Banking business data change influence analysis method and related equipment

Abstract

The invention provides a banking business data change influence analysis method and related equipment. The method comprises the following steps: acquiring a business data blood relationship map constructed for a banking business system; each node in the business data blood relationship map corresponds to each bank data entity, and each edge in the business data blood relationship map corresponds to a data flow relationship among the bank data entities; based on the change type, determining a target sub-graph corresponding to the change type from preset sub-graphs; taking the change node as a starting point, and screening out candidate nodes matched with the root node attribute of the target sub-graph from the business data blood relationship graph; based on the target sub-graph and each candidate node, performing sub-graph matching in the business data blood relationship graph to obtain a matching result isomorphic with the target sub-graph; based on a preset influence scoring rule, scoring each matching path in the matching result; and on the basis of each matching path and the score corresponding to the matching path, a change influence analysis result is generated, so that the change influence analysis efficiency and accuracy are improved.

Description

Technical Field

[0001] This invention relates to the field of financial technology, specifically to a method and related equipment for analyzing the impact of changes in banking business data. Background Technology

[0002] With the informatization transformation of banks, the types of business are constantly enriched, the business areas are constantly expanding, and the amount of business data is surging. Bank data governance faces a variety of challenges, such as large volume, complex dependencies, and frequent changes, which traditional data change impact analysis methods are difficult to cope with.

[0003] Existing technologies are unable to accurately identify key impact links in massive, multi-layered data dependencies when changes occur in banking business data, resulting in low efficiency and poor accuracy in change impact analysis. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and related equipment for analyzing the impact of changes in banking business data, so as to improve the efficiency and accuracy of change impact analysis.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of this invention discloses a method for analyzing the impact of changes in banking business data, the method comprising:

[0007] Obtain a business data lineage diagram pre-constructed for the banking business system; each node in the business data lineage diagram corresponds to a bank data entity, and each edge in the business data lineage diagram corresponds to the data flow relationship between the bank data entities;

[0008] Based on the change type input by the user, the target subgraph corresponding to the change type is determined from each preset subgraph;

[0009] Starting from the change node, each candidate node that matches the root node attribute of the target subgraph is selected in the business data lineage graph;

[0010] Based on the target subgraph and each of the candidate nodes, subgraph matching is performed in the business data lineage graph to obtain a matching result that is isomorphic to the target subgraph;

[0011] Each matching path in the matching results is scored based on a preset impact scoring rule.

[0012] Based on each matching path and the corresponding score, change impact analysis results are generated.

[0013] Optionally, the process of constructing a business data lineage diagram for a banking business system includes:

[0014] Obtain database logs and API gateway interface logs from the banking business system;

[0015] The database logs and API gateway interface logs are parsed to determine the data flow relationships between each bank data entity and the data flow relationships between each bank data entity.

[0016] A business data lineage diagram is constructed based on the data flow relationships between the various bank data entities.

[0017] Optionally, the attribute information of each edge in the business data lineage graph includes dependency strength; the dependency strength includes strong dependency and weak dependency.

[0018] Accordingly, the step of performing subgraph matching in the business data lineage graph based on the target subgraph and each of the candidate nodes to obtain a matching result isomorphic to the target subgraph includes:

[0019] Starting from the change node in the business data lineage graph, traverse each edge with a strong dependency strength, and determine the candidate node connected by each edge with a strong dependency strength as a strong dependency node.

[0020] From each edge and each strongly dependent node, extract the matching results that are isomorphic to the target subgraph.

[0021] Optionally, the step of scoring each matching path in the matching results based on a preset impact scoring rule includes:

[0022] For each matching path in the matching results, a score is calculated based on the preset dependency weights corresponding to each edge in the matching path, the preset business weights corresponding to each node, and the preset scoring rules to obtain the score corresponding to the matching path.

[0023] Optionally, generating change impact analysis results based on each matching path and the corresponding score includes:

[0024] Based on each matching path and its corresponding score, the matching paths are sorted to obtain the change impact analysis results.

[0025] Optionally, after obtaining a matching result isomorphic to the target subgraph, the method further includes:

[0026] For each matching path in the matching result, determine whether the matching path satisfies the preset constraint conditions corresponding to the target subgraph;

[0027] If not, the matching path is removed from the matching results.

[0028] Optionally, the method further includes:

[0029] Based on the results of the change impact analysis and the preset incremental algorithm, the business data lineage diagram is updated.

[0030] A second aspect of this invention discloses a device for analyzing the impact of changes in banking business data, the device comprising:

[0031] The acquisition unit is used to acquire a business data lineage diagram pre-constructed for the banking business system; each node in the business data lineage diagram corresponds to a bank data entity, and each edge in the business data lineage diagram corresponds to the data flow relationship between the bank data entities.

[0032] The determining unit is used to determine the target subgraph corresponding to the change type from preset subgraphs based on the change type input by the user.

[0033] The filtering unit is used to filter out candidate nodes that match the root node attributes of the target subgraph in the business data lineage graph, starting from the change node.

[0034] A subgraph matching unit is used to perform subgraph matching in the business data lineage graph based on the target subgraph and each of the candidate nodes to obtain a matching result that is isomorphic to the target subgraph;

[0035] The scoring unit is used to score each matching path in the matching result based on a preset impact scoring rule;

[0036] The output unit is used to generate change impact analysis results based on each matching path and the score corresponding to the matching path.

[0037] Optionally, the device further includes:

[0038] The building unit is used to obtain database logs and API gateway interface logs from the banking business system;

[0039] The database logs and API gateway interface logs are parsed to determine the data flow relationships between each bank data entity and the data flow relationships between each bank data entity.

[0040] A business data lineage diagram is constructed based on the data flow relationships between the various bank data entities.

[0041] Optionally, the attribute information of each edge in the business data lineage graph includes dependency strength; the dependency strength includes strong dependency and weak dependency.

[0042] Accordingly, the subgraph matching unit is specifically used for:

[0043] Starting from the change node in the business data lineage graph, traverse each edge with a strong dependency strength, and determine the candidate node connected by each edge with a strong dependency strength as a strong dependency node.

[0044] From each edge and each strongly dependent node, extract the matching results that are isomorphic to the target subgraph.

[0045] Based on the above embodiments of the present invention, a method and related equipment for analyzing the impact of changes in banking business data are provided. The method involves obtaining a pre-constructed business data lineage graph for the banking business system. Each node in the business data lineage graph corresponds to a bank data entity, and each edge in the business data lineage graph corresponds to the data flow relationship between the bank data entities. Based on the change type input by the user, a target subgraph corresponding to the change type is determined from preset subgraphs. Starting from the change node, candidate nodes matching the root node attributes of the target subgraph are selected in the business data lineage graph. Based on the target subgraph and each candidate node, subgraph matching is performed in the business data lineage graph to obtain a matching result isomorphic to the target subgraph. Based on preset impact scoring rules, each matching path in the matching result is scored. Based on each matching path and the score corresponding to the matching path, a change impact analysis result is generated. In this solution, by modeling the dependency relationships of the entire lifecycle of banking business data as a business data lineage graph, and matching predefined target subgraphs representing the scope of data change impact according to the change type, the impact paths of different change scenarios are accurately captured, thereby improving the efficiency and accuracy of change impact analysis. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0047] Figure 1 This is a flowchart of a method for analyzing the impact of changes in banking business data, as disclosed in an embodiment of the present invention.

[0048] Figure 2 This is a structural diagram of a banking business data change impact analysis device disclosed in an embodiment of the present invention;

[0049] Figure 3 This is a structural diagram of an electronic device disclosed in an embodiment of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0052] First, the technical terms appearing in this application will be explained as follows:

[0053] Node V: Data entity and processing unit.

[0054] Edge E: Data dependency and flow relationship.

[0055] A directed graph is a graph with directionality, where each edge in a direction connects to a pair of ordered vertices. In a lineage graph, the directed edges represent the direction of data flow, and the corresponding ordered vertices represent the source data table or field and the target data table or field, respectively.

[0056] Data changes: Bank data changes are divided into various types, including structural changes, content changes, and permission changes. Structural changes include adding or deleting fields in database tables and adjusting indexes. Content changes include correcting data values ​​and cleaning data. Permission changes include adjusting data access permissions and modifying data encryption strategies.

[0057] Subgraph isomorphism: Given two graphs P and Q, they are completely identical in their one-to-one correspondence of nodes and edges, that is, every node and edge in the pattern graph can be found in the target graph with the same structure.

[0058] Subgraph matching: Given a data graph G and a query graph Q, subgraph matching finds all subgraphs in G that are isomorphic to Q.

[0059] As can be seen from the background technology, existing technologies are unable to accurately identify key impact links in massive, multi-level data dependencies when banking business data changes, resulting in low efficiency and poor accuracy in change impact analysis.

[0060] Therefore, this invention discloses a method and related equipment for analyzing the impact of changes in banking business data. It constructs a banking data lineage graph, abstracting data entities as nodes and data flow relationships as edges, adding attributes to nodes and edges. Multiple data change impact subgraph patterns are predefined, each containing node structure and constraints. When a data change is detected, the corresponding subgraph pattern is selected and matching is performed in the data lineage graph. Based on the matching results, a change impact path is generated, and the impact risk level (score) is assessed. In this solution, by modeling the dependencies throughout the entire lifecycle of banking business data as a business data lineage graph, and matching predefined target subgraphs representing the scope of data change impact according to the change type, the impact paths of different change scenarios are accurately captured, thereby improving the efficiency and accuracy of change impact analysis.

[0061] like Figure 1 The diagram shown is a flowchart of a method for analyzing the impact of changes in banking business data, as disclosed in an embodiment of the present invention. It mainly includes the following steps:

[0062] Step S101: Obtain the business data lineage diagram pre-built for the banking business system.

[0063] In the business data lineage graph, each node corresponds to a bank data entity, and each edge corresponds to the data flow relationship between the bank data entities.

[0064] In the specific implementation of step S101, the database logs and API gateway interface logs of the banking business system are obtained; the database logs and API gateway interface logs are parsed to determine the data flow relationships between each bank data entity; and a business data lineage diagram is constructed based on the data flow relationships between each bank data entity.

[0065] In this embodiment of the invention, targeted banking business data modeling incorporates the characteristics of banking business data, such as high compliance and multi-source heterogeneity, into the lineage graph construction, supporting special types of nodes such as sensitive data and reports.

[0066] It should be noted that read and write relationships are extracted from database logs, and interface call dependencies are extracted from API gateway logs. These are then parsed and processed to determine the data flow relationships between various bank data entities. Based on these relationships, a business data lineage graph is constructed, and a large-scale data lineage graph is stored in the TGDB database.

[0067] For example, bank data entities, such as database tables, API interfaces, and reports, are abstracted as nodes, and data flow relationships, such as ETL processing and permission dependencies, are abstracted as edges. A directed graph G=(V, E) (i.e., a business data lineage graph) is constructed as the basic carrier for subsequent subgraph matching.

[0068] The node attributes include sensitivity level, business area, and update frequency. Sensitivity level is divided into three levels: high, medium, and low. Business area can be divided into personal finance, risk control, etc. Update frequency can be divided into real-time, daily, monthly, etc.

[0069] Edge attributes include dependency strength and compliance tags. Dependency strength is divided into two levels: strong and weak. Compliance tags are divided into two types: requiring approval and not requiring approval.

[0070] Optionally, a hash index can be created for key node attributes. For example, if the sensitivity level is high, the hash index can be used to quickly filter out irrelevant nodes (i.e., nodes with a low sensitivity level). A range index can be created for key edge attributes. For example, if the dependency strength is strong, the range index can be used to quickly find all edges with strong dependency strength.

[0071] Step S102: Based on the change type input by the user, determine the target subgraph corresponding to the change type from the preset subgraphs.

[0072] In step S102, various subgraph patterns are designed, such as structural changes, content changes, and permission changes, to cover all scenarios of bank data changes. Each subgraph pattern corresponds to a subgraph. The corresponding subgraph pattern is determined according to the change type input by the user, and then the subgraph corresponding to the subgraph pattern is determined as the target subgraph.

[0073] For example, a predefined subgraph mode can be selected based on the type of data change. For instance, field deletion can be handled by selecting structure change mode P1, and permission adjustment can be handled by selecting permission change mode P2.

[0074] Step S103: Starting from the change node, filter out each candidate node in the business data lineage graph that matches the root node attribute of the target subgraph.

[0075] In step S103, starting from the changed node, candidate nodes that meet the root node attributes of the target subgraph are filtered by index. For example, in the permission change mode, data source nodes with a high sensitivity level and a permission type of query need to be filtered.

[0076] In one embodiment, pruning optimization is performed during the candidate node screening process. For example, offline nodes, test environment nodes, and other non-production environment nodes are excluded, and low-risk nodes, such as historical archived data, are given lower priority.

[0077] Step S104: Based on the target subgraph and each candidate node, perform subgraph matching in the business data lineage graph to obtain matching results that are isomorphic to the target subgraph.

[0078] In step S104, a suitable matching strategy is selected, including forward matching and reverse matching. Forward matching starts from the changed node and traverses downstream nodes along the data flow (e.g., change table - ETL - report - interface). Reverse matching traces upstream dependencies from the faulty node. Both matching strategies can obtain matching results that are isomorphic to the target subgraph.

[0079] The attribute information of each edge in the aforementioned business data lineage graph includes dependency strength; dependency strength includes strong dependency and weak dependency.

[0080] Accordingly, in the specific implementation of step S104, starting from the change node in the business data lineage graph, each edge with strong dependency strength is traversed, and the candidate nodes connected by each edge with strong dependency strength are determined as strong dependency nodes; from each edge with strong dependency strength and each strong dependency node, the matching result isomorphic to the target subgraph is extracted.

[0081] In this embodiment of the invention, strong dependency nodes are selected first to obtain matching results consisting of matching paths composed of strong dependency nodes. Strong dependency nodes include core interface calls. This allows for priority analysis of the impact of changes.

[0082] In another specific implementation of step S104, a layered traversal is performed based on the dependency strength of the edges. The first layer matches nodes with strong dependencies, such as core interface calls; the second layer matches nodes with weak dependencies, such as log transmission, which are considered secondary influences; and the third layer matches nodes with indirect dependencies. Implicit dependencies are identified using SQL parsing tools.

[0083] In one embodiment, each subgraph pattern includes not only the corresponding target subgraph, but also the constraints corresponding to the target subgraph.

[0084] After obtaining the matching results that are isomorphic to the target subgraph, for each matching path in the matching results, it is determined whether the matching path satisfies the preset constraints corresponding to the target subgraph; if not, the matching path is removed from the matching results.

[0085] For example, constraints such as time window constraints stipulate that only dependency links within a specified time before and after the change occur, and compliance constraints stipulate that permission changes must verify the department's filing status and approval records, etc.

[0086] In this embodiment of the invention, the constraint-enhanced subgraph matching improves matching accuracy and reduces false alarms through attribute constraints, time constraints, and dependency strength constraints.

[0087] Optionally, multi-mode cross-validation is used to match multiple subgraph patterns in parallel. If the same node is matched by multiple target subgraphs, it is marked as a high-risk node.

[0088] Step S105: Based on the preset impact scoring rules, score each matching path in the matching results.

[0089] In the specific implementation of step S105, for each matching path in the matching result, the score corresponding to the matching path is calculated based on the preset dependency weights corresponding to each edge in the matching path, the preset business weights corresponding to each node, and the preset scoring rules.

[0090] The preset dependency weights for each edge can be obtained from the edge attributes, and the preset business weights for each node can be obtained from the node attributes.

[0091] Specifically, the scoring rules are as follows:

[0092] The score corresponding to the matching path = ⅀ (dependency weight × business weight)

[0093] In this embodiment of the invention, the matching results are converted into quantifiable risk scores to support differentiated response strategies.

[0094] Step S106: Generate change impact analysis results based on each matching path and the score corresponding to the matching path.

[0095] In the specific implementation of step S106, each matching path is sorted based on each matching path and the score corresponding to the matching path to obtain the change impact analysis results.

[0096] In one embodiment, the business data lineage diagram is updated based on the results of change impact analysis and a preset incremental algorithm.

[0097] It should be noted that incremental algorithms, such as IncIsoMatch (continuous subgraph matching), only update the local subgraph affected by the change. For example, when adding a new data table, only the upstream and downstream dependent edges of that data table are reconstructed, rather than the entire graph is reconstructed.

[0098] Based on the above embodiments of the present invention, a method for analyzing the impact of changes in banking business data accurately identifies the scope of impact of changes on downstream business systems, reports, and compliance when banking business data changes. By modeling the dependencies throughout the entire lifecycle of banking business data as a business data lineage diagram, and matching predefined target subgraphs representing the scope of data change impact according to the change type, the method accurately captures the impact paths of different change scenarios, thereby improving the efficiency and accuracy of change impact analysis. In banking data governance, this method can support risk assessment, compliance approval, and secure implementation of data changes, addressing the challenges of complex dependencies and hidden impacts of changes in banking business data, and improving the efficiency of change impact analysis.

[0099] Based on the above embodiments of the present invention, a method for analyzing the impact of changes in banking business data is disclosed, such as... Figure 2 The diagram shown is a structural diagram of a banking business data change impact analysis device disclosed in an embodiment of the present invention. The device includes: an acquisition unit 201, a determination unit 202, a filtering unit 203, a subgraph matching unit 204, a scoring unit 205, and an output unit 206.

[0100] The acquisition unit 201 is used to acquire a business data lineage diagram pre-constructed for the banking business system; each node in the business data lineage diagram corresponds to a bank data entity, and each edge in the business data lineage diagram corresponds to the data flow relationship between the bank data entities.

[0101] The determining unit 202 is used to determine the target subgraph corresponding to the change type from preset subgraphs based on the change type input by the user;

[0102] The filtering unit 203 is used to filter out each candidate node that matches the root node attribute of the target subgraph in the business data lineage graph, starting from the change node.

[0103] The subgraph matching unit 204 is used to perform subgraph matching in the business data lineage graph based on the target subgraph and each of the candidate nodes to obtain a matching result that is isomorphic to the target subgraph;

[0104] Scoring unit 205 is used to score each matching path in the matching result based on a preset impact scoring rule;

[0105] Output unit 206 is used to generate change impact analysis results based on each of the matching paths and the scores corresponding to the matching paths.

[0106] In one embodiment, the device further includes:

[0107] The building unit is used to obtain database logs and API gateway interface logs from the banking business system;

[0108] The database logs and API gateway interface logs are parsed to determine the data flow relationships between each bank data entity and the data flow relationships between each bank data entity.

[0109] A business data lineage diagram is constructed based on the data flow relationships between the various bank data entities.

[0110] In one embodiment, the attribute information of each edge in the business data lineage graph includes dependency strength; the dependency strength includes strong dependency and weak dependency.

[0111] Accordingly, the subgraph matching unit 204 is specifically used for:

[0112] Starting from the change node in the business data lineage graph, traverse each edge with a strong dependency strength, and determine the candidate node connected by each edge with a strong dependency strength as a strong dependency node.

[0113] From each edge and each strongly dependent node, extract the matching results that are isomorphic to the target subgraph.

[0114] In one embodiment, the scoring unit 205 is specifically used for:

[0115] For each matching path in the matching results, a score is calculated based on the preset dependency weights corresponding to each edge in the matching path, the preset business weights corresponding to each node, and the preset scoring rules to obtain the score corresponding to the matching path.

[0116] In one embodiment, the output unit 206 is specifically used for:

[0117] Based on each matching path and its corresponding score, the matching paths are sorted to obtain the change impact analysis results.

[0118] In one embodiment, the device further includes:

[0119] The constraint unit is used to determine, after obtaining a matching result isomorphic to the target subgraph, whether each matching path in the matching result satisfies the preset constraint conditions corresponding to the target subgraph; if not, the matching path is removed from the matching result.

[0120] In one embodiment, the device further includes:

[0121] The update unit is used to update the business data lineage diagram based on the change impact analysis results and a preset incremental algorithm.

[0122] Based on the above embodiments of the present invention, a device for analyzing the impact of changes in banking business data accurately identifies the scope of impact of changes on downstream business systems, reports, and compliance when banking business data changes. By modeling the dependencies throughout the entire lifecycle of banking business data as a business data lineage diagram, and matching predefined target subgraphs representing the scope of data change impact according to the change type, the device accurately captures the impact paths of different change scenarios, thereby improving the efficiency and accuracy of change impact analysis. This method can support risk assessment, compliance approval, and secure implementation of data changes in bank data governance, addressing the challenges of complex dependencies and hidden impacts of changes in banking business data, and improving the efficiency of change impact analysis.

[0123] This invention also provides an electronic device, please refer to... Figure 3 The electronic device includes a memory 301 and a processor 302.

[0124] Among them, memory 301 is used to store computer programs;

[0125] The processor 302 is used to execute computer programs, specifically to implement a method for analyzing the impact of changes in banking business data provided in any embodiment of this application.

[0126] This application also provides a computer storage medium for storing a computer program, which, when executed, is specifically used to implement a banking business data change impact analysis method provided in any embodiment of this application.

[0127] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0128] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0129] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing the impact of changes in banking business data, characterized in that, The method includes: Obtain a business data lineage diagram pre-constructed for the banking business system; each node in the business data lineage diagram corresponds to a bank data entity, and each edge in the business data lineage diagram corresponds to the data flow relationship between the bank data entities; Based on the change type input by the user, the target subgraph corresponding to the change type is determined from each preset subgraph; Starting from the change node, each candidate node that matches the root node attribute of the target subgraph is selected in the business data lineage graph; Based on the target subgraph and each of the candidate nodes, subgraph matching is performed in the business data lineage graph to obtain a matching result that is isomorphic to the target subgraph; Each matching path in the matching results is scored based on a preset impact scoring rule. Based on each matching path and the corresponding score, change impact analysis results are generated.

2. The method according to claim 1, characterized in that, The process of constructing a business data lineage diagram for a banking business system includes: Obtain database logs and API gateway interface logs from the banking business system; The database logs and API gateway interface logs are parsed to determine the data flow relationships between each bank data entity and the data flow relationships between each bank data entity. A business data lineage diagram is constructed based on the data flow relationships between the various bank data entities.

3. The method according to claim 1, characterized in that, The attribute information of each edge in the business data lineage graph includes dependency strength; the dependency strength includes strong dependency and weak dependency. Accordingly, the step of performing subgraph matching in the business data lineage graph based on the target subgraph and each of the candidate nodes to obtain a matching result isomorphic to the target subgraph includes: Starting from the change node in the business data lineage graph, traverse each edge with a strong dependency strength, and determine the candidate node connected by each edge with a strong dependency strength as a strong dependency node. From each edge and each strongly dependent node, extract the matching results that are isomorphic to the target subgraph.

4. The method according to claim 1, characterized in that, The method of scoring each matching path in the matching results based on the preset influence scoring rules includes: For each matching path in the matching results, a score is calculated based on the preset dependency weights corresponding to each edge in the matching path, the preset business weights corresponding to each node, and the preset scoring rules to obtain the score corresponding to the matching path.

5. The method according to claim 1, characterized in that, The step of generating change impact analysis results based on each matching path and the corresponding score includes: Based on each matching path and its corresponding score, the matching paths are sorted to obtain the change impact analysis results.

6. The method according to claim 1, characterized in that, After obtaining a matching result isomorphic to the target subgraph, the method further includes: For each matching path in the matching result, determine whether the matching path satisfies the preset constraint conditions corresponding to the target subgraph; If not, the matching path is removed from the matching results.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the results of the change impact analysis and the preset incremental algorithm, the business data lineage diagram is updated.

8. A device for analyzing the impact of changes in banking business data, characterized in that, The device includes: The acquisition unit is used to acquire a business data lineage diagram pre-constructed for the banking business system; each node in the business data lineage diagram corresponds to a bank data entity, and each edge in the business data lineage diagram corresponds to the data flow relationship between the bank data entities. The determining unit is used to determine the target subgraph corresponding to the change type from preset subgraphs based on the change type input by the user. The filtering unit is used to filter out candidate nodes that match the root node attributes of the target subgraph in the business data lineage graph, starting from the change node. A subgraph matching unit is used to perform subgraph matching in the business data lineage graph based on the target subgraph and each of the candidate nodes to obtain a matching result that is isomorphic to the target subgraph; The scoring unit is used to score each matching path in the matching result based on a preset impact scoring rule; The output unit is used to generate change impact analysis results based on each matching path and the score corresponding to the matching path.

9. The apparatus according to claim 8, characterized in that, The device further includes: The building unit is used to obtain database logs and API gateway interface logs from the banking business system; The database logs and API gateway interface logs are parsed to determine the data flow relationships between each bank data entity and the data flow relationships between each bank data entity. A business data lineage diagram is constructed based on the data flow relationships between the various bank data entities.

10. The apparatus according to claim 8, characterized in that, The attribute information of each edge in the business data lineage graph includes dependency strength; the dependency strength includes strong dependency and weak dependency. Accordingly, the subgraph matching unit is specifically used for: Starting from the change node in the business data lineage graph, traverse each edge with a strong dependency strength, and determine the candidate node connected by each edge with a strong dependency strength as a strong dependency node. From each edge and each strongly dependent node, extract the matching results that are isomorphic to the target subgraph.

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