CN111179052A - Method and system for identifying actual control person - Google Patents
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- CN111179052A CN111179052A CN201911299393.XA CN201911299393A CN111179052A CN 111179052 A CN111179052 A CN 111179052A CN 201911299393 A CN201911299393 A CN 201911299393A CN 111179052 A CN111179052 A CN 111179052A
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The embodiment of the invention discloses a method and a system for identifying an actual control person, wherein the method comprises the following steps: determining an actual controller candidate set from a pre-stored equity network according to the client identifier to be identified; and determining the actual controller of the client to be identified from the actual controller candidate set according to a predefined actual controller mining model. Therefore, the actual control person of the client can be identified from the massive stock right relation, and the identification efficiency of the actual control person is improved.
Description
Technical Field
The embodiment of the invention relates to a data mining technology, in particular to a method and a system for identifying an actual control person.
Background
The actual controller generally refers to a natural person, a legal person or other economic organization which can actually govern the behavior of a company through investment relations, protocols or other arrangements, and the actual controller of the medium-sized and small-sized enterprises is basically a natural person at present. The actual control people are souls of enterprises and are also the key of enterprise development and risk prevention and control, so the quality of the actual control people determines the future of the enterprises to a certain extent. In the actual credit work, a plurality of enterprise employers can be diligent and reluctant in the early development stage of enterprises, and once the enterprises develop to be strong, the enterprises are easy to lose themselves and pay more attention to personal enjoyment. The phenomena that an enterprise actually controls people to buy luxury cars, participate in gambling, buy stocks in large quantities and transfer large funds of the enterprise to a personal account are often encountered, and even the enterprise finally cares nothing about the development of the enterprise, so that the enterprise continuously walks down a slope, and the operation condition is gradually changed, thereby bringing greater risk potential for credit funds of commercial banks. Therefore, in credit risk prevention and control, it is important to analyze the actual controller of a small enterprise, and it is important to effectively identify and grasp the risk of the actual controller in the credit management of the bank.
At present, in order to identify an actual controller, a bank adopts a scheme of identifying the actual controller by using a Structured database as a core and adopting a storage process sql (Structured Query Language) based on the existing system and data. The main idea of the scheme is to traverse a Graph (Graph) generated by the stock right relationship by adopting a Depth First Search (DFS) algorithm, perform exhaustive Search on all the stock right relationships until all nodes meeting the rules are touched, so as to form a final stock control path and identify an actual controller of each client. For example, according to the above traversal process, the specific steps of actually controlling the human recognition are as follows: first, the equity relationship is extracted and a graph is generated. All the stock right relations are derived from the database (the repeated stock right relations are removed), one stock right relation is represented by (x, y), x is a control node (a control person), and y is a controlled node (a controlled person). The stock control relationship is used as an edge, and enterprises (natural people or legal people) are used as nodes to form a graph. Since the stock-holding relationship is directional, it is represented as a directed graph. Second, the graph is represented as an adjacency matrix (X, Y). The adjacency matrix is a two-dimensional array in which each dimension is all nodes in the graph (i.e., business names, or natural or legal persons). When an edge exists between the node i and the node j (namely, a stock control relationship exists), the values of the elements corresponding to the ith row and the jth column are 1, otherwise, the values are 0. The adjacency matrix represents the complex strand right relationship as a clear two-dimensional matrix, which is beneficial to fast searching all adjacent nodes of any node in the graph by the DFS and ensures the high efficiency and accuracy of searching. And thirdly, adopting a DFS algorithm to identify the actual control person. And (5) searching and traversing the adjacent matrixes (X and Y) by using a DFS algorithm to obtain an actual control person identification result.
However, as the number of bank enterprise customers rapidly increases, along with a large number of constantly changing equity relationships, the number of equity relationships constructed in such a way sharply increases, and it is generally difficult to meet the performance requirements of complex graph mining based on the existing actual controller identification scheme. Therefore, based on the actual controller identification scheme, due to the complexity of the stock right relationship network, the current actual controller identification strategy is difficult to meet the requirement of massive stock right relationship data mining. For example, the depth-first algorithm implemented by the conventional sql storage process is difficult to represent a graph as an adjacency matrix in a large data environment; a large number of iterations cause performance and memory bottlenecks, and the search of the graph data is difficult to realize; in addition, the adjacent matrix representation method realized by the database technology has the problem of data sparsity under the condition that the stock right relationship is increased, and the increase of the entities and the relationship brings high complexity and is difficult to meet the representation requirement.
Disclosure of Invention
In view of this, an embodiment of the present invention provides a method for identifying an actual control person, including:
determining an actual controller candidate set from a pre-stored equity network according to the client identifier to be identified;
and determining the actual controller of the client to be identified from the actual controller candidate set according to a predefined actual controller mining model.
The embodiment of the invention also provides a system for identifying the actual control person, which comprises the following steps:
the first determining unit is used for determining an actual control person candidate set from a pre-stored equity network according to the identification of the client to be identified;
a second determining unit, configured to determine an actual controller of the customer to be identified from the actual controller candidate set according to a predefined actual controller mining model.
The embodiment of the invention also provides a system for identifying the actual control person, which comprises the following steps: a memory, a processor and a computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out the above-mentioned method of identifying an actual controlling person.
An embodiment of the present invention further provides a computer-readable storage medium, in which an information processing program is stored, and the information processing program, when executed by a processor, implements the steps of the method for identifying an actual control person.
The technical scheme provided by the embodiment of the invention can identify the actual controller of the client from the mass stock right relationship, and improves the identification efficiency of the actual controller.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. Other advantages of the present application may be realized and attained by the instrumentalities and combinations particularly pointed out in the specification and the drawings.
Drawings
The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.
Fig. 1 is a schematic flow chart illustrating a method for identifying an actual controller according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a method for identifying an actual controller according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a method for identifying an actual controller according to another embodiment of the present invention;
FIG. 4 is a schematic diagram of a system for identifying an actual controlling person according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a system for identifying an actual control person according to another embodiment of the present invention;
FIG. 6 is a schematic illustration of an identified real controller in accordance with an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a system for identifying an actual control person according to another embodiment of the present invention.
Detailed Description
The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
Fig. 1 is a schematic flowchart of a method for identifying an actual controller according to an embodiment of the present invention, as shown in fig. 1, the method includes:
and 102, determining the actual controller of the client to be identified from the actual controller candidate set according to a predefined actual controller mining model.
Optionally, the equity network is a point-edge relationship graph formed by directional connections between points, where a point relationship stores attributes of corresponding clients, and an edge relationship connected between points stores attributes of corresponding associated clients and associated relationships;
the type of the actual control person in the actual control person mining model comprises at least one of the following:
the share right of two or more related clients commonly controlled by the third-party client exceeds 50%, and the third-party client is an actual controller of the two or more related clients; a third party client which directly or indirectly controls the client with the stock right more than 50% is an actual controller of the client; the third party client to which the client's stock chain analysis ultimately converges is the actual controller of the client.
Optionally, the determining an actual candidate set of controllers from a pre-stored equity network according to the to-be-identified client identifier includes:
loading a pre-saved equity network by using a graph calculation engine;
and identifying the stock right sub-network associated with the to-be-identified customer identifier from the stock network by using a maximum connected graph algorithm to serve as an actual controller candidate set of the to-be-identified customer.
Optionally, the determining the actual controller of the customer to be identified from the actual controller candidate set according to a predefined actual controller mining model includes:
and traversing the actual controller candidate set according to the actual controller mining model by using a graph calculation algorithm, and identifying all actual controllers corresponding to the customer to be identified.
Optionally, before determining the actual candidate set of control persons from the pre-stored equity networks according to the customer identification to be identified, the method further comprises:
and extracting the point-edge relationship which is combed in advance from the stock control data and the relationship data by using a map extraction tool to form the stock right network, and storing the stock right network in a database.
Optionally, the graph computation engine is a spark graph computation engine, the maximum connected graph algorithm is a depth-first graph search algorithm, the graph computation algorithm is a Pregel based on spark graph, the graph extraction tool is a hive sql graph extraction tool, and the database is a hive database.
Optionally, the method further comprises:
and displaying the actual controller of the customer to be identified through a graph display tool.
The technical scheme provided by the embodiment of the invention can identify the actual controller of the client from the mass stock right relationship, and improves the identification efficiency of the actual controller.
Fig. 2 is a schematic flowchart of a method for identifying an actual controller according to another embodiment of the present invention, as shown in fig. 2, the method includes:
the equity network is a point-edge relationship graph formed by directed connection between points, wherein the point relationship stores attributes of corresponding clients, and the edge relationship connected between the points stores attributes of corresponding associated clients and associated relationships. For example, the storage manner of the dot edges may be as shown in tables 1 and 2 below.
Table 1:
| vertex point | Vertex attribute set |
Table 2:
| vertex 1 | Vertex 2 | Edge property set |
The table 1 stores a point relationship, the vertex item corresponds to a name or an identifier of a client corresponding to each point in the equity network, the vertex attribute set corresponds to an attribute of a corresponding point, for example, an identity attribute of a point is a natural person or a legal person or other organization, for example, the vertex 1 represents a client a, the corresponding attribute is a natural person, the vertex 2 represents a client B, and the corresponding attribute is a legal person. In table 2, edge relationships are stored, for example, vertex 1 and vertex 2 have an association relationship, and the connected edge relationships have corresponding edge attribute sets, for example, the association relationship of vertex 1 and vertex 2 is a relationship between a natural person shareholder and a business and accounts for 20% of the equity, which means that the customer a corresponding to vertex 1 is the natural person shareholder of the customer B corresponding to vertex 2 and accounts for 20% of the equity of the business B.
Optionally, the atlas extraction tool is any atlas extraction tool in the prior art, such as a hive sql atlas extraction tool, and the database is any database in the prior art, such as a hive database. For example, the point-side relationship can be extracted by a hive sql map extraction tool based on the point-side relationship which is combed in advance, and the stock right map is formed and stored in hive.
optionally, the graph computation engine is any one of graph computation engines in the prior art, such as a spark graph computation engine. For example, the point-edge relationship data stored in hive is loaded by taking spark graph x as a graph computation engine.
optionally, the maximum connected graph algorithm is any maximum connected graph algorithm in the prior art, for example, a depth-first graph search algorithm, for example, a maximum connected graph algorithm such as a depth-first graph search algorithm is used, a share relation subgraph in a graph with the minimum share right is identified, and a node id is used as a subgraph identifier and stored in a node attribute, so that irrelevant enterprises and relevant relations are removed, and an actual control person candidate set is obtained.
Wherein the customer to be identified refers to an entity to be identified, such as a customer requesting a loan. The client identifier to be identified refers to an entity identifier to be identified, such as an identifier representing the identity of the client, such as a client id or a name. The entity may be a natural person or a legal person or other organization.
optionally, the type of actual controller in the actual controller mining model includes at least one of:
the share right of two or more related clients commonly controlled by the third-party client exceeds 50%, and the third-party client is an actual controller of the two or more related clients; a third party client which directly or indirectly controls the client with the stock right more than 50% is an actual controller of the client; the third party client to which the client's stock chain analysis ultimately converges is the actual controller of the client.
Wherein the share right of two or more associated clients commonly controlled by the third party client exceeds 50%, the third party client is the actual controller of the two or more associated clients: for example, the shareholder B of the enterprise A, but the B also serves as the shareholder high-management of other stock-controlling enterprises of the enterprise A, such as the D, and the cumulative share right of the B + D exceeds 50 percent, so the shareholder B is the actual controller of the enterprise A;
wherein a third party client that directly or indirectly controls a client for more than 50% of its equity is the actual controller of the client: for example, the shareholder B and the shareholder C of the enterprise A, and the shareholder B and the shareholder C have close association, and the accumulated shareholder right exceeds 50 percent, so that the shareholder B and the shareholder C are actual controllers of the enterprise A;
the third-party client to which the stock control chain analysis of the client is finally converged is an actual controller of the client: for example, the analysis of stock control chain (including cross stock control) of enterprise a is finally converged to a certain stockholder B, and B is the actual controller of enterprise a, and then stockholder B is the actual controller of enterprise a.
Optionally, the graph computation algorithm is any graph computation algorithm in the prior art, such as Pregel based on sparkgraphx. For example, based on the actual controller candidate set obtained in step 203, in combination with the actual controller mining model, a candidate set traversal is implemented through pregel, and the candidate set is screened to obtain all the actual controllers of the customer to be identified.
Optionally, the specific implementation step of traversing the actual control person candidate set by pregel includes:
step 1, traversing all nodes in a candidate set of an actual controller, and setting the nodes as actual controllers of current nodes;
wherein, the attribute (i.e. point relationship attribute) of the node in the stock right relationship graph comprises at least one of the following: identification id, corresponding customer name, etc. The edge relationship connected between points in the equity relationship graph stores corresponding associated client attributes and associated relationship attributes, wherein the associated relationship attributes are, for example, actual controllers, relatives, and the like, and the edge relationship is directional, for example, the edge relationship between the node a and the node B points from the node a to the node B, indicating that the relationship between the node a and the node B is that the client corresponding to the node a is the actual controller of the client corresponding to the node B.
Step 2, the source node sends self attribute information to the destination node, and the information is the actual controller attribute of the stock control path attribute and the iteration identification of the round;
and 3, combining the received pieces of information by the destination node, and processing the condition that the path has the coincident node. Processing the iteration message of the current round according to the actual control person mining model, and identifying a new actual control person; if the source node information already exists in the destination node, the message is not sent;
by analogy, based on the iteration, the actual controller of the target node can be obtained.
And step 205, displaying the actual controller of the customer to be identified through a graph display tool.
Alternatively, the graph presentation tool may be any one of the prior art graph presentation tools, such as echarts and the like.
The technical scheme provided by the embodiment of the invention can identify the actual controller of the client from the mass stock right relationship, and improves the identification efficiency of the actual controller.
Fig. 3 is a schematic flowchart of a method for identifying an actual controller according to another embodiment of the present invention, as shown in fig. 3, the method includes:
the right map refers to the right network in the previous embodiment.
Specifically, the stock right and the relationship data are combed, and related entities, attributes and association relations in the data are extracted. And uniformly expressing the related stock right relationship, and constructing a stock right map by taking the enterprise as an entity and the stock right relationship as an edge.
the stock right minimum map refers to a stock right sub-network in the previous embodiment, and is used as an actual candidate set of the controller.
Specifically, the share right map is loaded by taking spark map as a map calculation engine. And identifying a stock right relation subgraph in the stock right graph through an implemented connected graph algorithm. Therefore, irrelevant enterprises and relevant relations are eliminated, and a candidate set of the actual controller is obtained.
In the step, irrelevant enterprises and relevant relations are removed from the equity network, and an actual candidate set of controllers is obtained.
specifically, for an obtained actual controller candidate set, a depth-first algorithm realized by pregel is used for traversing the candidate set by taking a customer to be identified as a starting point in combination with an actual controller mining model, and the candidate set is screened to obtain an actual controller.
And 304, displaying all actual control persons corresponding to the customer to be identified through a graph display tool.
Optionally, the graph displaying tool is any one of existing graph displaying tools, such as echarts and the like.
According to the technical scheme provided by the embodiment of the invention, the storage of the adjacency matrix is avoided through the storage of the map data in the hive database, the storage and representation problems of the mass map data are solved, the performance problems existing in the traditional sql traversal complex network are solved through the spark graph calculation engine, and therefore the actual controller of the target enterprise can be efficiently identified from the mass map data. The identification of the actual control person is beneficial to the commercial bank to finish the subsequent quality assessment of the actual control person or the control team. Through investigation on the aspects of the core personnel academic calendar, background, experience, quality, credit, capability, achievement and the like of the enterprise, the risk assessment on the core management personnel is strengthened, and the influence of the moral risk possibly occurring in the enterprise on the bank credit is prevented.
Fig. 4 is a schematic structural diagram of a system for identifying an actual control person according to an embodiment of the present invention, as shown in fig. 4, the system includes:
the first determining unit is used for determining an actual control person candidate set from a pre-stored equity network according to the identification of the client to be identified;
a second determining unit, configured to determine an actual controller of the customer to be identified from the actual controller candidate set according to a predefined actual controller mining model.
Optionally, the equity network is a point-edge relationship graph formed by directional connections between points, where a point relationship stores attributes of corresponding clients, and an edge relationship connected between points stores attributes of corresponding associated clients and associated relationships;
the type of the actual control person in the actual control person mining model comprises at least one of the following:
the share right of two or more related clients commonly controlled by the third-party client exceeds 50%, and the third-party client is an actual controller of the two or more related clients; a third party client which directly or indirectly controls the client with the stock right more than 50% is an actual controller of the client; the third party client to which the client's stock chain analysis ultimately converges is the actual controller of the client.
Optionally, the first determining unit is specifically configured to load a pre-stored equity network by using the graph computation engine;
and identifying the stock right sub-network associated with the to-be-identified customer identifier from the stock network by using a maximum connected graph algorithm to serve as an actual controller candidate set of the to-be-identified customer.
Optionally, the second determining unit is specifically configured to traverse the actual controller candidate set according to the actual controller mining model by using a graph calculation algorithm, and identify all actual controllers corresponding to the customer to be identified.
Optionally, the system further comprises:
and the third determining unit is used for extracting the point-edge relationship which is combed in advance from the stock control data and the relationship data by using a map extraction tool to form the stock right network and storing the stock right network in a database before determining the actual control person candidate set from the stock right network which is stored in advance according to the identification of the client to be identified.
Optionally, the graph computation engine is a spark graph computation engine, the maximum connected graph algorithm is a depth-first graph search algorithm, the graph computation algorithm is a Pregel based on spark graph, the graph extraction tool is a hive sql graph extraction tool, and the database is a hive database.
Optionally, the system further comprises: and the display unit is used for displaying the actual controller of the client to be identified through a graph display tool.
The technical scheme provided by the embodiment of the invention can identify the actual controller of the client from the mass stock right relationship, and improves the identification efficiency of the actual controller.
Fig. 5 is a schematic structural diagram of a system for identifying an actual control person according to another embodiment of the present invention, as shown in fig. 5, the system includes:
a connectivity graph API (Application Programming Interface) and an actual controller filtering API;
wherein the connected graph API corresponds to the first determining unit in the above-described embodiment, and the actual controller filter API corresponds to the second determining unit in the above-described embodiment.
The system comprises a communication graph API, a user identity identification and a user identity identification, wherein the communication graph API is used for determining an actual control person candidate set from a pre-stored equity network according to the user identity to be identified;
optionally, the equity network is a point-edge relationship graph formed by directional connections between points, where a point relationship stores attributes of corresponding clients, and an edge relationship connected between points stores attributes of corresponding associated clients and associated relationships;
the type of the actual control person in the actual control person mining model comprises at least one of the following:
the share right of two or more related clients commonly controlled by the third-party client exceeds 50%, and the third-party client is an actual controller of the two or more related clients; a third party client which directly or indirectly controls the client with the stock right more than 50% is an actual controller of the client; the third party client to which the client's stock chain analysis ultimately converges is the actual controller of the client.
Optionally, the connected graph API is specifically configured to load a pre-stored equity network using the graph computation engine;
and identifying the stock right sub-network associated with the identification of the client to be identified from the stock right network by utilizing a maximum connected graph algorithm, wherein the stock right sub-network is used as an actual controller candidate set of the client to be identified.
Optionally, the graph computation engine is any one of existing graph computation engines, such as a spark graph computation engine, and the maximum connected graph algorithm is any one of existing maximum connected graph algorithms, such as a depth-first graph search algorithm. For example, the above-mentioned equity network is used as an input of the connectivity graph API, and the actual candidate set of controllers in the equity network is calculated.
Wherein, this system still includes:
and the third determining unit is used for extracting the point-edge relationship which is combed in advance from the stock control data and the relationship data by using the map extraction tool to form the stock right network and storing the stock right network in the database.
Optionally, the atlas extraction tool is any kind of existing atlas extraction tool, such as a hive sql atlas extraction tool, and the database is any kind of existing database, such as a hive database.
For example, in this embodiment, description is made based on stock control data and relationship data of a certain commercial bank client, point-side relationships in the stock control data and relationship data are extracted by a hive sql tool from the defined point-side definitions, and a stock right network is constructed to store the point-side relationships and the relationship-side relationships, respectively. The point relation stores the client and the related attribute thereof, and the edge relation stores the information with the related client id as the main body and the related attribute. Then, the stock right network in the hive database is used as the input of the API, and the actual control person candidate set in the stock right network is calculated.
And the actual controller filtering API is used for determining the actual controller of the client to be identified from the actual controller candidate set according to a predefined actual controller mining model.
Optionally, the actual controller filtering API is specifically configured to traverse the actual controller candidate set according to the actual controller mining model by using a graph calculation algorithm, and identify all actual controllers corresponding to the customer to be identified.
Optionally, the graph computation algorithm is any one of the existing graph computation algorithms, such as Pregel based on spark graph x.
For example, in this embodiment, a client to be identified (i.e., an entity to be identified) and an actual controller candidate set are used as input, an actual controller filtering API is called, and all actual controllers of the client to be identified are filtered.
Wherein, this system still includes: a display unit for displaying the image of the object,
and the display unit is used for displaying all the identified actual control persons through the graph display tool.
Optionally, the graph displaying tool is any one of existing graph displaying tools, such as echarts and the like. For example, as shown in fig. 6, which is an exemplary diagram of an actual controller shown as a diagram showing tool, A, B, C, D, E, F, where each node in the diagram may represent a different entity (customer), and each entity may represent a different customer, where an edge relationship between each two nodes represents an association relationship between the two nodes, for example, an edge relationship between a (nature person) and B (enterprise) is a relationship between a nature person shareholder and an enterprise, and the shareholder a accounts for 20% of the equity of the enterprise B.
The technical scheme provided by the embodiment of the invention can identify the actual controller of the client from the mass stock right relationship, and improves the identification efficiency of the actual controller.
Fig. 7 is a schematic structural diagram of a system for identifying an actual control person according to another embodiment of the present invention, as shown in fig. 7, the system includes:
the system comprises a stock right map API, a connection map API, an actual controller filtering API and a display unit;
wherein the stock right map API corresponds to the third determination unit in the above-described embodiment.
The stock right map API is used for extracting point-edge relations from the stock control data and the relationship data based on the point-edge relations which are combed in advance through a hive sql map extraction tool to form a stock right map which is stored in the hive;
the right map refers to the right network in the above embodiment.
The connection graph API is used for loading the stock right graph stored in the hive by taking spark graph x as a graph calculation engine, and identifying the minimum stock right graph of the client to be identified through a maximum connection graph algorithm;
the stock right minimum map refers to a stock right sub-network in the previous embodiment, and is used as an actual candidate set of the controller.
In the step, the equity network is used as the input of the API, irrelevant enterprises and relevant relations are removed from the equity network, an actual controller candidate set in the equity network is calculated, and the actual controller candidate set is obtained.
The actual controller filtering API is used for traversing the stock right minimum map through a pregel according to an actual controller mining model, and identifying all actual controllers corresponding to the customer to be identified;
in the step, the actual control person of the client to be identified is screened out by using the actual control person candidate of the client to be identified and the actual control person mining model seat actual control person filtering API.
And the display unit is used for displaying all the actual control persons corresponding to the customer to be identified through a graph display tool.
Optionally, the graph displaying tool is any one of existing graph displaying tools, such as echarts and the like.
According to the technical scheme provided by the embodiment of the invention, the stock right stock control data is combed to extract entities related to services and related attributes, the stock control relationship of a client is combed and combined and perfected, a stock right map is constructed, hive is used as a map storage medium, an actual control person mining model is designed by combining service rules on the basis of the constructed stock right map, and the actual control person mining model is engineered on the basis of Pregel of spark graph x; therefore, the mining of the share right map is realized, and the identification of the actual control person is completed.
The embodiment of the invention also provides a system for identifying the actual control person, which comprises the following steps: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing any of the above methods of identifying an actual control person.
The embodiment of the invention also provides a computer readable storage medium, wherein an information processing program is stored on the computer readable storage medium, and when the information processing program is executed by a processor, the information processing program realizes the steps of any one of the methods for identifying the actual control person.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Claims (10)
1. A method of identifying a physically controlling person, comprising:
determining an actual controller candidate set from a pre-stored equity network according to the client identifier to be identified;
and determining the actual controller of the client to be identified from the actual controller candidate set according to a predefined actual controller mining model.
2. The method of claim 1,
the equity network is a point-edge relation graph formed by directed connection between points, wherein the point relation stores attributes of corresponding clients, and the edge relation connected between the points stores attributes of corresponding associated clients and associated relations;
the type of the actual control person in the actual control person mining model comprises at least one of the following:
the share right of two or more related clients commonly controlled by the third-party client exceeds 50%, and the third-party client is an actual controller of the two or more related clients; a third party client which directly or indirectly controls the client with the stock right more than 50% is an actual controller of the client; the third party client to which the client's stock chain analysis ultimately converges is the actual controller of the client.
3. The method of claim 1, wherein determining the actual candidate set of directors from the pre-stored equity networks based on the customer identification to be identified comprises:
loading a pre-saved equity network by using a graph calculation engine;
and identifying the stock right sub-network associated with the to-be-identified customer identifier from the stock network by using a maximum connected graph algorithm to serve as an actual controller candidate set of the to-be-identified customer.
4. The method of claim 1, wherein determining the actual controller of the customer to be identified from the set of actual controller candidates according to a predefined actual controller mining model comprises:
and traversing the actual controller candidate set according to the actual controller mining model by using a graph calculation algorithm, and identifying all actual controllers corresponding to the customer to be identified.
5. The method of claim 1, wherein prior to determining the actual candidate set of directors from the pre-stored equity networks based on the customer identification to be identified, the method further comprises:
and extracting the point-edge relationship which is combed in advance from the stock control data and the relationship data by using a map extraction tool to form the stock right network, and storing the stock right network in a database.
6. The method according to any one of claims 3 to 5,
the map calculation engine is a spark graph calculation engine, the maximum connected graph algorithm is a depth-first graph search algorithm, the map calculation algorithm is a Pregel based on spark graph, the map extraction tool is a hive sql map extraction tool, and the database is a hive database.
7. The method of claim 1, further comprising:
and displaying the actual controller of the customer to be identified through a graph display tool.
8. A system for identifying a physically controlling person, comprising:
the first determining unit is used for determining an actual control person candidate set from a pre-stored equity network according to the identification of the client to be identified;
a second determining unit, configured to determine an actual controller of the customer to be identified from the actual controller candidate set according to a predefined actual controller mining model.
9. A system for identifying a physically controlling person, comprising: memory, processor and computer program stored on the memory and executable on the processor, which computer program, when executed by the processor, carries out a method of identifying an actual controlling person according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that an information processing program is stored thereon, which when executed by a processor implements the steps of the method of identifying an actual controlling person according to any one of claims 1 to 7.
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