Data processing method and apparatus

Abstract

The present application relates to a data processing method, comprising: receiving transaction data; parsing the transaction data to obtain a plurality of business parameters; determining a merging parameter according to comparison of the plurality of business parameters with a pre-set first parameter configuration table; and determining a first rule in a second parameter configuration table based on the merging parameter, and processing the transaction data using the first rule. The present application also relates to a data processing device, a computer storage medium, a computer program product and a clearing system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, in particular to a data processing method and device, a computer storage medium, a computer program product and a clearing system. BACKGROUND

[0002] With the impact of the Internet on traditional payment, the processing mode of transaction data (for example, the clearing mode) has undergone tremendous changes. For example, traditional government pricing is gradually changing to market pricing. The pricing relationship between clearing institutions, acquirers, card issuers and merchants has become diversified due to reasons such as industry, cooperation and local economy.

[0003] With the development of business, more and more merchants need personalized data processing rules (for example, personalized clearing rules), but the amount of parameters related to the personalized data processing rules increases in the form of Cartesian product as the number of accessed merchants and different acquirer card business increases, and the amount of parameters will reach tens of millions. Under such a large parameter scale, for example, after adjusting the clearing rules once and adding new parameters, the background parameters must also be modified synchronously, which is prone to parameter recording errors and update errors and other operation risks of operation personnel.

[0004] Moreover, when a large number of concurrent transactions are processed simultaneously, the massive parameter matching is prone to performance problems, such as a sharp increase in CPU usage, which drags down the efficiency of all transaction processing. SUMMARY

[0005] According to one aspect of the present application, a data processing method is provided, the method comprising: receiving transaction data; parsing the transaction data to obtain a plurality of business parameters; determining a merged parameter according to a comparison of the plurality of business parameters with a pre-set first parameter configuration table; and determining a first rule in a second parameter configuration table based on the merged parameter, and processing the transaction data using the first rule.

[0006] As a supplement or alternative to the above-mentioned scheme, in the above-mentioned method, the business parameters are data processing factors adaptively grouped according to data processing rules.

[0007] As a supplement or alternative to the above-mentioned scheme, in the above-mentioned method, the first parameter configuration table includes a plurality of parameters scattered in dimensions.

[0008] As a supplement or alternative to the above-mentioned scheme, in the above-mentioned method, the first parameter configuration table includes merchants, acquirers and card issuers.

[0009] As a supplement or alternative to the above scheme, in the above method, determining the merging parameter according to the comparison of the plurality of service parameters with the pre-set first-level parameter configuration table comprises: determining the merging parameter according to the similarity of the plurality of service parameters with parameters in the first-level parameter configuration table.

[0010] As a supplement or alternative to the above scheme, in the above method, determining the merging parameter according to the comparison of the plurality of service parameters with the pre-set first-level parameter configuration table comprises: determining the merging parameter as a matched parameter set when a part of the plurality of service parameters matches a corresponding parameter set in the first-level parameter configuration table.

[0011] As a supplement or alternative to the above scheme, in the above method, determining the first rule in the second-level parameter configuration table based on the merging parameter comprises: performing parameter matching in a multi-level cache using the merging parameter to determine a corresponding second-level parameter set in the second-level parameter configuration table; and determining the first rule according to the matching of at least a part of the plurality of service parameters with the second-level parameter set.

[0012] As a supplement or alternative to the above scheme, in the above method, the multi-level cache comprises a first-level cache, a second-level cache and a third-level cache, wherein a first part of the second-level parameter configuration table is stored in the first-level cache, a second part of the second-level parameter configuration table is stored in the second-level cache, and the entire second-level parameter configuration table is stored in the third-level cache.

[0013] As a supplement or alternative to the above scheme, in the above method, hot parameters in the second-level parameter configuration table are not wildcard cached in the first-level cache; all parameters in the second-level parameter configuration table are wildcard cached in the second-level cache.

[0014] As a supplement or alternative to the above scheme, in the above method, performing parameter matching in a multi-level cache using the merging parameter comprises: performing parameter matching in the first-level cache using the merging parameter using a binary search-like method; when the matching is unsuccessful, performing parameter matching in the second-level cache in a trie manner; and when the matching is still unsuccessful, finally performing parameter matching in the third-level cache.

[0015] According to another aspect of the present application, there is provided a data processing device, comprising: receiving means for receiving transaction data; parsing means for parsing the transaction data to obtain a plurality of service parameters; determining means for determining a merging parameter according to the comparison of the plurality of service parameters with a pre-set first-level parameter configuration table; and processing means for determining a first rule in a second-level parameter configuration table based on the merging parameter, and processing the transaction data using the first rule.

[0016] As a supplement or alternative to the above solutions, in the above device, the service parameter is a data processing factor after adaptive grouping according to a data processing rule.

[0017] As a supplement or alternative to the above solutions, in the above device, the first-level parameter configuration table includes multiple parameters distributed in dimensions.

[0018] As a supplement or alternative to the above solutions, in the above device, the first-level parameter configuration table includes merchants, acquirers, and card issuers.

[0019] As a supplement or alternative to the above solutions, in the above device, the determination device is configured to determine the merging parameter according to the similarity between the multiple service parameters and the parameters in the first-level parameter configuration table.

[0020] As a supplement or alternative to the above solutions, in the above device, the determination device is configured to determine the matching parameter set as the merging parameter when a part of the multiple service parameters matches the corresponding parameter set in the first-level parameter configuration table.

[0021] As a supplement or alternative to the above solutions, in the above device, the processing device is configured to perform parameter matching in the multi-level cache using the merging parameter to determine the corresponding second-level parameter set in the second-level parameter configuration table, and determine the first rule according to the matching between at least a part of the multiple service parameters and the second-level parameter set.

[0022] As a supplement or alternative to the above solutions, in the above device, the multi-level cache includes a first-level cache, a second-level cache, and a third-level cache, wherein a first part of the second-level parameter configuration table is stored in the first-level cache, a second part of the second-level parameter configuration table is stored in the second-level cache, and the entire second-level parameter configuration table is stored in the third-level cache.

[0023] As a supplement or alternative to the above solutions, in the above device, hot parameters in the second-level parameter configuration table are not cached universally in the first-level cache, and all parameters in the second-level parameter configuration table are cached universally in the second-level cache.

[0024] As a supplement or alternative to the above solutions, in the above device, the processing device is configured to perform parameter matching in the first-level cache using the merging parameter using a binary-like search, perform parameter matching in the second-level cache in a dictionary tree manner when the matching is unsuccessful, and finally perform parameter matching in the third-level cache when the matching is still unsuccessful.

[0025] According to yet another aspect of the present application, there is provided a computer storage medium comprising instructions which, when executed, perform the method as described above.

[0026] According to yet another aspect of the present application, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method as described above.

[0027] According to yet another aspect of the present application, there is provided a clearing system comprising the data processing device as described above.

[0028] The data processing scheme of the embodiments of the present application hierarchically manages parameters (in a first parameter configuration table and a second parameter configuration table), so that the total amount of parameters after grouping is reduced. In addition, the data processing scheme of the embodiments of the present application determines a merging parameter according to a comparison between a plurality of business parameters parsed from transaction data and a first parameter configuration table set in advance, and determines a first rule in a second parameter configuration table based on the merging parameter, so as to process the transaction data. In this way, the data processing scheme not only reduces the amount of parameter input, reduces the risk of manual parameter input error and update error, but also improves the parameter searching efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0029] The above and other objects and advantages of the present application will become more apparent by describing in detail the following embodiments thereof in connection with the accompanying drawings, in which like reference numerals refer to like elements throughout the several views.

[0030] Figure 1 A flowchart of a data processing method according to one embodiment of the present application is shown;

[0031] Figure 2 A structural diagram of a data processing device according to one embodiment of the present application is shown;

[0032] Figure 3 A diagram of a mapping relationship between multi-level parameter configuration tables according to one embodiment of the present application is shown; and

[0033] Figure 4 A diagram of a multi-level cache query scheme of a clearing system according to one embodiment of the present application is shown. DETAILED DESCRIPTION

[0034] In the following, although the exemplary embodiments are described as using a plurality of units to perform the exemplary processes, it should be understood that the exemplary processes can also be performed by one or more modules.

[0035] Also, the control logic of the present application can be embodied on a computer readable medium containing executable program instructions embodied in a non-transitory computer readable medium, which are executed by a processor or the like. Examples of computer readable media include, but are not limited to, ROM, RAM, compact discs, tape, floppy disks, flash memories, smart cards, and optical data storage devices. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable media is stored and implemented in a distributed fashion.

[0036] Unless specifically noted otherwise, as used herein, the term "about" is understood to be within normal tolerances in the art, for example within 2 standard deviations of the mean.

[0037] Hereinafter, the data processing scheme according to the exemplary embodiments of the present application will be described in detail with reference to the accompanying drawings.

[0038] Figure 1 A flowchart of a data processing method 1000 according to one embodiment of the present application is shown. As shown, the data processing method 1000 includes the following steps: Figure 1

[0039] In step S110, transaction data is received;

[0040] In step S120, the transaction data is parsed to obtain a plurality of service parameters;

[0041] In step S130, a merging parameter is determined according to a comparison of the plurality of service parameters with a pre-set first parameter configuration table; and

[0042] In step S140, a first rule is determined in a second parameter configuration table based on the merging parameter, and the transaction data is processed using the first rule.

[0043] In one embodiment, the "transaction data" in step S110 is data related to a transaction to be processed. For example, the transaction data is a successful transaction recorded in a transaction log, which is to be processed (e.g. clearing processing). In the context of the present application, "clearing" refers to calculating transaction costs and transaction fees (commission, distribution, etc.) for each successful transaction recorded in a transaction log, and then aggregating the differences to form receivable or payable amounts according to the clearing objects.

[0044] ​The transaction data can be obtained from merchants, acquirers, card issuers, etc. In one embodiment, step S110 can include receiving transaction information streams from various institutions such as merchants, acquirers, etc.; verifying the legitimacy of the transaction information streams (e.g., whether the information streams are complete, whether the indicated transactions have been successful, etc.); and pre-processing (e.g., format conversion) the qualified transaction information streams to obtain transaction data to be processed (e.g., cleared).

[0045] In step S120, the transaction data is parsed to obtain a plurality of business parameters. In one embodiment, the plurality of business parameters are data processing factors adaptively grouped according to data processing rules. For example, the plurality of business parameters are various elements related to clearing, including merchant, institution 1 (e.g., field 32), institution 2 (e.g., field 33), institution 3 (e.g., field 100), transaction code, etc.

[0046] Next, in step S130, a merging parameter is determined according to a comparison between the parsed plurality of business parameters and a pre-set first parameter configuration table. After the merging parameter is determined, a first rule is determined in a second parameter configuration table based on the merging parameter in step S140. The first parameter configuration table and the second parameter configuration table are pre-set. In one embodiment, the first parameter configuration table is composed of a plurality of different first parameters. The second parameter configuration table is composed of a plurality of merging parameters, a plurality of second parameters, and a plurality of data processing rules. In addition, the settings of the first parameters and the second parameters can be set according to different businesses. For example, parameters with dispersed dimensions and important businesses can be set as first parameters, such as elements of merchants. The merging parameter is determined according to the similarity of the first parameters, and the merging parameter can be used to determine the data processing rules.

[0047] Reference is made to Figure 3 , which shows a schematic diagram of mapping relationships between multi-level parameter configuration tables according to one embodiment of the present application. As shown in Figure 3 , a first parameter configuration table 310 and a second parameter configuration table 320 are shown. The first parameter configuration table 310 includes a plurality of parameter combinations, such as parameter 11 and parameter 21 in the first row, parameter 12 and parameter 22 in the second row, parameter 13 and parameter 32 in the third row, etc. The second parameter configuration table 320 includes a plurality of merging parameters, second parameters, and clearing rules, such as merging parameter 1, parameter m1, and clearing rule 1 in the first row, merging parameter 2, parameter m2, and clearing rule 2 in the second row, etc.

[0048] Continuing to refer to Figure 3The parameters (parameter 11 and parameter 21) in the first row of the first-level parameter configuration table 310 are mapped to merged parameter 1 in the second-level parameter configuration table 320, the parameters (parameter 12 and parameter 22) in the second row of the first-level parameter configuration table 310 are mapped to merged parameter 1 in the second-level parameter configuration table 320, and the parameters (parameter 13 and parameter 23) in the third row of the first-level parameter configuration table 310 are mapped to merged parameter 2 in the second-level parameter configuration table 320. It should be noted that the above parameter configuration tables (i.e., the first-level parameter configuration table 310 and the second-level parameter configuration table 320) are only used as examples, and the specific form of the configuration table can be changed according to actual needs, and is not limited to Figure 3 the specific form listed. For example, each row in the first-level parameter configuration table 310 can include more first-level parameters. For another example, each row in the second-level parameter configuration table 320 can include more second-level parameters.

[0049] In addition, although Figure 3 Although only two-level parameter configuration tables (i.e., the first-level parameter configuration table 310 and the second-level parameter configuration table 320) are shown in the above embodiment, those skilled in the art can understand that more levels of parameter configuration tables can be set according to needs, and are not limited to two levels.

[0050] In one embodiment, the first-level parameter configuration table in step S130 includes multiple parameters distributed in dimensions. For example, the first-level parameter configuration table includes merchants, acquirers, and card issuers.

[0051] In one embodiment, step S130 can include determining the merged parameter according to the similarity between the multiple business parameters and the parameters in the first-level parameter configuration table. For example, when a part of the multiple business parameters match a corresponding parameter set in the first-level parameter configuration table, the matched parameter set is determined as the merged parameter.

[0052] In this way, by hierarchically managing parameters, the total amount of parameters can be reduced to one percent or even one thousandth, and the amount of parameter entry can be reduced. In this way, not only can the risk of manual parameter entry errors and update errors be reduced, but the quality of external services can be improved. Moreover, the complexity of operation and maintenance is reduced.

[0053] Moreover, the data processing method 1000 determines a merged parameter according to the comparison between the multiple business parameters parsed from the transaction data and the first-level parameter configuration table, and determines a first rule in a second-level parameter configuration table based on the merged parameter, so as to process the transaction data. In this way, the data processing method 1000 is also conducive to the acquisition of subsequent data processing rules.

[0054] In one embodiment, step S140 comprises: performing parameter matching in the multi-level cache using the merging parameter, determining a corresponding second parameter set in the second parameter configuration table; and determining the first rule according to matching of at least part of the plurality of service parameters with the second parameter set.

[0055] In one embodiment, the multi-level cache can comprise a first level cache, a second level cache and a third level cache, wherein a first part of the second parameter configuration table is stored in the first level cache, a second part of the second parameter configuration table is stored in the second level cache, and the entire second parameter configuration table is stored in the third level cache. By employing a multi-level cache scheme, parameter lookup efficiency and application processing capability can be improved under limited hardware conditions and in high concurrency transactions. For example, the first level cache is a hotspot parameter cache, the second level cache is a non-hotspot and wildcard cache, and the third level cache is a redis full-amount parameter cache. For example, hotspot parameter non-wildcard caching in the second parameter configuration table is performed in the first level cache; and wildcard caching of all parameters in the second parameter configuration table is performed in the second level cache.

[0056] In one practical application, a data processing device (such as a clearing engine) accesses the first level, second level and third level caches in sequence. For example, first, parameter matching is performed in the first level cache using the merging parameter using a binary search-like method; when no match is found, parameter matching is performed in the second level cache in a dictionary tree manner; and when no match is still found, finally, parameter matching is performed in the third level cache.

[0057] Reference Figure 4 , which shows a multi-level cache query scheme of a clearing system according to one embodiment of the present application. As shown in FIG. 1, the multi-level cache query scheme comprises a merging parameter, a first level cache, a second level cache and a third level cache. Figure 4As shown, the clearing system can include a clearing engine 410, a first level cache 420, and a second level cache 430, wherein the first level cache 420 and the second level cache 430 are both located locally (hence, the two levels of cache can also be referred to as local first level cache 420 and local second level cache 430, respectively). In one embodiment, the clearing engine 410 is a highly parameterized and high performance clearing engine, and a process of clearing a transaction needs to match nearly a hundred parameter tables to complete the entire clearing process, which is also the result of multiple clustering. The query efficiency of each parameter is also efficient, which is the characteristic of the clearing engine. The first level cache 410 is a main factor non-wildcard cache, which is arranged in descending order according to the index field, and can be searched according to the traditional binary search. The second level cache 420 is a wildcard cache for all factors, and since the parameter amount of the second level cache is small, it can be stored and searched in the form of a dictionary tree. The third level cache 430 is a redis full parameter cache, and the parameters in the first level cache 410, the second level cache 420, and the third level cache 430 can be obtained from the parameter library 440.

[0058] Redis (Remote Dictionary Server), i.e., a remote dictionary server, is a key-value storage system. It supports relatively more types of stored values, including string, list, set, zset, and hash. These data types support push / pop, add / remove, intersection, union, and difference, and more rich operations, and these operations are atomic. On this basis, redis supports various ways of sorting. In order to ensure efficiency, data is cached in memory. Redis periodically writes updated data to disk or writes modification operations to an appended record file, and on this basis, it implements master-slave synchronization.

[0059] Those skilled in the art can understand that the aforementioned multi-level cache scheme can not be limited to "three levels of cache", but can be changed according to actual needs, for example, changed to four levels of cache. When the parameter amount is in the order of millions, the multi-level cache scheme can greatly improve the performance of parameter matching and query.

[0060] According to another aspect of the present application, a computer readable storage medium is provided, wherein instructions are stored, when executed by a processor, cause the processor to perform the data processing method 1000 as described above. The computer readable medium referred to in the present application includes various types of computer storage media, which can be any available medium that can be accessed by a general or special purpose computer. For example, the computer readable medium can include RAM, ROM, EPROM, EEPROM, E 2PROM, a register, a hard disk, a removable disk, a CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium of any nature that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. As used herein, a disk typically magnetically reproduces data, while a disc optically reproduces data using a laser. Combinations of the above should also be included within the computer-readable medium. The exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor and the storage medium can reside as discrete components in a user terminal.

[0061] In addition, those skilled in the art can easily understand that the data processing method 1000 provided by one or more embodiments of the present application can be implemented by a computer program. For example, the computer program is contained in a computer program product, and the computer program is executed by a processor to implement the data processing method of one or more embodiments of the present application. For another example, when a computer storage medium (such as a U disk) containing the computer program is connected to a computer, the computer program is executed to implement the data processing method of one or more embodiments of the present application.

[0062] Figure 2 A structural schematic diagram of a data processing device 2000 according to an embodiment of the present application is shown. As shown in the figure, the data processing device 2000 includes a receiving apparatus 210, an analyzing apparatus 220, a determining apparatus 230, and a processing apparatus 240. The receiving apparatus 210 is configured to receive transaction data; the analyzing apparatus 220 is configured to analyze the transaction data to obtain a plurality of service parameters; the determining apparatus 230 is configured to determine a merging parameter according to a comparison between the plurality of service parameters and a pre-set first parameter configuration table; and the processing apparatus 240 is configured to determine a first rule in a second parameter configuration table based on the merging parameter, and process the transaction data using the first rule. Figure 2

[0063] ​In one embodiment, the "transaction data" is the data related to a transaction to be processed. For example, the transaction data is a successful transaction recorded in a transaction log, which is to be processed (e.g. clearing and settlement) by the data processing system. In the context of the present application, "clearing and settlement" refers to the process of calculating the transaction cost and transaction fee (commission, spread, etc.) for each successful transaction recorded in the transaction log, and then aggregating the net difference to form the amount to be received or paid according to the clearing object. The transaction data can be obtained from various sources, such as merchants, acquirers, card issuers, etc. In one embodiment, the receiving device 210 can be configured to receive transaction information streams from various sources, such as merchants, acquirers, etc.; verify the legitimacy of the transaction information streams (e.g. whether the information stream is complete, whether the transaction indicated has been successful, etc.); and pre-process (e.g. format conversion) the transaction information streams that meet the requirements to obtain the transaction data to be processed (e.g. clearing and settlement).

[0064] The parsing device 220 is configured to parse the transaction data to obtain a plurality of business parameters. In one embodiment, the plurality of business parameters are data processing factors that are adaptively grouped according to the data processing rules. For example, the plurality of business parameters are various elements related to clearing and settlement, including merchant, institution 1 (e.g. field 32), institution 2 (e.g. field 33), institution 3 (e.g. field 100), transaction code, etc.

[0065] Next, the determining device 230 determines the merging parameter according to the comparison between the parsed plurality of business parameters and the pre-set first parameter configuration table. After determining the merging parameter, the processing device 240 determines the first rule in the second parameter configuration table based on the merging parameter. The first parameter configuration table and the second parameter configuration table are pre-set. In one embodiment, the first parameter configuration table is composed of a plurality of different first parameters. The second parameter configuration table is composed of a plurality of merging parameters, a plurality of second parameters, and a plurality of data processing rules. In addition, the settings of the first parameters and the second parameters can be set according to different businesses. For example, parameters that are dimensionally dispersed and important to the business can be set as first parameters, such as the element of merchant. The merging parameter is determined according to the similarity of the first parameters, and the merging parameter can be used to determine the data processing rule.

[0066] Reference is made to FIG. 3, which shows a schematic diagram of the mapping relationship between the multi-level parameter configuration tables according to one embodiment of the present application. As shown in FIG. 3, the first parameter configuration table 300 is composed of a plurality of different first parameters, such as the element of merchant, the element of institution 1 (e.g. field 32), the element of institution 2 (e.g. field 33), the element of institution 3 (e.g. field 100), the element of transaction code, etc. The second parameter configuration table 310 is composed of a plurality of merging parameters, a plurality of second parameters, and a plurality of data processing rules. In one embodiment, the first parameter configuration table 300 and the second parameter configuration table 310 are pre-set. In one embodiment, the first parameter configuration table 300 is composed of a plurality of different first parameters. The second parameter configuration table 310 is composed of a plurality of merging parameters, a plurality of second parameters, and a plurality of data processing rules. In addition, the settings of the first parameters and the second parameters can be set according to different businesses. For example, parameters that are dimensionally dispersed and important to the business can be set as first parameters, such as the element of merchant. The merging parameter is determined according to the similarity of the first parameters, and the merging parameter can be used to determine the data processing rule. Figure 3 Figure 3 ​As shown, a primary parameter configuration table 310 and a secondary parameter configuration table 320 are shown. The primary parameter configuration table 310 includes a plurality of parameter combinations, such as parameter 11 and parameter 21 in the first row, parameter 12 and parameter 22 in the second row, parameter 13 and parameter 23 in the third row, and so on. The secondary parameter configuration table 320 includes a plurality of merged parameters, secondary parameters, and clearing rules, such as merged parameter 1, parameter m1, and clearing rule 1 in the first row, merged parameter 2, parameter m2, and clearing rule 2 in the second row, and so on.

[0067] With continued reference to Figure 3 The parameters in the first row (parameter 11 and parameter 21) of the primary parameter configuration table 310 are mapped to merged parameter 1 in the secondary parameter configuration table 320, the parameters in the second row (parameter 12 and parameter 22) of the primary parameter configuration table 310 are mapped to merged parameter 1 in the secondary parameter configuration table 320, and the parameters in the third row (parameter 13 and parameter 23) of the primary parameter configuration table 310 are mapped to merged parameter 2 in the secondary parameter configuration table 320. It should be noted that the above parameter configuration tables (i.e., the primary parameter configuration table 310 and the secondary parameter configuration table 320) are merely used as examples, and the specific form of the configuration tables can be changed according to actual needs, and is not limited to Figure 3 the specific form listed above. For example, each row in the primary parameter configuration table 310 can include more primary parameters. For another example, each row in the secondary parameter configuration table 320 can include more secondary parameters.

[0068] In addition, although Figure 3 only two-level parameter configuration tables (i.e., the primary parameter configuration table 310 and the secondary parameter configuration table 320) are shown in the above embodiment, those skilled in the art can understand that more levels of parameter configuration tables can be set according to needs, and are not limited to two levels.

[0069] In one embodiment, the primary parameter configuration table includes a plurality of parameters that are dimensionally dispersed. For example, the primary parameter configuration table includes merchants, acquirers, and card issuers.

[0070] In one embodiment, the determining device 230 is configured to determine the merged parameter according to the similarity between the plurality of business parameters and the parameters in the primary parameter configuration table. For example, the determining device 230 is configured to determine a matched set of parameters as the merged parameter when a portion of the plurality of business parameters matches a corresponding set of parameters in the primary parameter configuration table.

[0071] In this way, by hierarchically managing the parameters, the total number of parameters can be reduced to one percent or even one thousandth, and the amount of parameter entry is reduced. This not only reduces the risk of manual parameter entry errors and update errors, and improves the quality of external services. Moreover, the complexity of operation and maintenance is reduced.

[0072] Further, the data processing device 2000 determines the merging parameter according to comparison between the plurality of business parameters parsed from the transaction data and the first level parameter configuration table, and determines the first rule in the second level parameter configuration table based on the merging parameter, so as to process (e.g., to perform clearing) the transaction data. In this way, the data processing device 2000 is also conducive to obtaining subsequent data processing rules (e.g., clearing rules).

[0073] In one embodiment, the processing apparatus 240 is configured to: perform parameter matching in a multi-level cache using the merging parameter, to determine a corresponding second level parameter set in the second level parameter configuration table; and determine the first rule according to matching between at least part of the plurality of business parameters and the second level parameter set.

[0074] In one embodiment, the multi-level cache can include a first level cache, a second level cache, and a third level cache, wherein a first part of the second level parameter configuration table is stored in the first level cache, a second part of the second level parameter configuration table is stored in the second level cache, and the entire second level parameter configuration table is stored in the third level cache. By adopting a multi-level cache scheme, the parameter lookup efficiency and application processing capability can be improved under limited hardware conditions and in high concurrency transactions. For example, the first level cache is a hot parameter cache, the second level cache is a non-hot and wildcard cache, and the third level cache is a redis full parameter cache. For example, in the first level cache, non-wildcard caching of hot parameters in the second level parameter configuration table is performed; in the second level cache, wildcard caching of all parameters in the second level parameter configuration table is performed.

[0075] In one practical application, the processing apparatus 240 is configured to access the first level, the second level, and the third level cache in sequence. For example, the processing apparatus 240 is configured to first perform parameter matching in the first level cache using the merging parameter using a binary search-like method; when the matching is unsuccessful, perform parameter matching in the second level cache in a dictionary tree manner; and when the matching is still unsuccessful, finally perform parameter matching in the third level cache.

[0076] In one or more embodiments, the above-mentioned data processing device 2000 can be included in a clearing system.

[0077] The data processing process (i.e., the clearing process) will be further introduced below in combination with a clearing engine.

[0078] The expansion of transaction channels brings about explosive growth of transaction information flow, and clearing information is no longer a fixed element of traditional clearing. For example, in an embodiment of the present application, transaction data includes a transaction primary key segment, a transaction basic information segment, and a transaction additional information segment. In order to meet the individualized needs of member institutions, we obtain clearing information from more additional fields. For example, in the above embodiment, the transaction additional information segment includes a first field (e.g., a general segment), a second field (e.g., a points segment), a third field (e.g., a coupon), and the like.

[0079] The transaction basic information segment includes a business type, a merchant code, an acquirer code, and the like. Generally, in order to comply with technical specifications, the business type provided externally cannot be changed. In order to provide better service quality, the business type can be virtualized, for example, by extending a principal business into multiple sub-principal businesses (e.g., principal 1, principal 2, and the like), so as to meet the individualized clearing needs of various institutions.

[0080] In addition, the clearing engine is a highly parameterized and high-performance clearing engine. Each clearing party in the clearing process is a member institution, i.e., a real role. Often, these real roles need to be mapped. Therefore, a virtual role can be introduced in the clearing process, and a relationship between the virtual role and the real role is established.

[0081] In the clearing process of the principal multi-party, the institution enters corresponding parameters according to the parameter hierarchical management method, and the background parameter quantity will be greatly reduced, and the operation risk of the operation and maintenance personnel can be controlled to the minimum. Next, the clearing engine divides multiple levels of cache according to the parameter rules, and provides an efficient query scheme for subsequent transaction clearing; then, according to the business rules, the transaction information flow with the extension field is obtained, and various elements related to clearing are parsed. According to the transaction attribute, the clearing algorithm and the clearing bitmap of the principal are queried in the multi-level cache; then, according to the principal algorithm and each clearing bitmap, the principal clearing is performed. For the calculation of the virtual business commission, the same as the foregoing clearing steps, the clearing algorithm and the clearing bitmap of the fee are queried in the multi-level cache according to the transaction attribute; then, according to the fee algorithm and each clearing bitmap, the principal selection and fee clearing are performed. In order to maintain a more flexible clearing party decision relationship, after the principal and each type of fee clearing, the role institution mapping table is matched to obtain the final fund clearing party. In one implementation mode, a detailed extension field can be added, and each role or virtual role information and fee is counted into the field in TLV format. Since the current clearing details are large, the TLV format can control the actual size of each detail as much as possible, and reduce the I / O of subsequent file, summary, and the like.

[0082] The core of the clearing engine is a billing factor, different billing factors are selected according to billing rules to configure business parameters conforming to business rules, so that the clearing engine has universality, the code development is reduced in repeatability, and the configuration steps of the business parameters are simplified. Different from the traditional business types, the virtual business types are added in the scheme, such as multiple principal clearing types, and the principal clearing of multiple modes such as coupons and points can be supported. The subsequent cost clearing can depend on the previous principal, or can depend on the personalized principal mode.

[0083] To sum up, the data processing scheme of the embodiment of the present application performs hierarchical management on parameters (in the first-level parameter configuration table and the second-level parameter configuration table), so that the total amount of parameters after grouping is reduced. In addition, according to the comparison between the plurality of business parameters parsed from the transaction data and the first-level parameter configuration table, the data processing scheme of the embodiment of the present application determines the merged parameters and determines the first rule in the second-level parameter configuration table based on the merged parameters, so as to process the transaction data. In this way, the data processing scheme not only reduces the parameter input amount, reduces the risk of manual parameter input error and update error, but also improves the parameter searching efficiency.

[0084] The above merely describes specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Those skilled in the art can think of other feasible changes or replacements according to the technical range disclosed in the present application, and such changes or replacements are all covered in the protection scope of the present application. The embodiments and features in the embodiments can be combined with each other without conflict. The protection scope of the present application is subject to the description of the claims.

Claims

1. A data processing method, characterized by, The method comprises: receiving transaction data; parsing the transaction data to obtain a plurality of business parameters; determining a merged parameter according to a comparison between the plurality of business parameters and a pre-set first parameter configuration table; and determining a first rule in a second parameter configuration table based on the merged parameter, and processing the transaction data using the first rule.

2. The method of claim 1, wherein, The business parameters are data processing factors adaptively grouped according to a data processing rule.

3. The method of claim 1, wherein, The first parameter configuration table comprises a plurality of parameters scattered in dimensions.

4. The method of claim 3, wherein, The first parameter configuration table comprises a merchant, an acquirer and an issuer.

5. The method of claim 1, wherein, The determining of the merged parameter according to the comparison between the plurality of business parameters and the pre-set first parameter configuration table comprises: determining the merged parameter according to a similarity between the plurality of business parameters and parameters in the first parameter configuration table.

6. The method of claim 5, wherein, The determining of the merged parameter according to the comparison between the plurality of business parameters and the pre-set first parameter configuration table comprises: when a part of the plurality of business parameters matches a corresponding parameter set in the first parameter configuration table, determining the matched parameter set as the merged parameter.

7. The method of claim 1, wherein, The determining of the first rule in the second parameter configuration table based on the merged parameter comprises: performing parameter matching in a multi-level cache using the merged parameter to determine a corresponding second parameter set in the second parameter configuration table; and determining the first rule according to a matching between at least a part of the plurality of business parameters and the second parameter set.

8. The method of claim 7, wherein, The multi-level cache comprises a first-level cache, a second-level cache and a third-level cache, wherein a first part of the second parameter configuration table is stored in the first-level cache, a second part of the second parameter configuration table is stored in the second-level cache, and the entire second parameter configuration table is stored in the third-level cache.

9. The method of claim 8, wherein, Hot parameters in the second parameter configuration table are not cached universally in the first-level cache, and all parameters in the second parameter configuration table are cached universally in the second-level cache.

10. The method of claim 9, wherein, The parameter matching in the multi-level cache using the merged parameter comprises: performing parameter matching in the first-level cache using the merged parameter using a binary search-like method; when the matching is unsuccessful, performing parameter matching in the second-level cache in a dictionary tree manner; and when the matching is still unsuccessful, finally performing parameter matching in the third-level cache. The device comprises:

11. A data processing device, characterized by receiving means for receiving transaction data; parsing means for parsing the transaction data to obtain a plurality of business parameters; determining means for determining a merged parameter according to a comparison between the plurality of business parameters and a pre-set first parameter configuration table; and processing means for determining a first rule in a second parameter configuration table based on the merged parameter, and processing the transaction data using the first rule. The business parameters are data processing factors adaptively grouped according to a data processing rule.

12. The apparatus of claim 11, wherein, The first parameter configuration table comprises a plurality of parameters scattered in dimensions.

13. The apparatus of claim 11, wherein, The first parameter configuration table comprises a merchant, an acquirer and an issuer.

14. The apparatus of claim 13, wherein, The determining means are configured to:

15. The apparatus of claim 11, wherein, ​ According to similarity between the plurality of service parameters and parameters in the primary parameter configuration table, the merging parameter is determined.

16. The apparatus of claim 15, wherein, The determining device is configured to: When a part of the plurality of service parameters matches a corresponding parameter set in the primary parameter configuration table, the matched parameter set is determined as the merging parameter.

17. The apparatus of claim 11, wherein, The processing device is configured to: Perform parameter matching in a multi-level cache using the merging parameter to determine a corresponding secondary parameter set in the secondary parameter configuration table; and According to matching between at least a part of the plurality of service parameters and the secondary parameter set, the first rule is determined.

18. The apparatus of claim 17, wherein, The multi-level cache includes a first-level cache, a second-level cache and a third-level cache, wherein a first part of the secondary parameter configuration table is stored in the first-level cache, a second part of the secondary parameter configuration table is stored in the second-level cache, and the entire secondary parameter configuration table is stored in the third-level cache.

19. The apparatus of claim 18, wherein, Hot parameters in the secondary parameter configuration table are not wildcard cached in the first-level cache, and all parameters in the secondary parameter configuration table are wildcard cached in the second-level cache.

20. The apparatus of claim 19, wherein, The processing device is configured to: Perform parameter matching in the first-level cache using the merging parameter using a binary search-like method; When the matching is unsuccessful, perform parameter matching in the second-level cache in a dictionary tree manner; and When the matching is still unsuccessful, finally perform parameter matching in the third-level cache.

21. A computer storage medium, comprising, The medium includes instructions which, when executed, perform the method of any one of claims 1 to 10.

22. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method of any one of claims 1 to 10.

23. A clearing system characterized by The system includes the data processing device of any one of claims 11 to 20.

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