US-20260127312-A1 - OBJECT MATCHING METHOD, APPARATUS, DEVICE, SYSTEM, MEDIUM AND PROGRAM PRODUCT

US20260127312A1US 20260127312 A1US20260127312 A1US 20260127312A1US-20260127312-A1

Abstract

The present application discloses an object matching method, apparatus, device, system, medium and program product. The method includes: selecting one of first feature vectors of first objects in a first device as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; obtaining, based on the first target feature vector and a second feature vector of a second object in a second device, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; selecting a first feature vector to be matched according to the first similarity parameter, the second similarity parameter, and a similarity matching threshold; and determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection.

Inventors

Shuo He
Hongbao Liu
Pengfei Gao
Zhenyao QIU
Tao Tang
Jianbin Zheng

Assignees

CHINA UNIONPAY CO., LTD.

Dates

Publication Date: 20260507
Application Date: 20231107
Priority Date: 20221128

Claims (17)

1 . An object matching method applicable to a first device, the method comprising: selecting any one of first feature vectors of two or more first objects in the first device as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; obtaining, based on the first target feature vector and a second feature vector of a second object in a second device, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; selecting a first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and a preset similarity matching threshold, wherein a number of the first feature vector to be matched is less than a number of the first feature vectors; and determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection.
2 . The method according to claim 1 , wherein selecting the first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and the preset similarity matching threshold comprises: calculating a first difference and a first sum of the second similarity parameter and the similarity matching threshold; selecting the first similarity parameter closest to the first difference as a first boundary similarity parameter; selecting the first similarity parameter closest to the first sum as a second boundary similarity parameter; selecting the first feature vector for which the first similarity parameter is greater than the first boundary similarity parameter and less than the second boundary similarity parameter as a candidate feature vector for matching, and determining the first feature vector to be matched from the candidate feature vector for matching.
3 . The method according to claim 1 , wherein selecting the first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and the preset similarity matching threshold comprises: sorting the first feature vectors in an order of the first similarity parameters from biggest to smallest or from smallest to biggest; calculating a first difference and a first sum of the second similarity parameter and the similarity matching threshold; selecting the first feature vector for which the first similarity parameter is closest to the first difference as a first boundary feature vector; selecting the first feature vector for which the first similarity parameter is closest to the first sum as a second boundary feature vector; and selecting the first feature vector positioned between the first boundary feature vector and the second boundary feature vector as a candidate feature vector for matching, and determining the first feature vector to be matched from the candidate feature vector for matching.
4 . The method according to claim 2 , wherein the candidate feature vector for matching is the first feature vector to be matched.
5 . The method according to claim 2 , wherein a number of the candidate feature vector for matching is greater than 1, determining the first feature vector to be matched from the candidate feature vector for matching comprises: selecting any one of the candidate feature vectors for matching as the first target feature vector, and respectively calculating a third similarity parameter between the first target feature vector and each of the candidate feature vectors for matching; obtaining, based on the first target feature vector and the second feature vector, a fourth similarity parameter between the second feature vector and the first target feature vector by the vector similarity calculation method with privacy protection; and determining a new candidate feature vector for matching according to the third similarity parameter, the fourth similarity parameter, and the preset similarity matching threshold until the number of the candidate feature vectors for matching is less than or equal to 1.
6 . The method according to claim 1 , wherein determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the similarity calculation method with privacy protection comprises: obtaining, based on the first feature vector to be matched and the second feature vector, a fifth similarity parameter of the first feature vector to be matched and the second feature vector respectively by the vector similarity calculation method with privacy protection; and determining that the first object corresponding to the first feature vector to be matched is successfully matched with the second object under a condition that the fifth similarity parameter is less than or equal to the similarity matching threshold.
7 . The method according to claim 1 , further comprising: based on a selected first feature vector and a second target feature vector in the second device and by the vector similarity calculation method with privacy protection, enabling the second device to obtain a sixth similarity between the first feature vector and the second target feature vector, wherein the second target feature vector is any second feature vector selected by the second device from second feature vectors of two or more second objects; based on the first feature vector and a second feature vector to be matched selected by the second device according to the sixth similarity, a seventh similarity, and the similarity matching threshold and by the vector similarity calculation method with privacy protection, enabling the second device to determine the second object successfully matched with the first object according to the similarity matching threshold, wherein the seventh similarity include a similarity between the second target feature vector and each of the second feature vectors calculated by the second device.
8 . The method according to claim 7 , wherein the selected first feature vector comprises the first feature vector that is not successfully matched with the second feature vector.
9 . The method according to claim 1 , wherein after selecting any one of the first feature vectors as the first target feature vector, and calculating the first similarity parameter between the first target feature vector and each of the first feature vectors, the method further comprises: generating a similarity parameter matrix according to the first similarity parameters obtained in a way that each of the first feature vectors acts as the first target feature vector, wherein the first similarity parameters in a same row of the similarity parameter matrix correspond to a same first target feature vector, or the first similarity parameters in a same column of the similarity parameter matrix correspond to a same first target feature vector.
10 . The method according to claim 1 , wherein the similarity parameter comprises Euclidean distance or cosine similarity.
11 . (canceled)
12 . An electronic device comprising a processor and a memory storing computer program instructions; wherein the processor, when executing the computer program instructions, implements operations that include: selecting any one of first feature vectors of two or more first objects in the electronic device as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; obtaining, based on the first target feature vector and a second feature vector of a second object in a second device, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; selecting a first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and a preset similarity matching threshold, wherein a number of the first feature vector to be matched is less than a number of the first feature vectors; and determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection.
13 . An object matching system comprising: a first device; and a second device configured to perform a vector similarity calculation method with privacy protection with the first device, wherein the first device is configured to perform operations that include: selecting any one of first feature vectors of two or more first objects in the first device as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; obtaining, based on the first target feature vector and a second feature vector of a second object in the second device, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; selecting a first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and a preset similarity matching threshold, wherein a number of the first feature vector to be matched is less than a number of the first feature vectors; and determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection.
14 . A non-transitory computer readable storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement the object matching method according to claim 1 .
15 . A computer program product, wherein instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the object matching method according to claim 1 .
16 . The method according to claim 3 , wherein the candidate feature vector for matching is the first feature vector to be matched.
17 . The method according to claim 3 , wherein a number of the candidate feature vector for matching is greater than 1, determining the first feature vector to be matched from the candidate feature vector for matching comprises: selecting any one of the candidate feature vectors for matching as the first target feature vector, and respectively calculating a third similarity parameter between the first target feature vector and each of the candidate feature vectors for matching; obtaining, based on the first target feature vector and the second feature vector, a fourth similarity parameter between the second feature vector and the first target feature vector by the vector similarity calculation method with privacy protection; and determining a new candidate feature vector for matching according to the third similarity parameter, the fourth similarity parameter, and the preset similarity matching threshold until the number of the candidate feature vectors for matching is less than or equal to 1.

Description

CROSS REFERENCE TO RELATED APPLICATION This application claims priority to Chinese Patent Application No. 202211497502.0 entitled “OBJECT MATCHING METHOD, APPARATUS, DEVICE, SYSTEM, MEDIUM AND PROGRAM PRODUCT” filed on Nov. 28, 2022, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD The present application relates to the field of data processing, and particularly to an object matching method, apparatus, device, system, medium and program product. BACKGROUND With the development of big data technology, object matching needs to be performed on two or more parties in more and more business scenarios. Object matching may occur between a user and a servicer, or between servicers. The data required for object matching may include private data. In order to ensure data security, two or more parties on which object matching is performed will not exchange original data. In order to perform object matching on the basis of ensuring data security, two or more parties may encrypt the data respectively, and compare the encrypted data in pairs to determine a matched object. Under a condition that multiple parties each have a large amount of data, object matching will result in a large amount of computation, thus reducing the efficiency of object matching. SUMMARY Embodiments of the present application provide an object matching method, apparatus, device, system, medium, and program product capable of improving the efficiency of object matching. In a first aspect, embodiments of the present application provide an object matching method applicable to a first device, the method including: selecting any one of first feature vectors of two or more first objects in the first device as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; obtaining, based on the first target feature vector and a second feature vector of a second object in a second device, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; selecting a first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and a preset similarity matching threshold, in which a number of the first feature vector to be matched is less than a number of the first feature vectors; and determining, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection. In a second aspect, embodiments of the present application provide an object matching apparatus, including: a calculation module configured to select any one of first feature vectors of two or more first objects in the object matching apparatus as a first target feature vector, and calculating a first similarity parameter between the first target feature vector and each of the first feature vectors; an interactive calculation module configured to obtain, based on the first target feature vector and a second feature vector of a second object in another apparatus, a second similarity parameter between the second feature vector and the first target feature vector by a vector similarity calculation method with privacy protection; a selection module configured to select a first feature vector to be matched from the first feature vectors according to the first similarity parameter, the second similarity parameter, and a preset similarity matching threshold, in which a number of the first feature vector to be matched is less than a number of the first feature vectors; and a matching module configured to determine, based on the first feature vector to be matched and the second feature vector, the first object successfully matched with the second object according to the similarity matching threshold by the vector similarity calculation method with privacy protection. In a third aspect, embodiments of the present application provide an electronic device, including: a processor and a memory storing computer program instructions; in which the processor, when executing the computer program instructions, implements the object matching method in the first aspect. In a fourth aspect, embodiments of the present application provide an object matching system, including: a first device configured to execute the object matching method in the first aspect; and a second device configured to perform privacy interaction with the first device. In a fifth aspect, embodiments of the present application provide a computer readable storage medium having computer program instructions stored thereon, in which the computer program instructions, when executed by a processor, implement the object matching method in the first aspect. In a sixth