CN109978560A - E-commerce product quality safety Risk Monitoring and method for early warning - Google Patents
E-commerce product quality safety Risk Monitoring and method for early warning Download PDFInfo
- Publication number
- CN109978560A CN109978560A CN201811484489.9A CN201811484489A CN109978560A CN 109978560 A CN109978560 A CN 109978560A CN 201811484489 A CN201811484489 A CN 201811484489A CN 109978560 A CN109978560 A CN 109978560A
- Authority
- CN
- China
- Prior art keywords
- control
- value
- moving range
- sample
- control line
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/018—Certifying business or products
Landscapes
- Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Finance (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a kind of e-commerce product quality safety Risk Monitoring and method for early warning, first determine product to be sampled, and then timing extraction quantity is the sample of n, detect to the quality for the sample being drawn into, obtain each data of the sample；According to the data that detection obtains, Risk Monitoring is carried out to sample using control figure, early warning is carried out to sample using formula calculation risk coefficient；The Risk Monitoring of sample includes the risk monitoring and control of percent defective and the risk monitoring and control of Critical to quality.The present invention have the characteristics that intuitively to reflect security risk and timely and accurately early warning is carried out to product quality.
Description
Technical field
The present invention relates to a kind of e-commerce product quality monitoring method, especially a kind of e-commerce product quality safety
Risk Monitoring and method for early warning.
Background technique
With the fast development of e-commerce, for the quality of e-commerce product increasingly by the concern of consumer, having must
The quality of e-commerce product is monitored, and early warning is made to corresponding product quality, so as to law enfrocement official can and
When Information, protect consumers' rights and interests.But it is mainly logical for the quality safety Risk Monitoring of e-commerce product at present
It crosses online shopping product to be detected, the data detected intuitively can not effectively reflect the risk；And for e-commerce product
Early warning, mainly bias toward information retrieval, it is intended to help user to find valuable information, lack further early warning calculating and grind
Study carefully, lead to not promptly and accurately to product quality carry out early warning.Therefore, there is can not intuitively reflect safety for existing technology
Risk and can not timely and accurately to product quality carry out early warning the problem of.
Summary of the invention
The object of the present invention is to provide a kind of e-commerce product quality safety Risk Monitoring and method for early warning.This hair
It is bright have the characteristics that intuitively to reflect security risk and timely and accurately early warning is carried out to product quality.
Technical solution of the present invention: e-commerce product quality safety Risk Monitoring and method for early warning are first determined wait sample
Product, then timing extraction quantity be n sample, the quality for the sample being drawn into is detected, each of the sample is obtained
A data；According to the data that detection obtains, Risk Monitoring is carried out to sample using control figure, utilizes formula calculation risk coefficient pair
Sample carries out early warning；The Risk Monitoring of sample includes the risk monitoring and control of percent defective and the risk monitoring and control of Critical to quality；
The calculation formula of the risk factor of early warning is carried out to sample are as follows:
Wherein, RiFor quality risk coefficient, yAFor the frequency of the unqualified generation of A class
Secondary, yBThe frequency, y for the unqualified generation of B classCFor the frequency of the unqualified generation of C class；yA=KA×wA, wAFor the underproof power of A class
Weight, wA=3, KAFor the underproof stringency of A class；yB=KB×wB, wBFor the underproof weight of B class, wB=2, KBDo not conform to for B class
The stringency of lattice；yC=KC×wC, wCFor the underproof weight of C class, wC=1；KCFor the underproof stringency of C class；The value of m is 0.1, PAFor A class percent defective standard value, PBIt is unqualified for B class
Product rate standard value, PCFor C class percent defective standard value.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, the risk monitoring and control of percent defective
Specific steps are as follows:
A, it using sample group number as abscissa, using sample disqualification rate as ordinate, draws, obtains rejected product
Rate control figure, and upper control line, center line and the lower control line parallel with abscissa are set in defective number chart；
B, by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number work
For abscissa, the practical disqualification rate of respective sample is drawn into idea as ordinate in defective number chart；
C, the risk monitoring and control of percent defective is carried out to sample according to the idea distribution situation in defective number chart；
When the percent defective standard value of given product:
The occurrence UCL of the ordinate of upper control line in step A are as follows:Wherein, P0Given
Disqualification rate standard value, n are sample size；
The occurrence CL of the ordinate of center line in step A are as follows: CL=P0；Wherein, P0Given disqualification rate standard
Value；
The occurrence LCL of the ordinate of lower control line in step A are as follows:Wherein, P0It is given
Disqualification rate standard value, n is sample size；
When the disqualification rate standard value of not given product:
The occurrence UCL of the ordinate of upper control line in step A are as follows:Wherein,It is multiple
The average value of the practical percent defective of accumulation selective examination sample, n is sample size；
The occurrence CL of the ordinate of center line in step A are as follows:Wherein,For multiple accumulation selective examination samples
The average value of practical percent defective；
The occurrence LCL of the ordinate of lower control line in step A are as follows:Wherein,It is more
The average value of the practical percent defective of a accumulation selective examination sample, n is sample size.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, the risk monitoring and control of Critical to quality
Specific steps are as follows: according to the sample size n of selective examination select Critical to quality risk monitoring and control method: as n=1, application
The risk monitoring and control of Individual-moving range control chart progress Critical to quality；As 1 < n≤10, using average value-movement pole
Poor control figure carries out the risk monitoring and control of Critical to quality；As n > 10, crucial matter is carried out using average value-Standard Deviation Charts
The risk monitoring and control of flow characteristic.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, controlled using monodrome-moving range
The method that figure carries out the risk monitoring and control of Critical to quality are as follows: first using sample group number as abscissa, with the Key Quality of sample
Characteristic value is drawn as ordinate, obtains monodrome control figure, and setting is parallel with abscissa in monodrome control figure
Control line under monodrome upper control line, monodrome center line and monodrome；Using sample group number as abscissa, with the moving range value of sample
As ordinate, moving range control figure is drawn, and the mobile pole parallel with abscissa is set in moving range control figure
Control line under poor upper control line, moving range center line and moving range；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number
As abscissa, using the monodrome of respective sample and moving range value as ordinate respectively in monodrome control figure and moving range control
Idea is drawn in drawing；
Critical to quality is carried out to sample according to the idea distribution situation in monodrome control figure and moving range control figure
Risk monitoring and control；
When providing standard value:
The UCL of the monodrome upper control line of monodrome control figured, monodrome center line CLdWith control line LCL under monodromedCalculating it is public
Formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permission
Deviation.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula: R0, the standard value of given moving range；σ0, given tolerance value；Coefficient
D1, D2For constant value；
When not providing standard value:
The UCL of the monodrome upper control line of monodrome control figured, monodrome center line CLdWith control line LCL under monodromedCalculating it is public
Formula are as follows:
In formula:It is averaged to accumulate the Critical to quality value of at least 25 samples
Value；Average moving range when n=2；E2For constant value.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula:Average moving range when n=2；Coefficient D3, D4For constant value；
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, using average value-moving range control
The method that drawing carries out the risk monitoring and control of Critical to quality are as follows: first using sample group number as abscissa, with Critical to quality
The average value of value is drawn as ordinate, obtains X-control chart, and setting and abscissa on X-control chart
Control line under parallel average value upper control line, average value center line and average value；Using sample group number as abscissa, with sample
This moving range value draws moving range control figure as ordinate, and setting and abscissa in moving range control figure
Control line under parallel moving range upper control line, moving range center line and moving range；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number
As abscissa, using the average value of each corresponding sample and moving range value as ordinate respectively X-control chart with
Idea is drawn in moving range control figure；
It is special that Key Quality is carried out to sample according to the idea distribution situation on X-control chart and moving range control figure
The risk monitoring and control of property；
When providing standard value:
The UCL of the average value upper control line of X-control chartP, average value center line CLPWith control line LCL under average valueP
Calculation formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permits
Perhaps deviation；A, constant value.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula: R0, the standard value of given moving range；σ0, given tolerance value；Coefficient
D1、D2For constant value；
When not providing standard value:
The UCL of the average value upper control line of X-control chartd, average value center line CLdWith control line LCL under average valued
Calculation formula are as follows:
In formula:To accumulate at least 25 sample Critical to quality value average value
Average value；For the average value for accumulating at least 25 ranges；A2For constant value.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula:For the average value for accumulating at least 25 ranges；Coefficient D3、D4For constant
Value.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, average value-Standard Deviation Charts into
The method of the risk monitoring and control of row Critical to quality are as follows: first using sample group number as abscissa, with the flat of Critical to quality value
Mean value is drawn as ordinate, obtains X-control chart, and setting is parallel with abscissa on X-control chart
Average value upper control line, control line under average value center line and average value；Using sample group number as abscissa, with the mark of sample
Quasi- difference draws Standard Deviation Charts as ordinate, and the standard parallel with abscissa is arranged on Standard Deviation Charts
Control line under poor upper control line, standard deviation center line and standard deviation；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number
As abscissa, using the average and standard deviation value of each corresponding sample as ordinate respectively in X-control chart and mark
Idea is drawn in quasi- difference control figure；
Critical to quality is carried out to sample according to the idea distribution situation on X-control chart and Standard Deviation Charts
Risk monitoring and control；
When providing standard value:
The UCL of the average value upper control line of X-control chartP, average value center line CLPWith control line LCL under average valueP
Calculation formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permits
Perhaps deviation；A, constant value.
The moving range upper control line UCL of Standard Deviation ChartsB, moving range center line CLBWith control line under moving range
LCLBCalculation formula are as follows:
In formula: S0, the standard value of given moving range；σ0, given tolerance value；Coefficient
S5、S6For constant value；
When not providing standard value:
The UCL of the average value upper control line of X-control chartd, average value center line CLdWith control line LCL under average valued
Calculation formula are as follows:
In formula:To accumulate at least 25 sample Critical to quality value average value
Average value；For the average value for accumulating at least 25 sample standard deviations；A3For constant value.
The moving range upper control line UCL of Standard Deviation ChartsB, moving range center line CLBWith control line under moving range
LCLBCalculation formula are as follows:
In formula:For the average value for accumulating at least 25 sample standard deviations；Coefficient B3、B4For constant
Value.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, for general goods, risk factor
Calculation formula are as follows:
As N=1, i.e., a batch is only examined, at this time formula are as follows:
As N >=2, calculation formula at this time are as follows:
As P 'Cj< PCWhen, enable nC=0；
For important goods, the calculation formula of risk factor are as follows:
As N=1, i.e., a batch is only examined, at this time formula are as follows:
As N >=2, calculation formula at this time are as follows:
As P 'Cj< PCWhen, P 'CjCorresponding nCIt is 0.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, wind is carried out to sample using control figure
The method of the judgement nearly monitored are as follows: if 25 continuous ideas are fallen between upper control line and lower control line in control figure, Huo Zhelian
The continuous most ideas of 35 ideas are fallen in outside upper control line or lower control line or most two ideas of continuous 100 ideas
Fall in outside upper control line or lower control line, and idea arrangement be it is random, then show that e-commerce product is normal, indicate should
E-commerce commodity are in controllable state；If idea is fallen in except upper control line or lower control line or idea is in upper control line
Arrangement between lower control line be it is nonrandom, then showing the e-commerce product, there are systematicness risks.
In e-commerce product quality safety Risk Monitoring above-mentioned and method for early warning, work as Ri=R1When >=0.75, for red
Early warning risk factor；As 0.5≤Ri=R2It is orange warning risk factor when < 0.75；As 0.25≤Ri=R3When < 0.5, it is
Yellow early warning risk factor；As 0.1≤Ri=R4When < 0.25, for blue early warning risk factor.
Compared with prior art, the obtained each data of sampling are depicted in control figure by the present invention, so as to by sample
This quality information intuitively shows, and then can intuitively reflect the security risk of product；Meanwhile it being produced in the present invention
The percent defective and Critical to quality of product in different control figures by showing, so as to more preferable, more fully anti-
Mirror the different security risks of product.The present invention calculates the risk factor of product by formula, according to risk factor come into
The corresponding grading of row accurately carries out early warning so as to intuitive.In conclusion the present invention, which has, can intuitively reflect safety wind
Danger and timely and accurately to product quality carry out early warning the characteristics of.
Detailed description of the invention
Fig. 1 is defective number chart；
Fig. 2 is monodrome control figure；
Fig. 3 is moving range control figure；
Fig. 4 is X-control chart；
Fig. 5 is Standard Deviation Charts.
Specific embodiment
The present invention is further illustrated with reference to the accompanying drawings and examples, but be not intended as to the present invention limit according to
According to.
Embodiment.E-commerce product quality safety Risk Monitoring and method for early warning are constituted as shown in Figures 1 to 5, first really
Fixed product to be sampled, then timing extraction quantity is the sample of n, detects, is somebody's turn to do to the quality for the sample being drawn into
Each data of sample；According to the data that detection obtains, Risk Monitoring is carried out to sample using control figure, calculates wind using formula
Dangerous coefficient carries out early warning to sample；The Risk Monitoring of sample includes the risk monitoring and control of percent defective and the wind of Critical to quality
Danger monitoring；
The calculation formula of the risk factor of early warning is carried out to sample are as follows:
Wherein, RiFor quality risk coefficient, yAFor the frequency of the unqualified generation of A class
Secondary, yBThe frequency, y for the unqualified generation of B classCFor the frequency of the unqualified generation of C class；yA=KA×wA, wAFor the underproof power of A class
Weight, wA=3, KAFor the underproof stringency of A class；yB=KB×wB, wBFor the underproof weight of B class, wB=2, KBDo not conform to for B class
The stringency of lattice；yC=KC×wC, wCFor the underproof weight of C class, wC=1；KCFor the underproof stringency of C class；The value of m is 0.1, PAFor A class percent defective standard value, PBIt is unqualified for B class
Product rate standard value, PCFor C class percent defective standard value.
The specific steps of the risk monitoring and control of percent defective are as follows:
A, it using sample group number as abscissa, using sample disqualification rate as ordinate, draws, obtains rejected product
Rate control figure, and upper control line, center line and the lower control line parallel with abscissa are set in defective number chart；
B, by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number work
For abscissa, the practical disqualification rate of respective sample is drawn into idea as ordinate in defective number chart；
C, the risk monitoring and control of percent defective is carried out to sample according to the idea distribution situation in defective number chart；
When the percent defective standard value of given product:
The occurrence UCL of the ordinate of upper control line in step A are as follows:Wherein, P0Given
Disqualification rate standard value, n are sample size；
The occurrence CL of the ordinate of center line in step A are as follows: CL=P0；Wherein, P0Given disqualification rate standard
Value；
The occurrence LCL of the ordinate of lower control line in step A are as follows:Wherein, P0It is given
Disqualification rate standard value, n is sample size；
When the disqualification rate standard value of not given product:
The occurrence UCL of the ordinate of upper control line in step A are as follows:Wherein,It is multiple
The average value of the practical percent defective of accumulation selective examination sample, n is sample size；
The occurrence CL of the ordinate of center line in step A are as follows:Wherein,Sample is spot-check for multiple accumulations
Practical percent defective average value；
The occurrence LCL of the ordinate of lower control line in step A are as follows:Wherein,It is more
The average value of the practical percent defective of a accumulation selective examination sample, n is sample size.
The specific steps of the risk monitoring and control of Critical to quality are as follows: Critical to quality is selected according to the sample size n of selective examination
Risk monitoring and control method: as n=1, using Individual-moving range control chart carry out Critical to quality risk monitoring and control；
As 1 < n≤10, the risk monitoring and control of Critical to quality is carried out using average value-moving range control figure；When n > 10, application
Average value-Standard Deviation Charts carry out the risk monitoring and control of Critical to quality.
The method of the risk monitoring and control of Critical to quality is carried out using Individual-moving range control chart are as follows: first with sample group
It number is drawn using the Critical to quality value of sample as ordinate as abscissa, obtains monodrome control figure, and in list
It is worth control line under the monodrome upper control line, monodrome center line and monodrome that setting is parallel with abscissa in control figure；With sample group
Number be used as abscissa, using the moving range value of sample as ordinate, draw moving range control figure, and moving range control
Control line under the setting moving range upper control line parallel with abscissa, moving range center line and moving range on figure；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number
As abscissa, using the monodrome of respective sample and moving range value as ordinate respectively in monodrome control figure and moving range control
Idea is drawn in drawing；
Critical to quality is carried out to sample according to the idea distribution situation in monodrome control figure and moving range control figure
Risk monitoring and control；
When providing standard value:
The UCL of the monodrome upper control line of monodrome control figured, monodrome center line CLdWith control line LCL under monodromedCalculating it is public
Formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permission
Deviation.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula: R0, the standard value of given moving range；σ0, given tolerance value；Coefficient
D1, D2The constant value searched in slave table 1 when for n=2；
When not providing standard value:
The UCL of the monodrome upper control line of monodrome control figured, monodrome center line CLdWith control line LCL under monodromedCalculating it is public
Formula are as follows:
In formula:It is averaged to accumulate the Critical to quality value of at least 25 samples
Value；Average moving range when n=2；E2For the constant value searched from table when n=2.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula:Average moving range when n=2；Coefficient D3, D4To be searched from table when n=2
Obtained constant value；
The method of the risk monitoring and control of Critical to quality is carried out using average value-moving range control figure are as follows: first with sample
Group number is drawn as abscissa using the average value of Critical to quality value as ordinate, and X-control chart is obtained,
And it is arranged under the average value upper control line parallel with abscissa, average value center line and average value on X-control chart and controls
Line processed；Using sample group number as abscissa, using the moving range value of sample as ordinate, moving range control figure is drawn, and
The moving range upper control line parallel with abscissa, moving range center line and mobile pole are set in moving range control figure
The lower control line of difference；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number
As abscissa, using the average value of each corresponding sample and moving range value as ordinate respectively X-control chart with
Idea is drawn in moving range control figure；
It is special that Key Quality is carried out to sample according to the idea distribution situation on X-control chart and moving range control figure
The risk monitoring and control of property；
When providing standard value:
The UCL of the average value upper control line of X-control chartP, average value center line CLPWith control line LCL under average valueP
Calculation formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permits
Perhaps deviation；A, the constant value searched from table 1.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula: R0, the standard value of given moving range；σ0, given tolerance value；Coefficient
D1、D2For the constant value searched from table 1；
When not providing standard value:
The UCL of the average value upper control line of X-control chartd, average value center line CLdWith control line LCL under average valued
Calculation formula are as follows:
In formula:To accumulate at least 25 sample Critical to quality value average value
Average value is (i.e. at least 25 accumulativeAverage value)；For the average value for accumulating at least 25 ranges；A2It is n from table
It is middle to search obtained constant value.
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith controlled under moving range
Line LCLyCalculation formula are as follows:
In formula:For the average value for accumulating at least 25 ranges；Coefficient D3、D4For from table
Search obtained constant value.
The method that average value-Standard Deviation Charts carry out the risk monitoring and control of Critical to quality are as follows: first with sample group number work
It is drawn for abscissa using the average value of Critical to quality value as ordinate, obtains X-control chart, and flat
Control line under the setting average value upper control line parallel with abscissa, average value center line and average value on mean chart；
Using sample group number as abscissa, using the standard deviation of sample as ordinate, Standard Deviation Charts are drawn, and in standard deviation control
Control line under the setting standard deviation upper control line parallel with abscissa, standard deviation center line and standard deviation in drawing；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, with sample group number
As abscissa, using the average and standard deviation value of each corresponding sample as ordinate respectively in X-control chart and mark
Idea is drawn in quasi- difference control figure；
Critical to quality is carried out to sample according to the idea distribution situation on X-control chart and Standard Deviation Charts
Risk monitoring and control；
When providing standard value:
The UCL of the average value upper control line of X-control chartP, average value center line CLPWith control line LCL under average valueP
Calculation formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permits
Perhaps deviation；A, the constant value searched from table 1.
The moving range upper control line UCL of Standard Deviation ChartsB, moving range center line CLBWith control line under moving range
LCLBCalculation formula are as follows:
In formula: S0, the standard value of given moving range；σ0, given tolerance value；Coefficient
S5、S6For the constant value searched from table 1；
When not providing standard value:
The UCL of the average value upper control line of X-control chartd, average value center line CLdWith control line LCL under average valued
Calculation formula are as follows:
In formula:To accumulate at least 25 sample Critical to quality value average value
Average value；For the average value for accumulating at least 25 sample standard deviations；A3The constant value searched from table 1 for n.
The moving range upper control line UCL of Standard Deviation ChartsB, moving range center line CLBWith control line under moving range
LCLBCalculation formula are as follows:
In formula:For the average value for accumulating at least 25 sample standard deviations；Coefficient B3、B4For from table
The constant value searched in 1.
For general goods, the calculation formula of risk factor are as follows:
As N=1, i.e., a batch is only examined, at this time formula are as follows:
As N >=2, calculation formula at this time are as follows:
As P 'Cj< PCWhen, enable nC=0；
For important goods, the calculation formula of risk factor are as follows:
As N=1, i.e., a batch is only examined, at this time formula are as follows:
As N >=2, calculation formula at this time are as follows:
As P 'Cj< PCWhen, P 'CjCorresponding nCIt is 0.
The method of the judgement of Risk Monitoring is carried out to sample using control figure are as follows: if 25 continuous ideas are fallen in control figure
Between upper control line and lower control line or the most ideas of continuous 35 ideas are fallen in outside upper control line or lower control line,
Or continuous most two ideas of 100 ideas are fallen in outside upper control line or lower control line, and idea arrangement is random, then table
Bright e-commerce product is normal, indicates that the e-commerce commodity are in controllable state；If idea fall in upper control line or
Except lower control line or arrangement of the idea between upper control line and lower control line be it is nonrandom, then show the e-commerce
There are systematicness risks for product.
Work as Ri=R1It is red early warning risk factor when >=0.75；As 0.5≤Ri=R2It is orange warning wind when < 0.75
Dangerous coefficient；As 0.25≤Ri=R3It is yellow early warning risk factor when < 0.5；As 0.1≤Ri=R4It is pre- for blue when < 0.25
Alert risk factor.
1 control figure of table calculates the coefficient table of control limit
R1: red early warning risk factor；(yA1,yB1,yC1) be corresponding A class when red early warning risk factor calculates, B class,
The frequency of the unqualified generation of C class；
R2: orange warning risk factor；(yA2,yB2,yC2) be corresponding A class when orange warning risk factor calculates, B class,
The frequency of the unqualified generation of C class；
R3: yellow early warning risk factor；(yA3,yB3,yC3) be corresponding A class when yellow early warning risk factor calculates, B class,
The frequency of the unqualified generation of C class；
R4: blue early warning risk factor；(yA4,yB4,yC4) be corresponding A class when blue early warning risk factor calculates, B class,
The frequency of the unqualified generation of C class；
The Critical to quality value X of each product in number of non-compliances d and the sample in the sample is obtained, according to formula meter
Calculate samplePractical disqualification rate P', then using control figure to sample carry out Risk Monitoring, utilize Early-warning Model pair
Sample carries out early warning.
Claims (9)
1. e-commerce product quality safety Risk Monitoring and method for early warning, it is characterised in that: first determine product to be sampled, so
Timing extraction quantity is the sample of n afterwards, detects to the quality for the sample being drawn into, obtains each data of the sample；Root
According to the data that detection obtains, Risk Monitoring is carried out to sample using control figure, sample is carried out using formula calculation risk coefficient
Early warning；The Risk Monitoring of sample includes the risk monitoring and control of percent defective and the risk monitoring and control of Critical to quality；
The calculation formula of the risk factor of early warning is carried out to sample are as follows:
Wherein, RiFor quality risk coefficient, yAThe frequency, y for the unqualified generation of A classBThe frequency, y for the unqualified generation of B classCFor C
The frequency of the unqualified generation of class；yA=KA×wA, wAFor the underproof weight of A class, wA=3, KAFor the underproof stringency of A class；
yB=KB×wB, wBFor the underproof weight of B class, wB=2, KBFor the underproof stringency of B class；yC=KC×wC, wCNot for C class
Qualified weight, wC=1；KCFor the underproof stringency of C class； The value of m is
0.1, PAFor A class percent defective standard value, PBFor B class percent defective standard value, PCFor C class percent defective standard value.
2. e-commerce product quality safety Risk Monitoring according to claim 1 and method for early warning, it is characterised in that: no
The specific steps of the risk monitoring and control of accepted product percentage are as follows:
A, it using sample group number as abscissa, using sample disqualification rate as ordinate, draws, obtains percent defective control
Drawing, and upper control line, center line and the lower control line parallel with abscissa are set in defective number chart；
B, by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, using sample group number as cross
The practical disqualification rate of respective sample is drawn idea as ordinate by coordinate in defective number chart；
C, the risk monitoring and control of percent defective is carried out to sample according to the idea distribution situation in defective number chart；
When the percent defective standard value of given product:
The occurrence UCL of the ordinate of upper control line in step A are as follows:Wherein, P0Given does not conform to
Lattice rate standard value, n are sample size；
The occurrence CL of the ordinate of center line in step A are as follows: CL=P0；Wherein, P0Given disqualification rate standard value；
The occurrence LCL of the ordinate of lower control line in step A are as follows:Wherein, P0It is given not
Yield criterion value, n are sample size；
When the disqualification rate standard value of not given product:
The occurrence UCL of the ordinate of upper control line in step A are as follows:Wherein,For multiple accumulations
The average value of the practical percent defective of sample is spot-check, n is sample size；
The occurrence CL of the ordinate of center line in step A are as follows:Wherein,For the reality of multiple accumulation selective examination samples
The average value of percent defective；
The occurrence LCL of the ordinate of lower control line in step A are as follows:Wherein,It is multiple tired
The average value of the practical percent defective of product selective examination sample, n is sample size.
3. e-commerce product quality safety Risk Monitoring according to claim 1 and method for early warning, it is characterised in that: close
The specific steps of the risk monitoring and control of key mass property are as follows: the risk monitoring and control of Critical to quality is selected according to the sample size n of selective examination
Method: as n=1, using Individual-moving range control chart carry out Critical to quality risk monitoring and control；When 1 n≤10 <
When, the risk monitoring and control of Critical to quality is carried out using average value-moving range control figure；As n > 10, using average value-mark
Quasi- difference control figure carries out the risk monitoring and control of Critical to quality.
4. e-commerce product quality safety Risk Monitoring according to claim 3 and method for early warning, it is characterised in that: answer
The method of the risk monitoring and control of Critical to quality is carried out with Individual-moving range control chart are as follows: first using sample group number as horizontal seat
Mark, using the Critical to quality value of sample as ordinate, draws, obtains monodrome control figure, and in monodrome control figure
Control line under the monodrome upper control line parallel with abscissa, monodrome center line and monodrome is set；Using sample group number as horizontal seat
Mark, using the moving range value of sample as ordinate, draw moving range control figure, and in moving range control figure setting and
Control line under the parallel moving range upper control line of abscissa, moving range center line and moving range；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, using sample group number as
Abscissa, using the monodrome of respective sample and moving range value as ordinate respectively in monodrome control figure and moving range control figure
Upper drafting idea；
The wind of Critical to quality is carried out to sample according to the idea distribution situation in monodrome control figure and moving range control figure
Danger monitoring；
When providing standard value:
The UCL of the monodrome upper control line of monodrome control figured, monodrome center line CLdWith control line LCL under monodromedCalculation formula
Are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given tolerance
Value；
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith control line under moving range
LCLyCalculation formula are as follows:
In formula: R0, the standard value of given moving range；σ0, given tolerance value；Coefficient D1, D2
For constant value；
When not providing standard value:
The UCL of the monodrome upper control line of monodrome control figured, monodrome center line CLdWith control line LCL under monodromedCalculation formula
Are as follows:
In formula:For the average value of the Critical to quality value of at least 25 samples of accumulation；Average moving range when n=2；E2For constant value；
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith control line under moving range
LCLyCalculation formula are as follows:
In formula:Average moving range when n=2；Coefficient D3, D4For constant value.
5. e-commerce product quality safety Risk Monitoring according to claim 3 and method for early warning, it is characterised in that: answer
The method of the risk monitoring and control of Critical to quality is carried out with average value-moving range control figure are as follows: first using sample group number as cross
Coordinate is drawn using the average value of Critical to quality value as ordinate, obtains X-control chart, and in average value
Control line under the setting average value upper control line parallel with abscissa, average value center line and average value in control figure；With sample
This group number is used as abscissa, using the moving range value of sample as ordinate, draws moving range control figure, and in moving range
It is arranged under the moving range upper control line parallel with abscissa, moving range center line and moving range in control figure and controls
Line；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, using sample group number as
Abscissa, using the average value of each corresponding sample and moving range value as ordinate respectively in X-control chart and movement
Idea is drawn on range chart；
Critical to quality is carried out to sample according to the idea distribution situation on X-control chart and moving range control figure
Risk monitoring and control；
When providing standard value:
The UCL of the average value upper control line of X-control chartP, average value center line CLPWith control line LCL under average valuePMeter
Calculate formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permission is inclined
Difference；A, constant value；
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith control line under moving range
LCLyCalculation formula are as follows:
In formula: R0, the standard value of given moving range；σ0, given tolerance value；Coefficient D1、D2
For constant value；
When not providing standard value:
The UCL of the average value upper control line of X-control chartd, average value center line CLdWith control line LCL under average valuedMeter
Calculate formula are as follows:
In formula:To accumulate being averaged at least 25 sample Critical to quality value average value
Value；For the average value for accumulating at least 25 ranges；A2For constant value；
The moving range upper control line UCL of moving range control figurey, moving range center line CLyWith control line under moving range
LCLyCalculation formula are as follows:
In formula:For the average value for accumulating at least 25 ranges；Coefficient D3、D4For constant value.
6. e-commerce product quality safety Risk Monitoring according to claim 3 and method for early warning, it is characterised in that: flat
The method that mean-standard deviation control figure carries out the risk monitoring and control of Critical to quality are as follows: first using sample group number as abscissa, with
The average value of Critical to quality value is drawn as ordinate, obtains X-control chart, and on X-control chart
Control line under the average value upper control line parallel with abscissa, average value center line and average value is set；With sample group number work
For abscissa, using the standard deviation of sample as ordinate, draw Standard Deviation Charts, and on Standard Deviation Charts setting with
Control line under the parallel standard deviation upper control line of abscissa, standard deviation center line and standard deviation；
Then by timing extraction to sample arrange to obtain corresponding sample group number with chronological order, using sample group number as
Abscissa, using the average and standard deviation value of each corresponding sample as ordinate respectively in X-control chart and standard deviation
Idea is drawn in control figure；
The wind of Critical to quality is carried out to sample according to the idea distribution situation on X-control chart and Standard Deviation Charts
Danger monitoring；
When providing standard value:
The UCL of the average value upper control line of X-control chartP, average value center line CLPWith control line LCL under average valuePMeter
Calculate formula are as follows:
In formula: X0, the standard value of given Critical to quality value；σ0, given permission is inclined
Difference；A, constant value；
The moving range upper control line UCL of Standard Deviation ChartsB, moving range center line CLBWith control line LCL under moving rangeB
Calculation formula are as follows:
In formula: S0, the standard value of given moving range；σ0, given tolerance value；Coefficient S5、S6
For constant value；
When not providing standard value:
The UCL of the average value upper control line of X-control chartd, average value center line CLdWith control line LCL under average valuedMeter
Calculate formula are as follows:
In formula:To accumulate being averaged at least 25 sample Critical to quality value average value
Value；For the average value for accumulating at least 25 sample standard deviations；A3For constant value.
The moving range upper control line UCL of Standard Deviation ChartsB, moving range center line CLBWith control line LCL under moving rangeB
Calculation formula are as follows:
In formula:For the average value for accumulating at least 25 sample standard deviations；Coefficient B3、B4For constant value.
7. e-commerce product quality safety Risk Monitoring according to claim 1 and method for early warning, it is characterised in that: right
In general goods, the calculation formula of risk factor are as follows:
As N=1, i.e., a batch is only examined, at this time formula are as follows:
As N >=2, calculation formula at this time are as follows:
As P 'Cj< PCWhen, enable nC=0；
For important goods, the calculation formula of risk factor are as follows:
As N=1, i.e., a batch is only examined, at this time formula are as follows:
As N >=2, calculation formula at this time are as follows:
As P 'Cj< PCWhen, P 'CjCorresponding nCIt is 0.
8. e-commerce product quality safety Risk Monitoring according to claim 1 and method for early warning, it is characterised in that: benefit
The method of the judgement of Risk Monitoring is carried out to sample with control figure are as follows: if in control figure 25 continuous ideas fall in upper control line and
Perhaps the most ideas of continuous 35 ideas are fallen in outside upper control line or lower control line or continuous 100 between lower control line
A most two ideas of idea are fallen in outside upper control line or lower control line, and idea arrangement be it is random, then show e-commerce
Product is normal, indicates that the e-commerce commodity are in controllable state；If idea fall in upper control line or lower control line it
Outside or arrangement of the idea between upper control line and lower control line be it is nonrandom, then show that the e-commerce product has system
System property risk.
9. e-commerce product quality safety Risk Monitoring according to claim 1 and method for early warning, it is characterised in that: when
Ri=R1It is red early warning risk factor when >=0.75；As 0.5≤Ri=R2It is orange warning risk factor when < 0.75；When
0.25≤Ri=R3It is yellow early warning risk factor when < 0.5；As 0.1≤Ri=R4When < 0.25, for blue early warning risk system
Number.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811484489.9A CN109978560A (en) | 2018-12-06 | 2018-12-06 | E-commerce product quality safety Risk Monitoring and method for early warning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811484489.9A CN109978560A (en) | 2018-12-06 | 2018-12-06 | E-commerce product quality safety Risk Monitoring and method for early warning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109978560A true CN109978560A (en) | 2019-07-05 |
Family
ID=67076152
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811484489.9A Pending CN109978560A (en) | 2018-12-06 | 2018-12-06 | E-commerce product quality safety Risk Monitoring and method for early warning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109978560A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111679641A (en) * | 2020-06-10 | 2020-09-18 | 重庆中烟工业有限责任公司涪陵卷烟厂 | Method for controlling moisture of finished cigarette |
| CN113627699A (en) * | 2020-05-07 | 2021-11-09 | 中国移动通信集团山西有限公司 | Information early warning method, device, equipment and storage medium |
| CN113837515A (en) * | 2020-10-13 | 2021-12-24 | 常州铭赛机器人科技股份有限公司 | Online mounting process capacity determination and evaluation method and device |
| CN116090910A (en) * | 2023-04-10 | 2023-05-09 | 鸣启数字科技(山东)有限公司 | Lean sample data alarm method and system |
| CN117035563A (en) * | 2023-10-10 | 2023-11-10 | 河北省产品质量监督检验研究院 | Product quality safety risk monitoring method, device, monitoring system and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521675A (en) * | 2011-12-02 | 2012-06-27 | 吴福平 | Statistics and measurement method for dynamic quality control early warning coefficient and application thereof |
| CN103632232A (en) * | 2013-12-04 | 2014-03-12 | 华为技术有限公司 | Method and equipment for detecting products |
| CN105676817A (en) * | 2016-01-14 | 2016-06-15 | 西安电子科技大学 | Statistical process control method of mean-standard deviation control charts of samples of different sizes |
| CN106022668A (en) * | 2016-08-04 | 2016-10-12 | 石正国 | Risk assessment system for confirmation and verification |
-
2018
- 2018-12-06 CN CN201811484489.9A patent/CN109978560A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102521675A (en) * | 2011-12-02 | 2012-06-27 | 吴福平 | Statistics and measurement method for dynamic quality control early warning coefficient and application thereof |
| CN103632232A (en) * | 2013-12-04 | 2014-03-12 | 华为技术有限公司 | Method and equipment for detecting products |
| CN105676817A (en) * | 2016-01-14 | 2016-06-15 | 西安电子科技大学 | Statistical process control method of mean-standard deviation control charts of samples of different sizes |
| CN106022668A (en) * | 2016-08-04 | 2016-10-12 | 石正国 | Risk assessment system for confirmation and verification |
Non-Patent Citations (2)
| Title |
|---|
| 张春华 等: "《数理统计方法》", 31 August 1992, 济南：山东大学出版社 * |
| 马燕 等: "《航海技术与航海教育论文集 2007》", 31 May 2008, 大连：大连海事大学出版社 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113627699A (en) * | 2020-05-07 | 2021-11-09 | 中国移动通信集团山西有限公司 | Information early warning method, device, equipment and storage medium |
| CN111679641A (en) * | 2020-06-10 | 2020-09-18 | 重庆中烟工业有限责任公司涪陵卷烟厂 | Method for controlling moisture of finished cigarette |
| CN113837515A (en) * | 2020-10-13 | 2021-12-24 | 常州铭赛机器人科技股份有限公司 | Online mounting process capacity determination and evaluation method and device |
| CN116090910A (en) * | 2023-04-10 | 2023-05-09 | 鸣启数字科技(山东)有限公司 | Lean sample data alarm method and system |
| CN117035563A (en) * | 2023-10-10 | 2023-11-10 | 河北省产品质量监督检验研究院 | Product quality safety risk monitoring method, device, monitoring system and medium |
| CN117035563B (en) * | 2023-10-10 | 2023-12-26 | 河北省产品质量监督检验研究院 | Product quality safety risk monitoring method, device, monitoring system and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109978560A (en) | E-commerce product quality safety Risk Monitoring and method for early warning | |
| CN109583680B (en) | Power stealing identification method based on support vector machine | |
| CN107220906A (en) | Multiple Time Scales multiplexing electric abnormality analysis method based on electricity consumption acquisition system | |
| CN108629525A (en) | It is a kind of to consider that Severity method temporarily drops in the node voltage of load significance level | |
| US10746768B2 (en) | System and method for detecting theft of electricity | |
| US10598702B2 (en) | Similarity detection of abnormal waveforms using PMU measurement | |
| CN102855588B (en) | Method for detection of transaction data, device and server | |
| CN103971182B (en) | The online risk monitoring method of a kind of distribution feeder | |
| CN110889441B (en) | Power transformation equipment data anomaly identification method based on distance and point density | |
| CN108734602B (en) | User electricity stealing behavior identification system based on high-dimensional random matrix | |
| CN106776480B (en) | A kind of elimination method of radio interference in-site measurement exceptional value | |
| CN103488800A (en) | SVM (Support Vector Machine)-based power consumption abnormality detection method | |
| CN109697574A (en) | Small customer electricity Risk Identification Method in a kind of electric power | |
| CN109542742A (en) | Database server hardware health evaluating method based on expert model | |
| CN102945222A (en) | Poor information measurement data gross error discrimination method based on Grey System Theory | |
| CN103389427B (en) | GIS equipment operational condition online test method and system | |
| CN110232061A (en) | A kind of power distribution network multi-source data method of quality control | |
| CN103400308B (en) | GIS equipment operational condition online test method and system | |
| Liu et al. | Data-driven condition monitoring of data acquisition for consumers’ transformers in actual distribution systems using t-statistics | |
| Wu et al. | Analysis of electricity stealing based on user electricity characteristics of electricity information collection system | |
| CN112015724A (en) | Method for analyzing metering abnormality of electric power operation data | |
| CN106709623B (en) | Power grid marketing inspection risk control method based on risk calculation model | |
| CN105823964B (en) | Power transmission line comprehensive Fault Locating Method towards intelligent substation | |
| CN108845285A (en) | Electric energy metering device detection method and system | |
| Chen et al. | An ARL-unbiased approach to setting control limits of CCC-r chart for high-yield processes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190705 |