CN110781251A - Insurance knowledge map generation method, device, equipment and storage medium - Google Patents
Insurance knowledge map generation method, device, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a device, equipment and a storage medium for generating an insurance knowledge graph, wherein the method comprises the following steps: collecting a plurality of insurance information; determining a plurality of combination relations among the plurality of sub-risk category according to the plurality of sub-risk category and the sub-risk attribute corresponding to each sub-risk category; taking the risk category as a node entity and the plurality of sub-risk categories as respective sub-nodes of the node entity; generating an insurance knowledge graph according to the node entity and each child node; and displaying the thumbnail so that a user clicks the thumbnail according to the sub-risk description information to inquire premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category. The method provided by the embodiment can intuitively and effectively inquire insurance business knowledge.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method, a device, equipment and a storage medium for generating an insurance knowledge graph.
Background
Insurance business knowledge has considerable expertise, business system models are often complex, core systems even have hundreds of tables, and aiming at problems of the core systems, new users (new business personnel) often cannot solve the problems, even users who are quite familiar with business and systems may refer to various architecture diagrams and diagram model introduction, and consume considerable resources and manpower.
At present, some similar knowledge sharing platforms or regular organization training appear in the prior art to enable newly-enrolled staff to work as soon as possible. At present, introduction of some service models and knowledge points is mainly drawn and stored in an open source code version control system (Subversion, svn) or multi-person collaborative writing system wiki by using visio, word, excel and the like, and can help some users to know insurance services.
However, the prior art cannot intuitively and effectively inquire insurance business knowledge.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for generating an insurance knowledge graph, which aim to solve the problem that insurance business knowledge cannot be intuitively and effectively inquired in the prior art.
In a first aspect, an embodiment of the present invention provides an insurance knowledge graph generating method, including:
collecting a plurality of insurance information, wherein each insurance information comprises an insurance category, a plurality of sub-insurance categories corresponding to the insurance category and sub-insurance attributes corresponding to each sub-insurance category, and the sub-insurance attributes comprise sub-insurance description information and premium information of the sub-insurance categories;
determining a plurality of combination relations among the plurality of sub-risk category according to the plurality of sub-risk category and the sub-risk attribute corresponding to each sub-risk category;
taking the risk category as a node entity and the plurality of sub-risk categories as respective sub-nodes of the node entity;
generating an insurance knowledge graph according to the node entities and each child node, wherein the insurance knowledge graph comprises thumbnails of the node entities and each child node, and the thumbnails comprise child risk species description information corresponding to the child nodes;
and displaying the thumbnail so that a user clicks the thumbnail according to the sub-risk description information to inquire premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category.
Optionally, the sub-risk category description information includes a name and an identification of the sub-risk category.
Optionally, the determining, according to the plurality of sub-risk categories and the sub-risk attribute corresponding to each sub-risk category, a plurality of combination relationships between the plurality of sub-risk categories includes:
obtaining a plurality of combination relations among the plurality of sub-risk categories through a preset classification model according to the plurality of sub-risk category and the premium information corresponding to each sub-risk category;
each combination relation comprises a relation graph between premium information corresponding to at least two corresponding sub-risk categories and a premium accounting formula corresponding to the relation graph.
Optionally, the premium information includes: child node gross premium, child node net premium and tax rate;
the premium accounting formula comprises a first premium accounting formula corresponding to each sub-node in the relational graph and a total second premium accounting formula corresponding to each sub-node in the relational graph;
wherein the first payment accounting formula is as follows: the sub-node net premium is the sub-node gross premium/(1+ tax rate), and the second premium accounting formula is: and the total net premium is the total gross premium/(1+ tax rate), and the total gross premium is the sum of the gross premiums of the sub nodes corresponding to the sub nodes in the relationship graph.
Optionally, the generating an insurance knowledge graph according to the node entity and each child node includes:
and generating an insurance knowledge graph through a graph database according to the node entity and each child node.
Optionally, the thumbnail is a hyperlink configured with a query function;
after the generating an insurance knowledgegraph, the method further comprises:
receiving a policy number sent by a target terminal;
receiving a policy number input by a user, and acquiring policy information matched with the policy number from a preset database, wherein the policy information comprises the policy number, the dangerous type category, the sub-dangerous type category, the identifier of the sub-dangerous type category, the multiple combination relations and the premium information corresponding to the policy number;
identifying the policy number, the risk category, the sub-risk category, and the identity of the sub-risk category and configuring them in the hyperlink;
calculating target premium information according to the premium accounting formula configured in the hyperlink;
and auditing the premium information corresponding to the insurance policy number and the target premium information, determining whether the insurance policy information is correct, and displaying an auditing result.
In a second aspect, an embodiment of the present invention provides an insurance knowledge graph generating apparatus, including:
the insurance information acquisition module is used for acquiring a plurality of insurance information, wherein each insurance information comprises an insurance category, a plurality of sub-insurance categories corresponding to the insurance category and sub-insurance attributes corresponding to each sub-insurance category, and the sub-insurance attributes comprise sub-insurance description information and insurance cost information of the sub-insurance categories;
the combination relation determining module is used for determining a plurality of combination relations among the plurality of sub risk types according to the plurality of sub risk types and the sub risk attribute corresponding to each sub risk type;
a node determining module, configured to use the risk category as a node entity, and use the plurality of sub-risk categories as respective sub-nodes of the node entity;
the insurance knowledge graph generating module is used for generating an insurance knowledge graph according to the node entities and the child nodes, the insurance knowledge graph comprises thumbnails of the node entities and the child nodes, and the thumbnails comprise child risk species description information corresponding to the child nodes;
and the thumbnail display module is used for displaying the thumbnail so that a user clicks the thumbnail according to the sub-risk description information to inquire the premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories related to the sub-risk category.
Optionally, the sub-risk category description information includes a name and an identification of the sub-risk category.
In a third aspect, an embodiment of the present invention provides an insurance knowledge graph generating apparatus, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the insurance knowledgegraph generation method as described above in the first aspect and in various possible designs of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the method for generating an insurance knowledgegraph according to the first aspect and various possible designs of the first aspect is implemented.
The insurance knowledge graph generation method, apparatus, device, and storage medium provided by this embodiment first acquire a plurality of insurance information, and provide an information source for building an insurance knowledge graph, where each insurance information includes an insurance category, a plurality of sub-insurance categories corresponding to the insurance category, and a sub-insurance attribute corresponding to each sub-insurance category, and the sub-insurance attribute includes sub-insurance description information and premium information of the sub-insurance categories, and then determine a plurality of combination relationships among the sub-insurance categories according to the plurality of sub-insurance categories and the sub-insurance attribute corresponding to each sub-insurance category, so as to split and combine the sub-insurance categories when generating an insurance policy, and provide a plurality of insurance schemes for a user, and then start forming the insurance knowledge graph, that is, first, using the insurance categories as node entities, and using the plurality of sub-insurance categories as sub-nodes of the node entities, forming a tree structure or a mesh structure, generating an insurance knowledge graph, wherein the insurance knowledge graph comprises thumbnails of the node entities and each child node, the thumbnails comprise child risk type description information corresponding to the child nodes, and a query interface is provided for a user by displaying the thumbnails, so that the user clicks the thumbnails according to the child risk type description information to query premium information of the child risk type corresponding to the child nodes and the multiple combination relations between the child risk type and other child risk types related to the child risk type, so that the user can know insurance knowledge more intuitively, and can effectively query insurance business knowledge for a certain insurance business. The scheme collects a plurality of insurance information in the insurance knowledge map generation method, then analyzes a plurality of combination relations among a plurality of sub-insurance categories, generating an insurance knowledge graph through the determination of the node entities and each sub-node corresponding to each node entity, by displaying the node entities and the thumbnails of all the sub-nodes contained in the insurance knowledge graph, the user can click on the thumbnails according to the sub-risk description information, the premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category are inquired, the user can know the insurance knowledge more intuitively, and the insurance business knowledge can be effectively inquired aiming at a certain insurance business, so that the efficiency of solving the insurance business problem is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for generating an insurance knowledge graph according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an insurance knowledge graph generation system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a relationship between premium information in a method for generating an insurance knowledgegraph according to another embodiment of the present invention;
FIG. 4 is a schematic flow chart of a method for generating an insurance knowledge graph according to yet another embodiment of the present invention;
FIG. 5 is a schematic flow chart of a method for generating an insurance knowledge graph according to another embodiment of the present invention;
FIG. 6 is a schematic diagram of an insurance knowledgegraph generation method according to yet another embodiment of the invention;
FIG. 7 is a schematic diagram of an insurance knowledge graph generation method according to yet another embodiment of the present invention;
FIG. 8 is a schematic structural diagram of an insurance knowledge map generation apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an insurance knowledgebase generation apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the prior art, users usually consult before or after sale for a certain product or a certain product, or train insurance business for insurance business personnel, and the introduction to the user or the insurance business personnel for business models and knowledge points is mainly drawn and stored in svn or wiki by visio, word, excel and the like, but the knowledge organized by the prior art is not intuitive enough, is not perfect enough and is not easy to be inquired in classification, and especially when individual cases are solved, the work is difficult to be carried out efficiently by spending more time in the aspects of knowledge training and the like for business personnel. In order to solve the above technical problem, an embodiment of the present invention provides an insurance knowledge graph generating method to solve the above problem.
Fig. 1 is a schematic flow diagram of an insurance knowledge graph generation method according to an embodiment of the present invention, where an execution subject in this embodiment may be a terminal or a server, and the execution subject is not limited herein.
Referring to fig. 1, the insurance knowledge graph generation method includes:
s101, collecting a plurality of insurance information, wherein each insurance information comprises an insurance category, a plurality of sub-insurance categories corresponding to the insurance category and sub-insurance attributes corresponding to each sub-insurance category, and the sub-insurance attributes comprise sub-insurance description information and premium information of the sub-insurance categories.
Wherein the sub-risk category description information comprises the name and the identification of the sub-risk category.
In this embodiment, a terminal or a server collects multiple categories of insurance information from a database or each service terminal, where the insurance information may be an insurance policy or may be previous-stage data that does not generate the insurance policy, and specifically, each insurance information may include an insurance type, multiple sub-insurance type categories corresponding to the insurance type category, and sub-insurance type attribute information corresponding to each sub-insurance type category, where the sub-insurance type attributes include sub-insurance type description information and insurance cost information of the sub-insurance type categories. Wherein, the user can be a service person or any common user who wants to consult a question.
S102, determining various combination relations among the sub risk types according to the sub risk types and the sub risk attribute corresponding to each sub risk type.
In this embodiment, according to each sub-risk category and the sub-risk category attribute corresponding to each sub-risk category, for example, according to the sub-risk description information and the premium information of the sub-risk category, multiple sub-risk categories may be combined or split in a historical policy to obtain multiple combination modes corresponding to multiple sub-risk categories, and each combination mode may generate a policy to provide a basis for learning or consultation for a user.
S103, taking the risk category as a node entity, and taking the plurality of sub risk categories as each sub node of the node entity.
In this embodiment, to generate a tree-like or mesh-like visual graph, that is, an insurance knowledge graph, a parent node (risk category as a node entity) and child nodes (child risk categories) are first determined, that is, the risk category is used as a node entity, the plurality of child risk categories are used as child nodes of the node entity, and a tree-like or mesh-like visual graph is formed through association between the node entity and the child nodes. For example, a health risk as a risk is a node entity, wherein the health risk further specifically includes a sub-risk category 1, a sub-risk category 2, a sub-risk category 3, and so on.
And S104, generating an insurance knowledge graph according to the node entities and the child nodes, wherein the insurance knowledge graph comprises thumbnails of the node entities and the child nodes, and the thumbnails comprise child risk species description information corresponding to the child nodes.
In this embodiment, in order to intuitively embody the insurance intellectual graph, specifically, the insurance intellectual graph may be generated according to the node entities and each child node, where the insurance intellectual graph includes thumbnails of the node entities and thumbnails of each child node, and the thumbnails include child risk type description information corresponding to the child nodes and risk type description information corresponding to the node entities, so as to intuitively provide the overall architecture and content of the insurance intellectual graph for the user.
S105, displaying the thumbnail to enable a user to click the thumbnail according to the sub-risk description information to inquire premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories related to the sub-risk category.
In practical application, in combination with the schematic structural diagram of the insurance knowledgeable map generating system provided by the embodiment shown in fig. 2, the insurance knowledgeable map generating system includes a terminal (or server) 20 and a user terminal 10, the terminal (or server) is configured to generate an insurance knowledgeable map and provide a display insurance knowledgeable map to the user terminal, and the user terminal is configured to click on a thumbnail included in the insurance knowledgeable map, query premium information of a sub-risk category corresponding to a sub-node on the thumbnail, and query the multiple combination relationships between the sub-risk category and other sub-risk categories associated with the sub-risk category.
In order to achieve the above purpose, firstly, the use of the graph data NEO4J is introduced, and NEO4J can provide a visual interface suitable for storing the insurance knowledge graph.
In this embodiment, a plurality of insurance information are collected first, and an information source is provided for building an insurance knowledge graph, wherein each insurance information includes an insurance category, a plurality of sub-insurance category corresponding to the insurance category, and a sub-insurance attribute corresponding to each sub-insurance category, and the sub-insurance attribute includes sub-insurance description information and premium information of the sub-insurance category, and then, according to the plurality of sub-insurance categories and the sub-insurance attribute corresponding to each sub-insurance category, a plurality of combination relations among the plurality of sub-insurance categories are determined, so as to split and combine the sub-insurance categories when generating an insurance policy, and to provide a plurality of insurance schemes for a user, and then, an insurance knowledge graph is formed, that is, firstly, the insurance category is used as a node entity, and the plurality of sub-insurance category are used as each sub-node of the node entity, forming a tree structure or a mesh structure, generating an insurance knowledge graph, wherein the insurance knowledge graph comprises thumbnails of the node entities and each child node, the thumbnails comprise child risk type description information corresponding to the child nodes, and a query interface is provided for a user by displaying the thumbnails, so that the user clicks the thumbnails according to the child risk type description information to query premium information of the child risk type corresponding to the child nodes and the multiple combination relations between the child risk type and other child risk types related to the child risk type, so that the user can know insurance knowledge more intuitively, and can effectively query insurance business knowledge for a certain insurance business. The scheme collects a plurality of insurance information in the insurance knowledge map generation method, then analyzes a plurality of combination relations among a plurality of sub-insurance categories, generating an insurance knowledge graph through the determination of the node entities and each sub-node corresponding to each node entity, by displaying the node entities and the thumbnails of all the sub-nodes contained in the insurance knowledge graph, the user can click on the thumbnails according to the sub-risk description information, the premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category are inquired, the user can know the insurance knowledge more intuitively, and the insurance business knowledge can be effectively inquired aiming at a certain insurance business, so that the efficiency of solving the insurance business problem is improved.
The invention provides a visual and unified knowledge map learning platform, integrates and persists the existing knowledge points dispersed in each document into a database to generate an insurance knowledge map, so that the human resource cost invested in knowledge training and case solving can be reduced, and the operation and maintenance cost of an old system is reduced.
When a model of nodes and relationships in an insurance knowledge graph is established, in order to intuitively embody a business model, it is necessary to determine various combination relationships among a plurality of sub-insurance category, as shown in fig. 3, and fig. 3 is a relationship diagram among premium information. In the present embodiment, S102 is explained in detail based on the above-described embodiment, for example, based on the embodiment shown in fig. 1. The determining, according to the multiple sub-risk category and the sub-risk attribute corresponding to each sub-risk category, multiple combination relationships among the multiple sub-risk category includes:
obtaining a plurality of combination relations among the plurality of sub-risk categories through a preset classification model according to the plurality of sub-risk category and the premium information corresponding to each sub-risk category; each combination relation comprises a relation graph between premium information corresponding to at least two corresponding sub-risk categories and a premium accounting formula corresponding to the relation graph.
In this embodiment, a plurality of sub-risk category and premium information corresponding to each sub-risk category are input into a trained preset classification model, so as to obtain a plurality of classification modes, that is, a plurality of combination relationships among the plurality of sub-risk category, where each combination relationship includes a visual graph corresponding to a relationship between the premium information corresponding to at least two corresponding sub-risk category and a premium accounting formula corresponding to the combination relationship. The integration and uniform persistence of all knowledge documents are realized, the learning is convenient to view, and problem data are quickly positioned through a visual interface.
Wherein the premium information includes: child node gross premium, child node net premium and tax rate; the premium accounting formula comprises a first premium accounting formula corresponding to each sub-node in the relational graph and a total second premium accounting formula corresponding to each sub-node in the relational graph; wherein the first payment accounting formula is as follows: the sub-node net premium is the sub-node gross premium/(1+ tax rate), and the second premium accounting formula is: and the total net premium is the total gross premium/(1+ tax rate), and the total gross premium is the sum of the gross premiums of the sub nodes corresponding to the sub nodes in the relationship graph.
In practical application, when a model of nodes and relations in an insurance knowledge graph is established, in order to intuitively embody a business model, a detection method is convenient to develop after specific data are collected, the individual case problem is solved, for a standard business model, a unified tag printing mode can be used, query is convenient, and tags are displayed on a page of a NEO4J database.
Specifically, the example tax removing process: the risk category is used as a node entity, wherein the node entity contains various premium (gross premium, risk gross premium, tax, etc.), that is, actually, various premium is used as the node entity, the node description and the specific value are used as the node attribute, the relationship (such as addition, division, etc.) between the premium is used as the relationship entity, the specific calculation expression is used as the relationship attribute, a premium splitting calculation relationship graph is established, the relationship graph is created through a CYPHER statement, and the relationship graph is shown in FIG. 3. For example, equal, internet zone premium equals core gross premium; the chargeable premium is the financial chargeable premium of the dangerous level and is equal to the dangerous gross premium; splitting, such as splitting from a gross insurance premium to a net insurance premium and a tax at risk; splitting items, wherein the net premium of the risk classification to the tax fee of the risk classification are mutually split items; comprises, can be a split;
optionally, how to generate the insurance knowledge graph, the embodiment describes step S104 in detail based on the above embodiments, for example, based on the embodiment described in fig. 1. Generating an insurance knowledge graph according to the node entities and each child node comprises the following steps:
and generating an insurance knowledge graph through a graph database according to the node entity and each child node.
In this embodiment, the node entities and the sub-nodes are generated and displayed to the user by using an image database technology, that is, an NEO4J technology, so that the user can conveniently view and learn, and the problem data is quickly located through a visual interface.
In order to detect data of an insurance policy based on a learning platform of a generated insurance knowledgebase, thumbnails in the insurance knowledgebase are configured with a query function, specifically, referring to fig. 4, fig. 4 is a flowchart of a method for generating an insurance knowledgebase according to still another embodiment of the present invention, which is based on the above-described embodiment, for example, based on the embodiment described in fig. 1, the present embodiment describes a method for generating an insurance knowledgebase in detail. The thumbnail is a hyperlink configured with a query function; after the generating of the insurance knowledgebase, the method for generating the insurance knowledgebase further comprises:
s401, receiving a policy number sent by a target terminal;
s402, receiving a policy number input by a user, and acquiring policy information matched with the policy number from a preset database, wherein the policy information comprises the policy number, the dangerous type category, the sub-dangerous type category, the identifier of the sub-dangerous type category, the multiple combination relations and the premium information corresponding to the policy number.
In this embodiment, after the insurance knowledge map is generated, in order to spot check or detect the accuracy of the insurance information corresponding to the historical insurance policy, the insurance policy number sent by the service terminal needs to be received or collected, where the service terminal may be regarded as a target terminal corresponding to the insurance policy to be audited, and then a user corresponding to the user side inputs the insurance policy number at an inquiry interface position of the hyperlink or directly recognizes the insurance policy number and configures the insurance policy number at the inquiry interface position of the hyperlink to perform automatic inquiry, that is, obtains the insurance policy information matching with the insurance policy number from a preset database, where the insurance policy information includes the insurance policy number, the risk type, the sub-risk type, the identifier of the sub-risk type, the multiple combination relationships, and the insurance information corresponding to the insurance policy number.
After the policy information matching with the policy number is queried, the queried policy information may be checked or audited, as shown in fig. 5, fig. 5 is a flowchart illustrating a method for generating an insurance knowledgegraph according to another embodiment of the present invention, and this embodiment describes the method for generating the insurance knowledgegraph in detail on the basis of the above-described embodiment, for example, on the basis of the embodiment described in fig. 4. After the obtaining policy information matching the policy number, the method further comprises:
s501, identifying the insurance policy number, the dangerous seed category, the sub dangerous seed category and the identification of the sub dangerous seed category, and configuring the identification in the hyperlink;
s502, calculating target premium information according to the premium accounting formula configured in the hyperlink;
s503, auditing the premium information corresponding to the insurance policy number and the target premium information, determining whether the insurance policy information is correct, and displaying an auditing result.
In this embodiment, the policy number, the dangerous type, the sub-dangerous type and the identifier of the sub-dangerous type are identified and automatically configured in the hyperlink, and since the hyperlink is configured with the premium accounting formula, the target premium information corresponding to the policy can be automatically calculated based on the premium accounting formula, by comparing the target premium information with the premium information corresponding to the policy number, if the target premium information is consistent with the premium information corresponding to the policy number, the premium information of the policy is accurate, and if the target premium information is not consistent with the premium information corresponding to the policy number, the premium information of the policy is incorrect, and regardless of the comparison result (audit result), the comparison result can be displayed to the user, so that the user performs subsequent maintenance work and the like according to the comparison result.
Optionally, after the determining whether the policy information is correct, the method further includes:
and if the audit result shows that the premium information in the policy information is incorrect, replacing the target premium information with the premium information in the policy information.
In the embodiment, the content of the insurance knowledge graph can be continuously updated through auditing the premium information, so that the effectiveness of the insurance knowledge graph is ensured.
In practical applications, the query function configured for the hyperlink may be: node risk classification gross insurance fee (which is the total gross insurance fee): configured with name attribute core premium, configured with policy number attribute (for storing value and then querying), configured with specific value consistent with the core system model for storing tax itself later, and configured with specific value consistent with the core system model for storing tax rate later.
Wherein, the node risk classification net premium (child node net premium): a risk net premium configured with a name attribute, a policy number attribute (for storing values for later queries), and an attribute consistent with the core system model for later storing specific values. Relationship between a gross at risk premium and a net at risk premium in a relationship entity: configured with the attribute of a Chinese formula, the Chinese formula is a premium accounting formula: the risk net premium is a risk gross premium/(1+ tax rate), wherein the risk net premium is a child node net premium, and the risk gross premium is a child node gross premium; configuring an English formula attribute, wherein the English formula is as follows: wplicopymetrozine. notaxprimum. wpreminum/(1 + ggrisktaxmapping. addtaxrate). Other nodes, and relationships are configured with similar attributes. In the page of the insurance knowledge graph, the user can check the attributes by clicking specific nodes or relations, so that the definition and the relation of the premium in the tax dismantling process can be clearly understood.
Referring to fig. 6, for the detection and verification of key service data, a simple import procedure is used to convert the core data model into the NEO4J model, and then import the data into the NEO4J database (inserted into the above-mentioned preset attributes), query a certain policy to obtain the core gross insurance fee, the premium, the tax fee, and the net insurance fee, and use the NEO4J page to select other attributes of the display node.
As shown in fig. 6, verification by this visual interface assistance is facilitated for the user. For example, the premium 10 Yuan policy is divided into two risks, one risk of 0.97 Yuan and one 9.03 Yuan, calculated by a tax rate of 0.06, 0.97 Risk premium, 0.05 tax, 0.92 net premium, 9.03 Risk premium, 0.52 tax, and 8.52 net premium. Through such a visual interface, it is easy to find which part of the problem is present. Meanwhile, the idea is applied to an automatic detection program, the program searches the node and the relation of each policy by accessing the NEO4J database, and automatically calculates according to a premium accounting formula stored in a relation entity, and when problems occur in each step, exceptions are thrown out, so that the data are verified or the reason of the position of the problem is searched.
For another example, referring to fig. 7, the core product model is arranged into a graph database: some basic configuration strong checks are carried out through the attributes in the relationship entities, such as the relationship from the risk class to the risk type of 1-to-many, so that the basic configuration of the product is prevented from being mistakenly matched by the user. The integration and uniform persistence of all knowledge documents are realized, the learning is convenient to view, and problem data are quickly positioned through a visual interface. The labor cost of training business and system knowledge is reduced, and the operation and maintenance cost for solving individual case problems is reduced.
For example, in FIG. 7, the subsumption, for example, the risk class contains a plurality of risk types, the relationship is 1: n, and the rest is similar; corresponding to each other, if the dangerous species is matched with the target, the relationship is 1:1, and the rest is similar; composition, for example, a plurality of dangerous categories form a plurality of rating schemes, dangerous seeds 1 and 2 form a scheme 1, dangerous seeds 1 and 3 form a scheme 2, the relationship is m: n, the rest are also compositions, and the relationship can be n: 1. All words displayed on relationship lines between the NEO4J nodes are labels, and may be further specified to specific field attribute descriptions as references for relationships between nodes or child nodes, and a user may view the attributes by clicking on the specific nodes or relationships in the NEO4J page.
In order to implement the insurance knowledgebase generation method, the embodiment provides an insurance knowledgebase generation device. Referring to fig. 8, fig. 8 is a schematic structural diagram of an insurance knowledge map generating apparatus according to an embodiment of the present invention; the insurance knowledge map generation device comprises: an insurance information acquisition module 801, a combination relation determination module 802, a node determination module 803, an insurance knowledge graph generation module 804 and a thumbnail display module 805; an insurance information collecting module 801, configured to collect a plurality of insurance information, where each insurance information includes an insurance category, a plurality of sub-insurance categories corresponding to the insurance category, and a sub-insurance attribute corresponding to each sub-insurance category, where the sub-insurance attribute includes sub-insurance description information and premium information of the sub-insurance categories; a combination relation determining module 802, configured to determine, according to the multiple sub-risk categories and the sub-risk attribute corresponding to each sub-risk category, multiple combination relations among the multiple sub-risk categories; a node determining module 803, configured to use the risk category as a node entity, and use the plurality of sub-risk categories as respective sub-nodes of the node entity; an insurance knowledge graph generating module 804, configured to generate an insurance knowledge graph according to the node entities and the child nodes, where the insurance knowledge graph includes thumbnails of the node entities and the child nodes, and the thumbnails include child risk type description information corresponding to the child nodes; a thumbnail display module 805, configured to display the thumbnail, so that the user clicks the thumbnail according to the sub-risk description information, so as to query premium information of the sub-risk category corresponding to the sub-node and the multiple combination relationships between the sub-risk category and other sub-risk categories associated with the sub-risk category.
In this embodiment, an insurance information acquisition module 801, a combination relation determination module 802, a node determination module 803, an insurance knowledge graph generation module 804, and a thumbnail display module 805 are used to acquire a plurality of insurance information, and provide an information source for building an insurance knowledge graph, wherein each insurance information includes an insurance type, a plurality of sub-insurance type categories corresponding to the insurance type category, and a sub-insurance type attribute corresponding to each sub-insurance type category, and the sub-insurance type attribute includes sub-insurance type description information and premium information of the sub-insurance type, and then according to the sub-insurance type categories and the sub-insurance type attributes corresponding to each sub-insurance type category, a plurality of combination relations between the sub-insurance type categories are determined, so as to split and combine the sub-insurance type categories when generating a policy, so as to provide a plurality of insurance schemes for a user, then, forming an insurance knowledge graph, namely, firstly, taking the insurance category as a node entity, and taking the plurality of sub-insurance categories as each sub-node of the node entity to form a tree structure or a mesh structure to generate the insurance knowledge graph, wherein, the insurance knowledge graph comprises the node entity and thumbnails of all the sub-nodes, the thumbnails comprise sub-risk species description information corresponding to the sub-nodes, providing a query interface for a user by displaying the thumbnail so that the user clicks the thumbnail according to the sub-risk description information, the premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category are inquired, the user can know the insurance knowledge more intuitively, and the insurance business knowledge can be effectively inquired aiming at a certain insurance business.
The invention collects a plurality of insurance information in the insurance knowledge map generating method, then analyzes a plurality of combination relations among a plurality of sub-insurance categories, generating an insurance knowledge graph through the determination of the node entities and each sub-node corresponding to each node entity, by displaying the node entities and the thumbnails of all the sub-nodes contained in the insurance knowledge graph, the user can click on the thumbnails according to the sub-risk description information, the premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category are inquired, the user can know the insurance knowledge more intuitively, and the insurance business knowledge can be effectively inquired aiming at a certain insurance business, so that the efficiency of solving the insurance business problem is improved.
The apparatus provided in this embodiment may be used to implement the technical solutions of the above method embodiments, and the implementation principles and technical effects are similar, which are not described herein again.
Optionally, the sub-risk category description information includes a name and an identification of the sub-risk category.
Optionally, the combination relation determining module is specifically configured to: obtaining a plurality of combination relations among the plurality of sub-risk categories through a preset classification model according to the plurality of sub-risk category and the premium information corresponding to each sub-risk category; each combination relation comprises a relation graph between premium information corresponding to at least two corresponding sub-risk categories and a premium accounting formula corresponding to the relation graph.
Optionally, the premium information includes: child node gross premium, child node net premium and tax rate; the premium accounting formula comprises a first premium accounting formula corresponding to each sub-node in the relational graph and a total second premium accounting formula corresponding to each sub-node in the relational graph; wherein the first payment accounting formula is as follows: the sub-node net premium is the sub-node gross premium/(1+ tax rate), and the second premium accounting formula is: and the total net premium is the total gross premium/(1+ tax rate), and the total gross premium is the sum of the gross premiums of the sub nodes corresponding to the sub nodes in the relationship graph.
Optionally, the correlation group determining unit is specifically configured to: inquiring all interactive information containing the same user identification in the plurality of pieces of interactive information; and aiming at each same user identifier, taking the interactive information of the sending time of the interactive information in all the interactive information containing the same user identifier in a preset time period as an information group.
Optionally, the insurance knowledge graph module is specifically configured to: and generating an insurance knowledge graph through a graph database according to the node entity and each child node.
Optionally, the thumbnail is a hyperlink configured with a query function; the device further comprises: a query module; the query module is configured to:
after the insurance knowledge graph is generated, receiving an insurance policy number sent by a target terminal; receiving a policy number input by a user, and acquiring policy information matched with the policy number from a preset database, wherein the policy information comprises the policy number, the dangerous type category, the sub-dangerous type category, the identifier of the sub-dangerous type category, the multiple combination relations and the premium information corresponding to the policy number.
Optionally, the apparatus further comprises: an auditing module; an audit module for identifying and configuring in the hyperlink the identification of the policy number, the risk category, the sub-risk category and the sub-risk category after the acquiring policy information matching the policy number; calculating target premium information according to the premium accounting formula configured in the hyperlink; and auditing the premium information corresponding to the insurance policy number and the target premium information, determining whether the insurance policy information is correct, and displaying an auditing result.
Optionally, the apparatus further comprises: an update module; and the updating module is used for replacing the target premium information with the premium information in the policy information if the audit result shows that the premium information in the policy information is incorrect after the policy information is determined to be correct.
In order to implement the insurance knowledgebase generation method, the embodiment provides insurance knowledgebase generation equipment. Fig. 9 is a schematic structural diagram of an insurance knowledgebase generation apparatus according to an embodiment of the present invention. As shown in fig. 9, the insurance knowledgemap generating apparatus 90 of the present embodiment includes: a processor 901 and a memory 902; a memory 902 for storing computer-executable instructions; a processor 901 for executing computer executable instructions stored in the memory to implement the steps performed in the above embodiments. Reference may be made in particular to the description relating to the method embodiments described above.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer executing instruction is stored in the computer-readable storage medium, and when a processor executes the computer executing instruction, the method for generating an insurance knowledgegraph as described above is implemented.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form. In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each module may exist alone physically, or two or more modules are integrated into one unit. The unit formed by the modules can be realized in a hardware form, and can also be realized in a form of hardware and a software functional unit.
The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present invention are not limited to only one bus or one type of bus. The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device or host device.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An insurance knowledge graph generation method, comprising:
collecting a plurality of insurance information, wherein each insurance information comprises an insurance category, a plurality of sub-insurance categories corresponding to the insurance category and sub-insurance attributes corresponding to each sub-insurance category, and the sub-insurance attributes comprise sub-insurance description information and premium information of the sub-insurance categories;
determining a plurality of combination relations among the plurality of sub-risk category according to the plurality of sub-risk category and the sub-risk attribute corresponding to each sub-risk category;
taking the risk category as a node entity and the plurality of sub-risk categories as respective sub-nodes of the node entity;
generating an insurance knowledge graph according to the node entities and each child node, wherein the insurance knowledge graph comprises thumbnails of the node entities and each child node, and the thumbnails comprise child risk species description information corresponding to the child nodes;
and displaying the thumbnail so that a user clicks the thumbnail according to the sub-risk description information to inquire premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories associated with the sub-risk category.
2. The method according to claim 1, wherein the determining a plurality of combination relationships between the plurality of sub-risk categories according to the plurality of sub-risk categories and the sub-risk attribute corresponding to each of the sub-risk categories comprises:
obtaining a plurality of combination relations among the plurality of sub-risk categories through a preset classification model according to the plurality of sub-risk category and the premium information corresponding to each sub-risk category;
each combination relation comprises a relation graph between premium information corresponding to at least two corresponding sub-risk categories and a premium accounting formula corresponding to the relation graph.
3. The method of claim 2, wherein the premium information comprises: child node gross premium, child node net premium and tax rate;
the premium accounting formula comprises a first premium accounting formula corresponding to each sub-node in the relational graph and a total second premium accounting formula corresponding to each sub-node in the relational graph;
wherein the first payment accounting formula is as follows: the sub-node net premium is the sub-node gross premium/(1+ tax rate), and the second premium accounting formula is: and the total net premium is the total gross premium/(1+ tax rate), and the total gross premium is the sum of the gross premiums of the sub nodes corresponding to the sub nodes in the relationship graph.
4. The method according to any one of claims 1-3, wherein generating an insurance knowledge graph from the node entities and respective child nodes comprises:
and generating an insurance knowledge graph through a graph database according to the node entity and each child node.
5. The method of claim 2, wherein the thumbnail is a hyperlink configured with a query function;
after the generating an insurance knowledgegraph, the method further comprises:
receiving a policy number sent by a target terminal;
receiving a policy number input by a user, and acquiring policy information matched with the policy number from a preset database, wherein the policy information comprises the policy number, the dangerous type category, the sub-dangerous type category, the identifier of the sub-dangerous type category, the multiple combination relations and the premium information corresponding to the policy number.
6. The method of claim 5, wherein after said obtaining policy information matching said policy number, said method further comprises:
identifying the policy number, the risk category, the sub-risk category, and the identity of the sub-risk category and configuring them in the hyperlink;
calculating target premium information according to the premium accounting formula configured in the hyperlink;
and auditing the premium information corresponding to the insurance policy number and the target premium information, determining whether the insurance policy information is correct, and displaying an auditing result.
7. The method of claim 6, wherein after said determining whether said policy information is correct, said method further comprises:
and if the audit result shows that the premium information in the policy information is incorrect, replacing the target premium information with the premium information in the policy information.
8. An insurance knowledge map generating apparatus, comprising:
the insurance information acquisition module is used for acquiring a plurality of insurance information, wherein each insurance information comprises an insurance category, a plurality of sub-insurance categories corresponding to the insurance category and sub-insurance attributes corresponding to each sub-insurance category, and the sub-insurance attributes comprise sub-insurance description information and insurance cost information of the sub-insurance categories;
the combination relation determining module is used for determining a plurality of combination relations among the plurality of sub risk types according to the plurality of sub risk types and the sub risk attribute corresponding to each sub risk type;
a node determining module, configured to use the risk category as a node entity, and use the plurality of sub-risk categories as respective sub-nodes of the node entity;
the insurance knowledge graph generating module is used for generating an insurance knowledge graph according to the node entities and the child nodes, the insurance knowledge graph comprises thumbnails of the node entities and the child nodes, and the thumbnails comprise child risk species description information corresponding to the child nodes;
and the thumbnail display module is used for displaying the thumbnail so that a user clicks the thumbnail according to the sub-risk description information to inquire the premium information of the sub-risk category corresponding to the sub-node and the multiple combination relations between the sub-risk category and other sub-risk categories related to the sub-risk category.
9. An insurance knowledge graph generating apparatus, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the insurance knowledgegraph generation method of any of claims 1 to 7.
10. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the insurance knowledgegraph generating method of any one of claims 1 to 7.
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