海洋數(shù)據(jù)集的質(zhì)量檢查驗(yàn)收抽樣方法——翻譯.

1、Acceptance sampling plan of quality inspection for ocean datasetCompared with the dataset of industrial products, ocean datasets have several distinct characteristics,such as large quantities and being multi-source,multi-dimension and multi-type.Based on the acceptance quality level (AQL) and limit

2、quality level (LQL), we designed an acceptance sampling plan of quality inspection for ocean which solvesthsetripcrtonbelsesm ofdatasets(ASP-OD),used this plan to inspect ocean dataset quality,and evaluated its advantage.ASP-OD has a consistent and stable discriminatory power in dependent of lot siz

3、efor large lot size, toleration for small lot size in the percent sam-pOlinDgepsltanb.liAshSePs a relationshipbetween lot size and sampling size,and provides a plan for a given lot size.This plan o vercomes the de?ciency of ISO2859-based sampling plans,different lot size corresponding to the same sa

4、mpling plan, in the quality inspection of ocean datasets. Collectively, this study suggests that ASP-OD is a suitable sampling plan for the inspection of ocean dataset quality.Keywords: ocean dataset;quality inspection;AQL;LQL;acceptance sampling plan1.IntroductionWith the rapid development of ocean

5、 monitoring technology,huge amounts of ocean data have been collected from various sources,such as remote sensing images, buoys, cruise data and underwater observation data.Thus,ocean datasets have gradually become a classic example of multi-modal big data.However,the biggest obstacle for preparing

6、an ocean atlas is how to control the quality of data.The quality control of ocean datasets is an important part of any ocean analysis/forecasting system.Using or accepting erroneous data could lead to an invalid conclusion or an incorrect analysis.By contrast,rejecting extreme but valid data sometim

7、es could cause the missing of key events and anomalous features.To date,a growing number of scientists have begun to focus on the quality inspection of ocean data.An automated quality control system was proposed to inspect oceanic temperature and temperature-salinity profiles. The Surface Ocean CO 2

8、 Atlas(SOCA T) project was performed to investigate the global dataset of marine surface CO2. During this project, all data were designed to be put in a uniform format following a strict protocol. Quality control was conducted according to clearly defined criteria.In addition,the quality and consist

9、ency of NASA ocean colour data,including spectral water-leavi ng reflecta nce,chlorophyll- a concen trati on, and diffuse atte nu ati on, were exam ined using com mon第1 頁共1 頁 algorithms and improved instrument calibration knowledge.These studies have put forward several quality inspection plans for

10、ocean data,especially for one or a few elements. Ocean datasets are usually composed of multi-element,multi-scale and multi-temporal geo-information elements.Moreover,there is a potential interplay between different elements in an ocean dataset.Thus,it is required to propose a novel acceptance sampl

11、ing plan to inspect the quality of ocean data as a complete and indivisible dataset.The goal of quality inspection is to judge whether the data reach the required quality through a sampling plan.Currently, the optimisation of acceptance sampling plans has been conducted to satisfy the balance betwee

12、n inspection risk and inspection cost for the quality inspection of industrial products. Some acceptance sampling plans have been designed based on inspection risk, which aimed to minimise either the producersrisk or the consumer s risk.Some other acceptance sampling plans were designed based on the

13、 inspection cost, which aimed to reduce the sampling number.These existing plans are mainly used to inspect the quality of industrial products.Generally,industrial products are produced in a controlled and consistent manner, and usually have certain items and uniform units.Compared with industrial p

14、roducts,ocean data have some distinct characteristics, such as being multi-source,multi-dimensional multi-type,multi-time-state,with different accuracy and nonlinearity.Thus,these existing acceptance sampling plans are not suitable for the quality inspection of ocean datasets.In this paper, we desig

15、ned an acceptance sampling plan of quality inspection for an ocean dataset (ASP-OD). In section 2, the conceptual framework,derivation process and the formulas of ASP-OD are shown.In section 3,we apply the ASP-OD to inspect the quality of ocean data,and compare its advantages over existing acceptanc

16、e sampling plans.In section 4,we summarise this study,and propose that ASP-OD is a suitable acceptance sampling plan for the quality inspection of ocean datasets.2. Design of ASP-ODThe theory of ASP-ODThe acceptance sampling plan of quality inspection for ocean datasets was designed as S(N,n,c).Here

17、,N is the lot size and comprises all inspected ocean data from which the sample is to be taken;n is the sample size and consists of a number of sampling units selected from the lot size,which is a compromise between the accuracy of product inspection and the cost of the inspection;c,the acceptance n

18、umber,is used to judge whether the inspected ocean data meet the requirement of the ocean data consumer.The process of quality inspection is shown as below:(1)n-sampled data are extracted from the lot size N;(2)the quality of extracted data is inspected one by one;(3)if the number of non-conforming

19、data (d) is larger than the acceptance number (c),the 第2 頁 共19 頁quality of in spected ocea n data is con sidered to be non-conformin g.Otherwise,the quality of in spected data is con sidered to be conforming.Based on the accepta nee sampli ng pla n S(N, n, c), the perce nt non-conforming (P ) is cal

20、culated byDP 100%(1)Nwhere D is the number of non-conforming ocean data in the total ocean dataset.Gen erally,it is difficult to obta in the values of D and P uni ess the total data are100 perce nt in spected.Sampled ocea n data are used to estimate the parameters for lot size.Thus,P is usually esti

21、mated using theperce nt non-conforming estimator(p);p is calculated by-Jp = d 100%nwhere d is the nu mber of non-conforming ocea n data in the sampled dataset.Based on the above-mentioned parameters,the acceptance quality probability(L(p)of the acceptaneesampli ng pla n S(N, n, c) can be calculated

22、bycL(p)八 h(d, n,D,N)d=0小N - Dcd =02 人n-d%、2NpN _Npcd =0Id 八 nd(0 w dW n,dW D,n-d w N-Np )Operati ng characteristic curves (OC-curve)are powerful tool sin the field of quality con trol, as they display the discrim in atory power of an accepta nce sampli ng pla n. Here,we con sidered the quality level

23、 as the horiz on tal axis and the corresp onding accepta nce probability as the vertical axis.The relati on ship betwee n L(p) and the proporti on p of non-conforming items was represe nted as the OC-curve of sampli ng in specti on in a recta ngular coord in ate system.Gen erally,c on sideri ng the

24、in terests of both the producers and con sumers, accepta nce quality level(AQL)and limiting quality level (LQL)were adopted to design the acceptance sampling plan. LQL is amaximum quality level of defectives tolerated in the inspection data. When the quality level is worse than LQL, 第3頁共19頁the con s

25、umers tend to reject the in spected data.AQL represe nts a mea n quality level of defective samples tolerated in the in spect ion.lf the quality level of the in spected data is better tha n AQL,the producers tend toaccept the in spected data.To meet the requireme nt of both producers and con sumers,

26、AQL and LQL were take n into acco unt in the/=tuE-4t-fe*E- =JHFIIFigured OC-curve of the accepta nee sampli ng pla n(po,1-a )p, i.e. AQL, is the proportion of non-conforming items that can be tolerated to judge that theentire lot can be accepted.a ,the producer s risk,is the probability of rejection

27、 of the inspected lot even thoughthe quality level of the lot is equal to or better than AQL.The second point is denoted as(p 1, 3 )ip.e.LQL,is the proporti on of nonconforming items that can be tolerated to judge that the en tire lot can be rejected.consumer s risk,is the probability of acceptanee

28、of the inspcted lot even though the quality level of the lot isequal to or worse tha n LQL.Un der the con diti on of the two points on the OC-curve,the relati on ship betwee n the lot size,the samplesize and the accepta nee nu mber is calculated.The problem could be formulated as a non li near progr

29、am ming problem.The ASP-OD modelFrom the perspective of the producer, the accepta nee sampli ng pla n should satisfy the follow ingcon diti on:(9)第7頁共19頁czd -0卞Dlnd 丿dDi, a positive integer, is the number of non-conforming data elements in the inspected ocean dataset.When the proportion of non-confo

30、rming data is equal to AQL,the value of D 1 is calculated byD1=round(N p”From the perspective of the con sumer,the accepta nee sampli ng pla n should satisfy the followi ng con diti on(N - D2、lnd 丿d丿D2 , a positive in teger, is the nu mber of nonconforming data eleme nts in the in spected ocea n dat

31、aset.When the proportion of nonconforming data is worse than the limiting quality level (LQL), the value of D 2 is calculated byD2=round(N p2)The total residual error, $means the sum of residual errors of the acceptanee probability at both AQL andLQL.The role of eis used for the calculation of the v

32、alue of n and c in the acceptance sampling plan.Here,we chose the minimal eto determine the optimal n and c for the acceptance sampling plan at AQL and LQL.The optimal acceptance sampling plan is formulated as the following nonlinear optimisation problemminn,cc(8)s.t?d =0*N - D2 d2czd z0n d 八dIn2ei

33、is the residual error of the acceptance probability based on the producer risk. & is the residual error of the acceptance probability based on the consumer risk. The nonlinear optimisation problem is solved based on the iterative algorithm.The iterative algorithm is implemented in Matlab software.3.

34、 Case studyIn this sectio n, we employed ASP-OD,the perce nt sampli ng pla n (PSP) and the ISO 2859-based sampli ng pla n (ISO2859)to in spect the quality of ocea n datasets,a nd discussed whether the proposed ASP-OD has a sig ni fica nt adva ntage in quality in spect ion for ocea n datasets.Study a

35、rea and datasetThe study area is located in a cultivation area in Southern China, and contains 5093 monitoring sites (Figure 2). The datasets con sist of three differe nt characteristics, attribute character, spatial character and temporal character (Table 1). Here, deposit sedime nt data were colle

36、cted using research vessels with an un certa in collect ion cycle. Hydrometeor-ological data were from remote sensing once a day or twice a day. Water quality data,megalobe nthos, zoopla nkton and phytopla nkton data were collected using buoys per 10 mi nu tes.Study Ocean?* * -p * Mud Flai025 50100

37、MUetFigure2. Studied ocean area with the monitoring sitesThe ocean dataset contains the location (X/Y coord inates) and attribute information that is representativeof the corresponding location. Here we used the x i/yi value to represent the latitude and Iongitude of themon itor sites (Table 2). How

38、ever, these parameters were collected using differe nt mon itori ng tools and methods. It is difficult to guara ntee the accuracy of data acquisiti on ,the complete ness of the dataset and the consistency of the data.Undoubtedly,a lot of abnormal datasets have arisen. For example,the salinity of loc

39、ati on (x 15,y15)is 70.751,which is sig ni fica ntly higher tha n that of the n eighbouri ng site. The reactive silicate of location(x 12,y12)is null.The total phosphorus of the locations (x 4,y4),(X5,y5),(X6,y6), and(X7,y7)is sig ni fica ntly differe nt from the value of other locati on s.Thus,it i

40、s required to con duct a quality in specti on of the ocea n dataset.ble 1. SpuhaJ. tempcriLl and altribuie data in the studied ocean areaDun typeSpatial dauTemporal dataAltribute deltaPhytophnkrnnLongitudetitudcPer 10 minutesBiomass, wcighi. densny of living creatures, biodiversiryZooplanktonLongitu

41、deLaricudcPer 10 minutesBiomass, density ot living crcanires. bitxliversity, weightMcgalobenthosLongitudeLatitudePer 10 minutesBiomass, biodiversity, density of living creatures, weightDeposit sedimemLongitudeLatitudeUncertainDepih、color, Temperature, transparency, petroleum, TOC, suliide, mercury,

42、chrome, cadmium, copper, Lead, phosphorus, nitrogen, tec al coJiform. arienicH ydrometeorologic aLongitudeLatitudeOnceAwiee a dayWaler temperatureh wave height, sure height, wind dirCLlion wind speeds tutil cloud eovcrh jinounl of prccipildUunh jit lcinpeiutureh trEiiispaienc. water colort atniusphe

43、ric pressureWater qualityLt)ni：iTudcLatiludcPer JO minuics.depthh colur+ salinityh pH. oxygen, suspended solids, phosphate, nilRLlcRkbk 2. Ocean paraineters in isoirie moniluring sitesLongitudeLatitudeSalinity |rng/L)pHTSP(mg/L)RtEictivc silicaic (mg心Total nitrogen (mg/L)ToiaJ phtwphorus (mg/L)COD (

44、mg/L)BOD(mg/LlV/31.52&221&50.4360.08540W5460.992L91y?3137721 10,4990.1330X)124().7920.0936巧v?31.296SJ724.5().396().1310.01731.020.796為31.405&23也04190()669&94O.M72J fifi731.359S.3422 J).4940.(17578430.5920328工&v*31.494KJ423.8039204360.01390.712045631,445&08270+4010.1 IN(H)ISKD.72X(J.363砂V/630.673S351

45、230.628().06580.01581.040.07023ya3(k7()4&216A)7260.101D.OIKK1.12(1.2225y/230.804S.1124.10.1220.0 ISS1.350.7253V/J30.782SJ J11.50J960.08670.01531.273633畑30,77KJ 113,S03250.102（H）1731.270.751&114.50.3670.120.0272L120.445Accepta nee sampli ng pla nsWe desig ned ASP-OD to in spect the quality of the oce

46、a n dataset. Here, five lots of ocea n datasets were collected for differe nt mon itori ng sites. Mea nwhile, the quality of these datasets was evaluated using two other kinds of pla n, PSP and ISO 2859. Fin ally, we determ ined which pla n had the adva ntage in the quality in spect ion of ocea n da

47、ta.The results of the perce nt sampli ng pla n with differe nt sampli ng rates are show n in Table3,Figure 3andFigure 4. n is the sample size.Here,we adopted differe nt sampli ng rates of 10 perce nt, 20 perce nt and 30 perce nt of the lot size for in spect ion.c is the accepta nee nu mber, and 1 pe

48、rce nt, 2 perce nt and 3 perce nt of the sample size were used (Table3).Operat ing characteristic curves(OC-curves) are powerful tools in the field of quality con trol, as they display the discrim in atory power of an accepta nce sampli ng pla n. Figure3 shows the OC-curves of five lots of ocean dat

49、asets with sample size (n i=N*10%) and acceptance number(C2=n *2%).Figure 4 shows the OC-curves of one lot of ocea n datasets (N5093) at differe nt sample sizes(ni=N*10%,n 2=N*20% and n 3=N*30%) and acceptance numbers(ci= n*1%,C2=n*2%,c 3=n*3%). Taken together, these results suggest the follow ing.T

50、able 3. Result ot perecnlige sampling pln willi dillcicnl sample rjtc10% sampling plan20% sampling plan30% sampling planLot No. Total number n Cr c； 匚# nfj n ct ooX)455CXI 3598478624550099()6K13 5 4 29213030154 5 5 3uo 2 o o 62 3 3 17 o2 75 41 I5 o43儲(chǔ)12豁454524第13頁共19頁氣NimpH血官 pl筑p niih 10%SaimpbejE

51、pln with 10%Npl-HiH nith1aooHwmu40wH1pQuulily kt cl p%Figure 3.OC-curves of five lots of ocean datasets with the sample size (n=10%*N) and acceptance number(c 2=2%*n)rx*E二 7UEE 弭c=-e-e9# .二已Figure 4. OC-curves of one lot of ocean datasets(N=5093)with different sample sizes(n=10%*N ,20%*N and 30%*N)

52、and acceptance numbers(c=1%*n,2%*n and 3%*n)施何警H Wilh犢二14韌Min|ii|iH|EMilh 詈料nHn卩Mun ilh 出-|4ii ilhA SHrnpliHv iriiin wilh 1MuMndi piflja h ifihof MMpIlHt ipl iM ill(1) The problem with this method is that the sample take n from small lots may not be restrictive eno ugh and the sample take n from lar

53、ge lots may be too restrictive. For example, with the quality level cha nges occurri ng, the accepta nee probability of large lots (N5093) decreases faster tha n that of small lots (N1450)(Figure3).(2) Differe nt sample sizes and accepta nee nu mbers could gen erate differe nt accepta nee sampli ng

54、pla ns, which could lead to varied discrimi natory power for the perce nt accepta nee of the sampli ng pla n( Figure4).To overcome these limitai ons of PSP,we proposed a new method called ASP-OD for the quality in spect ion of ocea n data. ISO 2859 provides a sampli ng pla n to assess whether a spec

55、ific lot of in dustrial products will meet the customers expectati on s.Here, we aksot eom phsyeuality in spect ion of ocea ndata. Furthermore, we compared their differe nces on discrimi natory power and sample rate. The results of the accepta nee sampli ng pla ns shows the follow ing.Compared with

56、ISO 2859, ASP-OD is more flexible and allows a saving in experimental time and cost.As for ISO 2859,different lot sizes may have the same acceptanee sampling plan. For example, a product with a lot size of 3500,5093or4895 has the same sample size (315) and same accepta nee nu mber(11)(Table 4 andFigure 5).lable 4. Result of ASP-OD. ISO 2859 and PSP sampling plansLotTaiiilASP-ODISO20%