CN104766215B - A kind of comprehensive, various dimensions owner of cargo selects quantization method - Google Patents
A kind of comprehensive, various dimensions owner of cargo selects quantization method Download PDFInfo
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Abstract
The present invention discloses a kind of comprehensive, various dimensions owner of cargo and selects quantization method, and objective, comprehensive project evaluation chain is carried out to the owner of cargo from multiple dimensions suitable for member.The invention takes into full account that the owner of cargo loads and unloads goods on time, acknowledgement of consignment business number, number is complained by platform member, payment for goods payment and the promptness rate of information issue, once offer into multiple influence selected metric indexs such as single rate, the index weights division methods of Multiple Attribute Decision Model based on combination least square method and ELECTRE II methods quantitatively determine each index weights, the e-commerce platform preferably handled using logistics service as trading object by accurate big data mining analysis and model algorithm rationally evaluates owner of cargo's reliability disadvantages, platform member is can interpolate that the comprehensive condition of the owner of cargo to be selected and make the selection for meeting oneself demand.
Description
Technical field
The present invention relates to owner of cargo side's credibility quantization in a kind of logistics e-commerce platform and overall evaluation system, fit
Quantify for carrying out objective, comprehensive assessment to the owner of cargo from multiple dimensions, belong to data analysis technique field.
Background technology
The continuous development of information technology, ecommerce has been promoted to be combined with the depth of logistic industry, however it is a kind of creating
While brand-new business model, the transaction of this non-face-to-face also brings some potential risks.In the differentiation to risk, detect,
During management, establish a credit security mechanism, allow user have to the credibility of the owner of cargo, platform member etc. one it is comprehensive
Close, select the suitable owner of cargo, be just particularly important so as to save cost of transportation and improve logistic efficiency.
Existing logistics e-commerce platform indiscriminately imitates the credit assessment method of commodity electron-like business platform, evaluation index mostly
The foundation of system is also relatively unsound, can not it is objective, fully reflect the real load level of the owner of cargo, logistics e-commerce platform
Other users can not know true, reliable owner of cargo's load situation.
The content of the invention
Goal of the invention：It is flat in transaction for problems and shortcomings present in existing logistics e-commerce platform appraisement system
In all history evaluation and transaction record data basis in platform, the present invention provides a kind of based on combination least square method and more category
Property decision model owner of cargo side load level credibility quantify and overall merit system of selection.
Technical scheme：A kind of comprehensive, various dimensions owner of cargo selects quantization method, suitable for providing physical distribution trading service
Quantitatively Selecting is carried out to the owner of cargo in e-commerce platform.Specifically comprise the following steps：
(1) data analysis chooses confidence level and relatively comments scheme attribute, structural scheme collection and property set
The confidence level of reference database and industry selecting index this platform needs relatively comments scheme attribute, is relatively commented according to confidence level
Correlation between scheme and each attribute, establish scheme collection and property set.Confidence level relatively comments scheme collection：It is average credible in the same industry
Degree, commented owner of cargo's confidence level, platform datum confidence level.Property set：Consignor freights on time, consignee unloads on time, goods for selection
Main acknowledgement of consignment business number, by platform member complain number, information issue promptness rate, owner of cargo's payment for goods pay promptness rate, once offer
Attribute into single seven evaluation indexes of rate as influence scheme.
(2) property set standardization
According to attribute to confidence level compared with from the point of view of the influence for commenting scheme, the profitable type of attribute and the class of cost type two of scheme.Effect
Beneficial its property value of type attribute is the bigger the better, conversely, its property value of cost type attribute is the smaller the better.And the dimension of each scheme attribute
It is also different with dimensional unit, it is therefore necessary to which that nondimensionalization processing, profit evaluation model attribute and cost type are carried out to each attribute of scheme
The nondimensionalization processing of attribute is as follows：
The processing of profit evaluation model attribute：bij=(aij-aj min)/(aj max-aj min)
The processing of cost type attribute：bij=(aj max-aij)/(aj max-aj min)
Wherein, aijIt is the property value of scheme i j-th of attribute, bijIt is aijValue after standardization, aj maxIt is j-th
Attribute PjMaximum, aj minIt is PjMinimum value.bij∈ (0,1), i=1,2 ..., m, j=1,2 ..., n, normalized matrix
B=(bij)m×n。
(3) multidimensional analysis is carried out, builds the polynary Optimized model of attribute weight
The reasonability of validity and information processing in view of feedback of the information, introduces polynary Optimized model herein, from acknowledgement of consignment
Two different dimensions of people member and platform, their Different Effects to owner of cargo's confidence level of comprehensive consideration, and analyzed in big data
On the basis of to they carry out mathematics portray.Carrier member and platform are different therefore credible to the owner of cargo from the contact point of the owner of cargo
The emphasis of degree shows different attribute evaluation values.
1. polynary weight determines
In order to reduce the influence of single preference and cognition limitation to result, platform is by all carrier members to each finger of the owner of cargo
The marking of target history evaluation is weighted averagely, and drawing carrier member, most intuitively overall merit is given a mark to the owner of cargo；In order to subtract
The deviation that small single dimension evaluation is brought, introduces platform decision-making member from another dimension herein --- based on platform historical trading number
Supplementary evaluation marking is carried out to owner of cargo's confidence level according to trading activity.Then marking is standardized to obtain each attribute
Weighted value, it is that two decision-making members set significance level coefficient to be finally based on the integrality of data and authenticity.
The decision-making member T that two dimensions determinek, k=1,2；Wherein T1Represent carrier member's decision-making member, T2Platform is represented to determine
Plan member.
The attribute weight of two decision-making member assignment is：wk=(w1 k,w2 k,...,wn k)T, k=1,2
The significance level of each decision-making member is：Z=(z1,z2)T, wherein, z1+z2=1, zk≥0。
2. build unitary weight optimization model
The subjective factor that may be carried to owner of cargo's evaluation in view of carrier member, determines method angle from subjective weight herein
Degree, comprehensive big data digging technology, structure attribute weight Optimized model are as follows：
Wherein N=1,2 ..., and n } it is property set.
Model, which is meant that, tries to achieve a wjSo that wjAnd wj kTotal partial variance quadratic sum L1It is minimum.
3. build binary weight optimization model
In order to reduce human factor in model as far as possible, method angle is determined from objective weight herein, multidimensional point is carried out to data
Analysis, structure attribute weight Optimized model are as follows：
G-minL=(l1,l2,...lm)
Wherein,aj *=max { a1j,a2j,...amjIt is attribute PjIdeal value, with etc. power line
Property weighting method, can be by model simplification：
For wherein M={ 1,2 ..., m } relatively to comment scheme collection, N={ 1,2 ..., n } is the property set of scheme.
4. synthesize single object optimization model
Two above Optimized model is carried out to be integrated into G=min (L1,L2), and by linear weighting method, problem is turned
Turn to following single object optimization model：
Wherein, for M={ 1,2 ..., m } relatively to comment scheme collection, N={ 1,2 ..., n } is the property set of scheme.Pass through structure
Lagrangian, it can obtain：
Wherein,
(4) attribute weight is solved
Due to multiple decision-makings member (Tk:K=1,2) it is to attribute j comprehensive weight imparting value：
For scheme attribute.
The weight vectors of the polynary Optimized model of attribute weight are：w*=[w1,w2,...wj,...,wn], wherein
Property set final weight matrix is A*=(Aij)m×n=Bw*T
Wherein, AijFor final weight value of the confidence level compared with j-th of attribute for commenting scheme i, B=(bij)m×nFor property set mark
Standardization matrix.
(5) calculate and relatively comment draft norm confidence score, record the when m- confidence score curve of transaction count
It is A according to gained property set final weight matrix*=(Aij)m×n=Bw*T, calculate and relatively comment draft norm confidence level
Score, and draw the when m- confidence score curve based on transaction count.Main calculation procedure is as follows：
1. calculate confidence level is compared with the confidence score for commenting scheme i, formula
Wherein, bijIt is the property value a of scheme i j-th of attributeijValue after standardization,To build j-th of the attribute of each scheme drawn after the polynary Optimized model of attribute weight
Weight.
Scheme confidence score is relatively commented 2. standardizing
Confidence level relatively comments the scheme collection to be：(average credibility, is commented owner of cargo's confidence level, platform datum is credible in the same industry
Degree), so relatively commenting the scheme confidence score collection to be：
M=(M1,M2,M3)=(MAverage credibility in the same industry, MCommented owner of cargo's confidence level, MPlatform datum confidence level)
Wherein, average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datum confidence levels
With platform datum confidence level MPlatform datum confidence levelIt is for standardized benchmark confidence level point, then each credible compared with the standardization of scheme is commented
Spend score mi(i=1,2,3,
Average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datums confidence level) be：
3. calculate and relative normalized relatively comment scheme confidence score
With standardized platform benchmark confidence level mPlatform datum confidence levelBenchmark letter is judged for relative score, then respectively relatively comments the relative of scheme
Standardize confidence score
mi *(i=1,2,3,
Average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datums confidence level) be：
m1 *=m1-m3
m2 *=m2-m3
m3 *=m3-m3=0
m*=(m1 *,m2 *,m3 *) it is required respectively relatively to comment scheme final confidence score.
Wherein, average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datum confidence levels.
4. draw the when m- confidence score curve based on transaction count
Because confidence score can update with the renewal of platform owner of cargo's transaction count, it can be seen from above-mentioned result of calculation
Platform datum confidence score is that 0 holding is constant, has practical significance；Average credibility can be with different industries in the same industry
The transaction data of (or whole platform) is accumulated and constantly updated；Owner of cargo's confidence level is commented as the owner of cargo is traded time in platform
Several increase and change.Therefore, using the time as transverse axis, confidence score is the longitudinal axis, it can be deduced that based on transaction count when it is m-
Confidence score curve, the owner of cargo that (or whole platform) in the same industry in a period of time can be intuitively obtained very much according to curve can
Reliability situation and the confidence level change conditions of some specific owner of cargo, preferably can be controlled risk and selected with helpdesk member
The owner of cargo carries out order.
(6) relative score for relatively commenting harmonious sex index and each attribute between scheme is calculated
Platform is in order to meet the risk partiality of different platform member or selection preference, by calculating harmonious sex index and each category
The relative normalized score of property, more diversified and more humane selection is provided for different platform member.Main calculation procedure is such as
Under：
1. calculate harmonious sex index
Harmonious sex index JikThe attribute weight sum for referring to relatively comment scheme i not to be inferior to scheme k is in the summation of all weights
Shared ratio, according to attribute j (j=1,2 ..., n), if scheme i better than scheme k (i=1,2 ..., m, k=1,
2 ..., m, i ≠ k), i ＞ k are designated as, so as to which all attribute j for meeting i ＞ k set is designated as into U+(i, k), it can equally obtain
To U=(i, j), it is specific as follows：
U+(i, k)=j 1≤j≤n, Aij＞ Aik}
U=(i, k)=j 1≤j≤n, Aij=Aik}
Then concordance exponential formula is：
2. calculate the relative score of each attribute of the owner of cargo
It is A according to gained property set final weight matrix*=(Aij)m×n=Bw*T, the relative normalized of each attribute of the owner of cargo must
It is divided into a2j *=A2j-A3j, wherein, j=1,2 ..., n
The relative standard for finally obtaining each attribute of the owner of cargo obtains diversity a2 *=(a21 *,a22 *,...a2n *)。
Beneficial effect：Compared with prior art, the present invention is suitable for the e-commerce platform for providing physical distribution trading service
Trust evaluation is carried out to the owner of cargo.Its feature is to connect the numerous attributes for influenceing owner of cargo's confidence level by being divided into each other
Orderly level, is allowed to methodization, according to carrier member and the mass data of platform, sets platform datum confidence level, will be a certain
The average credibility of industry (or whole platform) and the confidence level of some owner of cargo carry out therewith contrast draw it is relative normalized credible
Score is spent, result is presented more directly perceived.Compared with the confidence level attribute of the owner of cargo is carried out two-by-two with platform datum attribute score, so
Its each attribute is quantitatively described afterwards, being easy to platform member, preferably selection and risk are held.According to evaluation result, Neng Gouke
The synthetic reliability situation that the owner of cargo is known on ground is seen, some information asymmetries between customer and businessman is solved to a certain extent and asks
Topic, platform member or other members quickly, is objectively found the businessman of high credit in countless businessmans and is made and meet certainly
The selection of own demand.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 relatively comments scheme and its attribute model for the embodiment of the present invention；
Fig. 3 is the when m- confidence score curve based on transaction count.
Embodiment
With reference to specific embodiment, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate the present invention
Rather than limitation the scope of the present invention, after the present invention has been read, various equivalences of the those skilled in the art to the present invention
The modification of form falls within the application appended claims limited range.
(1) data analysis chooses confidence level and relatively comments scheme attribute, structural scheme collection and property set
The confidence level of reference database and industry selecting index this platform needs relatively comments scheme attribute, is relatively commented according to confidence level
Correlation between scheme and each attribute, establish scheme collection and property set, such as Fig. 2.Confidence level relatively comments scheme collection：Put down in the same industry
Equal confidence level, commented owner of cargo's confidence level, platform datum confidence level.Property set：Consignor freights on time, consignee unloads on time for selection
Goods, the owner of cargo carry business number, complain number, information to issue promptness rate by platform member, owner of cargo's payment for goods pays promptness rate, once
Offer into attribute of single seven evaluation indexes of rate as influence scheme.The present embodiment relatively comments scheme and the evaluation situation of each attribute
It is shown in Table 1.
The embodiment confidence level of table 1 relatively comments scheme attribute evaluation table
(2) property set standardization
According to attribute to confidence level compared with from the point of view of the influence for commenting scheme, the profitable type of attribute and the class of cost type two of scheme.Effect
Beneficial its property value of type attribute is the bigger the better, conversely, its property value of cost type attribute is the smaller the better.And the dimension of each scheme attribute
It is also different with dimensional unit, it is therefore necessary to which that nondimensionalization processing, profit evaluation model attribute and cost type are carried out to each attribute of scheme
The nondimensionalization processing of attribute is as follows：
The processing of profit evaluation model attribute：bij=(aij-aj min)/(aj max-aj min)
The processing of cost type attribute：bij=(aj max-aij)/(aj max-aj min)
Wherein, aijIt is the property value of scheme i j-th of attribute, bijIt is aijValue after standardization, aj maxIt is j-th
Attribute PjMaximum, aj minIt is PjMinimum value.bij∈ (0,1), i=1,2 ..., m, j=1,2 ..., n, normalized matrix
B=(bij)m×n。
It is as follows compared with scheme attribute decision matrix is commented that confidence level is obtained according to table 1：
Wherein, the owner of cargo carries business number, information issues promptness rate, owner of cargo's payment for goods pays promptness rate, once offers into list
Rate is profit evaluation model attribute, and evaluation of estimate is the bigger the better；And consignor freights on time, consignee unloads on time, is complained by platform member
Number is then cost type attribute, and evaluation of estimate is the smaller the better.After being standardized to matrix A, obtained normalized matrix is：
(3) multidimensional analysis is carried out, builds the polynary Optimized model of attribute weight
The reasonability of validity and information processing in view of feedback of the information, introduces polynary Optimized model herein, from acknowledgement of consignment
Two different dimensions of people member and platform, their Different Effects to owner of cargo's confidence level of comprehensive consideration, and analyzed in big data
On the basis of to they carry out mathematics portray.Carrier member and platform are different therefore credible to the owner of cargo from the contact point of the owner of cargo
The emphasis of degree shows different attribute evaluation values.
Determine polynary weight
In order to reduce the influence of single preference and cognition limitation to result, the sea that platform is evaluated the owner of cargo carrier member
Measure big data and carry out depth excavation and analysis, draw carrier member to the owner of cargo's most intuitively overall merit；It is single in order to reduce
The deviation that dimension evaluation is brought, introduce platform decision-making member from another dimension herein --- based on platform historical trading data and friendship
Easy is to carry out supplementary evaluation to owner of cargo's confidence level.And to be that two decision-makings members are set reasonable for integrality based on data and authenticity
Significance level coefficient.
This example is weighted averagely to the marking of carrier member and platform to each attribute, is then standardized
Arrive：
The attribute weight of carrier member's decision-making member assignment is：w1=(0.53,0.13,0.1,0.6,0.1,0.64,
0.64)T
The attribute weight of platform decision-making member assignment is：w2=(0.2,0.2,0.4,0.75,0.15,0.9,0.4)T
The significance level of each decision-making member is：Z=(0.45,0.55)T。
(4) attribute weight is solved
In order that attribute weight both includes subjective preferences, contain objective information again, can be by subjective weight optimization model and visitor
Weight optimization model is seen to carry out being integrated into G=min (L1,L2), and by linear weighting method, problem is converted into following list
Objective optimization model：
Wherein, for M={ 1,2 ..., m } relatively to comment scheme collection, N={ 1,2 ..., n } is the property set of scheme.
By building Lagrangian, can obtain：
C=(0.89,0.89,0.33,0.67,0.4,0.22,0.1)T
D=(0.15,0.1,0.11,0.31,0.06,0.38,0.25)T
According to formula, then it can obtain while reflect carrier member and the weight vectors of platform information are：
w*=(0.23,0.19,0.07,0.28,0.07,0.11,0.04)T
(5) calculate and relatively comment draft norm confidence score, record the when m- confidence score curve of transaction count
It is A according to gained property set final weight matrix*=(Aij)m×n=Bw*T, calculate and relatively comment draft norm confidence level
Score, and draw the when m- confidence score curve based on transaction count.Main calculation procedure is as follows：
Calculate confidence level is compared with the confidence score for commenting scheme i, formula
Confidence level relatively comments the scheme collection to be：(average credibility, is commented owner of cargo's confidence level, platform datum is credible in the same industry
Degree), so relatively commenting the scheme confidence score collection to be：
M=(M1,M2,M3)=(MAverage credibility in the same industry, MCommented owner of cargo's confidence level, MPlatform datum confidence level)
Wherein, average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datum confidence levels
Tried to achieve according to formula：
M=(M1,M2,M3)=(MAverage credibility in the same industry, MCommented owner of cargo's confidence level, MPlatform datum confidence level)=(0.5,0.75,0.28)
Scheme confidence score is relatively commented in standardization
With platform datum confidence level MPlatform datum confidence levelIt is for standardized benchmark confidence level point, then each credible compared with the standardization of scheme is commented
Spend score mi(i=1,2,3,
Average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datums confidence level) be：
Institute is in the hope of m=(1.76,2.64,1)
Calculate and relative normalized relatively comment scheme confidence score
With standardized platform benchmark confidence level mPlatform datum confidence levelBenchmark letter is judged for relative score, then respectively relatively comments the relative of scheme
Standardize confidence score
mi *(i=1,2,3,
Average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datums confidence level) be：
m1 *=m1-m3
m2 *=m2-m3
m3 *=m3-m3=0
Obtain m*=(m1 *,m2 *,m3 *)=(0.76,1.64,0) it is required respectively relatively to comment scheme final confidence score.Its
In, average credibility in the 1=same industries, 2=is commented owner of cargo's confidence level, 3=platform datum confidence levels.
As shown in figure 3, draw the when m- confidence score curve based on transaction count
Because confidence score can update with the renewal of platform owner of cargo's transaction count, it can be seen from above-mentioned result of calculation
Platform datum confidence score is that 0 holding is constant, has practical significance；Average credibility can be with different industries in the same industry
The transaction data of (or whole platform) is accumulated and constantly updated；Owner of cargo's confidence level is commented as the owner of cargo is traded time in platform
Several increase and change.Therefore, using the time as transverse axis, confidence score is the longitudinal axis, it can be deduced that based on transaction count when it is m-
Confidence score curve, the owner of cargo that (or whole platform) in the same industry in a period of time can be intuitively obtained very much according to curve can
Reliability situation and the confidence level change conditions of some specific owner of cargo, preferably can be controlled risk and selected with helpdesk member
The owner of cargo carries out order.
(6) relative score of each attribute of the owner of cargo is calculated
It is A according to gained property set final weight matrix*=(Aij)m×n=Bw*T, the relative normalized of each attribute of the owner of cargo must
It is divided into a2j *=A2j-A3j, wherein, j=1,2 ..., 7,1=consignor freight on time, 2=consignee unloads on time, the 3=owner of cargo
Acknowledgement of consignment business number, 4=complain number, 5=information issue promptness rate, 6=owner of cargo's payment for goods to pay promptness rate, 7 by platform member
=once offer into single rate.
The relative standard for finally obtaining each attribute of the owner of cargo is scored at：
a2 *=(a21 *,a22 *,...a2n *)=(0.2,0.1,0.04,0,0.07,0,0.04)
Wherein, a24 *=0, a26 *=0 shows to be commented owner of cargo's " complaining number by platform member " and " owner of cargo's payment for goods is paid in time
The two attribute scores of rate " are identical with platform datum score, herein for 0 defend oneself it is bright relative to the datum-plane owner of cargo in this attribute
On perform poor, just just pass, platform member in selection should it is noted that this owner of cargo exist in terms of the two it is higher credible
Risk is spent, should be careful.
Claims (2)
1. a kind of comprehensive, various dimensions owner of cargo selects quantization method, it is characterised in that specifically comprises the following steps：
(1) data analysis chooses confidence level and relatively comments scheme attribute, structural scheme collection and property set
(2) property set standardization
Nondimensionalization processing is carried out to each attribute of scheme, the nondimensionalization processing of profit evaluation model attribute and cost type attribute is as follows：
The processing of profit evaluation model attribute：bij=(aij-aj min)/(aj max-aj min)
The processing of cost type attribute：bij=(aj max-aij)/(aj max-aj min)
Wherein, aijIt is the property value of scheme i j-th of attribute, bijIt is aijValue after standardization, aj maxIt is j-th of attribute
PjMaximum, aj minIt is PjMinimum value；bij∈ (0,1), i=1,2 ..., m, j=1,2 ..., n, normalized matrix B=
(bij)m×n；
(3) multidimensional analysis is carried out, builds the polynary Optimized model of attribute weight
Polynary Optimized model is introduced, from two different dimensions of carrier member and platform, they are credible to the owner of cargo for comprehensive consideration
The Different Effects of degree, and mathematics is carried out to them on the basis of big data analysis and portrayed；Carrier member and platform and the owner of cargo
Contact point it is different, therefore different attribute evaluation values is shown to the emphasis of owner of cargo's confidence level；
(4) attribute weight is solved
Because multiple decision-making members are to attribute j comprehensive weight imparting value：
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The weight vectors of the polynary Optimized model of attribute weight are：w*=[w1,w2,...wj,...,wn], wherein
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Property set final weight matrix is A*=(Aij)m×n=Bw*T
Wherein, AijFor final weight value of the confidence level compared with j-th of attribute for commenting scheme i, B=(bij)m×nStandardized for property set
Matrix；
(5) calculate and relatively comment draft norm confidence score, record the when m- confidence score curve of transaction count
It is A according to gained property set final weight matrix*=(Aij)m×n=Bw*T, calculate and relatively comment draft norm confidence score,
And draw the when m- confidence score curve based on transaction count；
(6) relative score for relatively commenting harmonious sex index and each attribute between scheme is calculated
Platform is in order to meet the risk partiality of different platform member or selection preference, by calculating harmonious sex index and each attribute
Relative normalized score, more diversified and more humane selection is provided for different platform member；
Step 3 concretely comprises the following steps：
1. polynary weight determines
In order to reduce the influence of single preference and cognition limitation to result, platform is by all carrier members to each index of the owner of cargo
History evaluation marking is weighted averagely, and drawing carrier member, most intuitively overall merit is given a mark to the owner of cargo；In order to reduce list
The deviation brought of dimension evaluation, introduces platform decision-making member from another dimension herein --- based on platform historical trading data and
Trading activity carries out supplementary evaluation marking to owner of cargo's confidence level；Then marking is standardized to obtain the weight of each attribute
Value, it is that two decision-making members set significance level coefficient to be finally based on the integrality of data and authenticity；
The decision-making member T that two dimensions determinek, k=1,2；Wherein T1Represent carrier member's decision-making member, T2Represent platform decision-making member；
The attribute weight of two decision-making member assignment is：wk=(w1 k,w2 k,...,wn k)T, k=1,2
The significance level of each decision-making member is：Z=(z1,z2)T, wherein, z1+z2=1, zk≥0；
2. build unitary weight optimization model
The subjective factor that may be carried to owner of cargo's evaluation in view of carrier member, determines method angle from subjective weight herein, comprehensive
Big data digging technology is closed, structure attribute weight Optimized model is as follows：
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Wherein N={ 1,2 ..., n } is the property set of scheme
Model, which is meant that, tries to achieve a wjSo that wjAnd wj kTotal partial variance quadratic sum L1It is minimum；
3. build binary weight optimization model
Method angle is determined from objective weight herein, data are carried out with multidimensional analysis, structure attribute weight Optimized model is as follows：
G-minL=(l1,l2,...lm)
Wherein,aj *=max { a1j,a2j,...amjIt is attribute PjIdeal value, with etc. power linearly add
Quan Fa, can be by model simplification：
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For wherein M={ 1,2 ..., m } relatively to comment scheme collection, N={ 1,2 ..., n } is the property set of scheme
4. synthesize single object optimization model
Two above Optimized model is carried out to be integrated into G=min (L1,L2), and by linear weighting method, problem is converted into
Following single object optimization model：
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Wherein, for M={ 1,2 ..., m } relatively to comment scheme collection, N={ 1,2 ..., n } is the property set of scheme；By building glug
Bright day function, can be obtained：
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Wherein,
The main calculation procedure of step (5) is as follows：
1. calculate confidence level is compared with the confidence score for commenting scheme i, formula
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Wherein, bijIt is the property value a of scheme i j-th of attributeijValue after standardization,
To build the weight of j-th of the attribute of each scheme drawn after the polynary Optimized model of attribute weight；
Scheme confidence score is relatively commented 2. standardizing
Confidence level relatively comments the scheme collection to be：Average credibility in the same industry, is commented owner of cargo's confidence level, platform datum confidence level, so
The scheme confidence score collection is relatively commented to be：
M=(M1,M2,M3)=(MAverage credibility in the same industry, MCommented owner of cargo's confidence level, MPlatform datum confidence level)
Wherein, average credibility in the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datum confidence levels
With platform datum confidence level MPlatform datum confidence levelFor standardized benchmark confidence level point, then respectively the standardization confidence level of scheme is relatively commented to obtain
Divide miFor：
I=1, average credibility in 2,3, the 1=same industries, 2=are commented owner of cargo's confidence level, 3=platform datum confidence levels；
3. calculate and relative normalized relatively comment scheme confidence score
With standardized platform benchmark confidence level mPlatform datum confidence levelBenchmark letter is judged for relative score, then each relative standard for relatively commenting scheme
Change confidence score
mi *For：
m1 *=m1-m3
m2 *=m2-m3
m3 *=m3-m3=0
m*=(m1 *,m2 *,m3 *) it is required respectively relatively to comment scheme final confidence score；
4. draw the when m- confidence score curve based on transaction count
Because confidence score can update with the renewal of platform owner of cargo's transaction count, platform is understood according to above-mentioned result of calculation
Benchmark confidence score is that 0 holding is constant, has practical significance；Average credibility can be with different industries or whole in the same industry
The transaction data of platform is accumulated and constantly updated；The increase for being commented owner of cargo's confidence level to be traded in platform with the owner of cargo number
And change；Therefore, using the time as transverse axis, confidence score is the longitudinal axis, it can be deduced that the when m- confidence level based on transaction count obtains
Component curve, can intuitively be obtained very much in a period of time in of the same trade according to curve or entirely owner of cargo's confidence level situation of platform with
And the confidence level change conditions of some specific owner of cargo, it can preferably be controlled risk with helpdesk member and select the owner of cargo to be connect
It is single.
2. comprehensive, various dimensions the owner of cargo as claimed in claim 1 selects quantization method, it is characterised in that step (6) is main
Calculation procedure is as follows：
1. calculate harmonious sex index
Harmonious sex index JikThe attribute weight sum for referring to relatively comment scheme i not to be inferior to scheme k is shared in the summation of all weights
Ratio, according to attribute j, j=1,2 ..., n, if scheme i is better than scheme k, i=1,2 ..., m, k=1,2 ..., m, i
≠ k, i ＞ k are designated as, so as to which all attribute j for meeting i ＞ k set is designated as into U+(i, k), it can equally obtain U=(i, k),
It is specific as follows：
U+(i, k)=j 1≤j≤n, Aij＞ Aik}
U=(i, k)=j 1≤j≤n, Aij=Aik}
Then concordance exponential formula is：
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2. calculate the relative score of each attribute of the owner of cargo
It is A according to gained property set final weight matrix*=(Aij)m×n=Bw*T, relative normalized being scored at of each attribute of the owner of cargo
a2j *=A2j-A3j, wherein, j=1,2 ..., n are scheme attribute；
The relative standard for finally obtaining each attribute of the owner of cargo obtains diversity a2 *=(a21 *,a22 *,...a2n *)。
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| CN106327138A (en) * | 2016-11-07 | 2017-01-11 | 南京大学 | Ship carrier aptitude evaluation method based on big data analysis |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7650315B2 (en) * | 2003-07-31 | 2010-01-19 | Swiss Reinsurance Company | Transaction server and computer programme product |
| CN101840534A (en) * | 2010-01-22 | 2010-09-22 | 同济大学 | Integrated supply chain performance index evaluation method |
| CN102208072A (en) * | 2010-03-31 | 2011-10-05 | 南京为真网络技术有限公司 | Digital goods transaction method and system for e-commerce platform |
| CN104463380A (en) * | 2014-12-31 | 2015-03-25 | 国家电网公司 | Energy source electricity export willingness analyzing method and equipment |
-
2015
- 2015-04-13 CN CN201510173407.9A patent/CN104766215B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7650315B2 (en) * | 2003-07-31 | 2010-01-19 | Swiss Reinsurance Company | Transaction server and computer programme product |
| CN101840534A (en) * | 2010-01-22 | 2010-09-22 | 同济大学 | Integrated supply chain performance index evaluation method |
| CN102208072A (en) * | 2010-03-31 | 2011-10-05 | 南京为真网络技术有限公司 | Digital goods transaction method and system for e-commerce platform |
| CN104463380A (en) * | 2014-12-31 | 2015-03-25 | 国家电网公司 | Energy source electricity export willingness analyzing method and equipment |
Non-Patent Citations (3)
| Title |
|---|
| 基于FQFD的电子商务顾客满意度测评研究;吴星星;《中国优秀硕士学位论文全文数据库 经济与管理科学辑》;20131215;J152-931，第25-32页 * |
| 基于行为分析的货物运输方式选择模型研究;罗俊;《中国博士学位论文全文数据库 经济与管理科学辑》;20121115;J151-1，第59-60，66-68页 * |
| 多元价值观下的公共项目规划方案选择研究;徐祎琳;《中国优秀硕士学位论文全文数据库 经济与管理科学辑》;20100515;J146-4，第78-98页 * |
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