Abnormal account matching method and device, computer device and storage medium

Abstract

The application relates to an abnormal account matching method and device, computer equipment and a storage medium. The method comprises the following steps: inputting account interaction information corresponding to a target check account into an extended filter according to an abnormal query request corresponding to each target check account, and obtaining a filter matching result; in the case that the filter matching result represents that the target check account is an abnormal account, matching the account interaction information corresponding to the target abnormal account with each account interaction information in a cache query library, and obtaining each sub-cache matching result corresponding to the cache query library; in the case that each sub-cache matching result represents that the target check account is not an abnormal account, matching the account interaction information corresponding to the target check account with the account interaction information corresponding to each account in a second abnormal account list in an abnormal account database, and obtaining an abnormal account matching result corresponding to the target check account. The method can improve the system stability under the premise of ensuring the accuracy of abnormal account matching.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data, and particularly relates to an abnormal account matching method and device, computer equipment and a storage medium. BACKGROUND

[0002] With the development of big data technology, an account query technology appears. With the management requirements of account information interaction between different industries in China becoming more and more strict, in order to strengthen the management level, when different accounts access the account information interaction platform, the accounts need to be matched with an abnormal account list according to account number, institution number, region number and other account interaction information, and the accounts with abnormal interaction information are blocked.

[0003] In the traditional technology, the abnormal account list is generally issued by a management institution or generated by a system corresponding to the account information interaction platform. At present, the abnormal account list is mostly saved to a database, and whether there is a matching result is found from the database when information is interacted online. In recent years, the online information interaction volume of multiple special activities and festivals has started to grow explosively. The way of accessing the database every time information is interacted will cause great access pressure on the database, and even cause the database to be down and lead to the risk of system service stop. Therefore, the traditional abnormal account matching method cannot meet the real-time query requirement under high concurrency. SUMMARY

[0004] Therefore, it is necessary to provide an account matching method, device, computer equipment, computer readable storage medium and computer program product capable of quickly identifying abnormal accounts in view of the above technical problems.

[0005] In a first aspect, the application provides an account matching method. The method comprises: in response to an abnormal query request for each target check account, inputting account interaction information corresponding to the target check account into an extended filter to obtain a filter matching result; the filter matching result is obtained by matching the account interaction information corresponding to the target check account with account interaction information corresponding to each account in a first abnormal account list in the extended filter; in the case that the filter matching result indicates that the target check account is an abnormal account, matching the account interaction information corresponding to the target check account with corresponding account interaction information in a cache query library to obtain each sub-cache matching result corresponding to the cache query library; the cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponds to a preset sub-cache query list, and the preset sub-cache query list has the account interaction information corresponding to the abnormal account; in the case that each sub-cache matching result indicates that the target check account is not the abnormal account, matching the account interaction information corresponding to the target check account with account interaction information corresponding to each account in a second abnormal account list in an abnormal account database to obtain an abnormal account matching result corresponding to the target check account; the abnormal account matching result determines whether the target check account is an abnormal account.

[0006] In one embodiment, each sub-cache matching result corresponding to the cache query library is a local cache matching result and a distributed cache matching result, and the matching of the account interaction information corresponding to the target check account with corresponding account interaction information in the cache query library to obtain each sub-cache matching result corresponding to the cache query library comprises: matching the account interaction information corresponding to the target check account with account interaction information corresponding to each account in a third abnormal account list in a local cache to obtain a local cache matching result corresponding to the local cache; in the case that the local cache matching result indicates that there is no abnormal account, matching the account interaction information corresponding to the target check account with account interaction information corresponding to each account in a fourth abnormal account list in a distributed cache to obtain a distributed cache matching result corresponding to the distributed cache.

[0007] In one of the embodiments, the method further comprises: in the case that the local cache matching result is abnormal, outputting the abnormal account matching result; or, in the case that the distributed cache matching result is abnormal, writing the account interaction information corresponding to the target check account into the third abnormal account list and outputting the abnormal account matching result; or, in the case of performing the step of matching the account interaction information corresponding to the target check account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target check account, writing the account interaction information corresponding to the target check account into the third abnormal account list and the fourth abnormal account list and outputting the abnormal account matching result.

[0008] In one of the embodiments, the filter matching result comprises a plurality of sub-filter matching results, and the step of inputting the account interaction information corresponding to the target check account into the extended filter to obtain the filter matching result comprises: grouping the extended filter according to a discrete algorithm to obtain at least two grouped extended filters, any one of the grouped extended filters having a corresponding filter data shard location value; performing data shard location calculation according to the account number corresponding to the account interaction information to obtain at least two account number shard location values; based on each sub-account number obtained by data sharding based on the account number, splitting the account interaction information according to each of the sub-account numbers to obtain each sub-account interaction information; based on the matching result of the filter data shard location value and the account number shard location value, inputting each of the sub-account interaction information into the corresponding grouped extended filter to obtain a sub-filter matching result; the grouped extended filter is configured to match each of the sub-account interaction information with the account interaction information of the preset first abnormal account list; and determining the filter matching result according to each of the sub-filter matching results.

[0009] In one of the embodiments, the step of grouping the Bloom filter according to a discrete algorithm to obtain a plurality of grouped extended filters comprises: based on the hash value space calculated based on the discrete algorithm, splitting the hash value space into a plurality of segments in the manner of a hash ring, and determining the corresponding data shard location according to the plurality of sub-hash value spaces after splitting; and inserting the corresponding data shard location into the corresponding extended filter for each of the sub-hash value spaces to obtain a plurality of the grouped extended filters.

[0010] In one embodiment, the method further includes: inserting supplementary query data into the first abnormal account list of the corresponding group expansion filter based on several account number sharding position values ​​corresponding to the account number, to obtain a supplemented first abnormal account list; and updating the first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list according to the supplemented first abnormal account list.

[0011] In one embodiment, the method further includes: based on several account number sharding position values ​​corresponding to the account number, deleting data from the first abnormal account list of the corresponding group expansion filter according to the deletion query data, to obtain a first abnormal account list after deletion; and updating the first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list according to the first abnormal account list after deletion.

[0012] Secondly, this application also provides an account matching apparatus. The apparatus includes: a filter matching result obtaining module, configured to, in response to an anomaly query request corresponding to each target checked account, input account interaction information corresponding to the target checked account into an extended filter to obtain a filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in a first anomaly account list in the extended filter; and a cache matching result obtaining module, configured to, when the filter matching result indicates that the target checked account is an anomaly account, match the account interaction information corresponding to the target checked account with the corresponding account interaction information in a cache query database to obtain the cache query result. The query database contains matching results for each sub-cache; the cache query database has at least two levels of sub-cache query databases, each sub-cache query database corresponds to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account; the abnormal account matching result obtaining module, when all the sub-cache matching results indicate that the target checked account is not the abnormal account, matches the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account; the abnormal account matching result determines whether the target checked account is an abnormal account.

[0013] In one embodiment, the cache matching result obtaining module is further configured to: match the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the third abnormal account list in the local cache to obtain the local cache matching result corresponding to the local cache; if the local cache matching result is that there is no abnormal account, then match the account interaction information corresponding to the target abnormal account with the account interaction information corresponding to each account in the fourth abnormal account list in the distributed cache to obtain the distributed cache matching result corresponding to the distributed cache.

[0014] In one embodiment, the abnormal account matching result obtaining module is further configured to: output the abnormal account matching result if the local cache matching result is abnormal; or, if the distributed cache matching result is abnormal, write the account interaction information corresponding to the target checked account to the third abnormal account list and output the abnormal account matching result; or, when performing the step of matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account, write the account interaction information corresponding to the target checked account to the third abnormal account list and the fourth abnormal account list, and output the abnormal account matching result.

[0015] In one embodiment, the filter matching result obtaining module is further configured to: group the extended filters according to a discrete algorithm to obtain at least two grouped extended filters, each of the grouped extended filters having a corresponding filter data fragmentation position value; calculate the data fragmentation position based on the account number corresponding to the account interaction information to obtain at least two account number fragmentation position values; divide the account interaction information according to each sub-account number obtained by data fragmentation based on the account number to obtain each sub-account interaction information; input each sub-account interaction information into the corresponding grouped extended filter based on the matching result of the filter data fragmentation position value and the account number fragmentation position value to obtain a sub-filter matching result; the grouped extended filter is configured to match each sub-account interaction information with the account interaction information of the first abnormal account list in each grouped extended filter; and determine the filter matching result based on the sub-filter matching results.

[0016] In one embodiment, the filter matching result obtaining module is further configured to: divide the hash value space calculated by the discrete algorithm into several segments according to the hash ring method, and determine the corresponding data fragment position according to the several sub-hash value spaces after division; insert the corresponding extended filter into the corresponding data fragment position of each sub-hash value space to obtain several grouped extended filters.

[0017] In one embodiment, the filter matching result obtaining module is further configured to: insert supplementary query data into the first abnormal account list of the corresponding grouping extended filter based on several account number sharding position values ​​corresponding to the account number, to obtain the supplemented first abnormal account list; and update the first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list according to the supplemented first abnormal account list.

[0018] In one embodiment, the filter matching result obtaining module is further configured to: based on several account number sharding position values ​​corresponding to the account number, delete from the first abnormal account list of the corresponding grouping extended filter according to the deletion query data, to obtain a first abnormal account list after deletion; and update the first abnormal account list, the second abnormal account list, the third abnormal account list and the fourth abnormal account list according to the first abnormal account list after deletion.

[0019] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, performs the following steps: in response to an anomaly query request corresponding to each target check account, inputting the account interaction information corresponding to the target check account into an extended filter to obtain a filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target check account with the account interaction information corresponding to each account in a first anomaly account list in the extended filter; if the filter matching result indicates that the target check account is an anomaly account, matching the account interaction information corresponding to the target check account with the corresponding accounts in a cache query database. User interaction information is matched to obtain matching results for each sub-cache corresponding to the cache query library; the cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponds to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account; if all the sub-cache matching results indicate that the target checked account is not the abnormal account, then the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account; the abnormal account matching result determines whether the target checked account is an abnormal account.

[0020] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps: in response to an anomaly query request corresponding to each target inspection account, inputting account interaction information corresponding to the target inspection account into an extended filter to obtain a filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target inspection account with the account interaction information corresponding to each account in a first list of abnormal accounts in the extended filter; if the filter matching result indicates that the target inspection account is an abnormal account, matching the account interaction information corresponding to the target inspection account with the corresponding account interaction information in a cached query database. The information is matched to obtain the matching results of each sub-cache corresponding to the cache query library; the cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponds to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account; if all the sub-cache matching results indicate that the target checked account is not the abnormal account, then the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account; the abnormal account matching result determines whether the target checked account is an abnormal account.

[0021] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps: in response to an anomaly query request corresponding to each target check account, inputting account interaction information corresponding to the target check account into an extended filter to obtain a filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target check account with the account interaction information corresponding to each account in a first anomaly account list in the extended filter; if the filter matching result indicates that the target check account is an anomaly account, matching the account interaction information corresponding to the target check account with the corresponding account interaction information in a cached query library. The matching process is performed to obtain matching results for each sub-cache corresponding to the cache query library. The cache query library has at least two levels of sub-cache query libraries, each corresponding to a preset sub-cache query list. The preset sub-cache query list contains the account interaction information corresponding to the abnormal account. If all sub-cache matching results indicate that the target checked account is not the abnormal account, then the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list of the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account. The abnormal account matching result determines whether the target checked account is an abnormal account.

[0022] The aforementioned account matching method, apparatus, computer equipment, storage medium, and computer program product, in response to anomaly query requests corresponding to each target checked account, inputs the account interaction information corresponding to the target checked account into an extended filter to obtain a filter matching result. The filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the first abnormal account list within the extended filter. If the filter matching result indicates that the target checked account is an abnormal account, the account interaction information corresponding to the target checked account is matched with the corresponding account interaction information in a cache query library to obtain sub-cache matching results corresponding to the cache query library. The cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponding to a preset sub-cache query list, and the preset sub-cache query list contains account interaction information corresponding to abnormal accounts. If all sub-cache matching results indicate that the target checked account is not an abnormal account, the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account. The abnormal account matching result determines whether the target checked account is an abnormal account.

[0023] By performing multiple hash calculations on key account interaction information in resource interactions among multiple accounts, and checking for values ​​at different positions in the array based on each hash result, it can be determined whether the target account matches data corresponding to an abnormal account. If no data corresponding to an abnormal account is found, resource interaction proceeds directly. If an abnormal account exists, a three-level cache is used to confirm whether an abnormal account truly exists. This approach ensures accurate matching of abnormal accounts while further reducing direct database queries, significantly decreasing database access volume, and improving system stability. Attached Figure Description

[0024] Figure 1 This is a diagram illustrating the application environment of the account matching method in one embodiment;

[0025] Figure 2 This is a flowchart illustrating an account matching method in one embodiment;

[0026] Figure 3 This is a flowchart illustrating a method for obtaining matching results from a distributed cache in one embodiment.

[0027] Figure 4 This is a flowchart illustrating a method for outputting matching results for multiple abnormal accounts in one embodiment.

[0028] Figure 5 This is a flowchart illustrating a method for determining filter matching results in one embodiment;

[0029] Figure 6 This is a flowchart illustrating a method for obtaining a grouped extended filter in one embodiment;

[0030] Figure 7 This is a flowchart illustrating a method for inserting supplementary query data in one embodiment;

[0031] Figure 8 This is a flowchart illustrating a method for deleting or reducing query data in one embodiment;

[0032] Figure 9 This is a schematic diagram of a combination of several grouped extended filters in one embodiment;

[0033] Figure 10 This is a logical diagram of an account matching method in one embodiment;

[0034] Figure 11 This is a structural block diagram of an account matching device in one embodiment;

[0035] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0037] The account matching method provided in this application embodiment can be applied to, for example, Figure 1 In the application environment shown, terminal 102 acquires data, server 104 receives the data from terminal 102 in response to the terminal 102's instructions, performs calculations on the acquired data, and transmits the calculation results back to terminal 102 for display. Terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on other network servers. In response to anomaly query requests for each target account, the account interaction information corresponding to the target account is input into an extended filter to obtain a filter matching result. The filter matching result is obtained by matching the account interaction information corresponding to the target account with the account interaction information corresponding to each account in the first abnormal account list in the extended filter. If the filter matching result indicates that the target account is an abnormal account, the account interaction information corresponding to the target account is matched with the corresponding account interaction information in the cache query library to obtain the sub-cache matching results corresponding to the cache query library. The cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponding to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account. If all sub-cache matching results indicate that the target account is not an abnormal account, the account interaction information corresponding to the target account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target account. The abnormal account matching result determines whether the target account is an abnormal account. The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0038] In one embodiment, such as Figure 2 As shown, an account matching method is provided, which is applied to... Figure 1 Taking the server in the example, the following steps are included:

[0039] Step 202: In response to the anomaly query requests corresponding to each target inspection account, the account interaction information corresponding to the target inspection account is input into the extended filter to obtain the filter matching result.

[0040] The target inspection account can be an account that needs to be checked for anomalies, obtained from the terminal. Each target inspection account includes at least one piece of account interaction information, which includes information on resource exchange between the target inspection account and other accounts.

[0041] Among them, the abnormal query request can be an instruction to determine whether there is an anomaly in the target checked account. The instruction or the request is calculated and executed to compare the account interaction information in the target checked account with the account interaction information of the account with anomalies in the database and obtain the comparison result.

[0042] The account interaction information can be information indicating resource interaction between the target inspection account and the public platform or other accounts, or it can be the target inspection account's inherent information, such as the target inspection account holder, account opening date, account type, etc.

[0043] The extended filter can be a Bloom filter with an added counter. The Bloom filter can be a long binary vector and a series of random mapping functions. Bloom filters can be used to check whether an element is in a set. Its advantages are that its space efficiency and query time are much better than general algorithms, but its disadvantages include a certain false positive rate and difficulty in deletion.

[0044] The filter matching result can be obtained by matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the pre-defined account list formed by the extended filter to represent accounts with abnormalities.

[0045] Specifically, server 104 responds to input from terminal 102 of anomaly query requests for each target check account. The anomaly query request may specify that the query includes at least one target check account, and may include multiple different query targets. Based on the computer language carried in the anomaly query request, the account interaction information corresponding to the target account is input into an extended filter. The extended filter then matches the account interaction information corresponding to the target check account with the account interaction information corresponding to each account in the first anomaly account list in the extended filter, and outputs the matching result, which is the filter matching result. This result may indicate that no anomaly exists or that an anomaly exists.

[0046] For example, in response to the anomaly query request for each target inspection account, the account interaction information 1-100 corresponding to the target inspection account A is input into the expanded filter. Then, the expanded filter matches the account interaction information 1-100 corresponding to the target inspection account with the account interaction information 1-100 corresponding to each account in the first list of abnormal accounts in the expanded filter to obtain the filter matching result. This result can be either no anomaly or an anomaly exists.

[0047] Step 204: If the filter matching result indicates that the target checked account is an abnormal account, match the account interaction information corresponding to the target checked account with the corresponding account interaction information in the cache query library to obtain the matching results of each sub-cache corresponding to the cache query library.

[0048] The cache query library can be a three-level cache query library, meaning that the cache query library includes three sub-cache query libraries with different operating principles. Each sub-cache query library has its own account list, and each account query list stores the corresponding account interaction information, which can be used to check the target account.

[0049] Among them, the sub-cache matching result can be the result of matching the account interaction information corresponding to each account query list in the cache query library with the account interaction information corresponding to the target checked account. The set of all sub-cache matching results is the cache matching result.

[0050] Specifically, after matching the account interaction information in the expanded filter with the account interaction information in the target checked account, if the filter matching result indicates that the target checked account is abnormal, the account interaction information in the target checked account is further input into the cache query library. Based on the abnormal account list corresponding to each sub-cache query library in the cache query library, the account interaction information in the target checked account is compared with the account interaction information in the abnormal account list corresponding to each sub-cache query library, to obtain the sub-cache matching results corresponding to each sub-cache query library in the cache query library. The cache query library has at least two levels of sub-cache query libraries, and each sub-cache query library corresponds to a preset sub-cache query list, which contains the account interaction information corresponding to the abnormal accounts.

[0051] For example, if the extended filter matches the account interaction information 1-100 corresponding to the target checked account with the account interaction information 1-100 corresponding to each account in the first abnormal account list in the extended filter, and the filter matching result is that there is an abnormal situation, then the account interaction information 1-100 corresponding to the target checked account and the account interaction information are matched with the corresponding account interaction information 1-100 in the cache query library to obtain the matching results of each sub-cache query library in the cache query library.

[0052] Step 206: If the matching results of each sub-cache indicate that the target checked account is not an abnormal account, then the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account.

[0053] The second abnormal account list can be a table in the abnormal account database used to record the account interaction information corresponding to each account, which facilitates comparison with the account interaction information corresponding to the target account being checked.

[0054] The abnormal account matching result can be the result of matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database. The output result can be either determined to be an abnormal account or determined not to be an abnormal account.

[0055] Specifically, after matching the account interaction information corresponding to the target checked account with the corresponding account interaction information in the cache query database, if the result is that the target checked account is not an abnormal account in each sub-cache matching result, then the account interaction information in the target checked account is further input into the abnormal account database. Based on the second abnormal account list in the abnormal account database, the account interaction information in the target checked account is compared with the account interaction information in the abnormal account list corresponding to the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account. The abnormal account matching result determines whether the target checked account is an abnormal account.

[0056] For example, the account interaction information 1-100 corresponding to the target checked account is matched with the corresponding account interaction information 1-100 in the cache query database to obtain the sub-cache matching results 1-n corresponding to each sub-cache query database. If each sub-cache matching result 1-n is not an abnormal account, the account interaction information 1-100 corresponding to the target checked account is matched with the account interaction information 1-100 corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result N corresponding to the target checked account.

[0057] In the above account matching method, in response to the abnormal query requests corresponding to each target checked account, the account interaction information corresponding to the target checked account is input into the extended filter to obtain the filter matching result. The filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the first abnormal account list in the extended filter. If the filter matching result indicates that the target checked account is an abnormal account, the account interaction information corresponding to the target checked account is matched with the corresponding account interaction information in the cache query library to obtain the sub-cache matching results corresponding to the cache query library. The cache query library has at least two levels of sub-cache query libraries, and each sub-cache query library corresponds to a preset sub-cache query list, which contains the account interaction information corresponding to the abnormal account. If all sub-cache matching results indicate that the target checked account is not an abnormal account, the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account. The abnormal account matching result determines whether the target checked account is an abnormal account.

[0058] By performing multiple hash calculations on key account interaction information in resource interactions among multiple accounts, and checking for values ​​at different positions in the array based on each hash result, it can be determined whether the target account matches data corresponding to an abnormal account. If no data corresponding to an abnormal account is found, resource interaction proceeds directly. If an abnormal account exists, a three-level cache is used to confirm whether an abnormal account truly exists. This approach ensures accurate matching of abnormal accounts while further reducing direct database queries, significantly decreasing database access volume, and improving system stability.

[0059] In one embodiment, such as Figure 3 As shown, the matching results of each sub-cached instance in the cache query library, the local cache matching results, and the distributed cache matching results are obtained by matching the account interaction information corresponding to the target account with the corresponding account interaction information in the cache query library. These results include:

[0060] Step 302: Match the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the third abnormal account list in the local cache to obtain the local cache matching result.

[0061] The local cache can be one of the sub-cached query libraries in the cache query library, and the information corresponding to the local cache is cached on a container on the local server. Since there can be multiple containers, the local cache can match multiple target check accounts at the same time.

[0062] The third abnormal account list can be a table in the local cache used to record the account interaction information of each account, which is convenient for comparison with the account interaction information of the target account being checked.

[0063] Specifically, the account interaction information in the target account is input into the local cache. Based on the third abnormal account list in the abnormal local cache, the account interaction information in the target account is compared with the account interaction information in the abnormal account list corresponding to the local cache to obtain the local cache matching result. The local cache matching result determines whether the target account is an abnormal account.

[0064] For example, the account interaction information 1-100 corresponding to the target checked account is matched with the account interaction information 1-100 corresponding to each account in the third abnormal account list in the local cache to obtain the local cache matching result N1.

[0065] Step 304: If the local cache matching result shows that there is no abnormal account, then the account interaction information corresponding to the target abnormal account is matched with the account interaction information corresponding to each account in the fourth abnormal account list in the distributed cache to obtain the distributed cache matching result.

[0066] The distributed cache can be one of the sub-cached query libraries in the cache query library, and the information corresponding to the distributed cache is cached on a container on a cloud server. Since there can be multiple containers, the distributed cache can simultaneously match multiple target check accounts.

[0067] The fourth abnormal account list can be a table in a distributed cache used to record the account interaction information corresponding to each account, which facilitates comparison with the account interaction information corresponding to the target checked account.

[0068] Specifically, after matching the account interaction information corresponding to the target account with the corresponding account interaction information in the local cache, if the result indicates that the target account does not have an abnormal account in the local cache matching result, then the account interaction information in the target account is further input into the distributed cache. Based on the fourth abnormal account list in the distributed cache, the account interaction information in the target account is compared with the account interaction information in the abnormal account list corresponding to the distributed cache to obtain the distributed cache matching result. The distributed cache matching result determines whether the target account is an abnormal account.

[0069] For example, the account interaction information 1-100 corresponding to the target checked account is matched with the corresponding account interaction information 1-100 in the local cache to obtain the corresponding local cache matching result 1-n. If the local cache matching result 1-n does not contain any abnormal accounts, then the account interaction information 1-100 corresponding to the target checked account is matched with the account interaction information 1-100 corresponding to each account in the fourth abnormal account list in the distributed cache to obtain the abnormal account matching result N2 corresponding to the target checked account.

[0070] In this embodiment, by refining the cache query library into multiple different sub-cache query libraries, and obtaining the respective sub-cache matching results based on the different sub-cache query libraries, the use of a multi-level caching mechanism improves database query efficiency and reduces database access pressure, enabling the system to achieve higher matching efficiency between the account interaction information of the target checked account and the system's preset account interaction information.

[0071] In one embodiment, such as Figure 4 As shown, the method also includes:

[0072] Step 402: If the local cached matching result is abnormal, then the abnormal account matching result is output.

[0073] Specifically, the account interaction information in the target checked account is compared with the account interaction information in the third abnormal account list corresponding to the local cache. If the local cache matching result is found to be abnormal, the local cache matching result is output as the abnormal account matching result corresponding to the target checked account and displayed on the visualization device.

[0074] For example, the account interaction information 1-100 corresponding to the target checked account is matched with the account interaction information 1-100 corresponding to each account in the third abnormal account list in the local cache. If the local cache matching result N1 is found to be abnormal, then the local cache matching result N1 is output as the abnormal account matching result N corresponding to the target checked account and displayed on the visualization device.

[0075] Step 404, or, if the distributed cache matching result is abnormal, then write the account interaction information corresponding to the target checked account to the third abnormal account list and output the abnormal account matching result.

[0076] Specifically, the account interaction information in the target checked account is compared with the account interaction information in the fourth abnormal account list corresponding to the distributed cache. If the local cache matching result corresponding to the distributed cache is found to be abnormal, the distributed cache matching result is returned and written to the third abnormal account list corresponding to the local cache executed in the previous step, and output as the abnormal account matching result corresponding to the target checked account to the visualization device for display.

[0077] For example, the account interaction information 1-100 corresponding to the target checked account is matched with the account interaction information 1-100 corresponding to each account in the fourth abnormal account list in the distributed cache. If the distributed cache matching result N2 corresponding to the distributed cache is found to be abnormal, the distributed cache matching result N2 is returned and written to the third abnormal account list corresponding to the local cache executed in the previous step, and output as the abnormal account matching result N corresponding to the target checked account to the visualization device for display.

[0078] Step 406, or, if the step of matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account is performed, then the account interaction information corresponding to the target checked account is written to the third abnormal account list and the fourth abnormal account list, and the abnormal account matching result is output.

[0079] Specifically, when performing the steps of "inputting the account interaction information in the target checked account into the abnormal account database, comparing the account interaction information in the target checked account with the account interaction information in the corresponding abnormal account list in the abnormal account database based on the second abnormal account list in the abnormal account database, obtaining the abnormal account matching result corresponding to the target checked account, and determining whether the target checked account is an abnormal account based on the abnormal account matching result", the account interaction information corresponding to the target checked account is returned and written to the third abnormal account list corresponding to the local cache and the fourth abnormal account list corresponding to the distributed cache executed above, and output as the abnormal account matching result corresponding to the target checked account to the visualization device for display.

[0080] For example, when performing the step of "matching the account interaction information 1-100 corresponding to the target checked account with the account interaction information 1-100 corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result N corresponding to the target checked account", the account interaction information corresponding to the target checked account is returned and written to the third abnormal account list corresponding to the local cache and the fourth abnormal account list corresponding to the distributed cache executed in the previous step, and is output to the visualization device as the abnormal account matching result N corresponding to the target checked account for display.

[0081] In this embodiment, by using local cache, distributed cache and abnormal account database to match the target account to be checked, and returning the matching results to the corresponding abnormal account lists of local cache, distributed cache and abnormal account database, the information of abnormal accounts can be supplemented, making the subsequent abnormal account check more efficient and the check error rate lower.

[0082] In one embodiment, such as Figure 5 As shown, the filter matching results include several sub-filter matching results. The account interaction information corresponding to the target account is input into the extended filter to obtain the filter matching results, including:

[0083] Step 502: Group the extended filters according to the discrete algorithm to obtain at least two grouped extended filters.

[0084] Among them, the discrete algorithm can be a Secure Hash Algorithm (SHA), a family of cryptographic hash functions that can calculate a fixed-length string corresponding to a digital message. Furthermore, different input messages have a high probability of corresponding to different strings.

[0085] Specifically, the Murmurhash hash algorithm (a non-cryptographic hashing algorithm) is used as the standard. Before insertion, the key value is hashed to obtain a number between 0 and (2^63-1). The hash value space [0 to (2^63-1)] is divided into N segments using a hash ring method. The segment in which the hash value falls determines the data shard it belongs to. After calculating the shard position corresponding to the key value, the corresponding extended filter is used for insertion.

[0086] For example, using the unencrypted hash algorithm Murmurhash, the calculated hash value space can be divided into N segments. Then, based on the segmentation of the hash values, the extended filter K is grouped to obtain at least two grouped extended filter position values ​​(K1, K2, K3...K). N ).

[0087] Step 504: Calculate the data sharding position based on the account number corresponding to the account interaction information to obtain at least two account number sharding position values.

[0088] The account number fragment position value can be a value used to represent the fragment position for any group of account numbers after the account number corresponding to the account interaction information is calculated by fragmentation.

[0089] Specifically, the Murmurhash hash algorithm (a non-encrypted hash algorithm) is used as the standard. Before insertion, the key value is hashed to obtain a number from 0 to (2^63-1). The hash value space [0 to (2^63-1)] is divided into N segments using a hash ring method. The segment in which the hash value obtained above falls determines which data segment it belongs to.

[0090] For example, using the unencrypted hash algorithm Murmurhash, the calculated hash value space can be divided into N segments. Then, based on the segmentation of the hash values, the account number L is grouped to obtain at least two sub-account number position values ​​(L1, L2, L3...L...). N ).

[0091] Step 506: Based on the account number, the data is sharded to obtain the sub-account numbers. The account interaction information is then segmented according to the sub-account numbers to obtain the sub-account interaction information.

[0092] Specifically, after calculating the shard position corresponding to the key value, the data is sharded based on the account number to obtain the sub-account numbers. When a query is needed, the shard position is first calculated based on the customer code, and then the account interaction information is divided according to each sub-account number to obtain the interaction information for each sub-account. To ensure that the data in each shard is evenly distributed, the number of shards can be configured to 2^n, and the number of sub-account interaction information will also be 2^n, such as 128.

[0093] For example, the sub-account numbers L1, L2, L3...L are obtained by data sharding based on account numbers. N Account interaction information is categorized by sub-account number L1, L2, L3...L N The data is divided into segments, resulting in 2^n interaction messages for each sub-account.

[0094] Step 508: Based on the matching results of the filter data fragment position value and the account number fragment position value, input the interaction information of each sub-account into the corresponding group expansion filter to obtain the sub-filter matching results.

[0095] The sub-filter matching result can be the filter matching result obtained by inputting the interaction information of each sub-account into the corresponding grouped extended filter.

[0096] Specifically, a discrete algorithm is used to match the filter data fragment position values ​​calculated by combining key values ​​with the account number fragment position values ​​to obtain a one-to-one matching result between the filter data fragment position values ​​and the account number fragment position values. Based on the above matching result, the interaction information of each sub-account corresponding to the number fragment position value is input into each group extended filter corresponding to the filter data fragment position value for calculation to obtain the sub-filter matching result. The group extended filter is used to match the interaction information of each sub-account with the account interaction information of the first abnormal account list in each group extended filter.

[0097] For example, the filter data fragment position values ​​are (K1, K2, K3...K... N Correspondingly insert grouped extended filters so that for any filter data sharding position value, there is a corresponding grouped extended filter, setting the account number sharding position value to (L1, L2, L3...L...). N ) and (K1, K2, K3...K N ) and (L1, L2, L3...L N The system performs matching and further inputs the interaction information of each sub-account into the corresponding group expansion filter based on the matching results to obtain the sub-filter matching results.

[0098] Step 510: Determine the filter matching result based on the matching results of each sub-filter.

[0099] Specifically, the matching results of each sub-filter are combined in a preset order to obtain the filter matching results corresponding to the expanded filter formed by the combination of all grouped expanded filters.

[0100] In this embodiment, the customer ID from the account interaction information is used as the primary key for data sharding. Since the customer ID is a unique identifier for each customer in the account interaction information system, it offers better distinguishability. Using a single account interaction information entry instead of all account interaction information to calculate the sharding position further reduces computational load without affecting the filter matching results, thus improving the matching efficiency for the target account.

[0101] In one embodiment, such as Figure 6 As shown, the computational Bloom filter is grouped according to the discrete algorithm to obtain several grouped expanded filters, including:

[0102] Step 602: Based on the hash value space calculated by the discrete algorithm, the hash value space is divided into several segments according to the hash ring method, and the corresponding data fragment positions are determined according to the several sub-hash value spaces after the division.

[0103] In this context, the hash value space can be defined as the space where an input of arbitrary length (also called a pre-image) is transformed into a fixed-length output using a hash algorithm. This output is the hash value, and the space corresponding to the hash values ​​is the hash value space. This transformation is a compression mapping, meaning that the space of hash values ​​is usually much smaller than the space of inputs. Different inputs may hash to the same output, so it is impossible to determine a unique input value from the hash value.

[0104] In this context, a hash ring can be a virtual ring that organizes the entire hash value space. For example, if the value space of a certain hash function is 0-2^32-1 (that is, the hash value is a 32-bit unsigned integer), the entire hash space ring is formed by mapping all input values ​​to the range 0-2^32-1 to form a ring.

[0105] Specifically, the Murmurhash hash algorithm (a non-encrypted hash algorithm) is used as the standard. Before insertion, the key value is hashed to obtain a number from 0 to (2^63-1). The hash value space [0 to (2^63-1)] is divided into N segments using a hash ring method. The segment in which the hash value obtained above falls determines which data segment it belongs to.

[0106] For example, using the unencrypted hash algorithm Murmurhash, the calculated hash value space can be divided into N segments. Then, based on the segmentation of the hash values, the extended filter K is grouped to obtain at least two grouped extended filter position values ​​(K1, K2, K3...K). N ).

[0107] Step 604: Determine the corresponding data fragment position for each sub-hash value space and insert the corresponding extended filter to obtain several grouped extended filters.

[0108] Specifically, such as Figure 9 As shown, after calculating the shard position corresponding to the key value, the same filter as the aforementioned extended filter is inserted into the data shard position determined by each sub-hash value space as the filter corresponding to each shard position, resulting in several group extended filters. Among them, the group extended filters are the same extended filters as the extended filters. That is to say, multiple group extended filters form an extended filter with the same performance and effect.

[0109] For example, using the unencrypted hash algorithm Murmurhash, the calculated hash value space can be divided into N segments. Then, based on the segmentation of the hash values, the extended filter K is grouped to obtain at least two grouped extended filter position values ​​(K1, K2, K3...K). NFor any extended filter position value (K1, K2, K3...K), N Insert an expansion filter as a group expansion filter accordingly to obtain several group expansion filters.

[0110] In this embodiment, by using a discrete algorithm to calculate the hash value space and further determining the data fragment positions where the extended filter needs to be inserted based on the hash value space, multiple grouped extended filters can be obtained, resulting in a more balanced load during data processing and improving the matching efficiency of the target checked accounts.

[0111] In one embodiment, such as Figure 7 As shown, the method also includes:

[0112] Step 702: Based on the sharding position values ​​of several account numbers corresponding to the account numbers, insert the supplementary query data into the first abnormal account list of the corresponding group expansion filter to obtain the supplemented first abnormal account list.

[0113] Among them, supplementing query data can be done by adding query data to the original list of first abnormal accounts, so that the account interaction information corresponding to the abnormal accounts in the list of first abnormal accounts is more complete.

[0114] The supplemented first abnormal account list can be a new abnormal account list formed after the supplemented query data has been added to the original first abnormal account list and used by the expanded filter.

[0115] Specifically, based on the sharding position values ​​of several account numbers corresponding to the account number, the specific position of the account number in the first abnormal account list is determined. Then, based on the specific position of different account numbers, the position for inserting supplementary query data is determined. The supplementary query data is then inserted into the first abnormal account list corresponding to the group expansion filter that needs to supplement query data. The first abnormal account list that has already added data becomes the supplemented first abnormal account list.

[0116] For example, based on the account number corresponding to several account number sharding position values, the account number sharding position values ​​are (L1, L2, L3...L... N ) i Where i is the number of account numbers, after determining the position of any account number, insert the supplementary query data X into the place where the supplementary query data needs to be added in the first abnormal account list, and obtain the supplemented first abnormal account list.

[0117] Step 704: Update the first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list according to the supplemented first abnormal account list.

[0118] Specifically, based on the supplemented first abnormal account list with added supplementary query data, supplementary query data is added to the second, third, and fourth abnormal account lists accordingly, and the data in the second, third, and fourth abnormal account lists is updated to satisfy the query for the target checked account.

[0119] For example, based on the supplemented first abnormal account list, supplementary query data X is added to the data in the second, third, and fourth abnormal account lists, and all lists are updated synchronously to ensure that business needs are met.

[0120] In this embodiment, by using the account number segmentation position value to determine the insertion position of the supplementary query data, the first abnormal account list is updated, and the second, third, and fourth abnormal account lists are also updated. This ensures that all abnormal account lists process the added data synchronously, improving the accuracy of the system.

[0121] In one embodiment, such as Figure 8 As shown, the method also includes:

[0122] Step 802: Based on the sharding position values ​​of several account numbers corresponding to the account numbers, delete the first abnormal account list from the corresponding grouping expansion filter according to the deletion query data to obtain the first abnormal account list after deletion.

[0123] Specifically, based on the sharding position values ​​of several account numbers corresponding to the account number, the specific position of the account number in the first abnormal account list is determined. Then, based on the specific position of different account numbers, the position to be deleted corresponding to the data to be deleted is determined. The data to be deleted is then removed from the first abnormal account list corresponding to the group expansion filter. The first abnormal account list with deleted data is the first abnormal account list after deletion.

[0124] For example, based on the account number corresponding to several account number sharding position values, the account number sharding position values ​​are (L1, L2, L3...L... N ) i Where i is the number of account numbers, after determining the position of any account number, delete the deletion query data Y from the place where the deletion and addition query data needs to be deleted in the first abnormal account list, and obtain the first abnormal account list after deletion.

[0125] Step 804: Update the first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list based on the first abnormal account list after deletion.

[0126] Specifically, based on the first abnormal account list after the deletion of the deletion query data, the deletion query data is correspondingly deleted from the second, third, and fourth abnormal account lists, and the data in the second, third, and fourth abnormal account lists is updated to satisfy the query for the target checked account.

[0127] For example, based on the first abnormal account list after deletion, the data in the second, third, and fourth abnormal account lists is deleted. The query data Y has been updated synchronously to ensure that business needs are met.

[0128] In this embodiment, by using the account number segmentation position value to determine the deletion position, the deletion query data is deleted, the first abnormal account list is updated, and the second, third, and fourth abnormal account lists are also updated. This ensures that all abnormal account lists process the deleted data synchronously, improving the accuracy of the system.

[0129] The specific implementation process for the account matching method described above is as follows: Figure 10 As shown.

[0130] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0131] Based on the same inventive concept, this application also provides an account matching apparatus for implementing the account matching method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more account matching apparatus embodiments provided below can be found in the limitations of the account matching method described above, and will not be repeated here.

[0132] In one embodiment, such as Figure 11 As shown, an account matching device is provided, including: a filter matching result obtaining module 1102, a cached matching result obtaining module 1104, and an abnormal account matching result obtaining module 1106, wherein:

[0133] The filter matching result acquisition module 1102 is used to respond to the abnormal query request corresponding to each target check account, input the account interaction information corresponding to the target check account into the extended filter, and obtain the filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target check account with the account interaction information corresponding to each account in the first abnormal account list in the extended filter;

[0134] The cache matching result acquisition module 1104, when the filter matching result indicates that the target checked account is an abnormal account, matches the account interaction information corresponding to the target checked account with the corresponding account interaction information in the cache query library to obtain the sub-cache matching results corresponding to the cache query library; the cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponds to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account;

[0135] The abnormal account matching result module 1104, if all sub-cached matching results indicate that the target checked account is not an abnormal account, then matches the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account; the abnormal account matching result determines whether the target checked account is an abnormal account.

[0136] In one embodiment, the cache matching result obtaining module is further configured to: match the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the third abnormal account list in the local cache to obtain the local cache matching result corresponding to the local cache; if the local cache matching result is that there is no abnormal account, then match the account interaction information corresponding to the target abnormal account with the account interaction information corresponding to each account in the fourth abnormal account list in the distributed cache to obtain the distributed cache matching result corresponding to the distributed cache.

[0137] In one embodiment, the abnormal account matching result obtaining module is further configured to: output the abnormal account matching result if the local cache matching result indicates an anomaly; or, if the distributed cache matching result indicates an anomaly, write the account interaction information corresponding to the target checked account to the third abnormal account list and output the abnormal account matching result; or, if the step of matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account is performed, then the account interaction information corresponding to the target checked account is written to the third abnormal account list and the fourth abnormal account list, and the abnormal account matching result is output.

[0138] In one embodiment, the filter matching result obtaining module is further configured to: group the extended filters according to a discrete algorithm to obtain at least two grouped extended filters, each grouped extended filter having a corresponding filter data fragment position value; calculate the data fragment position based on the account number corresponding to the account interaction information to obtain at least two account number fragment position values; divide the account interaction information according to each sub-account number obtained by data fragmentation based on the account number to obtain each sub-account interaction information; input each sub-account interaction information into the corresponding grouped extended filter based on the matching result of the filter data fragment position value and the account number fragment position value to obtain the sub-filter matching result; the grouped extended filter is used to match each sub-account interaction information with the account interaction information of the first abnormal account list in each grouped extended filter; and determine the filter matching result based on the sub-filter matching result.

[0139] In one embodiment, the filter matching result obtaining module is further used to: divide the hash value space calculated based on the discrete algorithm into several segments according to the hash ring method, and determine the corresponding data fragment position according to the several sub-hash value spaces after division; insert the corresponding extended filter into the corresponding data fragment position of each sub-hash value space to obtain several grouped extended filters.

[0140] In one embodiment, the filter matching result obtaining module is further configured to: insert supplementary query data into the first abnormal account list of the corresponding grouped extended filter based on several account number sharding position values ​​corresponding to the account number, to obtain the supplemented first abnormal account list; and update the first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list according to the supplemented first abnormal account list.

[0141] In one embodiment, the filter matching result obtaining module is further configured to: based on several account number sharding position values ​​corresponding to the account number, delete from the first abnormal account list of the corresponding grouping extended filter according to the deletion query data, to obtain the first abnormal account list after deletion; and update the first abnormal account list, the second abnormal account list, the third abnormal account list and the fourth abnormal account list according to the first abnormal account list after deletion.

[0142] Each module in the aforementioned account matching device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can invoke and execute the corresponding operations of each module.

[0143] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores server data. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements an account matching method.

[0144] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0145] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0146] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0147] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0148] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0149] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0150] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An account matching method, characterized in that, The method includes: In response to an anomaly query request for each target inspection account, the account interaction information corresponding to the target inspection account is input into an extended filter to obtain a filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target inspection account with the account interaction information corresponding to each account in the first anomaly account list in the extended filter; If the filter matching result indicates that the target checked account is an abnormal account, the account interaction information corresponding to the target checked account is matched with the corresponding account interaction information in the cache query library to obtain the matching results of each sub-cache corresponding to the cache query library; the cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponds to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account; If all the sub-cache matching results indicate that the target checked account is not the abnormal account, then the account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account; the abnormal account matching result determines whether the target checked account is an abnormal account. The filter matching result includes several sub-filter matching results. The step of inputting the account interaction information corresponding to the target checked account into the extended filter to obtain the filter matching result includes: The extended filters are grouped according to the discrete algorithm to obtain at least two grouped extended filters, and each of the grouped extended filters has a corresponding filter data fragment position value. Based on the account number corresponding to the account interaction information, the data fragmentation position is calculated to obtain at least two account number fragmentation position values; Based on the account number, data is segmented to obtain sub-account numbers. The account interaction information is then divided according to each sub-account number to obtain the sub-account interaction information. Based on the matching result between the filter data fragment position value and the account number fragment position value, the interaction information of each sub-account is input into the corresponding group expansion filter to obtain the sub-filter matching result; the group expansion filter is used to match the interaction information of each sub-account with the account interaction information of the first abnormal account list in each group expansion filter; The filter matching result is determined based on the matching results of each sub-filter.

2. The method according to claim 1, characterized in that, The matching results of each sub-cache corresponding to the cache query library, the local cache matching results, and the distributed cache matching results, wherein the matching of the account interaction information corresponding to the target checked account with the corresponding account interaction information in the cache query library to obtain the matching results of each sub-cache corresponding to the cache query library, include: The account interaction information corresponding to the target checked account is matched with the account interaction information corresponding to each account in the third abnormal account list in the local cache to obtain the local cache matching result. If the local cache matching result indicates that there is no abnormal account, then the account interaction information corresponding to the target abnormal account is matched with the account interaction information corresponding to each account in the fourth abnormal account list in the distributed cache to obtain the distributed cache matching result corresponding to the distributed cache.

3. The method according to claim 2, characterized in that, The method further includes: If the local cached matching result is abnormal, then the abnormal account matching result is output; Alternatively, if the distributed cache matching result indicates an anomaly, the account interaction information corresponding to the target checked account is written to the third abnormal account list, and the abnormal account matching result is output. Alternatively, when performing the step of matching the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account, the account interaction information corresponding to the target checked account is written to the third abnormal account list and the fourth abnormal account list, and the abnormal account matching result is output.

4. The method according to claim 1, characterized in that, The computational Bloom filter is grouped according to a discrete algorithm to obtain several grouped extended filters, including: Based on the hash value space calculated by the discrete algorithm, the hash value space is divided into several segments according to the hash ring method, and the corresponding data fragment position is determined according to the several sub-hash value spaces after the division. For each of the sub-hash value spaces, the corresponding data fragment position is determined and the corresponding extended filter is inserted to obtain a number of the grouped extended filters.

5. The method according to claim 4, characterized in that, The method further includes: Based on the sharding position values ​​of several account numbers corresponding to the account number, the supplementary query data is inserted into the first abnormal account list of the corresponding group expansion filter to obtain the supplemented first abnormal account list. The first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list are updated based on the supplemented first abnormal account list.

6. The method according to claim 4, characterized in that, The method further includes: Based on the sharding position values ​​of several account numbers corresponding to the account number, the first abnormal account list of the corresponding group expansion filter is deleted according to the deletion query data to obtain the first abnormal account list after deletion; The first abnormal account list, the second abnormal account list, the third abnormal account list, and the fourth abnormal account list are updated based on the first abnormal account list after deletion.

7. An account matching device, characterized in that, The device includes: The filter matching result acquisition module is used to respond to the abnormal query request corresponding to each target inspection account, input the account interaction information corresponding to the target inspection account into the extended filter, and obtain the filter matching result; the filter matching result is obtained by the extended filter matching the account interaction information corresponding to the target inspection account with the account interaction information corresponding to each account in the first abnormal account list in the extended filter; The cache matching result obtaining module, when the filter matching result indicates that the target checked account is an abnormal account, matches the account interaction information corresponding to the target checked account with the corresponding account interaction information in the cache query library to obtain the sub-cache matching results corresponding to the cache query library; the cache query library has at least two levels of sub-cache query libraries, each sub-cache query library corresponds to a preset sub-cache query list, and the preset sub-cache query list contains the account interaction information corresponding to the abnormal account; The abnormal account matching result obtaining module, when all the sub-cached matching results indicate that the target checked account is not the abnormal account, then matches the account interaction information corresponding to the target checked account with the account interaction information corresponding to each account in the second abnormal account list in the abnormal account database to obtain the abnormal account matching result corresponding to the target checked account; the abnormal account matching result determines whether the target checked account is an abnormal account; The module for obtaining the cached matching result is also used for: The extended filters are grouped according to the discrete algorithm to obtain at least two grouped extended filters, and each of the grouped extended filters has a corresponding filter data fragment position value. Based on the account number corresponding to the account interaction information, the data fragmentation position is calculated to obtain at least two account number fragmentation position values; Based on the account number, data is segmented to obtain sub-account numbers. The account interaction information is then divided according to each sub-account number to obtain the sub-account interaction information. Based on the matching result between the filter data fragment position value and the account number fragment position value, the interaction information of each sub-account is input into the corresponding group expansion filter to obtain the sub-filter matching result; the group expansion filter is used to match the interaction information of each sub-account with the account interaction information of the first abnormal account list in each group expansion filter; The filter matching result is determined based on the matching results of each sub-filter.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

Images