A data processing method and system based on artificial intelligence

Abstract

The present application provides a data processing method and system based on artificial intelligence, which belongs to the field of data processing technology, so as to realize the analysis of e-commerce transactions through artificial intelligence to ensure the security of e-commerce transactions. The method includes: the electronic device obtains the e-commerce transaction related information of the participants of the e-commerce transaction; the electronic device pre-processes the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants; wherein the e-commerce transaction vector information of the participants is the vector expression form of the e-commerce transaction related information of the participants; the electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether there is a risk in the e-commerce transaction.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method and system based on artificial intelligence. Background Art

[0002] E-commerce, also known as electronic commerce, is a model of commercial transactions through the Internet. Its development can be traced back to the 1990s. With the popularization and development of the Internet, e-commerce has gradually become an indispensable part of people's daily life. The emergence of e-commerce has made shopping more convenient. Consumers can easily buy goods from all over the world at home, and it also provides merchants with a new sales channel. With the rapid development of e-commerce, related technologies such as online payment, logistics distribution, and intelligent recommendation are also constantly improving, further enhancing the shopping experience of e-commerce. Now, e-commerce has become an important part of global business activities, and many traditional companies have also joined the e-commerce field to adapt to market changes. On this basis, the security of e-commerce transactions is one of the core of e-commerce, and protecting personal information and payment security is crucial. Consumers need to ensure the use of a safe network environment and payment methods to protect their rights and interests.

[0003] However, the security of e-commerce transactions currently relies mainly on the self-identification of both parties to the transaction, that is, both parties to the transaction determine on their own whether the transaction is risky, resulting in the security of e-commerce transactions cannot be truly guaranteed. Summary of the invention

[0004] The embodiments of the present application provide a data processing method and system based on artificial intelligence, so as to analyze e-commerce transactions through artificial intelligence to ensure the security of e-commerce transactions.

[0005] In order to achieve the above objectives, this application adopts the following technical solutions:

[0006] In a first aspect, a data processing method based on artificial intelligence is provided, which is applied to electronic devices, and the method includes: the electronic device obtains e-commerce transaction related information of participants in the e-commerce transaction; the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants; wherein the e-commerce transaction vector information of the participants is a vector expression of the e-commerce transaction related information of the participants; the electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether there is a risk in the e-commerce transaction.

[0007] Optionally, the electronic device obtains e-commerce transaction related information of the participants in the e-commerce transaction, including: the electronic device initiates the e-commerce transaction according to the request of the participant, and obtains the e-commerce transaction related information of the participant; wherein, if the participant is a seller entity, the participant's request to initiate the e-commerce transaction is the seller entity's request to initiate the e-commerce transaction to the buyer entity; if the participant is a buyer entity, the participant's request to initiate the e-commerce transaction is the buyer entity's request to initiate the e-commerce transaction to the seller entity.

[0008] Optionally, when the seller entity requests to initiate an e-commerce transaction to the buyer entity, the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: the electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity; the electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; wherein the dimension of the vector in the e-commerce transaction vector information of the seller entity is higher than the dimension of the vector in the e-commerce transaction vector information of the buyer entity.

[0009] Optionally, in the case where the seller entity requests to initiate an e-commerce transaction to the buyer entity, the vector dimension of the seller entity is N-dimensional, N is an integer greater than 1, and the electronic device performs a first preprocessing on the seller entity's e-commerce transaction related information to obtain the seller entity's e-commerce transaction vector information, including: the electronic device maps each of the S types of information in the seller entity's e-commerce transaction related information to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the seller entity's e-commerce transaction related information, and obtains a total of S 1-dimensional vectors of the seller entity, where S is an integer greater than 1; the electronic device upgrades each of the S 1-dimensional vectors of the seller entity to N-dimensional based on the vector dimension being N-dimensional, and obtains S N-dimensional vectors, wherein the S N-dimensional vectors are the seller entity's e-commerce transaction vector information;

[0010] Optionally, the vector dimension of the buyer entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: the electronic device maps S types of information in the e-commerce transaction related information of the buyer entity to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity; based on the vector dimension being M-dimensional, the electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to M-dimensional to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the buyer entity.

[0011] Optionally, when a buyer entity requests to initiate an e-commerce transaction to a seller entity, the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: the electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; the electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the seller entity; wherein the dimension of the vector in the e-commerce transaction vector information of the buyer entity is higher than the dimension of the vector in the e-commerce transaction vector information of the seller entity.

[0012] Optionally, in the case where the buyer entity requests to initiate an e-commerce transaction to the seller entity, the vector dimension of the buyer entity is N-dimensional, N is an integer greater than 1, and the electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: the electronic device maps each of the S types of information in the e-commerce transaction related information of the buyer entity to the vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity, S is an integer greater than 1; the electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to N dimensions based on the vector dimension being N-dimensional, and obtains S N-dimensional vectors, wherein the S N-dimensional vectors are the e-commerce transaction vector information of the buyer entity;

[0013] Optionally, the vector dimension of the seller entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device preprocesses the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: the electronic device maps S types of information in the e-commerce transaction related information of the seller entity to the vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, obtaining a total of S 1-dimensional vectors of the seller entity; the electronic device upgrades each of the S 1-dimensional vectors of the seller entity to M-dimensional based on the vector dimension being M-dimensional, and obtains S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the seller entity.

[0014] Optionally, the electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether there is a risk in the e-commerce transaction, including: the electronic device interweaves S M-dimensional vectors with S N-dimensional vectors according to the same information type to obtain S M+N-dimensional vectors; the electronic device analyzes the S M+N-dimensional vectors through a deep neural network to determine whether there is a risk in the e-commerce transaction.

[0015] Optionally, the e-commerce transaction vector information of the participant includes at least one of the following: the e-commerce transaction account information of the participant, the user information of the participant, the e-commerce transaction address information of the participant, or the e-commerce transaction device information of the participant.

[0016] In a second aspect, an artificial intelligence-based data processing system is provided, the system includes an electronic device, and the system is configured as follows: the electronic device obtains e-commerce transaction related information of a participant in the e-commerce transaction; the electronic device preprocesses the e-commerce transaction related information of the participant to obtain the e-commerce transaction vector information of the participant; wherein the e-commerce transaction vector information of the participant is a vector expression of the e-commerce transaction related information of the participant; the electronic device analyzes the e-commerce transaction vector information of the participant through a deep neural network to determine whether there is a risk in the e-commerce transaction.

[0017] Optionally, the electronic device obtains e-commerce transaction related information of the participants in the e-commerce transaction, including: the electronic device initiates the e-commerce transaction according to the request of the participant, and obtains the e-commerce transaction related information of the participant; wherein, if the participant is a seller entity, the participant's request to initiate the e-commerce transaction is the seller entity's request to initiate the e-commerce transaction to the buyer entity; if the participant is a buyer entity, the participant's request to initiate the e-commerce transaction is the buyer entity's request to initiate the e-commerce transaction to the seller entity.

[0018] Optionally, when the seller entity requests to initiate an e-commerce transaction to the buyer entity, the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: the electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity; the electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; wherein the dimension of the vector in the e-commerce transaction vector information of the seller entity is higher than the dimension of the vector in the e-commerce transaction vector information of the buyer entity.

[0019] Optionally, in the case where the seller entity requests to initiate an e-commerce transaction to the buyer entity, the vector dimension of the seller entity is N-dimensional, N is an integer greater than 1, and the electronic device performs a first preprocessing on the seller entity's e-commerce transaction related information to obtain the seller entity's e-commerce transaction vector information, including: the electronic device maps each of the S types of information in the seller entity's e-commerce transaction related information to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the seller entity's e-commerce transaction related information, and obtains a total of S 1-dimensional vectors of the seller entity, where S is an integer greater than 1; the electronic device upgrades each of the S 1-dimensional vectors of the seller entity to N-dimensional based on the vector dimension being N-dimensional, and obtains S N-dimensional vectors, wherein the S N-dimensional vectors are the seller entity's e-commerce transaction vector information;

[0020] Optionally, the vector dimension of the buyer entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: the electronic device maps S types of information in the e-commerce transaction related information of the buyer entity to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity; based on the vector dimension being M-dimensional, the electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to M-dimensional to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the buyer entity.

[0021] Optionally, when a buyer entity requests to initiate an e-commerce transaction to a seller entity, the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: the electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; the electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the seller entity; wherein the dimension of the vector in the e-commerce transaction vector information of the buyer entity is higher than the dimension of the vector in the e-commerce transaction vector information of the seller entity.

[0022] Optionally, when the buyer entity requests to initiate an e-commerce transaction to the seller entity, the vector dimension of the buyer entity is N dimensions, where N is an integer greater than 1. The electronic device performs a first preprocessing on the buyer entity's e-commerce transaction related information to obtain the buyer entity's e-commerce transaction vector information, including: the electronic device maps each of the S types of information in the buyer entity's e-commerce transaction related information to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the buyer entity's e-commerce transaction related information, obtaining a total of S 1-dimensional vectors of the buyer entity, where S is an integer greater than 1; based on the vector dimension being N dimensions, the electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to N dimensions to obtain S N-dimensional vectors, where the S N-dimensional vectors are the buyer entity's e-commerce transaction vector information.

[0023] Optionally, the vector dimension of the seller entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device preprocesses the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: the electronic device maps S types of information in the e-commerce transaction related information of the seller entity to the vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, obtaining a total of S 1-dimensional vectors of the seller entity; the electronic device upgrades each of the S 1-dimensional vectors of the seller entity to M-dimensional based on the vector dimension being M-dimensional, and obtains S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the seller entity.

[0024] Optionally, the electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether there is a risk in the e-commerce transaction, including: the electronic device interweaves S M-dimensional vectors with S N-dimensional vectors according to the same information type to obtain S M+N-dimensional vectors; the electronic device analyzes the S M+N-dimensional vectors through a deep neural network to determine whether there is a risk in the e-commerce transaction.

[0025] Optionally, the e-commerce transaction vector information of the participant includes at least one of the following: the e-commerce transaction account information of the participant, the user information of the participant, the e-commerce transaction address information of the participant, or the e-commerce transaction device information of the participant.

[0026] In summary, the above method and system have the following technical effects:

[0027] The electronic device converts the e-commerce transaction related information of the participants into a vector expression, such as the e-commerce transaction vector information of the participants, through the e-commerce transaction related information of the participants of the e-commerce transaction. In this way, the e-commerce transaction vector information of the participants can be analyzed through a deep neural network to determine whether there is a risk in the e-commerce transaction, so as to achieve the analysis of e-commerce transactions through artificial intelligence to ensure the security of e-commerce transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flowchart of an artificial intelligence-based data processing method provided in an embodiment of the present application;

[0029] Figure 2 Schematic diagram of vector dimension increase;

[0030] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The present application will present various aspects, embodiments or features around a system that may include multiple devices, components, modules, etc. It should be understood and appreciated that each system may include additional devices, components, modules, etc., and / or may not include all of the devices, components, modules, etc. discussed in conjunction with the figures. In addition, combinations of these schemes may also be used.

[0032] In addition, in the embodiments of the present application, words such as "exemplary" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" in the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present concepts in a concrete way.

[0033] In the embodiments of the present application, "information", "signal", "message", "channel" and "signaling" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are matched. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings to be expressed are matched. In addition, the " / " mentioned in the present application can be used to express an "or" relationship.

[0034] The network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0035] The method of the embodiment of the present application can be executed by an electronic device, which can be a terminal, a terminal, or a chip or chip system that can be set in the terminal. The terminal can also be called user equipment (UE), access terminal, subscriber unit, user station, mobile station (MS), mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device. The terminal in the embodiments of the present application can be a mobile phone, a cellular phone, a smart phone, a tablet computer, a wireless data card, a personal digital assistant (PDA), a wireless modem, a handset, a laptop computer, a machine type communication (MTC) terminal, a computer with wireless transceiver function, a virtual reality (VR) terminal, an augmented reality (AR) terminal, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, a vehicle-mounted terminal, a road side unit (RSU) with terminal function, etc. The terminal of the present application may also be a vehicle-mounted module, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit built into the vehicle as one or more components or units.

[0036] For ease of understanding, the following will combine Figure 3 The data processing method based on artificial intelligence provided in the embodiment of the present application is specifically described.

[0037] For example, Figure 1 The present invention provides a flowchart of a data processing method based on artificial intelligence. The method can be applied to the interaction of the above electronic devices.

[0038] like Figure 1 As shown, the process of the data processing method based on artificial intelligence is as follows:

[0039] S101, the electronic device obtains e-commerce transaction related information of the participants in the e-commerce transaction.

[0040] The electronic device can initiate an e-commerce transaction according to the request of the participant, and obtain the e-commerce transaction related information of the participant. Among them, the participant can be the seller of the transaction, that is, the seller entity, or the participant can be the buyer of the transaction, that is, the buyer entity. If the participant is a seller entity, the participant's request to initiate an e-commerce transaction is the seller entity's request to initiate an e-commerce transaction to the buyer entity. If the participant is a buyer entity, the participant's request to initiate an e-commerce transaction is the buyer entity's request to initiate an e-commerce transaction to the seller entity. The electronic device can be used as an execution device for the e-commerce transaction. When the seller entity requests to initiate an e-commerce transaction to the buyer entity, or when the buyer entity requests to initiate an e-commerce transaction to the seller entity, the electronic device can obtain the e-commerce transaction vector information of the seller entity or the buyer entity from the request of the seller entity or the buyer entity. For example, the seller entity or the buyer entity are both participants, and the e-commerce transaction vector information of the participant includes S-type information, S is an integer greater than 1, and specifically at least one of the following: the participant's e-commerce transaction account information, the participant's user information, the participant's e-commerce transaction address information, or the participant's e-commerce transaction device information, etc.

[0041] S102, the electronic device pre-processes the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants.

[0042] The e-commerce transaction vector information of the participants is a vector expression of the e-commerce transaction related information of the participants.

[0043] Case 1: When the seller entity requests to initiate an e-commerce transaction with the buyer entity, the electronic device may perform a first preprocessing on the seller entity's e-commerce transaction related information to obtain the seller entity's e-commerce transaction vector information. The electronic device may perform a second preprocessing on the buyer entity's e-commerce transaction related information to obtain the buyer entity's e-commerce transaction vector information; wherein the dimension of the vector in the seller entity's e-commerce transaction vector information is higher than the dimension of the vector in the buyer entity's e-commerce transaction vector information.

[0044] Specifically, the vector dimension of the seller entity is N-dimensional, where N is an integer greater than 1. The electronic device can map each of the S types of information in the seller entity's e-commerce transaction related information to the vector space, and obtain a 1-dimensional vector corresponding to each of the S types of information in the seller entity's e-commerce transaction related information, and obtain a total of S 1-dimensional vectors of the seller entity, where S is an integer greater than 1. Among them, a mapping relationship can be established in advance between the information and the vector in the vector space, and the electronic device can determine the vector corresponding to each of the S types of information by traversing the mapping relationship. Alternatively, the electronic device can also dynamically determine the vector. The electronic device can convert the S type of information in the e-commerce transaction related information into a binary bit string, and divide the binary bit string into 3 segments in equal proportion. The decimal value corresponding to each segment is the coordinate of a term, and a three-dimensional coordinate point is obtained, that is, a 1-dimensional vector in the 3-dimensional vector space. The electronic device can upgrade each of the S 1-dimensional vectors of the seller entity to N dimensions based on the vector dimension being N dimensions, and obtain S N-dimensional vectors, where the S N-dimensional vectors are the e-commerce transaction vector information of the seller entity. For example Figure 2 As shown, it is easy to understand. Taking a one-dimensional vector as an example, the one-dimensional vector is recorded as vector a1, and the vector a1 is projected to the x-axis plane, y-axis plane, and z-axis plane in the three-dimensional vector space. Taking the x-axis plane as an example, the line segment in the projection direction is divided into N-1 segments. The endpoint of each segment pointed to by point O is a one-dimensional vector, and there are N-1 one-dimensional vectors in total, such as vector a2 to vector aN. The vector a2 to vector aN combined with a1 is an N-dimensional vector, that is, (a1, a2...aN).

[0045] On this basis, the vector dimension of the buyer entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device can map each of the S types of information in the buyer entity's e-commerce transaction related information to the vector space, and obtain a 1-dimensional vector corresponding to each of the S types of information in the buyer entity's e-commerce transaction related information, and obtain a total of S 1-dimensional vectors of the buyer entity; the electronic device can upgrade each of the S 1-dimensional vectors of the buyer entity to M-dimensional based on the vector dimension being M-dimensional, and obtain S M-dimensional vectors, where the S M-dimensional vectors are the e-commerce transaction vector information of the buyer entity, where the principle is similar to the above and can be used as a reference for understanding.

[0046] Case 2: When a buyer entity requests to initiate an e-commerce transaction to a seller entity, the electronic device may perform a first preprocessing on the buyer entity's e-commerce transaction related information to obtain the buyer entity's e-commerce transaction vector information; the electronic device may perform a second preprocessing on the buyer entity's e-commerce transaction related information to obtain the seller entity's e-commerce transaction vector information; wherein, the dimension of the vector in the buyer entity's e-commerce transaction vector information is higher than the dimension of the vector in the seller entity's e-commerce transaction vector information.

[0047] Specifically, the electronic device can map each of the S types of information in the e-commerce transaction related information of the buyer entity to the vector space, and obtain a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtain a total of S 1-dimensional vectors of the buyer entity, where S is an integer greater than 1; the electronic device can upgrade each of the S 1-dimensional vectors of the buyer entity to N dimensions based on the vector dimension of N dimensions, and obtain S N-dimensional vectors, where the S N-dimensional vectors are the e-commerce transaction vector information of the buyer entity, where the principle is similar to the above and can be used as a reference for understanding.

[0048] On this basis, the vector dimension of the seller entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device can map each of the S types of information in the e-commerce transaction related information of the seller entity to the vector space, and obtain a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, and obtain a total of S 1-dimensional vectors of the seller entity; the electronic device can upgrade each of the S 1-dimensional vectors of the seller entity to M-dimensional based on the vector dimension of M-dimensional, and obtain S M-dimensional vectors, where the S M-dimensional vectors are the e-commerce transaction vector information of the seller entity, where the principle is similar to the above and can be used as a reference for understanding.

[0049] It can be understood that the purpose of vector dimensionality increase is to improve the robustness of deep neural network processing, which can be simply understood as complicating features or information and amplifying implicit risks, so that after deep neural network processing, it can more accurately analyze whether there are risks in e-commerce transactions. In addition, if the buyer entity requests to initiate an e-commerce transaction with the seller entity, it means that more attention needs to be paid to the risks of the buyer entity, so the dimensionality of the buyer entity's vector needs to be higher than that of the seller entity, and vice versa.

[0050] S103, the electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether there is any risk in the e-commerce transaction.

[0051] The electronic device can interleave S M-dimensional vectors with S N-dimensional vectors according to the same information type to obtain S M+N-dimensional vectors. For example, the e-commerce transaction account information of the seller entity and the e-commerce transaction account information of the buyer entity are the same type of information, the N-dimensional vector of the e-commerce transaction account information of the seller entity is (a1, a2...aN), and the M-dimensional vector of the e-commerce transaction account information of the buyer entity is (b1, b2...bM), and the interleaved M+N-dimensional vector is (a1, b1, a2, b2,...bM...aN).

[0052] Electronic devices can analyze S M+N dimensional vectors through deep neural networks to determine whether there are risks in e-commerce transactions. Among them, the S M+N dimensional vectors meet the input parameter format of the deep neural network. Through interleaving, the deep neural network can analyze the same type of information of both buyers and sellers, so as to more accurately determine whether there are risks in the transaction.

[0053] In summary, the electronic device converts the e-commerce transaction related information of the participants into a vector expression, such as the e-commerce transaction vector information of the participants, through the e-commerce transaction related information of the participants of the e-commerce transaction. In this way, the e-commerce transaction vector information of the participants can be analyzed through a deep neural network to determine whether there is a risk in the e-commerce transaction, so as to achieve the analysis of e-commerce transactions through artificial intelligence to ensure the security of e-commerce transactions.

[0054] Combination of the above Figure 1 The artificial intelligence-based data processing method provided by the embodiment of the present application is described in detail. The artificial intelligence-based data processing system for executing the artificial intelligence-based data processing method provided by the embodiment of the present application is described in detail below.

[0055] The system includes an electronic device, which is configured as follows: the electronic device obtains e-commerce transaction related information of a participant in the e-commerce transaction; the electronic device preprocesses the e-commerce transaction related information of the participant to obtain the e-commerce transaction vector information of the participant; wherein the e-commerce transaction vector information of the participant is a vector expression of the e-commerce transaction related information of the participant; the electronic device analyzes the e-commerce transaction vector information of the participant through a deep neural network to determine whether there is a risk in the e-commerce transaction.

[0056] Optionally, the electronic device obtains e-commerce transaction related information of the participants in the e-commerce transaction, including: the electronic device initiates the e-commerce transaction according to the request of the participant, and obtains the e-commerce transaction related information of the participant; wherein, if the participant is a seller entity, the participant's request to initiate the e-commerce transaction is the seller entity's request to initiate the e-commerce transaction to the buyer entity; if the participant is a buyer entity, the participant's request to initiate the e-commerce transaction is the buyer entity's request to initiate the e-commerce transaction to the seller entity.

[0057] Optionally, when the seller entity requests to initiate an e-commerce transaction to the buyer entity, the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: the electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity; the electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; wherein the dimension of the vector in the e-commerce transaction vector information of the seller entity is higher than the dimension of the vector in the e-commerce transaction vector information of the buyer entity.

[0058] Optionally, in the case where the seller entity requests to initiate an e-commerce transaction to the buyer entity, the vector dimension of the seller entity is N-dimensional, N is an integer greater than 1, and the electronic device performs a first preprocessing on the seller entity's e-commerce transaction related information to obtain the seller entity's e-commerce transaction vector information, including: the electronic device maps each of the S types of information in the seller entity's e-commerce transaction related information to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the seller entity's e-commerce transaction related information, and obtains a total of S 1-dimensional vectors of the seller entity, where S is an integer greater than 1; the electronic device upgrades each of the S 1-dimensional vectors of the seller entity to N-dimensional based on the vector dimension being N-dimensional, and obtains S N-dimensional vectors, wherein the S N-dimensional vectors are the seller entity's e-commerce transaction vector information;

[0059] Optionally, the vector dimension of the buyer entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: the electronic device maps S types of information in the e-commerce transaction related information of the buyer entity to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity; based on the vector dimension being M-dimensional, the electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to M-dimensional to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the buyer entity.

[0060] Optionally, when a buyer entity requests to initiate an e-commerce transaction to a seller entity, the electronic device preprocesses the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: the electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; the electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the seller entity; wherein the dimension of the vector in the e-commerce transaction vector information of the buyer entity is higher than the dimension of the vector in the e-commerce transaction vector information of the seller entity.

[0061] Optionally, when the buyer entity requests to initiate an e-commerce transaction to the seller entity, the vector dimension of the buyer entity is N dimensions, where N is an integer greater than 1. The electronic device performs a first preprocessing on the buyer entity's e-commerce transaction related information to obtain the buyer entity's e-commerce transaction vector information, including: the electronic device maps each of the S types of information in the buyer entity's e-commerce transaction related information to a vector space to obtain a 1-dimensional vector corresponding to each of the S types of information in the buyer entity's e-commerce transaction related information, obtaining a total of S 1-dimensional vectors of the buyer entity, where S is an integer greater than 1; based on the vector dimension being N dimensions, the electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to N dimensions to obtain S N-dimensional vectors, where the S N-dimensional vectors are the buyer entity's e-commerce transaction vector information.

[0062] Optionally, the vector dimension of the seller entity is M-dimensional, where M is an integer less than N and greater than 1. The electronic device preprocesses the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: the electronic device maps S types of information in the e-commerce transaction related information of the seller entity to the vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, obtaining a total of S 1-dimensional vectors of the seller entity; the electronic device upgrades each of the S 1-dimensional vectors of the seller entity to M-dimensional based on the vector dimension being M-dimensional, and obtains S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the seller entity.

[0063] Optionally, the electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether there is a risk in the e-commerce transaction, including: the electronic device interweaves S M-dimensional vectors with S N-dimensional vectors according to the same information type to obtain S M+N-dimensional vectors; the electronic device analyzes the S M+N-dimensional vectors through a deep neural network to determine whether there is a risk in the e-commerce transaction.

[0064] Optionally, the e-commerce transaction vector information of the participant includes at least one of the following: the e-commerce transaction account information of the participant, the user information of the participant, the e-commerce transaction address information of the participant, or the e-commerce transaction device information of the participant.

[0065] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. For example, the electronic device may be a terminal device, or a chip (system) or other component or assembly that can be provided in the terminal device. Figure 3As shown, the electronic device 400 may include a processor 401. Optionally, the electronic device 400 may also include a memory 402 and / or a transceiver 403. The processor 401 is coupled to the memory 402 and the transceiver 403, such as being connected via a communication bus. In addition, the electronic device 400 may also be a chip, such as including the processor 401, in which case the transceiver may be an output and input interface of the chip.

[0066] Combine the following Figure 3 The components of the electronic device 400 are specifically introduced as follows:

[0067] The processor 401 is the control center of the electronic device 400, and may be a processor or a general term for multiple processing elements. For example, the processor 401 is one or more central processing units (CPUs), or may be application specific integrated circuits (ASICs), or may be configured to implement one or more integrated circuits of the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0068] Optionally, the processor 401 can execute various functions of the electronic device 400 by running or executing the software program stored in the memory 402 and calling the data stored in the memory 402, such as executing the above Figure 3 The artificial intelligence-based data processing method shown.

[0069] In a specific implementation, as an embodiment, the processor 401 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0070] In a specific implementation, as an embodiment, the electronic device 400 may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer programs or instructions).

[0071] The memory 402 is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor 401. The specific implementation method can refer to the above method embodiment, which will not be repeated here.

[0072] Optionally, the memory 402 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 402 may be integrated with the processor 401, or may exist independently and access the computer through the interface circuit ( Figure 3 (not shown) is coupled to the processor 401, which is not specifically limited in the embodiment of the present application.

[0073] The transceiver 403 is used for communication with other electronic devices. For example, if the electronic device 400 is a terminal device, the transceiver 403 can be used to communicate with a network device, or with another terminal device. For another example, if the electronic device 400 is a network device, the transceiver 403 can be used to communicate with a terminal device, or with another network device.

[0074] Optionally, the transceiver 403 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0075] Optionally, the transceiver 403 may be integrated with the processor 401, or may exist independently and communicate with the electronic device 400 through an interface circuit ( Figure 3 (not shown) is coupled to the processor 401, which is not specifically limited in the embodiment of the present application.

[0076] Understandably, Figure 3 The structure of the electronic device 400 shown in the figure does not constitute a limitation on the electronic device, and the actual electronic device may include more or fewer components than those shown in the figure, or combine certain components, or arrange the components differently.

[0077] In addition, the technical effects of the electronic device 400 can refer to the technical effects of the methods described in the above method embodiments, which will not be repeated here.

[0078] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0079] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0080] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program or instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program or instruction can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0081] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0082] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0083] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0084] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0089] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method based on artificial intelligence, characterized in that: Applied to electronic equipment, the method comprises: The electronic device obtains e-commerce transaction related information of the participants in the e-commerce transaction; The electronic device pre-processes the e-commerce transaction related information of the participant to obtain the e-commerce transaction vector information of the participant; wherein the e-commerce transaction vector information of the participant is a vector expression form of the e-commerce transaction related information of the participant; The electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether the e-commerce transaction has risks; The electronic device obtains the e-commerce transaction related information of the participants of the e-commerce transaction, including: The electronic device initiates the e-commerce transaction according to the request of the participant, and obtains the e-commerce transaction related information of the participant; Wherein, if the participant is a seller entity, the participant's request to initiate the e-commerce transaction is the seller entity's request to initiate the e-commerce transaction to the buyer entity; if the participant is the buyer entity, the participant's request to initiate the e-commerce transaction is the buyer entity's request to initiate the e-commerce transaction to the seller entity; In the case where the seller entity requests to initiate the e-commerce transaction to the buyer entity, the electronic device pre-processes the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: The electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity; The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; The dimension of the vector in the e-commerce transaction vector information of the seller entity is higher than the dimension of the vector in the e-commerce transaction vector information of the buyer entity; In the case where the seller entity requests to initiate the e-commerce transaction to the buyer entity, the vector dimension of the seller entity is N, where N is an integer greater than 1, and the electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the seller entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, and obtains a total of S 1-dimensional vectors of the seller entity, where S is an integer greater than 1; The electronic device upgrades each of the S 1-dimensional vectors of the seller entity to N dimensions according to the vector dimension being N dimensions, thereby obtaining S N-dimensional vectors, wherein the S N-dimensional vectors are the e-commerce transaction vector information of the seller entity; The vector dimension of the buyer entity is M, where M is an integer less than N and greater than 1. The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the buyer entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity; The electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to M-dimensional according to the vector dimension being M-dimensional, to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the buyer entity; In the case where the buyer entity requests to initiate the e-commerce transaction to the seller entity, the electronic device pre-processes the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: The electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the seller entity; Wherein, the dimension of the vector in the e-commerce transaction vector information of the buyer entity is higher than the dimension of the vector in the e-commerce transaction vector information of the seller entity; In the case where the buyer entity requests to initiate the e-commerce transaction to the seller entity, the vector dimension of the buyer entity is N, where N is an integer greater than 1, and the electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the buyer entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity, where S is an integer greater than 1; The electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to N dimensions according to the vector dimension being N dimensions, to obtain S N-dimensional vectors, wherein the S N-dimensional vectors are the e-commerce transaction vector information of the buyer entity; The vector dimension of the seller entity is M, where M is an integer less than N and greater than 1. The electronic device preprocesses the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the seller entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, and obtains a total of S 1-dimensional vectors of the seller entity; The electronic device upgrades each of the S 1-dimensional vectors of the seller entity to M dimension based on the vector dimension being M dimension, to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the seller entity.

2. The method according to claim 1, characterized in that The electronic device analyzes the e-commerce transaction vector information of the participant through a deep neural network to determine whether the e-commerce transaction has risks, including: The electronic device interleaves the S M-dimensional vectors with the S N-dimensional vectors according to the same information type to obtain the S M+N-dimensional vectors; The electronic device analyzes the S M+N dimensional vectors through a deep neural network to determine whether there is a risk in the e-commerce transaction.

3. A data processing system based on artificial intelligence, characterized in that: The system comprises an electronic device, wherein the system is configured to: The electronic device obtains e-commerce transaction related information of the participants in the e-commerce transaction; The electronic device pre-processes the e-commerce transaction related information of the participant to obtain the e-commerce transaction vector information of the participant; wherein the e-commerce transaction vector information of the participant is a vector expression form of the e-commerce transaction related information of the participant; The electronic device analyzes the e-commerce transaction vector information of the participants through a deep neural network to determine whether the e-commerce transaction has risks; The electronic device obtains the e-commerce transaction related information of the participants of the e-commerce transaction, including: The electronic device initiates the e-commerce transaction according to the request of the participant, and obtains the e-commerce transaction related information of the participant; Wherein, if the participant is a seller entity, the participant's request to initiate the e-commerce transaction is the seller entity's request to initiate the e-commerce transaction to the buyer entity; if the participant is the buyer entity, the participant's request to initiate the e-commerce transaction is the buyer entity's request to initiate the e-commerce transaction to the seller entity; In the case where the seller entity requests to initiate the e-commerce transaction to the buyer entity, the electronic device pre-processes the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: The electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity; The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; The dimension of the vector in the e-commerce transaction vector information of the seller entity is higher than the dimension of the vector in the e-commerce transaction vector information of the buyer entity; In the case where the seller entity requests to initiate the e-commerce transaction to the buyer entity, the vector dimension of the seller entity is N, where N is an integer greater than 1, and the electronic device performs a first preprocessing on the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the seller entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, and obtains a total of S 1-dimensional vectors of the seller entity, where S is an integer greater than 1; The electronic device upgrades each of the S 1-dimensional vectors of the seller entity to N dimensions according to the vector dimension being N dimensions, thereby obtaining S N-dimensional vectors, wherein the S N-dimensional vectors are the e-commerce transaction vector information of the seller entity; The vector dimension of the buyer entity is M, where M is an integer less than N and greater than 1. The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the buyer entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity; The electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to M-dimensional according to the vector dimension being M-dimensional, to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the buyer entity; In the case where the buyer entity requests to initiate the e-commerce transaction to the seller entity, the electronic device pre-processes the e-commerce transaction related information of the participants to obtain the e-commerce transaction vector information of the participants, including: The electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity; The electronic device performs a second preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the seller entity; Wherein, the dimension of the vector in the e-commerce transaction vector information of the buyer entity is higher than the dimension of the vector in the e-commerce transaction vector information of the seller entity; In the case where the buyer entity requests to initiate the e-commerce transaction to the seller entity, the vector dimension of the buyer entity is N, where N is an integer greater than 1, and the electronic device performs a first preprocessing on the e-commerce transaction related information of the buyer entity to obtain the e-commerce transaction vector information of the buyer entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the buyer entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the buyer entity, and obtains a total of S 1-dimensional vectors of the buyer entity, where S is an integer greater than 1; The electronic device upgrades each of the S 1-dimensional vectors of the buyer entity to N dimensions according to the vector dimension being N dimensions, to obtain S N-dimensional vectors, wherein the S N-dimensional vectors are the e-commerce transaction vector information of the buyer entity; The vector dimension of the seller entity is M, where M is an integer less than N and greater than 1. The electronic device preprocesses the e-commerce transaction related information of the seller entity to obtain the e-commerce transaction vector information of the seller entity, including: The electronic device maps each of the S types of information in the e-commerce transaction related information of the seller entity to a vector space, and obtains a 1-dimensional vector corresponding to each of the S types of information in the e-commerce transaction related information of the seller entity, and obtains a total of S 1-dimensional vectors of the seller entity; The electronic device upgrades each of the S 1-dimensional vectors of the seller entity to M dimension based on the vector dimension being M dimension, to obtain S M-dimensional vectors, wherein the S M-dimensional vectors are the e-commerce transaction vector information of the seller entity.

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