Business data processing method, server and storage medium

Abstract

The invention discloses a business data processing method, a server and a storage medium, and relates to the technical field of data processing.The business data processing method comprises the steps that a multi-dimensional aggregation table is created in response to a creation request of the multi-dimensional aggregation table, dimension combination keys and stock occupation amounts corresponding to the dimension combination keys are stored in the multi-dimension aggregation table, and the dimension combination keys are formed by combining a plurality of dimensions; periodically receiving an incremental transaction value sent by the user equipment, and updating the incremental transaction table by adopting the incremental transaction value; in response to an online amount check request, combining the multi-dimensional aggregation table and the incremental transaction table to obtain a real-time stock occupation amount; updating the stock occupancy amount in the multi-dimensional aggregation table by adopting the real-time stock occupancy amount to obtain an updated multi-dimensional aggregation table, and performing limit application verification on the user equipment based on the multi-dimensional aggregation table; the technical problem of low quota application processing efficiency is solved, and the quota application processing efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a business data processing method, a server, and a storage medium. Background Art

[0002] In the credit limit management scenario of group enterprises, with the development of diversified business, the credit limit application processing of group members faces complex data management challenges.

[0003] In related technologies, group members' business data is typically stored in multiple, interconnected business detail tables, organized by different dimensions, such as customer type, product category, and institution type. During the credit application process for group members, it's necessary to query the database in real time for all group members' business detail data across different dimensions and calculate the amount of credit used. This amount is then used to determine whether the group member meets the credit application requirements.

[0004] However, due to the multiple dimensions of group credit control and the large business volume, when applying for credit, it is necessary to query the business detail data under the corresponding dimensions from different business detail tables respectively, resulting in reduced efficiency in credit application processing. Summary of the Invention

[0005] The main purpose of this application is to provide a business data processing method, server and storage medium, aiming to solve the technical problem of low efficiency in credit application processing.

[0006] To achieve the above objectives, the present application proposes a business data processing method, which is applied to a business data processing server, comprising:

[0007] In response to a request to create a multi-dimensional aggregate table, a multi-dimensional aggregate table is created, wherein the multi-dimensional aggregate table stores a dimension combination key and an inventory usage amount corresponding to the dimension combination key, where the dimension combination key is composed of multiple dimensions;

[0008] Periodically receive incremental transaction values ​​sent by user devices and use the incremental transaction values ​​to update the incremental transaction table, where the table structure of the incremental transaction table is the same as the multi-dimensional aggregation table;

[0009] In response to online quota check requests, the multi-dimensional aggregation table and incremental transaction table are merged to obtain the real-time inventory usage amount;

[0010] The real-time stock occupancy amount is used to update the stock occupancy amount in the multi-dimensional aggregation table to obtain an updated multi-dimensional aggregation table, so as to verify the quota application of the user device based on the multi-dimensional aggregation table.

[0011] In one embodiment, in response to an online quota check request, the multi-dimensional aggregation table and the incremental transaction table are merged to obtain the real-time inventory usage amount, including:

[0012] Responding to an online credit check request, obtaining check information;

[0013] Based on the inspection information, search for a matching first dimension composite key from the multi-dimensional aggregation table, and search for a matching second dimension composite key from the incremental transaction table;

[0014] Get the inventory usage associated with the first dimension key combination, and get the incremental transaction value associated with the second dimension key combination;

[0015] The real-time inventory occupancy amount is obtained based on the sum of the inventory occupancy amount and the incremental transaction value.

[0016] In one embodiment, the inspection information includes inspection object information and inspection business field information. According to the inspection information, searching for a matching first dimension composite key from the multi-dimensional aggregation table includes:

[0017] Determine a first cosine similarity between the inspection object information and the preset inspection object information in each dimension combination key in the multi-dimensional aggregation table, and obtain target preset inspection object information that matches the inspection object information based on the first cosine similarity;

[0018] and determining a second cosine similarity between the inspection business field information and the preset inspection business field information in each dimension combination key in the multi-dimensional aggregation table, and obtaining target preset inspection business field information that matches the inspection business field information based on the second cosine similarity;

[0019] According to the target preset inspection object information and the target preset inspection business field information, the first dimension combination key is obtained.

[0020] In one embodiment, after using the real-time inventory occupancy amount to update the inventory occupancy amount in the multi-dimensional aggregation table and obtaining the updated multi-dimensional aggregation table, the following steps are further included:

[0021] In response to a credit application request sent by a user device, the credit application request is parsed to obtain application information;

[0022] Based on the application information, search for the matching target dimension composite key from the multi-dimensional aggregation table;

[0023] Get the target inventory usage associated with the target dimension key combination;

[0024] Based on the target inventory occupancy amount, the quota application of user equipment is verified;

[0025] If the credit application is verified, the corresponding application credit will be sent to the user's device.

[0026] In one embodiment, the application information includes application object information and application business field information. According to the application information, searching for a matching target dimension composite key from the multi-dimensional aggregation table includes:

[0027] Based on the application object information and application business field information, search for the matching target dimension composite key from the multi-dimensional aggregation table.

[0028] In one embodiment, searching for a matching target dimension composite key from a multi-dimensional aggregation table based on application object information and application business field information includes:

[0029] Determine the third cosine similarity between the applicant information and the preset applicant information in each dimension composite key in the multi-dimensional aggregation table, and obtain target preset applicant information that matches the applicant information based on the third cosine similarity;

[0030] and determining the fourth cosine similarity between the application business field information and the preset application business field information in each dimension combination key in the multi-dimensional aggregation table, and obtaining target preset application business field information that matches the application business field information based on the fourth cosine similarity;

[0031] The target dimension composite key is obtained according to the target preset application object information and the target preset application business field information.

[0032] In one embodiment, the application object information includes the group enterprise number and the member enterprise number, and the application business field information includes the organization name and the business type name. The group enterprise number, the member enterprise number, the organization name, and the business type name respectively represent different dimensions and together constitute a dimension composite key. Based on the application information, searching for a matching target dimension composite key from the multi-dimensional aggregation table includes:

[0033] Based on the group enterprise number, member enterprise number, organization name, and business type name, a matching dimension composite key is searched from the multi-dimensional aggregation table and used as the target dimension composite key.

[0034] In one embodiment, the application information further includes an application quota. Based on the target inventory usage amount, verifying the quota application of the user device includes:

[0035] The total occupancy value of the user equipment is obtained based on the sum of the target inventory occupancy amount and the application amount;

[0036] If the total occupancy value is less than or equal to the credit limit of the user corresponding to the user device, the application verification is determined to be successful;

[0037] Alternatively, if the total occupancy value is greater than the credit limit of the user corresponding to the user device, it is determined that the application verification has failed.

[0038] In addition, to achieve the above objectives, the present application also proposes a business data processing server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the business data processing method as described above.

[0039] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the business data processing method as described above are implemented.

[0040] This application responds to a request to create a multi-dimensional aggregation table and creates a multi-dimensional aggregation table, which stores dimension combination keys composed of multiple dimensions and corresponding inventory occupancy amounts. By periodically receiving the gain transaction value sent by the user device, and using the gain transaction value to update the incremental transaction table; during the online credit check, the data in the multi-dimensional aggregation table and the incremental transaction table are merged to obtain the real-time inventory occupancy amount; finally, the real-time inventory occupancy amount is used to update the inventory occupancy amount in the multi-dimensional aggregation table. Therefore, compared to related technologies, when verifying the credit application of the user device, this application only needs to verify based on the data in the multi-dimensional aggregation table, without the need to query data from multiple business detail tables, thereby improving the efficiency of credit application processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0043] Figure 1 A flowchart of the first embodiment of the business data processing method of this application is provided;

[0044] Figure 2 This is a detailed flowchart of step S30 of the first embodiment of the business data processing method of this application;

[0045] Figure 3 This is a detailed flowchart of step S32 of the first embodiment of the business data processing method of this application;

[0046] Figure 4 A flowchart of the second embodiment of the business data processing method of this application is provided;

[0047] Figure 5 This is a detailed flowchart of step S121 of the second embodiment of the business data processing method of this application;

[0048] Figure 6 This is a detailed flowchart of step S140 of the second embodiment of the business data processing method of this application;

[0049] Figure 7 This is a schematic diagram of the first interaction between the user equipment, the service processing device and the service processing server of the present application;

[0050] Figure 8 This is a schematic diagram of the second interaction between the user equipment, the service processing device and the service processing server of this application;

[0051] Figure 9 This is a structural diagram of the business data processing server involved in the business data processing method of this application.

[0052] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] In the area of ​​credit line management and control for group-type enterprises, with the diversification of corporate businesses and the increasing complexity of organizational structures, the credit line application and approval processes of group members are facing significant data management challenges. Traditional credit line management and control systems are usually designed based on a single entity or simple dimension, making it difficult to adapt to the dynamic control needs across members, businesses, and regions in group operations. Specifically, credit line applications from group members require a comprehensive assessment of their capital utilization across different business dimensions, such as business data split by customer type such as core enterprises, upstream and downstream supply chain enterprises, product categories such as working capital loans, trade financing, project loans, and institutional levels such as headquarters, branches, and subsidiaries. These data are usually stored in multiple related business detail tables, and the table structure and data granularity are highly heterogeneous due to differences in business scenarios.

[0056] Under the existing technical framework, credit application processing must follow the following process: When a group member initiates a credit application, the system must perform a real-time cross-table query from the database to obtain the member's detailed business data in all relevant dimensions, such as historical credit records, current occupied amounts, risk exposure, etc., and determine its comprehensive funds occupation status through complex aggregation calculations. Finally, it will determine whether the application is approved based on the preset credit control rules.

[0057] Due to the multiple dimensions of group credit control and the large business volume, when applying for credit, it is necessary to query the business detail data under the corresponding dimensions from different business detail tables, resulting in reduced efficiency in credit application processing.

[0058] In response to the above problems, the present application proposes a business data processing method, the main technical solutions of which include: in response to a request to create a multi-dimensional aggregation table, creating a multi-dimensional aggregation table, wherein the multi-dimensional aggregation table stores dimension combination keys and the inventory occupancy corresponding to the dimension combination keys, and the dimension combination keys are composed of multiple dimensions; regularly receiving incremental transaction values ​​sent by user devices, and using incremental transaction values ​​to update the incremental transaction table, wherein the table structure of the incremental transaction table is the same as that of the multi-dimensional aggregation table; in response to an online quota check request, merging the multi-dimensional aggregation table and the incremental transaction table to obtain a real-time inventory occupancy; using the real-time inventory occupancy to update the inventory occupancy in the multi-dimensional aggregation table to obtain an updated multi-dimensional aggregation table, so as to verify the quota application of the user device based on the multi-dimensional aggregation table.

[0059] In response to the request to create a multi-dimensional aggregation table, a multi-dimensional aggregation table is created, which stores dimension combination keys composed of multiple dimensions and corresponding inventory occupancy amounts. By periodically receiving the gain transaction value sent by the user device, and using the gain transaction value to update the incremental transaction table; during the online credit check, the data in the multi-dimensional aggregation table and the incremental transaction table are merged to obtain the real-time inventory occupancy amount; finally, the real-time inventory occupancy amount is used to update the inventory occupancy amount in the multi-dimensional aggregation table. Therefore, compared to related technologies, when verifying the credit application of the user device, the present application only needs to verify based on the data in the multi-dimensional aggregation table, without the need to query data from multiple business detail tables, thereby improving the efficiency of credit application processing.

[0060] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the above functions, a business data processing server, etc. The following uses a business data processing server as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, an embodiment of the present application provides a service data processing method, which is applied to a service data processing server, and the service data processing server is connected to a user device. Figure 1 and Figure 7 , Figure 1 This is a flowchart of the first embodiment of the business data processing method of this application. Figure 7 Schematic diagram of a first interaction between a user device, a service processing device, and a service processing server; in this embodiment, the service data processing method includes steps S10 to S40:

[0062] Step S10: In response to a request to create a multi-dimensional aggregation table, a multi-dimensional aggregation table is created, wherein the multi-dimensional aggregation table stores dimension combination keys and inventory occupancy amounts corresponding to the dimension combination keys, and the dimension combination keys are composed of multiple dimensions.

[0063] A multidimensional aggregation table is a database table that stores multidimensional composite keys and their associated inventory usage. This table is used to quickly query the inventory usage corresponding to multidimensional conditions. This multidimensional aggregation table can include multiple sets of dimension composite keys, each with a corresponding inventory usage.

[0064] The dimension combination key is composed of multiple dimensions and is used to uniquely identify an aggregate dimension. These dimensions can be group enterprise number, member enterprise number, organization, business type, and inventory usage amount.

[0065] Among them, the inventory occupancy amount refers to the amount or resource used historically, reflecting the real-time occupancy status of the system under a certain dimension combination.

[0066] For example, the multi-dimensional aggregation table is shown in Table 1:

[0067] Table 1

[0068] Group Enterprise Number Member Enterprise Number mechanism Business Types Inventory occupied C001 A x a 10w C001 A z a 20w C001 C x b 30w C001 C y b 20w C001 D w c 30w

[0069] For example, taking Table 1 above as an example, the dimension combination key is obtained by splicing multiple dimensions (group enterprise number, member enterprise number, organization, business type). There are 5 groups of dimension combination keys in Table 1 above, and each group of dimension combination keys has a corresponding inventory occupancy amount.

[0070] In one feasible implementation, MySQL can be used to create a multi-dimensional aggregation table, define dimension fields and inventory usage fields, and create a unique index for each dimension key combination to ensure the uniqueness of the dimension key combination. The corresponding dimension key combination can then be queried based on this unique index.

[0071] In another feasible implementation, Redis or HBase is used to hash the dimension combination key into a unique string as the Key, and the inventory occupancy amount as the Value, thereby realizing distributed key-value storage. Distributed key-value storage provides millisecond-level response and is suitable for high-concurrency reading and writing.

[0072] Step S20: regularly receive the incremental transaction value sent by the user equipment, and use the incremental transaction value to update the incremental transaction table, wherein the table structure of the incremental transaction table is the same as the multi-dimensional aggregation table.

[0073] Among them, the incremental transaction value refers to the single transaction or batch transaction data submitted by the user device, which needs to be merged into the existing amount to update the existing amount.

[0074] Among them, the incremental transaction table is a table that temporarily stores incremental transaction values. The table structure of the incremental transaction table is basically the same as the table structure of the multi-dimensional aggregation table, and is used to isolate incremental transaction values ​​from inventory occupancy.

[0075] In one feasible implementation, incremental transactions can be received periodically through Kafka, consumed in batches using Flink, and written to the incremental table. Specifically, configure Kafka consumer parameters and create a Flink Kafka source. The regularly received incremental transaction values ​​are then transformed and filtered; batch consumption is achieved using window functions or triggers; and the processed incremental transaction values ​​are written to the incremental transaction table, achieving the goal of regularly updating the incremental transaction values.

[0076] Step S30: In response to the online quota check request, the multi-dimensional aggregation table and the incremental transaction table are merged to obtain the real-time inventory usage amount.

[0077] The online credit check request can be a real-time check triggered by the business data processing server when a user initiates a credit application, requiring the merging of existing and incremental data to verify the available credit. The online credit check request can also be triggered on a scheduled basis, for example, sending an online credit check request to the business data processing server every 15 minutes so that the business data processing server can respond to the online credit check request.

[0078] Among them, the real-time inventory occupancy amount refers to the latest occupancy amount after combining the inventory occupancy amount and incremental transactions, which is used for real-time quota verification.

[0079] In one feasible implementation, in response to an online quota check request, the multi-dimensional aggregation table and the incremental transaction table can be loaded into a Redis in-memory database, and a Lua script can be used to merge and calculate the multi-dimensional aggregation table and the incremental transaction table to obtain the real-time inventory occupancy amount. Specifically, the Lua script can map and match the fields of the same dimension in the multi-dimensional aggregation table and the incremental transaction table, search for two dimension combination keys that match all dimension fields in the multi-dimensional aggregation table and the incremental transaction table, and then add the incremental transaction value associated with the matching dimension combination key in the incremental transaction table to the incremental transaction value associated with the matching dimension combination key in the multi-dimensional aggregation table, thereby obtaining the real-time inventory occupancy amount.

[0080] For example, if we search the multi-dimensional aggregate table for the dimension key combination (C001, A, x, a, 10w), the corresponding inventory usage is 10w. At the same time, we search the incremental transaction table for a dimension key combination that matches (C001, A, x, a, 10w). In the incremental transaction table, the incremental transaction value corresponding to this dimension key combination is 1w. Therefore, the real-time inventory usage is: 10w + 1w = 11w.

[0081] Step S40: Use the real-time stock occupancy amount to update the stock occupancy amount in the multi-dimensional aggregation table to obtain an updated multi-dimensional aggregation table, so as to verify the quota application of the user device based on the multi-dimensional aggregation table.

[0082] Among them, credit application verification refers to the business logic that determines whether the user's credit application is approved based on the real-time inventory usage.

[0083] In a feasible implementation, in the process of using real-time inventory occupancy to update the inventory occupancy in the multi-dimensional aggregate table, the inventory occupancy can be updated in batches using database transactions, and optimistic locking can be achieved through version numbers or timestamps to avoid concurrency conflicts.

[0084] In this embodiment, in response to a request to create a multi-dimensional aggregation table, a multi-dimensional aggregation table is created, and the multi-dimensional aggregation table stores dimension combination keys composed of multiple dimensions and the corresponding inventory occupancy amounts. By periodically receiving the gain transaction value sent by the user device, and using the gain transaction value to update the incremental transaction table; during the online credit check, the data in the multi-dimensional aggregation table and the incremental transaction table are merged to obtain the real-time inventory occupancy amount; finally, the real-time inventory occupancy amount is used to update the inventory occupancy amount in the multi-dimensional aggregation table. Therefore, compared to related technologies, when verifying the credit application of the user device, the present application only needs to verify based on the data in the multi-dimensional aggregation table, without the need to query data from multiple business detail tables, thereby improving the efficiency of credit application processing.

[0085] Further, based on the above content, please refer to Figure 2, step S30 includes:

[0086] Step S31: Responding to the online credit limit check request, obtaining check information.

[0087] The check information refers to the set of dimension values ​​carried in the online credit check request, used to determine credit usage in a specific business scenario. For example, the check information may include check object information and check business field information. The check object information may specifically include the group enterprise number and member enterprise number, and the check business field information may specifically include the organization name and business type name.

[0088] In a feasible implementation, the parameters of the HTTP request or RPC call, such as the JSON body, are directly parsed to extract the inspection information. This approach can adapt to scenarios with fixed dimensions.

[0089] In another feasible implementation, a rule engine such as Drools can be used to dynamically parse and obtain inspection information based on predefined rules. This approach can adapt to businesses with dynamically changing dimensions.

[0090] Step S32: Based on the inspection information, searching for a matching first dimension composite key from the multi-dimensional aggregation table, and searching for a matching second dimension composite key from the incremental transaction table.

[0091] The first dimension composite key refers to a unique key matched from a multi-dimensional aggregation table, which is composed of dimension values ​​in the inspection information and is associated with the inventory occupancy amount.

[0092] The second dimension composite key refers to the unique key matched from the incremental transaction table. Its structure is consistent with the first dimension composite key and is associated with the unmerged incremental transaction values.

[0093] In one feasible implementation, the similarity between the inspection information and the data in the multi-dimensional aggregation table can be calculated to search for a matching first dimension combination key from the multi-dimensional aggregation table. Specifically, the inspection information and each dimension combination key of the multi-dimensional aggregation table can be vectorized to obtain an inspection information vector and a dimension combination key vector. The inspection information vector and the dimension combination key vector are used to calculate the similarity, and the matching first dimension combination key is searched based on the similarity. If the inspection information consists of multiple parts, each part can be vectorized and then concatenated to obtain an inspection information vector. At the same time, each dimension in the dimension combination key can be vectorized and then concatenated to obtain a dimension combination key vector. The similarity here can be cosine similarity, Euclidean distance, etc. Similarly, the similarity between the inspection information and the data in the incremental transaction table is calculated to search for a matching second dimension combination key from the incremental transaction table. The specific search method is similar to that for the first dimension combination key and will not be repeated here. Searching for the first dimension combination key and the second dimension combination key by matching based on similarity makes the first dimension combination key and the second dimension combination key more accurate.

[0094] In another feasible implementation, a clustering analysis algorithm can be used to search for matching first-dimension key combinations in the multi-dimensional aggregation table based on the inspection information. Similarly, a clustering analysis algorithm can be used to search for matching second-dimension key combinations in the incremental transaction table. The clustering analysis algorithm used here is not specifically limited and can be any clustering analysis algorithm. Searching for first-dimension key combinations and second-dimension key combinations using a clustering approach can improve the efficiency of searching for first-dimension key combinations and second-dimension key combinations.

[0095] Step S33: Obtain the inventory usage amount associated with the first dimension combination key, and obtain the incremental transaction value associated with the second dimension combination key.

[0096] Since each dimension key combination has a corresponding index identifier, after obtaining the first dimension key combination, the inventory usage associated with the first dimension key combination can be obtained based on the index identifier corresponding to the first dimension key combination. After obtaining the second dimension key combination, the incremental transaction value associated with the second dimension key combination can be obtained based on the index identifier corresponding to the second dimension key combination.

[0097] Step S34: Obtain the real-time inventory occupancy amount based on the sum of the inventory occupancy amount and the incremental transaction value.

[0098] In this embodiment, it is possible to respond to an online quota check request and obtain check information; based on the check information, a matching first dimension combination key is searched from the multi-dimensional aggregation table, and a matching second dimension combination key is searched from the incremental transaction table; finally, the real-time inventory occupancy amount is obtained based on the sum of the inventory occupancy amount associated with the first dimension combination key and the incremental transaction value associated with the second dimension combination key. In this way, the inventory occupancy amount and the incremental transaction value can be merged to achieve accurate statistics of transaction data.

[0099] Furthermore, the check information includes the check object information and the check business field information. Among them, the check object information refers to the user object that currently needs to perform online credit check, which may include the group enterprise number and the member enterprise number. The check business field information refers to the business information that currently needs to perform online credit check, including the organization name and business type name. Please refer to Figure 3 , step S32 includes:

[0100] Step S321 : determining the first cosine similarity between the inspection object information and the preset inspection object information in each dimension combination key in the multi-dimensional aggregation table, and acquiring target preset inspection object information matching the inspection object information based on the first cosine similarity.

[0101] In a feasible implementation, the first sub-cosine similarity between the group enterprise number and the preset group enterprise number in each dimension combination key in the multi-dimensional aggregation table can be determined; and the target preset group enterprise number matching the group enterprise number is obtained based on the first sub-cosine similarity. At the same time, the second sub-cosine similarity between the member enterprise number and the preset member enterprise number in each dimension combination key in the multi-dimensional aggregation table is determined. The target preset member enterprise number matching the member enterprise number is obtained based on the second sub-cosine similarity. The target preset inspection object information includes the target preset group enterprise number and the target preset member enterprise number. The first cosine similarity includes the first sub-cosine similarity and the second sub-cosine similarity. Since the inspection object information can be further subdivided into the group enterprise number and the member enterprise number, the target preset inspection object information is jointly determined by the group enterprise number and the member enterprise number, thereby improving the accuracy of the obtained target preset inspection object information.

[0102] And, in step S322, determine the second cosine similarity between the inspection business field information and the preset inspection business field information in each dimension combination key in the multi-dimensional aggregation table, and obtain the target preset inspection business field information that matches the inspection business field information based on the second cosine similarity.

[0103] In a feasible implementation, the third sub-cosine similarity between the organization name and the preset organization name in each dimension combination key in the multi-dimensional aggregation table can be determined; and the target preset organization name that matches the organization name is obtained based on the third sub-cosine similarity. At the same time, the fourth sub-cosine similarity between the business variety name and the preset business variety name in each dimension combination key in the multi-dimensional aggregation table is determined. The target preset business variety name that matches the business variety name is obtained based on the fourth sub-cosine similarity. Among them, the target preset inspection business field information includes the target preset organization name and the target preset business variety name. The second cosine similarity includes the third sub-cosine similarity and the fourth sub-cosine similarity. Since the inspection business field information can be further subdivided into the organization name and the business variety name, the target preset inspection business field information is jointly determined by the organization name and the business variety name, thereby improving the accuracy of the obtained target preset inspection business field information.

[0104] Step S323: Obtain a first dimension combination key according to the target preset inspection object information and the target preset inspection business field information.

[0105] After obtaining the target preset inspection object information and the target preset inspection business field information, these two parts are combined to obtain the first dimension combination key.

[0106] In this embodiment, similarity calculation is performed by checking object information and checking business field information to obtain a first-dimensional combination key, thereby improving the accuracy of the obtained first-dimensional combination key.

[0107] It should be noted that, since the table structure of the incremental transaction table is the same as that of the multi-dimensional aggregation table, the second dimension composite key can be calculated based on the same calculation method as the first dimension composite key.

[0108] Based on the above embodiments, in the second embodiment of the present application, the same or similar contents as those in the above embodiments can be referred to the above introduction and will not be described in detail later. Figure 4 as well as Figure 8 , Figure 4 This is a flow chart of the second embodiment of the present application. Figure 8 This is a schematic diagram of the second interaction between the user equipment, the service processing device, and the service processing server of the present application. After step S40, the following steps are also included:

[0109] Step S110: In response to the credit application request sent by the user equipment, the credit application request is parsed to obtain application information.

[0110] Application information refers to the set of dimension values ​​included in a credit application request, used to identify credit utilization in a specific business scenario. For example, this application information may include information about the applicant and the business application fields. The applicant information may specifically include the group enterprise number and member enterprise number, while the business application fields may specifically include the organization name and business type name. This application information may also include the requested credit amount.

[0111] In one feasible implementation, a credit application request is received from a user device via HTTP / HTTPS. If the request is in JSON / XML format, it is deserialized into a memory object using a Java library; if the request is in a binary format such as Protobuf, it is directly parsed into a structured object using a pre-compiled IDL file.

[0112] In another feasible implementation, after parsing the credit application request, signature verification may be performed, and after successful signature verification, application information may be obtained.

[0113] Step S120: Search the multi-dimensional aggregation table for a matching target dimension composite key according to the application information.

[0114] Among them, the target dimension combination will refer to the unique key matched from the multi-dimensional aggregation table, which is composed of the dimension values ​​in the application information and the associated inventory occupancy amount.

[0115] In a feasible implementation, the similarity between the application information and the data in the multi-dimensional aggregation table can be calculated to find the matching target dimension combination key from the multi-dimensional aggregation table. Specifically, the application information and each dimension combination key of the multi-dimensional aggregation table can be vectorized to obtain the application information vector and the dimension combination key vector, and the application information vector and the dimension combination key vector are used to calculate the similarity, and the matching target dimension combination key is found according to the similarity. Among them, if the application information consists of multiple parts, each part can be vectorized and then spliced ​​to obtain the application information vector. At the same time, each dimension in the dimension combination key can be vectorized and then spliced ​​to obtain the dimension combination key vector. The similarity here can be cosine similarity, Euclidean distance, etc. The target dimension combination key is found by matching the similarity, so that the found target dimension combination key is more accurate.

[0116] In another feasible implementation, a clustering analysis algorithm can be used to search for matching target dimension key combinations in a multi-dimensional aggregation table based on the application information. The clustering analysis algorithm used here is not specifically limited and can be any clustering analysis algorithm. Searching for target dimension key combinations using a clustering approach can improve search efficiency for target dimension key combinations.

[0117] Step S130: Obtain the target inventory occupancy amount associated with the target dimension combination key.

[0118] In a feasible implementation, the target dimension combination key has a corresponding index identifier, and the target inventory occupancy amount associated with the target dimension combination key can be obtained based on the index identifier.

[0119] Step S140: Based on the target stock usage amount, verify the quota application of the user equipment.

[0120] Step S150: If the credit application is verified to be successful, the corresponding application credit is sent to the user device.

[0121] In this embodiment, it is possible to respond to a credit application request and obtain application information; based on the application information, search for a matching target dimension combination key from a multi-dimensional aggregation table; obtain the target inventory occupancy amount associated with the target dimension combination key; use the target inventory occupancy amount to verify the credit application on the user device; if the credit application verification is passed, the corresponding application credit amount is issued to the user device. In this way, when a credit application request is received, the corresponding target inventory occupancy amount can be quickly searched from a multi-dimensional aggregation table to verify the credit application, thereby improving the efficiency of credit application verification, and after the credit application verification is passed, the corresponding application credit amount is issued to the user device, thereby improving the efficiency of issuing the application credit amount.

[0122] Furthermore, the application information includes application object information and business field information. The application object information refers to the user object that currently needs to apply for a credit limit, which may include the group enterprise number and the member enterprise number. The application business field information refers to the business information that currently needs to apply for a credit limit, including the organization name and business type name. Step S120 includes:

[0123] Step S121 : searching for a matching target dimension composite key from the multi-dimensional aggregation table according to the application object information and the application business field information.

[0124] In a feasible implementation, the similarity between the application object information and the data in the multi-dimensional aggregation table can be calculated, as well as the similarity between the application business field information and the data in the multi-dimensional aggregation table. Based on the similarity of these two parts, a matching target dimension combination key can be found from the multi-dimensional aggregation table. Specifically, the application object information, the application business field information, and each dimension combination key of the multi-dimensional aggregation table can be vectorized to obtain the application object information vector, the application business field information vector, and the dimension combination key vector. The application object information vector, the application business field information vector, and the dimension combination key vector are used to calculate the similarity, and the matching target dimension combination key is obtained according to the similarity. The similarity here can be cosine similarity, Euclidean distance, etc. The target dimension combination key is found by matching the similarity, so that the found target dimension combination key is more accurate.

[0125] In another feasible implementation, a clustering analysis algorithm can be used to search for matching target dimension key combinations in a multi-dimensional aggregation table based on the application object information and the application business field information. The clustering analysis algorithm used here is not specifically limited and can be any clustering analysis algorithm. Searching for target dimension key combinations using a clustering approach can improve search efficiency for target dimension key combinations.

[0126] In this embodiment, the matching target dimension combination key is searched from the multi-dimensional aggregation table through the application object information and the application business field information, thereby improving the accuracy of the obtained target dimension combination key.

[0127] For further information, please refer to Figure 5 , step S121 includes:

[0128] Step S1211 , determining the third cosine similarity between the applicant information and the preset applicant information in each dimension combination key in the multi-dimensional aggregation table, and obtaining target preset applicant information matching the applicant information based on the third cosine similarity.

[0129] In one feasible implementation, the third sub-cosine similarity between the group enterprise number and the preset group enterprise number in each dimension combination key in the multi-dimensional aggregation table can be determined; based on the third sub-cosine similarity, a target preset group enterprise number matching the group enterprise number is obtained. Simultaneously, the fourth sub-cosine similarity between the member enterprise number and the preset member enterprise number in each dimension combination key in the multi-dimensional aggregation table is determined. Based on the fourth sub-cosine similarity, a target preset member enterprise number matching the member enterprise number is obtained. The target preset application object information includes a target preset group enterprise number and a target preset member enterprise number. The third cosine similarity includes the third sub-cosine similarity and the fourth sub-cosine similarity. Since the application object information can be further divided into the group enterprise number and the member enterprise number, the target preset application object information is determined by combining the group enterprise number and the member enterprise number, thereby improving the accuracy of the obtained target preset application object information.

[0130] And, step S1212, determining the fourth cosine similarity between the business field information and the preset business field information in each dimension combination key in the multi-dimensional aggregation table, and obtaining target preset business field information matching the business field information based on the fourth cosine similarity.

[0131] In a feasible implementation, the fifth sub-cosine similarity between the organization name and the preset organization name in each dimension combination key in the multi-dimensional aggregation table can be determined; and the target preset organization name that matches the organization name is obtained based on the fifth sub-cosine similarity. At the same time, the sixth sub-cosine similarity between the business variety name and the preset business variety name in each dimension combination key in the multi-dimensional aggregation table is determined. The target preset business variety name that matches the business variety name is obtained based on the sixth sub-cosine similarity. The target preset application business field information includes the target preset organization name and the target preset business variety name. The fourth cosine similarity includes the fifth sub-cosine similarity and the sixth sub-cosine similarity. Since the application business field information can be further subdivided into the organization name and the business variety name, the target preset application business field information is determined by jointly determining the organization name and the business variety name, thereby improving the accuracy of the obtained target preset application business field information.

[0132] Step S1213: Obtain the target dimension combination key according to the target preset application object information and the target preset business field information.

[0133] After obtaining the target preset application object information and the target preset application business field information, combine these two parts to obtain the target dimension combination key.

[0134] In this embodiment, similarity calculation is performed on the application object information and the application business field information to obtain the target dimension combination key, thereby improving the accuracy of the obtained target dimension combination key.

[0135] Furthermore, the application object information includes the group enterprise number and the member enterprise number, and the application business field information includes the organization name and the business variety name. The group enterprise number, the member enterprise number, the organization name and the business variety name respectively represent different dimensions and together constitute a dimension combination key; step S120 includes: according to the group enterprise number, the member enterprise number, the organization name and the business variety name, searching for a matching dimension combination key from the multi-dimensional aggregation table as the target dimension combination key.

[0136] In this embodiment, the group enterprise number, member enterprise number, organization name, and business type name are used to search for a matching target dimension combination key from the multi-dimensional aggregation table, thereby accurately obtaining the target dimension combination key in this business scenario.

[0137] For further information, please refer to Figure 6 The application information also includes the application amount. Step S140 includes:

[0138] Step S141 : Obtain the total occupancy value of the user equipment according to the sum of the target stock occupancy amount and the applied amount.

[0139] Step S142: If the total occupancy value is less than or equal to the credit limit of the user corresponding to the user device, it is determined that the application verification is passed.

[0140] The credit limit can be determined based on actual circumstances, for example, based on the user's historical credit situation and historical credit limit application situation.

[0141] Alternatively, in step S143, if the total occupancy value is greater than the credit limit of the user corresponding to the user device, it is determined that the application verification has failed.

[0142] In this embodiment, after obtaining the target inventory occupancy amount through a multi-dimensional aggregation table query, the target inventory occupancy amount is added to the application amount to obtain the total occupancy amount. If the total occupancy amount is less than or equal to the credit limit, it means that the application verification is passed, otherwise the application verification fails, thereby achieving the purpose of user credit limit.

[0143] It should be noted that the above examples are only used to understand this application and do not constitute a limitation on the business data processing method of this application. More simple transformations based on this technical concept are all within the scope of protection of this application.

[0144] Based on the same inventive concept, the present application provides a business data processing server, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the business data processing method in the above embodiment.

[0145] Reference below Figure 9 , which shows a schematic diagram of the structure of a business data processing server suitable for implementing an embodiment of the present application. The business data processing server in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), tablet computers (Portable Application Descriptions, PADs), portable multimedia players (PMPs), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 9 The business data processing server shown is merely an example and should not impose any limitations on the functions and scope of use of the embodiments of the present application.

[0146] like Figure 9 As shown, the business data processing server may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for the operation of the business data processing server are also stored in the random access memory 1004. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the business data processing server to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a business data processing server with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0147] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising 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 via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0148] The business data processing server provided in this application, employing the business data processing method of the aforementioned embodiment, can resolve the technical issue of low efficiency in credit limit application processing. Compared to the prior art, the beneficial effects of the business data processing server provided in this application are the same as those of the business data processing method provided in the aforementioned embodiment, and the other technical features of the business data processing server are the same as those disclosed in the method of the aforementioned embodiment, and are not further elaborated here.

[0149] Based on the same inventive concept, the present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, and the computer-readable program instructions are used to execute the business data processing method in the above embodiment.

[0150] The computer-readable storage medium provided in this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable storage media can 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, a read-only memory, an erasable programmable read-only memory (EPROM), an optical fiber, a portable compact disk read-only memory (CD-Read Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0151] The computer-readable storage medium may be included in the business data processing server; or it may exist independently without being assembled into the business data processing server.

[0152] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the business data processing server, the business data processing server can improve the efficiency of credit application processing.

[0153] The computer program code for performing the operations of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" 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 (e.g., using an Internet service provider to connect via the Internet).

[0154] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0155] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0156] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned business data processing method, thereby resolving the technical issue of low efficiency in credit application processing. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the business data processing method provided in the aforementioned embodiment, and are not further elaborated here.

[0157] The above are only some embodiments of the present application and are not intended to limit the patent scope of the present application. All equivalent structural transformations made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A business data processing method, characterized in that: Applied to a business data processing server, the business data processing method includes: In response to a request to create a multi-dimensional aggregate table, create a multi-dimensional aggregate table, wherein the multi-dimensional aggregate table stores a dimension combination key and an inventory usage amount corresponding to the dimension combination key, wherein the dimension combination key is composed of multiple dimensions; Periodically receiving incremental transaction values ​​sent by user equipment, and using the incremental transaction values ​​to update an incremental transaction table, wherein the table structure of the incremental transaction table is the same as that of the multi-dimensional aggregation table; In response to an online quota check request, merging the multi-dimensional aggregation table and the incremental transaction table to obtain a real-time inventory usage amount; The real-time stock occupancy amount is used to update the stock occupancy amount in the multi-dimensional aggregation table to obtain an updated multi-dimensional aggregation table, so as to verify the quota application of the user device based on the multi-dimensional aggregation table.

2. The business data processing method according to claim 1, wherein: In response to the online quota check request, merging the multi-dimensional aggregation table and the incremental transaction table to obtain the real-time inventory usage amount includes: Responding to the online credit limit check request, obtaining check information; According to the inspection information, searching for a matching first dimension composite key from the multi-dimensional aggregation table, and searching for a matching second dimension composite key from the incremental transaction table; Obtaining the inventory usage associated with the first dimension combination key, and obtaining the incremental transaction value associated with the second dimension combination key; The real-time inventory occupancy amount is obtained according to the sum of the inventory occupancy amount and the incremental transaction value.

3. The business data processing method according to claim 2, wherein: The inspection information includes inspection object information and inspection business field information. Searching for a matching first dimension composite key from the multi-dimensional aggregation table based on the inspection information includes: Determine a first cosine similarity between the inspection object information and preset inspection object information in each dimension combination key in the multi-dimensional aggregation table, and acquire target preset inspection object information matching the inspection object information based on the first cosine similarity; and determining a second cosine similarity between the inspection business field information and the preset inspection business field information in each dimension combination key in the multi-dimensional aggregation table, and acquiring target preset inspection business field information matching the inspection business field information based on the second cosine similarity; The first dimension combination key is obtained according to the target preset inspection object information and the target preset inspection business field information.

4. The business data processing method according to claim 1, wherein: After the real-time inventory occupancy amount is used to update the inventory occupancy amount in the multi-dimensional aggregation table to obtain the updated multi-dimensional aggregation table, the method further includes: In response to the credit application request sent by the user equipment, parsing the credit application request to obtain application information; According to the application information, searching for a matching target dimension composite key from the multi-dimensional aggregation table; Obtain the target inventory occupancy amount associated with the target dimension combination key; Based on the target inventory occupancy amount, verifying the quota application of the user equipment; If the credit application is verified to be successful, the corresponding application credit will be sent to the user device.

5. The business data processing method according to claim 4, wherein: The application information includes application object information and application business field information. The searching for a matching target dimension composite key from the multi-dimensional aggregation table based on the application information includes: According to the application object information and the application business field information, a matching target dimension composite key is searched from the multi-dimensional aggregation table.

6. The business data processing method according to claim 5, characterized in that: The searching for a matching target dimension combination key from the multi-dimensional aggregation table according to the application object information and the application business field information includes: Determining a third cosine similarity between the applicant information and preset applicant information in each dimension combination key in the multi-dimensional aggregation table, and obtaining target preset applicant information matching the applicant information based on the third cosine similarity; and determining a fourth cosine similarity between the application business field information and the preset application business field information in each dimension combination key in the multi-dimensional aggregation table, and obtaining target preset application business field information that matches the application business field information based on the fourth cosine similarity; The target dimension combination key is obtained according to the target preset application object information and the target preset application business field information.

7. The business data processing method according to claim 5, wherein: The application object information includes a group enterprise number and a member enterprise number, and the application business field information includes an organization name and a business type name. The group enterprise number, the member enterprise number, the organization name, and the business type name respectively represent different dimensions and together constitute a dimension composite key. Searching for a matching target dimension composite key from the multi-dimensional aggregation table based on the application information includes: According to the group enterprise number, the member enterprise number, the organization name, and the business type name, a matching dimension combination key is searched from the multi-dimensional aggregation table as the target dimension combination key.

8. The business data processing method according to claim 4, wherein: The application information also includes an application amount, and the verification of the application amount of the user equipment based on the target stock usage amount includes: Obtaining a total occupancy value of the user equipment according to the sum of the target stock occupancy amount and the applied amount; If the total occupancy value is less than or equal to the credit limit of the user corresponding to the user device, it is determined that the application verification is passed; Alternatively, if the total occupancy value is greater than the credit limit of the user corresponding to the user equipment, it is determined that the application verification has failed.

9. A business data processing server, characterized in that: The business data processing server includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the business data processing method according to any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the business data processing method according to any one of claims 1 to 8 are implemented.

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