Insurance proposal generation method and device, computer equipment and storage medium

Abstract

The embodiments of the present application provide a method and apparatus for generating an insurance proposal, a computer device, and a storage medium, which relate to the field of personalized data generation technology and are applicable to the field of financial technology. The method includes: obtaining user basic information, insurance question and answer data, and user current behavior data; predicting insurance preference prediction data of the target user based on the insurance question and answer data and the user current behavior data; obtaining historical insurance proposals, current insurance product information, and insurance claims data; extracting candidate insurance key fields from historical insurance proposals, current insurance product information, and insurance claims data based on key field extraction rules; filtering out target insurance key fields from candidate insurance key fields based on user basic information and insurance preference prediction data; and generating a proposal based on user basic information and target insurance key fields to obtain the target insurance proposal. The embodiments of the present application can reduce the professional requirements and manpower required when generating insurance proposals.

Description

Technical Field

[0001] The present application relates to the field of personalized data generation technology, applicable to the field of financial technology, and in particular to a method and device for generating an insurance proposal, a computer device, and a storage medium. Background Art

[0002] In the insurance industry, insurance proposals must be generated based on the client's current situation, needs, and the product landscape of the insurance market, providing professional insurance advice. Previously, insurance proposals were primarily generated manually by insurance agents after conversations with clients, based on the client's needs, current situation, and relevant information about insurance products. This not only requires extensive experience and knowledge in insurance, but also consumes a significant amount of time and effort. Therefore, reducing the professional requirements and manpower required for insurance agents to prepare insurance proposals has become a pressing technical challenge. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a method and apparatus for generating an insurance proposal, a computer device, and a storage medium, aiming to realize the automated generation of insurance proposals and reduce the professional requirements and manpower required when generating insurance proposals.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for generating an insurance proposal, the method comprising:

[0005] Obtaining the target user's basic information, insurance question-and-answer data, and current user behavior data; wherein the current user behavior data represents the target user's operating behavior on the insurance business platform;

[0006] Predicting the preferences of the target user based on the insurance question-and-answer data and the user's current behavior data to obtain insurance preference prediction data;

[0007] Access historical insurance proposals, current insurance product information, and insurance claims data;

[0008] Extracting key fields from the historical insurance proposals, the current insurance product information, and the insurance claims data according to preset key field extraction rules to obtain candidate insurance key fields; wherein the candidate insurance key fields are key content for constructing the insurance proposal;

[0009] Filtering target insurance key fields from the candidate insurance key fields according to the user basic information and the insurance preference prediction data;

[0010] The target insurance proposal is obtained by generating a proposal based on a preset original proposal generation model, the user basic information and the target insurance key fields.

[0011] In some embodiments, the key field extraction rules include: field splitting rules and key field classification rules;

[0012] The key field extraction rules are used to separate the historical insurance proposals, the current insurance product information, and the insurance claim data into key fields to obtain candidate insurance key fields, including:

[0013] Performing field splitting on the historical insurance proposal, the current insurance product information, and the insurance claim data according to the field splitting rule to obtain candidate fields;

[0014] Performing category identification on each candidate field to obtain a field category;

[0015] The candidate fields are constructed into key fields according to the key field classification rules and the field categories to obtain the candidate insurance key fields.

[0016] In some embodiments, the step of selecting a target insurance key field from the candidate insurance key fields based on the user basic information and the insurance preference prediction data includes:

[0017] Acquire field tag information of the candidate insurance key field; wherein the field tag information includes: candidate user information and candidate insurance attribute information;

[0018] The candidate insurance key fields are screened according to the user basic information, the insurance preference prediction data, the candidate user information and the candidate insurance attribute information to obtain the target insurance key fields.

[0019] In some embodiments, the screening of the candidate insurance key fields based on the user basic information, the insurance preference prediction data, the candidate user information, and the candidate insurance attribute information to obtain the target insurance key fields includes:

[0020] Performing vector conversion on the user basic information and the insurance preference prediction data to obtain a query vector;

[0021] Performing vector conversion on the candidate user information and the candidate insurance attribute information to obtain a reference vector;

[0022] Calculating similarity between the query vector and the reference vector to obtain vector similarity;

[0023] The candidate insurance key fields are screened according to the vector similarity to obtain the target insurance key fields.

[0024] In some embodiments, performing preference prediction on the target user based on the insurance question-and-answer data and the user's current behavior data to obtain insurance preference prediction data includes:

[0025] Performing feature extraction on the insurance question and answer data to obtain insurance question and answer features;

[0026] Extracting features from the user's current behavior data to obtain features of the user's current behavior;

[0027] Preference prediction is performed using a preset preference prediction model, the insurance question-and-answer features, and the user's current behavior features to obtain the insurance preference prediction data.

[0028] In some embodiments, after generating a target insurance proposal by using a preset original proposal generation model, the user's basic information, and the target insurance key fields to obtain the target insurance proposal, the method further includes:

[0029] Performing a quality assessment on the target insurance proposal based on a preset verification insurance proposal and preset quality assessment rules to obtain quality assessment data; wherein the quality assessment data represents the degree of match between the target insurance proposal and the verification insurance proposal, and the degree of match between the target insurance proposal and the quality assessment rules;

[0030] Optimizing the original proposal generation model according to the quality assessment data to obtain a target proposal generation model;

[0031] The target insurance proposal is optimized by using the target proposal generation model, the user basic information and the target insurance key fields to obtain an updated insurance proposal.

[0032] In some embodiments, performing a quality assessment on the target insurance proposal according to a preset verified insurance proposal and preset quality assessment rules to obtain quality assessment data includes:

[0033] Calculating a matching degree based on the verification insurance proposal and the target insurance proposal to obtain a content matching degree;

[0034] Extracting evaluation reference indicator data from the target insurance proposal; wherein the evaluation reference indicator data includes: insurance product information, insurance service information and insured user information;

[0035] Calculating the matching degree of the insurance product information, the insurance service information, and the insured user information according to the quality assessment rules to obtain an information matching degree;

[0036] The content matching degree and the information matching degree are spliced ​​together to obtain the quality assessment data.

[0037] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides an insurance proposal generating device, the device comprising:

[0038] A user data acquisition module is used to acquire basic user information, insurance question-and-answer data, and current user behavior data of a target user; wherein the current user behavior data represents the target user's operating behavior on the insurance business platform;

[0039] a preference prediction module, configured to predict the preferences of the target user based on the insurance question-and-answer data and the user's current behavior data, and obtain insurance preference prediction data;

[0040] Historical data acquisition module, used to obtain historical insurance proposals, current insurance product information and insurance claims data;

[0041] A key field splitting module is used to split the key fields of the historical insurance proposal, the current insurance product information and the insurance claim data according to a preset key field extraction rule to obtain candidate insurance key fields;

[0042] A key field screening module, configured to screen target insurance key fields from the candidate insurance key fields based on the user basic information and the insurance preference prediction data;

[0043] The proposal screening module is used to generate a proposal based on a preset original proposal generation model, the user basic information and the target insurance key fields to obtain a target insurance proposal.

[0044] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0045] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0046] The insurance proposal generation method and apparatus, computer device, and storage medium proposed in this application predict the target user's insurance preference data based on their current user behavior data and insurance question-and-answer data on an insurance business platform. The method then pre-segments historical insurance proposals, current insurance product information, and insurance claims data on the insurance business platform into candidate insurance key fields for constructing the insurance proposal. Therefore, when constructing the insurance proposal, target insurance key fields suitable for constructing the insurance proposal are screened from the candidate insurance key fields based on the user's basic information and insurance preference prediction data. The target insurance key fields are then combined into the target insurance proposal using the original proposal generation model and the user's basic information. Therefore, by automating the generation of insurance proposals based on the target user's basic information and insurance preferences, not only is the professionalism and manpower required to generate the insurance proposal reduced, but it can also generate insurance proposals tailored to the target user, improving the quality of insurance services. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flowchart of the method for generating an insurance proposal provided by an embodiment of the present application;

[0048] Figure 2 yes Figure 1 Flowchart of step S102 in FIG.

[0049] Figure 3 yes Figure 1 Flowchart of step S104 in FIG.

[0050] Figure 4 yes Figure 1 Flowchart of step S105 in FIG.

[0051] Figure 5 yes Figure 4 Flowchart of step S402 in FIG.

[0052] Figure 6 is a flowchart of a method for generating an insurance proposal provided by another embodiment of the present application;

[0053] Figure 7 yes Figure 6 Flowchart of step S601 in FIG.

[0054] Figure 8 This is a schematic diagram of an interface for generating a target insurance proposal in the insurance proposal generation method provided in an embodiment of the present application;

[0055] Figure 9 This is a model structure diagram of the insurance proposal generation method provided in an embodiment of the present application;

[0056] Figure 10This is a schematic diagram of the structure of the insurance proposal generation device provided in an embodiment of the present application;

[0057] Figure 11 This is a schematic diagram of the hardware structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0059] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, used in the specification, claims, and accompanying drawings, are intended to distinguish similar users and are not necessarily intended to describe a specific sequence or precedence.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0061] First, let’s analyze some of the terms used in this application:

[0062] Artificial intelligence (AI) is a new technical discipline that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. A branch of computer science, AI seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. AI can simulate the information processes of human consciousness and thinking. It also encompasses the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0063] Large Language Models (LLMs): These are AI models designed to understand and generate human language. Trained on large amounts of text data, LLMs can perform a wide range of tasks, including text summarization, translation, and sentiment analysis. LLMs are characterized by their massive size, containing billions of parameters, which help them learn complex patterns in language data. These models are often based on deep learning architectures, such as transformers, which contribute to their impressive performance on various natural language processing (NLP) tasks.

[0064] Prompt: In the field of artificial intelligence (AI), a prompt is a text or instruction that provides input to a model to guide it to produce a specific output. It is a text paragraph provided by the user when interacting with the model, describing the information, answer, text, etc. that the user wants from the model. The purpose of a prompt is to guide the model to produce a desired response, thereby providing greater control over the generated output.

[0065] Insurance proposal: also known as insurance plan, refers to a written document in which insurance practitioners recommend suitable insurance products to customers based on the customers' own financial status and financial management requirements, design the best insurance plan, seek the maximum insurance benefits for customers, and at the same time help customers understand and accept insurance products.

[0066] With the advancement of technology, the traditional insurance industry's business model faces challenges such as inefficiency and information asymmetry. For example, in the case of insurance proposal generation, insurance agents primarily engage with clients and create proposals based on their needs, introducing insurance products to them. Therefore, having insurance agents complete these tasks requires not only extensive experience and knowledge, but also a significant amount of their time and effort.

[0067] Based on this, the embodiments of the present application provide an insurance proposal generation method and apparatus, computer equipment, and storage medium. This method predicts the target user's insurance preference prediction data based on the target user's current user behavior data and insurance question-and-answer data on the insurance business platform, and then pre-segments the historical insurance proposals, current insurance product information, and insurance claims data on the insurance business platform into candidate insurance key fields for constructing the insurance proposal. Therefore, when constructing the insurance proposal, the target insurance key fields for constructing the insurance proposal are screened from the candidate insurance key fields based on the user's basic information and insurance preference prediction data, and the target insurance key fields are combined into the target insurance proposal using the original proposal generation model and the user's basic information. Therefore, by automatically generating the insurance proposal, and the generation process being based on the target user's basic information and insurance preferences, not only is the professional requirements and manpower required for generating the insurance proposal reduced, but it is also possible to generate insurance proposals that meet the target user's needs, thereby improving the quality of insurance services.

[0068] The insurance proposal generation method and apparatus, computer equipment, and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the insurance proposal generation method in the embodiments of the present application is described.

[0069] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. 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 achieve optimal results.

[0070] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0071] The insurance proposal generation method provided in the embodiment of the present application relates to the field of personalized data generation technology and is applicable to the field of financial technology. The insurance proposal generation method provided in the embodiment of the present application can be applied in a terminal, can be applied in a server side, or can be software running in a terminal or a 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 as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as 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; the software can be an application that implements the insurance proposal generation method, etc., but is not limited to the above forms.

[0072] 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 computer devices, 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, users, 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.

[0073] 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 user's identity or characteristics, such as user information, user current behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0074] Figure 1 This is an optional flowchart of the insurance proposal generation method provided in the embodiment of the present application. Figure 1The method may include but is not limited to steps S101 to S106.

[0075] Step S101: Obtain basic user information, insurance question-and-answer data, and current user behavior data of a target user; wherein the current user behavior data represents the target user's operating behavior on the insurance business platform;

[0076] Step S102: Predicting the preferences of the target user based on the insurance question-and-answer data and the user's current behavior data to obtain insurance preference prediction data;

[0077] Step S103, obtaining historical insurance proposals, current insurance product information, and insurance claim data;

[0078] Step S104: splitting the historical insurance proposals, current insurance product information, and insurance claim data into key fields according to preset key field extraction rules to obtain candidate insurance key fields;

[0079] Step S105, filtering out target insurance key fields from candidate insurance key fields based on user basic information and insurance preference prediction data;

[0080] Step S106 , generating a proposal using a preset original proposal generation model, basic user information, and target insurance key fields to obtain a target insurance proposal.

[0081] In steps S101 to S106 shown in the embodiment of the present application, the target user's insurance preferences are predicted based on the target user's current user behavior data and insurance question-and-answer data on the insurance business platform to obtain insurance preference prediction data. Before generating an insurance proposal, candidate insurance key fields for constructing the insurance proposal are extracted in advance from existing historical insurance proposals, current insurance product information, and insurance claims data. Therefore, during the automatic generation of the insurance proposal, target insurance key fields that match the target user's insurance preferences and user basic information are extracted from the candidate insurance key fields, and the target insurance key fields are combined into a target insurance proposal using the original proposal generation model and user basic information. Therefore, by automatically generating insurance proposals using a model, and the insurance proposal generation process is completed in combination with the target user's insurance preferences, personal information, and historical insurance-related data, not only is the professional requirements and manpower required for generating the insurance proposal reduced, but an insurance proposal that meets the target user's needs is also constructed to improve the quality of insurance services.

[0082] In step S101 of some embodiments, the basic user information is the basic attribute information of the target user, specifically including: the target user's age, gender, income, occupation, risk tolerance and disease information, etc., and this embodiment does not impose specific restrictions on the content of the basic user information. Insurance Q&A data is collected by using an intelligent insurance Q&A robot to conduct Q&A with the target user before the insurance proposal is generated, and the data generated during the Q&A is used as insurance Q&A data. The user's current behavior data is the target user's behavior data on the insurance business platform, which is generated through operations such as browsing, clicking and purchasing on the insurance business platform. The user's current behavior data includes the user's current browsing behavior data, policy validity data, etc. This embodiment does not impose specific restrictions on the content of the user's current behavior data.

[0083] Specifically, the target user's basic information, insurance Q&A data, and current user behavior data can also be generated through face-to-face or online communication between the insurance agent and the target user, and the audio and video data can be analyzed and learned. In addition, a virtual insurance agent robot can be trained using the audio and video data provided by professional insurance agents during customer meetings. The virtual insurance agent robot can replace professional insurance agents to complete insurance business communication with the target user, collect insurance-related audio and video data in advance, and save manpower for audio and video data collection. It should be noted that the data collection and processing before the insurance proposal is generated is mainly achieved through the pre-prompt generation module. Specifically, the prompt generation module collects the target user's basic information, insurance Q&A data, and current user behavior data in advance.

[0084] It should be noted that after collecting basic user information, insurance Q&A data, and current user behavior data, these data are preprocessed. This preprocessing includes removing duplicate content, filling in missing values, formatting dates, and formatting data categories. This preprocessing ensures the quality and consistency of the collected basic user information, insurance Q&A data, and current user behavior data, facilitating subsequent preference prediction and insurance recommendation generation.

[0085] See also Figure 2 In some embodiments, step S102 may include but is not limited to steps S201 to S203:

[0086] Step S201: extracting features from insurance question and answer data to obtain insurance question and answer features;

[0087] Step S202: extracting features from the user's current behavior data to obtain the user's current behavior features;

[0088] Step S203: Preference prediction is performed using a preset preference prediction model, insurance question-and-answer features, and user current behavior features to obtain insurance preference prediction data.

[0089] In some embodiments, steps S201 and S202 extract insurance Q&A features from the insurance Q&A data, and extract user current behavior features from the user current behavior data. The insurance Q&A features and user current behavior features represent the target user's behavior and Q&A status. Specifically, insurance Q&A features may include the number of Q&A queries, question categories, and Q&A sentiment, while user current behavior features may include the number of user views, insurance information browsed by the user, and the time period during which the user was online. This embodiment does not impose specific restrictions on the content of insurance Q&A features and user current behavior features.

[0090] In step S203 of some embodiments, the preference prediction model is pre-trained and can predict insurance preference data, specifically predicting preferred insurance types and the degree of preference for each insurance type. Therefore, the preference prediction model is used to input insurance question-and-answer features and the user's current behavioral characteristics into the preference prediction model to perform preference prediction, thereby outputting insurance preference data. The insurance preference data includes the target user's preferred insurance types and the preference probability for each preferred insurance type. The preferred insurance types and preference probabilities can be used to determine the target user's preferred insurance types, thereby enhancing the target user's experience by creating an insurance proposal based on the target user's preferred insurance types.

[0091] In steps S201 to S203 shown in this embodiment, by extracting insurance question and answer features and user current behavior features, and inputting the insurance question and answer features and user current behavior features into the preference prediction model to make preference predictions, the prediction operation of the type of insurance that the target user is interested in is simple and accurate, so as to facilitate the preparation of insurance recommendations that meet the preferences of the target user based on the predicted insurance types.

[0092] In step S103 of some embodiments, to generate a more professional and targeted insurance proposal, historical insurance proposals, current insurance product information, and insurance claims data are obtained from the insurance business platform in advance. Historical insurance proposals primarily include those manually uploaded by insurance agents and those previously generated by the insurance business platform. Therefore, historical insurance proposals serve as a reference for automatically generating insurance proposals. Current insurance product information refers to information about insurance products available for purchase on the insurance business platform and is stored in an insurance product database. It should be noted that the insurance product database is regularly updated, with delisted insurance product information removed and newly added to the database to reduce the use of delisted insurance product information as current insurance product information. Insurance claims data primarily consists of insurance claim plans, which include information on policy type, risk category, payout amount, case description, and commentary. Claim plans provide insights into the claims outcomes of different insurance products under different circumstances, allowing target users to gain a deeper understanding of insurance product claims.

[0093] As disclosed above, after obtaining historical insurance proposals, current insurance product information, and insurance claims data, it is necessary to extract content that can construct a professional insurance proposal from the historical insurance proposals, current insurance product information, and insurance claims data to serve as material for constructing the insurance proposal. Therefore, keyword extraction rules are set in advance, and the keyword extraction rules represent the key fields that need to be extracted, serving as operational instructions for extracting key fields. Among them, the keyword extraction rules include: field splitting rules and keyword classification rules, the field splitting rules serve as operational instructions for splitting historical insurance proposals, current insurance product information, and insurance claims data, and the keyword classification rules serve as operational instructions for classifying the fields after splitting. Therefore, by setting the field splitting rules and keyword classification rules, it is possible to determine how to complete the extraction of key fields from historical insurance proposals, current insurance product information, and insurance claims data.

[0094] See also Figure 3 In some embodiments, step S104 may include but is not limited to steps S301 to S303:

[0095] Step S301: splitting the fields of historical insurance proposals, current insurance product information, and insurance claim data according to field splitting rules to obtain candidate fields;

[0096] Step S302: perform category identification on each candidate field to obtain a field category;

[0097] Step S303 : constructing candidate fields into key fields according to key field classification rules and field categories to obtain candidate insurance key fields.

[0098] In step S301 of some embodiments, historical insurance proposals, current insurance product information, and insurance claim data are split separately according to field splitting rules. Specifically, historical insurance proposals, current insurance product information, and insurance claim data are split separately according to field splitting rules such as entities, attributes, and relationships to obtain entity fields, attribute fields, and relationship fields, and the entity fields, attribute fields, and relationship fields are combined into candidate fields.

[0099] Specifically, entity fields include: user fields, insurance product fields, disease fields, medical facility fields, and insurance terms fields, etc. Attribute fields are the attributes of entities in historical insurance proposals, current insurance product information, and insurance claims data, and include: user age fields, gender fields, occupation fields, insurance product name fields, category fields, premium fields, and insurance amount fields, etc. Relationship fields are the relationships between entity fields, such as the purchase relationship between users and insurance products, the relationship between diseases and insurance product coverage, etc. Therefore, by extracting entity fields, attribute fields, and relationship fields from historical insurance proposals, current insurance product information, and insurance claims data, we can understand various aspects of insurance knowledge in the insurance field.

[0100] In steps S302 to S303 of some embodiments, in order to facilitate the subsequent screening of key fields, it is necessary to classify the candidate fields into key content categories, also known as knowledge point division. Specifically, the field category of the candidate field is first identified, and the field category mainly characterizes which knowledge category in the insurance field the candidate field involves. Among them, the knowledge categories divided in the insurance field mainly include: product knowledge category, user knowledge category, disease knowledge category, claim knowledge category and question and answer knowledge category. Therefore, the candidate fields are divided according to the field category and keyword classification rules to obtain candidate insurance key fields, so the candidate insurance key fields are key fields divided according to the field category. Specifically, the candidate fields are divided into different preset knowledge bases according to the insurance category to obtain the target knowledge base, and then the fields stored in each target knowledge base are used as candidate insurance key fields.

[0101] It should be noted that the target knowledge base includes: product knowledge base, user knowledge base, disease knowledge base, claims knowledge base and question and answer knowledge base, and the product knowledge base covers candidate fields for all insurance products, and the corresponding content of the candidate fields includes: product name, product category, premium, insured amount, coverage and terms, etc. The user knowledge base contains candidate fields corresponding to user information, and the corresponding content of the candidate fields includes: user's age, gender, occupation and health history, etc. The disease knowledge base includes candidate fields related to diseases, and the corresponding content of the candidate fields includes: disease name, definition, common treatment methods, related insurance products, etc. The claims knowledge base includes candidate fields related to claims, and the corresponding content of the candidate fields includes: claims process, claims materials and claims introduction, etc. The question and answer knowledge base includes candidate fields related to questions and answers, and the corresponding content of the candidate fields includes: question and answer questions and answers, such as insurance, claims and product comparison, etc.

[0102] In steps S301 to S303 of this embodiment, when constructing the materials needed for generating an insurance proposal, existing historical insurance proposals, current insurance product information, and insurance claims data are first split into candidate fields. These fields are then classified according to the key field classification rules and the candidate field categories to obtain candidate insurance key fields. Therefore, splitting the fields before classifying them makes the generation of candidate insurance key fields simpler and more accurate.

[0103] See also Figure 4 In some embodiments, step S105 may include but is not limited to steps S401 to S402:

[0104] Step S401: Acquire field tag information of candidate insurance key fields; wherein the field tag information includes: candidate user information and candidate insurance attribute information;

[0105] Step S402 , screening candidate insurance key fields according to user basic information, insurance preference prediction data, candidate user information and candidate insurance attribute information to obtain target insurance key fields.

[0106] In step S401 of some embodiments, field tag information is set for each candidate insurance key field, and the field tag information serves as reference indicator information for screening candidate insurance key fields. The field tag information includes candidate user information and candidate insurance attribute information, and the candidate user information represents the user information associated with the candidate insurance key field, and the candidate insurance attribute information represents the insurance attribute information associated with the candidate insurance key field. It should be noted that the candidate user information specifically includes: the user's age, gender, occupation, disease information, and tolerable risk information, etc., and the candidate insurance attribute information includes: the name and category of the insurance, applicable group information, applicable disease information, etc. Therefore, the user information and insurance type that match the candidate insurance key field can be determined through the candidate user information and the candidate insurance attribute information.

[0107] In step S402 of some embodiments, the target insurance key fields are filtered out from the candidate insurance key fields in combination with the user basic information, insurance preference prediction data, candidate user information and candidate insurance attribute information. Then, the filtered target insurance key fields are consistent with the basic information and insurance preferences of the target user, so the target insurance proposal constructed through the target insurance key fields can also improve the target user's satisfaction.

[0108] In steps S401 and S402 of this embodiment, candidate user information and candidate insurance attribute information for candidate insurance key fields are obtained. Then, target insurance key fields are filtered from the candidate insurance key fields by combining the candidate user information, candidate insurance attribute information, user basic information, and insurance preference prediction data. This accurately selects target insurance key fields that match the target user's basic information and preferences, and constructs a target insurance proposal that meets the target user's preferences and basic circumstances.

[0109] See also Figure 5 In some embodiments, step S402 may include but is not limited to steps S501 to S504:

[0110] Step S501: converting user basic information and insurance preference prediction data into vectors to obtain a query vector;

[0111] Step S502: Perform vector conversion on the candidate user information and the candidate insurance attribute information to obtain a reference vector;

[0112] Step S503: Calculate similarity between the query vector and the reference vector to obtain vector similarity;

[0113] Step S504 , screening the candidate insurance key fields according to the vector similarity to obtain the target insurance key fields.

[0114] In step S501 of some embodiments, basic user features are extracted from the user's basic information, insurance preference features are extracted from the insurance preference prediction data, and the user basic features and insurance preference features are concatenated into a user query feature. The user query feature is then encoded by an encoder to generate a query vector, representing the user basic features and insurance preference features in vector form, facilitating the screening of candidate insurance key fields based on the query vector.

[0115] In step S502 of some embodiments, candidate user features are extracted from the candidate user information, candidate insurance attribute features are extracted from the candidate insurance attribute information, and then the candidate user features and the candidate insurance attribute features are concatenated into reference features. The reference features are encoded by an encoder to obtain a reference vector represented by a vector.

[0116] In step S503 of some embodiments, similarity is calculated between the query vector and the reference vector. This similarity calculation can be implemented using a vector similarity function. In this embodiment, the vector similarity function includes any of the following: cosine similarity function, Euclidean distance function, Manhattan distance function, and Hamming distance function. This embodiment does not impose any specific restrictions on the vector similarity function. It should be noted that vector similarity can represent the degree of match between the candidate insurance key fields and the user's basic information and insurance preference prediction data. The greater the vector similarity, the higher the degree of match between the candidate insurance key fields and the user's basic information and insurance preference prediction data, and vice versa.

[0117] In step S504 of some embodiments, the candidate insurance key fields of each field category are sorted according to vector similarity, and a preset number of candidate insurance key fields before sorting of each field category are obtained as target insurance key fields to screen out target insurance key fields that highly match the preferences and basic information of the target user.

[0118] In steps S501 to S504 shown in this embodiment, the user basic information, insurance preference prediction data, candidate user information and candidate insurance attribute information are represented in vector form, and then the screening operation in the candidate insurance key fields is completed in a vector similarity manner, so that the screening operation of the target insurance key fields is simple and consistent with the basic information and preferences of the target user.

[0119] See also Figure 6 In some embodiments, after step S106, the insurance proposal generation method further includes but is not limited to steps S601 to S603:

[0120] Step S601: Perform a quality assessment on the target insurance proposal based on the preset verification insurance proposal and preset quality assessment rules to obtain quality assessment data; wherein the quality assessment data represents the degree of match between the target insurance proposal and the verification insurance proposal, and the degree of match between the target insurance proposal and the quality assessment rules;

[0121] Step S602: Optimize the original proposal generation model based on the quality assessment data to obtain a target proposal generation model.

[0122] Step S603: Optimize the target insurance proposal using the target proposal generation model, user basic information, and target insurance key fields to obtain an updated insurance proposal.

[0123] In some embodiments, in step S601, during the initial stages of insurance proposal generation, a verification insurance proposal document is pre-configured to verify the quality of the target insurance proposal. Therefore, the initially generated target insurance proposal is not directly output to the target user but is first sent to the insurance agent, who then verifies the insurance proposal document and quality assessment rules based on the target insurance proposal feedback. The target insurance proposal is then quality-assessed using the verification insurance proposal document and quality assessment rules to determine quality assessment data for the target insurance proposal. It should be noted that quality assessment data represents the content quality of the target insurance proposal, and content quality includes, among other factors, completeness, clarity, insurance type matching, product rationality, readability, and regulatory compliance. Specifically, higher quality assessment data indicates a higher quality content of the target insurance proposal, while lower quality assessment data indicates a lower quality content of the target insurance proposal.

[0124] It should be noted that the original proposal generation model is trained by a large language model with the ability to generate proposals, and the prompt generation module will input the collected user basic information and the filtered target insurance key fields into the original proposal generation model to generate proposals.

[0125] In step S602 of some embodiments, the quality assessment data can also characterize the generation effect of the original proposal generation model. Therefore, the model parameters of the original proposal generation model are adjusted according to the quality assessment data. The quality assessment data is continuously updated and the original proposal generation model is optimized until the updated quality assessment data converges. The corresponding original proposal generation model is then used as the target proposal generation model.

[0126] In step S603 of some embodiments, an updated insurance proposal is generated using a target proposal generation model, basic user information, and target insurance key fields. The target insurance proposal is replaced with the updated insurance proposal, and the updated insurance proposal is sent to the target user, thereby achieving high-quality and automated insurance proposal generation.

[0127] In steps S601 to S603 of this embodiment, the quality of the target insurance proposal is evaluated based on the insurance proposal verification and quality assessment rules to obtain quality assessment data, and the original proposal generation model is adjusted based on the quality assessment data to obtain the target proposal generation model. Then, an optimized updated insurance proposal is regenerated using the target proposal generation model, basic user information, and key fields of the target insurance to obtain an insurance proposal of higher quality.

[0128] See also Figure 7 In some embodiments, step S601 may include but is not limited to steps S701 to S704:

[0129] Step S701, calculating the matching degree based on the verification insurance proposal and the target insurance proposal to obtain the content matching degree;

[0130] Step S702: extracting evaluation reference indicator data from the target insurance proposal; wherein the evaluation reference indicator data includes: insurance product information, insurance service information, and insured user information;

[0131] Step S703: Calculate the matching degree of the insurance product information, insurance service information, and insured user information according to the quality assessment rules to obtain the information matching degree;

[0132] Step S704: The content matching degree and the information matching degree are combined to obtain quality evaluation data.

[0133] It should be noted that when evaluating the content quality of the target insurance proposal, the content quality assessment can be completed from aspects such as the degree of demand matching, product rationality, plan completeness, information accuracy, content readability, content clarity, content compliance, content practicality and subsequent serviceability of the target insurance proposal, so as to achieve a more refined quality assessment.

[0134] In step S701 of some embodiments, as previously disclosed, evaluating the content quality of the target insurance proposal involves determining the degree of matching. Therefore, content features of the verification insurance proposal are extracted as verification content features, and content features of the target insurance proposal are then extracted as target content features. The degree of matching between the verification content features and the target content features is calculated to determine the content matching degree. It should be noted that the content matching degree can represent the degree of similarity between the target insurance proposal and the verification insurance proposal, and thus the professionalism of the target insurance proposal.

[0135] In steps S702 to S703 of some embodiments, in order to further evaluate the product adaptability, service adaptability and user information accuracy in the target insurance proposal, insurance product information, insurance service information and insured user information are extracted from the target insurance proposal in advance, and the quality assessment rules include verifying product information, verifying service information and verifying user information. The product matching degree is obtained by matching the insurance product information and the verified product information, the service matching degree is obtained by matching the insurance service information and the verified service information, and the user matching degree is obtained by matching the insured user information and the verified user information. Then, the product matching degree, service matching degree and user matching degree are weightedly summed to obtain the information matching degree.

[0136] It's important to note that product fit indicates whether the target insurance proposal meets the target user's product needs, risk tolerance, and cost affordability. Service fit indicates whether the insurance service provided by the target insurance proposal meets the target user's insurance service needs, such as insurance terms, claims ratios, compensation methods, coverage period, and claims process. User fit indicates the accuracy of the user information in the target insurance proposal. Additionally, additional evaluation data, such as the presence of practical content and subsequent service support instructions, can be added to the quality assessment data.

[0137] In step S704 of some embodiments, a first weight is obtained by performing an importance evaluation on the content matching degree, and a second weight is obtained by performing an importance evaluation on the information matching degree. The content matching degree and the information matching degree are spliced ​​into quality evaluation data based on the first weight and the second weight.

[0138] In steps S701 to S704 shown in this embodiment, the quality assessment of the target insurance proposal is completed by determining the degree of match between the target insurance proposal and the verification insurance proposal, as well as the degree of match between the insurance products, insurance services and insured users in the target insurance proposal and the quality assessment rules, so that the quality assessment of the target insurance proposal is more refined and the representation of content quality is more accurate.

[0139] Please refer to Figure 8During the insurance proposal generation process, the original proposal generation model generates multiple target insurance proposals. Each target insurance proposal is then evaluated for content quality, generating quality assessment data. These multiple target insurance proposals and their quality assessment data are then displayed together on the insurance business platform, allowing target users to select them. The quality assessment data is represented by a score, with higher scores indicating higher content quality. For example, if target insurance proposals A, B, and C are generated, the quality assessment data for target insurance proposal A is 85, for target insurance proposal B is 79, and for target insurance proposal C is 92.

[0140] Please refer to Figure 9 , Figure 9 The model structure used in the insurance proposal generation method provided in an embodiment of the present application is shown. It includes a prompt generation module and a large language model. The prompt generation module collects data required for the insurance proposal in advance, then pre-processes the collected data. The prompt generation module also collects information uploaded in advance by insurance agents, including historical proposals, insurance product libraries, and claim case libraries. These libraries are then divided into a product knowledge base, a user knowledge base, a disease knowledge base, a claim knowledge base, and a question-and-answer knowledge base. When generating an insurance proposal, the prompt generation module extracts knowledge base fragments from multiple knowledge bases that match the target user's user profile. The user profile records the target user's basic information, insurance preferences, and medical conditions. The prompt generation module then inputs the knowledge base fragments and user profile into the large language model, which automatically outputs the target insurance proposal. Once generated, the target insurance proposal is not sent directly to the target user. Instead, it is first sent to the insurance agent's terminal, where the agent reviews the proposal and sends the approved proposal to the target user. Therefore, not only can the automatic generation of insurance proposals be achieved, but also insurance proposals that meet the basic information and preferences of target users can be generated, thereby improving the target users' satisfaction with the insurance proposals and thus improving the professionalism of insurance services.

[0141] See also Figure 10 The present application also provides an insurance proposal generation device that can implement the above-mentioned insurance proposal generation method. The device includes:

[0142] The user data acquisition module 1001 is used to acquire the target user's basic information, insurance question and answer data, and the user's current behavior data; wherein the user's current behavior data represents the target user's operating behavior on the insurance business platform;

[0143] The preference prediction module 1002 is used to predict the preferences of target users based on the insurance question-and-answer data and the user's current behavior data to obtain insurance preference prediction data;

[0144] The historical data acquisition module 1003 is used to obtain historical insurance proposals, current insurance product information and insurance claims data;

[0145] A key field splitting module 1004 is used to split the key fields of historical insurance proposals, current insurance product information, and insurance claim data according to preset key field extraction rules to obtain candidate insurance key fields;

[0146] The key field screening module 1005 is used to screen target insurance key fields from candidate insurance key fields based on user basic information and insurance preference prediction data;

[0147] The proposal screening module 1006 is used to generate a proposal based on a preset original proposal generation model, basic user information, and target insurance key fields to obtain a target insurance proposal.

[0148] The specific implementation of the insurance proposal generating device is substantially the same as the specific embodiment of the above-mentioned insurance proposal generating method, and will not be described in detail here.

[0149] The present application also provides a computer device comprising a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described insurance proposal generation method. The computer device can be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0150] See also Figure 11 , Figure 11 The hardware structure of a computer device according to another embodiment is shown. The computer device includes:

[0151] The processor 1101 can be implemented as a general-purpose CPU (Central Processing Unit), 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;

[0152] The memory 1102 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 1102 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1102 and is called by the processor 1101 to execute the insurance proposal generation method of the embodiments of this application.

[0153] Input / output interface 1103, used to implement information input and output;

[0154] Communication interface 1104, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0155] Bus 1105 , which transmits information between various components of the device (e.g., processor 1101 , memory 1102 , input / output interface 1103 , and communication interface 1104 );

[0156] The processor 1101 , the memory 1102 , the input / output interface 1103 and the communication interface 1104 are connected to each other in communication within the device via a bus 1105 .

[0157] An embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned insurance proposal generation method when executed by a processor.

[0158] 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 arranged 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.

[0159] The insurance proposal generation method and apparatus, computer equipment, and storage medium provided in the embodiments of the present application predict the insurance preference prediction data of the target object based on the current user behavior data and insurance question-and-answer data of the target user on the insurance business platform, and then pre-segment the historical insurance proposals, current insurance product information, and insurance claims data on the insurance business platform into candidate insurance key fields for constructing the insurance proposal. Therefore, when constructing the insurance proposal, the target insurance key fields for constructing the insurance proposal are screened from the candidate insurance key fields based on the user's basic information and insurance preference prediction data, and the target insurance key fields are combined into the target insurance proposal using the original proposal generation model and the user's basic information. Therefore, by automatically generating the insurance proposal, and the generation process is constructed based on the basic information and insurance preferences of the target user, not only the professional requirements and manpower for generating the insurance proposal are reduced, but also insurance proposals that meet the target user's needs can be generated, thereby improving the quality of insurance services.

[0160] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this 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 this application are also applicable to similar technical problems.

[0161] Those skilled in the art will understand 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.

[0162] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0163] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0164] The terms "first," "second," "third," "fourth," and the like (if any) in the specification of the present application and the accompanying drawings are used to distinguish similar users and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are 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 variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus 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 not clearly listed or inherent to such processes, methods, products, or apparatus.

[0165] It should be understood that in this 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 users, 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 previous and subsequent associated users 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.

[0166] In the several embodiments provided in this 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 merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, 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.

[0167] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0169] 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, which is stored in a storage medium and includes multiple instructions for enabling 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: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0170] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for generating an insurance proposal, characterized in that: The method comprises: Obtaining the target user's basic information, insurance question-and-answer data, and current user behavior data; wherein the current user behavior data represents the target user's operating behavior on the insurance business platform; Predicting the preferences of the target user based on the insurance question-and-answer data and the user's current behavior data to obtain insurance preference prediction data; Access historical insurance proposals, current insurance product information, and insurance claims data; Extracting key fields from the historical insurance proposals, the current insurance product information, and the insurance claims data according to preset key field extraction rules to obtain candidate insurance key fields; wherein the candidate insurance key fields are key content for constructing the insurance proposal; Filtering target insurance key fields from the candidate insurance key fields according to the user basic information and the insurance preference prediction data; Generate a proposal using a preset original proposal generation model, the user's basic information, and the target insurance key fields to obtain a target insurance proposal; The step of selecting a target insurance key field from the candidate insurance key fields according to the user basic information and the insurance preference prediction data includes: Acquire field tag information of the candidate insurance key field; wherein the field tag information includes: candidate user information and candidate insurance attribute information; Screening the candidate insurance key fields according to the user basic information, the insurance preference prediction data, the candidate user information, and the candidate insurance attribute information to obtain the target insurance key fields; The screening process of the candidate insurance key fields according to the user basic information, the insurance preference prediction data, the candidate user information, and the candidate insurance attribute information to obtain the target insurance key fields includes: Performing vector conversion on the user basic information and the insurance preference prediction data to obtain a query vector; Performing vector conversion on the candidate user information and the candidate insurance attribute information to obtain a reference vector; Calculating similarity between the query vector and the reference vector to obtain vector similarity; The candidate insurance key fields are screened according to the vector similarity to obtain the target insurance key fields.

2. The method according to claim 1, characterized in that The key field extraction rules include: field splitting rules and key field classification rules; The key field extraction rules are used to separate the historical insurance proposals, the current insurance product information, and the insurance claim data into key fields to obtain candidate insurance key fields, including: Performing field splitting on the historical insurance proposal, the current insurance product information, and the insurance claim data according to the field splitting rule to obtain candidate fields; Performing category identification on each candidate field to obtain a field category; The candidate fields are constructed into key fields according to the key field classification rules and the field categories to obtain the candidate insurance key fields.

3. The method according to any one of claims 1 to 2, characterized in that The step of predicting the target user's preferences based on the insurance question-and-answer data and the user's current behavior data to obtain insurance preference prediction data includes: Performing feature extraction on the insurance question and answer data to obtain insurance question and answer features; Extracting features from the user's current behavior data to obtain features of the user's current behavior; Preference prediction is performed using a preset preference prediction model, the insurance question-and-answer features, and the user's current behavior features to obtain the insurance preference prediction data.

4. The method according to any one of claims 1 to 2, characterized in that After generating a target insurance proposal by using a preset original proposal generation model, the user basic information, and the target insurance key fields, the method further includes: Performing a quality assessment on the target insurance proposal based on a preset verification insurance proposal and preset quality assessment rules to obtain quality assessment data; wherein the quality assessment data represents the degree of match between the target insurance proposal and the verification insurance proposal, and the degree of match between the target insurance proposal and the quality assessment rules; Optimizing the original proposal generation model according to the quality assessment data to obtain a target proposal generation model; The target insurance proposal is optimized by using the target proposal generation model, the user basic information and the target insurance key fields to obtain an updated insurance proposal.

5. The method according to claim 4, characterized in that The quality assessment of the target insurance proposal is performed according to the preset verification insurance proposal and the preset quality assessment rules to obtain quality assessment data, including: Calculating a matching degree based on the verification insurance proposal and the target insurance proposal to obtain a content matching degree; Extracting evaluation reference indicator data from the target insurance proposal; wherein the evaluation reference indicator data includes: insurance product information, insurance service information and insured user information; Calculating the matching degree of the insurance product information, the insurance service information, and the insured user information according to the quality assessment rules to obtain an information matching degree; The content matching degree and the information matching degree are spliced ​​together to obtain the quality assessment data.

6. An insurance proposal generating device, characterized in that: The method for generating an insurance proposal according to any one of claims 1 to 5, wherein the device comprises: A user data acquisition module is used to acquire basic user information, insurance question-and-answer data, and current user behavior data of a target user; wherein the current user behavior data represents the target user's operating behavior on the insurance business platform; a preference prediction module, configured to predict the preferences of the target user based on the insurance question-and-answer data and the user's current behavior data, and obtain insurance preference prediction data; Historical data acquisition module, used to obtain historical insurance proposals, current insurance product information and insurance claims data; A key field splitting module is used to split the key fields of the historical insurance proposal, the current insurance product information and the insurance claim data according to a preset key field extraction rule to obtain candidate insurance key fields; A key field screening module, configured to screen target insurance key fields from the candidate insurance key fields based on the user basic information and the insurance preference prediction data; The proposal screening module is used to generate a proposal based on a preset original proposal generation model, the user basic information and the target insurance key fields to obtain a target insurance proposal.

7. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the insurance proposal generation method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the insurance proposal generating method according to any one of claims 1 to 5 is implemented.

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