CN106157045B - Method, device and server for identifying false transactions based on logistics data - Google Patents
Method, device and server for identifying false transactions based on logistics data Download PDFInfo
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Abstract
The application provides a method, a device and a server for identifying false transactions based on logistics data, wherein the method comprises the following steps: acquiring logistics data of online transaction about transaction commodities, wherein the logistics data comprises logistics order numbers of the transaction commodities and commodity pictures of the transaction commodities; determining a first commodity type corresponding to the transaction commodity according to the logistics sheet number; determining a second commodity type of the transaction commodity according to the commodity picture; and determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type. The technical scheme of the invention can avoid false transactions between the seller and the buyer who transact on the network in advance, and can prohibit illegal actions such as cash register, money laundering and the like of illegal sellers through an e-commerce platform, so that the online transaction action is more standard.
Description
Technical Field
The application relates to the technical field of internet, in particular to a method, a device and a server for identifying false transactions based on logistics data.
Background
With the rapid development of internet technology, electronic commerce makes people more convenient to shop. In order to rapidly increase the account credit and/or the commodity sales of a commodity provider (a store owner or a seller) based on an e-commerce platform in the early stage of opening a store, a fake transaction is usually adopted to increase the commodity sales and the account credit, and when a commodity purchaser (a buyer) refers to the account credit or the commodity sales of the seller, the rights and interests of the buyer are jeopardized because the parameters do not fully represent the true information of the seller. In addition, unreal online transactions can enable illegal sellers to perform illegal behaviors such as cash register, money laundering and the like through an e-commerce platform, so that extremely adverse effects are brought to the society. In the false transaction identification process in the prior art, an e-commerce platform provider supervises a seller by equipping related personnel so as to standardize the online transaction behavior of the seller, and the method consumes a large amount of human resources and brings unnecessary cost for the e-commerce platform provider.
Disclosure of Invention
In view of this, the present application provides a new technical solution, which can solve the technical problem of false transactions existing in online transactions.
In order to achieve the above purpose, the present application provides the following technical solutions:
according to a first aspect of the present invention, there is provided a method of identifying false transactions based on logistics data, comprising:
acquiring logistics data of online transaction about transaction commodities, wherein the logistics data comprises logistics order numbers of the transaction commodities and commodity pictures of the transaction commodities;
determining a first commodity type corresponding to the transaction commodity according to the logistics sheet number;
determining a second commodity type of the transaction commodity according to the commodity picture;
and determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.
According to a second aspect of the present invention, there is provided an apparatus for identifying false transactions based on logistics data, which is applied to a server, and comprises:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring logistics data of online transaction about transaction commodities, and the logistics data comprises logistics list numbers of the transaction commodities and commodity pictures of the transaction commodities;
the first determining module is used for determining a first commodity type corresponding to the transaction commodity according to the logistics list number acquired by the acquiring module;
the second determining module is used for determining a second commodity type of the transaction commodity according to the commodity picture acquired by the acquiring module;
a third determining module, configured to determine whether the online transaction is a false transaction according to the first commodity type determined by the first determining module and the second commodity type determined by the second determining module.
According to a third aspect of the present invention, there is provided a server comprising:
a processor; a memory for storing the processor-executable instructions;
the processor is used for acquiring logistics data of online transaction on transaction commodities, wherein the logistics data comprises logistics order numbers of the transaction commodities and commodity pictures of the transaction commodities; determining a first commodity type corresponding to the transaction commodity according to the logistics sheet number; and determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.
According to the technical scheme, whether the online transaction is false transaction is identified according to the logistics data, the logistics data are acquired through a third-party logistics company, so that the reliability of a data source can be ensured, false transaction of a seller and a buyer who transact online in advance can be avoided, the seller is prevented from increasing the account credit point or commodity sales through the false transaction, the seller information displayed by the e-commerce platform is ensured to be real information, a real reference basis is provided for the buyer when shopping online, and the rights and interests of the buyer when shopping online are ensured. In addition, whether online transaction is real or not is identified through logistics data and transaction information, and illegal actions such as cash register, money laundering and the like of an illegal seller through an e-commerce platform can be prohibited, so that online transaction actions are more standard.
Drawings
FIG. 1A shows a flow diagram of a method of identifying spurious transactions based on logistics data in accordance with an exemplary embodiment of the present invention;
fig. 1B illustrates a schematic diagram of logistics voucher information according to an exemplary embodiment of the present invention;
FIG. 1C shows a schematic diagram of a transaction commodity according to an exemplary embodiment of the present invention;
FIG. 2 shows a flow diagram of a method of identifying spurious transactions based on logistics data, according to yet another example embodiment of the present invention;
FIG. 3 shows a flow diagram of a method of identifying spurious transactions based on logistics data in accordance with yet another example embodiment of the present invention;
FIG. 4 shows a schematic block diagram of a server according to an exemplary embodiment of the present invention;
FIG. 5 shows a schematic structural diagram of an apparatus for identifying false transactions based on logistics data according to an exemplary embodiment of the present invention;
fig. 6 is a schematic structural diagram illustrating an apparatus for identifying a false transaction based on logistics data according to another exemplary embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The method and the system identify whether the online transaction is false transaction through the logistics data provided by the logistics company, and the logistics data is acquired through the logistics company of the third party, so that the reliability of the data source can be ensured, the false transaction of the online transaction by the seller and the buyer in advance is avoided, and the illegal transaction behavior can be prevented.
For further explanation of the present application, the following examples are provided:
referring to fig. 1A, a flow chart of a method for identifying false transactions based on logistics data according to an exemplary embodiment of the invention is shown, which includes the following steps:
In one embodiment, the server of the e-commerce platform provider may acquire logistics data on the transaction goods from the server of the logistics company in real time or near real time by communicatively connecting the server of the e-commerce platform provider with the server of the logistics company for managing logistics data of the shipped goods. In one embodiment, the commodity picture may be a color picture taken by the logistics company for the transaction commodity when the transaction commodity is received, in another embodiment, the commodity picture may be an infrared picture taken by the logistics company for security check of the transaction commodity, and the image characteristic of the transaction commodity may be determined through the color picture or the infrared picture.
And 102, determining a first commodity type corresponding to the transaction commodity according to the logistics list number.
In an embodiment, the commodity transaction information of the online transaction may be queried from a server of an e-commerce platform provider according to a logistics order number, please refer to fig. 1B, which shows the commodity transaction information in the embodiment of the present invention, where the commodity transaction information may include a logistics order number and a logistics company of the online transaction, and may further include information such as a commodity type of a transaction commodity, a commodity name, a commodity weight, consignee information, shipper information, a payment method, and online transaction time, and thus, a first commodity type of the transaction commodity may be determined according to the commodity transaction information. For example, as shown in fig. 1B, the type of the product in the online transaction is "smart phone" which is called "Apple (Apple) iPhone6Plus (a1524)16G silver mobile telecommunication 4G phone" which is queried from the server of the e-commerce platform provider according to the logistics order number.
And 103, determining a second commodity type corresponding to the transaction commodity according to the commodity picture.
In an embodiment, the picture feature information of the picture of the commodity may be calculated through an image detection technology, and the commodity type of the transaction commodity is determined according to the picture feature information, referring to fig. 1C, a schematic diagram of the transaction commodity according to an exemplary embodiment of the present invention is shown, which is exemplarily illustrated by taking an apple phone 10 as an example, for example, after the picture feature information of the transaction commodity is obtained through the image detection technology, the commodity type of the transaction commodity may be determined to be a smart phone according to the picture feature information.
And 104, determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.
For example, the first commodity type corresponding to the transaction commodity is determined to be a smart phone through the step 102, the commodity type of the transaction commodity is determined to be a smart phone through the step 103, and the transaction on the internet can be determined to be a real transaction if the first commodity type is matched with the second commodity type; if the first commodity type corresponding to the transaction commodity is determined to be the smart phone through the step 102, but the seller sends a packet of paper towels to the buyer to replace the smart phone, the second commodity type of the transaction on the network can be determined to be paper through the picture characteristic information of the transaction commodity, and the transaction can be determined to be false transaction because the smart phone is not matched with the paper.
As can be seen from the above description, the embodiment of the present invention identifies whether the online transaction is a false transaction according to the logistics data, and the logistics data is obtained by the third-party logistics company, so that the reliability of the data source can be ensured, thereby preventing the seller and the buyer in the online transaction from performing false transactions in advance, preventing the seller from increasing the account credit points or commodity sales through the false transactions, ensuring that the seller information displayed by the e-commerce platform is real information, providing a real reference for the buyer to purchase online, and ensuring the legitimate interest of the buyer in online shopping. In addition, whether the online transaction is real or not is identified through the logistics data, and illegal actions such as cash register, money laundering and the like of an illegal seller through an e-commerce platform can be prohibited, so that the online transaction action is more standard.
Referring to fig. 2, a flow chart of a method for identifying false transactions based on logistics data according to another exemplary embodiment of the invention is shown, which includes the following steps:
For the description of step 201, refer to the description of step 101 above, and will not be described in detail here.
The description of step 202 refers to the description of step 102 above, and is not detailed here.
And step 203, determining picture reference features corresponding to the first commodity type in a feature model library, wherein the picture reference features of the transaction commodities with the sales volume reaching a set number in a set time period are stored in the feature model library.
In an embodiment, a picture feature library may be set on a server of an e-commerce platform provider, and the sales volume of any commodity after being online is recorded, and when the sales volume within a set time period reaches a set value, the transaction commodity may be considered as a hot commodity, and further, the picture reference feature and the corresponding commodity model of the hot commodity may be recorded. In one embodiment, by comparing the feature information of the transaction commodity with the picture reference features stored in the picture feature library, the commodity model of the transaction commodity can be determined through the commodity picture, so that the transaction commodity can be identified more accurately.
And step 204, calculating picture characteristic information of the commodity picture.
In an embodiment, the picture feature information of the product picture may be implemented by an image recognition technology, and it can be understood by those skilled in the art that the picture feature information of different products is different, for example, the picture feature information of a smart phone is different from the picture feature information of a living user.
And step 206, if the two are consistent, determining that the online transaction is a real transaction, and determining the commodity model of the transaction commodity according to the picture reference characteristics.
For example, after the first commodity type of the transaction commodity is determined to be the smart phone through the logistics list number, the picture reference feature of the smart phone is searched from the picture feature library, and then the commodity model of the transaction commodity can be determined to be 'apple cell phone6 Plus'.
And step 207, if the transaction is consistent, determining that the online transaction is a false transaction.
On the basis of the beneficial technical effects of the embodiment shown in fig. 1A, the embodiment of the invention identifies whether the online transaction is a false transaction according to the commodity picture, and can identify the commodity model of the transaction commodity through the commodity picture, thereby realizing more accurate identification of the false online transaction.
Referring to fig. 3, a flow chart of a method for identifying false transactions based on logistics data according to still another exemplary embodiment of the invention is shown, which includes the following steps:
For the description of acquiring the stream data in step 301, refer to the description of step 101 above, and will not be described in detail here. In one embodiment, the weight of the commodity may be weight information of the commodity weighed by the logistics company.
In an embodiment, the commodity weight of the transaction commodity may be determined according to the logistics data shown in fig. 1B, and a normal weight range corresponding to the transaction commodity is determined, for example, the commodity type corresponding to the transaction commodity is determined as "apple cell phone" through the logistics list number, if the logistics company weighs 484 grams of the transaction commodity, 484 grams of the commodity is within the normal weight range [ 480-10, 480+10 ] corresponding to the apple cell phone, so that the transaction on the secondary network may be determined as a real transaction, and if the logistics company weighs 54 grams of the transaction commodity, the commodity weight is already out of the normal weight range.
For example, if the logistics company weighs 54 grams of the traded good, the weight of the good is already out of the normal weight range, and the difference between the weight of the good and the normal weight range is calculated to be 480-54-426 grams, which is 426/480-0.8875 percent of the difference from the normal weight range.
In an embodiment, the preset threshold may be determined according to the supervision of the e-commerce platform on the online transaction commodity, when the preset threshold is larger, it indicates that a larger weight error is allowed, and when the preset threshold is smaller, it indicates that a larger weight error is not allowed, so that the specific size of the preset threshold is not limited in the embodiment of the present invention.
And step 306, if the percentage is larger than a preset threshold value, prompting that the online transaction is a false transaction.
And 307, if the percentage is smaller than a preset threshold value, prompting that the online transaction is a suspected false transaction.
In this embodiment, whether the online transaction is a false transaction or a suspected false transaction is prompted by the commodity weight of the transaction commodity, so that a supervisor of an e-commerce platform provider can classify the supervision level of the online transaction, if the online transaction is a real transaction, the supervision level is released, if the online transaction is a suspected false transaction, the supervision level is confirmed again in a manual mode, and if the online transaction is a false transaction, the online transaction is directly released to a corresponding penalty department, so that the supervision strength of the e-commerce platform on the online transaction is improved, the situation that a seller and a buyer of the online transaction have good trade volume in advance and the behavior of the online transaction is standardized.
By the embodiment, the logistics data provided by the logistics company is used as a basis in the false transaction identification process, and the commodity picture, the logistics certificate information and other data of the transaction commodity are acquired from the server of the logistics company, so that various commodity transaction behaviors on the e-commerce platform are identified by the embodiment, and the accuracy of the false transaction identification can be improved.
Corresponding to the above-mentioned method for identifying false transactions based on logistics data, the present application also proposes a schematic block diagram of a server according to an exemplary embodiment of the present application, shown in fig. 4. Referring to fig. 4, at the hardware level, the server includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, but may also include hardware required for other services. The processor reads a corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form a device for identifying false transactions based on the logistics data on a logic level. Of course, besides the software implementation, the present application does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.
Referring to fig. 5, in a software implementation, the device for identifying false transactions based on logistics data may include: an obtaining module 51, a first determining module 52, a second determining module 543, and a third determining module 54. Wherein:
the acquisition module 51 is used for acquiring logistics data of the transaction commodities in online transaction, wherein the logistics data comprises logistics list numbers of the transaction commodities and commodity pictures of the transaction commodities;
the first determining module 52 is configured to determine a first commodity type corresponding to the transaction commodity according to the logistics list number acquired by the acquiring module 51;
the second determining module 53 is configured to determine a second commodity type of the transaction commodity according to the commodity picture acquired by the acquiring module 51;
and a third determining module 54, configured to determine whether the online transaction is a false transaction according to the first commodity type determined by the first determining module 52 and the second commodity type determined by the second determining module 53.
Referring to fig. 6, based on the embodiment shown in fig. 5, in an embodiment, the second determining module 53 may include:
the first calculating unit 531 is configured to calculate picture feature information of the commodity picture acquired by the acquiring module 51;
the first determining unit 532 is configured to determine the second commodity type of the transaction commodity according to the picture feature information calculated by the first calculating unit 531.
In one embodiment, the third determination module 54 may include:
a second determination unit 541 for determining whether the first article type determined by the first determination module 52 matches the second article type determined by the second determination module 53;
a third determining unit 542, configured to determine that the online transaction is a real transaction if the second determining unit 541 determines that the online transaction is a match; if the second determination unit 541 determines that there is no match, it determines that the online transaction is a fake transaction.
In an embodiment, the apparatus may further comprise:
a fourth determining module 55, configured to determine, in a feature model library, a picture reference feature corresponding to the first commodity type determined by the first determining module 52, where the picture reference feature of a transaction commodity of which the sales volume reaches a set number in a set time period is stored in the feature model library;
a fifth determining module 56, configured to determine whether the commodity picture is consistent with the picture reference feature; and if the picture reference characteristics are consistent with the picture reference characteristics, determining the commodity model of the transaction commodity.
In an embodiment, the logistics data acquired by the acquiring module 51 further includes a commodity weight of the transaction commodity, and the apparatus further includes:
a fourth determining module 57, configured to determine whether a commodity weight corresponding to the traded commodity is within a normal weight range of the traded commodity;
and the prompting module 58 is used for determining the commodity weight of the transaction commodity according to the logistics order number, and determining whether the commodity weight exceeds the normal weight range determined by the fourth determining module 57 to prompt the authenticity of the online transaction.
In one embodiment, the prompt module 58 may include:
the first prompting unit 581 is used for prompting to determine that the online transaction is a real transaction if the weight of the commodity is within a normal weight range;
a second calculating unit 582 for calculating a percentage of a difference of the weight of the article out of the normal weight range to the normal weight range if the weight of the article is out of the normal weight range;
the second prompting unit 583 is configured to prompt that the online transaction is a false transaction if the percentage calculated by the second calculating unit 582 is greater than a preset threshold;
and the third prompting unit 584, configured to prompt the online transaction to be a suspected false transaction if the percentage calculated by the second calculating unit 582 is smaller than a preset threshold.
It can be seen from the above embodiments that in the false transaction identification process, the logistics data provided by the logistics company is used as a basis, the data such as the commodity weight of the transaction commodity and/or the scanned commodity picture is acquired from the server of the logistics company, and the data is used to identify various commodity transaction behaviors on the e-commerce platform.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (11)
1. A method for identifying false transactions based on logistics data, the method comprising:
acquiring logistics data of online transaction on transaction commodities, wherein the logistics data comprises logistics order numbers of the transaction commodities and commodity pictures of the transaction commodities, and the logistics data is acquired through a third-party logistics data server;
determining a first commodity type corresponding to the transaction commodity according to the logistics sheet number;
calculating picture characteristic information of the commodity picture, and determining a second commodity type of the transaction commodity according to the picture characteristic information, wherein the picture characteristic information of different transaction commodities is different;
and determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.
2. The method of claim 1, wherein said step of determining whether said online transaction is a fraudulent transaction based on said first article type and said second article type comprises:
determining whether the first item type matches the second item type;
if the online transaction is matched with the real transaction, determining that the online transaction is a real transaction;
and if not, determining that the online transaction is a false transaction.
3. The method of claim 1, further comprising:
determining picture reference features corresponding to the first commodity type in a feature model library, wherein the picture reference features of the transaction commodities with sales reaching a set number in a set time period are stored in the feature model library;
determining whether the commodity picture is consistent with the picture reference feature;
and if the picture reference characteristics are consistent with the picture reference characteristics, determining the commodity model of the transaction commodity.
4. The method of any one of claims 1-3, wherein the logistics data further comprises a commodity weight of the transaction commodity, the method further comprising:
determining the commodity weight corresponding to the transaction commodity according to the logistics sheet number;
determining whether the weight of the item is within a normal weight range for the transaction item;
and prompting the authenticity of the online transaction according to whether the weight of the commodity exceeds the normal weight range.
5. The method of claim 4, wherein said step of prompting the authenticity of said online transaction based on whether said weight of said item is outside said normal weight range comprises:
if the weight of the commodity is within the normal weight range, prompting that the online transaction is a real transaction;
if the weight of the good is outside the normal weight range, calculating a percentage of the difference in the weight of the good outside the normal weight range to the normal weight range;
if the percentage is larger than a preset threshold value, prompting that the online transaction is a false transaction;
and if the percentage is smaller than the preset threshold value, prompting that the online transaction is a suspected false transaction.
6. An apparatus for identifying false transactions based on logistics data, the apparatus comprising:
the system comprises an acquisition module, a transaction module and a display module, wherein the acquisition module is used for acquiring logistics data of online transaction about transaction commodities, the logistics data comprises logistics list numbers of the transaction commodities and commodity pictures of the transaction commodities, and the logistics data is acquired through a third-party logistics data server;
the first determining module is used for determining a first commodity type corresponding to the transaction commodity according to the logistics list number acquired by the acquiring module;
the second determining module is used for calculating picture characteristic information of the commodity picture and determining a second commodity type of the transaction commodity according to the picture characteristic information, wherein the picture characteristic information of different transaction commodities is different;
a third determining module, configured to determine whether the online transaction is a false transaction according to the first commodity type determined by the first determining module and the second commodity type determined by the second determining module.
7. The apparatus of claim 6, wherein the third determining module comprises:
a second determination unit configured to determine whether the first article type determined by the first determination module matches the second article type determined by the second determination module;
a third determining unit, configured to determine that the online transaction is a real transaction if the second determining unit determines that the online transaction matches the online transaction; and if the second determination unit determines that the online transaction is not matched, determining that the online transaction is a false transaction.
8. The apparatus of claim 6, further comprising:
a fourth determining module, configured to determine, in a feature model library, a picture reference feature corresponding to the first commodity type determined by the first determining module, where the picture reference feature of a transaction commodity whose sales volume reaches a set number in a set time period is stored in the feature model library;
the fifth determining module is used for determining whether the picture characteristic information of the commodity picture is consistent with the picture reference characteristic; and if the picture reference characteristics are consistent with the picture reference characteristics, determining the commodity model of the transaction commodity.
9. The apparatus according to any one of claims 6 to 8, wherein the logistics data acquired by the acquisition module further includes a product weight of the transaction product, the apparatus further comprising:
the fourth determining module is used for determining the commodity weight corresponding to the trading commodity according to the logistics order number and determining whether the commodity weight is in a normal weight range of the trading commodity;
and the prompting module is used for prompting the authenticity of the online transaction according to whether the weight of the commodity exceeds the normal weight range determined by the fourth determining module.
10. The apparatus of claim 9, wherein the prompting module comprises:
the first prompting unit is used for prompting and determining that the online transaction is a real transaction if the weight of the commodity is within the normal weight range;
a second calculation unit for calculating a percentage of a difference value of the weight of the article out of the normal weight range to the normal weight range if the weight of the article is out of the normal weight range;
the second prompting unit is used for prompting the online transaction to be a false transaction if the percentage calculated by the second calculating unit is larger than a preset threshold;
and the third prompting unit is used for prompting that the online transaction is a suspected false transaction if the percentage calculated by the second calculating unit is smaller than the preset threshold.
11. A server, characterized in that the server comprises:
a processor; a memory for storing the processor-executable instructions;
the processor is used for acquiring logistics data of online transaction on transaction commodities, wherein the logistics data comprises logistics order numbers of the transaction commodities and commodity pictures of the transaction commodities, and the logistics data is acquired through a third-party logistics data server; determining a first commodity type corresponding to the transaction commodity according to the logistics sheet number; calculating picture characteristic information of the commodity picture, and determining a second commodity type of the transaction commodity according to the picture characteristic information, wherein the picture characteristic information of different transaction commodities is different; and determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.
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| CN202111069264.9A CN113850610A (en) | 2015-03-26 | 2015-03-26 | Method, device and server for identifying false transactions based on logistics data |
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| CN107563885A (en) * | 2017-08-08 | 2018-01-09 | 阿里巴巴集团控股有限公司 | A kind of arbitrage recognition methods and device |
| CN111311411B (en) * | 2020-02-14 | 2022-03-08 | 北京三快在线科技有限公司 | Illegal behavior identification method and device |
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| CN106157045A (en) | 2016-11-23 |
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