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售后服務(wù)數(shù)據(jù)的運(yùn)用模型
售后服務(wù)數(shù)據(jù)的運(yùn)用模型
摘 要
本文以工廠提供的轎車某部件的千車故障數(shù)的數(shù)據(jù)表為研究對(duì)象,在分析了數(shù)據(jù)表的特征并剔除了表中不合理的數(shù)據(jù)后，根據(jù)數(shù)據(jù)的不同特點(diǎn)建立了多個(gè)模型，對(duì)其中3個(gè)生產(chǎn)批次的部件的質(zhì)量狀況進(jìn)行了預(yù)測，探討了如何改進(jìn)數(shù)據(jù)表以進(jìn)1步提高數(shù)據(jù)的有效性，并對(duì)廠家的質(zhì)量管理提出了1系列建設(shè)性意見。
本文的主要工作有:
1對(duì)數(shù)據(jù)進(jìn)行了分析，提出了原文中千車故障數(shù)的定義存在的幾種不合理性,并對(duì)其進(jìn)行了修正,給出了更加合理的千車故障數(shù)的概念；
2 采用橫向最小2乘擬合與縱向卡爾曼濾波方法的聯(lián)合預(yù)測方法對(duì)數(shù)據(jù)表進(jìn)行填充；
3 建立了灰色馬爾柯夫預(yù)測模型,并對(duì)0205批次使用月數(shù)18時(shí)﹑0306批次使用月數(shù)9時(shí)和0310批次使用月數(shù)12時(shí)的千車故障數(shù)進(jìn)行了預(yù)測。
預(yù)測結(jié)果為:
0205批次使用月數(shù)18時(shí)的千車故障數(shù)為 79.65；
0306批次使用月數(shù)9時(shí)的千車故障數(shù)為 32.78；
0310批次使用月數(shù)12時(shí)的千車故障數(shù)為 12.57；
關(guān)鍵詞： 最小2乘法；灰色預(yù)測；馬爾柯夫鏈
Post-sale service data utilization model
Abstract
This paper factories car a 1000 car parts fault of a few data tables for study , After has analyzed the data sheet characteristic and has rejected in the table the unreasonable data, Has established many models according to the data different characteristic, Has carried on the forecast to 3 productions raid of part quality condition, How discussed improved the data sheet to further to enhance the data the validity, And gave a series of constructive comment to the factory quality control.
The main work:
1 on the data analysis, The original 1000 car fault the definition of several unreasonable, And its amendments, Is a more reasonable number of 1,000 cars the concept of fault.
2 horizontal and vertical least squares fitting method of Kalman filtering method of predicting the data sheet filler
3 Gray established three Markov chain model. 0205 installment also use a few of 18:00 ﹑ 0,306 installment on the use of several 0900 and 0310 installments on the use of a few 2:00 fault of a few thousand cars a prediction.
Forecast results:
0,205 batches of 18 for the use of 1,000 cars at 79.65 Fault
0,306 installment on the use of the 0900 number 1000 Trouble at 32.78
0,310 installment on the use of several 1000 12:00 Trouble at 12.57
4 after-use method to predict the test data
5 Finally, the re-tabulation of the proposals
Keywords: least squares; Gray forecast; markov
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