Transaction matching method, system, and related device

Abstract

The present application provides a transaction matching method, a system, and a related device. The method comprises the following steps: obtaining transaction data, determining a plurality of matching rules matching the transaction data, and sorting the plurality of matching rules on the basis of the score of each matching rule to obtain a sorting result, wherein the score of each matching rule is in a positive proportional relationship with the granularity of a condition in each matching rule; and on the basis of the sorting result, determining a transaction account used by the transaction data. A score representing the granularity is configured for each rule, and when a transaction occurs, if there are a plurality of rules successfully matching transaction data, the rule having the highest granularity can be selected on the basis of the score to determine a transaction account, thereby solving the problem of transaction failures caused by matching results comprising a plurality of accounts; moreover, no human participation is required, thereby improving use experience of users.

Description

Transaction matching method, system and related device

[0001] The present application claims priority to the Chinese patent application No. 202410525726.0, filed on April 28, 2024, with the State Intellectual Property Office of China, and the Chinese patent application No. 202410525726.0 has the title of "Transaction matching method, system and related device", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0002] The present application relates to the field of big data, and in particular, to a transaction matching method, system and related device. BACKGROUND

[0003] An enterprise usually has multiple bank accounts to adapt to the needs of different settlement types or to meet the needs of parallel settlement processing. In order to meet different needs, financial personnel need to configure transaction rules according to business needs before a transaction occurs, and the transaction rules will include screening conditions and corresponding accounts. When a transaction occurs, if the transaction data meets the screening conditions, the transaction can be completed using the account corresponding to the screening conditions.

[0004] However, after the user configures a large number of transaction rules, there may be a problem of overlapping configuration conditions, such as the configured transaction rules including: account A is used for X type transactions, and account B is used for Y type transactions. If a transaction belongs to both X type and Y type, the matching result will include account A and account B, which will cause the transaction to fail, and the user needs to manually select an account to complete the transaction, which not only affects the transaction efficiency, but also consumes human cost and reduces the user experience. SUMMARY

[0005] The present application provides a transaction matching method, system and related device to solve the problem of manual selection of transaction accounts by an enterprise when determining the account used for a transaction, which affects the transaction efficiency.

[0006] In a first aspect, a transaction matching method is provided, the method is executed by a transaction matching system, and the method includes the following steps: obtaining transaction data, determining a plurality of matching rules matched with the transaction data, the matching rule including a condition and an account, the matching rule being used to complete a transaction corresponding to the transaction data using the account in the case that the transaction data meets the condition, sorting the plurality of matching rules based on a score of each matching rule in the plurality of matching rules, obtaining a sorting result, wherein the score of each matching rule is in a positive proportional relationship with a refinement degree of the condition in each matching rule, and determining a transaction account used by the transaction data based on the sorting result.

[0007] The method described in the first aspect is implemented by configuring a score representing the importance of each rule for each rule, and when a transaction occurs, if the number of matching rules is multiple, the multiple matching rules can be sorted according to the score, and the account used by the transaction is determined according to the sorting result, so that when the matching rules overlap, the transaction account can also be determined according to the score, solving the problem that the transaction cannot be performed due to the matching result including multiple accounts, and without human intervention, improving the user experience.

[0008] In a possible implementation, before the transaction matching system obtains the transaction data, the method further includes the following steps: obtaining a plurality of factors existing in the historical transaction data, the plurality of factors including field values in the historical transaction data, determining a score of each factor in the plurality of factors, the score of each factor being in a positive proportional relationship with the refinement degree of the factor, obtaining the matching rule input by the user through the configuration interface, determining at least one factor included in the matching rule, and determining the score of the matching rule according to the score of the at least one factor.

[0009] The above implementation determines a plurality of factors from the historical transaction data, configures a score for each factor, and then determines the score of the matching rule based on the score of each factor. The score of the rule is determined by comprehensively considering the scores of the plurality of factors, and the score of the rule can better reflect the importance of the matching rule. Meanwhile, the score of the factor is in a positive proportional relationship with the refinement degree of the factor, so that the score of the rule obtained using the refined factor will be higher. If the matching result shows that multiple matching rules meet the conditions, the account corresponding to the matching rule with the highest refinement degree can be selected to complete the transaction, which is more in line with the user's use demand and improves the user experience.

[0010] In a possible implementation, when the plurality of factors existing in the historical transaction data are obtained, a plurality of fields that have an impact on the determination of the transaction account can be obtained from the historical transaction data, a plurality of first factors of each field can be determined according to a plurality of field values of each field, each field can be summarized or refined by combining field professional knowledge to generate a plurality of second factors of each field, and the upper and lower relationships between the plurality of first factors and the plurality of second factors can be determined to obtain hierarchical relationship data of each field. The hierarchical relationship data includes a plurality of levels, each level includes one or more of the first factors and the second factors, the factor of the previous level is a summary of the factor of the next level, and the factor of the next level is a refinement of the factor of the previous level.

[0011] The above implementation manner establishes the hierarchical relationship data of each field, the hierarchical relationship data includes not only the field value (the first factor) under the field, but also the second factor for summarizing or refining the field value, so that the generated hierarchical relationship data covers most of the possible factors, thereby expanding the coverage of the business scenario. Meanwhile, the factors are divided into upper and lower levels, and the user can simplify the configuration operation based on the hierarchical relationship when configuring the rules. If multiple rules use the same account, the rules can be merged according to the hierarchical relationship, for example, accounts 001 are used for foreign exchange transactions of companies A to D. According to the hierarchical relationship of the company factor, it is determined that companies A to D are all domestic companies, so account 001 can be configured for foreign exchange transactions of domestic companies, and it is not necessary to configure matching rules for each company, thereby reducing the configuration pressure of the user and improving the use experience of the user.

[0012] In a possible implementation manner, when determining the score of each factor in the plurality of factors, the score of each field is determined based on the influence degree of each field on determining the transaction account, and the scores of the plurality of factors under each field are determined based on the hierarchical relationship data of each field, wherein the score of each factor under the field is greater than the score of the field, and the score of the next level factor is higher than the score of the previous level factor.

[0013] The above implementation manner allocates scores to the fields according to the importance of the fields, and then configures scores for each factor in the field according to the score of the field and the hierarchical relationship data of the field, so that the allocation of the factor score can be finely controlled, and the scores of the plurality of factors under the important field can be higher than the scores of the plurality of factors under the unimportant field, thereby ensuring that the matching can be performed according to the importance required by the user when matching the rules.

[0014] In a possible implementation manner, the score of each field includes a first power function, the base of the first power function of different fields is the same, and the exponents are different. When determining the score of each field based on the influence degree of each field on determining the transaction account, the plurality of fields are sorted based on the influence degree, and the exponents of the first power functions are sequentially decreased according to the sorting result to determine the score of each field. The score of each factor includes the first power function and a weight. The base and the exponent of the first power function of each factor are the same as the base and the exponent of the first power function of the field to which the factor belongs. The weight of each factor increases with the increase of the level of the factor. When determining the scores of the plurality of factors under each field based on the hierarchical relationship data of each field, the size of the weight is sequentially increased according to the level of the factor based on the first power function of the factor field to determine the score of each factor.

[0015] Compared with the manner of directly assigning scores, the scheme can have a more explicit quantification method to distinguish the importance of various fields and factors, the score assignment manner has interpretability, and the difference in importance of different fields and factors can be intuitively expressed. Due to the existence of the power function, the difference between the scores of two factors with adjacent importance is large, which can reduce the possibility that the scores of different levels are similar, and facilitate subsequent sorting of the matching result. Meanwhile, when a new field and factor appear, the exponent of the power function can be determined according to the importance of the new field, and then the score of the new field is obtained, so that the field expansion is very convenient, and the factor can also be easily expanded. The power exponent method has strong flexibility, expansibility and adjustability.

[0016] In a possible implementation, the score of each field includes a second power function, and the second power function is used to control the score interval of the plurality of factors under each field, so that the scores of the plurality of factors under the second field are not higher than the scores of the plurality of factors under the first field, wherein the score of the second field is lower than the score of the first field.

[0017] In the above implementation, the second power function is used to control the score interval of the plurality of factors under each factor, so that the scores of the plurality of factors under the second factor are not higher than the scores of the plurality of factors under the first factor, wherein the score of the second factor is lower than the score of the first factor. It should be understood that, since the factor score of each level is lower than the factor score of the next level, the more levels of the factor division, the higher the factor score of the bottom layer. It may appear that the score of the first factor is greater than the score of the second factor, but the score of the factor at the bottom layer of the second factor is greater than the score of the first factor. In order to avoid the above score crossing problem, the second power function can well separate the score intervals of various factors, and no matter how the levels of each factor are divided, the score crossing problem will not occur.

[0018] In a possible implementation, when determining the transaction account used by the transaction data based on the sorting result, the sorting result is sent to the user, and the transaction account of the user feedback is obtained; or, the matching rule with the highest importance is determined according to the sorting result, and the account of the matching rule with the highest importance is determined as the transaction account used by the transaction data.

[0019] Optionally, if the system automatically confirms the transaction account, the transaction matching system can store the sorting result of each transaction and the finally determined transaction account, for the user to query. By querying the sorting result of the historical transaction data and the used transaction account, the user can check whether the configuration of the matching rule is correct, and whether the score calculation rule is correct.

[0020] Optionally, if manual confirmation of the transaction account is required, the transaction matching system can display the ranking result, and the user can determine the account used based on the ranking result. If the first ranked account is temporarily abnormal, the user can select other accounts to complete the transaction. By displaying the ranking result to the user, the user can quickly determine the transaction account, and the user experience is improved.

[0021] In the above implementation, if the transaction data matches multiple matching rules, the matching rules can be ranked according to the scores, so that the system can determine the transaction account according to the ranking result, avoid transaction errors, and if manual selection of the transaction account is required, the user can be provided with a reference based on the ranking result, so that the user can quickly determine the transaction account and the user experience is improved.

[0022] In a second aspect, a transaction matching system is provided. The system includes an acquisition unit configured to acquire transaction data, a matching unit configured to determine multiple matching rules matched with the transaction data, the matching rules including conditions and accounts, the matching rules being used to complete a transaction corresponding to the transaction data using the accounts if the conditions are met, a ranking unit configured to rank the multiple matching rules based on scores of the matching rules to obtain a ranking result, wherein the score of each matching rule is in a positive proportional relationship with a refinement degree of the condition in each matching rule, and a determination unit configured to determine a transaction account used by the transaction data based on the ranking result.

[0023] The system described in the second aspect is implemented by configuring a score representing the importance of each rule for each rule. When a transaction occurs, if the number of matching rules matched with the transaction data is multiple, the multiple matching rules can be ranked according to the scores, and the transaction account used by the transaction can be determined according to the ranking result. When the conditions of the matching rules overlap, the transaction account can also be determined according to the scores. The problem that the transaction cannot be performed due to the multiple accounts in the matching result is solved, and the user experience is improved without manual intervention.

[0024] In a possible implementation, the system further includes a configuration unit and a calculation unit. The configuration unit is configured to acquire multiple factors existing in historical transaction data, the multiple factors including field values in the historical transaction data, to determine a score of each factor in the multiple factors, the score of each factor being in a positive proportional relationship with a refinement degree of the factor, and to acquire a matching rule input by a user through a configuration interface, and determine at least one factor included in the matching rule. The calculation unit is configured to determine a score of the matching rule according to the score of the at least one factor.

[0025] In a possible implementation, the configuration unit is configured to obtain a plurality of fields that have a certain influence on the transaction account from historical transaction data, determine a plurality of first factors of each field according to a plurality of field values of each field, summarize or refine the plurality of first factors of each field in combination with domain expertise to generate a plurality of second factors of each field, determine a hierarchical relationship between the plurality of first factors and the plurality of second factors to obtain hierarchical relationship data of each field, and the hierarchical relationship data includes a plurality of levels, each level includes one or more of the first factors and the second factors, a factor of a previous level is a summary of a factor of a next level, and a factor of the next level is a refinement of the factor of the previous level.

[0026] In a possible implementation, the configuration unit is configured to determine a score of each field based on an influence degree of each field on the determination of the transaction account, and determine a score of each factor under each field based on the hierarchical relationship data of each field, where the score of each factor under each field is greater than the score of the field, and the score of a factor of a next level is higher than the score of a factor of a previous level.

[0027] In a possible implementation, the score of each field includes a first power function, the base of the first power function of different fields is the same, and the exponents are different, the configuration unit is configured to sort the plurality of fields based on the influence degree of each field on the determination of the transaction account, and determine the score of each field by sequentially decreasing the exponents of the first power functions according to the sorting result, the score of each factor includes the first power function and a weight, the base and the exponent of the first power function of each factor are the same as the base and the exponent of the first power function of the field to which the factor belongs, and the weight of each factor increases with an increase in the level in which the factor is located, and the configuration unit is configured to determine the score of each factor by sequentially increasing the size of the weight according to the level of the factor based on the first power function of the field of the factor.

[0028] In a possible implementation, the score of each field includes a first power function, the base of the first power function of different fields is the same, and the exponents are different, the score of each factor includes the first power function and a weight, the base and the exponent of the first power function of each factor are the same as the base and the exponent of the first power function of the field to which the factor belongs, and the weight of each factor increases with an increase in the level in which the factor is located.

[0029] In a possible implementation, the score of each field includes a second power function, the second power function is used to control a score interval of a plurality of factors under each field, so that the scores of the plurality of factors under a second field are not higher than the scores of the plurality of factors under a first field, where the score of the second field is lower than the score of the first field.

[0030] In a possible implementation, the determining unit is configured to send the ranking result to a user, and obtain a transaction account of the user feedback; or the determining unit is configured to determine a matching rule with the highest importance degree according to the ranking result, and determine an account of the matching rule with the highest importance degree as the transaction account used by the transaction data.

[0031] In a third aspect, a computing device is provided, which includes a processor and a memory, the memory being configured to store instructions, and the processor being configured to execute the instructions to enable the computing device to implement the method described in the first aspect.

[0032] In a fourth aspect, a computer-readable storage medium is provided, which stores instructions, and the instructions are executed by a computing device or a computing device cluster to implement the method described in the first aspect.

[0033] In a fifth aspect, a computing device cluster is provided, which includes at least one computing device, and each of the at least one computing device includes a processor and a memory, and the processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to enable the computing device cluster to implement the method described in the first aspect.

[0034] In a sixth aspect, a computer program product including instructions is provided, the computer program product including instructions executable on a computing device or stored in any available medium or software or program product, and when the computer program product is executed on a computing device or a computing device cluster, the computing device or the computing device cluster performs the method described in the first aspect.

[0035] On the basis of the implementation manners of the aspects described above, the present application can be further combined to provide more implementation manners. BRIEF DESCRIPTION OF DRAWINGS

[0036] FIG. 1 is an architecture diagram of a transaction matching system provided by the present application;

[0037] FIG. 2 is an example diagram of a transaction matching system provided by the present application deployed in a cloud environment;

[0038] FIG. 3 is a step flow diagram of a configuration phase of a transaction matching method provided by the present application;

[0039] FIG. 4 is an example diagram of a hierarchical relationship of company factors provided by the present application;

[0040] FIG. 5 is an example diagram of factor score configuration in a transaction matching method provided by the present application;

[0041] FIG. 6 is a step flow diagram of a matching phase of a transaction matching method provided by the present application;

[0042] FIG. 7 is an example diagram of a configuration interface provided by the present application;

[0043] FIG. 8 is an example interface of a sorting result provided by the present application;

[0044] FIG. 9 is a structural schematic diagram of a transaction matching system provided by the present application;

[0045] FIG. 10 is a structural schematic diagram of a computing device provided by the present application;

[0046] FIG. 11 is an example diagram of a computing device cluster provided by the present application;

[0047] FIG. 12 is another example diagram of a computing device cluster provided by the present application. DETAILED DESCRIPTION

[0048] Enterprises face a variety of transaction demands, involving supply chain management, sales services, capital investment, and many other aspects. In order to effectively manage these transactions, reduce risks, and optimize costs, enterprises usually choose to hold multiple bank accounts, and the bank accounts used in different transaction scenarios also differ. Therefore, before a transaction occurs, financial personnel will configure multiple transaction rules according to business needs, each transaction rule can include a screening condition and a corresponding account, so that when a transaction occurs, the transaction data meets the screening condition, the corresponding account of the screening condition can be used to complete the transaction.

[0049] After the user configures a large number of transaction rules, there may be a problem of overlapping configuration conditions, such as rule A: all foreign exchange transactions use account 001 for payment, and rule B: all U.S. dollar transactions use account 002 for payment. If a transaction is a U.S. dollar foreign exchange transaction, the transaction meets both rule A and rule B, and the matching result includes account 001 and account 002, resulting in a transaction that cannot be performed, requiring manual selection of a transaction account, reducing the user's experience.

[0050] In summary, enterprises hold multiple bank accounts, and when a transaction occurs, the transaction data needs to be matched with the pre-configured transaction rules to determine which account to use to complete the transaction. Due to the complexity of the enterprise's transaction business, the more transaction rules, the more likely it is to have overlapping screening conditions, resulting in a matching result that includes multiple accounts, causing the transaction to fail, requiring manual selection of a transaction account, reducing the user's experience, and affecting transaction efficiency.

[0051] In order to solve the problem that the transaction efficiency is affected by manual selection of the transaction account when the enterprise determines the account used by the transaction, the application provides a transaction matching method, wherein a score representing the importance of each rule is configured for each rule, when the transaction occurs, if the number of rules matched successfully by the transaction data is multiple, at this time, the multiple rules matched successfully can be sorted according to the scores, and the account used by the transaction is determined according to the sorting result, so that when the matching rules overlap, the transaction account can also be determined according to the scores, the problem that the transaction cannot be performed due to the matching result including multiple accounts is solved, and manual participation is not required, thereby improving the user experience.

[0052] FIG. 1 is an architecture diagram of a transaction matching system provided by the application, as shown in FIG. 1, the architecture includes a client 100, a transaction matching system 200, a transaction processing system 300 and a storage system 400, wherein the client 100, the transaction matching system 200, the transaction processing system 300 and the storage system 400 are connected through a network to establish a communication connection, the communication connection can be a wired connection or a wireless connection, the network can be a public Internet, can also be an internal LAN, can also be a virtual private network (VPN), can also be a dedicated line such as a fiber line, a copper line, a satellite connection, etc., can also be a wireless network such as a wireless LAN (Wi-Fi), a cellular network, etc., the application does not make specific limitations. The number of clients 100 connected with the transaction matching system 200 can be one or more, the number of transaction processing systems 300 connected with the transaction matching system 200 can be one or more, and the number of storage systems 400 connected with the transaction matching system 200 can be one or more, the application does not make specific limitations.

[0053] The client 100 is deployed on a terminal device, and is used to realize human-computer interaction, the client 100 is used to send the data input by the user to the transaction matching system 200 for processing, and display the data generated by the transaction matching system 200 to the user. The terminal device includes a personal computer, a smart phone, a wearable device, a palm processing device, a tablet computer, a mobile notebook, an augmented reality (AR) device, a virtual reality (VR) device, a smart conference device, etc., which are not limited here. The description of the computing device can refer to the foregoing content, which is not repeated here. The client 100 can also be deployed on a physical server such as an ARM server or an X86 server, the application does not make specific limitations.

[0054] The transaction matching system 200 can be deployed on a computing device or a computing device cluster composed of multiple computing devices. The transaction matching system 200 is used to determine the account used for the transaction. The computing device can be a bare metal server (BMS), a virtual machine, a container or a storage device. The BMS refers to a general-purpose physical server, such as an ARM server or an X86 server; the virtual machine refers to a complete computer system simulated by software, which runs in a completely isolated environment and has complete hardware system functions. The work that can be completed in a physical computer can be realized in a virtual machine. When creating a virtual machine in a computing device, part of the hard disk and memory capacity of the physical machine needs to be used as the hard disk and memory capacity of the virtual machine. Each virtual machine has an independent basic input / output system (BIOS), hard disk and operating system, and can be operated like a physical machine; the container is a portable software unit that can combine an application and all its dependencies into a software package that is not limited by the underlying host operating system, so that the complex environment does not need to be built again, simplifying the application development to deployment process. In a specific implementation, the computing device cluster can be a cloud data center, and can also be an enterprise private cluster, and can also be a hybrid cloud environment, that is, a deployment mode in which a public cloud and a private cloud are used at the same time, which is not limited in the present application.

[0055] The transaction processing system 300 can be deployed on a computing device or a computing device cluster, and the above-mentioned computing device and computing device cluster are described in the foregoing, which will not be repeated here. The transaction processing system 300 is used to execute the transaction process according to the account determined by the transaction matching system 200, and the transaction processing system 300 can include various transaction businesses, such as bond transactions, currency transactions, foreign exchange transactions and the like shown in FIG. 1, which are not limited in the present application. When processing transaction business, the transaction processing system 300 can first send transaction data to the transaction matching system 200, and after obtaining the account fed back by the transaction matching system 200, the account is used to complete the transaction.

[0056] The storage system 400 can be deployed in a computing device or a computing device cluster, and can also be deployed in a storage device or a storage array. The storage system 400 supports storage of data and provides storage-related services for the transaction matching system 200 and the transaction processing system 300 to support the transaction matching system 200 and the transaction processing system 300 to complete respective businesses, such as the currency information, the exchange rate information, the historical transaction data, and the like shown in FIG. 1. The storage system 400 can be a file storage system, a database, or the like, which is not limited herein. The computing device and the computing device cluster are described above and will not be repeated here. The storage device can be a hard disk drive (HDD), a solid state disk (SSD), a mechanical hard disk (HDD), a universal serial bus (USB), a flash memory, a secure digital memory card (SD card), a memory stick, or the like, which is not limited herein. The storage array can be a redundant array of independent disks (RAID), a network attached storage (NAS), a storage area network (SAN), or the like, which is not limited herein.

[0057] In specific implementations, the transaction matching system 200, the transaction processing system 300, and the storage system 400 are respectively deployed in different computing devices or computing device clusters, for example, as shown in FIG. 1. Alternatively, at least two of the transaction matching system 200, the transaction processing system 300, and the storage system 400 can be deployed in the same computing device or the same computing device cluster, which is not limited herein.

[0058] In specific implementations, the client 100 can be a terminal device or a computing device controlled by a user, a software or an application running on the terminal device or the computing device, such as a personal computer (PC) client, a world wide web (web) client based on a browser, an application (APP) client running on a mobile terminal, or a console of a cloud platform, which is not limited herein. The user holding the client 100 is a personnel managing transaction business of an enterprise, such as a financial employee or an information technology (IT) employee of the enterprise, which is not limited herein.

[0059] Optionally, the client 100 can be a client specially used for account matching in a transaction process, such as a transaction account matching tool, used to recommend an optimal receiving or paying account for a transaction when the transaction occurs, which can be used by other systems actually processing the transaction, which is not limited in the present application.

[0060] Optionally, the client 100 can also be a comprehensive client including the above-mentioned account matching function, or a comprehensive client integrated with the above-mentioned transaction account matching tool. For example, the comprehensive client can be a financial management software client, which not only includes the above-mentioned transaction account matching tool, but also includes a transaction approval tool, a batch repayment tool, a transaction entry tool, an interest rate adjustment tool, etc., and these modules can be associated with the transaction account matching tool, such as the batch repayment tool needs to determine the account used for repayment through the transaction account matching tool before performing batch repayment. The above example is used for illustration, which is not limited in the present application.

[0061] Optionally, the client 100 can also be a client of a cloud platform, used to purchase and rent various cloud services by users, and the transaction matching scheme provided by the present application can be one of the cloud services, and the user can purchase the cloud service to realize the function of matching the transaction account; or the cloud platform provides a comprehensive service to the user, and the transaction account matching function can be a sub-service in the comprehensive cloud service, such as the user purchases a cloud service of foreign exchange transaction management, and the transaction account matching function can be a sub-service in the cloud service, which is not limited in the present application.

[0062] The possible deployment manners of the client 100, the transaction matching system 200, the transaction processing system 300 and the storage system 400 are described in detail above, and in actual deployment, they can be flexibly deployed in combination with specific application scenarios and business requirements. The actual deployment manners of the client 100, the transaction matching system 200, the transaction processing system 300 and the storage system 400 are exemplarily illustrated below in combination with specific application scenarios.

[0063] In an application scenario, the client 100, the transaction matching system 200, the transaction processing system 300 and the storage system 400 can be deployed on office equipment within an enterprise, for example, the transaction matching system 200 and the transaction processing system 300 are deployed on a server or a server cluster purchased by the enterprise, the client 100 is deployed on an office computer of the enterprise, the storage system 400 is deployed on a database server of the enterprise, and a financial staff of the enterprise can use the office computer to run the client 100, obtain a recommended transaction account of the transaction matching system 200 through the client 100, send the recommended transaction account to the transaction processing system 300 by the transaction matching system 200, and complete a transaction according to the recommended transaction account by the transaction processing system 300 and store transaction data in the storage system 400. The above example is used for illustration, and the present application is not limited specifically.

[0064] In another application scenario, the transaction matching system 200, the transaction processing system 300 and the storage system 400 can be deployed in a cloud environment, and the client 100 is a console of a cloud platform. For example, FIG. 2 is an example diagram of a transaction matching system deployed in a cloud environment according to the present application. As shown in FIG. 2, a user can initiate a purchase request of a cloud service through the client 100, and after the client 100 sends the purchase request to the cloud platform, the cloud platform can provide the client 100 with a cloud service use right of the transaction matching system, so that the user can determine an account used in a transaction through the client 100. The cloud platform also maintains various basic resources, including computing resources, storage resources, network resources and security resources, to meet the computing requirements of the transaction matching system 200 in different scales and loads, and these computing resources can be dynamically scaled according to the use requirements of the transaction matching system 200 to ensure stable operation of the transaction matching system 200. The storage system 400 can be a basic resource in the data center, such as an elastic cloud service or a cloud storage service, and the transaction processing system 300 can also be a service in the data center. The above example is used for illustration, and the present application is not limited specifically.

[0065] It should be understood that the above application scenarios are used for illustration, and the client, the transaction matching system 200, the transaction processing system 300 and the storage system 400 can be flexibly deployed according to actual business requirements, which will not be illustrated one by one here.

[0066] Further, the transaction matching system 200 can include a configuration layer 210 and a matching layer 220, wherein the configuration layer 210 and the matching layer 220 can be implemented by software or hardware. For example, the implementation of the configuration layer 210 is described below. Similarly, the implementation of the matching layer 220 can also refer to the implementation of the configuration layer 210.

[0067] As an example of a software functional unit, the configuration layer 210 can include code running on a compute instance. Wherein the compute instance can include at least one of a physical host (computing device), a virtual machine, a container. Further, the above-mentioned compute instance can be one or more. For example, the configuration layer 210 can include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers for running the code can be distributed in the same region, or distributed in different regions. Further, the multiple hosts / virtual machines / containers for running the code can be distributed in the same availability zone (AZ), or distributed in different AZs, each AZ including a data center or multiple data centers with similar geographical locations. Wherein, usually one region can include multiple AZs.

[0068] Similarly, the multiple hosts / virtual machines / containers for running the code can be distributed in the same virtual private cloud (VPC), or distributed in multiple VPCs. Wherein, usually one VPC is set in one region, and communication between two VPCs in the same region, and between VPCs in different regions needs to set a communication gateway in each VPC to realize the interconnection between VPCs through the communication gateway.

[0069] As an example of a hardware functional unit, the configuration layer 210 can include at least one computing device, such as a server, etc. Alternatively, the configuration layer 210 can also be a device implemented by a central processing unit (CPU), or implemented by an application-specific integrated circuit (ASIC), or implemented by a programmable logic device (PLD), etc. The PLD can be implemented by a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), a data processing unit (DPU), a neural network processing unit (NPU), a system on chip (SoC), an offload card, an acceleration card, or any combination thereof.

[0070] The plurality of computing devices included in the configuration layer 210 can be distributed in the same region, or distributed in different regions. The plurality of computing devices included in the configuration layer 210 can be distributed in the same AZ, or distributed in different AZs. Similarly, the plurality of computing devices included in the configuration layer 210 can be distributed in the same VPC, or distributed in multiple VPCs. The plurality of computing devices can be any combination of servers, ASICs, PLDs, CPLDs, FPGAs, GALs, DPUs, NPUs, SoCs, offload cards, acceleration cards, etc.

[0071] It should be noted that in other embodiments, the configuration layer 210 can be used to perform any step of the transaction matching method provided by the present application, and the matching layer 220 can be used to perform any step of the transaction matching method provided by the present application. The steps implemented by the configuration layer 210 and the matching layer 220 can be specified as needed, and the entire function of the transaction matching system 200 can be implemented by the configuration layer 210 and the matching layer 220 respectively implementing different steps of the transaction matching method provided by the present application.

[0072] The functions of the configuration layer 210 and the matching layer 220 will be explained and described below.

[0073] The configuration layer 210 is configured to obtain a matching rule input by a user, and generate a score corresponding to each matching rule according to a score calculation rule.

[0074] In the embodiments of the present application, the matching rule is input by the user, the matching rule includes a condition and a corresponding account, and the score generated by the configuration layer 210 is used to indicate the importance of the matching rule. The higher the importance of the matching rule, the higher the priority of the account of the matching rule. The score of the matching rule is related to the refinement degree of the condition in the matching rule, for example, in a positive proportional relationship. In this way, when determining the account used for a transaction, if the transaction data meets the conditions of multiple rules, the score can be used to indicate the use order of the multiple rules, and the account corresponding to the rule with the highest refinement degree is used as the account used for the transaction. The refinement degree refers to the degree of concretization of the rule. For example, rule A is: all foreign exchange transactions use account 001 for payment, and rule B is: all U.S. dollar transactions use account 002 for payment. U.S. dollar transactions are a type of foreign exchange transactions, so the refinement degree of rule B is higher than that of rule A. The above examples are used for illustration, and the present application is not limited in this regard.

[0075] In specific implementations, the matching rule can include multiple factors, and the factors can be fields in the matching rule or fields in historical transaction data. The score calculation rule includes a score corresponding to each factor. In this way, according to the score of each factor in each matching rule, the score corresponding to each matching rule can be obtained. For example, in the example shown in FIG. 1, rule A includes factor A11 and factor A21, the score of factor A11 is a1, and the score of factor A21 is a3. Therefore, the score x1 of rule A is a1+a3. In the above example, the score of the rule is determined according to the sum of the scores of the fields. The calculation method of this example is used for illustration, and in specific implementations, the score can also be determined according to other calculation methods such as weighted sum, weighted average, variance, etc. The present application is not limited in this regard.

[0076] It should be understood that by configuring a score for each factor, the matching rule can determine the score of the matching rule based on the factors it contains. In this way, the score of the matching rule obtained is obtained according to a unified standard, which can ensure the accuracy of the matching process. The matching importance of important factors is higher, thereby better meeting the business requirements. At the same time, if the business changes, the matching rule can also be adjusted by modifying the score, without the need to modify the overall rule, thereby making the system more flexible and adaptable.

[0077] The matching layer 220 is configured to obtain transaction data, and then match the transaction data with the matching rules to obtain multiple matching rules that match successfully. Then, the multiple matching rules that match successfully are sorted according to the scores of the matching rules to obtain a sorting result, and the account used for the transaction data is determined based on the sorting result. The transaction data can be obtained from the transaction processing system 300.

[0078] For example, as shown in FIG. 1, it is assumed that after the transaction data matches the rules A-E, it is determined that the transaction data meets the rules A, B and E, wherein the score x1 of the rule A is the highest, at this time, the account 001 corresponding to the rule A can be used as the finally determined account for transaction, and the transaction matching system 200 can send the information of the account 001 to the transaction processing system 300.

[0079] In a specific implementation, the matching rules are set according to the accounting and related business requirements. The more detailed and specific the matching rules are set, the higher the importance is, and the higher the score can be configured. In this way, even if the conditions overlap, the rule with more details and more specific can be used to determine the transaction account. Not only can the problem that the matching result has multiple accounts and the system cannot determine which account to use for the next transaction be avoided, but also the determined account is the most suitable account, improving the user experience.

[0080] For example, rule 1 is set: the foreign exchange transaction uses the account 001, and rule 2 is set: the U.S. dollar transaction uses the account 002. Rule 2 is a more detailed rule than rule 1, so the score of rule 1 is lower than that of rule 2. When the U.S. dollar transaction occurs, the matching result includes rule 1 and rule 2, but the sorting result shows that the score of rule 2 is higher than that of rule 1, and the final recommended result is only rule 2, that is, the U.S. dollar transaction uses the account 002, which meets the user's use demand. The whole process does not need manual intervention, reduces the labor cost, and improves the user experience.

[0081] In summary, the transaction matching system provided by the present application configures a score representing the importance of each rule. When the transaction occurs, if the number of rules that match the transaction data is multiple, the multiple rules that match the transaction data can be sorted according to the scores, and the account used for the transaction is determined according to the sorting result. Even if the conditions of the matching rules overlap, the transaction account can be determined according to the scores. The problem that the transaction cannot be performed due to the matching result including multiple accounts is solved, and manual intervention is not required, improving the user experience.

[0082] The transaction matching system provided by the present application is explained and described above in combination with FIGS. 1 and 2. The transaction matching method provided by the present application is explained and described below in combination with FIGS. 3-8, which can be applied to the transaction matching system as shown in FIGS. 1 and 2. Among them, FIGS. 3-5 explain and describe the step flow in the configuration stage. FIG. 6 explains and describes the step flow in the application stage, and FIGS. 7 and 8 exemplarily illustrate possible interface diagrams in the configuration stage and the application stage.

[0083] FIG. 3 is a schematic diagram of a method for transaction matching provided by the present application in a configuration stage, which can be implemented by the configuration layer 210 in FIG. 1. As shown in FIG. 3, the method can include the following steps:

[0084] S310: The transaction matching system 200 obtains historical transaction data from the storage system 400 and determines a plurality of factors.

[0085] In specific implementations, a factor can be a field in the historical transaction data that has an impact on the determination of a transaction account. For example, a company subject, a transaction product, a transaction object, a currency, a payment type, and the like, without limitation. A field in the transaction data that has no impact on the determination of a transaction account can not be determined as a factor. For example, a transaction number, a note, a contract number, and the like, without limitation, which can not participate in the process of determining a transaction account, and can not be used as a factor.

[0086] Optionally, the transaction matching system 200 can obtain factors by a machine learning method. A sample set is established by using historical transaction data, and the sample set is used to train a machine learning model, so that the trained machine learning model has the ability to generate factors according to transaction data. The machine learning method includes, but is not limited to, supervised learning, semi-supervised learning, unsupervised learning, and the like, and the structure of the machine learning model includes, but is not limited to, a convolutional neural network (CNN), a recurrent neural network (RNN), a long short-term memory (LSTM), and the like, without limitation.

[0087] Optionally, the transaction matching system 200 can obtain factors from historical transaction data by configuring a white list or a black list. For example, the white list includes a plurality of fields, and if a field in the historical transaction data exists in the white list, the field can be determined as a factor. The above examples are used for illustration, without limitation. For example, the white list can include some fields that have an impact on the determination of a transaction account, such as a company subject, a transaction product, a transaction object, a currency, a payment type, a transaction date, a risk assessment, and the like, without limitation. The black list can include some fields that have no relationship with the determination of a transaction account, such as a transaction description, transaction audit information, and the like, without limitation.

[0088] Optionally, the transaction matching system 200 can provide a configuration interface for the user to upload factors, for example, after the user's business changes, the user needs to temporarily configure a new matching rule, which includes a new field that does not exist in the historical transaction data, at this time the user can configure a new factor corresponding to the new field through the configuration interface. The above example is used for illustration, and the present application is not limited in this way.

[0089] It should be understood that the above examples give several possible ways to obtain factors, and the present application can also obtain factors from historical transaction data in other ways, which are not limited by the present application.

[0090] S320: The transaction matching system 200 generates hierarchical relationship data of each factor.

[0091] In a specific implementation, the transaction matching system 200 can determine the hierarchical relationship data according to the field value of each factor corresponding to the field in the historical transaction data, wherein the hierarchical relationship data can include multiple hierarchical data, each hierarchical data includes multiple factors, and the factors include not only the field value of the field, but also the upper generalization of the field value of the field. Each level of factors is the upper generalization of part of the factors of the next level. Taking the company factor as an example, the m-level factor is a domestic company, the m+1-level factor is a Shenzhen company and an Dongguan company, and the m+2-level factor is a Shenzhen terminal company and a Dongguan cloud technology company. Taking the transaction object as an example, the m-level factor is a Shenzhen branch, the m+1-level factor is an X area branch and a Y area branch, and the m+2-level factor is an X1 road branch and a Y1 road branch. It should be understood that the above examples are used for illustration, and the present application is not limited in this way. In short, the higher the level of the factor, the higher the degree of generalization, and the lower the level of the factor, the higher the degree of refinement. Among them, the factor can be used as the factor of the uppermost level of the hierarchical relationship data.

[0092] In a specific implementation, the transaction matching system 200 can first determine a plurality of first factors according to the field values of each factor in the historical transaction data, then summarize or refine the plurality of first factors in combination with domain professional knowledge to generate a plurality of second factors, and then determine the superior-inferior relationship between the plurality of first factors and the plurality of second factors to form the hierarchical relationship data. In simple terms, the first factor is the original field value in the historical transaction data, for example, the first factor can include A company, B company, etc., and the second factor is a new field value obtained by summarizing or refining the original field value in the historical transaction data, for example, A company and B company are both in Shenzhen area, so the second factor can include Shenzhen area, Dongguan area, etc., of course, the second factor can be further summarized or refined, for example, the second factor can also include China area, overseas area, etc., and the second factor can be generated according to actual business needs, which is not limited in the present application. The domain professional knowledge can include industry characteristics, geographical location, laws and regulations, financial knowledge, etc., and the source of the domain professional knowledge data can include legal and regulatory databases, professional research reports, literature, expert experience, geographic information databases, financial knowledge databases, etc., which are not limited in the present application.

[0093] Optionally, the factor can be a value contained in the field corresponding to the factor, it should be understood that the factor is a field that can be contained in the historical transaction record and the matching rule, so the factor can be a value contained in the field, for example, the factor: company is a field in the historical transaction record, and the field includes A company, B company and C company, so A company, B company and C company can be factors corresponding to the factor: company. The above example is used for illustration, which is not limited in the present application.

[0094] Optionally, the factor can be a summary of the value contained in the field corresponding to the factor, for example, the company field includes the following field values: A company, B company and C company, wherein A company and B company are domestic companies, and C company is an overseas company, at this time the transaction matching system 200 can create the factor "domestic company" and "overseas company", wherein the factor "domestic company" includes the factors: A company and B company, and the factor "overseas company" includes the factor: C company. The above example is used for illustration, which is not limited in the present application.

[0095] In a specific implementation, the transaction matching system 200 can pre-configure some hierarchical division templates of factors, for example, the factor corresponding to the company field can be hierarchically divided according to the region, or can be hierarchically divided according to the business type. For example, the factor corresponding to the transaction object can be hierarchically divided according to the bank type, or can be hierarchically divided according to the region. In this way, after the transaction matching system 200 obtains the factor, the transaction matching system 200 can perform hierarchical division on the factor according to the pre-configured target to obtain a plurality of factors corresponding to the factor.

[0096] Alternatively, the transaction matching system 200 can train a model based on a machine learning algorithm, so that the model has the ability to generate hierarchical relationship data of the factor according to the factor and the value of the field corresponding to the factor. The machine learning algorithm includes decision tree, random forest, K-means clustering algorithm, etc. For example, after obtaining the values contained in the field corresponding to the factor, the values are clustered to obtain multiple categories. Each category can correspond to a factor of m levels, and multiple values under each category correspond to a factor of m+1 levels. Then, the name of each category is inferred as the factor name by the machine learning model. Similarly, the factor of m levels can also obtain multiple factors of m-1 levels by clustering, and so on, to obtain hierarchical relationship data. The above example is used for illustration, and the present application is not limited specifically.

[0097] Optionally, the hierarchical relationship data further includes an association relationship between the factors. Specifically, the factors having a superior generalization relationship can be associated. The first factor is associated with multiple second factors of the next level, indicating that the first factor is a superior generalization of the multiple second factors. By establishing the association relationship, the hierarchical relationship between the factors can be made clearer, facilitating user management. When the user configures a matching rule, if multiple rules use the same account, the rules can be merged according to the hierarchical relationship, such as companies A-D using account 001 for foreign exchange transactions. According to the hierarchical relationship of the company factor, it is determined that companies A-D are all domestic companies. Therefore, account 001 can be configured for foreign exchange transactions of domestic companies, and it is not necessary to configure a matching rule for each company, thereby reducing the configuration pressure of the user and improving the user experience.

[0098] Optionally, the hierarchical relationship data is stored in a structure tree. If stored in a structure tree, the structure tree can include multiple levels, each level including multiple nodes, each node corresponding to a factor, and each parent node in the structure tree being a superior generalization of the child nodes.

[0099] For example, FIG. 4 is an example diagram of hierarchical relationship data of a company factor provided by the present application, each field in the example diagram is a plurality of factors under the company factor, and the hierarchical relationship data can include a plurality of hierarchical data, each hierarchical data can include a plurality of factors. As shown in FIG. 4, the company factor can include three levels, the L1 level includes China region factors and overseas region factors, the L2 level includes Shenzhen region factors, Dongguan region factors, France region factors and Germany region factors, and the L3 level includes companies A-E. FIG. 4 is used for example and illustration, and the present application is not limited in this regard. Among them, the more the level goes down or the deeper the level, the higher the factor refinement degree, and the more the level goes up or the shallower the level, the higher the factor generalization degree, such as the refinement degree of "X Company" is higher than that of "Shenzhen Region", and the refinement degree of "Shenzhen Region" is higher than that of "China Region". Similarly, a structure tree corresponding to the hierarchical relationship data can also be established in the manner shown in FIG. 4 for other factors, which will not be illustrated one by one here. It should be noted that in the hierarchical relationship data of each factor, the first level can be the factor, such as the company factor shown in FIG. 4 as the factor of the L0 level.

[0100] Alternatively, the hierarchical relationship data is stored in the form of a table, the hierarchical relationship data table includes a plurality of fields, each field corresponds to a level, and the values contained in each field represent the factors in the level. For example, Table 1 is an example of storing the hierarchical relationship data of a transaction product factor in the form of a table after hierarchical division provided by the present application, as shown in Table 1, the transaction product factor can be divided into three levels, the L1 level includes currency transaction, foreign exchange transaction and transfer transaction, the L2 level includes investment, financing, spot foreign exchange, forward, non-delivery forward, same subject allocation and cross-subject allocation, and the L3 level includes fixed deposit, large-denomination deposit certificate and structured deposit, etc. For specific content, please refer to Table 1 below. Among them, the factors with a superior generalization relationship can be adjacent fields and in the same row, such as the currency transaction factor in the L1 level is a superior generalization of the investment factor and the financing factor in the L2 level, so the investment factor and the financing factor are in the same row as the currency transaction factor. It should be understood that Table 1 is used for example and illustration, and the present application is not limited in this regard.

[0101] Table 1: Hierarchical relationship data of transaction product factors

[0102] Alternatively, the transaction matching system 200 can also provide a configuration interface for the user to upload the hierarchical relationship data of the factors, such as uploading the structure tree shown in FIG. 4 or the table shown in Table 1, and the present application is not limited in this regard.

[0103] Alternatively, the hierarchical relationship data can be stored in the form of a table, a database or a data structure, or other ways, and the present application does not limit this.

[0104] S330: The transaction matching system 200 configures scores for each factor of each factor based on the hierarchical relationship data, and generates a score calculation rule.

[0105] In specific implementation, the score of each factor is in positive proportion to the refinement degree of the factor. It can be known from the hierarchical relationship data that the factor of each layer is an upper generalization of the factor of the next layer, so the refinement degree of the factor of the next layer is greater than that of the factor of the previous layer. In simple terms, the deeper the level of the factor, the higher the score.

[0106] In specific implementation, the transaction matching system 200 can first configure scores for each factor, and then configure scores for each factor in the factor according to the score of the factor and the hierarchical relationship data, and further generate a score calculation rule for calculating the score of the matching rule. The score calculation rule can include a calculation logic for generating the score of the matching rule according to the scores of the factors and the factors, such as summing, weighted summing or weighted averaging the scores of the factors and the factors contained in the matching rule, or other calculation logics, which are not limited in the present application.

[0107] Optionally, the score of each factor is determined based on the score of the factor. When configuring scores for each factor, scores are allocated according to the importance of the factor. The greater the importance, the higher the score of the factor. For example, the importance of the three factors of company, transaction product and transaction object is ranked as company > transaction product > transaction object, so the score of the company is x1, the score of the transaction product is x2, and the score of the transaction object is x3, x1 > x2 > x3. The above example is used for illustration, which is not limited in the present application.

[0108] It should be noted that the importance of the factor refers to the importance of the matching rule containing the factor in determining the transaction account. The importance of the factor can be determined in combination with the actual application scenario, which is not limited in the present application. The importance of the factor can be input by the user or configured by default by the system, which is not limited in the present application.

[0109] It should be understood that by allocating scores to each factor according to the importance of the factor, and then configuring scores for each factor in the factor according to the score of the factor and the hierarchical relationship data of the factor, the allocation of the factor score can be finely controlled, and the scores of multiple factors under important factors can be higher than those of multiple factors under unimportant factors, so as to ensure that the matching can be performed according to the importance required by the user when the matching rule is matched.

[0110] Further, the score of each factor is determined, and when the score of each factor is configured, the score of each factor can be configured according to the hierarchy in the hierarchy data, and each hierarchy is configured in sequence, and the score of each factor in a hierarchy is higher than the score of a factor in a previous hierarchy and lower than the score of a factor in a next hierarchy. For example, in the example shown in FIG. 4, the score of a factor in the L1 hierarchy is higher than the score of a factor in the L0 hierarchy (i.e., a factor), the score of a factor in the L2 hierarchy is higher than the score of a factor in the L1 hierarchy, and the score of a factor in the L3 hierarchy is higher than the score of a factor in the L2 hierarchy.

[0111] It should be understood that the higher the hierarchy of a factor, the more general the factor is, and the larger the scope of the rule configured using the general factor is, and the lower the hierarchy of a factor, the more detailed and specific the factor is, and the smaller the scope of the rule configured using the detailed factor is, and if multiple rules match the result, the user is more likely to use the rule configured in detail and with a small scope to determine the transaction account, and therefore, the factor is divided into hierarchies according to the generality, the lower score is configured for a general factor, and the higher score is configured for a detailed factor, so that if multiple rules match the result, the system can recommend a rule configured in more detail and with more pertinence to determine the transaction account.

[0112] Optionally, the scores of multiple factors in the same hierarchy can be the same, or the scores of multiple factors in the same hierarchy can be divided according to the importance of the factors to the matching result, and the higher the importance, the higher the score of the factor, and in a specific implementation, when the first factor is associated with multiple second factors in a next hierarchy, the scores of the multiple second factors can be divided according to the importance of the second factors to the matching result, and the higher the importance, the higher the score of the second factor.

[0113] For example, in the example shown in FIG. 4, the scores of different factors can be configured according to the division of administrative regions. For example, the score of a factor in the L2 hierarchy is higher than the score of a factor in the L1 hierarchy, and the score of the Shenzhen region is the same as the score of the Dongguan region, and both the score of the Shenzhen region and the score of the Dongguan region are greater than the score of China, so the score of the Shenzhen region can be the same as the score of the Dongguan region, or if the importance of the Shenzhen region is greater, the score of the Shenzhen region is greater than the score of the Dongguan region. Similarly, the scores of A Company, B Company, and C Company in the L3 hierarchy are greater than the score of the Shenzhen region, and if the importance of A Company is greater than the importance of B Company, and the importance of B Company is greater than the importance of C Company, the score of A Company is greater than the score of B Company, and the score of B Company is greater than the score of C Company. The above examples are used for illustration, and the present application is not limited in this regard.

[0114] Briefly, in the example shown in FIG. 4, along the direction from bottom to top, the generality of the factors is higher and higher, and the scores are lower and lower, and along the direction from top to bottom, the generality of the factors is lower and lower, and the specificity is higher and higher, and the scores are higher and higher.

[0115] It should be understood that the score is allocated according to the importance of the factors, and the score of each factor is allocated, so that the user can manage the factors hierarchically, clearly understand and maintain the importance relationship between the factors; secondly, the score of the factor is less than the score of each factor contained in it, so that the score of the factor gives a score range to the multiple factors it includes, and realizes fine score control of the factors; finally, the score of the factors is determined according to the importance in the horizontal direction, and the score of the factors is determined according to the level in the vertical direction. Through this way, the score of each factor can be obtained, and the higher the generalization degree of the factor, the lower the score of the factor, and the higher the detailed degree of the factor, the higher the score of the factor. Because the user configures the matching rule, the more generalized rule uses the lower importance, which is in line with the user's use demand, so as to ensure that the matching rule can be matched according to the importance required by the user.

[0116] Optionally, the score of each factor and factor can be determined by power index method. Specifically, the score of each factor includes a first power function, the base of the first power function of each factor is the same, and the index is different, which controls the score of each factor. That is, the greater the importance, the greater the index, and the higher the score of the factor. Specifically, the factors can be sorted according to the importance from large to small, the factor with the highest importance is configured with the index and base of the first power function, and then the index is gradually decreased according to the arrangement order, and the decreasing difference is not limited, such as gradually decreasing 1 or gradually decreasing 2, which is not limited in the application.

[0117] For example, n is the total number of factors, i is the position of a certain factor in the importance order (from 1 to n, 1 is the most important), the base of the first power function is b, the initial index is e, and the decreasing difference is d, then the score of the ith factor is S i = b e-(i-1)d . For example, the score of the factor with the highest importance S1 = b e , the score of the second factor S2 = b e-d , and the score of the third factor S3 = b e-2d . The above examples are used for illustration, and the application is not limited.

[0118] Further, the score of each level factor includes a first power function and a weight, wherein the weight increases with the increase of the level, and the increasing difference is not limited, the first power function of multiple levels of the same factor is the same, the base of the first power function in the score of each factor is the same, and the index is different, the index of each factor is higher than the index of the last level factor and lower than the index of the next level factor.

[0119] Still taking the above example as an example, the score of the ith factor is S i = b e-(i-1)d, assuming j is the level of a certain factor in the hierarchical relationship data, the weight is A, and the score of each factor under this factor is S ij = b e-(i-1)d + jA, for example, the score of the factor ranked first is S1= b e , the score of the factor in the first level under this factor is S11= b e + A, the score of the factor in the second level is S12= b e + 2A, and so on, which is not limited in the present application.

[0120] For example, if the importance of the company factor shown in FIG. 4 is greater than the importance of the transaction product shown in Table 1, the score of the company factor x1= 2 15 , the score of the transaction product factor x2= 2 14 , as shown in FIG. 4, the score of the China region factor in the L1 layer x 11 = 2 15 + 1, the score of the Shenzhen region factor in the L2 layer x 12 = 2 15 + 2, the score of the A company factor in the L3 layer x 13 = 2 15 + 3. Similarly, the score of the currency transaction factor in the L1 layer in Table 1 is x 21 = 2 14 + 1, the score of the investment factor in the L2 layer is x 22 = 2 14 + 2, the score of the fixed deposit factor in the L3 layer is x 23 = 2 14 + 3, and so on, which is not limited in the present application.

[0121] It should be understood that some of the numerical values in the above examples are used for illustration, and the present application is not limited to specific numerical values. For example, in the above example, when the factors are ranked according to the importance and the scores of each factor are configured, the difference between the exponents of the first power function of the two factors adjacent in the importance ranking is 1, for example, the score of the company factor x1= 2 15 , the score of the transaction product factor x2= 2 14 , and the difference between the exponents is 1. The difference between the exponents is an example, and the difference between the exponents of the first power function of the two factors adjacent in the importance ranking can also be other numerical values, for example, the difference can be 2, in which case the score of the company factor x1= 2 15 , the score of the transaction product factor x2= 2 13 . For example, in the above example, the difference between the scores of the factors in the adjacent levels is 1, for example, the score of the China region factor in the L1 layer x 11 = 2 15 + 1, the score of the Shenzhen region factor in the L2 layer x 12 = 2 15+ 2, the score x of the factor of the L3 layer A company 13 = 2 15 + 3, the score difference is also an example, the score difference between the factors of adjacent levels can also be other values, such as 100, at this time, the score x of the factor of the L1 layer China region 11 = 2 15 + 100, the score x of the factor of the L2 layer Shenzhen region 12 = 2 15 + 200, the score x of the factor of the L3 layer A company 13 = 2 15 + 300. For example, the base number 2 in the above example and the power index of the factor with the largest score are 15, these values are for example, and in actual business scenarios, they can be set as required, and the present application does not make specific limitations.

[0122] Further, the score of each factor and factor can also include a second power function, the second power function is used to control the score interval of multiple factors under each factor, so that the score of multiple factors under the second factor is not higher than the score of multiple factors under the first factor, wherein the score of the second factor is lower than the score of the first factor. It should be understood that since the factor score of each level is lower than the factor score of the next level, the more levels the factor is divided into, the higher the factor score of the lower layer. It may be the case that the score of the first factor is greater than the score of the second factor, but the score of the factor of the second factor at the bottom layer is greater than the score of the first factor. In order to avoid the above score crossing problem, the second power function can well separate the score interval of each factor, and no matter how the level of each factor is divided, the score crossing problem will not occur.

[0123] In a specific implementation, the base number and index of the second power function of multiple factors under each factor are the same as the base number and index of the factor to which they belong, and the indexes of different factors are allocated according to importance, the greater the importance, the greater the index, and the higher the score of the factor. Specifically, the factors can be sorted in descending order of importance, the factor with the highest importance is configured with the index and base number of the second power function, and then the index is gradually decreased according to the arrangement order, and the decrease difference is not limited, such as gradually decreasing by 1 or gradually decreasing by 2, which is not limited by the present application.

[0124] Still in the above example, n is the total number of factors, i is the position of a certain factor in the importance order (from 1 to n, 1 is the most important), the base number of the first power function is b1, the initial index is e, the decrease difference is d, the base number of the second power function is b2, the initial index is g, and the decrease difference is c. Then the score of the ith factor is S i = b1 e-(i-1)d b2 g-(i-1)cFor example, the score of the factor ranked first in importance is S1 = b1 e b2 g The score of the factor ranked second in importance is S2 = b1 e-d b2 g-c The score of the factor ranked third in importance is S2 = b1 e-2d b2 g-2c The above examples are for illustration only, and the application is not limited in this regard.

[0125] It should be understood that the above examples obtain the score of the factor by multiplying the first power function and the second power function, and in specific implementations, the first power function and the second power function can also be obtained by addition, weighted sum, weighted average, or other calculation methods, and the application is not limited in this regard.

[0126] It should be noted that the second power function of each factor under the same factor is the same, because the purpose of the second power function is to avoid the problem of score crossing, and if the second power function also increases with the increase of the level, it may instead aggravate the problem of score crossing, so that the score of some bottom-level factors is larger than the score of factors of higher importance, so the second power function remains unchanged among multiple factors under the same factor.

[0127] Still taking the above example, if the importance of the company factor shown in FIG. 4 is greater than the importance of the transaction product shown in Table 1, then the score of the company factor x1 = 2 15 10 5 The score of the transaction product factor x2 = 2 14 10 4 Referring to FIG. 4, the score of the China region factor in the L1 layer x 11 =(2 15 +1)10 5 The score of the Shenzhen region factor in the L2 layer x 12 =(2 15 +2)10 5 The score of the A company factor in the L3 layer x 13 =(2 15 +3)10 5 Similarly, the score of the currency transaction factor in the L1 layer in Table 1 is x 21 =(2 14 +1)10 4 The score of the investment factor in the L2 layer is x 22 =(2 14 +2)10 4 The score of the fixed deposit factor in the L3 layer is x 23 =(2 14 +3)10 4 and so on, which will not be illustrated one by one here.

[0128] In order to make the present application can be better understood, the following with reference to figure 5 on each factor and factor score configuration process are illustrated.

[0129] Figure 5 is a transaction matching method provided by the present application in the factor score configuration example, as shown in figure 5, the importance of the company factor is greater than the transaction product factor, the importance of the transaction product factor is greater than the transaction object factor, so the score of the company factor is 2 15 10 5 = 3276800000, the importance of the transaction product factor is second, so the score of the transaction product factor is 2 14 10 4 = 163840000, the importance of the transaction object factor is second, so the score of the transaction object factor is 2 13 10 3 = 8192000.

[0130] Then configure the score of each factor, according to the hierarchical relationship data of each factor, configure the score of the factor under the company factor, the score of the first layer of China region factor is (2 15 +1)10 5 = 3276900000, the score of the second layer of Shenzhen region factor is (2 15 +2)10 5 = 3277000000, the score of China region factor is greater than the company factor of the last layer, less than the Shenzhen region factor of the next layer.

[0131] Similarly, when configuring the score of the factor under the transaction product factor, the score of the first layer of currency transaction factor is (2 14 +1)10 4 = 163850000, the score of the second layer of investment factor is (2 14 +2)10 4 = 163860000, the score of the third layer of fixed deposit factor is (2 14 +3)10 4 = 163870000, the score of the investment factor is greater than the score of the currency transaction factor of the last layer, less than the score of the fixed deposit factor of the next layer. And because of the existence of the second power function 10 n , the score difference between factors is large, no matter how the transaction product factor is divided into layers, the factor with the highest score under the transaction product factor will not exceed the factor score under the company factor, and there will be no score crossing problem.

[0132] Similarly, the score of each factor in the transaction object can be allocated, the score of the first layer of Shenzhen region factor is (2 13+1)10 3 = 819300, the score of the Nanshan Branch factor of the second layer is (2 13 +2)10 3 = 8194000, the score of the Shenzhen area factor is greater than the score of the transaction object factor of the previous layer and less than the score of the Nanshan Branch factor of the next layer, and the score is much smaller than the score of each factor under the transaction product factor, and there is no score crossing problem.

[0133] It should be understood that FIG. 5 exemplarily shows the score configuration process of 3 factors and the score configuration process of multiple factors under each factor, and other factors and factors can be configured in the same way, which will not be illustrated one by one here.

[0134] In the above example, the base of the second power function is 10, the exponent of the second power function of the factor with the largest score is 5, for example, the score of the company factor x1 = 2 15 10 5 The value is used for illustration, and the application is not limited.

[0135] It should be understood that, compared with the direct allocation of scores, the power exponent method provided by the application can have a more explicit quantification method to distinguish the importance of each factor and factor, and the score allocation method has interpretability and can intuitively express the difference in importance between different factors and factors. The difference between the scores of two factors with adjacent importance can be large, which can reduce the possibility of similar scores between different levels, facilitating subsequent sorting of matching results. At the same time, when new factors and factors appear, the exponent of the power function can be determined according to the importance of the new factor, and then the score of the new factor can be obtained, so that the expansion of the factor is very convenient, and the factor can also be easily expanded. The power exponent method has strong flexibility, expansibility and adjustability.

[0136] It should be noted that in the scheme provided by the application, the scores of the factors and factors at least include the first power function, so as to explicitly quantify and distinguish the importance of each factor and factor. Then, according to the business requirements, the scores of the factors and factors can also include more power functions, such as the second power function for solving the score crossing problem. If there are other problems, new power functions can be configured, and the number of power functions is not limited by the application.

[0137] Optionally, the transaction matching system 200 can provide a configuration interface for the user to upload the importance relationship of the factors, such as the importance of the company factor being greater than the transaction product, and the importance of the transaction product being greater than the transaction object. The system can automatically generate the score of each factor according to the importance relationship, and automatically generate the score of each factor according to the hierarchical relationship data of the factors.

[0138] Alternatively, the user uploads the importance relationship of the factors and the related data of the power function in the score, such as the base number of the power function and the index of the power function, and the difference between the indexes of the first power functions of the two factors adjacent in the importance ranking, and the system can automatically generate the score of each factor and factor according to the importance relationship and the related data of the power function input by the user, which is not limited in the present application.

[0139] Alternatively, the user uploads the score of each factor and factor, which is not limited in the present application.

[0140] It should be understood that in the technical solutions provided by the present application, when the factors and factors are scored, the factors are scored according to their importance, the greater the importance, the higher the score of the factor, and the factors are scored according to their factor level, the score of each level of factor is higher than that of the factor of the previous level and lower than that of the factor of the next level. Any solution that can achieve the above score allocation requirements belongs to the scope of protection of the present application, and each possible implementation is not exemplified here.

[0141] S340: The client 100 sends the matching rule to the transaction matching system 200.

[0142] In specific implementation, the matching rule is preset according to the conditions and the corresponding relationship of the corresponding accounts according to business needs, wherein the conditions include factors or factors, for example, the matching rule can be: the account 001 is used for foreign exchange transaction of A company, the condition here is A company and foreign exchange transaction, A company is a factor under the company factor, and foreign exchange transaction is a factor under the transaction product factor. The above examples are used for illustration, and the present application is not limited.

[0143] In specific implementation, the system can provide some factors for the user to select, generate the matching rule according to the selected factors of the user, or store the matching rule in the form of a form, and the user inputs the form corresponding to the matching rule, which includes multiple fields, and each field can correspond to a factor or a factor.

[0144] For example, Table 2 is an example table of a matching rule provided by the present application, and each row of data in the table represents a matching rule. For example, rule No. 001 indicates that when a company in China region performs foreign exchange transaction, if the transaction product is spot foreign exchange and the transaction object is a bank in Shenzhen region, use transaction account 001 to complete. It should be understood that by storing the matching rule in the form of a form, the factors and factors in the matching rule can be clearly obtained, and the score of each matching rule can be determined.

[0145] Table 2: Matching rule example table

[0146] It should be understood that, since each factor is hierarchically classified in the present application, the factor of the upper level is a generalization of the factor of the lower level, so the value of each field in the matching rule configured by the user can be a generalization value, such as the company in the Chinese region in Table 1, and it is not necessary to configure each company one by one, such as the transaction object can be configured according to the Shenzhen region and the Nanshan branch, and it is not necessary to configure each bank in the Shenzhen region one by one, thereby reducing the number of configuration items of the matching rule and improving the user experience.

[0147] S350: The transaction matching system 200 determines the score of each matching rule based on the score calculation rule.

[0148] In a specific implementation, the transaction matching system 200 can determine the factors and factors contained in each matching rule, and determine the score of the matching rule according to the scores of the factors and factors. For example, the sum of the scores of the factors and factors is determined as the score of the matching rule, or the weighted sum of the scores of the factors and factors is determined as the score of the matching rule, or the weighted average of the scores of the factors and factors is determined as the score of the matching rule, or other calculation methods are used to determine the score of the matching rule, which is not limited in the present application. Wherein, the score of each matching rule can be stored through the rule score table in FIG. 1.

[0149] Still taking the example in S340 as an example, assuming that the score of the company factor x1=2 15 10 5 , the score of the transaction product factor x2=2 14 10 4 . Referring to FIG. 4, the score of the L1 layer Chinese region factor x 11 =(2 15 +1)10 5 , the score of the L2 layer Shenzhen region factor x 12 =(2 15 +2)10 5 , the score of the L3 layer A company factor x 13 =(2 15 +3)10 5 . Similarly, the score of the L1 layer currency transaction factor x 21 =(2 14 +1)10 4 , the score of the L2 layer investment factor x 22 =(2 14 +2)10 4 , the score of the L3 layer fixed deposit factor x 23 =(2 14 +3)10 4 .

[0150] If the matching rule A is: the account 001 is used by the company in Shenzhen to conduct currency transaction. The score of the rule A is x 12 +x 21 = (2 15 + 2)10 5 + (2 14 + 1)10 4 = 3440860000. If the matching rule B is: the account 002 is used by the company in Shenzhen to conduct fixed deposit. The score of the rule B is x 12 +x 23 = (2 15 + 2)10 5 + (2 14 + 3)10 4 = 3440870000. It should be understood that the above examples are used for illustration, and the present application is not limited in this way.

[0151] For example, if the score of each factor and each factor is obtained in the way shown in Fig. 5, and the score of each rule is determined according to the rules shown in Table 2, the score of each rule can be shown in Table 3, which has an additional "score" field compared with Table 2, which is used to fill in the score of the matching rule. As shown in Table 3, the matching rule numbered 001 is that the bank in China pays fixed deposit to the bank in Shenzhen through the transaction account 001, which includes the factor of China, the factor of fixed deposit and the factor of Shenzhen, so the score of the matching rule is (2 15 + 1)10 5 + (2 14 + 3)10 4 + (2 13 + 1)10 3 = 3448963000. In the same way, the score of each rule can be obtained. Here, the detailed description is not given.

[0152] Table 3: score of rule

[0153] It should be understood that through S310-S350, the system can obtain the score of each matching rule, such as the score shown in Table 2, so that when the transaction occurs, the most suitable transaction account can be selected according to the score of the matching rule to complete the transaction.

[0154] The above describes in detail how the configuration layer 210 determines the score of each matching rule in combination with Figs. 3-5. The following describes in detail how the matching layer 220 determines the transaction account according to the score of each matching rule in combination with Figs. 6-8.

[0155] FIG. 6 is a flowchart of steps in a matching stage of a transaction matching method provided by the present application, which can be implemented by the matching layer 220 in the embodiment of FIG. 1. As shown in FIG. 6, the method can include the following steps:

[0156] S610: The transaction processing system 300 sends transaction data to the transaction matching system 200.

[0157] In a specific implementation, the transaction data refers to the related data of the transaction to be processed. The transaction data can include multiple fields, at least including the fields included in the matching rules, such as the transaction object, the transaction product, the company name, etc. in Table 3, and can further include some additional fields, such as the currency, the type of payment and receipt, etc. The contents included in the transaction data can be determined according to actual business requirements, which are not limited in the present application.

[0158] S620: The transaction matching system 200 determines one or more matching rules matched with the transaction data.

[0159] Optionally, the transaction matching system 200 can determine the multiple field values included in the transaction data, and determine one or more matching rules existing for the multiple field values, such as the transaction data is that the company A pays the fixed deposit to the bank B, and the matching rule has a rule X including the company A factor, the bank B factor and the fixed deposit factor. At this time, the rule X can be directly determined as the matching successful matching rule.

[0160] Optionally, if there is no matching rule completely consistent with the field values in the transaction data, the matching can be completed by combining the hierarchical relationship data of each factor at this time, determining the factor corresponding to the field value in the hierarchical relationship data, and then obtaining the factor at the upper level of the factor, and determining whether there is a matching rule including the factor at the upper level. If not, the factor at the upper level is continuously obtained, that is, the upper layer is traversed along the hierarchical relationship tree or the hierarchical relationship table, and finally the matching successful rule is obtained.

[0161] For example, it is assumed that the hierarchical relationship tree of the company factor is shown in FIG. 4, and the matching rule X is that the account 001 is used when the transaction is processed by the company in the Chinese region. If the company A has a transaction, the account used when the transaction is processed by the company A is not configured in the matching rule. Therefore, according to the hierarchical relationship tree of the company factor, the factor "A company" corresponding to the company A is obtained first, the factor at the upper level of the factor is "Dongguan region", and the account used when the transaction is processed by the Dongguan region is not configured in the matching rule. Therefore, the factor at the upper level of the factor is determined as "Chinese region", and the account used when the transaction is processed by the Chinese region is 001 in the matching rule. It can be determined that the rule X is the matching successful matching rule. The above example is used for illustration, which is not limited in the present application.

[0162] S630: The transaction matching system 200 obtains a ranking result based on the scores of one or more matching rules.

[0163] In a specific implementation, the ranking can be performed in a descending order of the scores or in an ascending order of the scores, which is not limited in the present application. If a higher score indicates a higher importance when the scores are configured, the ranking can be performed in a descending order of the scores, and the first matching rule is the one with the highest score, i.e., the one with the highest importance. If a lower score indicates a higher importance when the scores are configured, the ranking can be performed in an ascending order of the scores, and the first matching rule is the one with the lowest score, i.e., the one with the highest importance.

[0164] It should be noted that if there is only one matching rule in the matching result, the ranking can be omitted, and the account of the matching rule is determined as the transaction account, and S650 is performed.

[0165] It should be understood that in the above description, the scores of the factors, the scores of the factors, and the scores of the matching rules in S310-S350 are all exemplified by using the scores in a manner that a higher score indicates a higher importance. For a manner that a lower score indicates a higher importance, reference can be made to the related description above, which is not exemplified herein.

[0166] S640A: The transaction matching system 200 sends the ranking result to the client 100, and receives the transaction account selected by the user from the client 100.

[0167] It should be understood that the transaction matching system 200 can send the ranking result to the user for reference, and the user can select an account to complete the transaction according to the ranking result, for example, the first ranked account is in a bank temporarily notified of system upgrade and cannot perform the transaction operation, and the user can select the second ranked account to complete the transaction. It should be understood that the present application can improve the efficiency of the user in selecting the transaction account by providing the ranking result to the user, and the user does not need to check the multiple matching rules of the matching success again, but can directly determine the transaction account according to the ranking result and the account information, thereby improving the use efficiency of the user.

[0168] S640B: The transaction matching system 200 determines the account of the first ranked matching rule in the ranking result as the transaction account.

[0169] It should be understood that the account of the first ranked matching rule is the account of the matching rule with the highest importance, and S640B is performed, so that the transaction matching system can automatically select the optimal transaction account from the multiple matching rules of the matching success, thereby improving the efficiency of determining the transaction account.

[0170] It should be understood that S640A and S640B are two parallel schemes, the transaction matching system 200 can send the matched multiple accounts and the corresponding scores to the user for reference through S640A, so as to facilitate the user to quickly determine the transaction account; the transaction matching system 200 can also automatically determine the account with the highest score as the transaction account through S640B, and the two ways can be selected to execute.

[0171] Optionally, S640A and S640B can also be performed at the same time, and if the transaction account selected by the user is different from the transaction account determined by the system, the system can also record it and remind the user whether to modify the score calculation rule.

[0172] It should be noted that if the number of matching rules ranked first in the sorting result is multiple, that is, the scores of the multiple matching rules are the same, then the account corresponding to any one of the matching rules can be determined as the transaction account.

[0173] For example, taking the score calculation rule shown in FIG. 5, the matching rules shown in Table 2 and Table 3 as an example, assuming that the transaction processing system 300 currently has a transaction: a company in Shenzhen area wants to pay a large amount of deposit to Nanshan branch, the transaction data contains the fields “Shenzhen area”, “Nanshan branch”, “large amount of deposit”, match the fields according to the factors, the matching consistent factors include the company factor, the transaction product factor and the transaction object factor, after determining the existing factors, match according to the hierarchical relationship tree of the company factor in FIG. 5, successfully match “China area” and “Shenzhen area”, match according to the hierarchical relationship table of the transaction product factor in FIG. 5, successfully match “currency transaction” and “investment”, match according to the hierarchical relationship tree of the transaction object factor, successfully match “Shenzhen area” and “Nanshan branch”, after combining these matching factors, the matching successful rules can be obtained, including rule 002 and rule 003, then sort based on the scores of rule 002 and rule 002, obtain the sorting result example table shown in Table 4 as follows, and determine the transaction account as account 002. The above example is used for illustration, and the present application is not limited in specific.

[0174] Table 4: Sorting result example table

[0175] S650: The transaction matching system 200 sends the transaction account to the transaction processing system 300.

[0176] In a specific implementation, after the transaction processing system 300 receives the transaction account sent by the transaction processing system 300, the transaction account can be used to complete the transaction, so that each transaction can be completed using the optimal account according to the business needs of the user, improving the user experience.

[0177] It should be noted that the above embodiments of FIG. 3 to FIG. 6 are described in the process of describing the technical solutions of the present application, and the score of the matching rule is in a positive proportional relationship with the refinement degree of the matching rule, that is, the more refined the matching rule is, the greater the score is, so the account of the matching rule with the largest score is determined as the transaction account. If the score of the matching rule is in an inverse proportional relationship with the refinement degree, that is, the more refined the matching rule is, the smaller the score is, at this time, the account of the matching rule with the smallest score can be determined as the transaction account, and the present application does not limit this.

[0178] In order to enable the present application to be better understood, the user interface used by the technical solutions provided by the present application will be exemplarily described below in combination with FIG. 7 and FIG. 8. It should be understood that FIG. 7 and FIG. 8 are an exemplary interface, and the present application does not specifically limit this.

[0179] FIG. 7 is an example diagram of a configuration interface provided by the present application, as shown in FIG. 7, the configuration interface includes a retrieval area 710 and a display area 720.

[0180] The retrieval area 710 is used for the user to input a retrieval condition. Exemplarily, FIG. 7 gives several possible retrieval fields, such as rule number, configuration time, rule state, company, transaction product and transaction object, etc. According to the retrieval condition input by the user, the display area 720 can display the matching rules meeting the retrieval condition.

[0181] The display area 720 is used to display the matched matching rules and the corresponding rule scores to the user. For example, the configuration time of the matching rule with rule number 001 is June 2023, the rule stipulates that the company in China region uses 001A account to complete the transaction when paying the regular deposit to the bank in Shenzhen region, the current state of the rule is enabled and effective, the score of the rule is 3348963000, and the rules with other numbers can be known in the same way, which will not be explained one by one here.

[0182] Further, the display area 720 can also include a tool bar 721, and the tool bar 721 can include a plurality of tool controls. Exemplarily, FIG. 7 gives an example of 5 controls, the first control is used to fold and unfold the tool bar, the second control is used to add a new matching rule, the third control is used to delete the matching rule, the fourth control is used to sort the matching rules according to the score size, and the fifth control is used to modify the existing matching rule. It should be understood that the tool bar shown in FIG. 7 is an example of illustration, and the present application does not specifically limit this.

[0183] It should be understood that through the interface shown in FIG. 7, the user can clearly know the content of the configured matching rule, and can complete the screening of the matching rule through the search area 710, and can also perform operations such as adding a new matching rule, modifying an existing matching rule, and deleting an existing matching rule through the toolbar 721.

[0184] Meanwhile, each matching rule can be configured using a higher-level general factor when being configured, so as to reduce the number of matching rule configurations. For example, the rule 001 is used when all companies in the Chinese region pay regular deposits to banks in the Shenzhen region using the account 001A. If the companies in the Chinese region include A-D companies, a total of 4 companies, and the banks in the Shenzhen region include E-F banks, a total of 4 banks, using the traditional configuration method, 16 matching rules need to be configured, and the present application only needs to configure one rule 001 shown in FIG. 7, which greatly improves the configuration efficiency of the user and improves the user experience. The score of each matching rule is obtained by the transaction matching system 200 performing the above S310-S350, which is not repeated here.

[0185] Further, after the matching rule is configured through the interface shown in FIG. 7, when a transaction occurs, the system can match the transaction data with the matching rule to obtain a ranking result. The specific process can be referred to the above S610-S650. The system can directly use the account of the matching rule ranked first as the transaction account and send it to the transaction processing system 300 for processing, or can display the ranking result to the user. For example, FIG. 8 is an example interface of a ranking result provided by the present application. As shown in FIG. 8, the interface can include a transaction data display area 810 and a ranking result display area 820.

[0186] The transaction data display area 810 is used to display transaction data, and the user can select transaction data that needs to confirm the transaction account. For example, in the example of FIG. 8, the user selects the first row of transaction data to confirm the transaction account, the transaction number of this transaction is 22013901, the occurrence event is June 8, 2023, and A company pays regular deposits to Nanshan Branch. The area can include a "confirm transaction account" control, and after the user clicks the control, the ranking result display area 820 can display the ranking result of one or more accounts matched with the transaction data.

[0187] The ranking result display area 820 is used to display the ranking result, which includes a plurality of matching rules matched with the transaction data, and is ranked according to the scores of the matching rules. For example, in the example shown in FIG. 8, there are two matching rules that satisfy the transaction number 22013901, which are rule 006 and rule 007, and the rule score of rule 006 is ranked first.

[0188] Optionally, if the system automatically confirms the transaction account, the transaction matching system 200 can take the account 002A as the transaction account of this transaction. The ranking result of each transaction and the finally determined transaction account can be stored, and the interface shown in FIG. 7 can be used for the user to query. By querying the ranking result of the historical transaction data and the transaction account used, the user can check whether the configuration of the matching rule is correct and whether the score calculation rule is correct.

[0189] Optionally, if manual confirmation of the transaction account is required, the transaction matching system 200 can display the interface shown in FIG. 7, and the user can determine the account used based on the ranking result. If the account 002A ranked first temporarily appears abnormal, the user can select the account 001B to complete the transaction. By displaying the ranking result to the user, the user can quickly determine the transaction account, and the user experience is improved.

[0190] In summary, the transaction matching method provided in the present application configures a score representing the importance of each rule. When the transaction data matches multiple rules, the multiple rules that match the transaction data can be ranked according to the scores, and the account used for the transaction is determined according to the ranking result. In this way, even when the matching rules overlap, the transaction account can be determined according to the scores. The problem that the transaction cannot be performed because the matching result includes multiple accounts is solved, and the user experience is improved without human intervention.

[0191] Meanwhile, the transaction matching method provided in the present application classifies each factor that may be included in the matching rule into levels. The factors classified into levels include multiple level factors. The factor of the upper level is a generalization of the factor of the lower level. Therefore, when the user configures the matching rule, if some matching rules use the same account for transactions, the factors can be configured by generalization. For example, if multiple companies in the same region use the same account for transactions, the matching rules can be configured according to the region. In this way, the number of matching rules that the user needs to configure is reduced, and the user experience is improved.

[0192] The transaction matching method provided in the present application is described in detail above. The transaction matching system provided in the present application is described below in conjunction with FIG. 9. The transaction matching system is the transaction matching system 200 described in FIGS. 1-9.

[0193] FIG. 9 is a structural schematic diagram of a transaction matching system provided by the present application. As shown in FIG. 9, the transaction matching system 200 can include a configuration unit 211, a calculation unit 212, an acquisition unit 221, a matching unit 222, a sorting unit 223, and a determination unit 224. The configuration unit 211, the calculation unit 212, the acquisition unit 221, the matching unit 222, the sorting unit 223, and the determination unit 224 can be implemented by software or by hardware. For example, the implementation of the configuration unit 211 is described below. Similarly, the implementation of the calculation unit 212, the acquisition unit 221, the matching unit 222, the sorting unit 223, and the determination unit 224 can refer to the implementation of the configuration unit 211.

[0194] As an example of the software function unit, the configuration unit 211 can include code running on a computing instance. The computing instance can include at least one of a physical host (computing device), a virtual machine, and a container. Further, the computing instance can be one or more. For example, the configuration unit 211 can include code running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers for running the code can be distributed in the same region or in different regions. Further, the multiple hosts / virtual machines / containers for running the code can be distributed in the same AZ or in different AZs, and each AZ includes one data center or multiple data centers with similar geographical locations. Generally, one region can include multiple AZs.

[0195] Similarly, the multiple hosts / virtual machines / containers for running the code can be distributed in the same VPC or in multiple VPCs. Generally, one VPC is set in one region, and a communication gateway needs to be set in each VPC for cross-region communication between two VPCs in the same region or between VPCs in different regions, and the interconnection between VPCs is realized through the communication gateway.

[0196] As an example of the hardware function unit, the configuration unit 211 can include at least one computing device, such as a server. Alternatively, the configuration unit 211 can be a device implemented by CPU, ASIC, or PLD. The PLD can be CPLD, FPGA, GAL, DPU, NPU, SoC, offload card, acceleration card, or any combination thereof.

[0197] The multiple computing devices included in the configuration unit 211 can be distributed in the same region or in different regions. The multiple computing devices included in the A module can be distributed in the same AZ or in different AZs. Similarly, the multiple computing devices included in the configuration unit 211 can be distributed in the same VPC or in multiple VPCs. The multiple computing devices can be any combination of servers, ASICs, PLDs, CPLDs, FPGAs, GALs, DPUs, NPUs, SoCs, offload cards, acceleration cards, and the like.

[0198] It should be noted that in other embodiments, the steps implemented by the configuration unit 211, the computing unit 212, the obtaining unit 221, the matching unit 222, the sorting unit 223, and the determining unit 224 can be specified as needed, and the overall function of the transaction matching system 200 can be implemented by the configuration unit 211, the computing unit 212, the obtaining unit 221, the matching unit 222, the sorting unit 223, and the determining unit 224 respectively implementing different steps in the transaction matching method.

[0199] The functions of the configuration unit 211, the computing unit 212, the obtaining unit 221, the matching unit 222, the sorting unit 223, and the determining unit 224 will be explained and described below.

[0200] The obtaining unit 221 is configured to obtain transaction data, and specifically configured to perform S610 shown in FIG. 6.

[0201] The matching unit 222 is configured to determine a plurality of matching rules matched with the transaction data, the matching rules including conditions and accounts, and the matching rules being used to complete a transaction corresponding to the transaction data using the accounts in a case where the transaction data satisfies the conditions, and specifically configured to perform S620 shown in FIG. 6.

[0202] The sorting unit 223 is configured to sort the plurality of matching rules based on a score of each matching rule (a rule score is taken as an example in FIG. 9), to obtain a sorting result, wherein the score of each matching rule is in a positive proportional relationship with a refinement degree of the condition in each matching rule, and specifically configured to perform S630 shown in FIG. 6.

[0203] The determining unit 224 is configured to determine a transaction account used by the transaction data based on the sorting result, and specifically configured to perform S640A, S640B, and S650 shown in FIG. 6.

[0204] The configuration unit 211 is configured to obtain a plurality of factors existing in the historical transaction data, the plurality of factors including field values in the historical transaction data, determine a score of each factor in the plurality of factors, the score of each factor being in a positive proportional relationship with a refinement degree of the factor, provide a configuration interface for a user, obtain a matching rule input by the user, and determine at least one factor included in the matching rule, and specifically configured to perform S310-S340 in FIG. 3.

[0205] The calculation unit 212 is configured to determine a score of the matching rule according to the score of the at least one factor, and specifically configured to perform S350 in FIG. 3.

[0206] In a possible implementation, the plurality of factors further include summarized words of the field values in the historical transaction data, the configuration unit 211 is configured to obtain, from the historical transaction data, a plurality of fields (that is, “factors” in the embodiments of FIGS. 1-7) that have a certain influence on the transaction account and field values under each field, and generate hierarchical relationship data of each field according to the field values under each field, where the hierarchical relationship data includes a plurality of hierarchical data, each hierarchical data includes a plurality of factors, a factor in a previous layer is a summary of a factor in a next layer, and the factor in the next layer is a refinement of the factor in the previous layer.

[0207] In a possible implementation, the configuration unit 211 is configured to obtain, from the historical transaction data, a plurality of fields that have a certain influence on the transaction account, and determine a plurality of first factors of each field according to a plurality of field values of each field, summarize or refine the plurality of first factors of each field in combination with domain expertise to generate a plurality of second factors of each field, determine a superior-inferior relationship between the plurality of first factors and the plurality of second factors, and obtain hierarchical relationship data of each field, where the hierarchical relationship data includes a plurality of layers, each layer includes one or more of the first factors and the second factors, a factor in a previous layer is a summary of a factor in a next layer, and the factor in the next layer is a refinement of the factor in the previous layer.

[0208] In a possible implementation, the configuration unit 211 is configured to determine a score of each field based on an influence degree of each field on the determination of the transaction account, and determine scores of a plurality of factors under each field based on the hierarchical relationship data of each field, where the score of each factor under each field is greater than the score of the field, and the score of a factor in a next layer is higher than the score of a factor in a previous layer.

[0209] In a possible implementation, the score of each field includes a first power function, the base of the first power function of different fields is the same, and the exponents are different, the score of each factor includes the first power function and a weight, the base and the exponent of the first power function of each factor are the same as those of the first power function of the field to which the factor belongs, and the weight of each factor increases with an increase of the level in which the factor is located.

[0210] In a possible implementation, the score of each field includes a first power function, the base of the first power function of different fields is the same, and the exponents are different, the score of each factor includes the first power function and a weight, the base and the exponent of the first power function of each factor are the same as those of the first power function of the field to which the factor belongs, and the weight of each factor increases with an increase of the level in which the factor is located.

[0211] In a possible implementation, the score of each field further includes a second power function, the second power function is used to control the score interval of the factors under each field, so that the scores of the factors under the second field are not higher than the scores of the factors under the first field, where the score of the second field is lower than the score of the first field.

[0212] In a possible implementation, the determining unit 224 is configured to send the sorting result to the user and obtain a transaction account with user feedback, or the determining unit 224 is configured to determine a matching rule with the highest importance degree according to the sorting result, and determine the account of the matching rule with the highest importance degree as the transaction account used by the transaction data.

[0213] In summary, the transaction matching system provided in this application configures a score representing the importance degree of each rule, when the transaction data matches multiple rules, the multiple rules that match the transaction data are sorted according to the scores, and the account used by the transaction is determined according to the sorting result, so that the account used by the transaction can be determined according to the scores when the matching rules overlap, the problem that the transaction cannot be performed due to the multiple accounts in the matching result is solved, and the user experience is improved without human intervention.

[0214] Meanwhile, the transaction matching system provided in the application classifies each factor possibly included in the matching rule into a hierarchy, and the factors after the hierarchical classification include multiple levels of factors. The factors of the upper level are the generalization of the factors of the lower level. Therefore, when the user configures the matching rule, if some matching rules use the same account for transaction, the user can use the generalization of the factors to combine and configure the matching rules. For example, if multiple companies in the same region use the same account for transaction, the user can configure the matching rules according to the region, so as to reduce the number of matching rules that the user needs to configure and improve the user experience.

[0215] The transaction matching method and the transaction matching system provided in the application are described in detail above. The computing device provided in the application is explained and described below in combination with FIGS. 10-12.

[0216] FIG. 10 is a structural schematic diagram of a computing device provided in the application. As shown in FIG. 10, the computing device 900 includes a bus 902, a processor 904, a memory 906, and a communication interface 908. The processor 904, the memory 906, and the communication interface 908 communicate with each other through the bus 902. The computing device 900 can be a server or a terminal device. It should be understood that the number of processors and memories in the computing device 900 is not limited in the application. The computing device 900 can be the transaction matching system 200 in the foregoing embodiments of FIGS. 1-9.

[0217] The bus 902 can be a peripheral component interconnect Express (PCIe) bus or an extended industry standard architecture (EISA) bus, a unified bus (Ubus or UB), a compute express link (CXL), a cache coherent interconnect for accelerators (CCIX), etc. The unified bus is also referred to as a smart bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one line is used in FIG. 9, but it does not mean that there is only one bus or only one type of bus. The bus 902 can include a path for transmitting information between various components (for example, the memory 906, the processor 904, and the communication interface 908) of the computing device 900. The unified bus can also be referred to as a smart bus.

[0218] The processor 904 can include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP), an ASIC, an FPGA, a CPLD, an NPU, a SoC, an offload card, an acceleration card, or other computing device.

[0219] The memory 906 can include volatile memory, such as random access memory (RAM) 906. The processor 904 can further include non-volatile memory, such as read-only memory (ROM), flash memory, a hard disk drive (HDD), or a solid-state drive (SSD), for example. Additionally, the memory 906 can be implemented by storage class memory (SCM), phase change memory (PCM), or other types of storage media.

[0220] It is worth noting that the same type of storage media can be configured to implement the memory 906 function in the same computing device, or two or more types of storage media can be configured to implement the memory 906 function, which is not limited in the present application.

[0221] The memory 906 stores executable program code, and the processor 904 executes the executable program code to implement the functions of the aforementioned transaction matching system 200, including the functions of the acquisition unit, the matching unit, the sorting unit, and the determination unit shown in FIG. 10, thereby implementing the transaction matching method provided by the present application. That is, the memory 906 stores instructions for executing the transaction matching method.

[0222] Alternatively, the memory 906 stores executable program code, and the processor 904 executes the executable program code to respectively implement the functions of the configuration unit, the calculation unit, the acquisition unit, the matching unit, the sorting unit, and the determination unit, thereby implementing the transaction matching method provided by the present application. That is, the memory 906 stores instructions for executing the transaction matching method.

[0223] The communication interface 908 uses a transceiver module such as, but not limited to, a network interface card, a transceiver, to implement communication between the computing device 900 and other devices or communication networks.

[0224] As a possible implementation, the computing device 900 can also include a chip system including a processor and a power supply circuit for performing power supply for the processor, and the processor is configured to perform the operation steps corresponding to the transaction matching method. For brevity, details are not repeated here. The processor can be implemented by a CPU, or by a GPU, a DPU, an NPU, an XPU, a SoC, an offload card, an acceleration card, or other computing devices or AI chips.

[0225] As a possible implementation, the computing device 900 can include multiple types of processors 904, i.e., the computing device 900 is a heterogeneous device, for example, the computing device 900 includes a CPU and a GPU, and at least one of the processors 904 can perform the operation steps corresponding to the transaction matching method. For brevity, details are not repeated here.

[0226] Embodiments of the present application also provide a computing device cluster. As shown in FIG. 11, FIG. 11 is an example diagram of a computing device cluster provided by the present application, which includes at least one computing device. The computing device can be a server, for example, a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a desktop computer, a notebook computer, or a terminal device such as a smart phone.

[0227] As shown in FIG. 11, the computing device cluster includes at least one computing device 900. The memory 906 in one or more computing devices 900 in the computing device cluster can have the same instructions for executing the transaction matching method.

[0228] In some possible implementations, the memory 906 of one or more computing devices 900 in the computing device cluster can also respectively store partial instructions for executing the transaction matching method. In other words, the combination of one or more computing devices 900 can collectively execute the instructions for executing the transaction matching method.

[0229] It should be noted that the memories 906 in different computing devices 900 in the computing device cluster can store different instructions, respectively for executing part of the functions of the transaction matching system 200. That is, the instructions stored in the memories 906 in different computing devices 900 can implement the functions of one or more modules in the transaction matching system 200.

[0230] In some possible implementation manners, one or more of the computing devices in the computing device cluster can be connected through a network. The network can be a wide area network, a local area network, or the like. FIG. 12 shows one possible implementation manner. As shown in FIG. 12, FIG. 12 is a structural schematic diagram of another computing device cluster provided in the present application. Two computing devices 900A and 900B are connected through a network. Specifically, the computing devices are connected to the network through communication interfaces in the computing devices. In this type of possible implementation manner, the memory 906 in the computing device 900A stores instructions for performing the functions of the computing unit and the configuration unit. Meanwhile, the memory 906 in the computing device 900B stores instructions for performing the functions of the obtaining unit, the matching unit, the sorting unit, and the determining unit.

[0231] The connection manner between the computing device cluster shown in FIG. 12 can be that, considering that the transaction matching method provided in the present application has a configuration stage and a matching stage, the unit modules are distributed on different computing devices, the functions of the configuration unit and the computing unit in the configuration stage are performed by the computing device 900A, and the functions of the obtaining unit, the matching unit, the sorting unit, and the determining unit in the matching stage are performed by the computing device 900B.

[0232] It should be understood that the functions of the computing device 900A shown in FIG. 12 can also be completed by multiple computing devices 900. Similarly, the functions of the computing device 900B can also be completed by multiple computing devices 900.

[0233] In some possible implementation manners, the memory 906 of one or more of the computing devices 900 in the computing device cluster can also respectively store partial instructions for performing the transaction matching method. In other words, the combination of one or more computing devices 900 can collectively execute the instructions for performing the transaction matching method.

[0234] The embodiments of the present application further provide a computer program product containing instructions. The computer program product can be a software or program product containing instructions, which can run on a computing device or be stored in any available medium. When the computer program product runs on at least one computing device, the at least one computing device is caused to perform the transaction matching method.

[0235] The embodiments of the present application further provide a computer readable storage medium. The computer readable storage medium can be any available medium or data storage device that can store the instructions of the present application and be accessed by a computing device. The available medium can be a magnetic medium (e.g., floppy diskette, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk), etc. The computer readable storage medium includes instructions instructing the computing device to execute the transaction matching method, or instructing the computing device to execute the transaction matching method. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the protection scope of the technical solutions of the embodiments of the present application.

Claims

1. A transaction matching method, characterized by, The method is executed by a transaction matching system, and the method comprises: obtaining transaction data; determining a plurality of matching rules matched with the transaction data, the matching rules comprising conditions and accounts, the matching rules being used to complete a transaction corresponding to the transaction data using the accounts if the conditions are met by the transaction data; sorting the plurality of matching rules based on scores of each matching rule in the plurality of matching rules, to obtain a sorting result, wherein the score of each matching rule is in a positive proportional relationship with a refinement degree of the condition in each matching rule; determining a transaction account used by the transaction data based on the sorting result.

2. The method of claim 1, wherein, Before the transaction matching system obtains the transaction data, the method further comprises: obtaining a plurality of factors existing in historical transaction data, the plurality of factors comprising field values in the historical transaction data; determining a score of each factor in the plurality of factors, the score of each factor being in a positive proportional relationship with a refinement degree of the factor; obtaining a matching rule input by a user through a configuration interface, and determining at least one factor included in the matching rule; determining a score of the matching rule according to the score of the at least one factor.

3. The method of claim 2, wherein, The obtaining of the plurality of factors existing in the historical transaction data comprises: obtaining a plurality of fields having an influence on a transaction account from the historical transaction data; determining a plurality of first factors of each field according to a plurality of field values of each field; generating a plurality of second factors of each field by summarizing or refining the plurality of first factors of each field in combination with domain expertise; determining a hierarchical relationship between the plurality of first factors and the plurality of second factors to obtain hierarchical relationship data of each field, the hierarchical relationship data comprising a plurality of hierarchies, each hierarchy comprising one or more of the first factors and the second factors, a factor of a previous hierarchy being a summary of a factor of a next hierarchy, and the factor of the next hierarchy being a refinement of the factor of the previous hierarchy.

4. The method of claim 3, wherein, The determining of the score of each factor in the plurality of factors comprises: determining the score of each field based on an influence degree of the field on determining a transaction account; determining the scores of the plurality of factors under each field based on the hierarchical relationship data of each field, wherein the score of a factor under each field is greater than the score of the field, and the score of the factor of the next hierarchy is higher than the score of the factor of the previous hierarchy.

5. The method of claim 3 or 4, wherein the score of each field comprises a first power function, the base number of the first power function of different fields being the same and the exponents being different, and the determining of the score of each field based on the influence degree of the field on determining a transaction account comprises: sorting the plurality of fields based on the influence degree, and sequentially decreasing the exponents of the first power functions according to the sorting result to determine the score of each field. ​ The score of each factor includes the first power function and a weight value, the base and exponent of the first power function of each factor are the same as the base and exponent of the first power function of the field to which the factor belongs, and the weight value of each factor increases with the increase of the level of the factor. Based on the first power function of the factor in the field, the size of the weight value is sequentially increased according to the level of the factor, and the score of each factor is determined.

6. A transaction matching system, characterized by, The system comprises: An acquisition unit configured to acquire transaction data; A matching unit configured to determine a plurality of matching rules matched with the transaction data, the matching rules comprising conditions and accounts, and the matching rules being used to complete a transaction corresponding to the transaction data using the accounts if the conditions are met by the transaction data; An ordering unit configured to order the plurality of matching rules based on a score of each matching rule in the plurality of matching rules, and obtain an ordering result, wherein the score of each matching rule is in a positive proportional relationship with a refinement degree of the condition in each matching rule; A determination unit configured to determine a transaction account used by the transaction data based on the ordering result.

7. The system of claim 6, wherein, The system further comprises a configuration unit and a calculation unit, The configuration unit is configured to acquire a plurality of factors existing in historical transaction data, and the plurality of factors include field values in the historical transaction data; The configuration unit is configured to determine a score of each factor in the plurality of factors, and the score of each factor is in a positive proportional relationship with a refinement degree of the factor; The configuration unit is configured to acquire a matching rule input by a user through a configuration interface, and determine at least one factor included in the matching rule; The calculation unit is configured to determine a score of the matching rule according to the score of the at least one factor.

8. The system of claim 7, wherein The configuration unit is configured to acquire a plurality of fields that have an influence on the determination of a transaction account from historical transaction data; The configuration unit is configured to determine a plurality of first factors of each field according to a plurality of field values of each field; The configuration unit is configured to summarize or refine the plurality of first factors of each field in combination with domain expertise, and generate a plurality of second factors of each field; The configuration unit is configured to determine a superior-inferior relationship between the plurality of first factors and the plurality of second factors, and obtain hierarchical relationship data of each field, the hierarchical relationship data comprising a plurality of levels, each level comprising one or more of the first factors and the second factors, a factor of a previous level being a summary of a factor of a next level, and a factor of the next level being a refinement of the factor of the previous level.

9. The system of claim 8, wherein The configuration unit is configured to determine a score of each field based on an influence degree of each field on the determination of a transaction account. The configuration unit is configured to determine scores of factors under each field based on hierarchical relationship data of each field, wherein the score of a factor under each field is greater than the score of the field, and the score of a next-level factor is higher than the score of a previous-level factor.

10. The system of claim 8 or 9, wherein, The score of each field comprises a first power function, the base of the first power function of different fields is the same, and the exponent is different, the configuration unit is configured to sort the plurality of fields based on an influence degree of each field on determining the transaction account, and determine the score of each field by sequentially decreasing the exponent of the first power function according to the sorting result; The score of each factor comprises the first power function and a weight, the base and the exponent of the first power function of each factor are the same as the base and the exponent of the first power function of the field to which the factor belongs, the weight of each factor increases with the increase of the level of the factor, and the configuration unit is configured to determine the score of each factor by sequentially increasing the weight according to the level of the factor based on the first power function of the field of the factor.

11. A computer program product comprising instructions, characterized in that, When the instructions are executed by the computing device, the computing device is caused to perform the operation steps of the method of any one of claims 1 to 5.

12. A computing device, comprising: The computing device comprises a processor and a memory, the memory is configured to store instructions, and the processor is configured to execute the instructions to cause the computing device to implement the method of any one of claims 1 to 5.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions, and the instructions are executed by a computing device or a cluster of computing devices to implement the method of any one of claims 1 to 5.

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