降到1100ms,降低了35%。
[Abstract]:Voluntary filling in the college entrance examination is about the fate of the candidates' life. However, every year, many candidates are responsible for "high scores, low marks" and even "high scores". Voluntary filling in the college entrance examination is a huge and complex system project. Because of the tight time and information, the parents and candidates can not fill out a reasonable volunteer, so development A set of voluntary reporting guidance systems is of great practical significance. Accurate prediction of the grading line of colleges and universities is the most critical problem. There are three main methods of prediction: two line difference, admission difficulty coefficient and fractional ordering. These three methods are simple, but all are linear. In this paper, the prediction method based on neural network is proposed. Based on this, a set of voluntary reporting guidance system for college entrance examination is designed and implemented. The work of this paper mainly includes two aspects: (1) neural network based on Neural Network In this paper, a method based on neural network is proposed to predict the filing fraction line of colleges and Universities Based on neural network. Firstly, the method of particle swarm optimization is used to optimize the neural network model, and the particle swarm optimization algorithm is used in the iterative process. In order to solve the problem that the neural network algorithm is easy to fall into the local minimum, the problem that the neural network algorithm is easy to fall into the local minimum is solved by the cognition of its own historical ability and the global environment cognition. The experiment results of 2011-2015 years' data in Jiangsu province show that compared with the line difference method, the neural network directly uses the neural network to predict the error of the two colleges and universities. More than 1 points, the accuracy rate of 2 points increased by 12%, 14% respectively. The accuracy of this three, higher vocational college prediction error not exceeding 3 points was raised by 43%, which indicates that the accuracy rate of the neural network is higher. Compared with the direct use of neural network, the optimized network model will further this, and the prediction error of the 2 colleges and universities is not more than the prediction error. The accuracy of 1 points and 2 points is raised by 4%, 8%, and the accuracy of this three, higher vocational college prediction error not exceeding 3 points is raised by 4%, indicating that the optimized model has a higher prediction accuracy. (2) the C4.5 based six file professional recommendation method. The Jiangsu provincial college entrance examination voluntary reporting rules set each examinee to fill in 8 colleges and universities with the same classes. The school selects 6 specialties. Aiming at the shortage of the six grade professional recommendation based on the existing line difference method, this paper first puts forward the six file professional recommendation method based on the C4.5 algorithm. This method has been greatly improved in the accuracy rate than the line difference method professional recommendation method. On this basis, the C4.5 selection based on the equivalent infinitesimal theory is used. The optimization of the split attribute makes the calculation process shorter than the original C4.5 algorithm. Experimental results show that based on C4.5 and line difference based six professional recommendation method, this one, this two, this three, and higher professional batches have increased 11%, 17%, 20%, and 26% in the error range, respectively. Compared with the C4.5 six gear professional recommendation method, the optimization of C4.5's six gear classification method, with the constant precision, its time complexity changed from O (9) 27) to 2 2n) to O (9) 2), and improved the efficiency. The work of this paper in the system implementation includes two aspects: (1) to meet the business needs of the system and the later service. In order to solve the problem of data change, this paper puts forward the method of dividing tables according to the change frequency of data, and puts forward different enrolment codes corresponding to colleges and universities to be the parents for the same college in the same batch. In order to improve the access efficiency of the system for Web requests, a Web efficient access control scheme based on the Apache Shiro is proposed in order to improve the access efficiency of the system for Web requests. This scheme adopts the access control module in the Web application. Encapsulation, and form the form of a filter chain, allows the communication of any connected module in the chain to improve the system's access efficiency. The experimental results show that when the number of concurrent threads reaches 5000, the system improves the throughput from 24rps to 41rps, increases the throughput by 70%, reduces the average response time from 1700ms to 1100ms, and reduces the 35%.

【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP311.52

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本文編號(hào)：1810550