Quota data processing methods, devices, equipment and their storage media

Abstract

This application discloses a method, apparatus, device, and storage medium for credit limit data processing, belonging to the field of financial technology (Fintech). The credit limit data processing method includes: obtaining credit limit trees from various data sources provided by an associated credit limit system; and generating a credit limit data table corresponding to each credit limit tree based on the credit limit nodes in each credit limit tree, wherein each cell in the credit limit data table corresponds to a credit limit node in the credit limit tree, and credit limit nodes from the same data source are located in the same row of the credit limit data table. This application solves the technical problem of high computational requirements when extracting credit limit data from a credit limit tree in conventional solutions.

Description

Technical Field

[0001] This application relates to the field of financial technology (Fintech), and in particular to a method, apparatus, device and storage medium for credit limit data processing. Background Technology

[0002] In recent years, with the continuous development of financial technology (Fintech), especially internet finance, data update technology has been introduced into the daily services of banks and other financial institutions. In the daily operations of these institutions, to assess a customer's creditworthiness across various financial products and to consider their repayment ability and avoid post-loan risks, banks typically grant customers a certain credit limit (i.e., credit line) for each financial product based on their credit information, such as repayment history. Examples include a 300,000 yuan car loan and a 200,000 yuan consumer loan. These limits constitute a tree-like logical structure of the credit limit held by the customer at the financial institution.

[0003] In conventional methods, banks and other financial institutions store credit limit data in a tree structure, with each node forming a row, creating a credit limit tree. When managing customer credit, they only need to query whether the individual credit limit node corresponding to the transaction request meets the credit requirements. However, in credit limit data analysis scenarios, it's not just about considering a single credit limit node; it's necessary to compare and analyze the credit limit information of multiple nodes simultaneously. Therefore, analysts need to extract credit limit data from multiple target nodes one by one from the credit limit tree. The computational load required for data extraction is directly proportional to the number of rows of data stored, resulting in a high computational cost when extracting credit limit data from the credit limit tree.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a method, apparatus, device and storage medium for processing quota data, which aims to solve the technical problem of high computational load required when extracting quota data from the quota tree in conventional solutions.

[0006] To achieve the above objectives, this application provides a credit limit data processing method, the credit limit data processing method comprising:

[0007] Retrieve the credit limit tree from each data source provided by the associated credit limit system;

[0008] Based on the quota nodes in each quota tree, a quota data table corresponding to each quota tree is generated, wherein the cell of the quota data table corresponds to the quota node of the quota tree, and the quota nodes of the same data source are located in the same row of the quota data table.

[0009] Optionally, the step of generating a credit limit data table corresponding to each credit limit tree based on the credit limit nodes in each credit limit tree includes:

[0010] Use the data source as the first column of the quota data table;

[0011] Based on a preset preorder traversal order, starting from the root node of the credit limit tree, the cells of the credit limit data table are configured sequentially according to the credit limit nodes of the credit limit tree to obtain the credit limit data table. In this table, any credit limit node of the credit limit tree and its sibling node correspond to adjacent table columns in the same table row of the credit limit data table.

[0012] Optionally, the method further includes:

[0013] Determine the target node and its target child node of the credit limit tree, and identify the target cell and target sub-cell corresponding to the target node and the target child node in the credit limit data table respectively;

[0014] Based on the preset function relationship, establish the target function relationship between the target cell and the target sub-cell, and update the quota data table based on the target function relationship.

[0015] Optionally, the method further includes:

[0016] Identify the target credit limit chain of the credit limit tree, and based on the target credit limit chain, determine the credit limit cell in the credit limit data table corresponding to the node in the target credit limit chain, and establish the target association relationship between each credit limit cell;

[0017] Update the quota data table according to the target association.

[0018] Optionally, the historical data and current data in the quota data table are both set in the same preset partition.

[0019] Optionally, the method further includes:

[0020] Determine the target data source corresponding to at least one table row of the quota data table;

[0021] Identify the target customer number of the target data source, and extract the target mapping relationship that matches the target customer number from the preset mapping relationship between customer number and ID number;

[0022] The quota data table is updated based on the target mapping relationship.

[0023] Optionally, after the step of obtaining the credit limit tree of each data source provided by the associated credit limit system, the method further includes:

[0024] The quota data in the quota tree is preprocessed, wherein the preprocessing includes at least one of the following: data extraction, data cleaning, data integration, and data synchronization.

[0025] This application also provides a credit limit data processing device, the credit limit data processing device comprising:

[0026] The acquisition module is used to obtain the credit limit tree from each data source provided by the associated credit limit system;

[0027] The generation module is used to generate a quota data table corresponding to each quota tree based on the quota nodes in each quota tree, wherein the cells of the quota data table correspond to the quota nodes of the quota trees, and the quota nodes of the same data source are located in the same row of the quota data table.

[0028] This application also provides an electronic device, the electronic device comprising: a memory, a processor, and a credit limit data processing program stored in the memory and executable on the processor, the credit limit data processing program being configured to implement the steps of the above-described credit limit data processing method.

[0029] This application also provides a storage medium, which is a computer-readable storage medium, on which a quota data processing program is stored, and the quota data processing program is executed by a processor to implement the steps of the quota data processing method described above.

[0030] This application discloses a method for processing credit limit data. It obtains credit limit trees from various data sources provided by a related credit limit system; then, based on the credit limit nodes in each credit limit tree, it generates a credit limit data table corresponding to each credit limit tree. The cells in the credit limit data table correspond to the credit limit nodes in the credit limit trees, and the credit limit nodes in the credit limit trees of the same data source are located in the same row of the credit limit data table. This application converts credit limit data (credit limit trees) stored in a tree structure into a credit limit data table, integrating multiple rows of credit limit data belonging to the same data source into a single row. Therefore, when analysts need to extract credit limit data from a target data source, they can directly extract the required credit limit data from a single row of the credit limit data table. Since the computational load required for data extraction is positively correlated with the number of rows of stored data, while the number of columns of stored data has a relatively small impact on the computational load required for data extraction, this application can significantly reduce the computational load required for credit limit data extraction, improve the efficiency of credit limit data extraction, facilitate analysis, and enhance user convenience and user experience. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of the structure of an electronic device in the hardware operating environment involved in the embodiments of this application;

[0032] Figure 2 This is a flowchart illustrating the quota data processing method involved in the embodiments of this application;

[0033] Figure 3 This is a schematic diagram illustrating a scenario of the quota data processing method involved in the embodiments of this application;

[0034] Figure 4 This is a schematic diagram of the credit limit tree involved in the embodiments of this application;

[0035] Figure 5 This is a schematic representation of the credit limit data involved in the embodiments of this application;

[0036] Figure 6 This is a schematic diagram illustrating the traversal of the quota tree involved in the embodiments of this application;

[0037] Figure 7 This is a schematic diagram of the framework structure of the quota data processing device involved in the embodiments of this application.

[0038] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0039] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0040] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the term "and / or" throughout the text includes three solutions; taking A and / or B as an example, it includes technical solution A, technical solution B, and a technical solution that simultaneously satisfies A and B. Furthermore, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0041] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of this application.

[0042] like Figure 1As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0043] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0044] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a quota data processing program.

[0045] exist Figure 1 In the illustrated electronic device, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the electronic device of this application can be disposed in the electronic device, and the electronic device calls the quota data processing program stored in the memory 1005 through the processor 1001 and performs the following operations:

[0046] Retrieve the credit limit tree from each data source provided by the associated credit limit system;

[0047] Based on the quota nodes in each quota tree, a quota data table corresponding to each quota tree is generated, wherein the cell of the quota data table corresponds to the quota node of the quota tree, and the quota nodes of the same data source are located in the same row of the quota data table.

[0048] Furthermore, the operation of generating a quota data table corresponding to each quota tree based on the quota nodes in each quota tree includes:

[0049] Use the data source as the first column of the quota data table;

[0050] Based on a preset preorder traversal order, starting from the root node of the credit limit tree, the cells of the credit limit data table are configured sequentially according to the credit limit nodes of the credit limit tree to obtain the credit limit data table. In this table, any credit limit node of the credit limit tree and its sibling node correspond to adjacent table columns in the same table row of the credit limit data table.

[0051] Furthermore, the method also includes:

[0052] Determine the target node and its target child node of the credit limit tree, and identify the target cell and target sub-cell corresponding to the target node and the target child node in the credit limit data table respectively;

[0053] Based on the preset function relationship, establish the target function relationship between the target cell and the target sub-cell, and update the quota data table based on the target function relationship.

[0054] Furthermore, the method also includes:

[0055] Identify the target credit limit chain of the credit limit tree, and based on the target credit limit chain, determine the credit limit cell in the credit limit data table corresponding to the node in the target credit limit chain, and establish the target association relationship between each credit limit cell;

[0056] Update the quota data table according to the target association.

[0057] Furthermore, the historical data and current data in the quota data table are both set in the same preset partition.

[0058] Furthermore, the method also includes:

[0059] Determine the target data source corresponding to at least one table row of the quota data table;

[0060] Identify the target customer number of the target data source, and extract the target mapping relationship that matches the target customer number from the preset mapping relationship between customer number and ID number;

[0061] The quota data table is updated based on the target mapping relationship.

[0062] Furthermore, the processor 1001 can call the quota data processing program stored in the memory 1005 and also perform the following operations:

[0063] Following the operation of obtaining the credit tree of each data source provided by the associated credit system, the method further includes:

[0064] The quota data in the quota tree is preprocessed, wherein the preprocessing includes at least one of the following: data extraction, data cleaning, data integration, and data synchronization.

[0065] Based on the above structure, various embodiments of the quota data processing method are proposed.

[0066] Reference Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the quota data processing method of this application.

[0067] In this embodiment, the executing entity of the credit limit data processing method can be an electronic device, which can be a local device or a network device. No limitation is imposed in this embodiment. For ease of description, the executing entity is omitted from the description of each embodiment below. In this embodiment, the credit limit data processing method includes:

[0068] Step S10: Obtain the credit limit tree of each data source provided by the associated credit limit system;

[0069] In this embodiment, it should be noted that the credit limit data processing method can be applied to a credit limit data processing system. This system can be a data warehouse, comprising at least a source layer (Ods layer) and a detail layer (DWD layer), and may also include an application layer for assembling data and displaying trend indicators according to application requirements. Each layer is sequentially ordered and operates unidirectionally to maintain logical coherence and reduce the probability of operational errors. Furthermore, the source layer can communicate with the associated credit limit system, which can interface with multiple external data sources (hereinafter referred to as data sources for distinction). The source layer then obtains the credit limit tree from each data source provided by the credit limit system, referring to... Figure 3 The credit limit tree is a credit limit information in a data source tree structure. The data source can be understood as a customer, which can be a company or an individual. This embodiment does not impose any restrictions on this.

[0070] In one feasible implementation, after step S10, which involves obtaining the credit tree of each data source provided by the associated credit system, the method further includes:

[0071] Step S11: Preprocess the quota data in the quota tree, wherein the preprocessing includes at least one of data extraction, data cleaning, data integration and data synchronization.

[0072] Since the associated credit system can connect to multiple data sources, and the credit trees of each data source may have different data formats, structures, semantics, and quality, a series of data processing and transformations are required before they can be effectively integrated into the data warehouse. Therefore, after obtaining the credit trees from each data source provided by the associated credit system, the credit data in the credit tree is preprocessed. This preprocessing includes at least one of the following: data extraction, data cleaning, data integration, and data synchronization. The credit data from different data sources is then integrated into a unified and standardized dataset, while ensuring data quality and availability as much as possible.

[0073] Step S20: Based on the quota nodes in each quota tree, generate a quota data table corresponding to each quota tree, wherein the cell of the quota data table corresponds to the quota node of the quota tree, and the quota nodes of the same data source are located in the same row of the quota data table.

[0074] In conventional methods, banks and other financial institutions store credit limit data in a tree structure, with each node forming a row, creating a credit limit tree. However, in credit limit data analysis scenarios, it's necessary to compare and analyze credit limit information from multiple nodes simultaneously. Therefore, analysts need to extract credit limit data from multiple target nodes one by one from the credit limit tree. The computational load required for data extraction is directly proportional to the number of rows stored, resulting in a high computational load when extracting credit limit data from the credit limit tree. To reduce the computational load and improve the efficiency of credit limit data extraction, a detailed layer in the credit limit data processing system can be used to configure the credit limit data table according to each node in the credit limit tree (hereinafter referred to as a credit limit node for distinction). The system configures each cell in the credit limit data table to correspond with the credit limit nodes in the credit limit tree. Furthermore, when configuring cells, credit limit nodes belonging to the same data source are placed in the same row of the credit limit data table. This compresses the granularity of credit limit data from various data sources from the credit limit tree granularity to the customer granularity. Consequently, when analysts need to extract credit limit data from a target data source, they can directly extract the required credit limit data from a single row of the credit limit data table. Since the computational load required for data extraction is positively correlated with the number of rows in the data storage, while the number of columns in the data storage has a relatively small impact on the computational load required for data extraction, this application can significantly reduce the computational load required for credit limit data extraction and improve the efficiency of credit limit data extraction.

[0075] Optionally, the quota data table may contain quota data from multiple data sources, i.e., quota data from each data source is in one row; alternatively, each data source may correspond to a separate quota data table. This embodiment does not impose any restrictions on this.

[0076] Optionally, each quota node in the quota tree can store a single quota parameter, or it can store multiple quota parameters at the same time, such as three types of quota parameters: total quota, occupied quota and available quota, etc. This embodiment does not impose any restrictions on this.

[0077] Optionally, each cell in the quota data table contains: the node identifier and quota parameters of each quota node in the quota tree; the node identifier can determine the relationship between any node and the remaining nodes, that is, whether they are parent nodes, child nodes or sibling nodes.

[0078] In this embodiment, it should be noted that the credit limit nodes of the credit limit tree include: leaf nodes and non-leaf nodes. A leaf node refers to the lowest level node in the credit limit tree, that is, a leaf node has no child nodes. All other nodes in the credit limit tree are non-leaf nodes. Furthermore, among the non-leaf nodes, the starting node of the credit limit tree is the root node.

[0079] For example, refer to Figure 4 and 5 , Figure 4 The credit limit tree for data source A (customer A) contains 8 credit limit nodes. Customer A's total credit limit is the root node, and products A through E are leaf nodes. Then, based on each credit limit node in the credit limit tree, the cells in the credit limit data table are configured to correspond to the credit limit nodes in the credit limit tree. Furthermore, when configuring cells, credit limit nodes belonging to the same data source are placed in the same row of the credit limit data table, resulting in the credit limit data table. (Refer to...) Figure 5 .

[0080] In this embodiment, the credit trees of each data source provided by the associated credit system are obtained; then, based on the credit nodes in each credit tree, a credit data table corresponding to each credit tree is generated, wherein the cell of the credit data table corresponds to the credit node of the credit tree, and the credit nodes in the credit trees of the same data source are located in the same row of the credit data table; this application converts the credit data (credit tree) stored in a tree structure into a credit data table, so that multiple rows of credit data belonging to the same data source are integrated into one row; then, when the analyst needs to extract the credit data of the target data source, the required credit data can be directly extracted from one row of the credit data table; since the amount of computation required for data extraction is positively correlated with the number of rows of data stored, while the number of columns of data stored has little impact on the amount of computation required for data extraction, this application can greatly reduce the amount of computation required for credit data extraction, improve the efficiency of credit data extraction, facilitate the use of analysts, and enhance the user's ease of use and user experience.

[0081] In one feasible implementation, the historical data and current data in the quota data table are both set in the same preset partition.

[0082] In conventional data warehouse storage, multiple partitions are created based on the data's time point (e.g., date). For example, the data warehouse stores a first partition corresponding to January 1st, a second partition corresponding to January 2nd, and a third partition corresponding to the current day (January 3rd). The first and second partitions are historical partitions, and the third partition is the current partition. Since the quota data may change daily, it is necessary to maintain the historical information of each data source for data refresh. Therefore, if the data stored in the data warehouse is divided into multiple partitions, data refresh needs to be performed sequentially according to the partition order, resulting in a large computational load and long maintenance time for the data warehouse. Therefore, this application sets both the historical data and the current data in the quota data table of the data warehouse in the same preset partition, so that the computation of the data warehouse can be parallelized, thereby enabling synchronous maintenance of quota data at multiple time points and greatly improving data maintenance efficiency.

[0083] Furthermore, based on the first embodiment described above, a second embodiment of the credit limit data processing method of this application is proposed. In this embodiment, step S20, the step of generating a credit limit data table corresponding to each credit limit tree based on the credit limit nodes in each credit limit tree, includes:

[0084] Step S21: Use the data source as the first column of the quota data table;

[0085] When generating a quota data table corresponding to each quota tree based on the quota nodes in each quota tree, the data source can be set as the first column of the quota data table, that is, the first column in the table.

[0086] Optionally, the root node in the credit limit tree corresponding to each data source can be used to represent each data source, and then the root node in the credit limit tree corresponding to each data source can be used as the first column of the credit limit data table.

[0087] Step S22: Based on the preset preorder traversal order, starting from the root node of the credit limit tree, configure the cells of the credit limit data table sequentially according to the credit limit nodes of the credit limit tree to obtain the credit limit data table. In this case, any credit limit node of the credit limit tree and its sibling node correspond to adjacent table columns in the same table row of the credit limit data table.

[0088] Based on the preset preorder traversal order, the root node of the credit limit tree is visited first, and then the root node is set in the second column of the credit limit data table (i.e., the column below the first column of the table). If the root node represents the data source, then the root node is set in the first column of the credit limit data table. Then, the remaining credit limit nodes are visited in order from left to right and from top to bottom, and the cells in the credit limit data table are set according to the visited credit limit nodes. Any credit limit node in the credit limit tree and its sibling node correspond to the adjacent column of the same table row in the credit limit data table, thus obtaining the credit limit data table.

[0089] For example, refer to Figure 6 The credit limit tree contains 8 credit limit nodes, where the total credit limit of customer A is the root node, and products A to E are leaf nodes. Then, based on the preset preorder traversal order, starting from the root node of the credit limit tree (total credit limit of customer A), the credit limit nodes of the credit limit tree are traversed in sequence, in the following order: 1 Total credit limit of customer A → 2 First category credit limit → Second category credit limit → Product A → Product B → Product C → Product D → Product E. And according to the above traversal order, the cells in the credit limit data table are configured.

[0090] In this embodiment, the data source is used as the first column of the credit limit data table; then, based on a preset preorder traversal order, the cells of the credit limit data table are configured sequentially according to the credit limit nodes of the credit limit tree, starting from the root node of the credit limit tree, to obtain the credit limit data table. In this embodiment, any credit limit node of the credit limit tree and its sibling node correspond to adjacent columns of the same table row of the credit limit data table. Based on a preset preorder traversal order, table columns for each row in the credit limit data table are constructed to preserve the relationships between credit limit nodes in the credit limit tree. This prevents update errors when updating the credit limit data table and allows analysts to compare credit limit nodes with the same parent node (i.e., sibling nodes) during credit limit data analysis. Therefore, by mapping any credit limit node in the credit limit tree and its sibling nodes to adjacent table columns in the same row of the credit limit data table, the target data requested by the user can be grouped together when retrieving credit limit data from the target data source. This facilitates intuitive data retrieval and improves ease of use and user experience.

[0091] In one feasible implementation, the method further includes:

[0092] Step A10: Determine the target node and its target child node of the credit limit tree, and identify the target cell and target sub-cell corresponding to the target node and the target child node in the credit limit data table respectively;

[0093] Since the credit limit data may change every day, it is necessary to update the data in the credit limit data table regularly in order to determine the target node in the credit limit tree. The target node can be any credit limit node in the credit limit tree. At the same time, the child nodes of the target node (hereinafter referred to as target child nodes for distinction) are determined. Then, the target cell corresponding to the target node in the credit limit data table and the target sub-cell corresponding to the target child node in the credit limit data table are identified.

[0094] Step A20: Based on the preset function relationship, establish the target function relationship between the target cell and the target sub-cell, and update the quota data table based on the target function relationship.

[0095] Based on a preset functional relationship, a functional relationship (hereinafter referred to as the target functional relationship) is established between the target cell and the target sub-cells. The target functional relationship can be a summation function, for example, the quota parameter in the target cell is equal to the sum of the quota parameters in the target sub-cells. Then, the quota data table is updated based on the target functional relationship. The update can be achieved by adding the target functional relationship to the target cell and the target sub-cells so that when the quota parameter in the target sub-cell changes, the quota parameter in the target cell can be updated accordingly.

[0096] In this embodiment, the target node and its target child node of the credit limit tree are determined, and the target cell and target sub-cell corresponding to the target node and the target child node in the credit limit data table are identified respectively. Then, according to the preset function relationship, the target function relationship between the target cell and the target sub-cell is established, and the credit limit data table is updated based on the target function relationship. This ensures that when the credit limit parameter in the target sub-cell changes, the credit limit parameter in the target cell can be updated accordingly, thereby improving the update efficiency and accuracy of the credit limit data table.

[0097] Furthermore, based on the first and / or second embodiments described above, a third embodiment of the quota data processing method of this application is proposed. In this embodiment, the method further includes:

[0098] Step B10: Identify the target credit limit chain of the credit limit tree, and based on the target credit limit chain, determine the credit limit cell in the credit limit data table corresponding to the node in the target credit limit chain, and establish the target association relationship between each credit limit cell;

[0099] When using the credit limit data table, users may need to quickly query multiple interrelated credit limit data. Since there are numerous credit limit nodes with relationships (e.g., parent-child, sibling, etc.) in the credit limit tree, users may find it difficult to accurately remember the node identifiers of multiple credit limit nodes. Therefore, a target credit limit chain can be identified in the credit limit tree. This target credit limit chain can be a node link consisting of leaf nodes, the root node, and nodes between leaf nodes and the root node in the credit limit tree; that is, the number of credit limit chains corresponds to the number of leaf nodes in the credit limit tree. Alternatively, a credit limit chain can be a node link formed by taking any credit limit node in the credit limit tree as the target node and determining all credit limit nodes within a preset number of nodes from the target node to the root node and / or leaf nodes. This can be set according to requirements, and this embodiment does not impose any limitations on it. Then, based on the target credit limit chain, the cells in the credit limit data table corresponding to the nodes in the target credit limit chain (hereinafter referred to as credit limit cells for distinction) are determined, and the target relationships between credit limit cells are established, including parent-child relationships, etc.

[0100] Step B20: Update the quota data table according to the target association relationship.

[0101] The quota data table is updated based on the target association. The update may involve setting comments, tags, etc., of the target association in the quota cells included in the target association. This allows users to quickly identify other cells that are associated with the quota cells by using the target association in the comments and / or tags set in the quota cells. This makes it easier for users to intuitively obtain data and improves the convenience and user experience.

[0102] In this embodiment, by identifying the target credit limit chain of the credit limit tree, and based on the target credit limit chain, determining the credit limit cells in the credit limit data table corresponding to the nodes in the target credit limit chain, and establishing target association relationships between each credit limit cell; then updating the credit limit data table based on the target association relationships; enabling users to quickly identify other cells that are associated with the credit limit cells through the target association relationships set in the annotations and / or tags of the credit limit cells when using the credit limit data table, making it easier for users to intuitively obtain data and improving ease of use and user experience.

[0103] In one feasible implementation, the method further includes:

[0104] Step C10: Determine the target data source corresponding to at least one table row of the quota data table;

[0105] Step C20: Identify the target customer number of the target data source, and extract the target mapping relationship that matches the target customer number from the preset mapping relationship between customer number and ID number;

[0106] Step C30: Update the quota data table based on the target mapping relationship.

[0107] When querying credit limit data, users may use the customer number or associated identification document number from the data source. Since there may be a many-to-many relationship between customer numbers and identification document numbers—for example, a corporate customer has one corporate customer number, while the identification document number can include multiple forms such as tax identification number, unified social credit code, and business registration code; and when different business teams receive applications from the same customer, multiple different customer numbers can correspond to one identification document number—this process facilitates analysis by identifying the data source corresponding to at least one row of the credit limit data table (hereinafter referred to as the target data source for distinction). Then, the target customer number of the target data source is identified, and a target mapping relationship matching the target customer number is extracted from the pre-set mapping relationship between customer numbers and identification document numbers. Finally, the credit limit data table is updated based on the target mapping relationship, where the update may include adding the target mapping relationship to a table row.

[0108] In this embodiment, a target data source corresponding to at least one row of the credit limit data table is determined; then, the target customer number of the target data source is identified, and a target mapping relationship matching the target customer number is extracted from the preset mapping relationship between customer number and ID number; then, the credit limit data table is updated based on the target mapping relationship; so that when analysts use the credit limit data, they can quickly retrieve the target credit limit data based on the customer number and / or ID number, improving ease of use and user experience.

[0109] Furthermore, embodiments of this application also provide a quota data processing device, referring to... Figure 7 The credit limit data processing device is applied to electronic devices, and the credit limit data processing device includes:

[0110] The acquisition module 10 is used to acquire the quota tree of each data source provided by the associated quota system;

[0111] The generation module 20 is used to generate a quota data table corresponding to each quota tree based on the quota nodes in each quota tree, wherein the cells of the quota data table correspond to the quota nodes of the quota trees, and the quota nodes of the same data source are located in the same row of the quota data table.

[0112] The generation module 20 is also used to use the data source as the first column of the quota data table;

[0113] Based on a preset preorder traversal order, starting from the root node of the credit limit tree, the cells of the credit limit data table are configured sequentially according to the credit limit nodes of the credit limit tree to obtain the credit limit data table. In this table, any credit limit node of the credit limit tree and its sibling node correspond to adjacent table columns in the same table row of the credit limit data table.

[0114] The generation module 20 is further configured to determine the target node and its target child node of the quota tree, and to identify the target cell and target child cell corresponding to the target node and the target child node in the quota data table respectively;

[0115] Based on the preset function relationship, establish the target function relationship between the target cell and the target sub-cell, and update the quota data table based on the target function relationship.

[0116] The generation module 20 is further configured to identify the target quota chain of the quota tree, and based on the target quota chain, determine the quota cell in the quota data table corresponding to the node in the target quota chain, and establish the target association relationship between each quota cell;

[0117] Update the quota data table according to the target association.

[0118] The generation module 20 is also used to determine the target data source corresponding to at least one table row of the quota data table;

[0119] Identify the target customer number of the target data source, and extract the target mapping relationship that matches the target customer number from the preset mapping relationship between customer number and ID number;

[0120] The quota data table is updated based on the target mapping relationship.

[0121] The acquisition module 10 is further configured to preprocess the quota data in the quota tree, wherein the preprocessing includes at least one of data extraction, data cleaning, data integration and data synchronization.

[0122] The specific implementation method of the quota data processing device in this application is basically the same as that of the above-described quota data processing method embodiments, and will not be repeated here.

[0123] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0124] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0125] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for processing credit limit data, characterized in that, The credit data processing method includes the following steps: Retrieve the credit limit tree from each data source provided by the associated credit limit system; Use the data source as the first column of the quota data table; Based on a preset preorder traversal order, starting from the root node of the credit limit tree, the cells of the credit limit data table are configured sequentially according to the credit limit nodes of the credit limit tree to obtain the credit limit data table. The cells of the credit limit data table correspond to the credit limit nodes of the credit limit tree, and the credit limit nodes of the same data source are located in the same row of the credit limit data table. Any credit limit node of the credit limit tree and its sibling nodes correspond to adjacent table columns in the same table row of the credit limit data table.

2. The quota data processing method as described in claim 1, characterized in that, The method further includes: Determine the target node and its target child node of the credit limit tree, and identify the target cell and target sub-cell corresponding to the target node and the target child node in the credit limit data table respectively; Based on the preset function relationship, establish the target function relationship between the target cell and the target sub-cell, and update the quota data table based on the target function relationship.

3. The quota data processing method as described in claim 1, characterized in that, The method further includes: Identify the target credit limit chain of the credit limit tree, and based on the target credit limit chain, determine the credit limit cell in the credit limit data table corresponding to the node in the target credit limit chain, and establish the target association relationship between each credit limit cell; Update the quota data table according to the target association.

4. The quota data processing method as described in claim 1, characterized in that, The historical data and current data in the quota data table are both set in the same preset partition.

5. The quota data processing method as described in claim 1, characterized in that, The method further includes: Determine the target data source corresponding to at least one table row of the quota data table; Identify the target customer number of the target data source, and extract the target mapping relationship that matches the target customer number from the preset mapping relationship between customer number and ID number; The quota data table is updated based on the target mapping relationship.

6. The quota data processing method as described in claim 1, characterized in that, Following the step of obtaining the credit tree of each data source provided by the associated credit system, the method further includes: The quota data in the quota tree is preprocessed, wherein the preprocessing includes at least one of the following: data extraction, data cleaning, data integration, and data synchronization.

7. A quota data processing device, characterized in that, The device includes: The acquisition module is used to obtain the credit limit tree from each data source provided by the associated credit limit system; The generation module is used to use the data source as the first column of the credit limit data table; based on a preset preorder traversal order, starting from the root node of the credit limit tree, the cells of the credit limit data table are configured sequentially according to the credit limit nodes of the credit limit tree to obtain the credit limit data table. The cells of the credit limit data table correspond to the credit limit nodes of the credit limit tree, and the credit limit nodes of the same data source are located in the same row of the credit limit data table. Any credit limit node of the credit limit tree and its sibling node correspond to adjacent table columns in the same table row of the credit limit data table.

8. An electronic device, characterized in that, The device includes: a memory, a processor, and a quota data processing program stored in the memory and executable on the processor, the quota data processing program being configured to implement the steps of the quota data processing method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the storage medium stores a quota data processing program, which, when executed by a processor, implements the steps of the quota data processing method as described in any one of claims 1 to 6.

Images