Mechanism data processing method and device

Abstract

The invention provides an institution data processing method and device, and the method comprises the steps: obtaining the institution data of a target enterprise, the institution data at least comprising the institution attributes and the relation data between the institutions; constructing a plurality of scenarized relation models based on the mechanism data; and constructing an organization knowledge graph corresponding to the target enterprise by using the scenarized relation model. According to the scheme, the plurality of scene relation models are constructed by utilizing the organization data of the target enterprise, then the organization knowledge graph corresponding to the target enterprise is constructed by utilizing the scene relation models, and the organization data are stored and verified by utilizing the organization knowledge graph, so that the processing effect of the organization data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for processing institutional data. Background Art

[0002] Organizations are an important part of enterprise operations. The same organization has multiple attributes and functions, and the types and number of relationships between organizations are numerous. At present, organization data is usually stored directly in the database, but this method is resource-consuming and unable to verify the correctness of the stored organization data, resulting in poor processing effect of organization data. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a method and device for processing organization data to improve the processing effect of organization data.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0005] A first aspect of an embodiment of the present invention discloses a method for processing organization data, the method comprising:

[0006] Acquire the organization data of the target enterprise, wherein the organization data at least includes organization attributes and inter-organization relationship data;

[0007] Based on the institutional data, multiple scenario-based relationship models are constructed;

[0008] The scenario-based relationship model is used to construct an organizational knowledge graph corresponding to the target enterprise.

[0009] Preferably, multiple scenario-based relationship models are constructed based on the organization data, including:

[0010] The institutional data are integrated according to the business of the target enterprise to construct a plurality of scenario-based relationship models, wherein the plurality of scenario-based relationship models at least include an administrative affiliation relationship model, a fund settlement relationship model and a data aggregation relationship model.

[0011] Preferably, after constructing the organizational knowledge graph corresponding to the target enterprise using the scenario-based relationship model, the method further includes:

[0012] Performing quality inspection on the organizational knowledge graph;

[0013] If the organizational knowledge graph passes the quality inspection, the organizational knowledge graph is stored in an enterprise-level organizational knowledge base.

[0014] Preferably, quality testing of the organizational knowledge graph includes:

[0015] Performing isolated node detection, cycle detection and edge missing detection on the organizational knowledge graph;

[0016] When the organizational knowledge graph passes the isolated node detection, the cycle detection and the edge missing detection, it is determined that the organizational knowledge graph passes the quality detection.

[0017] Preferably, before constructing a plurality of scenario-based relationship models based on the organization data, the method further includes:

[0018] The organization data is cleaned, and the cleaning process includes any one or more of the following: deleting duplicate data, deleting logical error data, and completing data.

[0019] A second aspect of an embodiment of the present invention discloses a device for processing organization data, the device comprising:

[0020] An acquisition unit, used to acquire the organization data of the target enterprise, wherein the organization data at least includes organization attributes and inter-organization relationship data;

[0021] A first construction unit, configured to construct a plurality of scenario-based relationship models based on the organization data;

[0022] The second construction unit is used to use the scenario-based relationship model to construct an organizational knowledge graph corresponding to the target enterprise.

[0023] Preferably, the first construction unit is specifically used to: integrate the institutional data according to the business of the target enterprise to construct multiple scenario-based relationship models, and the multiple scenario-based relationship models at least include an administrative affiliation model, a fund settlement relationship model and a data aggregation relationship model.

[0024] Preferably, it also includes:

[0025] A detection unit, used for performing quality detection on the organizational knowledge graph;

[0026] A storage unit is used to store the organizational knowledge graph into an enterprise-level organizational knowledge base if the organizational knowledge graph passes the quality inspection.

[0027] Preferably, the detection unit is specifically used to: perform isolated node detection, cycle detection and edge missing detection on the organizational knowledge graph; when the organizational knowledge graph passes the isolated node detection, the cycle detection and the edge missing detection, it is determined that the organizational knowledge graph passes the quality detection.

[0028] Preferably, it also includes:

[0029] A cleaning unit is used to perform cleaning processing on the organization data, and the cleaning processing includes any one or more of the following: deleting duplicate data, deleting logical error data, and completing data.

[0030] Based on the above-mentioned embodiment of the present invention, a method and device for processing institutional data are provided. The method comprises: obtaining the institutional data of the target enterprise, the institutional data at least including institutional attributes and inter-institutional relationship data; constructing multiple scenario-based relationship models based on the institutional data; and constructing the organizational knowledge graph corresponding to the target enterprise using the scenario-based relationship models. In this solution, multiple scenario-based relationship models are constructed using the institutional data of the target enterprise, and then the scenario-based relationship models are used to construct the organizational knowledge graph corresponding to the target enterprise. The organizational knowledge graph is used to store and verify the institutional data, thereby improving the processing effect of the institutional data. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0032] Figure 1 A flowchart of a method for processing institutional data provided by an embodiment of the present invention;

[0033] Figure 2 An example diagram of a construction scheme for an organizational knowledge graph provided in an embodiment of the present invention;

[0034] Figure 3 Another flow chart of a method for processing organization data provided by an embodiment of the present invention;

[0035] Figure 4 A structural block diagram of a device for processing mechanism data provided by an embodiment of the present invention;

[0036] Figure 5 Another structural block diagram of a device for processing mechanism data provided by an embodiment of the present invention;

[0037] Figure 6 Another structural block diagram of a device for processing mechanism data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0040] In order to better understand the following content, some terms are explained here:

[0041] Institutional code: It is the unique code for identifying an institution, plays the important role of an "identity card", and is the basis and foundation for the unified and centralized management of institutional information.

[0042] Graph: A graph data structure consists of a finite (possibly mutable) set of nodes and a set of unordered pairs (for undirected graphs) or ordered pairs (for directed graphs) of edges (also called arcs in directed graphs). Nodes can be part of the graph structure or external entities represented by integer subscripts or references.

[0043] Graph database: A graph database is a database that uses a graph structure for semantic queries. It uses nodes, edges, and attributes to represent and store data. The key concept of a graph database is the graph, which directly associates data items in storage with a collection of data nodes and edges representing relationships between nodes. These relationships allow data in storage areas to be directly linked together and, in many cases, retrieved with a single operation.

[0044] Knowledge graph: Knowledge graph is a new generation of structured information organization method based on semantics. It describes objective things in the form of graphs and is used to describe concepts and their relationships in the physical world in symbolic form. The basic component unit of the knowledge graph is the "entity-relationship-entity" triple, as well as entities and their related attribute value pairs. Entities are connected to each other through relationships to form a network knowledge structure.

[0045] Entity: refers to something that is distinguishable and exists independently. Entity is the most basic element in the knowledge graph, and different entities have different relationships.

[0046] Relationship: Relationships connect different entities and refer to the connection between entities. The nodes in the knowledge graph are connected through relationship nodes to form a large graph.

[0047] Organizational structure is an important part of enterprise operation. The same organizational structure has multiple attributes and multiple functions. The various relationships between organizational structures are numerous and huge in number. Taking a certain bank as an example, the internal organizational structure of the bank not only has public data items such as organization code, organization name, and organization address, but also has some special data such as organization operation status. At the same time, there are various affiliations between organizations, such as administrative affiliations, fund settlement relationships, data aggregation relationships, etc., which are an important attribute that is indispensable for the business management and system construction within the bank.

[0048] Currently, institutional data is usually stored directly in the database, but this method is resource-consuming and unable to verify the correctness of the stored institutional data. If an organization lacks a certain relationship, or if the relationship chain forms a closed loop, it may have a huge impact on the company's related business.

[0049] Therefore, the present invention provides a method and device for processing institutional data, which uses the institutional data of a target enterprise to construct multiple scenario-based relationship models, and then uses the scenario-based relationship models to construct an organizational knowledge graph corresponding to the target enterprise, so as to store and verify the institutional data in the organizational knowledge graph, thereby improving the processing effect of the institutional data.

[0050] See also Figure 1 , shows a flow chart of a method for processing institutional data provided by an embodiment of the present invention, the processing method comprising:

[0051] Step S101: Obtain the organizational data of the target enterprise.

[0052] In the specific implementation of step S101, the organization data of the target enterprise is obtained from a designated data source, and the organization data at least includes organization attributes and inter-organization relationship data.

[0053] For example: Obtain the target enterprise's organizational data from the target enterprise's system for storing organizational-related information.

[0054] Step S102: construct multiple scenario-based relationship models based on the organization data.

[0055] In the specific implementation of step S102, the institutional data is integrated according to the business of the target enterprise to construct multiple scenario-based relationship models, and the multiple scenario-based relationship models at least include an administrative affiliation relationship model, a fund settlement relationship model, and a data aggregation relationship model;

[0056] In some embodiments, before executing step S102, the organization data is cleaned, and the cleaning process includes any one or more of the following: deleting duplicate data, deleting logically erroneous data, and completing data.

[0057] In other words, the collected institutional data is cleaned by deleting duplicate data, deleting logically erroneous data, and completing data, and then the cleaned institutional data is integrated according to the business of the target enterprise, thereby constructing multiple scenario-based relationship models, each of which contains a scenario-based relationship data set.

[0058] Step S103: Use the scenario-based relationship model to construct an organizational knowledge graph corresponding to the target enterprise.

[0059] In the specific implementation of step S103, a single organization in the target enterprise is used as an "entity" node in the knowledge graph, the relationship between the organizations is used as a "relationship" in the knowledge graph, all entity nodes are set as graph nodes, and the relationship between the graph nodes is represented by directed line segments;

[0060] The relational data sets in the scenario-based relational model are classified and stored in the graph database for constructing and drawing the knowledge graph, thereby constructing the organizational knowledge graph corresponding to the target enterprise.

[0061] It should be noted that the knowledge graph consists of a data layer and a model layer. The model layer is the conceptual model and logical basis of the knowledge graph, and it regulates and constrains the data layer.

[0062] In the model layer of the knowledge graph, nodes represent ontology concepts and edges represent relationships between concepts. The data layer of the knowledge graph is an instance of the ontology, which is stored in triples of "entity-relationship-entity" or "entity-attribute-attribute value" to form a graph-like knowledge base, where entities are the basic elements of the knowledge graph. In this invention, entities refer to specific organizational names, and "relationships" are semantic relationships between two entities. In this invention, "relationships" refer to relationships between organizations.

[0063] When constructing a knowledge graph, there are two main ways to construct the knowledge graph: "top-down" and "bottom-up". The bottom-up approach refers to extracting concepts such as entities, relationships, and attributes based on the underlying data to summarize the framework of the upper-level knowledge graph.

[0064] Since the data to be constructed is enterprise-level organizational information, and the data source is a large number of structured organizational basic information tables and relationship information tables between organizations, this solution adopts a bottom-up approach to construct an organizational knowledge graph.

[0065] The construction scheme of organizational knowledge graph is as follows: Figure 2As shown in the figure, the organizational knowledge graph is constructed in a bottom-up manner.

[0066] In general, the construction of an organizational knowledge graph mainly includes two parts: the construction of the model layer and the construction of the data layer. The following explains the construction of the model layer and the construction of the data layer:

[0067] Construction of the model layer: Entities, attributes and relationships are represented in the form of triples. Entities, relationships and attributes are determined by analyzing the field definitions of the data table and the relationships between them. The specific contents of entity types and key attributes are shown in Table 1, and the specific contents of relationship types and key attributes are shown in Table 2.

[0068] Table 1:

[0069] Entity Type Key attributes Organization Organization code, organization name, organization address, etc.

[0070] Table 2:

[0071] Entities on both sides of the relationship Relationship Type Key attributes Organization-Organization Administrative affiliation Higher level administrative agency code Organization-Organization Funds settlement relationship Code of the superior clearing institution Organization-Organization Data aggregation relationship Code of superior data aggregation agency

[0072] Construction of the data layer: The processed triple data is stored in the graph database to construct the organizational knowledge graph.

[0073] In some embodiments, after the organizational knowledge graph is constructed, the organizational knowledge graph is subjected to quality inspection. When the accuracy value obtained by the quality inspection is greater than the accuracy threshold, it is determined that the organizational knowledge graph has passed the quality inspection, and the organizational knowledge graph is stored in the enterprise-level organizational knowledge base.

[0074] That is to say, the organizational knowledge graph is subjected to quality inspection; if the organizational knowledge graph passes the quality inspection, the organizational knowledge graph is stored in the enterprise-level organizational knowledge base.

[0075] Specifically, quality detection includes three parts: isolated node detection, cycle detection and missing edge detection. Isolated node detection, cycle detection and missing edge detection are performed on the organizational knowledge graph. When the organizational knowledge graph passes the isolated node detection, cycle detection and missing edge detection, it is determined that the organizational knowledge graph has passed the quality detection.

[0076] Among them, isolated node detection specifically means that the accuracy value obtained by isolated node detection is greater than the accuracy threshold. The same is true for cycle detection and edge missing detection, which will not be repeated here.

[0077] To better understand isolated node detection, cycle detection, and edge missing detection, the following explains isolated node detection, cycle detection, and edge missing detection respectively:

[0078] Isolated node detection: An isolated node refers to a node that is not connected to any other node in the graph. Corresponding to the organizational knowledge graph, an isolated node is an organization that has no relationship with any other organization.

[0079] Cycle detection: A cycle refers to a path in a graph that starts from a vertex, passes through a series of edges, and then returns to this vertex. In the knowledge graph of an enterprise organization, it is necessary to detect whether there are unhealthy cycles.

[0080] Missing edge detection: For organizations, some relationships must exist, such as basic administrative affiliations. If some nodes are expected to have edges but do not appear in the graph, this means that the data may be missing or entered incorrectly, and this situation also needs to be identified.

[0081] When the organizational knowledge graph passes the isolated node detection, cycle detection and edge missing detection, and is determined to have passed the quality inspection, the organizational knowledge graph is stored in the enterprise-level organizational knowledge base. The final enterprise-level organizational knowledge base can not only clearly and explicitly display the organizational attributes and relationships, but also ensure the accuracy of the data.

[0082] In an embodiment of the present invention, multiple scenario-based relationship models are constructed using the target enterprise's organizational data, and then the scenario-based relationship models are used to construct an organizational knowledge graph corresponding to the target enterprise. The organizational knowledge graph is used to store and verify the organizational data, thereby improving the processing effect of the organizational data.

[0083] See also Figure 3 , shows another flow chart of a method for processing institutional data provided by an embodiment of the present invention, the method for processing institutional data comprising:

[0084] Step S301: Obtain the organizational data of the target enterprise.

[0085] Step S302: Clean the organization data.

[0086] Step S303: construct multiple scenario-based relationship models based on the cleaned organizational data.

[0087] Step S304: Use the scenario-based relationship model to construct an organizational knowledge graph corresponding to the target enterprise.

[0088] Step S305: Perform quality inspection on the organizational knowledge graph; if the organizational knowledge graph passes the quality inspection, store the organizational knowledge graph in the enterprise-level organizational knowledge base.

[0089] It should be noted that the execution principle of step S301 to step S305 can be found in the above embodiment of the present invention. Figure 1 The contents in will not be repeated here.

[0090] This solution constructs an organizational knowledge graph through various scenario-based relationship models, and performs quality inspection on the organizational knowledge graph to determine whether the quality of the organization's data meets the standards. In this way, the attributes of the organization and the relationships between organizations can be made clearer and more visual. At the same time, the correctness of the stored organization data can be verified to meet the business needs of various scenarios.

[0091] Corresponding to the method for processing the organization data provided by the above embodiment of the present invention, see Figure 4 , an embodiment of the present invention also provides a structural block diagram of a device for processing mechanism data, the processing device comprising: an acquisition unit 100, a first construction unit 200 and a second construction unit 300;

[0092] The acquisition unit 100 is used to acquire the organization data of the target enterprise, where the organization data at least includes organization attributes and inter-organization relationship data.

[0093] The first construction unit 200 is used to construct multiple scenario-based relationship models based on the organization data.

[0094] In a specific implementation, the first construction unit 200 is specifically used to: integrate the institutional data according to the business of the target enterprise to construct multiple scenario-based relationship models, and the multiple scenario-based relationship models at least include an administrative affiliation relationship model, a fund settlement relationship model, and a data aggregation relationship model.

[0095] The second construction unit 300 is used to construct an organizational knowledge graph corresponding to the target enterprise by using a scenario-based relationship model.

[0096] In an embodiment of the present invention, multiple scenario-based relationship models are constructed using the target enterprise's organizational data, and then the scenario-based relationship models are used to construct an organizational knowledge graph corresponding to the target enterprise. The organizational knowledge graph is used to store and verify the organizational data, thereby improving the processing effect of the organizational data.

[0097] Preferably, combined Figure 4 , see Figure 5 , shows another structural block diagram of a device for processing mechanism data provided by an embodiment of the present invention, the processing device further comprising:

[0098] The detection unit 400 is used to perform quality detection on the organizational knowledge graph.

[0099] In a specific implementation, the detection unit 400 is specifically used to: perform isolated node detection, cycle detection and edge missing detection on the organizational knowledge graph; when the organizational knowledge graph passes the isolated node detection, cycle detection and edge missing detection, it is determined that the organizational knowledge graph has passed the quality detection.

[0100] The storage unit 500 is used to store the organizational knowledge graph into an enterprise-level organizational knowledge base if the organizational knowledge graph passes the quality inspection.

[0101] Preferably, combined Figure 5 , see Figure 6 , shows another structural block diagram of a device for processing mechanism data provided by an embodiment of the present invention, the processing device further comprising:

[0102] The cleaning unit 600 is used to perform cleaning processing on the organization data, and the cleaning processing includes any one or more of the following: deleting duplicate data, deleting logical error data, and completing data.

[0103] To sum up, the embodiments of the present invention provide a method and device for processing institutional data, which utilizes the institutional data of a target enterprise to construct multiple scenario-based relationship models, and then utilizes the scenario-based relationship models to construct an organizational knowledge graph corresponding to the target enterprise, so as to store and verify the institutional data using the organizational knowledge graph, thereby improving the processing effect of the institutional data.

[0104] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0105] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0106] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing institutional data, characterized in that: The method comprises: Acquire the organization data of the target enterprise, wherein the organization data at least includes organization attributes and inter-organization relationship data; Based on the institutional data, multiple scenario-based relationship models are constructed; The scenario-based relationship model is used to construct an organizational knowledge graph corresponding to the target enterprise.

2. The method according to claim 1, characterized in that Based on the organization data, multiple scenario-based relationship models are constructed, including: The institutional data are integrated according to the business of the target enterprise to construct a plurality of scenario-based relationship models, wherein the plurality of scenario-based relationship models at least include an administrative affiliation relationship model, a fund settlement relationship model and a data aggregation relationship model.

3. The method according to claim 1, characterized in that After constructing the organizational knowledge graph corresponding to the target enterprise using the scenario-based relationship model, the method further includes: Performing quality inspection on the organizational knowledge graph; If the organizational knowledge graph passes the quality inspection, the organizational knowledge graph is stored in an enterprise-level organizational knowledge base.

4. The method according to claim 3, characterized in that Performing quality inspection on the organizational knowledge graph, including: Performing isolated node detection, cycle detection and edge missing detection on the organizational knowledge graph; When the organizational knowledge graph passes the isolated node detection, the cycle detection and the edge missing detection, it is determined that the organizational knowledge graph passes the quality detection.

5. The method according to any one of claims 1 to 4, characterized in that: Before constructing multiple scenario-based relationship models based on the organization data, the method further includes: The organization data is cleaned, and the cleaning process includes any one or more of the following: deleting duplicate data, deleting logical error data, and completing data.

6. A device for processing institutional data, characterized in that: The device comprises: An acquisition unit, used to acquire the organization data of the target enterprise, wherein the organization data at least includes organization attributes and inter-organization relationship data; A first construction unit, configured to construct a plurality of scenario-based relationship models based on the organization data; The second construction unit is used to use the scenario-based relationship model to construct an organizational knowledge graph corresponding to the target enterprise.

7. The device according to claim 6, characterized in that The first construction unit is specifically used to: integrate the institutional data according to the business of the target enterprise to construct multiple scenario-based relationship models, and the multiple scenario-based relationship models at least include an administrative affiliation model, a fund settlement relationship model, and a data aggregation relationship model.

8. The device according to claim 6, characterized in that Also includes: A detection unit, used for performing quality detection on the organizational knowledge graph; A storage unit is used to store the organizational knowledge graph into an enterprise-level organizational knowledge base if the organizational knowledge graph passes the quality inspection.

9. The device according to claim 8, characterized in that The detection unit is specifically used to: perform isolated node detection, cycle detection and edge missing detection on the organizational knowledge graph; when the organizational knowledge graph passes the isolated node detection, the cycle detection and the edge missing detection, it is determined that the organizational knowledge graph passes the quality detection.

10. The device according to any one of claims 6 to 9, characterized in that: Also includes: A cleaning unit is used to perform cleaning processing on the organization data, and the cleaning processing includes any one or more of the following: deleting duplicate data, deleting logical error data, and completing data.

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