CN109118323A - The credit computing method of shared food provider - Google Patents
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- CN109118323A CN109118323A CN201810851645.4A CN201810851645A CN109118323A CN 109118323 A CN109118323 A CN 109118323A CN 201810851645 A CN201810851645 A CN 201810851645A CN 109118323 A CN109118323 A CN 109118323A
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- 235000013305 food Nutrition 0.000 title claims abstract description 32
- 238000004364 calculation method Methods 0.000 title claims abstract description 13
- 230000006378 damage Effects 0.000 claims abstract description 12
- 208000027418 Wounds and injury Diseases 0.000 claims abstract description 7
- 208000014674 injury Diseases 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 5
- 239000003795 chemical substances by application Substances 0.000 claims description 3
- 235000002864 food coloring agent Nutrition 0.000 claims description 3
- 239000004576 sand Substances 0.000 claims description 3
- 238000000926 separation method Methods 0.000 claims description 3
- 239000013056 hazardous product Substances 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 12
- 230000008901 benefit Effects 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 241000854350 Enicospilus group Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
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Abstract
The present invention is the credit computing method of shared food provider, the calculating of prestige is divided into individual and group's prestige, and credit value is investigated in terms of four, four aspects include public reputation, ecological environment prestige, social networks prestige and food coefficient of injury, each aspect are determined by specific prestige parameter again, finally thus prestige computation model obtains the provider in edible item shared platform and the credit value of reciever, guarantees shared safety and reliable.
Description
Technical field
The present invention is the credit computing method of shared food provider, and the calculating of prestige is divided into individual and group's prestige,
And credit value is investigated in terms of four, four aspects include public reputation, ecological environment prestige, social networks prestige and food danger
Evil coefficient, each aspect determine that finally thus prestige computation model is obtained in edible item by specific prestige parameter again
The credit value of provider and reciever in shared platform guarantee shared safety and reliable.Belong to community service and software work
Journey crossing domain.
Background technique
With the rapid development of the economy and culture of society, shared economy is appeared in the visual field of people, shares economy one
As refer to obtain definite remuneration as the main purpose, the new warp of the one kind temporarily shifted based on stranger and there are the article right to use
Ji mode, essence are to integrate unused article under line, labour, education and medical care resource, nowadays share edible article also gradually
Occur, the safety reliability using article that the focus of shared edible article is to provide.
This patent proposes the prestige calculating side of shared food provider for shared edible article platform security reliability
The calculating of prestige is divided into individual and group's prestige, and investigates credit value in terms of four by method, and four aspects include public letter
It praises, ecological environment prestige, social networks prestige and food coefficient of injury, each aspect is determined by specific prestige parameter again,
Finally thus prestige computation model obtains the provider in edible item shared platform and the credit value of reciever, guarantees altogether
The safety enjoyed and reliable.
Summary of the invention
Technical problem: nowadays sharing edible article and also gradually appear, and provider and connects when shared edible item
The safety and reliability that recipient has the worry of oneself, especially reciever more to worry edible item.
Technical solution: method of the invention is the credit computing method of shared food provider, and the calculating of prestige is divided into
Individual and group's prestige, and credit value is investigated in terms of four, four aspects include public reputation, and ecological environment prestige is social
Network credit and food coefficient of injury, each aspect determine by specific prestige parameter again, finally thus prestige computation model
The provider in edible item shared platform and the credit value of reciever are obtained, guarantees shared safety and reliable.
Architecture
Fig. 1 gives the architecture of the credit computing method of shared food provider, on the platform of shared edible item
When shared portion congee, what reciever (A) was most worried is the safety problem of food, and the prestige (REP) of provider (S) can be certain
The safety problem of degree reflection food；Provider (S) includes individual provider (SP) and group provider (SG), the open source area REP
Block chain is stored；Reputation model (REP) is divided into individual prestige (SP) and group prestige (SG) two parts, SPAnd SGAll common packet
Containing four parts, a part is public reputation (REPP), a part is ecological environment prestige (REPE), a part is social networks
Prestige (REPN) and last part food coefficient of injury (DF)；REPPThe social public reputation value (REP of S can be investigatedPS)
With bank credit value (REPPB), REPPSIt include again public security system, the public social framework such as family planning system and population census system deposits
The prestige record put, SPREPPSIt is the content in individual credit system, SGREPPSFor Credit System content.
REPERefer to that the historgraphic data recording for the edible item that provider (S) shares, such as S connect when sharing congee
Recipient (A) can consider that the congee of S shares historical record, and the How does it taste of congee, the composition quality good or not of congee, other once received
What the A that S shares has evaluate in the S congee shared；REPE(COM is evaluated including providerS), reciever evaluates (COMC) and matching degree
(M), evaluation content (COMCon) include food color, mouthfeel, price, freshness, temperature, the parameters such as shelf-life, each ginseng
One numerical value D of numberCEvaluated (DC∈ [0,31]), after by COMSAnd COMCThe COM being related toConIt is matched, calculates M,, M illustrates that edible food more meets the evaluation of S oneself closer to 1.
REPNRefer to social networks credit value locating for S, when S is SPWhen, REPNIncluding two parts, SPBetween individual
REPN, it is denoted as REPNPPAnd SPREP between groupN, it is denoted as REPNPG；REPNPPIt is discussed using six degrees of separation, a people and any
The people being spaced between one stranger does not exceed six, that is to say, that can at most be recognized by five go-betweens and be appointed
What stranger；In REPNPPIn network, everyone has a credit value (REPNPPi), according to SPIn the network of personal connections connected
REPNPPi, can be in the hope of all REPNPPiMean value obtain SPREPNPP；Such as Fig. 2 is the relationship between Xiao Wang and other individuals
Network, the REP of Xiao WangNPPCalculated result is small at, Xiao Liu, the REP of little Song, little Chen and Xiao Li directly by Xiao ZhangNPPIt influences, most
Xiao Wang REP is acquired eventuallyNPP=88；There is also a kind of situations, i.e. SPFrom other places, his/her REP can not be transferredN, such as Fig. 2 Xiao Wang
It is the student that other places is come, his REPNFor sky；This patent is provided with partner's supporting degree (FS) to help nonlocal SPIt obtains
REPN；SPIt can invite with REPNPartner help its carry out REPNValue record；Assuming that there is n partner to help other places SP, then, β is small by the trained obtained coefficient of relationship of mass data, such as Fig. 2
King is the student that other places is come, he has invited 7 companions to provide partner's supporting degree for him now, and Xiao Wang is calculated eventually by FS
REPNPP=88；REPNPG It is the social networks credit value between S and group；Provided such as shown in Fig. 3, in Fig. 3 Xiao Wang with
Relationship between each group, Xiao Wang are the student of 15 grades XX classes of the XX profession of China Hainan province Haikou City XX university；In this network
In, each layer of group's entity all has a credit value REPNPGi, the REP of each group's entityNPGAfter being trained by mass data
It collects and obtains, such as the REP of Chinese provinces and citiesNPGiIt is different, it is assumed that after data training, it is known that each provinces and cities
REPNPGiIt is worth as follows, Sichuan Province 10, Hainan Province 5, Shanghai City 20；Each professional grade REP simultaneouslyNPGi?
It is different, it is assumed that after data training, 15 grades where Xiao Wang have occurred the event of cheating loan, then 15 grades of REP at this timeNPGi
10 are reduced to by 20, specific coefficient of relationship obtains after being trained by mass data；When S is SGWhen, REPNValue calculating only include
REPNGG, i.e., REP between group and groupN。
This patent is also provided with a food coefficient of injury (DF), DFIncluding two parts, a part is that profession is closed with hazardous material
Connection degree (DP), it is searched for by forward and reverse, profession relevant to food harm is obtained, and compare with the profession of S, if S
Profession is relevant profession, obtains D according to hazard ratingPValue (DP∈ [0,10])；For example, the profession of S is specialty chemical, change
It learns profession and endangers profession correlation, the D of the S with foodP=10；Another part is the relationship degree (D between S and AR), in the present age
Often there is the event poisoned because of envying, according to REP in societyNPPNetwork, it is known that the relationship of S and A, if S and A has benefit
Beneficial relationship, such as room-mate, colleague etc., DRIt can be according to interest relations grade value (DR∈ [0,10])；Such as S and A are big four
It is raw, and be room-mate, A safeguarding the Graduate Record Examination, S is preparing for the postgraduate qualifying examination.Then DR=3, DRCoefficient by after collection mass data training obtain.
Final credit value (VREP) can be obtained by following, α, β, γ, δ are respectively REPP、REPE、REPNAnd DFRelationship system
Number obtains after mass data training:
(1)
The utility model has the advantages that
The method of the present invention proposes the credit computing method of shared food provider, this method and has the advantages that
1) the present invention provides the methods that the safety and reliability to shared edible item is ensured, can make to be shared
Premise guarantee is provided with edible article；
2) present invention ensures the safety and reliability of edible item with reputation model, and reputation model wide coverage provides
Guarantee it is more reliable.
Detailed description of the invention
Fig. 1 is the system assumption diagram of the credit computing method of shared food provider；
Fig. 2 is a specific implementation of social networks reputation module between the individual for sharing the credit computing method of food provider
Example；
Fig. 3 is a specific implementation of social networks reputation module between the group for sharing the credit computing method of food provider
Example；
Fig. 4 is the specific implementation flow chart of the credit computing method of shared food provider.
Specific embodiment
A kind of detailed process based on the visual foreign language learning method of deviation organ morphology behavior is as follows:
Shown in 001 in step 1) corresponding diagram 4, the identity information of provider (S)/reciever (A) is obtained；
Shown in 002 in step 2 corresponding diagram 4, public reputation value (REP is obtainedP), REPPBy social public reputation value (REPPS) and
Bank credit credit value (REPPB) constitute, REPPSAnd REPPBIt is obtained by transferring the data in Relational database；REPPValue can
It is calculated by formula (1), ρ and σ are respectively REPPSAnd REPPBCoefficient of relationship, obtained after being trained by mass data:
(1)
Shown in 003 in step 3) corresponding diagram 4, ecological environment credit value (REP is obtainedE), REPE(COM is mainly evaluated by providerS)
(COM is evaluated with recieverA) matching degree (M) determine, and constantly accumulated by historical data hereafter；Evaluation content
(COMCon) include food color, mouthfeel, price, freshness, temperature, the parameters such as shelf-life, one numerical value of each parameter
DCEvaluated (DC∈ [0,31]), after by COMSAnd COMCThe COM being related toConIt is matched, calculates M,, M more connects
Nearly 1, illustrate that edible food more meets the evaluation of S oneself；REPECalculating can be obtained by formula (2),For REPEPass
Coefficient obtains after being trained by mass data:
(2)
Shown in 004 in step 4) corresponding diagram 4, S and A are judged for individual or group, to obtain social networks credit value (REPN),
REPNIncluding two parts, individual (SP/AP) and individual between REPN, it is denoted as REPNPP, REP between groupN, it is denoted as
REPNPG；If it is and individual, enter step 5), if it is and group, enter step 6)；
Shown in 005 in step 5) corresponding diagram 4, the social networks credit value (REP of individual is obtainedNPP), REPNPPUsing six degrees of separation
By the people being spaced between a people and any one stranger does not exceed six, that is to say, that at most pass through five centres
People can recognize any one stranger；In REPNPPIn network, everyone has a credit value (REPNPPi), according to SPInstitute
REP in the network of personal connections of connectionNPPi, can be in the hope of all REPNPPiMean value obtain SPREPNPP；Such as Fig. 2 is Xiao Wang and other
Relational network figure between individual, the REP of Xiao WangNPPCalculated result is small at, Xiao Liu, little Song, little Chen and Xiao Li directly by Xiao Zhang
REPNPPIt influences, finally acquires Xiao Wang REPNPP=88；There is also a kind of situations, i.e. SPFrom other places, his/her can not be transferred
REPN, such as Fig. 2 Xiao Wang is the student that other places is come, his REPNFor sky, SPIt can invite with REPNPartner help its carry out REPN
Value record, this patent are provided with partner's supporting degree (FS) to help nonlocal SPObtain REPN, FS can calculate by formula (3)
It arrives, it is assumed that there is n partner to help other places S at this timeP, β is the coefficient of relationship obtained by mass data training:
(3)
Such as Fig. 2 Xiao Wang is the student that other places is come, he has invited 7 companions to provide partner's supporting degree for him now, eventually by
The REP of Xiao Wang is calculated in FSNPP=88；REPNPG It is the social networks credit value between S and group；Such as shown in Fig. 3, Fig. 3
In provide relationship between Xiao Wang and each group, Xiao Wang is 15 grades XX classes of the XX profession of China Hainan province Haikou City XX university
Student；In this network, each layer of group's entity all has a credit value REPNPGi, the REP of each group's entityNPGBy
It collects and obtains after mass data training, such as the REP of Chinese provinces and citiesNPGiIt is different, it is assumed that after data training, know
The REP of each provinces and cities in roadNPGiIt is worth as follows, Sichuan Province 10, Hainan Province 5, Shanghai City 20；Each professional simultaneously
Grade REPNPGiIt is also different, it is assumed that after data training, 15 grades where Xiao Wang have occurred the event of cheating loan, then at this time
15 grades of REPNPGi10 are reduced to by 20, specific coefficient of relationship obtains after being trained by mass data；
Shown in 006 in step 6) corresponding diagram 4, when S/A is SG/AGWhen, REPNValue calculating only include REPNGG, i.e. group and group
Between REPN；
Shown in 007 in step 7) corresponding diagram 4, food coefficient of injury (D is obtainedF), DFIncluding two parts, a part is profession and danger
The evil object degree of association (DP), it is searched for by forward and reverse, obtains profession relevant to food harm, and carried out pair with the profession of S
Than obtaining D according to hazard rating if S profession is relevant professionPValue (DP∈ [0,10])；For example, the profession of S is chemical special
Industry, specialty chemical endanger profession correlation, the D of the S with foodP=10；Another part is the relationship degree (D between S and AR),
Often there is the event poisoned because of envying, according to REP in contemporary societyNPPNetwork, it is known that the relationship of S and A, if S and A has
It has interests relations, such as room-mate, colleague etc., DRIt can be according to interest relations grade value (DR∈ [0,10])；Such as S and A are big
Four students, and be room-mate, A safeguarding the Graduate Record Examination, S preparing for the postgraduate qualifying examination, then DR=3, DRCoefficient by after collection mass data training obtain；
Shown in 008 in step 8) corresponding diagram 4, by step 2 to step 7), final credit value (V is obtainedREP), VREPBody can be passed through
Formula (4) in architecture obtains；α, β, γ, δ are respectively REPP、REPE、REPNAnd DFCoefficient of relationship, instructed through mass data
It is obtained after white silk:
(4)
Shown in 009 in step 9) corresponding diagram 4, compare VREPThe minimum V for needing to meetREP0Size, if VREPCompare VREP0Greatly, into
Enter step 10), otherwise enters step 11)；
Shown in 010 in step 10) corresponding diagram 4, by user VREPDeposit open source block chain, and other side is explicitly given, and by VREPIt carries out
Sequence, facilitates system when recommendation by VREPSequence from big to small is recommended；
Shown in 011 in step 11) corresponding diagram 4, by user VREPDeposit open source block chain, but do not give shared chance.
Claims (1)
1. the present invention is the credit computing method of shared food provider, the calculating of prestige is divided into individual and group's prestige, and
Credit value is investigated in terms of four, four aspects include public reputation, ecological environment prestige, social networks prestige and food harm
Coefficient；Each aspect is determined that finally thus prestige computation model obtains being total in edible item by specific prestige parameter again
Enjoy the provider on platform and the credit value of reciever；Guarantee shared safety and reliable；The prestige meter of shared food provider
The detailed process of calculation method is as follows:
Step 1) obtains the identity information of provider (S)/reciever (A)；
Step 2 obtains public reputation value (REPP), REPPBy social public reputation value (REPPS) and bank credit credit value
(REPPB) constitute, REPPSAnd REPPBIt is obtained by transferring the data in Relational database；REPPValue can be counted by formula (1)
It calculates, ρ and σ are respectively REPPSAnd REPPBCoefficient of relationship, obtained after being trained by mass data:
(1)
Step 3) obtains ecological environment credit value (REPE), REPE(COM is mainly evaluated by providerS) and reciever evaluation (COMA)
Matching degree (M) determine, and constantly accumulated by historical data hereafter；Evaluation content (COMCon) it include food
Color, mouthfeel, price, freshness, temperature, the parameters such as shelf-life, one numerical value D of each parameterCEvaluated (DC∈[0,
31]), by COM afterSAnd COMCThe COM being related toConIt is matched, calculates M,, M illustrates edible closer to 1
Food more meets the evaluation of S oneself；REPECalculating can be obtained by formula (2),For REPECoefficient of relationship, by largely counting
According to being obtained after training:
(2)
Step 4) judges S and A for individual or group, to obtain social networks credit value (REPN), REPNIt is a including two parts
Body (SP/AP) and individual between REPN, it is denoted as REPNPP, REP between groupN, it is denoted as REPNPG；If it is and individual, into
Enter step 5), if it is and group, enter step 6)；
Step 5) obtains the social networks credit value (REP of individualNPP), REPNPPIt is discussed using six degrees of separation, a people and any one
The people being spaced between a stranger does not exceed six, that is to say, that can at most be recognized by five go-betweens any
One stranger；In REPNPPIn network, everyone has a credit value (REPNPPi), according to SPIn the network of personal connections connected
REPNPPi, can be in the hope of all REPNPPiMean value obtain SPREPNPP；Such as Fig. 2 is the network of personal connections between Xiao Wang and other individuals
Network figure, the REP of Xiao WangNPPCalculated result is small at, Xiao Liu, the REP of little Song, little Chen and Xiao Li directly by Xiao ZhangNPPIt influences, finally
Acquire Xiao Wang REPNPP=88；There is also a kind of situations, i.e. SPFrom other places, his/her REP can not be transferredN, such as Fig. 2 Xiao Wang is
The student that other places is come, his REPNFor sky, SPIt can invite with REPNPartner help its carry out REPNValue record, this patent setting
One partner's supporting degree (FS) helps nonlocal SPObtain REPN, FS can be calculated by formula (3), it is assumed that have n partner at this time
With help other places SP, β is the coefficient of relationship obtained by mass data training:
(3)
Such as Fig. 2 Xiao Wang is the student that other places is come, he has invited 7 companions to provide partner's supporting degree for him now, eventually by
The REP of Xiao Wang is calculated in FSNPP=88；REPNPG It is the social networks credit value between S and group；Such as shown in Fig. 3, Fig. 3
In provide relationship between Xiao Wang and each group, Xiao Wang is 15 grades XX classes of the XX profession of China Hainan province Haikou City XX university
Student；In this network, each layer of group's entity all has a credit value REPNPGi, the REP of each group's entityNPGBy
It collects and obtains after mass data training, such as the REP of Chinese provinces and citiesNPGiIt is different, it is assumed that after data training, know
The REP of each provinces and cities in roadNPGiIt is worth as follows, Sichuan Province 10, Hainan Province 5, Shanghai City 20；Each professional simultaneously
Grade REPNPGiIt is also different, it is assumed that after data training, 15 grades where Xiao Wang have occurred the event of cheating loan, then at this time
15 grades of REPNPGi10 are reduced to by 20, specific coefficient of relationship obtains after being trained by mass data；
Step 6) is S as S/AG/AGWhen, REPNValue calculating only include REPNGG, i.e., REP between group and groupN；
Step 7) obtains food coefficient of injury (DF), DFIncluding two parts, a part is profession and the hazardous material degree of association (DP), lead to
Forward and reverse search is crossed, profession relevant to food harm is obtained, and compare with the profession of S, if S profession is relevant
Profession obtains D according to hazard ratingPValue (DP∈ [0,10])；For example, the profession of S is specialty chemical, specialty chemical is with food
Harm profession is related, the D of the SP=10；Another part is the relationship degree (D between S and AR), in contemporary society, often occur because
The event poisoned for envy, according to REPNPPNetwork, it is known that the relationship of S and A, if S and A has interest relations, such as room
Friend, colleague etc., DRIt can be according to interest relations grade value (DR∈ [0,10])；Such as S and A are senior, and are room-mate, A
Safeguarding the Graduate Record Examination, S are preparing for the postgraduate qualifying examination, then DR=3, DRCoefficient by after collection mass data training obtain；
Step 8), to step 7), obtains final credit value (V by step 2REP), VREPThe formula (4) in architecture can be passed through
It obtains；α, β, γ, δ are respectively REPP、REPE、REPNAnd DFCoefficient of relationship, through mass data training after obtain:
(4)
Step 9) compares VREPThe minimum V for needing to meetREP0Size, if VREPCompare VREP0Greatly, it enters step 10), otherwise enters
Step 11)；
Step 10) is by user VREPDeposit open source block chain, and other side is explicitly given, and by VREPIt is ranked up, system is facilitated to carry out
By V when recommendationREPSequence from big to small is recommended；
Step 11) is by user VREPDeposit open source block chain, but do not give shared chance.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429046A (en) * | 2020-06-12 | 2020-07-17 | 深圳大数据计算机信息股份有限公司 | User evaluation method and system based on block chain decentralization |
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2018
- 2018-07-30 CN CN201810851645.4A patent/CN109118323A/en active Pending
Non-Patent Citations (2)
| Title |
|---|
| 杨兴寿: "电子商务环境下的信用和信任机制研究", 《中国博士学位论文全文数据库 经济与管理科学辑》 * |
| 董成惠: "共享经济：理论与现实", 《广东财经大学学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429046A (en) * | 2020-06-12 | 2020-07-17 | 深圳大数据计算机信息股份有限公司 | User evaluation method and system based on block chain decentralization |
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