CN106157045A - Method, device and server for identifying false transactions based on logistics data - Google Patents

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

Method, device and server for identifying false transactions based on logistics data

technical field

The present application relates to the field of Internet technologies, and in particular to a method, device and server for identifying false transactions based on logistics data.

Background technique

With the rapid development of Internet technology, e-commerce makes people's shopping more convenient. Commodity providers (shop owners or sellers) based on e-commerce platforms usually use false transactions to increase their product sales and account credit points in order to quickly increase their account credit points and/or product sales at the initial stage of store opening. When the buyer (buyer) refers to the seller's account credit score or product sales, because the above parameters do not fully reflect the real information of the seller, thus hindering the buyer's rights and interests. In addition, unauthentic online transactions can enable illegal sellers to carry out illegal activities such as cashing out and money laundering through e-commerce platforms, which will have extremely adverse effects on society. In the process of identifying false transactions in the prior art, the provider of the e-commerce platform provides relevant personnel to supervise the sellers, so as to regulate the behavior of online transactions of the sellers. Businesses bring unnecessary costs.

Contents of the invention

In view of this, this 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 object, the application provides technical solutions as follows:

According to a first aspect of the present invention, a method for identifying false transactions based on logistics data is proposed, comprising:

Obtain online transaction logistics data about the traded commodities, the logistics data including the logistics order number of the traded commodities and the commodity pictures of the traded commodities;

determining the first commodity type corresponding to the transaction commodity according to the logistics order number;

determining the second commodity type of the traded commodity according to the commodity picture;

It is determined whether the online transaction is a false transaction according to the first commodity type and the second commodity type.

According to the second aspect of the present invention, a device for identifying false transactions based on logistics data is proposed, which is applied to a server, including:

An acquisition module, configured to acquire online transaction logistics data about traded commodities, said logistics data including logistics order numbers of said traded commodities and commodity pictures of said traded commodities;

A first determination module, configured to determine the first commodity type corresponding to the transaction commodity according to the logistics order number acquired by the acquisition module;

The second determination module is configured to determine the second commodity type of the transaction commodity according to the commodity picture acquired by the acquisition module;

A third determination module, configured to determine whether the online transaction is a false transaction according to the first commodity type determined by the first determination module and the second commodity type determined by the second determination module.

According to a third aspect of the present invention, a server is proposed, and the server includes:

a processor; memory for storing instructions executable by said processor;

Wherein, the processor is used to obtain online transaction logistics data about traded goods, the logistics data includes the logistics order number of the traded goods and the product picture of the traded goods; according to the logistics order number to determine the A first commodity type corresponding to the transaction commodity; determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.

It can be seen from the above technical solutions that this application identifies whether the online transaction is a false transaction based on the logistics data. Since the logistics data is obtained through a third-party logistics company, the reliability of the data source can be ensured, and the seller and buyer of the online transaction can be avoided. Negotiate in advance to carry out false transactions, prevent sellers from increasing their account credit points or product sales through false transactions, ensure that the seller information displayed on the e-commerce platform is true information, and provide buyers with real references when shopping online, thereby Ensure the rights and interests of buyers when shopping online. In addition, identifying whether online transactions are authentic through logistics data and transaction information can also prohibit illegal sellers from cashing out and money laundering through e-commerce platforms, making online transactions more standardized.

Description of drawings

FIG. 1A shows a schematic flowchart of a method for identifying false transactions based on logistics data according to an exemplary embodiment of the present invention;

Fig. 1B shows a schematic diagram of logistics voucher information according to an exemplary embodiment of the present invention;

Fig. 1C shows a schematic diagram of trading commodities according to an exemplary embodiment of the present invention;

Fig. 2 shows a schematic flow chart of a method for identifying false transactions based on logistics data according to yet another exemplary embodiment of the present invention;

FIG. 3 shows a schematic flowchart of a method for identifying false transactions based on logistics data according to yet another exemplary embodiment of the present invention;

Fig. 4 shows a schematic structural diagram of a server according to an exemplary embodiment of the present invention;

Fig. 5 shows a schematic structural diagram of a device for identifying false transactions based on logistics data according to an exemplary embodiment of the present invention;

Fig. 6 shows a schematic structural diagram of an apparatus for identifying false transactions based on logistics data according to yet 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, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present application as recited in the appended claims.

The terminology used in this application is for the purpose of describing particular embodiments only, and is not intended to limit the application. As used in this application and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of the present application, first information may also be called second information, and similarly, second information may also be called first information. Depending on the context, the word "if" as used herein may be interpreted as "at" or "when" or "in response to a determination."

This application uses the logistics data provided by the logistics company to identify whether the online transaction is a fake transaction. Since the logistics data is obtained through a third-party logistics company, the reliability of the data source can be ensured, and the seller and buyer of the online transaction can be avoided. It is possible to stop illegal transactions by agreeing to carry out false transactions in advance.

To further illustrate the application, the following examples are provided:

Please refer to FIG. 1A, which is a schematic flowchart of a method for identifying false transactions based on logistics data according to an exemplary embodiment of the present invention, including the following steps:

Step 101, acquiring online transaction logistics data about transaction commodities, wherein the logistics data includes online transaction logistics order numbers and commodity pictures of transaction commodities.

In one embodiment, the server of the e-commerce platform provider can communicate with the server used by the logistics company to manage the logistics data of the goods delivered, and the server of the e-commerce platform provider can obtain real-time or quasi-real-time Acquire the logistics data of online transactions about the commodities traded. In one embodiment, the product picture may be a color picture taken by the logistics company of the transaction product when it receives the transaction product. In another embodiment, the product picture may be an infrared image taken by the logistics company during the security inspection of the transaction product. Pictures, color pictures or infrared pictures can be used to determine the image characteristics of trading commodities.

Step 102, determine the first commodity type corresponding to the transaction commodity according to the logistics order number.

In one embodiment, the commodity transaction information of this online transaction can be queried from the server of the e-commerce platform provider according to the logistics order number. Please refer to FIG. 1B, which shows the commodity transaction information described in the embodiment of the present invention. Commodity transaction information can include the logistics order number and logistics company for online transactions, and can also include the commodity type, commodity name, commodity weight, consignee information, consignor information, payment method, and online transaction time of the commodity traded. Therefore, the first commodity type of the traded commodity can be determined through the commodity transaction information. For example, as shown in Figure 1B, it can be inquired from the server of the e-commerce platform provider according to the logistics order number that the product type of this online transaction is "smart phone" and the product name is "Apple (Apple) iPhone6Plus (A1524) 16G Silver Mobile Unicom Telecom 4G Mobile Phone".

Step 103: Determine the second commodity type corresponding to the transaction commodity according to the commodity picture.

In one embodiment, the picture feature information of the product picture can be calculated by image detection technology, and the product type of the transaction product can be determined according to the picture feature information. Referring to FIG. 1C, it shows the transaction product according to an exemplary embodiment of the present invention Schematic diagram, the schematic diagram is illustrated by taking Apple mobile phone 10 as an example. For example, after obtaining the picture feature information of the traded commodity through image detection technology, it can be determined according to the picture feature information that the commodity type of the traded commodity is a smart phone.

Step 104: Determine whether the online transaction is a false transaction according to the first commodity type and the second commodity type.

For example, through the above step 102, it is determined that the first product type corresponding to the transaction product is a smart phone, and through the above step 103, it is determined that the product type of the transaction product is a smart phone, and the first product type matches the second product type. The transaction is a real transaction; if it is determined through the above step 102 that the first product type corresponding to the transaction product is a smart phone, but the seller sends a pack of tissues to the buyer to replace the smart phone, then it can be determined through the image feature information of the transaction product that this time The second type of commodity traded online is "paper product". Since "smart phone" and "paper product" do not match, this transaction can be determined to be a false transaction.

As can be seen from the above description, the embodiment of the present invention identifies whether an online transaction is a false transaction based on the logistics data. Since the logistics data is obtained through a third-party logistics company, the reliability of the data source can be ensured, thereby avoiding the seller of the online transaction. The buyer negotiates in advance to carry out false transactions, which prevents the seller from increasing its account credit points or product sales through false transactions, ensures that the seller's information displayed on the e-commerce platform is true information, and provides buyers with real reference when shopping online. It ensures the legitimate rights and interests of buyers when shopping online. In addition, identifying whether online transactions are authentic through logistics data can also prohibit illegal sellers from cashing out and money laundering through e-commerce platforms, making online transactions more standardized.

Please refer to FIG. 2 , which is a schematic flowchart of a method for identifying false transactions based on logistics data according to another exemplary embodiment of the present invention, including the following steps:

Step 201 , acquiring online transaction logistics data about traded commodities, the logistics data including the logistics order number of the traded commodities and commodity pictures of the traded commodities.

For the description of step 201, please refer to the description of the above step 101, which will not be described in detail here.

Step 202, determine the first commodity type corresponding to the traded commodity according to the logistics order number.

For the description of step 202, please refer to the description of the above-mentioned step 102, which will not be described in detail here.

Step 203, determine the image reference features corresponding to the first commodity type in the feature model database, wherein the feature model database stores the image reference features of transaction commodities whose sales volume reaches a set number within a set period of time.

In one embodiment, a picture feature library can be set on the server of the e-commerce platform provider to record the sales volume of any product after it goes online. When the sales volume within a set time period reaches a set value , it can be considered that the traded commodity is a popular commodity, and then the picture reference feature and the corresponding commodity model of the popular commodity can be recorded. In one embodiment, by comparing the feature information of the traded commodity with the image reference features stored in the picture feature database, the commodity model of the traded commodity can be determined through the commodity picture, thereby identifying the traded commodity more accurately.

Step 204, calculating image feature information of the product image.

In one embodiment, the picture feature information of the product picture can be realized by image recognition technology. Those skilled in the art can understand that the picture feature information of different products is different. For example, for the picture feature information of smart phones and life User's image feature information is different.

Step 205, determine whether the picture feature information of the product picture is consistent with the picture reference feature, if they are consistent, go to step 206, if not, go to step 207.

Step 206, if they are consistent, determine that the online transaction is a real transaction, and determine the product model of the transaction product according to the picture reference feature.

For example, after determining that the first product type of the traded product is a smart phone through the logistics order number, the image reference feature of the smart phone is searched from the picture feature database, and then the product model of the traded product can be determined to be "iPhone 6Plus".

Step 207, if they are consistent, determine that the online transaction is a false transaction.

On the basis of the beneficial technical effect of the embodiment shown in Figure 1A above, the embodiment of the present invention identifies whether the online transaction is a false transaction according to the product picture, and can identify the product model of the transaction product through the product picture, thereby achieving more accurate To accurately identify false online transactions.

Please refer to FIG. 3 , which is a schematic flowchart of a method for identifying false transactions based on logistics data according to yet another exemplary embodiment of the present invention, including the following steps:

Step 301, acquiring online transaction logistics data about traded commodities, the logistics data including commodity weight and logistics order number.

For the description of obtaining logistics data in step 301, please refer to the description of step 101 above, and will not be described in detail here. In an embodiment, the commodity weight may be the weight information of the logistics company weighing the traded commodity.

Step 302, determine the commodity weight corresponding to the transaction commodity according to the logistics order number, determine whether the commodity weight is within the normal weight range of the transaction commodity, if the commodity weight is within the normal weight range, perform step 303, if the commodity weight exceeds the normal weight range, go to step 304.

In an embodiment, the commodity weight of the transaction commodity can be determined according to the logistics data shown in FIG. 1B, and the normal weight range corresponding to the transaction commodity can be determined. For example, the commodity type corresponding to the transaction commodity can be determined as "apple Mobile phone”, if the logistics company weighs the traded goods as 484 grams, then 484 is within the normal weight range [480-10, 480+10] corresponding to the Apple mobile phone, so it can be determined that the online transaction is a real transaction. If the logistics company weighs 54 grams of the traded goods, the weight of the goods has exceeded the normal weight range.

Step 303, prompting that the online transaction is a real transaction.

Step 304, calculate the percentage of the difference between the weight of the commodity beyond the normal weight range and the normal weight range.

For example, if the logistics company weighs 54 grams of the traded goods, the weight of the goods has exceeded the normal weight range, and the calculated difference in the weight of the goods beyond the normal weight range is 480-54=426 grams, which is different from the normal weight The percentage of range is 426/480 = 0.8875.

Step 305, determine whether the percentage is greater than a preset threshold, if the percentage is greater than the preset threshold, perform step 306, and if the percentage is less than the preset threshold, perform step 307.

In one embodiment, the preset threshold can be determined according to the supervision of online trading commodities by the e-commerce platform. When the preset threshold is larger, it means that a larger weight error is allowed; when the preset threshold is smaller, it means that there is no Larger weight errors are allowed, so this embodiment of the present invention does not limit the specific size of the preset threshold.

Step 306, if the percentage is greater than the preset threshold, prompting that the online transaction is a false transaction.

Step 307, if the percentage is less than the preset threshold, 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 through the commodity weight of the transaction commodity, so that the supervisory personnel of the e-commerce platform provider can divide the supervision level of the online transaction. If it is a real transaction, If it is a suspected false transaction, it will be reconfirmed manually. If it is a false transaction, the online transaction will be directly transferred to the corresponding punishment department, so as to improve the supervision of online transactions by e-commerce platforms and avoid online transactions. The seller and the buyer of the transaction negotiate in advance to carry out false transactions and regulate the behavior of online transactions.

Through the above-mentioned embodiment, in the process of false transaction identification, based on the logistics data provided by the logistics company, by obtaining data such as commodity pictures and logistics voucher information of the transaction goods from the server of the logistics company, using the above-mentioned embodiment to analyze the e-commerce platform Identifying various commodity trading behaviors on the Internet can improve the accuracy of false transaction identification.

Corresponding to the above-mentioned method for identifying false transactions based on logistics data, the present application also proposes a schematic structural diagram of a server according to an exemplary embodiment of the present application as shown in FIG. 4 . Please refer 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, and of course may include hardware required by other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for identifying false transactions based on logistics data at the logical level. Of course, in addition to the software implementation, this application does not exclude other implementations, such as logic devices or the combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, and can also be hardware or logic device.

Please refer to FIG. 5 , in a software implementation, the device for identifying false transactions based on logistics data may include: an acquisition module 51 , a first determination module 52 , a second determination module 543 , and a third determination module 54 . in:

The obtaining module 51 is used to obtain the online transaction logistics data about the transaction commodity, the logistics data includes the logistics order number of the transaction commodity and the commodity picture of the transaction commodity;

The first determination module 52 is configured to determine the first commodity type corresponding to the transaction commodity according to the logistics order number acquired by the acquisition module 51;

The second determination module 53 is used to determine the second commodity type of the transaction commodity according to the commodity picture acquired by the acquisition module 51;

The third determination module 54 is configured to determine whether the online transaction is a false transaction according to the first commodity type determined by the first determination module 52 and the second commodity type determined by the second determination module 53 .

Please refer to FIG. 6, on the basis of the above-mentioned embodiment shown in FIG. 5, in one embodiment, the second determining module 53 may include:

The first calculation unit 531 is used to calculate the picture feature information of the product picture acquired by the acquisition module 51;

The first determining unit 532 is configured to determine the second commodity type of the traded commodity according to the image characteristic information calculated by the first calculating unit 531 .

In an embodiment, the third determining module 54 may include:

The second determination unit 541 is configured to determine whether the first commodity type determined by the first determination module 52 matches the second commodity type determined by the second determination module 53;

The third determining unit 542 is configured to determine that the online transaction is a real transaction if the second determining unit 541 determines that they match; if the second determining unit 541 determines that they do not match, determine that the online transaction is a fake transaction.

In one embodiment, the device may also include:

The fourth determination module 55 is used to determine the picture reference features corresponding to the first commodity type determined by the first determination module 52 in the feature model library, wherein the sales volume within the set time period is stored in the feature model library Picture reference features of trading commodities that reach the set number;

The fifth determining module 56 is used to determine whether the product picture is consistent with the picture reference feature; if they are consistent, then determine the product model of the traded product according to the picture reference feature.

In one embodiment, the logistics data obtained by the acquisition module 51 also includes the commodity weight of the traded commodity, and the device may also include:

The fourth determination module 57 is used to determine whether the commodity weight corresponding to the transaction commodity is within the normal weight range of the transaction commodity;

The prompting module 58 is used to determine the commodity weight of the traded commodity according to the logistics order number, determine whether the commodity weight exceeds the normal weight range determined by the fourth determination module 57, and prompt the authenticity of the online transaction.

In one embodiment, the prompting module 58 may include:

The first prompting unit 581 is used to prompt and determine that the online transaction is a real transaction if the weight of the commodity is within the normal weight range;

The second calculation unit 582 is used to calculate the percentage of the difference between the weight of the product beyond the normal weight range and the normal weight range if the weight of the product exceeds the normal weight range;

The second prompting unit 583 is used to prompt that the online transaction is a false transaction if the percentage calculated by the second calculation unit 582 is greater than the preset threshold;

The third prompting unit 584 is configured to prompt that the online transaction is a suspected false transaction if the percentage calculated by the second calculation unit 582 is less than a preset threshold.

It can be seen from the above embodiments that in the process of identifying false transactions, the logistics data provided by the logistics company is used as the basis, and the data such as the weight of the traded goods and/or the scanned product pictures are obtained from the server of the logistics company, and the above data is used to identify The identification of various commodity trading behaviors on the e-commerce platform, since the above-mentioned different logistics data can be used to identify various types of online transactions, makes the identification of false transactions more comprehensive, thereby improving the accuracy of false transaction identification.

Other embodiments of the present application will be readily 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 modification, use or adaptation of the application, these modifications, uses or adaptations follow the general principles of the application and include common knowledge or conventional technical means in the technical field not disclosed in the application . The specification and examples are to be considered exemplary only, with a true scope and spirit of the application indicated by the following claims.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes Other elements not expressly listed, or elements inherent in the process, method, commodity, or apparatus are also included. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

The above is only a preferred embodiment of the application, and is not intended to limit the application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the application should be included in the application. within the scope of protection.

Claims (13)

1. A method for identifying false transactions based on logistics data, characterized in that the method comprises: Obtain online transaction logistics data about the traded commodities, the logistics data including the logistics order number of the traded commodities and the commodity pictures of the traded commodities; determining the first commodity type corresponding to the transaction commodity according to the logistics order number; determining the second commodity type of the traded commodity according to the commodity picture; It is determined whether the online transaction is a false transaction according to the first commodity type and the second commodity type. 2. The method according to claim 1, wherein the step of determining the second commodity type of the traded commodity according to the commodity picture comprises: calculating image feature information of the product image; A second commodity type of the traded commodity is determined according to the picture feature information. 3. The method according to claim 1, wherein the step of determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type comprises: determining whether the first item type matches the second item type; If they match, it is determined that the online transaction is a real transaction; If not, it is determined that the online transaction is a false transaction. 4. The method according to claim 1, wherein the method further comprises: Determining the picture reference features corresponding to the first commodity type in the feature model library, the picture reference features of the transaction commodities whose sales volume reaches a set number within a set period of time are stored in the feature model library; Determine whether the product picture is consistent with the picture reference feature; If they are consistent, the product model of the traded product is determined according to the picture reference features. 5. The method according to any one of claims 1-4, wherein the logistics data also includes the commodity weight of the traded commodity, and the method further comprises: Determine the commodity weight corresponding to the transaction commodity according to the logistics order number; determining whether the weight of the commodity is within the normal weight range for which the commodity is traded; The authenticity of the online transaction is prompted according to whether the weight of the commodity exceeds the normal weight range. 6. The method according to claim 5, wherein the step of prompting the authenticity of the online transaction according to whether the weight of the commodity exceeds the normal weight range comprises: If the weight of the commodity is within the normal weight range, prompt that the online transaction is a real transaction; If the weight of the product is outside the normal weight range, calculate the percentage of the difference between the weight of the product outside the normal weight range and the normal weight range; If the percentage is greater than a preset threshold, prompting that the online transaction is a false transaction; If the percentage is less than the preset threshold, it is prompted that the online transaction is a suspected false transaction. 7. A device for identifying false transactions based on logistics data, characterized in that the device comprises: An acquisition module, configured to acquire online transaction logistics data about traded commodities, said logistics data including logistics order numbers of said traded commodities and commodity pictures of said traded commodities; A first determination module, configured to determine the first commodity type corresponding to the transaction commodity according to the logistics order number acquired by the acquisition module; The second determination module is configured to determine the second commodity type of the transaction commodity according to the commodity picture acquired by the acquisition module; A third determination module, configured to determine whether the online transaction is a false transaction according to the first commodity type determined by the first determination module and the second commodity type determined by the second determination module. 8. The device according to claim 7, wherein the second determining module comprises: A first calculation unit, configured to calculate the picture feature information of the product picture acquired by the acquisition module; A first determining unit, configured to determine a second commodity type of the traded commodity according to the image feature information calculated by the first calculating unit. 9. The device according to claim 7, wherein the third determining module comprises: a second determination unit, configured to determine whether the first commodity type determined by the first determination module matches the second commodity type determined by the second determination module; The third determining unit is configured to determine that the online transaction is a real transaction if the second determining unit determines that it matches; if the second determining unit determines that it does not match, determine that the online transaction is a fake transaction. 10. The device according to claim 1, further comprising: The fourth determination module is used to determine the picture reference features corresponding to the first product type determined by the first determination module in the feature model library, and the feature model library stores the image reference features within a set period of time. Picture reference features of trading products whose sales volume reaches the set number; The fifth determination module is used to determine whether the picture feature information of the product picture is consistent with the picture reference feature; if they are consistent, determine the product model of the traded product according to the picture reference feature. 11. The device according to any one of claims 7-10, wherein the logistics data acquired by the acquisition module also includes the commodity weight of the traded commodity, and the device further includes: The fourth determining module is used to determine the weight of the product corresponding to the transaction product according to the logistics order number, and determine whether the weight of the product is within the normal weight range of the transaction product; A prompting module, configured to prompt 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. 12. The device according to claim 11, wherein the prompt module comprises: A first prompting unit, configured to prompt and determine that the online transaction is a real transaction if the weight of the commodity is within the normal weight range; A second calculation unit, configured to calculate the percentage of the difference between the weight of the commodity beyond the normal weight range and the normal weight range if the weight of the commodity exceeds the normal weight range; A second prompting unit, configured to prompt that the online transaction is a false transaction if the percentage calculated by the second calculation unit is greater than a preset threshold; A third prompting unit, configured to prompt that the online transaction is a suspected false transaction if the percentage calculated by the second calculation unit is less than the preset threshold. 13. A server, characterized in that the server comprises: a processor; memory for storing instructions executable by said processor; Wherein, the processor is used to obtain online transaction logistics data about traded goods, the logistics data includes the logistics order number of the traded goods and the product picture of the traded goods; according to the logistics order number to determine the A first commodity type corresponding to the transaction commodity; determining whether the online transaction is a false transaction according to the first commodity type and the second commodity type.