CN112613762B - Group rating method and device based on knowledge graph and electronic equipment - Google Patents
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Abstract
The invention provides a group rating method and device based on a knowledge graph and electronic equipment, and relates to the technical field of bank account management, wherein the method comprises the following steps: firstly, determining a group to be evaluated based on an investment relation knowledge graph, wherein the group to be evaluated comprises a plurality of target enterprises; then, determining influence weights of groups to be evaluated of each target enterprise based on an improved webpage ranking algorithm; and obtaining the default probability of the group to be evaluated according to each influence weight and the default probability of the target enterprise so as to determine the rating of the group to be evaluated. The method can solve the technical problem that the grading of the group clients is inaccurate in the prior art, and achieves the technical effect of improving the management and control of the group clients.
Description
Technical Field
The invention relates to the technical field of bank account management, in particular to a group rating method and device based on a knowledge graph and electronic equipment.
Background
The group clients bring great benefits to the banks and hide huge risks. Based on the prior banking experience, under the condition that the group does not combine the financial report, the quantitative analysis work of the group client rating is generally dependent on the rating results of all members of the group, and the initial default probability of the whole group is obtained according to the weighted average of the capital amount, so as to correspond to the internal rating result of the group. However, in view of the complexity of the group members in the group clients, this simple way of averaging in terms of capital amounts to evaluate the group members 'impact level on the group has the problem of not being rated accurately enough to be detrimental to the group clients' risk management.
Disclosure of Invention
The invention aims to provide a group rating method and device based on a knowledge graph and electronic equipment, so as to solve the technical problem that group client rating is inaccurate in the prior art.
In order to achieve the above object, the technical scheme adopted by the embodiment of the invention is as follows:
In a first aspect, an embodiment of the present invention provides a group rating method based on a knowledge graph, where the method includes: determining a group to be evaluated based on the investment relation knowledge graph; the group to be evaluated comprises a plurality of target enterprises; determining the influence weight of each target enterprise on the group to be evaluated based on an improved webpage ranking algorithm; and obtaining the default probability of the group to be evaluated according to each influence weight and the default probability of the target enterprise so as to determine the rating of the group to be evaluated.
In some possible embodiments, the nodes of the knowledge graph are full-scale enterprises, and the edges of the knowledge graph are investment relations of the full-scale enterprises.
In some possible implementations, the influence weights include relational weights; the relation weight is used for representing the investment proportion between any one target enterprise and the group to be evaluated.
In some possible implementations, the improved web page ranking algorithm is calculated as:
Wherein d is a damping factor, and N is the number of target enterprises; u is the group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of the any one target enterprise; p (v) is a relationship weight.
In some possible implementations, the influence weights further include node weights; the node weights are used to represent the registered capital of the target enterprise.
In some possible embodiments, the step of determining the node weight comprises: normalizing the registered capital of the whole enterprises, and determining the normalized registered capital of each enterprise as the node weight of the enterprise.
In some possible implementations, the improved web page ranking algorithm is calculated as:
Wherein d is a damping factor, and N is the number of target enterprises; u is the group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of the any one target enterprise; r (v) represents the normalized registered capital of any one of the target enterprises.
In a second aspect, an embodiment of the present invention provides a group rating device based on a knowledge graph, where the device includes: the group to be assessed determining module is used for determining the group to be assessed based on the investment relation knowledge graph; the group to be evaluated comprises a plurality of target enterprises; the influence weight determining module is used for determining influence weight of each target enterprise on the group to be evaluated based on an improved webpage ranking algorithm; and the breach probability acquisition module is used for acquiring the breach probability of the group to be evaluated according to each influence weight and the breach probability of the target enterprise so as to determine the rating of the group to be evaluated.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, and a processor, where the memory stores a computer program executable on the processor, and the processor implements the steps of the method according to any one of the first aspects when the processor executes the computer program.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of the first aspects.
The embodiment of the invention provides a group rating method and device based on a knowledge graph and electronic equipment, wherein the method comprises the following steps: firstly, determining a group to be evaluated based on an investment relation knowledge graph, wherein the group to be evaluated comprises a plurality of target enterprises; then, determining influence weights of groups to be evaluated of each target enterprise based on an improved webpage ranking algorithm; and obtaining the default probability of the group to be evaluated according to each influence weight and the default probability of the target enterprise so as to determine the rating of the group to be evaluated. The method can solve the technical problem that the grading of the group clients is inaccurate in the prior art, and achieves the technical effect of improving the management and control of the group clients.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a group rating method based on a knowledge graph according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another group rating method based on a knowledge graph according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a group rating device based on a knowledge graph according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The group clients bring great benefits to the banks and hide huge risks. The quantitative analysis of the customer ratings of the group by the bank usually adopts a standardized statistical model, and in the links of basic plane analysis and expert judgment, the bank comprehensively considers various condition factors in the group to carry out necessary correction on the quantitative initial ratings. The quantitative analysis part relies on the rating results of all members of the group under the condition that the financial report is not combined by the group, and obtains the initial default probability of the whole group according to the weighted average of capital amount, thereby corresponding to the internal rating result of the group.
Under the condition that the group does not incorporate the financial report, quantitative analysis of the group client rating is generally dependent on the rating results of all members of the group, and initial default probability of the whole group is obtained according to weighted average of capital amount, so that the internal rating results of the group are corresponding. However, in view of the complexity of the group members in the group clients, this simple way of averaging in terms of capital amounts to evaluate the group members' impact level on the group has the problem of insufficient precision in the rating, thereby compromising risk management for the group clients. In general, the internal rating and risk management of group clients by banks are quite weak, and a complete set of effective management systems and technical schemes are still lacking.
Based on the above, the embodiment of the invention provides a group rating method, a group rating device and electronic equipment based on a knowledge graph, so as to solve the technical problem that group client rating is inaccurate in the prior art.
For the understanding of the present embodiment, first, a detailed description will be given of a group rating method based on a knowledge graph disclosed in the present embodiment, referring to a flow chart of a group rating method based on a knowledge graph shown in fig. 1, the method may be executed by an electronic device, and mainly includes the following steps S110 to S130:
S110: determining a group to be evaluated based on the investment relation knowledge graph; the group to be evaluated comprises a plurality of target enterprises;
The nodes of the knowledge graph are full-scale enterprises, and the edges of the knowledge graph are investment relations of the full-scale enterprises.
S120: determining influence weights of groups to be evaluated of each target enterprise based on an improved webpage ranking algorithm;
S130: and obtaining the default probability of the group to be evaluated according to each influence weight and the default probability of the target enterprise so as to determine the rating of the group to be evaluated.
The page ranking algorithm (PR) is a technique based on a hyperlink algorithm between web pages, which is used to evaluate the relevance and importance of web pages, and is also the final output result of the algorithm, and is generally called a PageRank value or PR value. PR algorithms are often used in search engine optimization operations to evaluate web page optimization success. There are two basic assumptions for this algorithm: 1. if a web page is linked to by many other web pages, it is important to say that this web page is relatively high, i.e. the PageRank value is relatively high; 2. if a page with a high PageRank value links to another page, the PageRank value of the linked page is correspondingly increased.
And the impact weight of an enterprise on a group depends on two main aspects: firstly, the whole business ratio of the sub-group controlled by the system is controlled; and secondly, the business importance of the associated enterprises. In general, the more group companies that an enterprise associates, the more important it is to the group; meanwhile, the greater the importance of the enterprise-associated group company in the group, the more important it is to the group. Therefore, the PageRank algorithm is matched with the influence force scene of the enterprise on the group.
The embodiment of the application provides a group internal rating method using influence of enterprises as a weighting weight. The influence of enterprises comprehensively considers factors such as enterprise scale, organization structure and the like.
Only the impact of the relationship weights on the enterprise is considered in the web page ranking algorithm, and the impact of each relationship on the target node is the same. The influence of the relation weight on the enterprise is different, and each relation is assigned according to the actual relation weight, and the specific operation is as follows.
The PageRank algorithm iterates the formula:
wherein d is a damping factor for solving the convergence problem in some iterative processes. U is the page to be evaluated, BU is the in-chain set of page U. For any page V in the incoming chain set, the influence which can be brought to U is the influence PR (V) of the user divided by the outgoing chain quantity of the pages V, namely the page V distributes the influence PR (V) to the outgoing chains of the page V averagely, so that all the pages V which can bring links to U are counted, and the obtained sum is the influence of the web page U, namely PR.
The application improves the web page ranking algorithm to obtain an improved ranking algorithm for group ranking. In one embodiment, the influence weights include relational weights; the relationship weight is used to represent the investment ratio between any one target enterprise and the group to be assessed.
The improved web page ranking algorithm is calculated by the following formula:
Wherein d is a damping factor, and N is the number of target enterprises; u is a group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of any one target enterprise; p (v) is a relationship weight.
In addition, node weights are not considered in the traditional PageRank algorithm, and in the scene of enterprise influence, the influence of enterprise scale on the enterprise is different. In another embodiment, the impact weights of step S120 further include node weights; the node weights are used to represent the registered capital of the target enterprise.
Wherein the step of determining the node weight may comprise:
normalizing the registered capital of the whole enterprises, and determining the normalized registered capital of each enterprise as the node weight of the enterprise.
The improved web page ranking algorithm is calculated by the following formula:
Wherein d is a damping factor, and N is the number of target enterprises; u is a group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of any one target enterprise; r (v) represents the normalized registered capital of any one target enterprise.
The embodiment of the invention provides a group rating method based on a knowledge graph, which comprises the following steps: firstly, determining a group to be evaluated based on an investment relation knowledge graph, wherein the group to be evaluated comprises a plurality of target enterprises; then, determining influence weights of groups to be evaluated of each target enterprise based on an improved webpage ranking algorithm; and obtaining the default probability of the group to be evaluated according to each influence weight and the default probability of the target enterprise so as to determine the rating of the group to be evaluated. The method can solve the technical problem that the grading of the group clients is inaccurate in the prior art, and achieves the technical effect of improving the management and control of the group clients.
As a specific example, referring to fig. 2, an embodiment of the present application provides a group rating method based on a knowledge graph, the method including:
S210: constructing an investment relation knowledge graph and identifying group members;
and the full-quantity enterprises are taken as nodes, and the investment relationship is taken as a full-quantity industrial and commercial enterprise knowledge graph of the side. And identifying the groups of all enterprises of the industry and commerce by using a group algorithm based on the knowledge graph.
S220: calculating the influence weight of the enterprise on the group;
In each group subgraph, the pr value of the enterprise is calculated by adopting a modified PageRank algorithm.
S230: calculating the default probabilities of all members in the group according to a rating method of general company clients;
S240: and weighting according to the pr value of the enterprise to obtain the group rating.
According to the group rating method based on the knowledge graph, the group to be evaluated is determined based on the investment relation knowledge graph, then the influence weight of each target enterprise on the group to be evaluated is determined by utilizing an improved webpage ranking algorithm, and the offensiveness probability of the group to be evaluated is obtained according to each influence weight and the offensiveness probability of the target enterprise so as to determine the rating of the group to be evaluated. The method can solve the technical problem that the grading of the group clients is inaccurate in the prior art, and achieves the technical effect of improving the management and control of the group clients.
The embodiment of the invention also provides a group rating device based on the knowledge graph, referring to fig. 3, the device comprises:
A group under evaluation determination module 310 for determining a group under evaluation based on the investment relation knowledge graph; the group to be evaluated comprises a plurality of target enterprises;
an influence weight determination module 320, configured to determine an influence weight of each target enterprise to the group to be evaluated based on the improved web page ranking algorithm;
The breach probability obtaining module 330 is configured to obtain breach probability of the group to be evaluated according to each influence weight and breach probability of the target enterprise, so as to determine a rating of the group to be evaluated.
The group rating device based on the knowledge graph provided by the embodiment of the application can be specific hardware on equipment or software or firmware installed on the equipment and the like. The device provided by the embodiment of the present application has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein. The group rating device based on the knowledge graph provided by the embodiment of the application has the same technical characteristics as the group rating method based on the knowledge graph provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
The embodiment of the application also provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the embodiments described above.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application, where the electronic device 400 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, the processor 40, the communication interface 43 and the memory 41 being connected by the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The memory 41 may include a high-speed random access memory (RAM, randomAccessMemory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and the at least one other network element is achieved via at least one communication interface 43 (which may be wired or wireless), which may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 42 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 41 is configured to store a program, and the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40 or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 40. The processor 40 may be a general-purpose processor, including a Central Processing Unit (CPU), a network processor (NetworkProcessor NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 41 and the processor 40 reads the information in the memory 41 and in combination with its hardware performs the steps of the method described above.
Corresponding to the above method, embodiments of the present application also provide a computer readable storage medium storing machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the steps of the above method.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the various figures refer to like items and, thus, once an item is defined in one figure, no further definition or explanation of that in the subsequent figure is necessary, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (6)
1. The group rating method based on the knowledge graph is suitable for a group not merging financial statement scene, and is characterized by comprising the following steps of:
Determining a group to be evaluated based on the investment relation knowledge graph; the group to be evaluated comprises a plurality of target enterprises, wherein nodes of the knowledge graph are full-scale enterprises, and edges of the knowledge graph are investment relations of the full-scale enterprises;
Determining influence weights of each target enterprise on the groups to be evaluated based on an improved webpage ranking algorithm, wherein the influence weights comprise a relation weight and a node weight, the relation weight is used for representing investment proportions between any one target enterprise and the groups to be evaluated, and the node weight is used for representing registered capital of the target enterprise; the calculation formula of the improved webpage ranking algorithm is as follows:
Wherein d is a damping factor, and N is the number of target enterprises; u is the group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of the any one target enterprise; p (v) is a relationship weight;
And obtaining the default probability of the group to be evaluated according to each influence weight and the default probability of the target enterprise so as to determine the rating of the group to be evaluated.
2. The knowledge-graph-based clique ranking method of claim 1, wherein the step of determining the node weights comprises:
Normalizing the registered capital of the whole enterprises, and determining the normalized registered capital of each enterprise as the node weight of the enterprise.
3. The method for ranking groups based on knowledge-graph according to claim 2, wherein,
The calculation formula of the improved webpage ranking algorithm is as follows:
Wherein d is a damping factor, and N is the number of target enterprises; u is the group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of the any one target enterprise; r (v) represents the normalized registered capital of any one of the target enterprises.
4. A rating apparatus suitable for the knowledge-graph-based group rating method as claimed in any one of claims 1 to 3, comprising:
The group to be assessed determining module is used for determining the group to be assessed based on the investment relation knowledge graph; the group to be evaluated comprises a plurality of target enterprises, wherein nodes of the knowledge graph are full-scale enterprises, and edges of the knowledge graph are investment relations of the full-scale enterprises;
The influence weight determining module is used for determining influence weights of each target enterprise on the to-be-evaluated group based on an improved webpage ranking algorithm, wherein the influence weights comprise a relation weight and a node weight, the relation weight is used for representing investment proportion between any one target enterprise and the to-be-evaluated group, and the node weight is used for representing registered capital of the target enterprise; the calculation formula of the improved webpage ranking algorithm is as follows:
Wherein d is a damping factor, and N is the number of target enterprises; u is the group to be evaluated, B u is a set of target enterprises of the group to be evaluated, and v is any one target enterprise in the set of target enterprises; PR (v) represents the influence of the any one target enterprise; p (v) is a relationship weight;
and the breach probability acquisition module is used for acquiring the breach probability of the group to be evaluated according to each influence weight and the breach probability of the target enterprise so as to determine the rating of the group to be evaluated.
5. An electronic device comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method of any of the preceding claims 1 to 3.
6. A computer readable storage medium storing machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of claims 1 to 3.
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