Insurance clause division method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides an insurance clause division method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the steps of obtaining an insurance clause data set, wherein the insurance clause data set comprises initial insurance clauses and initial clause content data; performing clause similarity detection on any two initial clause content data to obtain a clause similarity value; determining an initial term pair from the initial insurance terms based on the term similarity value; performing edge betweenness detection on the initial clause pair to obtain an initial edge betweenness; based on the initial edge betweenness and a preset group number, performing term division on the first connection terms of the initial insurance term pair, and determining a candidate term group, the candidate term group comprising second connection terms; obtaining a group feedback score of the candidate term group; and performing group clause optimization on the second connection clauses based on the reinforcement learning model and the group feedback score to obtain a target clause group. According to the embodiment of the invention, the accuracy and efficiency of dividing the insurance terms can be improved.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a method and device for dividing insurance clauses, an electronic device, and a storage medium. Background Art

[0002] Insurance clauses refer to the various clauses and details stipulated in the insurance contract, which are an important part of the insurance contract and may include basic clauses, additional clauses, special agreements, etc. However, there are a large number of insurance clauses. By scientifically combining and adjusting similar and highly related insurance clauses, we can better achieve multiple goals such as improving the competitiveness of insurance products and customer needs. For example, in the insurance sales scenario of financial technology, by better dividing the clause combinations and providing combination discounts, we can meet the various needs of customers at the same time and improve the service quality.

[0003] However, related technologies usually use two methods to achieve insurance clause division. One is to simply classify auto insurance clauses by category, but this method does not deeply understand the potential associations and complex relationships between clauses, resulting in low accuracy in the division of insurance clauses. The other is to rely on expert experience or sales staff's sales experience to manually combine clauses, but this method is inefficient and easily misses subtle connections between clauses, resulting in low accuracy in the division of insurance clauses. Therefore, how to improve the accuracy and efficiency of the division of insurance clauses has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose an insurance clause division method and device, electronic device, and storage medium, aiming to improve the accuracy and efficiency of the division of insurance clauses.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for dividing insurance clauses, the method comprising:

[0006] Acquire an insurance clause data set, the insurance clause data set including initial insurance clauses and initial clause content data of the initial insurance clauses, the initial insurance clauses being used to represent nodes of a preset clause topology network;

[0007] Performing a clause similarity test on any two of the initial clause content data to obtain a clause similarity value;

[0008] Determining an initial clause pair from the initial insurance clauses based on the clause similarity value, the initial clause pair is used to represent an edge of the clause topology network, the initial insurance clause pair includes a first connection clause, and the edge of the clause topology network is used to represent a path between two connected first connection clauses;

[0009] Performing edge betweenness detection on the initial clause pair to obtain an initial edge betweenness, wherein the initial edge betweenness is used to represent the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two of the initial insurance clauses;

[0010] Dividing the first connection clause into clauses based on the initial edge betweenness and the preset number of groups to determine a candidate clause group, wherein the candidate clause group includes a second connection clause;

[0011] Obtaining a group feedback score of the candidate clause group;

[0012] The second connection terms are group-optimized based on a preset reinforcement learning model and the group feedback score to obtain a target term group.

[0013] In some embodiments, the performing group clause optimization on the second connection clause based on the preset reinforcement learning model and the group feedback score to obtain a target clause group includes:

[0014] extracting a demand feedback sub-score, an object feedback sub-score, and a group application sub-score from the group feedback score;

[0015] Based on the reinforcement learning model, an action decision is made on the demand feedback sub-score, the object feedback sub-score, the group application sub-score and the clause content data of the second connection clause to obtain clause action decision data, wherein the clause action decision data includes optimization feedback data;

[0016] Performing similarity calculation on the optimized feedback data and the preset target feedback data to obtain a feedback data similarity value;

[0017] Adjusting the clause data of the second connection clause based on the feedback data similarity value and the clause action decision data to obtain an optimized connection clause;

[0018] The target clause group is constructed based on the optimized connection clauses of the candidate clause group.

[0019] In some embodiments, the step of performing an action decision on the demand feedback sub-score, the object feedback sub-score, the group application sub-score, and the clause content data of the second connection clause based on the reinforcement learning model to obtain clause action decision data includes:

[0020] determining a candidate combined reward value for the candidate clause group based on the demand feedback sub-score, the object feedback sub-score, and the group application sub-score;

[0021] An action decision is made on the candidate combination reward value and the clause content data of the second connection clause based on the reinforcement learning model to obtain the clause action decision data.

[0022] In some embodiments, dividing the first connection terms based on the initial edge betweenness and the preset number of groups to determine the candidate term groups includes:

[0023] Determine a central clause pair from the initial clause pairs based on the initial edge betweenness, wherein the central clause pair is used to indicate the initial clause pair with the largest initial edge betweenness;

[0024] The first connection clause is divided into clauses based on the central clause pair and the preset number of groups to determine the candidate clause group.

[0025] In some embodiments, dividing the first connection clauses based on the central clause pair and the preset number of groups to determine the candidate clause groups includes:

[0026] Dividing the first connection clauses into clauses based on the central clause pair to determine a first candidate clause group and a second candidate clause group;

[0027] determining a first candidate group weight based on the initial edge betweenness included in the first candidate term group;

[0028] Determining a second candidate group weight based on the initial edge betweenness included in the second candidate term group;

[0029] Dividing the first connection clause into clauses based on the first candidate group weight and the second candidate group weight to obtain an initial clause group;

[0030] The initial clause group is updated based on the preset number of groups to determine the candidate clause group.

[0031] In some embodiments, dividing the first connection terms based on the first candidate group weight and the second candidate group weight to obtain an initial term group includes:

[0032] Determining a preset weight difference threshold based on the number of combinations of the first candidate clause group and the second candidate clause group;

[0033] If the weight difference between the first candidate group weight and the second candidate group weight is greater than the preset weight difference threshold, split the central clause pair to obtain a split central clause;

[0034] The first connection clause is divided into clauses based on the first candidate group weight, the second candidate group weight and the split center clause to obtain the initial clause group.

[0035] In some embodiments, the updating the initial clause group based on the preset number of groups to determine the candidate clause group includes:

[0036] If the number of the initial clause groups is less than the preset number of groups, edge betweenness detection is performed on the initial clause pairs in the initial clause groups to obtain candidate edge betweenness, where the candidate edge betweenness is used to represent the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two third connection clauses in the initial clause group;

[0037] Performing clause division on the third connection clause in the initial clause group based on the candidate edge betweenness to obtain a third candidate clause group;

[0038] The candidate clause group is determined based on the third candidate clause group and the undivided initial clause group.

[0039] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides an insurance clause division device, the device comprising:

[0040] A first acquisition module is used to acquire an insurance clause data set, wherein the insurance clause data set includes initial insurance clauses and initial clause content data of the initial insurance clauses, wherein the initial insurance clauses are used to represent nodes of a preset clause topology network;

[0041] A similarity detection module is used to perform a clause similarity detection on any two of the initial clause content data to obtain a clause similarity value;

[0042] A clause pair determination module, configured to determine an initial clause pair from the initial insurance clauses based on the clause similarity value, wherein the initial clause pair is used to represent an edge of the clause topology network, wherein the initial insurance clause pair includes a first connection clause, and the edge of the clause topology network is used to represent a path between two connected first connection clauses;

[0043] An edge betweenness detection module, used to perform edge betweenness detection on the initial clause pair to obtain an initial edge betweenness, wherein the initial edge betweenness is used to characterize the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two of the initial insurance clauses;

[0044] A clause division module, configured to divide the first connection clause into clauses based on the initial edge betweenness and a preset number of groups, and determine a candidate clause group, wherein the candidate clause group includes a second connection clause;

[0045] A second acquisition module is used to obtain a group feedback score of the candidate clause group;

[0046] A group clause optimization module is used to perform group clause optimization on the second connection clause based on a preset reinforcement learning model and the group feedback score to obtain a target clause group.

[0047] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0048] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0049] The present application proposes an insurance clause division method and device, electronic device, and storage medium. The method comprises: obtaining an insurance clause data set, wherein the insurance clause data set includes initial insurance clauses and initial clause content data of the initial insurance clauses, wherein the initial insurance clauses are used to represent nodes of a preset clause topology network; performing clause similarity detection on any two initial clause content data to obtain a clause similarity value; determining an initial clause pair from the initial insurance clauses based on the clause similarity value, wherein the initial clause pair is used to represent an edge of a clause topology network, wherein the initial insurance clause pair includes a first connection clause, and the edge of the clause topology network is used to represent a path between two connected first connection clauses; further, performing edge betweenness detection on the initial clause pair to obtain an initial edge betweenness, wherein the initial edge betweenness is used to represent the number of times an edge corresponding to the initial clause pair appears in the shortest path formed by any two initial insurance clauses; further, performing clause division on the first connection clause based on the initial edge betweenness and a preset number of groups to determine a candidate clause group, wherein the candidate clause group includes a second connection clause; further, obtaining a group feedback score of the candidate clause group; performing group clause optimization on the second connection clause based on a preset reinforcement learning model and the group feedback score to obtain a target clause group. Compared with the related art that simply classifies based on clause categories or combines clauses based on human experience, the present application can deeply understand the potential associations and complex relationships between clauses, first determine the initial clause pairs with similarity, and then realize the automatic classification of insurance clauses based on the edge betweenness and the preset number of clauses, and further realize the group clause optimization of insurance clauses based on group feedback scoring and reinforcement learning model, which can avoid missing the subtle connections between clauses, so as to effectively improve the accuracy of insurance clause division. Therefore, the embodiments of the present application can effectively improve the accuracy and efficiency of insurance clause division. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flow chart of the insurance clause division method provided in the embodiment of the present application;

[0051] Figure 2 It is a specific schematic diagram of a clause topology network provided in an embodiment of the present application;

[0052] Figure 3 yes Figure 1 A flowchart of step S150 in FIG.

[0053] Figure 4 yes Figure 3 A flowchart of step S320 in FIG.

[0054] Figure 5 yes Figure 4 A flowchart of step S440 in FIG.

[0055] Figure 6 yes Figure 4 A flowchart of step S450 in FIG.

[0056] Figure 7 yes Figure 1 A flowchart of step S170 in FIG.

[0057] Figure 8 yes Figure 7 A flowchart of step S720 in FIG.

[0058] Fig. 9 It is a structural schematic diagram of the insurance clause division device provided in an embodiment of the present application;

[0059] Fig.10 It is a hardware structure diagram of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0061] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0063] First, some nouns involved in this application are analyzed:

[0064] Artificial Intelligence (AI): It is a new technical science that studies and develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. AI is a branch of computer science. AI attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Research in this field includes robots, language recognition, image recognition, natural language processing and expert systems. AI can simulate the information process of human consciousness and thinking. AI is also a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0065] Natural Language Processing (NLP): NLP uses computers to process, understand, and apply human languages ​​(such as Chinese, English, etc.). NLP is a branch of artificial intelligence and an interdisciplinary subject between computer science and linguistics. It is often referred to as computational linguistics. Natural language processing includes grammatical analysis, semantic analysis, and text understanding. Natural language processing is commonly used in technical fields such as machine translation, handwritten and printed character recognition, speech recognition and text-to-speech conversion, information intent recognition, information extraction and filtering, text classification and clustering, public opinion analysis, and opinion mining. It involves data mining related to language processing, machine learning, knowledge acquisition, knowledge engineering, artificial intelligence research, and linguistic research related to language computing.

[0066] Knowledge Graph: An information system for storing and representing knowledge that can organize entities and their relationships in the form of a graph. Knowledge graphs are usually composed of nodes (representing entities, such as people, places, things, etc.) and edges (representing relationships between entities). This structure enables knowledge to be represented and queried in an intuitive way.

[0067] Topological network: is a term that describes the connection relationship between various elements in the network, involving the arrangement and layout of nodes (representing entities in the network, such as devices, items, etc.) and edges (representing the connection relationship between nodes) in the network. Topological network not only focuses on physical connections, but also includes logical connections, which is the basis for building and managing networks.

[0068] Girvan-Newman algorithm: It is a network analysis algorithm used for community discovery. It identifies the community structure in the network by calculating the edge betweenness centrality (EBC) of each edge in the network.

[0069] Betweenness Centrality: refers to the number of all shortest paths in the network that pass through a certain edge. Specifically, for each pair of nodes in the network, the shortest path refers to the shortest path connecting the two nodes, and the betweenness centrality of an edge is the number of paths that pass through the edge among these shortest paths.

[0070] Deep Q-Learning (DQL): is a model that combines deep learning and Q-Learning reinforcement learning algorithms. The DQL model is designed to solve decision-making and control problems. The main goal of the DQL model is to solve the computational difficulties of traditional Q-Learning when facing large-scale or continuous state spaces by using deep neural networks to approximate the Q function.

[0071] Insurance clauses refer to the various clauses and details stipulated in the insurance contract, which are an important part of the insurance contract and may include basic clauses, additional clauses, special agreements, etc. However, there are a large number of insurance clauses. By scientifically combining and adjusting similar and highly related insurance clauses, we can better achieve multiple goals such as improving the competitiveness of insurance products and customer needs. For example, in the insurance sales scenario of financial technology, there are a large number of insurance clauses. By better dividing the clause combinations and providing combination discounts, we can meet the various needs of customers at the same time and improve the quality of service.

[0072] However, related technologies usually use two methods to achieve insurance clause division. One is to simply classify auto insurance clauses by category, but this method does not deeply understand the potential associations and complex relationships between clauses, resulting in low accuracy in the division of insurance clauses. The other is to rely on expert experience or sales staff's sales experience to manually combine clauses, but this method is inefficient and easily misses subtle connections between clauses, resulting in low accuracy in the division of insurance clauses. Therefore, how to improve the accuracy and efficiency of the division of insurance clauses has become a technical problem that needs to be solved urgently.

[0073] Based on this, the embodiments of the present application provide a method and device for dividing insurance clauses, an electronic device, and a storage medium, aiming to improve the accuracy and efficiency of dividing insurance clauses.

[0074] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0075] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0076] The insurance clause division method provided in the embodiment of the present application relates to the field of artificial intelligence technology. The recommended method provided in the embodiment of the present application can be applied to the terminal, can also be applied to the server side, and can also be software running in the terminal or the server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services; the software can be an application that implements the insurance clause division method, etc., but is not limited to the above forms.

[0077] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0078] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to the identity or characteristics of the object, such as object operation information, object behavior data, and object feedback data, the permission or consent of the object will be obtained first, and the collection, use and processing of these data will comply with relevant laws, regulations and standards. In addition, when the embodiment of the present application needs to obtain sensitive personal information of the object, the separate permission or separate consent of the object will be obtained through a pop-up window or jump to a confirmation page. After clearly obtaining the separate permission or separate consent of the object, the necessary user-related data for enabling the normal operation of the embodiment of the present application is obtained.

[0079] See also Figure 1 , Figure 1 is an optional flow chart of the insurance clause division method provided in the embodiments of the present application. In some embodiments of the present application, Figure 1 The method may specifically include but is not limited to steps S110 to S170.

[0080] Step S110, obtaining an insurance clause data set;

[0081] Step S120, performing a clause similarity test on any two initial clause content data to obtain a clause similarity value;

[0082] Step S130, determining an initial clause pair from the initial insurance clauses based on the clause similarity value;

[0083] Step S140, performing edge betweenness detection on the initial clause pair to obtain an initial edge betweenness;

[0084] Step S150, dividing the first connection clause into clauses based on the initial edge betweenness and the preset number of groups, and determining a candidate clause group;

[0085] Step S160, obtaining a group feedback score of the candidate clause group;

[0086] Step S170, performing group clause optimization on the second connection clause based on a preset reinforcement learning model and group feedback score to obtain a target clause group.

[0087] In steps S110 to S170 of the present application, in the insurance sales scenario of financial technology, the number of insurance clauses is very large. By better dividing the clause combinations and providing combination discounts, it is possible to meet the various needs of customers at the same time and improve service quality. For example, the auto insurance clauses in the insurance industry can usually be divided into multiple types, such as basic insurance, third-party liability insurance, vehicle occupant liability insurance, etc. The existing clauses are very similar or even the same, but because it is not known what clauses there are, new clauses are often added, or how to combine the existing fewer clauses. Based on this, by dividing the insurance clauses into clause combinations and providing combination discounts, it is possible to better determine the insurance coverage, insurance amount and deductible information under different insurance combinations, thereby improving the convenience of the insured. Compared with the related art that simply classifies based on clause categories or combines clauses based on human experience, the present application can deeply understand the potential associations and complex relationships between clauses, first determine the initial clause pairs with similarity, and then realize the automatic classification of insurance clauses based on the edge betweenness and the preset number of clauses, and further realize the group clause optimization of insurance clauses based on group feedback scoring and reinforcement learning model, which can avoid missing the subtle connections between clauses, so as to effectively improve the accuracy of insurance clause division. Therefore, the embodiments of the present application can effectively improve the accuracy and efficiency of insurance clause division.

[0088] In step S110 of some embodiments, the insurance clause data set refers to a data set used to store all insurance clauses that currently need to be divided. The insurance clause data set includes multiple initial insurance clauses and initial clause content data for each initial insurance clause. The initial clause content data is used to characterize the specific content of the corresponding initial insurance clause. For example, in the financial technology auto insurance clause division combination scenario, the insurance clause data set may include all clauses related to auto insurance, such as basic insurance (such as vehicle loss insurance), third-party liability insurance, vehicle occupant liability insurance, spontaneous combustion insurance, etc., without specific limitation. In addition, the data in the insurance clause data set of the present application can be reasonably obtained from the publicly available insurance company databases, public policy documents or industry reports, etc., to ensure the integrity and accuracy of the data, without specific limitation.

[0089] It should be noted that in order to deeply understand the potential associations and complex relationships between clauses, the present application can introduce graph theory methods, that is, combine the Girvan-Newman algorithm to identify the association structure between insurance clauses. At this time, each initial insurance clause can be used to characterize the nodes of the preset clause topology network. The preset clause topology network describes the relationship and layout between the elements in the network by connecting nodes and edges, so as to perform targeted optimization.

[0090] In step S120 of some embodiments, in order to first understand whether there is a correlation between any two initial insurance clauses, the present application can perform clause similarity detection on any two initial clause content data to obtain a clause similarity value. The clause similarity value is used to characterize the similarity between the two corresponding initial insurance clauses. The higher the clause similarity value, the higher the similarity between the two corresponding initial insurance clauses. The similarity of the clauses can be reflected in the fact that the two clauses are usually purchased together, or have similarities that meet the conditions, the same number of accidents, etc., without specific limitation.

[0091] It should be noted that the clause similarity detection can use natural language processing technology, such as cosine similarity, word embedding model, etc. to quantify the similarity between any two initial clause contents. Specifically, by comparing the language structure, keywords, topics, purchase conditions, claims usage, etc. of the two initial clause content data, a similarity value can be obtained to indicate the degree of similarity between the two initial insurance clauses.

[0092] It should be noted that, when determining the initial clause pair, there may be initial insurance clauses that are not related to any initial insurance clauses, and they can be directly used as candidate clause groups that need to be optimized later.

[0093] In step S130 of some embodiments, after determining the clause similarity values ​​of multiple pairs of initial insurance clauses, at least one pair of initial insurance clauses whose clause similarity value is greater than or equal to a preset similarity threshold can be used as an initial clause pair. If a graph structure of a clause topology network is constructed, the initial clause pairs are used to characterize the edges of the clause topology network, the initial insurance clause pairs include first connection clauses, and the edges of the clause topology network are used to characterize the paths between the two connected first connection clauses. In this way, initial insurance clauses with similarities can be grouped together to facilitate subsequent analysis. For example, the initial clause pair includes clause A and clause B, then clause A and clause B are first connection clauses belonging to the same initial clause pair.

[0094] For example, Figure 2 As shown, Figure 2It is a specific schematic diagram of the clause topology network provided in the embodiment of the present application. Assuming that the insurance clause data set includes five initial insurance clauses, namely Clause A (basic insurance), Clause B (third party liability insurance), Clause C (vehicle personnel liability insurance), Clause D (whole vehicle theft and robbery insurance), and Clause E (spontaneous combustion insurance), after performing clause similarity detection on any two initial clause content data, 10 clause similarity values ​​can be obtained. Furthermore, by comparing the clause similarity value with the preset similarity threshold, it can be determined that the initial clause pairs with similar associations include clause pair (A, B) (i.e., clause A and clause B), clause pair (A, C), clause pair (B, C), clause pair (B, D), clause pair (B, E), clause pair (C, D), and clause pair (D, E).

[0095] In step S140 of some embodiments, after a plurality of initial clause pairs are determined, edge betweenness detection may be performed on the edges corresponding to the initial clause pairs to obtain the initial edge betweenness corresponding to each initial clause pair. The initial edge betweenness of the present application also represents the betweenness centrality of the initial clause pair, that is, it is used to characterize the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two initial insurance clauses. Specifically, the shortest path of the node pair formed by any two initial insurance clauses may be determined first, and then the initial edge betweenness may be determined based on the number of times the edges corresponding to the initial clause pairs appear in these shortest paths.

[0096] For example, Figure 2 As shown, the shortest path of clause pair (A,B) is (A,B) (i.e., from the node corresponding to clause A to the node corresponding to clause B), the shortest paths of clause pair (A,C), clause pair (B,C), clause pair (B,D), clause pair (B,E), clause pair (C,D) and clause pair (D,E) are all themselves, the shortest path of clause pair (A,D) is (A,B,D) (i.e., from the node corresponding to clause A to the node corresponding to clause B and then to the node corresponding to clause D) or (A,C,D), the shortest path of clause pair (A,E) is (A,B,E), and the shortest path of clause pair (C,E) is (C,B,E) or (C,D,E). In this way, we can get the initial edge betweenness of the initial clause pair (A,B) is 3, which appears in the shortest path of the clause pair (A,B), the shortest path of the clause pair (A,D) and the shortest path of the clause pair (A,E); the initial edge betweenness of the initial clause pair (A,C) is 2, the initial edge betweenness of the initial clause pair (B,C) is 1, the initial edge betweenness of the initial clause pair (B,D) is 1, the initial edge betweenness of the initial clause pair (B,E) is 3, the initial edge betweenness of the initial clause pair (C,D) is 3, and the initial edge betweenness of the initial clause pair (D,E) is 2.

[0097] In step S150 of some embodiments, after obtaining the initial edge betweenness corresponding to each initial clause pair, the first connection clause can be divided into clauses based on the initial edge betweenness and the preset number of groups to determine the candidate clause group. In addition, the initial insurance clause included in the candidate clause group can be used as the second connection clause. At this time, the number of second connection clauses included in the candidate clause group can be one, two, three, etc., without limitation.

[0098] See also Figure 3 , Figure 3 It is a specific flow chart of step S150 provided in an embodiment of the present application. In some embodiments of the present application, step S150 may specifically include but is not limited to steps S310 to S320.

[0099] Step S310, determining a central term pair from the initial term pairs based on the initial edge betweenness;

[0100] Step S320: dividing the first connection clause into clauses based on the central clause pair and the preset number of groups to determine candidate clause groups.

[0101] In step S310 of some embodiments, the central term pair is used to indicate the initial term pair with the largest initial edge betweenness, and is used to represent the edge that has the greatest impact on the graph structure. Figure 2 As shown, after determining that the initial edge betweenness of the initial clause pair (A, B) is 3, the initial edge betweenness of the initial clause pair (A, C) is 2, the initial edge betweenness of the initial clause pair (B, C) is 1, the initial edge betweenness of the initial clause pair (B, D) is 1, the initial edge betweenness of the initial clause pair (B, E) is 3, the initial edge betweenness of the initial clause pair (C, D) is 3, and the initial edge betweenness of the initial clause pair (D, E) is 2, by comparing the initial edge betweennesses, it can be determined that the largest initial clause pairs include the initial clause pair (A, B), the initial clause pair (B, E) and the initial clause pair (C, D), and then it can be determined that the central clause pairs in the current clause topology network include the clause pair (A, B), the clause pair (B, E) and the clause pair (C, D).

[0102] In step S320 of some embodiments, the preset number of groups refers to the preset number of candidate clause groups that need to be divided, which can also be referred to as the number of subgraphs that need to be divided. After determining the central clause pair, the first connection clause can be further divided into clauses based on the central clause pair and the preset number of groups to determine the preset number of candidate clause groups. It should be noted that the subgraph mentioned in this application is used to represent a clause combination method.

[0103] In some specific embodiments, after determining the central clause pair, any high-betweenness centrality edge can be removed, such as the edges of clause pair (A, B), clause pair (B, E), and clause pair (C, D). In this case, the entire graph can be divided into two subgraphs of various cases. For example, removing the edge of clause pair (A, B) can result in two subgraphs, including clause A and clause B. Figure 1 , and a sub-clause containing clause C, clause D, and clause E Figure 2 Alternatively, by removing the edge where the clause pair (B, E) is located, we can obtain two subgraphs, one containing clause B and the other containing clause E. Figure 3 , and a sub-clause containing clause A, clause C, and clause D Figure 4 Alternatively, by removing the edge where the clause pair (C, D) is located, we can obtain two subgraphs, one containing clause C and the other containing clause D. Figure 5 , and a sub-clause containing clause A, clause B, and clause E Figure 6 The specific division method can be adjusted according to the specific parameter settings of the Girvan-Newman algorithm on which it is based, and is not limited.

[0104] In other specific embodiments, the present application may consider the weight of the divided subgraphs when dividing the terms to improve the balance of the division. The weight of the divided subgraphs may be determined by the sum of the initial edge betweennesses of the edges corresponding to all initial terms pairs contained in the subgraphs.

[0105] See also Figure 4 , Figure 4 It is a specific flow chart of step S320 provided in an embodiment of the present application. In some embodiments of the present application, step S320 may specifically include but is not limited to steps S410 to S450.

[0106] Step S410, dividing the first connection clause into clauses based on the central clause pair to determine a first candidate clause group and a second candidate clause group;

[0107] Step S420, determining the weight of the first candidate group based on the initial edge betweenness included in the first candidate term group;

[0108] Step S430, determining the weight of the second candidate group based on the initial edge betweenness included in the second candidate term group;

[0109] Step S440, dividing the first connection clause based on the first candidate group weight and the second candidate group weight to obtain an initial clause group;

[0110] Step S450: updating the initial clause groups based on a preset number of groups to determine candidate clause groups.

[0111] In step S410 of some embodiments, the first connection clauses are divided into clauses based on the central clause pair, and the clause group containing only the central clause pair can be used as the first candidate clause group, and the clause group not containing the central clause pair can be used as the second candidate clause group. For example, by removing the edge where the central clause pair (A, B) is located, two subgraphs can be obtained, a subgraph containing clause A and clause B. Figure 1 , and a sub-clause containing clause C, clause D, and clause E Figure 2 , at this time Figure 1 The clause group formed is equivalent to the first candidate clause group. Figure 2 The formed clause group is equivalent to the second candidate clause group.

[0112] In step S420 and step S430 of some embodiments, further, since the edge betweenness can reflect the importance and intermediary role of the clause in the clause topology network, the higher the edge betweenness usually means that the connectivity and information flow of the clause to other clauses are more important. Therefore, by considering these edge betweenness, a weight value can be assigned to the candidate clause group so that its importance can be considered in subsequent optimization and evaluation. Specifically, the initial edge betweenness contained in the first candidate clause group can be added to obtain the weight of the first candidate group. The initial edge betweenness contained in the second candidate clause group is added to obtain the weight of the second candidate group. Figure 2 As shown, if the first candidate clause group includes clause A and clause B, and the initial edge betweenness of the initial clause pair (A, B) is 3, then the weight of the first candidate group can be determined to be 3. Similarly, if the second candidate clause group includes clause C, clause D, and clause E, and the initial edge betweenness of the initial clause pair (C, D) is 3, and the initial edge betweenness of the initial clause pair (D, E) is 2, then the weight of the second candidate group can be determined to be 5.

[0113] In step S440 of some embodiments, the first connection clause may be further divided into clauses based on the first candidate group weight and the second candidate group weight to obtain an initial clause group. That is, the present application may determine which clauses can be combined into a clause combination by comparing the weights of each clause group, emphasizing the need to find clause combinations that are complementary and interconnected, thereby enhancing overall performance and relevance.

[0114] In step S450 of some embodiments, after the initial clause group is determined, it is necessary to consider whether the number of subgraphs currently divided reaches the preset number of groups. If not, it is necessary to continue to divide the subgraphs to obtain more accurate and detailed clause combinations. In this way, it can be ensured that the final candidate clause group meets the preset number of groups and reaches the optimal state.

[0115] In the above embodiment, by considering the weights of the divided candidate clause groups to determine whether to evenly divide the graph corresponding to the clause topology network, the accuracy and comprehensiveness of the combination allocation can be improved.

[0116] See also Figure 5 , Figure 5 It is a specific flow chart of step S440 provided in an embodiment of the present application. In some embodiments of the present application, step S440 may specifically include but is not limited to steps S510 to S530.

[0117] Step S510, determining a preset weight difference threshold based on the number of combinations of the first candidate clause group and the second candidate clause group;

[0118] Step S520: if the weight difference between the first candidate group weight and the second candidate group weight is greater than a preset weight difference threshold, split the central clause pair to obtain a split central clause;

[0119] Step S530, dividing the first connection clause based on the first candidate group weight, the second candidate group weight and the split center clause to obtain an initial clause group.

[0120] In step S510 of some embodiments, when performing the specific division of clauses, the present application may first determine a preset weight difference threshold based on the number of combinations of the first candidate clause group and the second candidate clause group, and the preset weight difference threshold is used as a benchmark value for measuring the difference in importance between the two candidate clause groups. To ensure its scientificity and rationality, the setting of this threshold is usually based on data statistical analysis, and factors may include: the average weight of the candidate clause group, the number and complexity of the clauses in the group, industry standards or historical data in related fields, etc. When the number of combinations of candidate clause groups is large and involves a wide variety of clauses, the weight threshold should also be adjusted appropriately to reflect the complexity of the data.

[0121] In step S520 of some embodiments, the weight difference between the weight of the first candidate group and the weight of the second candidate group may be further calculated. If the weight difference is greater than or equal to the preset weight difference threshold, it means that the current division degree is insufficient. In this case, the central clause pair may be split to obtain the split central clause. In this way, the functions and responsibilities of different clause groups can be clearly divided and potential confusion caused by the weight difference between clauses can be eliminated. For example, Figure 2As shown, if the first candidate clause group includes clause A and clause B, and the initial edge betweenness of the initial clause pair (A, B) is 3, then the weight of the first candidate group can be determined to be 3. Similarly, if the second candidate clause group includes clause C, clause D, and clause E, and the initial edge betweenness of the initial clause pair (C, D) is 3, and the initial edge betweenness of the initial clause pair (D, E) is 2, then the weight of the second candidate group can be determined to be 5. If the preset weight difference threshold is 1, the central clause pair (C, D) can be clause-split to obtain the split central clause C and the split central clause D.

[0122] In step S530 of some embodiments, further, the first connection clause is divided based on the first candidate group weight, the second candidate group weight and the split center clause to obtain an initial clause group. As in the above example, the first candidate group weight of the initial clause pair (A, B) at this time is 3, the split center clause C can be directly used as a candidate clause group, and the second candidate group weight of the second candidate clause group composed of the split center clause D and clause E is 2. In this way, if the weight difference between the re-divided candidate clause groups meets the requirement of being less than or equal to the preset weight difference threshold, these candidate clause groups that meet the requirements can be used as the initial clause group. If the weight difference between the re-divided candidate clause groups still does not meet the requirement of being less than or equal to the preset weight difference threshold, the central clause pair can continue to be split, or the preset weight difference threshold can be re-determined without specific limitation.

[0123] In the above embodiments, the present application can flexibly respond to different situations and improve the overall effect by dynamically optimizing the clauses in the candidate clause group and considering the preset weight difference threshold and weight difference value.

[0124] It should be noted that due to the clause partitioning method based on the Girvan-Newman algorithm, some boundary conditions may occur during the partitioning process, causing nodes contained in node edges with high betweenness centrality to be easily assigned to subgraphs that do not conform to the actual partitioning algorithm. This is also caused by the subsequent edge removal leading to splitting and boundary fuzziness in the algorithm. Based on this, this application will introduce a reinforcement learning model in the subsequent steps to optimize the clauses of each combination to improve the accuracy of the clause combination.

[0125] See also Figure 6 , Figure 6 It is a specific flow chart of step S450 provided in an embodiment of the present application. In some embodiments of the present application, step S450 may specifically include but is not limited to steps S610 to S630.

[0126] Step S610, if the number of initial clause groups is less than the preset number of groups, edge betweenness detection is performed on the initial clause pairs in the initial clause groups to obtain candidate edge betweenness;

[0127] Step S620, dividing the third connection clause in the initial clause group based on the candidate edge betweenness to obtain a third candidate clause group;

[0128] Step S630 : determining a candidate clause group based on the third candidate clause group and the undivided initial clause group.

[0129] In step S610 of some embodiments, the candidate edge betweenness is used to characterize the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two third connected clauses in the initial clause group. It is understandable that if the number of initial clause groups is less than the preset number of groups, it indicates that the number of current clause groupings is insufficient to meet the target requirements. At this time, subgraph division can be continued for each initial clause group, that is, the initial clause pairs in the initial clause group are first tested for edge betweenness to obtain candidate edge betweenness. The specific detection of the candidate edge betweenness can refer to the detection of the initial edge betweenness in the above-mentioned embodiment, which will not be repeated here.

[0130] In step S620 of some embodiments, the third connecting clause refers to the insurance clause included in the initial clause group. Further, the third connecting clause in the initial clause group is divided into clauses based on the candidate edge betweenness to obtain the third candidate clause group. The above step S150 can be referred to, that is, the candidate edge betweenness is used as a basis to better identify the relationship between the third connecting clauses in the initial clause group and form a new clause group.

[0131] In step S630 of some embodiments, the third candidate clause group is merged with the undivided initial clause group to ensure that all relevant clauses are taken into consideration. The undivided initial clause group refers to initial insurance clauses that have no obvious association with other initial insurance clauses determined based on clause similarity values. In this way, a preset number of candidate clause groups can be obtained.

[0132] It should be noted that when determining the candidate clause group based on the third candidate clause group and the undivided initial clause group, if the determined candidate clause group is still less than the preset number of groups, the clause division of the determined candidate clause group will continue to be repeated until the determined candidate clause group is equal to the preset number of groups.

[0133] In step S160 of some embodiments, after determining a preset number of candidate clause groups, each candidate clause combination obtained is equivalent to a preliminary divided clause combination that is more in line with the needs. However, whether these clause combinations are more applicable in actual applications, it is also necessary to optimize each candidate clause group, that is, optimize the clause content and structure to ensure the consistency and coherence between the clauses. Based on this, the present application will obtain a group feedback score for each candidate clause group, and the source of the group feedback score is the group feedback data for the candidate clause group. Among them, the group feedback data is used to characterize the data combined with actual application feedback, and may include market demand data, customer feedback data, clause purchase frequency table and other data fed back by business objects. In order to facilitate the model to learn specific data, it can be quantified into demand feedback sub-scores (corresponding to market demand data), object feedback sub-scores (corresponding to customer feedback data) and group application sub-scores (corresponding to clause purchase frequency table).

[0134] In step S170 of some embodiments, further, the present application can optimize the second connection clauses based on the preset reinforcement learning model and group feedback score to obtain a target clause group. The group clause optimization includes adjusting the content of a clause, adding or reducing clauses, combining new clause combinations, etc. Among them, the reinforcement learning model of the present application can be constructed based on the DQL model, that is, combining DQL in reinforcement learning to achieve automated optimization of decision management.

[0135] See also Figure 7 , Figure 7 It is a specific flow chart of step S170 provided in an embodiment of the present application. In some embodiments of the present application, step S170 may specifically include but is not limited to steps S710 to S750.

[0136] Step S710, extracting a demand feedback sub-score, an object feedback sub-score, and a group application sub-score from the group feedback score;

[0137] Step S720, performing an action decision on the demand feedback sub-score, the object feedback sub-score, the group application sub-score, and the clause content data of the second connection clause based on the reinforcement learning model to obtain clause action decision data;

[0138] Step S730, performing similarity calculation on the optimized feedback data and the preset target feedback data to obtain a feedback data similarity value;

[0139] Step S740, adjusting the clause data of the second connection clause based on the feedback data similarity value and the clause action decision data to obtain an optimized connection clause;

[0140] Step S750: construct a target clause group based on the optimized connection clauses of the candidate clause group.

[0141] In step S710 and step S720 of some embodiments, the clause action decision data includes optimized feedback data, which is used to characterize the action taken by the agent in the next step. That is to say, the present application can use various attributes and historical data in the clause topology network as states, object purchase decisions and purchase results as reward functions, and use the DQL algorithm to learn how to take the best combination decision in different situations, so as to achieve faster and more accurate product combination sales. In this process, the entities and relationships in the graph can be regarded as a decision problem, and the attributes and association relationships of each entity can be regarded as a decision. Because the agent uses a deep neural network to estimate the Q value function, the network takes the state as input and outputs the Q value of each action. By randomly selecting actions and using experience replay technology to collect data, the agent can learn from the data how to take the best action in different states. At the same time, the value of taking each action in a specific state. By continuously iterating and updating the Q value function, the agent can learn how to take different actions in different states to maximize the cumulative return. Decision optimization is performed through deep neural networks and reinforcement learning algorithms.

[0142] Specifically, in the reinforcement learning model, the present application can set a memory D with an initialization capacity of N and a Q network using random parameters θ. The state s of the reinforcement learning model can include the clause content data of the second connected clause in the candidate clause group (i.e., the current state of the insurance clause and its combination), demand feedback sub-score, object feedback sub-score, group application sub-score and other data. The action a of the reinforcement learning model defines possible actions, such as adjusting the content of a clause, adding or reducing clauses, combining new clause packages, etc. The reward rt of the reinforcement learning model can be based on customer satisfaction, market response, sales data, etc., and a reward function can be designed to evaluate the effect of each action. Among them, the reward obtained based on the previous action can help the model make action decisions and obtain clause action decision data.

[0143] See also Figure 8 , Figure 8 It is a specific flow chart of step S720 provided in an embodiment of the present application. In some embodiments of the present application, step S720 may specifically include but is not limited to steps S810 to S820.

[0144] Step S810, determining a candidate combination reward value of a candidate clause group based on the demand feedback sub-score, the object feedback sub-score and the group application sub-score;

[0145] Step S820, making an action decision on the candidate combination reward value and the clause content data of the second connection clause based on the reinforcement learning model to obtain clause action decision data.

[0146] In steps S810 and S820 of some embodiments, the present application may update the demand feedback sub-score, the object feedback sub-score, and the group application sub-score after each action to determine the candidate combination reward value of the current action, and further make an action decision based on the candidate combination reward value and the clause content data of the second connected clause based on the reinforcement learning model to obtain the clause action decision data, that is, what to do in the next action.

[0147] It should be noted that the reinforcement learning model of the present application will conduct a cyclic training. For each candidate clause group, based on the current state s of the current clause combination, the agent uses random parameters θ to obtain the action a to be executed after the attributes and associations of the wandering entity, and determines whether the sales result is successfully sold. The reward is rt and is recorded. Each subsequent step is based on the previous state s, action a, and reward rt. At the same time, in order to obtain the best effect, except for the first data in the initial state, all other actions are taken according to the optimal decision, that is, a=arg maxQ(s). In addition, the above information can be stored in the memory pool Memory D, and the state that is most similar to this state can be extracted at any time. The network in this state can be used to reduce the deviation of action a caused by environmental influences to stabilize its environment. In addition, the deviation of random parameters caused by training can be reduced and the parameters of fixed parameter training can be used to maintain network stability. After the cycle ends, you can view the parameters and all records, and check whether the model's a is better, and adjust the parameters to continue training.

[0148] In steps S730 to S750 of some embodiments, in order to verify whether the clause content of the candidate clause group after the action adjustment reaches the predicted target, the optimized feedback data in the clause action decision data and the preset target feedback data can be calculated for similarity to obtain a feedback data similarity value, which characterizes the gap between the next action of the action decision and the expected target. Furthermore, based on the feedback data similarity value and the clause action decision data, the clause data of the second connection clause is adjusted, that is, the content of a certain clause is adjusted, clauses are added or reduced, new clause packages are combined, etc., to obtain the optimized connection clauses contained in the adjusted candidate clause group. Moreover, the optimized connection clauses at this time represent clauses whose feedback data similarity values ​​are less than or equal to the preset feedback similarity threshold, that is, the content contained in the currently adjusted candidate clause group is more in line with actual needs, so as to obtain the target clause group.

[0149] It is understandable that in actual applications, the present application can adjust the clause combination strategy in real time through the DQL model to respond to market changes and customer feedback.

[0150] It should be noted that the non-Company software tools or components appearing in the embodiments of the present application are merely examples and do not represent actual use.

[0151] The insurance clause division method provided in the embodiment of the present application reveals the potential correlation between insurance clauses through graph theory methods, helps to discover hidden clause groups, and performs targeted optimization. The subgraph partitioning algorithm is used to perform clause association analysis to reduce manual workload and improve analysis efficiency. In addition, the optimized clause structure is clearer, which can help actual application objects to more easily understand the functions and benefits of different types of insurance and improve customer satisfaction. Based on this, the present application can deeply understand the potential correlation and complex relationship between clauses, and combined with subgraph partitioning and reinforcement learning methods, it can better understand the complex relationship between clauses and make more informed decisions. Therefore, the embodiment of the present application can effectively improve the accuracy and efficiency of the division of insurance clauses.

[0152] See also Fig. 9 The embodiment of the present application further provides an insurance clause division device, which can implement the above-mentioned insurance clause division method, and the device includes:

[0153] A first acquisition module 910 is used to acquire an insurance clause data set, where the insurance clause data set includes initial insurance clauses and initial clause content data of the initial insurance clauses, where the initial insurance clauses are used to represent nodes of a preset clause topology network;

[0154] A similarity detection module 920 is used to perform a clause similarity detection on any two initial clause content data to obtain a clause similarity value;

[0155] A clause pair determination module 930, configured to determine an initial clause pair from the initial insurance clauses based on the clause similarity value, the initial clause pair being used to represent an edge of a clause topology network, the initial insurance clause pair including a first connection clause, and the edge of the clause topology network being used to represent a path between two connected first connection clauses;

[0156] The edge betweenness detection module 940 is used to perform edge betweenness detection on the initial clause pair to obtain an initial edge betweenness, where the initial edge betweenness is used to represent the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two initial insurance clauses;

[0157] A clause division module 950 is used to divide the first connection clause into clauses based on the initial edge betweenness and the preset number of groups, and determine a candidate clause group, where the candidate clause group includes the second connection clause;

[0158] The second acquisition module 960 is used to obtain the group feedback score of the candidate clause group;

[0159] The group clause optimization module 970 is used to perform group clause optimization on the second connection clause based on a preset reinforcement learning model and a group feedback score to obtain a target clause group.

[0160] The specific implementation of the insurance clause division device in the embodiment of the present application is basically the same as the specific implementation of the above-mentioned insurance clause division method, and will not be repeated here.

[0161] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above insurance clause division method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, a car computer, etc.

[0162] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0163] The processor 1010 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0164] The memory 1020 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1020 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1020, and the processor 1010 calls and executes the insurance clause division method of the embodiment of this application;

[0165] Input / output interface 1030, used to implement information input and output;

[0166] The communication interface 1040 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0167] Bus 1050 , which transmits information between various components of the device (e.g., processor 1010 , memory 1020 , input / output interface 1030 , and communication interface 1040 );

[0168] The processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 are connected to each other in communication within the device via a bus 1050 .

[0169] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned insurance clause division method is implemented.

[0170] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0171] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0172] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0173] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0175] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0176] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0177] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0178] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple 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.

[0179] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0180] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0181] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for dividing insurance clauses, characterized in that: The method comprises: Acquire an insurance clause data set, the insurance clause data set including initial insurance clauses and initial clause content data of the initial insurance clauses, the initial insurance clauses being used to represent nodes of a preset clause topology network; Performing a clause similarity test on any two of the initial clause content data to obtain a clause similarity value; Determining an initial clause pair from the initial insurance clauses based on the clause similarity value, the initial clause pair is used to represent an edge of the clause topology network, the initial insurance clause pair includes a first connection clause, and the edge of the clause topology network is used to represent a path between two connected first connection clauses; Performing edge betweenness detection on the initial clause pair to obtain an initial edge betweenness, wherein the initial edge betweenness is used to represent the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two of the initial insurance clauses; Dividing the first connection clause into clauses based on the initial edge betweenness and the preset number of groups to determine a candidate clause group, wherein the candidate clause group includes a second connection clause; Obtaining a group feedback score of the candidate clause group; The second connection terms are group-optimized based on a preset reinforcement learning model and the group feedback score to obtain a target term group.

2. The method according to claim 1, characterized in that The step of performing group clause optimization on the second connection clause based on the preset reinforcement learning model and the group feedback score to obtain a target clause group includes: extracting a demand feedback sub-score, an object feedback sub-score, and a group application sub-score from the group feedback score; Based on the reinforcement learning model, an action decision is made on the demand feedback sub-score, the object feedback sub-score, the group application sub-score and the clause content data of the second connection clause to obtain clause action decision data, wherein the clause action decision data includes optimization feedback data; Performing similarity calculation on the optimized feedback data and the preset target feedback data to obtain a feedback data similarity value; Adjusting the clause data of the second connection clause based on the feedback data similarity value and the clause action decision data to obtain an optimized connection clause; The target clause group is constructed based on the optimized connection clauses of the candidate clause group.

3. The method according to claim 2, characterized in that The step of performing an action decision on the demand feedback sub-score, the object feedback sub-score, the group application sub-score, and the clause content data of the second connection clause based on the reinforcement learning model to obtain clause action decision data includes: determining a candidate combined reward value for the candidate clause group based on the demand feedback sub-score, the object feedback sub-score, and the group application sub-score; An action decision is made on the candidate combination reward value and the clause content data of the second connection clause based on the reinforcement learning model to obtain the clause action decision data.

4. The method according to any one of claims 1 to 3, characterized in that: The step of dividing the first connection clause based on the initial edge betweenness and the preset number of groups to determine a candidate clause group includes: Determine a central clause pair from the initial clause pairs based on the initial edge betweenness, wherein the central clause pair is used to indicate the initial clause pair with the largest initial edge betweenness; The first connection clause is divided into clauses based on the central clause pair and the preset number of groups to determine the candidate clause group.

5. The method according to claim 4, characterized in that The dividing the first connection clauses based on the central clause pair and the preset number of groups to determine the candidate clause groups includes: Dividing the first connection clauses into clauses based on the central clause pair to determine a first candidate clause group and a second candidate clause group; determining a first candidate group weight based on the initial edge betweenness included in the first candidate term group; Determining a second candidate group weight based on the initial edge betweenness included in the second candidate term group; Dividing the first connection clause into clauses based on the first candidate group weight and the second candidate group weight to obtain an initial clause group; The initial clause group is updated based on the preset number of groups to determine the candidate clause group.

6. The method according to claim 5, characterized in that The step of dividing the first connection clause based on the first candidate group weight and the second candidate group weight to obtain an initial clause group includes: Determining a preset weight difference threshold based on the number of combinations of the first candidate clause group and the second candidate clause group; If the weight difference between the first candidate group weight and the second candidate group weight is greater than the preset weight difference threshold, split the central clause pair to obtain a split central clause; The first connection clause is divided into clauses based on the first candidate group weight, the second candidate group weight and the split center clause to obtain the initial clause group.

7. The method according to claim 5, characterized in that The updating the initial clause group based on the preset number of groups to determine the candidate clause group includes: If the number of the initial clause groups is less than the preset number of groups, edge betweenness detection is performed on the initial clause pairs in the initial clause groups to obtain candidate edge betweenness, where the candidate edge betweenness is used to represent the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two third connection clauses in the initial clause group; Performing clause division on the third connection clause in the initial clause group based on the candidate edge betweenness to obtain a third candidate clause group; The candidate clause group is determined based on the third candidate clause group and the undivided initial clause group.

8. An insurance clause division device, characterized in that: The device comprises: A first acquisition module is used to acquire an insurance clause data set, wherein the insurance clause data set includes initial insurance clauses and initial clause content data of the initial insurance clauses, wherein the initial insurance clauses are used to represent nodes of a preset clause topology network; A similarity detection module is used to perform a clause similarity detection on any two of the initial clause content data to obtain a clause similarity value; A clause pair determination module, configured to determine an initial clause pair from the initial insurance clauses based on the clause similarity value, wherein the initial clause pair is used to represent an edge of the clause topology network, wherein the initial insurance clause pair includes a first connection clause, and the edge of the clause topology network is used to represent a path between two connected first connection clauses; An edge betweenness detection module, used to perform edge betweenness detection on the initial clause pair to obtain an initial edge betweenness, wherein the initial edge betweenness is used to characterize the number of times the edge corresponding to the initial clause pair appears in the shortest path formed by any two of the initial insurance clauses; A clause division module, configured to divide the first connection clause into clauses based on the initial edge betweenness and a preset number of groups, and determine a candidate clause group, wherein the candidate clause group includes a second connection clause; A second acquisition module is used to obtain a group feedback score of the candidate clause group; A group clause optimization module is used to perform group clause optimization on the second connection clause based on a preset reinforcement learning model and the group feedback score to obtain a target clause group.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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