CN109360044A - A kind of cross-border e-commerce sale management system and method - Google Patents
A kind of cross-border e-commerce sale management system and method Download PDFInfo
- Publication number
- CN109360044A CN109360044A CN201811103064.9A CN201811103064A CN109360044A CN 109360044 A CN109360044 A CN 109360044A CN 201811103064 A CN201811103064 A CN 201811103064A CN 109360044 A CN109360044 A CN 109360044A
- Authority
- CN
- China
- Prior art keywords
- module
- loc
- track
- data
- route
- 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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0641—Shopping interfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
- G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0833—Tracking
-
- 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
- G06Q30/01—Customer relationship services
- G06Q30/015—Providing customer assistance, e.g. assisting a customer within a business location or via helpdesk
- G06Q30/016—After-sales
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Accounting & Taxation (AREA)
- Economics (AREA)
- Finance (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Bioethics (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Databases & Information Systems (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Medical Informatics (AREA)
- General Engineering & Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Human Resources & Organizations (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Technology Law (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention belongs to e-commerce fields, disclose a kind of cross-border e-commerce sale management system and method, it is provided with operator's platform, operator's platform includes language conversion module, video display module, manual service module, goods discharging module, after sale module, gathering module, order statistical module；Logistics platform includes safety check module, goods statistics module, route planning module, position locating module；Purchase platform includes logistics enquiring module, item property module, evaluation module, payment module, purchase platform, complains module, applies for module after sale；A kind of method is disclosed simultaneously.The present invention has ensured the interests of both sides in sales process, and can realize reimbursement without at one's own expense, so that cross border shopping is more convenient；The present invention is provided with complaint module, can ensure to the interests of purchaser, reduce fraud.
Description
Technical field
The invention belongs to e-commerce field more particularly to a kind of cross-border e-commerce sale management system and methods.
Background technique
In recent years, with the development of internet and e-commerce technology, the mode of commodity has been bought by network mode
Through more more and more universal, meanwhile, cross-border electric business development is very fast, and the support of policy more allows cross-border electric business further strengthened.Electricity at present
Sub- Electronic Business Technology has been usually present the behaviors such as fraud deception, and the equity between seller and purchaser can not ensure,
There is very big disagreement in aspect of returning goods, be unfavorable for the development of e-commerce technology.
In conclusion problem of the existing technology is:
E-commerce technology has been usually present the behaviors such as fraud deception at present, and between seller and purchaser
Equity can not ensure there is very big disagreement in terms of the return of goods, be unfavorable for the development of e-commerce technology.
The prior art exist (1) logistics platform circulation cargo to be clearly outlined unclear or scanning result inaccurate, to safety
Check that work is made troubles；(2) GPS locator causes positioning inaccurate or positioning vulnerable to extraneous and factor itself influence
Not normal situation；(3) when circulation cargo is excessive, easily there is goods statistics inaccuracy in statistic device, and statistical information reading capability is poor
The problems such as.
In the prior art, in handling to the route information of stream product, it not can be carried out track and data-privacy inhibited to have
Effect protection, is not avoided that route information is intervened by attacker, causes information leakage probability bigger.
Summary of the invention
In view of the problems of the existing technology, the present invention provides a kind of cross-border e-commerce sale management systems.
The invention is realized in this way a kind of cross-border e-commerce sales management method, the cross-border e-commerce sale
Management method includes:
The information of commodity is converted and shown to the language of country variant；
To the statistics and verification of order, cargo is handed into logistics platform；Logistics platform is by safety check module to product
Safety detect and count to shipment commodity；
Route planning module is determined route, is reported to businessman and buyer cargo location；Route planning mould
Block is handled the route information of stream product in being determined to route, is carried out track and is inhibited data-privacy protection, keeps away
Exempt from route information to be intervened by attacker；It specifically includes:
Initial data is collected and pre-processed to the first step, and ultimately forms the initial trace sequence of the route of several stream products
Column set；
Second step carries out anonymous processing to the initial trace arrangement set, including: in the initial trace sequence
The problematic set of projections VP for being unsatisfactory for the route privacy tolerance of stream product is found in column set；
All tracks in problem set of projections VP are dropped according to its frequency occurred in initial trace arrangement set
Sequence sequence, and result is saved in set FVP；
Third step, before searching in the set FVP | PS | a highest track projection record of the frequency of occurrences carries out it
Anonymity processing, wherein the anonymous processing is handled including track inhibition, untilOrTerminate anonymous processing；
4th step, to anonymous treated that track sets set is issued by described；
The present invention proposes a kind of scheme locally inhibited, by solving the relationship between the privacy degree of association and data effectiveness
Local inhibition is carried out to track data, in each anonymous treatment process, the inhibition of whole track record will be changed to inhibit rail
A certain position data in mark, is effectively promoted data effectiveness and performance.
The anonymous processing further includes local inhibition processing, in which: it is hidden that the smallest violation is found in the set FVP
The track sets collection of private demand, and it is saved in track set MVP；
According to the knowledge A of attackervCalculate the R (PG (loc of all tracing points in the track sets collection MVPi), UL
(loci)) value, R (PG (loc is found every timei), UL (loci)) the biggish tracing point loc of valuei, and be focused to find out in initial trace
Track collection corresponding with all track records comprising location information in MVP, the location information for inhibiting the track to concentrate
loci, this processing needs iteration to carry out, untilBeam；
The part inhibition processing includes:
1) IVPA is handled, and the privacy tolerance P for being unsatisfactory for the route of stream product is found from initial trace data set Tbr
Problematic set of projections VP；
2) FVPA is handled: the frequency that all tracks in problematic set of projections VP are occurred in the collection T of track according to it
It is ranked up, and result is saved in set FVP；
3) IMVA is handled: the smallest track sets collection for violating privacy requirements is found in problematic set of projections FVP,
And it is saved in the algorithm IMVA of track set MVP；
4) TAA_1 is handled: according to the knowledge A of attacker vvCalculate the R (PG of all tracing points in track sets collection MVP
(loci), UL (loci)) value, R (PG (loc is found every timei), UL (loci)) the biggish tracing point loc of valuei, and in initial trace
Track collection corresponding with all track records comprising location information in MVP is found in collection T, the track is inhibited to concentrate
Location information loci, this step needs iteration to carry out, untilTerminate；
The set FVP is empty set, then it represents that current initial trace arrangement set is safe condition, is issued；
Track data collection T is the set of the route track sequence of all stream products, formalization representation are as follows:
T=∪ ti, i=1,2...
Wherein, tiThe motion profile for indicating the route i of stream product, represents the history footprint of the route i of stream product.
To the route i of each stream product, motion profile tiIt is by n different moments timeiPosition sequence composition, table
It is shown as:
ti={ < loc1(x1, y1), timei> →...→ < locn(xn, yn), timen>}
Wherein < loci(xi, yi), timei> represent timeiSpecific location where the route i of moment stream product；
It carries out the satisfaction of judgement purchase article and makes evaluation；
Inquiry logistics message is carried out after purchase article；
Applied after sale, handling it to application by module after sale；
After buying freight charges danger, insurance platform will whether charges refund and tax pay logistics platform.
Further, IVPA, which is handled, includes:
To in anonymity treatment process used by initial trace data set T,
VPv: attacker v is inferred to other positions locjProbability be P (locj, tv, T ')；If P (locj, tv, T ') and > Pbr,
Then record tvFor the projection of problematic track, VPv={ tv|tv∈TPv∧P(locj, tv, T ') and > Pbr}；
Here VPvIt is the projection knowledge TP of attacker vvIn problematic set of projections, i.e., attacker can be higher than logistics
The privacy tolerance P of the route of productbrProbabilistic inference go out and VPvIn the corresponding initial trace of track record in other
Location information；Such track record for the route of stream product, be it is unsafe, anonymous place need to be carried out to it
Reason；There is m attacker, have:
For attacker a, b, problematic set of projections are as follows:
VPa={ a1→a3, a2→a3,
VPb={ b1, b1→b3, b2, b2→b3}
VP={ a1→a3, a2→a3, b1, b1→b3, b2, b2→b3}；
FVPA processing includes: based on IVPA, by the track sets in problematic set of projections VP according to it in initial trace
The number descending arrangement occurred in collection T, handles the higher track sets of the frequency of occurrences preferentially, passes through many experiments, hair
Now the algorithm can reduce repressed points to a certain extent.
To attacker a, track sets { a1→a2}、{a1→a3}、{a2→a3, the number occurred respectively is 4,1,3,
It is after sequence the result is that:
{a1→a2}→{a2→a3}→{a1→a3}。
IMVA algorithm includes:
MVPv:IfOrWhen, then
It will merge intoThen have
In order to promote the effectiveness of anonymous data, only by problematic set of projections FVPvIt merges, by set FVPvContracting
It is small, to obtain the smallest problematic set of projections MVPv；There is m attacker, have:
For attacker a, b, FVPa={ a2→a3, a1→a3, FVPb={ b1, b2, b1→b3, b2→b3}；Pass through algorithm
IMVA obtains MVPa={ a2→a3, a1→a3, MVPb={ b1, b2}；
TAA_1 is handled
Before carrying out anonymous processing to data set T, carry out:
R(PG(loci), UL (loci))=PG (loci)/(UL(loci)+1)
PG(loci): for position lociThe relevant privacy degree of association is represented by deleting point lociBrought privacy is received
Benefit, value are set MVPvIn include point lociDifferent track numbers；But when a certain location point is only associated with itself
When, the privacy degree of association is still defined as 1.Because if the privacy degree of association is defined as 0, when multiple positions are all associated with itself, lead
Cause multiple positions R value be it is identical, will cause the random erasure to location point, therefore, in order to avoid going out for this kind of situation
It is existing, it is defined as 1, then the less point of frequency of occurrence will be preferentially suppressed, to promote the effectiveness of data.UL
(loci): it represents by delete position point lociBrought information loss amount, value MVPvIn include in all track
Point lociSum；PG(loci) value it is bigger, represent by delete point lociBrought privacy income is bigger, and information loss
It measures smaller.
Anonymity algorithm track anonymity algorithm different from the past inhibits the side at the midpoint track collection MVP using part here
Method carries out anonymous processing to track data collection T；For the privacy income and higher data effectiveness obtained, in processing track
When collecting the location information in MVP, preferentially inhibit PG (loci) maximum point loci, so that one point loc of every deletioniInstitute's band
Secret protection and data effectiveness be all optimal simultaneously.Specific processing is described as follows: for example: for attacker a, for b,
MVPa={ a2→a3, a1→a3, MVPb={ b1, b2}.It is calculated according to above-mentioned definition and knows R (PG (a1), UL (a1)) maximum,
Due to track a1→a3Track a in corresponding T '1→a3→b1, so deleting track a1→a3→b1In point α1, i.e. a1→a3→
b1Become a3→b1, loop iteration, untilTerminate.
Further, track sets include the location information of the route of stream product, and location information is according to time timei
Ascending order arrangement；There are two attacker a, when b, the privacy tolerance P of the route of stream productbrIt is set as 0.5；
Track record: the record t that track record is n by the length that n location information forms sequentially in time
=< loc1, loc2..., locn>, wherein loci∈A；
A is all positions that data publication center is controlled, it is assumed that A={ a1, a2, a3, b1, b2, b3, A is divided into m mutually
Disjoint nonvoid subset, i.e.,There is A=A1∪A2, A1={ a1, a2, a3, A2={ b1, b2, b3Attacker's mould
Type；
It is assumed that potential attacker's quantity is m, then haveWherein V is attacker's set；Each attacker
viControl AiIn include all location informations, then have:AndFor each
Track record t ∈ T, each attacker vi∈ V is owned by a projection knowledge
Given initial trace data set T, T ' are the track data collection to be announced after treatment of T；IfEach is attacked
The person of hitting v cannot be to be higher than PbrProbability is accurately inferred to any position information locj,Then think T '
Be it is safe, publish, otherwise just it is dangerous, cannot publish.
Further, safety check module is provided with X-ray scanning, removes ray scanning mistake using non-local mean filtering algorithm
Discrete noise image in journey makes being clearly outlined for logistics platform circulation cargo, carries out safety inspection；It specifically includes:
Discrete noise image v=v (i) | and i ∈ I } to the estimated value NL [v] (i) of a pixel i, it is calculated as in image
The weighted average of all pixels, w (i, j) be weight, 0≤w (i, j)≤1 and
Gray vector v (Ni) and v (Nj) similitude indicate pixel i and pixel j between similitude,For square of the weighted euclidean distance in the region i, j, a (a > 0) indicates Gaussian kernel standard deviation, and h is control
System considers the coefficient of wave-path degree, all areas similarity summation within the scope of Z (i) picture search.
Further, position locating module is provided with GPS locator, and gray model is taken to assist GPS locator,
Real time GPS dynamic precision One-Point Location；It specifically includes:
Equipped with variable X(0)Original data sequence:
X(0)={ x(0)(1), x(0)(2) ..., x(0)(n)}
N is initial data number；The corresponding time is ti(n=1,2 ..., n)；
Single order accumulator module X is generated with A G O (Accumullated Generating Operation)(1)
X(1)={ x(1)(1), x(1)(2) ..., x(1)(n)}
By single order Grey Simulation X(1)The differential equation of composition are as follows:
According to derivative discrete form, the differential equation can be write as in the matrix form:
Y=AU
Wherein,
Using the principle of least square, estimates of parameters can be acquired are as follows:
Returning to the original differential equation has:
It must solve are as follows:
Discrete form are as follows:
Wherein, k is the initial data number for participating in positioning；
Form are as follows:
Wherein, P > 1 is anchor point；Original series after then positioning are as follows:
Or it simplifiedly expresses are as follows:
Entire gray model forecasting process expression are as follows:
Wherein, IAGO, AGO are respectively inverse accumulated generating sequence and Accumulating generation sequence；
Goods statistics module counts equipment using Full-automatic digital, using RFID technique, Full-automatic digital is driven to set
Standby information circuit sends out internal data, carries out information reading, specifically includes:
There are m information to be identified in system, is pitched and set using L, when search depth is 1, the identification probability of label are as follows:
P (1)=[1-L-1]m-1
In the statistical information batch reading process of Full-automatic digital statistics equipment, skill is identified using super high frequency radio frequency
Art obtains the mean value of the search depth of data are as follows:
Average timeslot number are as follows:
Empty Hash table data are sent into TABLE, using Binomial Trees, then Full-automatic digital counts equipment
Timeslot number is merged in the stratification that statistical information batch reads data are as follows:
Using RFID technique, drives the information circuit of Full-automatic digital equipment to send out internal data, read at this time
Device receives data by corresponding sequence, obtains the level of information fusion results of Full-automatic digital statistics equipment are as follows:
By processing, retain first Full-automatic digital facility information exported, filtered data keep original
Acquisition order, the information of Full-automatic digital equipment is read.
Another object of the present invention is to provide a kind of computers for realizing the cross-border e-commerce sales management method
Program.
Another object of the present invention is to provide a kind of Information Numbers for realizing the cross-border e-commerce sales management method
According to processing terminal.
Another object of the present invention is to provide a kind of computer readable storage mediums, including instruction, when it is in computer
When upper operation, so that computer executes the cross-border e-commerce sales management method.
Another object of the present invention is to provide a kind of cross-border electricity for realizing the cross-border e-commerce sales management method
Sub- business sales management system, the cross-border e-commerce sale management system include:
Operator's platform；
Operator's platform includes language conversion module, video display module, manual service module, goods discharging module, after sale mould
Block, gathering module, order statistical module are connect with logistics platform signal；
Logistics platform, include safety check module, goods statistics module, route planning module, position locating module with fortune
Seek the connection of quotient's bracket signal；
Buy platform, include logistics enquiring module, item property module, evaluation module, payment module, purchase platform,
Module, application module after sale is complained to connect with logistics platform signal；
Safety check module, is provided with X-ray scanning, during non-local mean filtering algorithm removal ray scanning
Discrete noise image carries out safety inspection so that logistics platform circulation cargo is clearly outlined；
Operator's platform, which delivers commodity to be transported through by logistics platform, gives purchase platform；
Operator's platform constitutes freight charges danger agreement with purchase platform and insurance platform；
Reimbursement freight cost and the expenses of taxation are transferred to logistics platform by insurance platform.
Another object of the present invention is to provide a kind of cross-border e-commerce sale equipment in internet, the internet cross
E-commerce sale equipment in border at least carries the cross-border e-commerce sale management system.
Advantages of the present invention and good effect are as follows:
The invention is provided with insurance platform, so that transaction of returning goods in cross-border electronic transaction is more convenient, so that retailer
With purchaser without worry in terms of back freight and the expenses of taxation, so that more convenient.The invention is provided with position locating module,
Purchaser is at any time determined cargo location.The invention is provided with complaint module, can be to the interests of purchaser
It is ensured, reduces fraud.
The invention has ensured the interests of both sides in sales process, and can realize reimbursement without at one's own expense, so that
Cross border shopping is more convenient.
Cross-border e-commerce sale management system provided by the invention, (1) are penetrated using the removal of non-local mean filtering algorithm
Discrete noise image during line light irradiation, so that being clearly outlined for logistics platform circulation cargo, is convenient for examining safely
It looks into；(2) it takes gray model to assist GPS locator, provides degree of precision for real time GPS dynamic precision One-Point Location
Data positioning；(3) equipment is counted using Full-automatic digital, using RFID technique, drives the letter of Full-automatic digital equipment
It ceases circuit to send out internal data, improves statistical information reading capability.
The present invention improves the anonymous quality of data, to some extent while meeting privacy of user demand significantly
Data effectiveness is improved, has been well solved in data publication between the privacy requirements of consumer articles information and data effectiveness
Equalization problem；The present invention is demonstrated in the case where same privacy requirements by many experiments, and the data effectiveness after anonymity is promoted
Nearly 40%, so that scheme more has realistic meaning when solving the problems, such as data publication.
Detailed description of the invention
Fig. 1 is cross-border e-commerce sale management system schematic diagram provided in an embodiment of the present invention；
In figure: 1, language conversion module；2, video display module；3, manual service module；4, logistics platform；5, safety check module；
6, goods statistics module；7, logistics enquiring module；8, item property module；9, evaluation module；10, payment module；11, it buys
Platform；12, module is complained；13, apply for module after sale；14, route planning module；15, position locating module；16, insurance is flat
Platform；17, goods discharging module；18, module after sale；19, gathering module；20, order statistical module；21, operator's platform.
Fig. 2 is cross-border e-commerce sales management method flow diagram provided in an embodiment of the present invention.
Specific embodiment
In order to further understand the content, features and effects of the present invention, the following examples are hereby given, and cooperate attached
Detailed description are as follows by Fig. 1.
Structure of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, cross-border e-commerce sale management system provided in an embodiment of the present invention, comprising: language converts mould
Block 1, video display module 2, manual service module 3, logistics platform 4, safety check module 5, goods statistics module 6, logistics enquiring module 7,
Item property module 8, payment module 10, purchase platform 11, complains module 12, applies for module 13, route after sale at evaluation module 9
Planning module 14, position locating module 15, insurance platform 16, goods discharging module 17, after sale module 18, gathering module 19, order system
Count module 20, operator's platform 21.
It specifically includes:
Operator's platform 21；
Operator's platform 21 include language conversion module 1, video display module 2, manual service module 3, goods discharging module 17,
Module 18, gathering module 19, order statistical module 20 after sale；
Logistics platform 4 includes safety check module 5, goods statistics module 6, route planning module 14, position locating module
15；
Purchase platform 11 includes logistics enquiring module 7, item property module 8, evaluation module 9, payment module 10, throws
It tells module 12, apply for module 13 after sale.
Operator's platform 21, which delivers commodity to be transported through by logistics platform 4, gives purchase platform 11.
Operator's platform 21 constitutes freight charges danger agreement with purchase platform 11 and insurance platform 16.
Reimbursement freight cost and the expenses of taxation are transferred to logistics platform 4 by insurance platform 16.
The working principle of the invention:
The invention converts the language of country variant by the realization of language module 1 of operator's platform 21, video display mould
Block 2 shows the information of commodity, is realized by order statistical module 20 to the statistics and verification of order, passes through manual service module 3
It holds consultation with client, cargo is handed to by logistics platform 4 by goods discharging module 17；Logistics platform 4 is right by safety check module 5
The safety of product is detected, and is counted by goods statistics module 6 to shipment commodity, pass course planning module 14
Route is determined, cargo location is reported to businessman and buyer by position locating module 15, sells quotient in sample platform
Product attribute module 8 makes buyer understand item property, is further judged by evaluation module 9, and can this 4 this purchase
It buys and makes evaluation；It is paid the bill by payment module 10, buyer is collected money by module 19 of collecting money, and can pass through object after purchase
Continuous query module 7 carries out inquiry logistics message, by applying for that module 13 is applied after sale after sale, by module 18 after sale to Shen
It please handle it, after freight charges danger is had purchased after payment, insurance platform 16 will pay the freight charges needed for reimbursement with tax
Logistics platform 4, which is realized, returns goods, and can be complained by buying platform 11 to businessman.
Below with reference to concrete analysis, the invention will be further described.
Safety check module provided in an embodiment of the present invention is provided with X-ray scanning, is removed using non-local mean filtering algorithm
Discrete noise image during ray scanning makes being clearly outlined for logistics platform circulation cargo, carries out safety inspection；Specifically
Include:
Discrete noise image v=v (i) | and i ∈ I } to the estimated value NL [v] (i) of a pixel i, it is calculated as in image
The weighted average of all pixels, w (i, j) be weight, 0≤w (i, j)≤1 and
Gray vector v (Ni) and v (Nj) similitude indicate pixel i and pixel j between similitude,For square of the weighted euclidean distance in the region i, j, a (a > 0) indicates Gaussian kernel standard deviation, and h is control
System considers the coefficient of wave-path degree, all areas similarity summation within the scope of Z (i) picture search.
Position locating module is provided with GPS locator, and gray model is taken to assist GPS locator, real time GPS
Dynamic precision One-Point Location；It specifically includes:
Equipped with variable X(0)Original data sequence:
X(0)={ x(0)(1), x(0)(2) ..., x(0)(n)}
N is initial data number；The corresponding time is ti(n=1,2 ..., n)；
Single order accumulator module X is generated with A G O (Accumullated Generating Operation)(1)
X(1)={ x(1)(1), x(1)(2) ..., x(1)(n)}
By single order Grey Simulation X(1)The differential equation of composition are as follows:
According to derivative discrete form, the differential equation can be write as in the matrix form:
Y=AU
Wherein,
Using the principle of least square, estimates of parameters can be acquired are as follows:
Returning to the original differential equation has:
It must solve are as follows:
Discrete form are as follows:
Wherein, k is the initial data number for participating in positioning；
Form are as follows:
Wherein, P > 1 is anchor point；Original series after then positioning are as follows:
Or it simplifiedly expresses are as follows:
Entire gray model forecasting process expression are as follows:
Wherein, IAGO, AGO are respectively inverse accumulated generating sequence and Accumulating generation sequence；
Goods statistics module counts equipment using Full-automatic digital, using RFID technique, Full-automatic digital is driven to set
Standby information circuit sends out internal data, carries out information reading, specifically includes:
There are m information to be identified in system, is pitched and set using L, when search depth is 1, the identification probability of label are as follows:
P (1)=[1-L-1]m-1
In the statistical information batch reading process of Full-automatic digital statistics equipment, skill is identified using super high frequency radio frequency
Art obtains the mean value of the search depth of data are as follows:
Average timeslot number are as follows:
Empty Hash table data are sent into TABLE, using Binomial Trees, then Full-automatic digital counts equipment
Timeslot number is merged in the stratification that statistical information batch reads data are as follows:
Using RFID technique, drives the information circuit of Full-automatic digital equipment to send out internal data, read at this time
Device receives data by corresponding sequence, obtains the level of information fusion results of Full-automatic digital statistics equipment are as follows:
By processing, retain first Full-automatic digital facility information exported, filtered data keep original
Acquisition order, the information of Full-automatic digital equipment is read.
Below with reference to concrete analysis, the invention will be further described.
As shown in Fig. 2, cross-border e-commerce sales management method provided in an embodiment of the present invention, comprising:
S101: the information of commodity is converted and shown to the language of country variant；
S102: to the statistics and verification of order, cargo is handed into logistics platform；Logistics platform passes through safety check module pair
The safety of product detect and count to shipment commodity；
S103: route planning module is determined route, is reported to businessman and buyer cargo location；Route
Planning module is handled the route information of stream product in being determined to route, carries out track and data-privacy is inhibited to protect
Shield, avoids route information from being intervened by attacker；It specifically includes:
Initial data is collected and pre-processed to the first step, and ultimately forms the initial trace sequence of the route of several stream products
Column set；
Second step carries out anonymous processing to the initial trace arrangement set, including: in the initial trace sequence
The problematic set of projections VP for being unsatisfactory for the route privacy tolerance of stream product is found in column set；
All tracks in problem set of projections VP are dropped according to its frequency occurred in initial trace arrangement set
Sequence sequence, and result is saved in set FVP；
Third step, before searching in the set FVP | PS | a highest track projection record of the frequency of occurrences carries out it
Anonymity processing, wherein the anonymous processing is handled including track inhibition, untilOrTerminate anonymous processing；
4th step, to anonymous treated that track sets set is issued by described；
The present invention proposes a kind of scheme locally inhibited, by solving the relationship between the privacy degree of association and data effectiveness
Local inhibition is carried out to track data, in each anonymous treatment process, the inhibition of whole track record will be changed to inhibit rail
A certain position data in mark, is effectively promoted data effectiveness and performance.
The anonymous processing further includes local inhibition processing, in which: it is hidden that the smallest violation is found in the set FVP
The track sets collection of private demand, and it is saved in track set MVP；
According to the knowledge A of attackervCalculate the R (PG (loc of all tracing points in the track sets collection MVPi), UL
(loci)) value, R (PG (loc is found every timei), UL (loci)) the biggish tracing point loc of valuei, and be focused to find out in initial trace
Track collection corresponding with all track records comprising location information in MVP, the location information for inhibiting the track to concentrate
loci, this processing needs iteration to carry out, untilBeam；
The part inhibition processing includes:
1) IVPA is handled, and the privacy tolerance P for being unsatisfactory for the route of stream product is found from initial trace data set Tbr
Problematic set of projections VP；
2) FVPA is handled: the frequency that all tracks in problematic set of projections VP are occurred in the collection T of track according to it
It is ranked up, and result is saved in set FVP；
3) IMVA is handled: the smallest track sets collection for violating privacy requirements is found in problematic set of projections FVP,
And it is saved in the algorithm IMVA of track set MVP；
4) TAA_1 is handled: according to the knowledge A of attacker vvCalculate the R (PG of all tracing points in track sets collection MVP
(loci), UL (loci)) value, R (PG (loc is found every timei), UL (loci)) the biggish tracing point loc of valuei, and in initial trace
Track collection corresponding with all track records comprising location information in MVP is found in collection T, the track is inhibited to concentrate
Location information loci, this step needs iteration to carry out, untilTerminate；
The set FVP is empty set, then it represents that current initial trace arrangement set is safe condition, is issued；
Track data collection T is the set of the route track sequence of all stream products, formalization representation are as follows:
T=∪ ti, i=1,2...
Wherein, tiThe motion profile for indicating the route i of stream product, represents the history footprint of the route i of stream product.
To the route i of each stream product, motion profile tiIt is by n different moments timeiPosition sequence composition, table
It is shown as:
ti={ < loc1(x1, y1), time1>→…→<locn(xn, yn), timen>}
Wherein < loci(xi, yi), timei> represent timeiSpecific location where the route i of moment stream product；
S104: it carries out the satisfaction of judgement purchase article and makes evaluation；
S105: inquiry logistics message is carried out after purchase article；
S106: applied after sale, handling it to application by module after sale；
S107: after buying freight charges danger, insurance platform will whether charges refund and tax pay logistics platform.
IVPA is handled
To in anonymity treatment process used by initial trace data set T,
VPv: attacker v is inferred to other positions locjProbability be P (locj, tv, T ')；If P (locj, tv, T ') and > Pbr,
Then record tvFor the projection of problematic track, VPv={ tv|tv∈TPv∧P(locj, tv, T ') and > Pbr}；
Here VPvIt is the projection knowledge TP of attacker vvIn problematic set of projections, i.e., attacker can be higher than logistics
The privacy tolerance P of the route of productbrProbabilistic inference go out and VPvIn the corresponding initial trace of track record in other
Location information；Such track record for the route of stream product, be it is unsafe, anonymous place need to be carried out to it
Reason；There is m attacker, have:
For attacker a, b, problematic set of projections are as follows:
VPa={ a1→a3, a2→a3,
VPb={ b1, b1→b3, b2, b2→b3}
VP={ a1→a3, a2→a3, b1, b1→b3, b2, b2→b3}；
FVPA processing includes: based on IVPA, by the track sets in problematic set of projections VP according to it in initial trace
The number descending arrangement occurred in collection T, handles the higher track sets of the frequency of occurrences preferentially, passes through many experiments, hair
Now the algorithm can reduce repressed points to a certain extent.
To attacker a, track sets { a1→a2}、{a1→a3}、{a2→a3, the number occurred respectively is 4,1,3,
It is after sequence the result is that:
{a1→a2}→{a2→a3}→{a1→a3}。
IMVA algorithm includes:
MVPv:IfOrWhen, then
It will merge intoThen have
In order to promote the effectiveness of anonymous data, only by problematic set of projections FVPvIt merges, by set FVPvContracting
It is small, to obtain the smallest problematic set of projections MVPv；There is m attacker, have:
For attacker a, b, FVPa={ a2→a3, a1→a3, FVPb={ b1, b2, b1→b3, b2→b3}；Pass through algorithm
IMVA obtains MVPa={ a2→a3, a1→a3, MVPb={ b1, b2}；
TAA_1 is handled
Before carrying out anonymous processing to data set T, carry out:
R(PG(loci), UL (loci))=PG (loci)/(UL(loci)+1)
PG(loci): for position lociThe relevant privacy degree of association is represented by deleting point lociBrought privacy is received
Benefit, value are set MVPvIn include point lociDifferent track numbers；But when a certain location point is only associated with itself
When, the privacy degree of association is still defined as 1.Because if the privacy degree of association is defined as 0, when multiple positions are all associated with itself, lead
Cause multiple positions R value be it is identical, will cause the random erasure to location point, therefore, in order to avoid going out for this kind of situation
It is existing, it is defined as 1, then the less point of frequency of occurrence will be preferentially suppressed, to promote the effectiveness of data.UL
(loci): it represents by delete position point lociBrought information loss amount, value MVPvIn include in all track
Point lociSum；PG(loci) value it is bigger, represent by delete point lociBrought privacy income is bigger, and information loss
It measures smaller.
Anonymity algorithm track anonymity algorithm different from the past inhibits the side at the midpoint track collection MVP using part here
Method carries out anonymous processing to track data collection T；For the privacy income and higher data effectiveness obtained, in processing track
When collecting the location information in MVP, preferentially inhibit PG (loci) maximum point loci, so that one point loc of every deletioniInstitute's band
Secret protection and data effectiveness be all optimal simultaneously.Specific processing is described as follows: for example: for attacker a, for b,
MVPa={ a2→a3, a1→a3, MVPb={ b1, b2}.It is calculated according to above-mentioned definition and knows R (PG (a1), UL (a1)) maximum,
Due to track a1→a3Track a in corresponding T '1→a3→b1, so deleting track a1→a3→b1In point a1, i.e. a1→a3→
b1Become a3→b1, loop iteration, untilTerminate.
Track sets include the location information of the route of stream product, and location information is according to time timeiAscending order row
Column；There are two attacker a, when b, the privacy tolerance P of the route of stream productbrIt is set as 0.5；
Track record: the record t that track record is n by the length that n location information forms sequentially in time
=< loc1, loc2..., locn>, wherein loci∈A；
A is all positions that data publication center is controlled, it is assumed that A={ a1, a2, a3, b1, b2, b3, A is divided into m mutually
Disjoint nonvoid subset, i.e.,There is A=A1∪A2, A1={ a1, a2, a3, A2={ b1, b2, b3Attacker's mould
Type；
It is assumed that potential attacker's quantity is m, then haveWherein V is attacker's set；Each attacker
viControl AiIn include all location informations, then have:AndFor each
Track record t ∈ T, each attacker vi∈ V is owned by a projection knowledge
Given initial trace data set T, T ' are the track data collection to be announced after treatment of T；IfEach is attacked
The person of hitting v cannot be to be higher than PbrProbability is accurately inferred to any position information locj,Then think T '
Be it is safe, publish, otherwise just it is dangerous, cannot publish.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When using entirely or partly realizing in the form of a computer program product, the computer program product include one or
Multiple computer instructions.When loading on computers or executing the computer program instructions, entirely or partly generate according to
Process described in the embodiment of the present invention or function.The computer can be general purpose computer, special purpose computer, computer network
Network or other programmable devices.The computer instruction may be stored in a computer readable storage medium, or from one
A computer readable storage medium is transmitted to another computer readable storage medium, for example, the computer instruction can be from
One web-site, computer, server or data center pass through wired (such as coaxial cable, optical fiber, Digital Logistics product
Route line (DSL) or wireless (such as infrared, wireless, microwave etc.) mode to another web-site, computer, server
Or data center is transmitted).The computer-readable storage medium can be any available Jie that computer can access
Matter either includes the data storage devices such as one or more usable mediums integrated server, data center.Described use is situated between
Matter can be magnetic medium, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as
Solid state hard disk Solid State Disk (SSD)) etc..
The above is only the preferred embodiments of the present invention, and is not intended to limit the present invention in any form,
Any simple modification made to the above embodiment according to the technical essence of the invention, equivalent variations and modification, belong to
In the range of technical solution of the present invention.
Claims (10)
1. a kind of cross-border e-commerce sales management method, which is characterized in that the cross-border e-commerce sales management method packet
It includes:
The information of commodity is converted and shown to the language of country variant；
To the statistics and verification of order, cargo is handed into logistics platform；Logistics platform is by safety check module to the safety of product
Property carry out detect and shipment commodity are counted；
Route planning module is determined route, is reported to businessman and buyer cargo location；Route planning module pair
During route is determined, the route information of stream product is handled, track is carried out and inhibits data-privacy protection, avoid route
Information is intervened by attacker；It specifically includes:
Initial data is collected and pre-processed to the first step, and ultimately forms the initial trace sequence sets of the route of several stream products
It closes；
Second step carries out anonymous processing to the initial trace arrangement set, including: in the initial trace arrangement set
In find the problematic set of projections VP for being unsatisfactory for the route privacy tolerance of stream product；
All tracks in problem set of projections VP are subjected to descending row according to the frequency that it occurs in initial trace arrangement set
Sequence, and result is saved in set FVP；
Third step, before searching in the set FVP | PS | a highest track projection record of the frequency of occurrences carries out anonymous place to it
Reason, wherein the anonymous processing includes track inhibition processing, untilOrTerminate anonymous processing；
4th step, to anonymous treated that track sets set is issued by described；
The anonymous processing further includes local inhibition processing, in which: the smallest violation privacy requirements are found in the set FVP
Track sets collection, and be saved in track set MVP；
According to the knowledge A of attackervCalculate the R (PG (loc of all tracing points in the track sets collection MVPi), UL (loci))
Value, finds R (PG (loc every timei), UL (loci)) the biggish tracing point loc of valuei, and initial trace be focused to find out in MVP
The corresponding track collection of all track records comprising location information, inhibit the track concentrate location information loci, herein
Reason needs iteration to carry out, untilBeam；
The part inhibition processing includes:
1) IVPA is handled, and the privacy tolerance P for being unsatisfactory for the route of stream product is found from initial trace data set TbrHave
The set of projections VP of problem；
2) FVPA is handled: all tracks in problematic set of projections VP are carried out according to the frequency that it occurs in the collection T of track
Sequence, and result is saved in set FVP；
3) IMVA is handled: being found the smallest track sets collection for violating privacy requirements in problematic set of projections FVP, and is saved
To the algorithm IMVA of track set MVP；
4) TAA_1 is handled: according to the knowledge A of attacker vvCalculate the R (PG (loc of all tracing points in track sets collection MVPi),
UL(loci)) value, R (PG (loc is found every timei), UL (loci)) the biggish tracing point loc of valuei, and looked in initial trace collection T
To track collection corresponding with all track records comprising location information in MVP, the location information for inhibiting the track to concentrate
loci, this step needs iteration to carry out, untilTerminate；
The set FVP is empty set, then it represents that current initial trace arrangement set is safe condition, is issued；
Track data collection T is the set of the route track sequence of all stream products, formalization representation are as follows:
T=∪ ti, i=1,2...
Wherein, tiThe motion profile for indicating the route i of stream product, represents the history footprint of the route i of stream product.
To the route i of each stream product, motion profile tiIt is by n different moments timeiPosition sequence composition, indicate are as follows:
ti={ < loc1(x1, y1), time1>→…→<locn(xn, yn), timen>}
Wherein < loci(xi, yi), timei> represent timeiSpecific location where the route i of moment stream product；
It carries out the satisfaction of judgement purchase article and makes evaluation；
Inquiry logistics message is carried out after purchase article；
Applied after sale, handling it to application by module after sale；
After buying freight charges danger, insurance platform will whether charges refund and tax pay logistics platform.
2. cross-border e-commerce sales management method as described in claim 1, which is characterized in that IVPA, which is handled, includes:
To in anonymity treatment process used by initial trace data set T,
VPv: attacker v is inferred to other positions locjProbability be P (locj, tv, T ')；If P (locj, tv, T ') and > Pbr, then remember
Record tvFor the projection of problematic track, VPv={ tv|tv∈TPvΛP(locj, tv, T ') and > Pbr}；
Here VPvIt is the projection knowledge TP of attacker vvIn problematic set of projections, i.e. attacker can be to be higher than stream product
The privacy tolerance P of routebrProbabilistic inference go out and VPvIn the corresponding initial trace of track record in other positions letters
Breath；Such track record for the route of stream product, be it is unsafe, anonymous processing need to be carried out to it；There are m to attack
The person of hitting has:
For attacker a, b, problematic set of projections are as follows:
VPa={ a1→a3, a2→a3,
VPb={ b1, b1→b3, b2, b2→b3}
VP={ a1→a3, a2→a3, b1, b1→b3, b2, b2→b3}；
FVPA processing includes: based on IVPA, by the track sets in problematic set of projections VP according to it in initial trace collection T
The number descending of appearance arranges, and handles the higher track sets of the frequency of occurrences preferentially；
To attacker a, track sets { a1→a2}、{a1→a3}、{a2→a3, the number occurred respectively is 4,1,3, after sequence
The result is that:
{a1→a2}→{a2→a3}→{a1→a3}。
IMVA algorithm includes:
MVPv:IfOrWhen, then it will merge
ForThen have
In order to promote the effectiveness of anonymous data, only by problematic set of projections FVPvIt merges, by set FVPvIt reduces,
To obtain the smallest problematic set of projections MVPv；There is m attacker, have:
For attacker a, b, FVPa={ a2→a3, a1→a3, FVPb={ b1, b2, b1→b3, b2→b3}；By algorithm IMVA,
Obtain MVPa={ a2→a3, a1→a3, MVPb={ b1, b2}；
TAA_1 is handled
Before carrying out anonymous processing to data set T, carry out:
R(PG(loci), UL (loci))=PG (loci)/UL(loci)+1)
PG(loci): for position lociThe relevant privacy degree of association is represented by deleting point lociBrought privacy income,
Value is set MVPvIn include point lociDifferent track numbers.
3. cross-border e-commerce sales management method as described in claim 1, which is characterized in that the miscarriage of track sets inclusion
The location information of the route of product, and location information is according to time timeiAscending order arrangement；There are two attacker a, when b, stream product
Route privacy tolerance PbrIt is set as 0.5；
Track record: the record t=that track record is n by the length that n location information forms sequentially in time <
loc1, loc2..., locn>, wherein loci∈A；
A is all positions that data publication center is controlled, it is assumed that A={ a1, a2, a3, b1, b2, b3, A is divided into m and mutually disjoints
Nonvoid subset, i.e.,There is A=A1∪A2, A1={ a1, a2, a3, A2={ b1, b2, b3The Attacker Model；
It is assumed that potential attacker's quantity is m, then haveWherein V is attacker's set；Each attacker viIt controls
AiIn include all location informations, then have:AndRemember for each track
Record t∈T, each attacker vi∈ V is owned by a projection knowledge
Given initial trace data set T, T ' are the track data collection to be announced after treatment of T；IfEach attacker
V cannot be to be higher than PbrProbability is accurately inferred to any position information locj,Then think that T ' is safety
, it publishes, it is otherwise just dangerous, it cannot publish.
4. cross-border e-commerce sales management method as described in claim 1, which is characterized in that safety check module is provided with X and penetrates
Line scanning is removed the discrete noise image during ray scanning using non-local mean filtering algorithm, logistics platform is made to circulate
Cargo is clearly outlined, and carries out safety inspection；It specifically includes:
Discrete noise image v=v (i) | and i ∈ I } to the estimated value NL [v] (i) of a pixel i, it is calculated as all in image
The weighted average of pixel, w (i, j) be weight, 0≤w (i, j)≤1 and
Gray vector v (Ni) and v (Nj) similitude indicate pixel i and pixel j between similitude,
For square of the weighted euclidean distance in the region i, j, a (a > 0) indicates Gaussian kernel standard deviation, and h is the coefficient that wave-path degree is considered in control, Z
(i) all areas similarity summation within the scope of picture search.
5. cross-border e-commerce sales management method as described in claim 1, which is characterized in that
Position locating module is provided with GPS locator, and gray model is taken to assist GPS locator, real time GPS dynamic essence
Close One-Point Location；It specifically includes:
Equipped with variable X(0)Original data sequence:
X(0)={ x(0)(1), x(0)(2) ..., x(0)(n)}
N is initial data number；The corresponding time is ti(n=1,2 ..., n)；
Single order accumulator module X is generated with A G O (Accumullated Generating Operation)(1)
X(1)={ x(1)(1), x(1)(2) ..., x(1)(n)}
By single order Grey Simulation X(1)The differential equation of composition are as follows:
According to derivative discrete form, the differential equation can be write as in the matrix form:
Y=AU
Wherein,
Using the principle of least square, estimates of parameters can be acquired are as follows:
Returning to the original differential equation has:
It must solve are as follows:
Discrete form are as follows:
Wherein, k is the initial data number for participating in positioning；
Form are as follows:
Wherein, P > 1 is anchor point；Original series after then positioning are as follows:
Or it simplifiedly expresses are as follows:
Entire gray model forecasting process expression are as follows:
Wherein, IAGO, AGO are respectively inverse accumulated generating sequence and Accumulating generation sequence；
Goods statistics module counts equipment using Full-automatic digital, using RFID technique, drives Full-automatic digital equipment
Information circuit sends out internal data, carries out information reading, specifically includes:
There are m information to be identified in system, is pitched and set using L, when search depth is 1, the identification probability of label are as follows:
P (1)=[1-L-1]m-1
In the statistical information batch reading process of Full-automatic digital statistics equipment, using super high frequency radio frequency identification technology, obtain
To the mean value of the search depth of data are as follows:
Average timeslot number are as follows:
Empty Hash table data are sent into TABLE, using Binomial Trees, then the statistics of Full-automatic digital statistics equipment is believed
Timeslot number is merged in the stratification that breath batch reads data are as follows:
Using RFID technique, the information circuit of Full-automatic digital equipment is driven to send out internal data, reader is pressed at this time
Corresponding sequence receives data, obtains the level of information fusion results of Full-automatic digital statistics equipment are as follows:
By processing, retain first Full-automatic digital facility information exported, filtered data keep original and adopt
Collection sequence reads the information of Full-automatic digital equipment.
6. a kind of computer program for realizing cross-border e-commerce sales management method described in Claims 1 to 5 any one.
7. a kind of information data processing for realizing cross-border e-commerce sales management method described in Claims 1 to 5 any one
Terminal.
8. a kind of computer readable storage medium, including instruction, when run on a computer, so that computer is executed as weighed
Benefit requires cross-border e-commerce sales management method described in 1-5 any one.
9. a kind of cross-border e-commerce sales management system for realizing cross-border e-commerce sales management method described in claim 1
System, which is characterized in that the cross-border e-commerce sale management system includes:
Operator's platform；
Operator's platform includes language conversion module, video display module, manual service module, goods discharging module, after sale module, gathering
Module, order statistical module are connect with logistics platform signal；
Logistics platform, include safety check module, goods statistics module, route planning module, position locating module and operator
Bracket signal connection；
Platform is bought, includes logistics enquiring module, item property module, evaluation module, payment module, purchase platform, complain
Module, application module after sale are connect with logistics platform signal；
Safety check module, is provided with X-ray scanning, removes discrete making an uproar during ray scanning using non-local mean filtering algorithm
Acoustic image carries out safety inspection so that logistics platform circulation cargo is clearly outlined；
Operator's platform, which delivers commodity to be transported through by logistics platform, gives purchase platform；
Operator's platform constitutes freight charges danger agreement with purchase platform and insurance platform；
Reimbursement freight cost and the expenses of taxation are transferred to logistics platform by insurance platform.
10. a kind of cross-border e-commerce sale equipment in internet, which is characterized in that the cross-border e-commerce sale in internet is set
Carry cross-border e-commerce sale management system as claimed in claim 9 less to the utmost.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811103064.9A CN109360044A (en) | 2018-09-20 | 2018-09-20 | A kind of cross-border e-commerce sale management system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811103064.9A CN109360044A (en) | 2018-09-20 | 2018-09-20 | A kind of cross-border e-commerce sale management system and method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109360044A true CN109360044A (en) | 2019-02-19 |
Family
ID=65351101
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811103064.9A Pending CN109360044A (en) | 2018-09-20 | 2018-09-20 | A kind of cross-border e-commerce sale management system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109360044A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109568047A (en) * | 2018-11-26 | 2019-04-05 | 焦建洪 | A kind of Cardiological intelligence bed special, control system and control method |
| CN109948489A (en) * | 2019-03-09 | 2019-06-28 | 闽南理工学院 | A kind of face identification system and method based on the fusion of video multiframe face characteristic |
| CN110774283A (en) * | 2019-10-29 | 2020-02-11 | 龙岩学院 | Robot walking control system and method based on computer vision |
| CN113313556A (en) * | 2021-06-18 | 2021-08-27 | 甘肃瑞恒易网络科技有限公司 | Cross-border electricity business affair middle platform system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914659A (en) * | 2014-03-12 | 2014-07-09 | 西安电子科技大学 | System and method for track restraining data publishing privacy protection based on frequency |
| CN106557904A (en) * | 2016-11-10 | 2017-04-05 | 夏飞 | A kind of cross-border electric business grid warehouse logistics system |
| CN107392699A (en) * | 2017-07-04 | 2017-11-24 | 深圳齐心集团股份有限公司 | A kind of cross-border ecommerce sale management system |
| CN107392701A (en) * | 2017-07-04 | 2017-11-24 | 深圳齐心集团股份有限公司 | A kind of cross-border e-commerce system |
-
2018
- 2018-09-20 CN CN201811103064.9A patent/CN109360044A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103914659A (en) * | 2014-03-12 | 2014-07-09 | 西安电子科技大学 | System and method for track restraining data publishing privacy protection based on frequency |
| CN106557904A (en) * | 2016-11-10 | 2017-04-05 | 夏飞 | A kind of cross-border electric business grid warehouse logistics system |
| CN107392699A (en) * | 2017-07-04 | 2017-11-24 | 深圳齐心集团股份有限公司 | A kind of cross-border ecommerce sale management system |
| CN107392701A (en) * | 2017-07-04 | 2017-11-24 | 深圳齐心集团股份有限公司 | A kind of cross-border e-commerce system |
Non-Patent Citations (4)
| Title |
|---|
| 罗春燕 等: ""生物医学图像去噪及分割方法研究"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 赵婧 等: "基于轨迹频率抑制的轨迹隐私保护方法", 《计算机学报》 * |
| 郑作亚 等: ""灰色模型修正及其在实时GPS卫星钟差预报中的应用研究"", 《天文学报》 * |
| 黄庆林: ""面向棉种质量安全溯源系统的RFID中间件技术的研究"", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109568047A (en) * | 2018-11-26 | 2019-04-05 | 焦建洪 | A kind of Cardiological intelligence bed special, control system and control method |
| CN109948489A (en) * | 2019-03-09 | 2019-06-28 | 闽南理工学院 | A kind of face identification system and method based on the fusion of video multiframe face characteristic |
| CN110774283A (en) * | 2019-10-29 | 2020-02-11 | 龙岩学院 | Robot walking control system and method based on computer vision |
| CN113313556A (en) * | 2021-06-18 | 2021-08-27 | 甘肃瑞恒易网络科技有限公司 | Cross-border electricity business affair middle platform system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11030622B2 (en) | Card systems and methods | |
| US8615439B2 (en) | Processing online transactions | |
| CN109360044A (en) | A kind of cross-border e-commerce sale management system and method | |
| Xu et al. | E-commerce reputation manipulation: The emergence of reputation-escalation-as-a-service | |
| US8712888B2 (en) | Methods and systems for assessing sales activity of a merchant | |
| US20130117154A1 (en) | Method and System of Evaluating Credibility of Online Trading User | |
| Hao et al. | Drops for stuff: An analysis of reshipping mule scams | |
| US20190180394A1 (en) | Method and system for evaluating commercial real estate pricing and location by leveraging transaction data | |
| CN103577987A (en) | Method and device for identifying risk users | |
| CN113469789A (en) | Abnormal order detection method and device and computer equipment | |
| JP2021513169A (en) | Fraud prevention devices and methods | |
| US20190295086A1 (en) | Quantifying device risk through association | |
| US20150112879A1 (en) | Systems and methods for evaluating pricing of real estate | |
| CN114170010A (en) | Matching transaction method and device, electronic equipment and storage medium | |
| Aliapoulios et al. | Swiped: Analyzing ground-truth data of a marketplace for stolen debit and credit cards | |
| CN112632004A (en) | User identity recognition method and device based on behavior data acquisition | |
| CN116228381B (en) | Money issuing method, money issuing device, computer equipment and readable storage medium | |
| CN109544299B (en) | Buyer identity rating method, device and storage medium based on transaction guarantee platform | |
| KR20140118235A (en) | Risk management method and apparatus for affiliated store | |
| CN115423574A (en) | Data processing method based on order and related device | |
| CN109685556B (en) | Trade integration method based on block chain technology | |
| ALGhamdi et al. | Electronic payment technology: Developing a taxonomy of factors to evaluate a fraud detection and prevention system for the airlines industry | |
| US10296918B1 (en) | Providing risk assessments to compromised payment cards | |
| JP2009163526A (en) | Service information management method | |
| Zuo | BIKE-SHARING PLATFORMS IN TWO-SIDED MARKETS |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190219 |
|
| WD01 | Invention patent application deemed withdrawn after publication |