Tax AI agent construction method based on large language model

Abstract

The invention provides a tax AI agent construction method based on a large language model. The method comprises the following steps: constructing a tax knowledge base, and based on the constructed tax knowledge base, executing large language model training and optimization to obtain a large language model; constructing a tax AI agent based on a large language model by using the designed agent architecture; collecting a standard session generated by the tax AI agent for the standard consultation, and training and optimizing the tax AI agent according to the generated standard session until multiple threshold parameters are met; deploying and applying the tax AI agent, and continuously optimizing and updating the tax AI agent according to the business session generated by the collected tax AI agent for the non-standard consultation and according to the generated business session. Therefore, efficient and low-cost tax consultation service can be provided.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method for constructing a tax AI agent based on a large language model. Background Art

[0002] With the continuous updating and improvement of tax laws and regulations, the demand for tax consulting by enterprises and individuals is growing. However, traditional tax consulting services have the following problems: tight human resources and a limited number of tax accountants, which make it difficult to meet the needs of the majority of users; the professional level of tax accountants varies, making it difficult to guarantee the quality of service; and the high cost of tax consulting is not conducive to popularization.

[0003] To solve the above problems, it is necessary to develop technical solutions that can provide efficient and low-cost tax consulting services. Summary of the invention

[0004] In view of this, this application proposes a tax AI agent construction method based on a large language model, aiming to solve the problem of lack of efficient and low-cost tax consulting services in the prior art.

[0005] In the first aspect, the present application provides a method for constructing a tax AI agent based on a large language model, comprising:

[0006] Build a tax knowledge base, and perform large language model training and optimization based on the built tax knowledge base to obtain a large language model;

[0007] Using the designed agent architecture, we build a tax AI agent based on a large language model.

[0008] Collect standard conversations generated by the tax AI agent for standard consultations, and train and optimize the tax AI agent based on the generated standard conversations until multiple threshold parameters are met;

[0009] Deploy and apply tax AI agents, and continuously optimize and update tax AI agents based on the collected business conversations generated by the tax AI agents for non-standard consultations.

[0010] Furthermore, the construction of the tax knowledge base includes:

[0011] Collect and organize tax-related laws, regulations, policy documents, and case materials to build a complete tax knowledge base to support the semantic understanding and reasoning of intelligent agents to clean, deduplicate, and segment the collected tax data to obtain pre-processed tax data.

[0012] Furthermore, the tax knowledge base constructed is used to perform large language model training and optimization to obtain a large language model, including:

[0013] Use deep learning to train large language models;

[0014] Input the preprocessed tax data into the large language model, adjust the model parameters and introduce external knowledge;

[0015] The pre-processed tax data comprises: voice, picture, video, text; the pre-processed tax data is carried out in the form of a question-and-answer dialogue.

[0016] Furthermore, the designed agent architecture includes: a data input layer, a processing layer, and an output layer;

[0017] The data input layer receives tax inquiries and tax declaration instructions input by users, and obtains relevant tax data from external data sources;

[0018] The processing layer combines the large language model and the tax knowledge base to process and analyze the input data and generate corresponding tax treatment suggestions or results;

[0019] The output layer displays tax processing suggestions or results to users in a user-friendly manner, and the tax processing suggestions or results include: tax reports and tax planning suggestions.

[0020] Furthermore, the construction of a tax AI agent based on a large language model includes:

[0021] Combine the large language model with the tax knowledge base, dialogue configuration unit, capability configuration unit, knowledge base configuration unit, configuration information management unit, user feedback unit, and knowledge update unit to form a tax AI agent;

[0022] The dialogue configuration unit is used to define the dialogue logic, question recognition, intent understanding and response generation of the intelligent agent;

[0023] The capability configuration unit allows developers to configure various tax-related capabilities for the agent according to business needs, including: tax calculation, policy interpretation, and risk assessment;

[0024] The knowledge base configuration unit is used to build and maintain the local proprietary tax knowledge base of the intelligent agent;

[0025] The configuration information management unit is used to store and manage all configuration information of the agent, including dialogue configuration, capability configuration and knowledge base configuration;

[0026] The user feedback unit collects feedback from users during use;

[0027] The knowledge updating unit is used to monitor changes in tax policies and automatically or assist developers in updating the knowledge base of the intelligent agent.

[0028] Furthermore, the capability configuration unit performs modular design to decompose the functions of the agent, and the decomposed independent functional modules include a tax consulting module, a tax planning module, and a tax declaration module;

[0029] The capability configuration unit adopts a plug-in architecture, allowing developers to add new functional modules as needed and supporting plug-in functional expansion;

[0030] The capability configuration unit integrates multiple API interfaces and provides API interfaces to allow third-party services and tools to be integrated with the agent;

[0031] The capability configuration unit adopts a microservice architecture so that each independent functional module can be independently deployed, expanded and maintained.

[0032] Furthermore, multiple threshold parameters are met, including: meeting at least one of the following threshold parameters: accuracy threshold; response time; user satisfaction; error rate; data update frequency.

[0033] Furthermore, the tax AI agent is collected to generate standard conversations for standard consultations, and the tax AI agent is trained and optimized according to the generated standard conversations until multiple threshold parameters are met, including:

[0034] Train the agent using a large amount of tax-related training data to improve its semantic understanding and generation capabilities;

[0035] During the training process, the model parameters and algorithms are continuously optimized based on the standard conversations generated for standard consultations to improve the performance and generalization ability of the agent; standard consultations are compiled using a large amount of tax data to generate conversation templates for standard consultations;

[0036] Based on the standard conversations and corresponding conversation templates generated by the tax AI agent for standard consultation, optimize the model parameters and algorithms until multiple threshold parameters are met;

[0037] Non-standard consulting includes: interpretation of tax policies, tax declaration guidance, and tax planning advice.

[0038] Furthermore, the deployment and application of the tax AI agent includes:

[0039] Deploy the trained agents to various actual management systems to provide users with intelligent tax services;

[0040] Supports multi-scenario applications, including: tax consulting, tax declaration, tax planning, and tax risk management.

[0041] Furthermore, the business conversations generated by the tax AI agent for non-standard consultations are collected, and the tax AI agent is continuously optimized and updated according to the generated business conversations, including:

[0042] Regularly evaluate the performance of the intelligent agent, and optimize and adjust the intelligent agent based on the evaluation results; track the latest changes in tax policies and regulations, and update the tax knowledge base in a timely manner to ensure the accuracy and timeliness of the intelligent agent.

[0043] In a second aspect, the present application provides a terminal, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect.

[0044] In a third aspect, the present application provides a computer storage medium storing computer executable instructions, wherein the computer executable instructions are used to execute the method described in the first aspect.

[0045] 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

[0046] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0047] Figure 1 A flowchart of a tax AI agent construction method based on a large language model according to an embodiment of the present application;

[0048] Figure 2 Another flowchart of the tax AI agent construction method based on a large language model according to an embodiment of the present application;

[0049] Figure 3 This is a schematic diagram of the implementation of the capability configuration unit in the tax AI agent construction method based on a large language model in an embodiment of the present application;

[0050] Figure 4 A schematic diagram of the composition of a terminal that applies the tax AI agent construction method based on a large language model according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0052] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0053] With the rapid advancement of technology, intelligent agents are currently undergoing unprecedented changes. Currently, intelligent agents are no longer just tools for executing preset instructions, but are gradually showing perception, understanding, reasoning, learning and decision-making capabilities similar to or even beyond those of humans.

[0054] Some intelligent agents can capture and analyze multimodal information from the environment in real time, including visual, auditory, tactile and even emotional signals, through highly integrated sensor networks, thus achieving comprehensive perception of complex environments. Intelligent agents are gradually being integrated into people's daily lives and work. They appear in more diverse forms, such as smart assistants, home robots, medical robots, self-driving cars, etc., bringing unprecedented convenience and experience to humans.

[0055] Referring to the above description, in order to provide efficient and low-cost tax consulting services, after fully understanding and considering different implementation methods, the present invention provides a technical solution for building a tax AI agent based on a large language model, including a tax AI agent construction method and a tax AI agent based on a large language model.

[0056] The tax AI agent based on the large language model is deployed on a web page, mini-program or app, and communicates with users through a GUI or interactive interface to form a tax consultation dialogue. Through the tax consultation dialogue, a large number of users can be provided with highly professional and consistent tax consultation services concurrently, which is efficient and low-cost.

[0057] like Figure 1 As shown, the tax AI agent construction method based on a large language model in an embodiment of the present invention includes:

[0058] S100: construct a tax knowledge base, and perform large language model training and optimization based on the constructed tax knowledge base to obtain a large language model;

[0059] S200: Using the designed agent architecture, build a tax AI agent based on a large language model;

[0060] S300: collecting standard conversations generated by the tax AI agent for standard consultation, and training and optimizing the tax AI agent according to the generated standard conversations until multiple threshold parameters are met;

[0061] S400: Deploy and apply tax AI agents, and continuously optimize and update the tax AI agents based on the collected business conversations generated by the tax AI agents for non-standard consultations.

[0062] Furthermore, the construction of the tax knowledge base includes:

[0063] Collect and organize tax-related laws, regulations, policy documents, and case materials to build a complete tax knowledge base to support the semantic understanding and reasoning of intelligent agents to clean, deduplicate, and segment the collected tax data to obtain pre-processed tax data.

[0064] Furthermore, the tax knowledge base constructed is used to perform large language model training and optimization to obtain a large language model, including:

[0065] Use deep learning to train large language models;

[0066] Input the preprocessed tax data into the large language model, adjust the model parameters and introduce external knowledge;

[0067] The pre-processed tax data comprises: voice, picture, video, text; the pre-processed tax data is carried out in the form of a question-and-answer dialogue.

[0068] Furthermore, the designed agent architecture includes: a data input layer, a processing layer, and an output layer;

[0069] The data input layer receives tax inquiries and tax declaration instructions input by users, and obtains relevant tax data from external data sources;

[0070] The processing layer combines the large language model and the tax knowledge base to process and analyze the input data and generate corresponding tax treatment suggestions or results;

[0071] The output layer displays tax processing suggestions or results to users in a user-friendly manner, and the tax processing suggestions or results include: tax reports and tax planning suggestions.

[0072] Furthermore, the construction of a tax AI agent based on a large language model includes:

[0073] Combine the large language model with the tax knowledge base, dialogue configuration unit, capability configuration unit, knowledge base configuration unit, configuration information management unit, user feedback unit, and knowledge update unit to form a tax AI agent;

[0074] The dialogue configuration unit is used to define the dialogue logic, question recognition, intent understanding and response generation of the intelligent agent;

[0075] The capability configuration unit allows developers to configure various tax-related capabilities for the agent according to business needs, including: tax calculation, policy interpretation, and risk assessment;

[0076] The knowledge base configuration unit is used to build and maintain the local proprietary tax knowledge base of the intelligent agent;

[0077] The configuration information management unit is used to store and manage all configuration information of the agent, including dialogue configuration, capability configuration and knowledge base configuration;

[0078] The user feedback unit collects feedback from users during use;

[0079] The knowledge updating unit is used to monitor changes in tax policies and automatically or assist developers in updating the knowledge base of the intelligent agent.

[0080] Furthermore, the capability configuration unit performs modular design to decompose the functions of the agent, and the decomposed independent functional modules include a tax consulting module, a tax planning module, and a tax declaration module;

[0081] The capability configuration unit adopts a plug-in architecture, allowing developers to add new functional modules as needed and supporting plug-in functional expansion;

[0082] The capability configuration unit integrates multiple API interfaces and provides API interfaces to allow third-party services and tools to be integrated with the agent;

[0083] The capability configuration unit adopts a microservice architecture so that each independent functional module can be independently deployed, expanded and maintained.

[0084] Furthermore, multiple threshold parameters are met, including: meeting at least one of the following threshold parameters: accuracy threshold; response time; user satisfaction; error rate; data update frequency.

[0085] Furthermore, the tax AI agent is collected to generate standard conversations for standard consultations, and the tax AI agent is trained and optimized according to the generated standard conversations until multiple threshold parameters are met, including:

[0086] Train the agent using a large amount of tax-related training data to improve its semantic understanding and generation capabilities;

[0087] During the training process, the model parameters and algorithms are continuously optimized based on the standard conversations generated for standard consultations to improve the performance and generalization ability of the agent; standard consultations are compiled using a large amount of tax data to generate conversation templates for standard consultations;

[0088] Based on the standard conversations and corresponding conversation templates generated by the tax AI agent for standard consultation, optimize the model parameters and algorithms until multiple threshold parameters are met;

[0089] Non-standard consulting includes: interpretation of tax policies, tax declaration guidance, and tax planning advice.

[0090] Furthermore, the deployment and application of the tax AI agent includes:

[0091] Deploy the trained agents to various actual management systems to provide users with intelligent tax services;

[0092] Supports multi-scenario applications, including: tax consulting, tax declaration, tax planning, and tax risk management.

[0093] Furthermore, the business conversations generated by the tax AI agent for non-standard consultations are collected, and the tax AI agent is continuously optimized and updated according to the generated business conversations, including:

[0094] Regularly evaluate the performance of the intelligent agent, and optimize and adjust the intelligent agent based on the evaluation results; track the latest changes in tax policies and regulations, and update the tax knowledge base in a timely manner to ensure the accuracy and timeliness of the intelligent agent.

[0095] In this way, the intelligent agent has an adaptive learning mechanism that can automatically adjust the parameters and strategies of its functional modules according to the user's interaction data and changes in tax policies.

[0096] In this way, the agent executes the personalized recommendation algorithm and is demonstrated as a personalized recommendation system that is able to recommend the most appropriate tax planning and filing strategies based on the user's tax history and preferences obtained from the conversation with the user.

[0097] In this way, the agent performs intelligent error detection and correction, which helps to improve the accuracy of tax declaration.

[0098] like Figure 2 As shown, the tax AI agent construction method based on a large language model of an embodiment of the present invention includes the following steps.

[0099] Specifically, the tax knowledge base is constructed, including:

[0100] Collect and organize tax-related laws, regulations, policy documents, and case materials to build a complete tax knowledge base to support the semantic understanding and reasoning of intelligent agents.

[0101] Naturally, the tax knowledge base includes aspects such as tax policies, tax declarations, and tax planning; accordingly, the collected tax data is pre-processed by cleaning, deduplication, word segmentation, etc. to improve data quality.

[0102] Specifically, based on the constructed tax knowledge base, a large language model is trained and optimized to obtain a large language model, including: using deep learning to train the large language model. During the training process, the pre-processed tax data is input into the large language model so that the model learns the knowledge and rules in the tax field; the generalization ability and accuracy of the model are improved by adjusting model parameters, introducing external knowledge, etc. The pre-processed tax data includes: voice, picture, video, text; the pre-processed tax data is used to conduct a conversation in the form of a question-and-answer dialogue.

[0103] Specifically, the designed intelligent agent architecture includes: a data input layer, a processing layer, and an output layer; the data input layer receives tax query instructions and tax declaration instructions input by users, and obtains relevant tax data from external data sources; the processing layer combines a large language model and a tax knowledge base to process and analyze the input data, and generate corresponding tax processing suggestions or results; the output layer displays the processing results to users in a user-friendly manner, such as generating tax reports, providing tax planning suggestions, etc.

[0104] Specifically, we build a tax AI agent based on a large language model, including:

[0105] Combine the large language model with the tax knowledge base, dialogue configuration unit, capability configuration unit, knowledge base configuration unit, configuration information management unit, user feedback unit, and knowledge update unit to form a tax AI intelligent entity.

[0106] Specifically, the dialogue configuration unit is used to define the dialogue logic, problem recognition, intent understanding and response generation of the intelligent agent; the capability configuration unit allows developers to configure various tax-related capabilities for the intelligent agent according to business needs, such as tax calculation, policy interpretation, risk assessment, etc.; the knowledge base configuration unit is used to build and maintain the local proprietary tax knowledge base of the intelligent agent; the configuration information management unit is used to store and manage all configuration information of the intelligent agent, including dialogue configuration, capability configuration and knowledge base configuration; the user feedback unit provides developers with improvement directions by collecting feedback from users during use (such as satisfaction evaluation, problem feedback, suggestions, etc.); the knowledge update unit is used to monitor changes in tax policies and automatically or assist developers in updating the knowledge base of the intelligent agent.

[0107] Among them, the capability configuration unit is the key to the expansion of intelligent agent functions, allowing the intelligent agent to expand and optimize its functions according to changes in user needs and tax environment.

[0108] like Figure 3 As shown, the capability configuration unit implements modular design and decomposes the functions of the intelligent body. The decomposed functional modules include independent modules such as tax consulting module, tax planning module, tax declaration module, etc. Each module can be independently upgraded and optimized.

[0109] like Figure 3 As shown, the capability configuration unit adopts a plug-in architecture, allowing developers to add new functional modules as needed and supporting plug-in functional expansion.

[0110] like Figure 3 As shown, the capability configuration unit integrates multiple API interfaces and provides API interfaces to allow third-party services and tools to be integrated with the intelligent agent to extend its functionality.

[0111] like Figure 3 As shown, the capability configuration unit adopts a microservice architecture, so that each independent functional module can be independently deployed, expanded and maintained.

[0112] Specifically, developing a capability configuration unit includes the following steps:

[0113] a. Demand analysis: Analyze the latest needs and trends in the tax field and determine the functions that need to be expanded.

[0114] b. Module development: Develop new functional modules or optimize existing modules based on the results of demand analysis.

[0115] c. Integration testing: Integrate new modules into the agent and perform tests to ensure the stability and compatibility of new functions.

[0116] d. User feedback: Collect user feedback during actual use and evaluate the practicality and effectiveness of new features.

[0117] e. Continuous iteration: Continuously iterate and optimize functional modules based on user feedback and market changes.

[0118] In the above, multiple implementation methods are provided for the capability configuration unit, including modular design, plug-in architecture, API integration and microservice architecture; the intelligent agent realizes features such as adaptive learning mechanism, personalized recommendation system and intelligent error detection and correction.

[0119] Specifically, the multiple threshold parameters include: an accuracy threshold; specifically, determining the accuracy threshold of the tax advice and declaration provided by the intelligent agent to be 98% to ensure high-quality service.

[0120] The multiple threshold parameters include: response time; specifically, the maximum time threshold for the agent to respond to user queries is determined to be 5 seconds, so that a response can be made within 5 seconds to provide fast service.

[0121] The multiple threshold parameters include: user satisfaction; specifically, the threshold of user satisfaction is determined to be 96%, which is used as an important indicator for evaluating the performance of the intelligent agent.

[0122] The multiple threshold parameters include: error rate; specifically, determining that the maximum error rate allowed by the intelligent agent in tax declaration is less than 0.1% to ensure tax compliance.

[0123] The multiple threshold parameters include: data update frequency; specifically, the frequency threshold for updating tax laws and policies data is determined to be at least once every two days to maintain the timeliness of the information.

[0124] Accordingly, the standard conversations generated by the tax AI agent for standard consultation are collected, and the tax AI agent is trained and optimized based on the generated standard conversations until multiple threshold parameters are met, including:

[0125] The agent is trained using a large amount of tax-related training data to improve its semantic understanding and generation capabilities. During the training process, the model parameters and algorithms are continuously optimized based on standard conversations generated for standard consultations to improve the performance and generalization capabilities of the agent.

[0126] Specifically, a large amount of tax data is used to compile standard consultations and generate conversation templates for standard consultations. Based on the standard conversations and corresponding conversation templates generated by the tax AI agent for standard consultations, the model parameters and algorithms are optimized until multiple threshold parameters are met.

[0127] Specifically, the non-standard consulting includes: interpretation of tax policies, tax declaration guidance, and tax planning advice.

[0128] Specifically, the deployment and application of tax AI agents include:

[0129] Deploy the trained intelligent agents to various actual management systems to provide users with intelligent tax services; support multi-scenario applications, including: tax consulting, tax declaration, tax planning, and tax risk management.

[0130] Specifically, based on the collected business conversations generated by the tax AI agent for non-standard consultations, the tax AI agent is continuously optimized and updated based on the generated business conversations, including:

[0131] Regularly evaluate the performance of the intelligent agent, and optimize and adjust the intelligent agent based on the evaluation results; track the latest changes in tax policies and regulations, and update the tax knowledge base in a timely manner to ensure the accuracy and timeliness of the intelligent agent.

[0132] In summary, the tax AI agent construction method based on a large language model in the embodiment of the present invention has the following characteristics:

[0133] (1) Improve tax processing efficiency: By automating the processing of tax affairs, manual operation time can be significantly reduced and processing efficiency can be improved.

[0134] (2) Reduce error rates: Utilize the precise semantic understanding and generation capabilities of large language models to reduce human errors and improve the accuracy of tax processing.

[0135] (3) Enhanced tax planning capabilities: Combined with the tax knowledge base, the intelligent agent can provide users with scientific and reasonable tax planning suggestions to help users optimize their tax structure.

[0136] (4) Improve user experience: Display processing results in a user-friendly manner to provide a convenient tax service experience.

[0137] Thus, the tax AI agent construction method based on the large language model of the embodiment of the present invention constructs a tax AI agent through extensive and in-depth data collection, data processing, model optimization, agent construction, user feedback and other links. The constructed tax AI agent based on the large language model has rich tax knowledge, can provide personalized tax consultation and services according to the actual needs of users, and can provide users with timely, accurate and efficient tax assistance, greatly improving the efficiency and accuracy of tax processing.

[0138] The tax AI agent construction method based on the large language model in the embodiment of the present invention constructs a tax AI agent based on a comprehensive analysis of the existing technology, through in-depth data collection, data processing, model optimization, agent construction, user feedback and other links. The agent not only has rich tax knowledge, but also has a high degree of personalized service capabilities, and can provide customized tax consultation and services according to the actual needs of users.

[0139] Furthermore, the tax AI agent of the present invention realizes accurate interpretation and rapid updating of tax policies through the application of deep learning and natural language processing technologies, ensuring that users can grasp the latest tax information in a timely manner.

[0140] At the same time, the intelligent agent can also provide personalized tax declaration guidance and tax planning suggestions based on the user's specific circumstances, helping users to reduce their tax burden legally and compliantly.

[0141] In addition, the tax AI agent also has the ability of self-learning and closed-loop feedback optimization. It can continuously learn from user feedback and actual operations, and continuously improve service quality and efficiency.

[0142] The present application also provides a terminal to execute the method. Figure 4 It shows a schematic diagram of a terminal provided by some embodiments of the present application. Figure 4 As shown, the terminal 8 includes: a processor 800, a memory 801, a bus 802 and a communication interface 803, and the processor 800, the communication interface 803 and the memory 801 are connected via the bus 802; the memory 801 stores a computer program that can be run on the processor 800, and the processor 800 executes the method provided by any embodiment described in the present application when running the computer program.

[0143] The memory 801 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 803 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0144] The bus 802 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 801 is used to store programs, and the processor 800 executes the program after receiving an execution instruction. The method disclosed in any implementation of the embodiment of the present application may be applied to the processor 800, or implemented by the processor 800.

[0145] The processor 800 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the method can be completed by the hardware integrated logic circuit or software instructions in the processor 800. The processor 800 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor are combined and executed. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 801, and the processor 800 reads the information in the memory 801 and completes the steps of the method in combination with its hardware.

[0146] The terminal provided in the embodiment of the present application and the method in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0147] The embodiments of the present application also provide a computer-readable storage medium corresponding to the method provided by the embodiments, wherein the computer-readable storage medium is a CD on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it will execute the method provided by any of the embodiments.

[0148] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0149] The computer-readable storage medium provided by the embodiments of the present application is based on the same inventive concept as the method of the embodiments of the present application, and has the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0150] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these adjustments and modifications of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these changes and modifications.

Claims

1. A tax AI agent construction method based on a large language model, characterized in that: include: Build a tax knowledge base, and perform large language model training and optimization based on the built tax knowledge base to obtain a large language model; Using the designed agent architecture, we build a tax AI agent based on a large language model. Collect standard conversations generated by the tax AI agent for standard consultations, and train and optimize the tax AI agent based on the generated standard conversations until multiple threshold parameters are met; Deploy and apply tax AI agents, and continuously optimize and update tax AI agents based on the collected business conversations generated by the tax AI agents for non-standard consultations.

2. The tax AI agent construction method based on a large language model as claimed in claim 1 is characterized in that: The construction of the tax knowledge base includes: Collect and organize tax-related laws, regulations, policy documents, and case materials to build a complete tax knowledge base to support the semantic understanding and reasoning of intelligent agents to clean, deduplicate, and segment the collected tax data to obtain pre-processed tax data.

3. The tax AI agent construction method based on a large language model as claimed in claim 2 is characterized in that: The tax knowledge base constructed above is used to perform large language model training and optimization to obtain a large language model, including: Use deep learning to train large language models; Input the preprocessed tax data into the large language model, adjust the model parameters and introduce external knowledge; The pre-processed tax data comprises: voice, picture, video, text; the pre-processed tax data is carried out in the form of a question-and-answer dialogue.

4. The tax AI agent construction method based on a large language model as claimed in claim 3 is characterized in that: The designed intelligent agent architecture includes: data input layer, processing layer, and output layer; The data input layer receives tax inquiries and tax declaration instructions input by users, and obtains relevant tax data from external data sources; The processing layer combines the large language model and the tax knowledge base to process and analyze the input data and generate corresponding tax treatment suggestions or results; The output layer displays tax processing suggestions or results to users in a user-friendly manner, and the tax processing suggestions or results include: tax reports and tax planning suggestions.

5. The tax AI agent construction method based on a large language model as claimed in claim 4 is characterized in that: The construction of a tax AI agent based on a large language model includes: Combine the large language model with the tax knowledge base, dialogue configuration unit, capability configuration unit, knowledge base configuration unit, configuration information management unit, user feedback unit, and knowledge update unit to form a tax AI agent; The dialogue configuration unit is used to define the dialogue logic, question recognition, intent understanding and response generation of the intelligent agent; The capability configuration unit allows developers to configure various tax-related capabilities for the agent according to business needs, including: tax calculation, policy interpretation, and risk assessment; The knowledge base configuration unit is used to build and maintain the local proprietary tax knowledge base of the intelligent agent; The configuration information management unit is used to store and manage all configuration information of the agent, including dialogue configuration, capability configuration and knowledge base configuration; The user feedback unit collects feedback from users during use; The knowledge updating unit is used to monitor changes in tax policies and automatically or assist developers in updating the knowledge base of the intelligent agent.

6. The tax AI agent construction method based on a large language model as claimed in claim 5 is characterized in that: The capability configuration unit performs modular design to decompose the functions of the intelligent agent, and the decomposed independent functional modules include a tax consulting module, a tax planning module, and a tax declaration module; The capability configuration unit adopts a plug-in architecture, allowing developers to add new functional modules as needed and supporting plug-in functional expansion; The capability configuration unit integrates multiple API interfaces and provides API interfaces to allow third-party services and tools to be integrated with the agent; The capability configuration unit adopts a microservice architecture so that each independent functional module can be independently deployed, expanded and maintained.

7. The tax AI agent construction method based on a large language model as claimed in claim 6 is characterized in that: Satisfy multiple threshold parameters, including: satisfying at least one of the following threshold parameters: accuracy threshold; response time; user satisfaction; error rate; data update frequency.

8. The tax AI agent construction method based on a large language model as claimed in claim 7 is characterized in that: The tax AI agent collects standard conversations generated for standard consultations, and trains and optimizes the tax AI agent based on the generated standard conversations until multiple threshold parameters are met, including: Train the agent using a large amount of tax-related training data to improve its semantic understanding and generation capabilities; During the training process, the model parameters and algorithms are continuously optimized based on the standard conversations generated for standard consultations to improve the performance and generalization ability of the agent; standard consultations are compiled using a large amount of tax data to generate conversation templates for standard consultations; Based on the standard conversations and corresponding conversation templates generated by the tax AI agent for standard consultation, optimize the model parameters and algorithms until multiple threshold parameters are met; Non-standard consulting includes: interpretation of tax policies, tax declaration guidance, and tax planning advice.

9. The tax AI agent construction method based on a large language model as claimed in claim 8, characterized in that: The deployment and application of tax AI agents include: Deploy the trained agents to various actual management systems to provide users with intelligent tax services; Supports multi-scenario applications, including: tax consulting, tax declaration, tax planning, and tax risk management.

10. The tax AI agent construction method based on a large language model as claimed in claim 9, characterized in that: The method of continuously optimizing and updating the tax AI agent based on the collected business conversations generated by the tax AI agent for non-standard consultations and the generated business conversations includes: Regularly evaluate the performance of the intelligent agent, and optimize and adjust the intelligent agent based on the evaluation results; track the latest changes in tax policies and regulations, and update the tax knowledge base in a timely manner to ensure the accuracy and timeliness of the intelligent agent.

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