Interaction scene simulation method for insurance business training, electronic equipment and medium

Abstract

The invention relates to the technical field of financial insurance, in particular to an interactive scene simulation method for insurance business training, electronic equipment and a medium. The method comprises the steps of firstly obtaining an interaction scene simulation request; generating a scene simulation script through a pre-trained insurance field large model; creating a virtual role based on the scene simulation script, and generating a service simulation role; making a service simulation behavior in response to the service simulation role, and detecting a service performance behavior; in response to the service performance behavior, controlling the service simulation role to update the service simulation behavior, and returning to execute the service simulation behavior in response to the service simulation role, and detecting the service performance behavior made by the target object until a preset simulation termination event is triggered; and simulating an insurance service scene through an interactive operation between the service simulation behavior and the service performance behavior. In this way, a business scene which is relatively close to a real situation can be simulated, and insurance employees can be helped to carry out business training.

Description

Technical Field

[0001] The present application relates to the field of financial insurance technology, and in particular to an interactive scenario simulation method, electronic equipment, and medium for insurance business training. Background Art

[0002] Insurance agents refer to professionals who are responsible for selling insurance products in the insurance industry. In the field of insurance training, there are already a large number of unified training materials for insurance agents. However, since each insurance agent has different business capabilities, customer groups, sales styles, etc., it is difficult for insurance agents to train their business skills. It is difficult for insurance agents to obtain learning content that is very suitable for their needs from unified training.

[0003] At present, insurance agents mainly rely on senior sales personnel for help and prepare for business through simulated sparring and customer business scenarios. However, this simulation method for insurance business training is inefficient. Even if suitable senior sales personnel are found for sparring, the simulation effect of real business scenarios is relatively limited. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes an interactive scenario simulation method, electronic device, and medium for insurance business training, which can simulate a business scenario that is more in line with the real situation, and help insurance practitioners to conduct business training.

[0005] According to the first aspect of the present application, an interactive scenario simulation method for insurance business training includes:

[0006] Get interactive scene simulation request;

[0007] Processing the interactive scenario simulation request through a pre-trained insurance field big model, so that the insurance field big model generates a scenario simulation script;

[0008] Creating a virtual character based on the scenario simulation script to generate a business simulation character; wherein the scenario simulation script is used to configure a variety of business simulation behaviors of the business simulation character;

[0009] In response to the business simulation role performing the business simulation behavior, detecting the business performance behavior performed by the target object;

[0010] In response to the business performance behavior, control the business simulation role to update the business simulation behavior based on the scenario simulation script, and return to execute in response to the business simulation role performing the business simulation behavior, detect the business performance behavior performed by the target object, until a preset simulation termination event is triggered;

[0011] The insurance business scenario is simulated through the interaction between the business simulation behavior and the business performance behavior.

[0012] According to some embodiments of the present application, before the interactive scenario simulation request is processed by the pre-trained insurance field big model so that the insurance field big model generates a scenario simulation script, the method further includes:

[0013] Acquire a business behavior characterization data set of the target object; wherein the business behavior characterization data set is used to characterize the individual business behavior of the target object;

[0014] The original insurance field big model is personalized and fine-tuned by using the business behavior characterization data set to obtain a pre-trained insurance field big model.

[0015] According to some embodiments of the present application, obtaining a target object's business behavior representation dataset includes:

[0016] Acquire historical business data of the target object;

[0017] Business features are extracted from the historical business data to obtain the business behavior representation data set.

[0018] According to some embodiments of the present application, the business behavior characterization dataset includes business behavior characterization samples and business behavior characterization labels corresponding to the business behavior characterization samples, and the personalized fine-tuning training of the original insurance field big model is performed through the business behavior characterization dataset to obtain the pre-trained insurance field big model, including:

[0019] Determine a model fine-tuning layer from each processing layer corresponding to the insurance field big model;

[0020] Iteratively train the insurance field big model based on the business behavior representation samples to obtain the current round of training results;

[0021] Compare the business behavior characterization label corresponding to the business behavior characterization sample with the current round training result to obtain the current round deviation data;

[0022] The model parameters of the model fine-tuning layer are updated based on the deviation data of this round, and the process returns to execute iterative training of the insurance field big model based on the business behavior characterization samples until the insurance field big model meets the preset fine-tuning conditions, thereby obtaining the pre-trained insurance field big model.

[0023] According to some embodiments of the present application, the step of creating a virtual character based on the scenario simulation script to generate a business simulation character includes:

[0024] Model the role image through the role modeling program to obtain the business role image;

[0025] Based on the scenario simulation script, corresponding voice mapping information is configured for the business role image to generate the business simulation role.

[0026] According to some embodiments of the present application, the scenario simulation script includes multiple groups of scenario task samples and example response samples; the configuring corresponding voice mapping information for the business role image based on the scenario simulation script to generate the business simulation role includes:

[0027] Based on each of the scenario task samples and the corresponding example response samples, performing conversation interaction training on the long short-term memory network model to obtain a scenario conversation model;

[0028] The corresponding voice mapping information is configured for the business role image according to the scenario conversation model to generate the business simulation role.

[0029] According to some embodiments of the present application, in response to the business performance behavior, controlling the business simulation role to update the business simulation behavior based on the scenario simulation script includes:

[0030] In response to the service performance behavior, obtaining a service performance dialogue;

[0031] Controlling the business simulation role to input the business performance dialogue into the scenario conversation model to generate a business response dialogue according to the voice mapping information;

[0032] The business simulation behavior is updated according to the business response dialogue.

[0033] According to some embodiments of the present application, configuring corresponding voice mapping information for the business role image based on the scenario simulation script to generate the business simulation role includes:

[0034] Based on the scenario simulation script, corresponding voice mapping information is configured for the business role image to obtain a transition simulation role;

[0035] Based on each of the voice mapping information, corresponding action mapping information is configured for the transition simulation role to generate the business simulation role.

[0036] In a second aspect, an embodiment of the present application provides an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements an interactive scenario simulation method for insurance business training as described in any one of the embodiments of the first aspect of the present application.

[0037] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement an interactive scenario simulation method for insurance business training as described in any one of the embodiments of the first aspect of the present application.

[0038] The interactive scenario simulation method, electronic device, and medium for insurance business training according to the embodiments of the present application have at least the following beneficial effects:

[0039] The interactive scenario simulation method for insurance business training in the embodiment of the present application needs to first obtain an interactive scenario simulation request; process the interactive scenario simulation request through a pre-trained insurance field big model so that the insurance field big model generates a scenario simulation script; create a virtual role based on the scenario simulation script to generate a business simulation role; wherein the scenario simulation script is used to configure a variety of business simulation behaviors of the business simulation role; in response to the business simulation role performing the business simulation behavior, detect the business performance behavior performed by the target object; in response to the business performance behavior, control the business simulation role to update the business simulation behavior based on the scenario simulation script, and return to execute the business simulation behavior in response to the business simulation role performing the business simulation behavior, detect the business performance behavior performed by the target object, until the preset simulation termination event is triggered; simulate the insurance business scenario through the interactive operation between the business simulation behavior and the business performance behavior. In this way, a business scenario that is more in line with the real situation can be simulated, which is helpful to help insurance practitioners conduct business training.

[0040] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0042] Figure 1 This is a flow chart of an interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0043] Figure 2 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0044] Figure 3 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0045] Figure 4 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0046] Figure 5 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0047] Figure 6 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0048] Figure 7 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0049] Figure 8 Another flowchart of the interactive scenario simulation method for insurance business training according to an embodiment of the present application;

[0050] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0052] In the description of this application, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0053] In the description of the present application, it should be understood that descriptions involving orientation, such as up, down, left, right, front, back, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0054] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0055] In the description of this application, it should be noted that, unless otherwise clearly defined, the terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application in combination with the specific content of the technical solution. In addition, the identification of specific steps below does not represent a limitation on the order of steps and execution logic. The execution order and execution logic between each step should be understood and inferred with reference to the contents described in the embodiment.

[0056] Insurance agents refer to professionals who are responsible for selling insurance products in the insurance industry. These insurance agents usually come into direct contact with customers, understand their needs, and recommend and sell insurance products to them. Insurance agents play an important role in the insurance industry because they are the bridge between insurance companies and customers.

[0057] The work of an insurance agent not only includes selling insurance products, but also involves many aspects of responsibilities such as customer service, product consultation, claims assistance, etc. They need to have good communication skills, product knowledge, market insight and sales skills in order to successfully connect with customers and facilitate transactions.

[0058] In the field of insurance training, although there are a large number of unified training materials for insurance agents, the business capabilities of insurance agents vary greatly, and the types of customers they face are also different, making it difficult for insurance agents to train their business skills. It is difficult for insurance agents to obtain learning content that is very consistent with their needs from unified training. Therefore, they need personalized training and guidance to improve their sales efficiency and customer satisfaction. Existing unified training materials often cannot meet such personalized needs.

[0059] At present, insurance agents mainly rely on seeking help from senior sales personnel and preparing for sales through simulated sparring and client sales scenarios. However, even if suitable senior sales personnel are found for sparring, the senior sales personnel may not fully understand the specific situation and capabilities of the insurance agents, so the training suggestions given are mostly based on the client's suggestions. Insurance agents lack scientific and systematic analysis of their own cognition and improvement, and the industry currently lacks intelligent training methods based on the insurance agents' own sales personality and capabilities. These problems have led to obstacles for insurance agents in improving their sales capabilities, making it difficult to achieve personalized and efficient training results.

[0060] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes an interactive scenario simulation method, electronic device, and medium for insurance business training, which can simulate a business scenario that is more in line with the real situation, and help insurance practitioners to conduct business training.

[0061] Further explanation is given below based on the accompanying drawings.

[0062] Reference Figure 1 According to the first aspect of the present application, the interactive scenario simulation method for insurance business training may include:

[0063] Step S101, obtaining an interactive scene simulation request;

[0064] Step S102, processing the interactive scenario simulation request through the pre-trained insurance field big model, so that the insurance field big model generates a scenario simulation script;

[0065] Step S103, creating a virtual role based on the scenario simulation script to generate a business simulation role; wherein the scenario simulation script is used to configure a variety of business simulation behaviors of the business simulation role;

[0066] Step S104, in response to the business simulation role performing a business simulation behavior, detecting the business performance behavior performed by the target object;

[0067] Step S105, in response to the business performance behavior, controlling the business simulation role to update the business simulation behavior based on the scenario simulation script, and returning to execute in response to the business simulation role performing the business simulation behavior, detecting the business performance behavior performed by the target object, until a preset simulation termination event is triggered;

[0068] Step S106, simulating the insurance business scenario through the interactive operation between the business simulation behavior and the business performance behavior.

[0069] The interactive scenario simulation method for insurance business training in the embodiment of the present application needs to first obtain an interactive scenario simulation request; process the interactive scenario simulation request through a pre-trained insurance field big model so that the insurance field big model generates a scenario simulation script; create a virtual role based on the scenario simulation script to generate a business simulation role; wherein the scenario simulation script is used to configure a variety of business simulation behaviors of the business simulation role; respond to the business simulation role to perform business simulation behaviors, detect the business performance behaviors performed by the target object; respond to the business performance behaviors, control the business simulation role to update the business simulation behaviors based on the scenario simulation script, and return to execute the business simulation role to perform business simulation behaviors, detect the business performance behaviors performed by the target object, until the preset simulation termination event is triggered; simulate the insurance business scenario through the interactive operation between the business simulation behavior and the business performance behavior. In this way, a business scenario that is more in line with the real situation can be simulated, which is helpful to help insurance practitioners conduct business training.

[0070] In some embodiments, step S101 is to obtain an interactive scene simulation request;

[0071] The starting point of the interactive scenario simulation method is to obtain interactive scenario simulation requests. This process involves receiving simulation requests from insurance agents, which can be submitted through a user interface through which insurance agents can select specific business scenarios they want to simulate or sales skills they want to improve. For example, insurance agents may want to simulate a scenario of meeting a potential customer for the first time, or they may want to practice handling customer objections.

[0072] In order to obtain these requests, the embodiments of the present application may provide an interactive interface that allows insurance agents to select different simulation scenarios, enter specific customer characteristics, or describe the specific problems they want to solve. This interface can be graphical, providing elements such as drop-down menus, check boxes, and text boxes, allowing insurance agents to easily express their needs.

[0073] In addition, the embodiments of the present application may also provide some preset simulation scenarios for the insurance agent to choose from, which may be based on common sales challenges or the sales process of insurance products. The insurance agent only needs to select a scenario, and the embodiments of the present application will automatically generate a corresponding simulation request.

[0074] In some cases, the process of obtaining interactive scenario simulation requests may also include analyzing the insurance agent's historical performance to determine in which areas they may need more practice. For example, if the analysis of the embodiment of the present application shows that the insurance agent has difficulty in handling customer objections, it may recommend that the insurance agent conduct relevant simulation training.

[0075] Once the insurance agent submits their request, the embodiment of the present application will process the request and proceed to the next step, which is to use the pre-trained insurance field big model to generate a scenario simulation script. This process ensures the personalization and pertinence of the simulation training, helping insurance agents to get effective practice in the areas where they need to improve the most.

[0076] In step S102 of some embodiments, the interactive scenario simulation request is processed by the pre-trained insurance field big model, so that the insurance field big model generates a scenario simulation script;

[0077] In some embodiments, processing interactive scenario simulation requests through a pre-trained insurance domain big model involves using an artificial intelligence model customized for the insurance field to process simulation requests submitted by insurance agents and generate a detailed scenario simulation script. This pre-trained insurance domain big model is obtained by learning from a large amount of insurance business data, and can cover a wealth of insurance product knowledge, sales strategies, customer communication skills, and various possible sales scenarios.

[0078] When insurance agents submit their interactive scenario simulation requests through the user interface, the request will be sent to the pre-trained insurance domain model. After receiving the request, the insurance domain model will analyze the key information in the request, such as the business scenario, customer characteristics, sales targets, etc. that the insurance agent wants to simulate. Then, the insurance domain model will use this information, combined with its internal knowledge base and learned patterns, to generate a scenario simulation script.

[0079] It should be noted that the scenario simulation script is a detailed guidance document that describes various possible situations in the simulation scenario and the behavior of the business simulation role. The script can include dialogue examples, customer reactions, sales strategy suggestions, etc., providing a comprehensive simulation environment for insurance agents. The script generation process is dynamic, and the model will customize the script according to the specific needs and historical performance of the insurance agent, ensuring that the simulation scenario is both challenging and can help insurance agents improve their sales skills in a targeted manner.

[0080] In addition, the pre-trained insurance field big model can also update the script content according to the latest market trends and sales strategies to ensure the timeliness and practicality of simulation training. In this way, insurance agents can practice in a constantly updated and optimized environment, so as to better adapt to the ever-changing insurance market.

[0081] The process of generating scenario simulation scripts not only helps to improve the personalization of training, but also makes training more efficient and targeted. Insurance agents can try different sales strategies and communication skills in a safe environment through simulation training, so that they can be more confident and professional in actual work.

[0082] Reference Figure 2 According to some embodiments of the present application, before the pre-trained insurance field big model processes the interactive scenario simulation request in step S102 so that the insurance field big model generates a scenario simulation script, the following may also be included:

[0083] Step S201, obtaining a business behavior characterization dataset of a target object; wherein the business behavior characterization dataset is used to characterize the individual business behavior of the target object;

[0084] Step S202, performing personalized fine-tuning training on the original insurance field big model through the business behavior characterization data set to obtain a pre-trained insurance field big model.

[0085] In step S201, the embodiment of the present application obtains a target object's business behavior characterization data set. This data set contains information such as the insurance agent's past sales records, customer feedback, and communication logs, and is used to characterize the insurance agent's individual business behavior. These data can help the embodiment of the present application understand the insurance agent's sales style, communication skills, strengths, weaknesses, and other personalized characteristics.

[0086] Next, in step S202, the embodiment of the present application uses these business behavior characterization data sets to perform personalized fine-tuning training on the original insurance field large model. This process involves using the personalized data of insurance agents to adjust and optimize the parameters of the model so that it can better understand and simulate the business behavior of insurance agents. Through this fine-tuning training, the model can learn the specific needs and preferences of insurance agents, thereby generating business simulation scripts that are more in line with the personality of insurance agents.

[0087] This process of personalized fine-tuning training can improve the pertinence and effectiveness of simulation training. By using the personalized data of insurance agents to adjust the model, the generated business simulation script will be closer to the actual business scenarios of insurance agents, thus providing a more realistic and valuable training experience. This can not only help insurance agents better practice and improve their skills in simulation, but also make them more engaged and involved in simulation training.

[0088] In addition, this personalized fine-tuning training can also improve the flexibility and scalability of simulation training. As the business behavior of insurance agents changes and grows, the embodiments of the present application can continuously update and adjust the business behavior characterization data set, thereby continuously optimizing the performance of the model. This enables simulation training to adapt to the needs of different insurance agents at different stages, providing continuous support and guidance.

[0089] In summary, adding steps S201 and S202 before step S102 can make the pre-trained insurance field model more personalized and accurate, thereby providing insurance agents with more effective and targeted business simulation training. This training method not only helps to improve the business skills of insurance agents, but also helps to enhance their confidence and adaptability, enabling them to interact with customers more effectively in a real business environment, thereby improving sales performance and customer satisfaction.

[0090] Reference Figure 3 According to some embodiments of the present application, step S201 of obtaining a target object's business behavior representation dataset may include:

[0091] Step S301, obtaining historical business data of the target object;

[0092] Step S302: extract business features from historical business data to obtain a business behavior representation data set.

[0093] In step S301, the embodiment of the present application obtains the historical business data of the target object. These historical business data may include the insurance agent's past sales records, customer interaction logs, transaction cases, customer feedback, and any relevant business activity information. These historical business data are important resources for understanding the business behavior of insurance agents because they record the performance and decision-making process of insurance agents in actual business scenarios.

[0094] In step S302, the embodiment of the present application will extract business features from these historical business data to obtain a business behavior characterization data set. Business feature extraction involves identifying and extracting key information and patterns in historical data, which can represent the business behavior characteristics of insurance agents. These business behavior characteristics may include sales skills, customer communication methods, problem-solving strategies, product knowledge application, etc.

[0095] The process of extracting business features can use various data analysis and machine learning techniques, such as cluster analysis, classification algorithms, association rule mining, etc., to identify significant features in the business behavior of insurance agents. For example, the embodiment of the present application may analyze the communication records between insurance agents and customers to extract their common communication strategies and language patterns; or analyze their sales records to identify the types of products they are best at selling or the most successful sales scenarios.

[0096] Through these feature extraction steps, the embodiment of the present application can construct a comprehensive and accurate business behavior characterization dataset, which can reflect the individual business behavior of insurance agents. Then, this business behavior characterization dataset will be used to perform personalized fine-tuning training on the original insurance field big model to generate a pre-trained insurance field big model that is more in line with the personality and needs of insurance agents.

[0097] This personalized fine-tuning training based on the target object's business behavior representation dataset can ensure that the simulation training is closer to the actual business situation of insurance agents, thereby providing a more effective and targeted training experience. This not only helps insurance agents better practice and improve their skills in simulation, but also makes them more engaged and involved in simulation training, ultimately improving their performance in real business environments.

[0098] Reference Figure 4 According to some embodiments of the present application, the business behavior characterization dataset includes business behavior characterization samples and business behavior characterization labels corresponding to the business behavior characterization samples. Step S202 performs personalized fine-tuning training on the original insurance field big model through the business behavior characterization dataset to obtain a pre-trained insurance field big model, which may include:

[0099] Step S401, determining a model fine-tuning layer from each processing layer corresponding to the insurance field big model;

[0100] Step S402, iteratively training the insurance field big model based on the business behavior representation samples to obtain the training results of this round;

[0101] Step S403, comparing the business behavior characterization label corresponding to the business behavior characterization sample with the current round training result to obtain the current round deviation data;

[0102] Step S404, update the model parameters of the model fine-tuning layer based on the current round of deviation data, return to execute iterative training of the insurance field big model based on the business behavior characterization samples, until the insurance field big model meets the preset fine-tuning conditions, and obtain the pre-trained insurance field big model.

[0103] In some embodiments of the present application, the construction and use of the business behavior characterization dataset is to perform personalized fine-tuning training on the original insurance field big model. The business behavior characterization dataset includes business behavior characterization samples and corresponding business behavior characterization labels. The business behavior characterization samples represent the specific business behaviors of insurance agents, while the business behavior characterization samples and the corresponding business behavior characterization labels describe the characteristics or results corresponding to these business behavior characterization samples. Through this process, a pre-trained, personalized insurance field big model can be generated to better adapt to the business behaviors of specific insurance agents.

[0104] First, in step S401, the embodiment of the present application determines the model fine-tuning layer from each processing layer of the insurance field large model. This is because large deep learning models are usually composed of multiple processing layers, each layer is responsible for different tasks. In the fine-tuning process, it is necessary to adjust a part of the insurance field large model instead of all layers, so as to maintain the general knowledge of the insurance field large model while adapting to specific personalized needs.

[0105] Next, in step S402, the embodiment of the present application iteratively trains the insurance field big model based on the business behavior characterization samples. This process involves inputting the business behavior characterization samples into the insurance field big model, and adjusting the model parameters according to the difference between the prediction results of the insurance field big model and the actual business behavior characterization labels. Each iterative training will make the insurance field big model more accurately reflect the business behavior of insurance agents.

[0106] In step S403, the embodiment of the present application compares the business behavior characterization label corresponding to the business behavior characterization sample with the current round training result to obtain the current round deviation data. This current round deviation data is a key indicator for evaluating model performance, which shows the gap between model prediction and actual results. By analyzing the current round deviation data, it is possible to identify which aspects of the insurance field big model need further improvement.

[0107] Then, in step S404, the embodiment of the present application will update the model parameters of the model fine-tuning layer based on the current round of deviation data. This update process is to reduce deviations and improve the accuracy of the model. After updating the parameters, the embodiment of the present application will perform iterative training again, and this process will be repeated until the insurance field large model meets the preset fine-tuning conditions.

[0108] Finally, when the model meets the fine-tuning conditions, the embodiment of the present application will obtain a pre-trained insurance field model. This model has been personalized and fine-tuned to more accurately simulate the business behavior of a specific insurance agent. The pre-trained insurance field model can be used to generate more realistic and personalized business simulation scripts, providing insurance agents with a more effective training experience.

[0109] Through this meticulous fine-tuning training process, the embodiments of the present application can ensure that the generated pre-trained model not only has general knowledge in the insurance field, but also reflects the unique business behaviors and needs of specific insurance agents. This helps to improve the relevance and effectiveness of simulation training, allowing insurance agents to obtain more valuable practice and feedback in a simulation environment.

[0110] In step S103 of some embodiments, a virtual role is created based on a scenario simulation script to generate a business simulation role; wherein the scenario simulation script is used to configure a variety of business simulation behaviors of the business simulation role;

[0111] In some embodiments, virtual characters are created based on scenario simulation scripts to generate business simulation characters, which involves converting scenario simulation scripts generated by pre-trained insurance domain big models into specific business simulation characters, which will be used to simulate real business interaction scenarios. Among them, the scenario simulation script contains detailed business simulation behavior configurations, which define various actions and reactions that the business simulation character may take during the simulation. For example, if the scenario simulation script describes a potential customer who is skeptical about insurance products, then the business simulation character will be configured to express this skepticism in the simulation and raise challenging questions or objections.

[0112] In the embodiment of the present application, the creation process of the business simulation role utilizes artificial intelligence technologies, such as natural language processing and machine learning, to ensure that the role's behavior is as close to the reaction of real customers as possible. These roles can not only conduct conversations according to the instructions in the script, but also dynamically adjust their behavior according to the insurance agent's response, thereby providing a more realistic and interactive simulation experience.

[0113] When creating a business simulation role, the embodiments of the present application may consider a variety of factors, such as the character's personality traits, communication style, decision-making process, etc., to ensure that each role is unique and realistic. In addition, the embodiments of the present application may also adjust the difficulty and complexity of the role's behavior according to the specific needs and training goals of the insurance agent, thereby providing different levels of challenge.

[0114] In this way, insurance agents can practice their sales skills and customer service capabilities in a highly simulated environment. They can interact with business simulation characters in real time and learn how to handle various customer reactions and sales challenges. This simulation training not only helps improve the business skills of insurance agents, but also enhances their confidence and ability to deal with complex sales scenarios.

[0115] In short, the process of creating virtual characters based on scenario simulation scripts provides insurance agents with a safe and controllable environment, allowing them to practice and improve their business capabilities without risk. The efficiency and personalization of this training method make it an effective tool for improving the sales skills of insurance agents.

[0116] Reference Figure 5 According to some embodiments of the present application, step S103 creates a virtual role based on a scenario simulation script to generate a business simulation role, which may include:

[0117] Step S501, performing role image modeling through a role modeling program to obtain a business role image;

[0118] Step S502: configuring corresponding voice mapping information for the business role image based on the scenario simulation script to generate a business simulation role.

[0119] First, in step S501, the embodiment of the present application performs character image modeling through a character modeling program to obtain a business character image. Character image modeling is a creative process that involves designing the appearance, clothing, expressions, and other visual elements of a virtual character so that the character looks as close as possible to a customer or business partner in the real world. This process may use 3D modeling software, graphic design tools, or specialized artificial intelligence algorithms to generate realistic character images. Among them, character image modeling is not limited to focusing on the visual appeal of the character, but also on its behavior and reaction in the simulated scene. Therefore, the business character image needs to match the background and objectives of the simulated scene to ensure the realism and immersion of the simulation training.

[0120] Next, in step S502, the embodiment of the present application configures corresponding voice mapping information for the business role image based on the scenario simulation script to generate a business simulation role. This step involves matching the visual image of the role with its voice and language response. The embodiment of the present application can use text-to-speech technology to convert the role's dialogue into natural-sounding voice output, or use pre-recorded voice clips.

[0121] In addition, the voice mapping information may also include the character's tone, speaking speed, accent, and other voice features, which are essential for conveying the character's personality and emotional state. For example, if the scenario simulation script requires the character to express a skeptical or uncertain attitude, the embodiment of the present application will configure the corresponding voice features to reflect this emotion.

[0122] By combining character image modeling with voice mapping information, the embodiment of the present application can generate a complete business simulation character that is not only visually attractive but also provides a real and convincing interactive experience in voice communication.

[0123] This meticulous character creation process ensures a high degree of realism and interactivity in the simulation training environment. Insurance agents can interact with these virtual characters in simulated scenarios to practice their communication skills, sales strategies, and customer service capabilities, thereby improving their business skills in a safe and controlled environment. This simulation training method not only helps improve the business skills of insurance agents, but also helps to enhance their confidence and adaptability, enabling them to interact with customers more effectively in a real business environment.

[0124] Reference Figure 6 According to some embodiments of the present application, the scenario simulation script includes multiple sets of scenario task samples and example response samples. Step S502 configures corresponding voice mapping information for the business role image based on the scenario simulation script to generate a business simulation role, which may include:

[0125] Step S601, based on each scenario task sample and the corresponding example response sample, the long short-term memory network model is trained for conversation interaction to obtain a scenario conversation model;

[0126] Step S602: configuring corresponding voice mapping information for the business role image according to the scenario conversation model to generate a business simulation role.

[0127] In some embodiments of the present application, the construction and use of scenario simulation scripts is to generate business simulation characters that can effectively interact with insurance agents in simulation training. The scenario simulation scripts include multiple sets of scenario task samples and sample response samples, which provide the necessary dialogue content and communication methods for the virtual characters.

[0128] First, in step S601, the embodiment of the present application performs conversational interaction training on the long short-term memory network (LSTM) model based on each scenario task sample and the corresponding example response sample. The long short-term memory network is a time series prediction model that is very suitable for processing and predicting sequence data, such as natural language. In this way, the model can learn the dialogue flow and logic in the scenario task sample, while absorbing the language style and response strategy in the example response sample.

[0129] The purpose of conversational interaction training is to teach the LSTM model how to generate appropriate responses in specific business scenarios. The LSTM model analyzes customer questions or statements in scenario task samples, and then predicts the best sales agent response based on the sample response. Through extensive training, the scenario conversation model can master various conversation scenarios and appropriate responses.

[0130] Next, in step S602, the embodiment of the present application configures corresponding voice mapping information for the business role image according to the scenario conversation model to generate a business simulation role. This step involves converting the text output of the scenario conversation model into voice output for verbal communication with the insurance agent in the simulation training. To this end, the embodiment of the present application may use text-to-speech technology to synthesize natural-sounding speech, or use a pre-recorded voice library to match the text generated by the model.

[0131] When configuring the voice mapping information, the embodiment of the present application will take into account the language characteristics of the character, such as intonation, speaking speed, pauses, etc., to ensure that the voice output of the character sounds natural and has personality. In addition, the embodiment of the present application may also adjust the voice mapping information according to the character's personality traits and emotional state to enhance the realism of the simulation training.

[0132] Through this method, the embodiment of the present application can generate highly interactive business simulation characters, which can have real conversations with insurance agents in simulation training. Insurance agents can practice their communication skills, sales strategies and customer service capabilities through interaction with these business simulation characters, thereby gaining valuable practical experience in a simulation environment.

[0133] In summary, by combining scenario simulation scripts and conversational interactive training of LSTM models, and configuring voice mapping information for business role images, the embodiments of the present application can provide a highly realistic and interactive simulation training environment. This not only helps to improve the business skills of insurance agents, but also helps to enhance their confidence and adaptability, enabling them to interact with customers more effectively in a real business environment.

[0134] Reference Figure 7 According to some embodiments of the present application, step S502 configures corresponding voice mapping information for the business role image based on the scenario simulation script to generate a business simulation role, which may include:

[0135] Step S701, configuring corresponding voice mapping information for the business role image based on the scenario simulation script to obtain a transition simulation role;

[0136] Step S702: Based on each voice mapping information, corresponding action mapping information is configured for the transition simulation role to generate a business simulation role.

[0137] First, in step S701, the embodiment of the present application configures corresponding voice mapping information for the business role image based on the scenario simulation script. The scenario simulation script describes in detail the conversation content and communication method in the simulation scenario, including the character's language style, intonation, speech speed, etc. The embodiment of the present application uses this information to generate a transition simulation character, which already has basic voice communication capabilities, but does not yet have complete action and expression mapping.

[0138] Next, in step S702, the embodiment of the present application configures corresponding action mapping information for the transition simulation role based on each voice mapping information to generate a complete business simulation role. The action mapping information includes body language, facial expressions, head movements, etc. that the role may use in the conversation. These action information are crucial to convey the emotional state and personality characteristics of the role.

[0139] When configuring the action mapping information, the embodiments of the present application may use animation technology or motion capture technology to generate the actions of the character. For example, the embodiments of the present application may automatically generate corresponding facial expressions and gestures based on the voice content and emotional state required by the character. These actions are combined with the voice mapping information so that the character can interact with the insurance agent in a more natural and realistic way in the simulation training.

[0140] In addition, the embodiment of the present application can also adjust the complexity and details of the action mapping information according to the character's personality traits and scene requirements. For example, if the character is an enthusiastic sales manager, the embodiment of the present application may configure more gestures and expressions to convey this enthusiasm. If the character is a serious potential customer, the embodiment of the present application may configure fewer actions and more serious expressions.

[0141] In this way, the embodiments of the present application can generate business simulation characters with high interactivity and realism. These characters can not only communicate by voice, but also interact with insurance agents in a richer and more realistic way through actions and expressions. This simulated training environment can help insurance agents practice their communication skills, sales strategies and customer service capabilities in a safe and controlled environment, thereby improving their performance in a real business environment.

[0142] In summary, by configuring voice mapping information and action mapping information for business role images, the embodiments of the present application can provide a highly realistic and interactive simulation training environment. This not only helps to improve the business skills of insurance agents, but also helps to enhance their confidence and adaptability, enabling them to interact with customers more effectively in a real business environment.

[0143] In step S104 of some embodiments, in response to the business simulation role performing a business simulation behavior, detecting a business performance behavior performed by the target object;

[0144] In some embodiments, step S104 involves responding to the business simulation behavior made by the business simulation role and detecting the business performance behavior made by the target object. This is a key link in the interactive scenario simulation method, which aims to evaluate the performance and reaction of the insurance agent in the simulated business environment in real time. Among them, the target object can be an insurance agent, other practitioners in the insurance field, or a training object for insurance business, etc.

[0145] When the business simulation role begins to perform its preset behavior according to the scenario simulation script, the embodiment of the present application will closely monitor the interaction of the insurance agent. These behaviors may include objections, questions or specific purchase signals raised by virtual customers. The embodiment of the present application will record how the insurance agent responds to these behaviors, which may include their communication skills, problem-solving strategies, product knowledge application and customer service capabilities.

[0146] Detecting the target's business performance behavior is a complex process that involves analyzing the insurance agent's language, tone, reaction time, and selected sales strategy. The present application embodiment can use natural language processing (NLP) technology to analyze the insurance agent's conversation content, use speech recognition technology to evaluate the tone, and use machine learning algorithms to evaluate the insurance agent's overall performance.

[0147] In addition, the embodiments of the present application can also provide real-time feedback to help insurance agents understand their performance in the simulation. For example, if the insurance agent is hesitant in handling customer objections, the embodiments of the present application can immediately provide advice or guidance to help the insurance agent improve their strategy. This instant feedback mechanism is crucial to improving the learning and training effects of insurance agents.

[0148] In this way, step S104 not only enables insurance agents to practice their skills in a simulated environment, but also enables them to get immediate feedback on how to improve. This training method helps insurance agents to be more confident and professional when facing real customers, thereby improving their sales performance and customer satisfaction.

[0149] In step S105 of some embodiments, in response to the business performance behavior, the business simulation role is controlled to update the business simulation behavior based on the scenario simulation script, and the execution is returned to detect the business performance behavior performed by the target object in response to the business simulation role performing the business simulation behavior, until a preset simulation termination event is triggered;

[0150] In some embodiments, step S105 is a dynamic and iterative process in the interactive scenario simulation method, which involves how the embodiment of the present application responds to and controls the feedback behavior of the business simulation role after the target object performs a business performance behavior.

[0151] When the insurance agent responds in the simulated environment, the embodiment of the present application analyzes these responses and adjusts the behavior of the business simulation role according to the preset rules and logic in the scenario simulation script to simulate the behavior of the customer in the actual business scenario. This process is real-time, which means that every action or decision of the insurance agent will result in a corresponding change in the business simulation role. For example, if the insurance agent successfully resolves the objection raised by the business simulation role, the embodiment of the present application may cause the business simulation role to show satisfaction or trust, thereby driving the simulation scenario forward.

[0152] In addition, the embodiment of the present application may adjust the difficulty of the challenge or provide additional scenarios based on the performance of the insurance agent. If the insurance agent performs well in the simulation, the embodiment of the present application may introduce more complex sales challenges to further test and improve their abilities. Conversely, if the insurance agent encounters difficulties, the embodiment of the present application may provide additional guidance or simplify the challenge to help them learn and improve.

[0153] It should be noted that this iterative process can continue until a preset simulation termination event is triggered. The simulation termination event may include a variety of situations, such as reaching a predetermined training time, achieving a specific sales target, or the insurance agent requesting to end the simulation. At the end of the simulation, the embodiment of the present application may provide a summary report to evaluate the overall performance of the insurance agent and provide improvement suggestions.

[0154] Through this dynamic interaction and feedback mechanism, step S105 ensures the real-time and adaptability of the simulation training, enabling insurance agents to continuously learn and adapt in the simulation environment, thereby improving their sales skills and customer service capabilities. This training method not only helps to improve the business skills of insurance agents, but also helps to enhance their ability to cope with various challenges in real sales scenarios.

[0155] Reference Figure 8 According to some embodiments of the present application, step S105, in response to the business performance behavior, controls the business simulation role to update the business simulation behavior based on the scenario simulation script, which may include:

[0156] Step S801, in response to the service performance behavior, obtaining the service performance dialogue;

[0157] Step S802, controlling the business simulation role to input the business performance dialogue into the scenario conversation model to generate a business response dialogue according to the voice mapping information;

[0158] Step S803: updating the business simulation behavior according to the business response dialogue.

[0159] In some embodiments of the present application, step S105 involves responding to the business performance behavior of the insurance agent and controlling the business simulation role to update its business simulation behavior based on the scenario simulation script. This process is dynamic, ensuring that the simulation training can reflect the behavior of the insurance agent in real time and provide corresponding feedback. Specifically, this process includes three key steps: step S801, step S802 and step S803.

[0160] First, in step S801, the embodiment of the present application obtains a business performance dialogue in response to a business performance behavior. When the insurance agent interacts with the business simulation role in the simulation training, their speech or action is regarded as a business performance behavior. The embodiment of the present application captures these behaviors in real time and analyzes them as input data. These inputs may include the insurance agent's voice, text input, or operations performed through an interface.

[0161] Next, in step S802, the embodiment of the present application controls the business simulation role to input the business performance dialogue into the scenario conversation model. The scenario conversation model is a long short-term memory network (LSTM) model based on previous training, which can understand and process the business performance dialogue of the insurance agent. The embodiment of the present application uses this model to analyze the input of the insurance agent and generate appropriate business response dialogues based on the scenario simulation script and the pre-defined dialogue flow. This step involves converting the business performance dialogue of the insurance agent into a format that the model can understand, and using the predictive ability of the model to generate a response.

[0162] Then, in step S803, the embodiment of the present application updates the business simulation behavior according to the business response dialogue. This means that the business simulation role will adjust its behavior and dialogue according to the response generated by the scenario conversation model. These updated behaviors and dialogues will be converted into the voice output and action performance of the role through voice mapping information and action mapping information, so as to be reflected in real time in the simulation training. In this way, the insurance agent can receive feedback from the simulated role and adjust his strategy and behavior according to this feedback.

[0163] In this way, the embodiments of the present application can ensure the dynamic and interactive nature of the simulation training. Every action of the insurance agent will receive an immediate response, making the simulation training closer to the real business scenario. This real-time feedback and adjustment mechanism not only helps insurance agents improve their business skills, but also helps them better understand the needs and reactions of customers, so as to communicate and interact with customers more effectively in a real business environment.

[0164] In summary, by responding to business performance behaviors and controlling business simulation roles to update their behaviors, the embodiments of the present application can provide a highly interactive and adaptable simulation training environment, which not only helps improve the business skills of insurance agents, but also helps to enhance their confidence and adaptability, enabling them to interact with customers more effectively in a real business environment.

[0165] In step S106 of some embodiments, an insurance business scenario is simulated through interaction between business simulation behavior and business performance behavior.

[0166] In some embodiments, step S106 is the core of the entire interactive scenario simulation method, which involves simulating real insurance business scenarios through interactive operations between business simulation behaviors and business performance behaviors. This process is intended to create a highly interactive and realistic simulation environment that enables insurance agents to practice and improve their sales skills in a safe and controlled environment.

[0167] During the simulation, the business simulation role will perform various business simulation behaviors according to the scenario simulation script, such as asking questions, expressing doubts, or showing purchase intentions. These behaviors are designed to simulate the possible reactions and needs of real customers, providing insurance agents with a real business challenge.

[0168] At the same time, insurance agents need to respond to these business simulation behaviors, which are called business performance behaviors. Insurance agents may try to explain the advantages of insurance products, solve customer concerns, or propose customized insurance plans. These responses not only demonstrate the insurance agents' sales skills, but also reflect their understanding of insurance product knowledge and customer needs.

[0169] The embodiment of the present application monitors and analyzes the business performance of insurance agents in real time, and evaluates their communication skills, problem-solving ability, and the effectiveness of their sales strategies. Through this interactive operation, insurance agents can test and improve their sales methods in a simulated environment while receiving instant feedback and guidance.

[0170] In addition, this simulation training also helps insurance agents better understand customer needs and psychology, and improve their empathy and customer service awareness. By interacting with virtual customers, insurance agents can learn how to adjust their communication methods and sales strategies in different business scenarios to meet the needs of different customers.

[0171] Ultimately, simulating insurance business scenarios through the interaction between business simulation behaviors and business performance behaviors can help target insurance agents gain valuable practical experience in simulated insurance business scenarios. This training not only helps improve their sales skills, but also helps enhance their confidence and adaptability, enabling them to interact with customers more effectively in a real business environment, improving sales performance and customer satisfaction.

[0172] Reference Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0173] The processor 901 may be implemented by 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;

[0174] The memory 902 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 902 can store and operate the embodiments of the present application and other application programs. When the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the relevant program codes are stored in the memory 902, and the processor 901 calls and executes the interactive scenario simulation method for insurance business training in the embodiments of the present application;

[0175] Input / output interface 903, used to implement information input and output;

[0176] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);

[0177] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0178] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0179] The embodiment of the present application further provides a computer program product, which includes a computer program. A processor of a computer device reads and executes the computer program, so that the computer device executes the above-mentioned interactive scenario simulation method for insurance business training.

[0180] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present disclosure 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 disclosure described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "comprises" and "comprising" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, embodiment of the present application, product or device that includes 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.

[0181] It should be understood that in the present disclosure, "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 items" or similar expressions refers to any combination of these items, which may include any combination of single items 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.

[0182] It should be understood that in the description of the embodiments of the present application, the meaning of multiple (or multiple items) is more than two, greater than, less than, exceed, etc. are understood to not include the number, and above, below, within, etc. are understood to include the number.

[0183] In the several embodiments provided in the present disclosure, it should be understood that the disclosed embodiments, devices and methods of the present application can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another embodiment of the present application, 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0184] The units described 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.

[0185] In addition, each functional unit in each embodiment of the present disclosure 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.

[0186] 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 disclosure, 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, which may include several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present disclosure. 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 program codes.

[0187] It should also be understood that the various implementations provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0188] The above is a specific description of the implementation methods of the present disclosure, but the present disclosure is not limited to the above implementation methods. Technical personnel familiar with the art can also make various equivalent modifications or substitutions without violating the spirit of the present disclosure. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present disclosure.

Claims

1. An interactive scenario simulation method for insurance business training, characterized in that: include: Get interactive scene simulation request; Processing the interactive scenario simulation request through a pre-trained insurance field big model, so that the insurance field big model generates a scenario simulation script; Creating a virtual character based on the scenario simulation script to generate a business simulation character; wherein the scenario simulation script is used to configure a variety of business simulation behaviors of the business simulation character; In response to the business simulation role performing the business simulation behavior, detecting the business performance behavior performed by the target object; In response to the business performance behavior, control the business simulation role to update the business simulation behavior based on the scenario simulation script, and return to execute in response to the business simulation role performing the business simulation behavior, detect the business performance behavior performed by the target object, until a preset simulation termination event is triggered; The insurance business scenario is simulated through the interaction between the business simulation behavior and the business performance behavior.

2. The method according to claim 1, characterized in that Before the interactive scenario simulation request is processed by the pre-trained insurance field big model so that the insurance field big model generates a scenario simulation script, the method further includes: Acquire a business behavior characterization data set of the target object; wherein the business behavior characterization data set is used to characterize the individual business behavior of the target object; The original insurance field big model is personalized and fine-tuned by using the business behavior characterization data set to obtain a pre-trained insurance field big model.

3. The method according to claim 2, characterized in that The step of obtaining a target object's business behavior representation dataset includes: Acquire historical business data of the target object; Business features are extracted from the historical business data to obtain the business behavior representation data set.

4. The method according to claim 2, characterized in that: The business behavior characterization dataset includes business behavior characterization samples and business behavior characterization labels corresponding to the business behavior characterization samples. The original insurance field big model is personalized fine-tuned and trained by the business behavior characterization dataset to obtain the pre-trained insurance field big model, including: Determine a model fine-tuning layer from each processing layer corresponding to the insurance field big model; Iteratively train the insurance field big model based on the business behavior representation samples to obtain the current round of training results; Compare the business behavior characterization label corresponding to the business behavior characterization sample with the current round training result to obtain the current round deviation data; The model parameters of the model fine-tuning layer are updated based on the deviation data of this round, and the process returns to execute iterative training of the insurance field big model based on the business behavior characterization samples until the insurance field big model meets the preset fine-tuning conditions, thereby obtaining the pre-trained insurance field big model.

5. The method according to claim 1, characterized in that The step of creating a virtual role based on the scenario simulation script to generate a business simulation role includes: Model the role image through the role modeling program to obtain the business role image; Based on the scenario simulation script, corresponding voice mapping information is configured for the business role image to generate the business simulation role.

6. The method according to claim 5, characterized in that The scenario simulation script includes multiple sets of scenario task samples and sample response samples; the configuring corresponding voice mapping information for the business role image based on the scenario simulation script to generate the business simulation role includes: Based on each of the scenario task samples and the corresponding example response samples, performing conversation interaction training on the long short-term memory network model to obtain a scenario conversation model; The corresponding voice mapping information is configured for the business role image according to the scenario conversation model to generate the business simulation role.

7. The method according to claim 6, characterized in that In response to the business performance behavior, controlling the business simulation role to update the business simulation behavior based on the scenario simulation script includes: In response to the service performance behavior, obtaining a service performance dialogue; Controlling the business simulation role to input the business performance dialogue into the scenario conversation model to generate a business response dialogue according to the voice mapping information; The business simulation behavior is updated according to the business response dialogue.

8. The method according to claim 5, characterized in that The configuring corresponding voice mapping information for the business role image based on the scenario simulation script to generate the business simulation role includes: Based on the scenario simulation script, corresponding voice mapping information is configured for the business role image to obtain a transition simulation role; Based on each of the voice mapping information, corresponding action mapping information is configured for the transition simulation role to generate the business simulation role.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the interactive scenario simulation method for insurance business training as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a program, and the program is executed by a processor to implement the interactive scenario simulation method for insurance business training as described in any one of claims 1 to 8.

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