Credit card data processing method and device, equipment, storage medium and product

Abstract

The invention discloses a credit card data processing method and device, equipment, a storage medium and a product. The invention relates to the technical field of big data. The method comprises the following steps: acquiring credit card data of at least one object; the credit card data of at least one object is updated to a distributed data storage system wide table, the distributed data storage system wide table comprises a real-time updating field column family and a static feature field column family, and the real-time updating field column family is used for storing the credit card data updated in real time; the static feature field column family is used for storing non-real-time updated credit card data; screening the credit card data in the updated wide table of the distributed data storage system to obtain a target object screening result; and under the condition that the target object screening result contains at least one target object, credit card service information is sent to equipment of the target object. According to the technical scheme, the technical effect of improving the accuracy of the credit card service information sending object can be achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of big data technology, and in particular, to a credit card data processing method, device, equipment, storage medium and product. Background Art

[0002] With the development of big data technology, it has been widely used in banking scenarios such as credit cards.

[0003] In the prior art, credit card data is often stored in a data warehouse, and data analysts can use data analysis software such as SAS to call the credit card data in the data warehouse for analysis to complete the screening of target objects. Since the data warehouse is updated once a day, it is impossible to obtain real-time credit card data in a timely manner, and thus it is impossible to accurately screen out target objects through credit card data. Summary of the invention

[0004] The embodiments of the present invention provide a credit card data processing method, device, equipment, storage medium and product to improve the accuracy of screening target objects.

[0005] In a first aspect, an embodiment of the present invention provides a credit card data processing method, comprising:

[0006] Get credit card data for at least one object;

[0007] Updating the credit card data of the at least one object into a distributed data storage system wide table, wherein the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data;

[0008] Filter the credit card data in the updated distributed data storage system wide table to obtain the target object filtering result;

[0009] In a case where at least one target object is included in the target object screening result, credit card business information is sent to a device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

[0010] In a second aspect, an embodiment of the present invention further provides a credit card data processing device, the device comprising:

[0011] A credit card data acquisition module, used to acquire credit card data of at least one object;

[0012] A distributed data storage system wide table update module, used to update the credit card data of the at least one object into a distributed data storage system wide table, wherein the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data;

[0013] A target object screening module, used to screen the credit card data in the updated distributed data storage system wide table to obtain the target object screening result;

[0014] The credit card business information sending module is used to send credit card business information to the device of the target object when at least one target object is included in the target object screening result, wherein the credit card business information is used to remind the target object to process the credit card business.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a credit card data processing method as described in any one of the embodiments of the present invention is implemented.

[0016] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a credit card data processing method as described in any one of the embodiments of the present invention.

[0017] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements a credit card data processing method as described in any one of the embodiments of the present invention.

[0018] In an embodiment of the present invention, by acquiring the credit card data of at least one object, the credit card data of at least one object is updated to a distributed data storage system wide table, the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data; the credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, and then, when the target object screening result contains at least one target object, the credit card business information is sent to the device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business. The above technical scheme realizes the real-time update of credit card data through the distributed data storage system wide table, improves the timeliness of the credit card data in the database, thereby providing reliable data for the target object screening, thereby improving the accuracy of the target object screening, and accurately reaching the credit card business information. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a credit card data processing method provided by an embodiment of the present invention;

[0021] Figure 2 A flowchart of a credit card data processing method provided by an embodiment of the present invention;

[0022] Figure 3 A flowchart of a credit card data processing method provided by an embodiment of the present invention;

[0023] Figure 4 A flowchart of a credit card data processing method provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of the structure of a credit card data processing device provided by an embodiment of the present invention;

[0025] Figure 6 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0027] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0028] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0029] Figure 1 This is a flowchart of a credit card data processing method provided by an embodiment of the present invention. This embodiment is applicable to the screening of credit card business processing objects. The method can be executed by a credit card data processing device. The credit card data processing device can be implemented in the form of hardware and / or software. The credit card data processing device can be configured in a computer terminal, server and other equipment. Figure 1 As shown, the method includes:

[0030] S110. Obtain credit card data of at least one object.

[0031] In the embodiment of the present invention, an object refers to a user who uses a credit card, and the number of objects may be one or more, which is not specifically limited here. Credit card data refers to data related to a credit card user, and credit card data may include one or more of object information, asset information, credit information, and consumer behavior information. Object information may include but is not limited to name, mobile phone number, age, residential address, and mailing address, etc., asset information may include but is not limited to annual income information and real estate information, etc., credit information may include but is not limited to overdue information and credit card bills, etc., and consumer behavior information may include but is not limited to online or offline shopping records, etc.

[0032] For example, the credit card data of one or more credit card users can be obtained by connecting to Kafka and using the Flink or Spark Streaming stream computing framework.

[0033] S120. Update the credit card data of the at least one object to a wide table of a distributed data storage system, wherein the wide table of the distributed data storage system includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the credit card data updated in real time, and the static feature field column family is used to store the credit card data updated in non-real time.

[0034] In the embodiment of the present invention, the distributed data storage system refers to HBase, which is a Key-Value database based on Hadoop. Compared with the large tables of existing SQL databases, the wide tables of the distributed data storage system can flexibly expand fields, overcoming the problem of difficulty in expanding fields. The wide tables of the distributed data storage system include real-time update field column families and static feature field column families, wherein the real-time update field column families are used to store real-time updated credit card data, and the static feature field column families are used to store non-real-time updated credit card data. In addition, the wide tables of the distributed data storage system also have horizontal expansion capabilities and can support TB or PB-level data volumes, so they are suitable for situations where the amount of credit card data is huge.

[0035] Exemplarily, the credit card data updated in real time may be consumption behavior information, etc., and the credit card data updated in non-real time may be object information, etc.

[0036] It should be noted that since the frequency of writing real-time updated credit card data and non-real-time updated credit card data into HBase is different, designing the wide table of the distributed data storage system into real-time updated field column families and static feature field column families can reduce the input and output (IO) of credit card data.

[0037] In some optional embodiments, updating the credit card data of at least one object to a wide table of a distributed data storage system includes: updating the real-time updated credit card data to the real-time updated field column family of the wide table of the distributed data storage system through a data adding component (PUT API) of the distributed data storage system; and updating the non-real-time updated credit card data to the static feature field column family of the wide table of the distributed data storage system through a batch loading component (Bulk Load) of the distributed data storage system.

[0038] It should be noted that through the PUT API, real-time updated credit card data such as consumption behavior information can be updated to the wide table of the distributed data storage system, ensuring the timeliness of the credit card data and thus improving the accuracy of target object screening.

[0039] S130. Screen the credit card data in the updated distributed data storage system wide table to obtain target object screening results.

[0040] In the embodiment of the present invention, the target object screening result refers to the result obtained by screening the credit card data, and may include one or more target objects that meet the screening conditions.

[0041] Specifically, by performing real-time and / or offline screening on the credit card data in the updated distributed data storage system wide table, the credit card data meeting the screening criteria can be obtained, and the target object can be determined based on the object to which the credit card data meeting the screening criteria belongs.

[0042] S140. When at least one target object is included in the target object screening result, send credit card business information to a device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

[0043] In an embodiment of the present invention, after determining the target object, credit card business information can be pushed to the target object's mobile phone, computer or other devices to remind the target object to handle credit card business. The credit card business can be a business such as increasing the credit card limit or credit card consumption discount, which is not specifically limited here.

[0044] In an embodiment of the present invention, by acquiring the credit card data of at least one object, the credit card data of at least one object is updated to a distributed data storage system wide table, the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data; the credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, and then, when the target object screening result contains at least one target object, the credit card business information is sent to the device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business. The above technical scheme realizes the real-time update of credit card data through the distributed data storage system wide table, improves the timeliness of the credit card data in the database, thereby providing reliable data for the target object screening, thereby improving the accuracy of the target object screening, and accurately reaching the credit card business information.

[0045] Figure 2A flowchart of a credit card data processing method provided in an embodiment of the present invention, the method of this embodiment can be combined with the various optional schemes in the credit card data processing method provided in the above embodiments. The credit card data processing method provided in this embodiment is further optimized. Optionally, the credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, including: taking a snapshot of the credit card data in the updated distributed data storage system wide table to obtain credit card offline screening data; and performing distributed offline screening on the credit card offline screening data to obtain the target object screening result.

[0046] like Figure 2 As shown, the method includes:

[0047] S210. Obtain credit card data of at least one object.

[0048] S220. Update the credit card data of at least one object to a wide table of a distributed data storage system, wherein the wide table of the distributed data storage system includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store real-time updated credit card data, and the static feature field column family is used to store non-real-time updated credit card data.

[0049] S230. Take a snapshot of the credit card data in the updated distributed data storage system wide table to obtain credit card offline screening data.

[0050] S240: Perform distributed offline screening on the credit card offline screening data to obtain target object screening results.

[0051] S250. When at least one target object is included in the target object screening result, credit card business information is sent to a device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

[0052] In the embodiment of the present invention, the credit card offline screening data refers to the credit card data selected from the wide table of the distributed data storage system through a snapshot.

[0053] Specifically, by taking a snapshot of the credit card data in the updated distributed data storage system wide table, the data range for offline screening can be locked, thereby obtaining the credit card offline screening data. It should be noted that the snapshot operation only creates a reference to the credit card data in the distributed data storage system wide table, rather than a copy. There is no need to refresh all the credit card data in the distributed data storage system wide table to the disk, which does not affect the online HBase read and write functions, thereby meeting the high throughput requirements of offline screening.

[0054] In an embodiment of the present invention, distributed offline screening is a big data screening method, which can effectively improve the credit card data screening rate.

[0055] In some optional embodiments, distributed offline screening is performed on the credit card offline screening data to obtain a target object screening result, including: based on a mapping code component of a map-reduce programming model and a pre-set first screening strategy script, the credit card offline screening data is screened to obtain an initial object screening result; based on a reduction code component of the map-reduce programming model, the initial object screening result is aggregated to obtain a target object screening result.

[0056] In the embodiment of the present invention, the first screening policy script can be customized by the business personnel according to the credit card business, which is not specifically limited here. For example, the screening policy set in the first screening policy script can be to retain credit card users who have been in business for more than three years and whose consumption amount is more than one thousand.

[0057] Exemplarily, in the mapping phase, the filtering strategy in the first filtering strategy script can be applied through the mapping code component of the map-reduce programming model (MapReduce model) to filter out initial object filtering results that meet the filtering strategy; in the reduction phase, the initial object filtering results can be aggregated through the reduction code component of the MapReduce model to obtain target object filtering results, and the target object filtering results can be written to a storage medium such as a Hadoop distributed file system or a data warehouse.

[0058] In an embodiment of the present invention, a snapshot of the credit card data in the updated distributed data storage system wide table is taken to reference the credit card data in the distributed data storage system wide table instead of copying it. This eliminates the need to refresh all the credit card data in the distributed data storage system wide table to disk, thereby not affecting the online HBase read and write functions, and thereby meeting the high throughput requirements of offline screening.

[0059] Figure 3 A flowchart of a credit card data processing method provided in an embodiment of the present invention, the method of this embodiment can be combined with the various optional schemes in the credit card data processing method provided in the above embodiments. The credit card data processing method provided in this embodiment has been further optimized. Optionally, the credit card data in the updated distributed data storage system wide table is filtered to obtain the target object filtering result, including: obtaining the object identifier of the active account; based on the object identifier of the active account, reading the credit card data corresponding to the object identifier from the updated distributed data storage system wide table; based on the pre-set second filtering policy script, judging whether the credit card data corresponding to the object identifier meets the filtering policy to obtain the target object filtering result.

[0060] like Figure 3 As shown, the method includes:

[0061] S310. Obtain credit card data of at least one object.

[0062] S320. Update the credit card data of at least one object to a wide table of a distributed data storage system, wherein the wide table of the distributed data storage system includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store real-time updated credit card data, and the static feature field column family is used to store non-real-time updated credit card data.

[0063] S330, obtaining the object identifier of the account incurred.

[0064] S340. Based on the object identifier of the account change, read the credit card data corresponding to the object identifier from the updated distributed data storage system wide table.

[0065] S350. Based on a preset second screening policy script, determine whether the credit card data corresponding to the object identifier satisfies the screening policy, and obtain a target object screening result.

[0066] S360. When at least one target object is included in the target object screening result, send credit card business information to the device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

[0067] In the embodiment of the present invention, the object identifier refers to a number that can characterize a credit card user and is unique. For example, the object identifier may be 0000000001. The second screening strategy script may be customized by business personnel according to the credit card business, and is not specifically limited here.

[0068] For example, consumption can be subscribed through Flink or Spark Streaming to obtain the credit card user number of the active account, and then the credit card data corresponding to the credit card user number can be read from the updated distributed data storage system wide table through the credit card user number, and then the credit card data corresponding to the credit card user number can be judged through the filtering policy script to determine whether the credit card data corresponding to the credit card user number meets the filtering policy, and then credit card business information including increase in credit limit or installment payment can be sent to the device of the credit card user who meets the filtering policy.

[0069] In some optional embodiments, the credit card data includes an object identifier; accordingly, after obtaining the credit card data of at least one object, the method further includes: determining a row key of a wide table of the distributed data storage system based on the object identifier.

[0070] In the embodiment of the present invention, the row key can be used to uniquely identify each row of data in Hbase. Specifically, the credit card data of the target object can be quickly located in Hbase through the row key to increase the acquisition speed of the credit card data of the target object.

[0071] Specifically, the object identifier may be hashed modulo to obtain an object identifier modulo result; and the row key of the wide table of the distributed data storage system may be determined based on the object identifier modulo result and the object identifier.

[0072] Exemplarily, the object identifier can be divided by 1024 to obtain the object identifier modulo result, and then the object identifier modulo result and the object identifier are added to obtain the row key of the wide table of the distributed data storage system, so that the physical distribution of the object can be broken up, thereby avoiding hot spot problems.

[0073] In the embodiment of the present invention, by obtaining the object identifier of the account change, and then based on the object identifier of the account change, reading the credit card data corresponding to the object identifier from the updated distributed data storage system wide table, and then judging whether the credit card data corresponding to the object identifier satisfies the screening strategy based on the pre-set second screening strategy script, real-time screening of credit card data is achieved, the real-time nature of the target object screening results is guaranteed, and the accuracy of the target object screening results is improved.

[0074] Figure 4 A flowchart of a credit card data processing method provided in an embodiment of the present invention, the method of this embodiment can be combined with the various optional schemes in the credit card data processing method provided in the above embodiment. The credit card data processing method provided in this embodiment is further optimized. Optionally, the credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, including: inputting the updated credit card data in the distributed data storage system wide table into a pre-trained target object screening model for screening to obtain the target object screening result.

[0075] like Figure 4 As shown, the method includes:

[0076] S410. Obtain credit card data of at least one object.

[0077] S420. Update the credit card data of at least one object to a wide table of a distributed data storage system, wherein the wide table of the distributed data storage system includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store real-time updated credit card data, and the static feature field column family is used to store non-real-time updated credit card data.

[0078] S430: Input the updated credit card data in the wide table of the distributed data storage system into the pre-trained target object screening model for screening to obtain the target object screening result.

[0079] S440. When at least one target object is included in the target object screening result, credit card business information is sent to a device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

[0080] In the embodiment of the present invention, the target object screening model refers to a pre-trained artificial intelligence model. The target object screening model can be obtained by training any machine learning model, which is not specifically limited here.

[0081] Specifically, the training steps of the target object screening model include: obtaining historical credit card data of multiple objects; training an initial decision tree model based on the historical credit card data of multiple objects to obtain a pre-trained decision tree model; and obtaining a trained target object screening model in response to a user's trimming operation on the pre-trained decision tree model.

[0082] Among them, historical credit card data refers to historical data related to credit card users, which may include but is not limited to object information, historical asset information, historical credit information, and historical consumption behavior information.

[0083] In some optional embodiments, after acquiring the historical credit card data of multiple objects, it also includes: taking the credit card data of objects that meet the target object screening conditions in the historical credit card data of the multiple objects as positive samples; taking the credit card data of objects that do not meet the target object screening conditions in the historical credit card data of the multiple objects as negative samples; accordingly, training the initial decision tree model based on the historical credit card data of the multiple objects to obtain a pre-trained decision tree model, including: training the initial decision tree model based on the positive samples and the negative samples to obtain a pre-trained decision tree model.

[0084] For example, taking the invitation to adjust the credit limit scenario as an example, the credit card data of the object who was invited to adjust the credit limit and the adjustment was successful can be selected as a positive sample, and the credit card data of the object who was invited to adjust the credit limit but did not adjust the credit limit can be selected as a negative sample. Then, based on the positive and negative samples, the initial decision tree model is trained to obtain a pre-trained decision tree model, which contains the feature combination conditions that lead to the adjustment of the credit limit and the feature combination conditions that lead to no adjustment of the credit limit. Furthermore, the pre-trained decision tree model can be manually trimmed according to restrictive conditions such as abnormal transactions to obtain a target object screening model with higher screening accuracy.

[0085] In some optional embodiments, during the sample data screening stage, historical credit card data may also be subjected to data cleaning operations such as missing value processing, outlier processing, duplicate value processing, data type conversion, standardization and normalization, and noise data removal to improve the reliability of the training sample data.

[0086] In the embodiment of the present invention, by introducing a target object screening model, compared with a fixed screening strategy, more abundant target objects can be mined, thereby improving the accuracy of the target object screening result.

[0087] Figure 5 A schematic diagram of the structure of a credit card data processing device provided by an embodiment of the present invention. Figure 5 As shown, the device comprises:

[0088] A credit card data acquisition module 510, used to acquire credit card data of at least one object;

[0089] A distributed data storage system wide table update module 520 is used to update the credit card data of the at least one object into a distributed data storage system wide table, wherein the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data;

[0090] A target object screening module 530 is used to screen the credit card data in the updated distributed data storage system wide table to obtain a target object screening result;

[0091] The credit card business information sending module 540 is used to send credit card business information to the device of the target object when at least one target object is included in the target object screening result, wherein the credit card business information is used to remind the target object to process the credit card business.

[0092] In an embodiment of the present invention, by acquiring the credit card data of at least one object, the credit card data of at least one object is updated to a distributed data storage system wide table, the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data; the credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, and then, when the target object screening result contains at least one target object, the credit card business information is sent to the device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business. The above technical scheme realizes the real-time update of credit card data through the distributed data storage system wide table, improves the timeliness of the credit card data in the database, thereby providing reliable data for the target object screening, thereby improving the accuracy of the target object screening, and accurately reaching the credit card business information.

[0093] In some optional implementations, the credit card data includes one or more of object information, asset information, credit information, and consumer behavior information.

[0094] In some optional implementations, the distributed data storage system wide table update module 520 may be specifically used to:

[0095] The real-time updated credit card data is updated to the real-time updated field column family of the wide table of the distributed data storage system through a data adding component of the distributed data storage system;

[0096] The non-real-time updated credit card data is updated to the static feature field column family of the wide table of the distributed data storage system through the batch loading component of the distributed data storage system.

[0097] In some optional implementations, the target object screening module 530 includes:

[0098] The offline screening unit is used to take a snapshot of the credit card data in the updated distributed data storage system wide table to obtain credit card offline screening data; and perform distributed offline screening on the credit card offline screening data to obtain target object screening results.

[0099] In some optional embodiments, the offline screening unit is further used for:

[0100] Based on a mapping code component of a map-reduce programming model and a preset first screening strategy script, the credit card offline screening data is screened to obtain an initial object screening result;

[0101] Based on the reduction code component of the map-reduce programming model, the initial object screening results are aggregated to obtain the target object screening results.

[0102] In some optional implementations, the target object screening module 530 includes:

[0103] The online screening unit is used to obtain the object identifier of the account change; based on the object identifier of the account change, read the credit card data corresponding to the object identifier from the updated distributed data storage system wide table; based on the pre-set second screening strategy script, determine whether the credit card data corresponding to the object identifier meets the screening strategy to obtain the target object screening result.

[0104] In some optional implementations, the target object screening module 530 includes:

[0105] The training model screening unit is used to input the updated credit card data in the wide table of the distributed data storage system into the pre-trained target object screening model for screening to obtain the target object screening result.

[0106] In some optional embodiments, the step of training the target object screening model includes:

[0107] Get historical credit card data for multiple objects;

[0108] Training an initial decision tree model based on the historical credit card data of the multiple objects to obtain a pre-trained decision tree model;

[0109] In response to the user's trimming operation on the pre-trained decision tree model, a trained target object screening model is obtained.

[0110] In some optional embodiments, the step of training the target object screening model specifically further includes:

[0111] The credit card data of the objects that meet the target object screening conditions in the historical credit card data of multiple objects are taken as positive samples;

[0112] The credit card data of the objects that do not meet the target object screening conditions in the historical credit card data of multiple objects are used as negative samples;

[0113] Accordingly, the initial decision tree model is trained based on the historical credit card data of the multiple objects to obtain a pre-trained decision tree model, including:

[0114] Based on the positive samples and the negative samples, an initial decision tree model is trained to obtain a pre-trained decision tree model.

[0115] In some optional embodiments, the credit card data includes an object identifier;

[0116] Accordingly, the credit card data processing device further includes:

[0117] A row key determination module is used to determine the row key of a wide table of a distributed data storage system based on the object identifier.

[0118] In some optional implementations, the row key determination module may also be specifically used to:

[0119] Performing hashing modulo on the object identifier to obtain an object identifier modulo result;

[0120] A row key of a wide table of a distributed data storage system is determined based on the object identifier modulo result and the object identifier.

[0121] The credit card data processing device provided in the embodiment of the present invention can execute the credit card data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0122] Figure 6 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Figure 6 , which shows an electronic device (eg, Figure 6 The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0123] like Figure 6 As shown, the electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An edit / output (I / O) interface 505 is also connected to the bus 504.

[0124] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0125] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0126] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0127] The electronic device provided in the embodiment of the present disclosure and the credit card data processing method provided in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0128] The embodiments of the present disclosure provide a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the credit card data processing method provided in the above embodiments is implemented.

[0129] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0130] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0131] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0132] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0133] Get credit card data for at least one object;

[0134] Updating the credit card data of the at least one object into a distributed data storage system wide table, wherein the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data;

[0135] Filter the credit card data in the updated distributed data storage system wide table to obtain the target object filtering result;

[0136] In a case where at least one target object is included in the target object screening result, credit card business information is sent to a device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

[0137] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0138] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0139] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0140] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0141] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0142] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the credit card data processing method provided in any embodiment of the present application.

[0143] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0144] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A credit card data processing method, characterized in that: include: Get credit card data for at least one object; Updating the credit card data of the at least one object into a distributed data storage system wide table, wherein the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data; Filter the credit card data in the updated distributed data storage system wide table to obtain the target object filtering result; In a case where at least one target object is included in the target object screening result, credit card business information is sent to a device of the target object, wherein the credit card business information is used to remind the target object to process the credit card business.

2. The method according to claim 1, characterized in that The credit card data includes one or more of object information, asset information, credit information and consumption behavior information.

3. The method according to claim 1, characterized in that The updating of the credit card data of the at least one object into the distributed data storage system wide table comprises: The real-time updated credit card data is updated to the real-time updated field column family of the wide table of the distributed data storage system through a data adding component of the distributed data storage system; The non-real-time updated credit card data is updated to the static feature field column family of the wide table of the distributed data storage system through the batch loading component of the distributed data storage system.

4. The method according to claim 1, characterized in that The credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, including: Take a snapshot of the credit card data in the updated distributed data storage system wide table to obtain the credit card offline screening data; Distributed offline screening is performed on the credit card offline screening data to obtain a target object screening result.

5. The method according to claim 4, characterized in that The step of performing distributed offline screening on the credit card offline screening data to obtain a target object screening result includes: Based on a mapping code component of a map-reduce programming model and a preset first screening strategy script, the credit card offline screening data is screened to obtain an initial object screening result; Based on the reduction code component of the map-reduce programming model, the initial object screening results are aggregated to obtain the target object screening results.

6. The method according to claim 1, characterized in that The credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, including: Get the object ID of the account that has been moved; Based on the object identifier of the account change, read the credit card data corresponding to the object identifier from the updated distributed data storage system wide table; Based on a preset second screening policy script, it is determined whether the credit card data corresponding to the object identifier satisfies the screening policy, and a target object screening result is obtained.

7. The method according to claim 1, characterized in that The credit card data in the updated distributed data storage system wide table is screened to obtain the target object screening result, including: The updated credit card data in the wide table of the distributed data storage system is input into the pre-trained target object screening model for screening to obtain the target object screening result.

8. The method according to claim 7, characterized in that The training step of the target object screening model includes: Get historical credit card data for multiple objects; Training an initial decision tree model based on the historical credit card data of the multiple objects to obtain a pre-trained decision tree model; In response to the user's trimming operation on the pre-trained decision tree model, a trained target object screening model is obtained.

9. The method according to claim 8, characterized in that After obtaining the historical credit card data of the plurality of objects, the method further includes: The credit card data of the objects that meet the target object screening conditions in the historical credit card data of multiple objects are taken as positive samples; The credit card data of the objects that do not meet the target object screening conditions in the historical credit card data of multiple objects are used as negative samples; Accordingly, the initial decision tree model is trained based on the historical credit card data of the multiple objects to obtain a pre-trained decision tree model, including: Based on the positive samples and the negative samples, an initial decision tree model is trained to obtain a pre-trained decision tree model.

10. The method according to claim 1, characterized in that The credit card data includes an object identifier; Accordingly, after obtaining the credit card data of at least one object, the method further includes: A row key of a wide table of a distributed data storage system is determined based on the object identifier.

11. The method according to claim 10, characterized in that Determining the row key of the wide table of the distributed data storage system based on the object identifier includes: Performing hashing modulo on the object identifier to obtain an object identifier modulo result; A row key of a wide table of a distributed data storage system is determined based on the object identifier modulo result and the object identifier.

12. A credit card data processing device, characterized in that: include: A credit card data acquisition module, used to acquire credit card data of at least one object; A distributed data storage system wide table update module, used to update the credit card data of the at least one object into a distributed data storage system wide table, wherein the distributed data storage system wide table includes a real-time update field column family and a static feature field column family, wherein the real-time update field column family is used to store the real-time updated credit card data, and the static feature field column family is used to store the non-real-time updated credit card data; A target object screening module, used to screen the credit card data in the updated distributed data storage system wide table to obtain the target object screening result; The credit card business information sending module is used to send credit card business information to the device of the target object when at least one target object is included in the target object screening result, wherein the credit card business information is used to remind the target object to process the credit card business.

13. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the credit card data processing method as claimed in any one of claims 1 to 11 when executing the computer program.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the credit card data processing method as described in any one of claims 1-11 is implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the credit card data processing method according to any one of claims 1 to 11.

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