Battery database access system and method based on Dify and large model question and answer mechanism

Abstract

The invention relates to a battery database access system and method based on Dify and a large model question and answer mechanism, and the system comprises a front end interaction module which is responsible for carrying out information interaction with a user, receiving a natural language question of the user, and transmitting the question to a rear end for processing; the Dify agent management module is responsible for task distribution and user intention recognition; the Prompt generation and scheduling module is responsible for converting the user problem into a query request which can be understood and processed by the system; the database interface module is responsible for being connected with a new energy battery database; the large model generation module is used for generating natural language answers of the user questions through a large language model; and the context management module is responsible for storing and managing context information in multiple rounds of conversations. A user can intelligently interact with the new energy battery database through a natural language, and can easily and efficiently access information in the real-time database.

Description

Technical Field

[0001] The present invention relates to the technical field of database query, and in particular to a battery database access system and method based on Dify and a large model question-and-answer mechanism. Background Art

[0002] New energy batteries, especially lithium batteries, solid-state batteries, and hydrogen fuel cells, have become a key enabling technology for the revolution in electric vehicles, energy storage systems, and mobile devices. With the continuous advancement of technology, battery products have become increasingly diverse, and the amount of data has increased dramatically. Multi-dimensional data on each battery, including charge and discharge efficiency, cycle life, material composition, performance degradation, and environmental adaptability, is recorded in dedicated databases. However, current access and utilization of new energy battery databases present certain challenges:

[0003] High operational threshold: Most current databases use traditional SQL, NoSQL, and other formats to store data. Ordinary users need to have certain database query capabilities to extract valid data.

[0004] Low data access efficiency: The data in the database is highly structured, but often users' needs are not limited to simple SQL queries, but more complex queries and analyses, which require users to have a high level of technical background.

[0005] Lack of intelligent question-answering: Existing database query systems mainly rely on fixed query templates and cannot adapt to the flexible and changing questions raised by users. Users cannot directly interact with the database through natural language. Summary of the Invention

[0006] The purpose of the present invention is to provide a battery database access system and method based on Dify and large model question-answering mechanism to solve the above-mentioned problems.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A battery database access method based on Dify and a large-model question-answering mechanism: First, the user inputs a natural language question; second, it is converted into a database query; finally, a natural language answer is generated through a large language model.

[0009] Furthermore, the method specifically includes the following steps: S1: the user inputs a natural language question through the front-end interaction module;

[0010] S2: The agent management module identifies the user's intention based on the question;

[0011] S3: Generates a request that matches the database query through Prompt based on the question type;

[0012] S4: The database interface module receives the query request, executes the SQL query, and returns the query results;

[0013] S5: The large model generation module combines the query results with the user's questions to generate natural language answers;

[0014] S6: Return the generated answer to the user.

[0015] Furthermore, after step S6, the user can continue to ask questions and continue to track the conversation with the support of the context management module.

[0016] A battery database access system based on Dify and a large model question-answering mechanism includes: a front-end interaction module responsible for information interaction with users, receiving users' natural language questions, and passing the questions to the back-end for processing;

[0017] Dify agent management module, responsible for task distribution and user intent recognition;

[0018] The prompt generation and scheduling module is responsible for converting user questions into query requests that the system can understand and process;

[0019] Database interface module, responsible for connecting with the new energy battery database;

[0020] The large model generation module generates natural language answers to user questions through a large language model;

[0021] The context management module is responsible for storing and managing context information in multiple rounds of dialogue.

[0022] Furthermore, in the Dify agent management module, intent recognition is performed through the BERT model.

[0023] Furthermore, multimodal input is combined to determine user intent, and key information is identified through entity recognition technology.

[0024] Furthermore, in the Prompt generation and scheduling module, a query request is dynamically generated according to the type of user question through a preset Prompt template.

[0025] Furthermore, the database interface module supports multiple types of databases, including relational databases and NoSQL databases.

[0026] Furthermore, the large model generation module receives the results of the database query and combines them with the user question to generate a complete answer.

[0027] Furthermore, in the context management module, a Redis database is used to store context information, and a sliding window technology is used to save the most recent N rounds of conversations.

[0028] Beneficial effects of the present invention:

[0029] This battery database access system, based on Dify and a large-scale model question-and-answer mechanism, comprises multiple modules. Each module intelligently processes user natural language input, converts it into database queries, and generates natural language responses using a large language model. The large language model, combined with the database, allows users to easily input queries and ask flexible and diverse questions. Users intelligently interact with the new energy battery database through natural language, efficiently accessing information in the real-time database. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a logical architecture diagram of the battery database access system based on Dify and the large model question-answering mechanism of the present invention;

[0031] Figure 2 It is a processing flow chart of the battery database access system based on Dify and large model question-answering mechanism of the present invention;

[0032] Figure 3 This is a screenshot of the overall answer content in scenario one;

[0033] Figure 4 This is a screenshot of the overall answer content in another scenario. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0035] Example:

[0036] like Figure 1-Figure 2 As shown, the battery database access system based on Dify and a large-scale model question-answering mechanism consists of multiple modules, each of which plays a different role in the entire system. Each module of the system intelligently processes the user's natural language input, converts it into a database query, and finally generates a natural language answer using a large language model.

[0037] 1. Front-end interaction module

[0038] The front-end interaction module is responsible for interacting with users, receiving natural language questions, and passing them to the back-end for processing. This module supports a web interface and can adapt to different interaction methods based on user input methods. The front-end interface design should be simple and intuitive, ensuring that users can easily enter questions and receive system-generated answers.

[0039] For example, on the web interface, the user enters the question "What is the cycle life of the X123 battery at room temperature?" The system recognizes the question and processes it through the back-end intelligent agent, and finally generates an answer and returns it to the user.

[0040] 2. Dify intelligent agent management module

[0041] The Dify Agent Management module is the core of the system, responsible for task distribution, user intent recognition, tool invocation, and more. Based on a multi-layered deep learning model and employing advanced natural language processing (NLP) technology, this module ensures efficient and flexible query processing by refining the task distribution mechanism.

[0042] Technical implementation:

[0043] (1) Intent Recognition:

[0044] The system first uses the BERT (Bidirectional Encoder Representations from Transformers) model for intent recognition. The BERT model effectively captures contextual information and performs fine-grained classification of user input based on a pre-trained semantic model. To improve recognition accuracy, the system combines multimodal input (such as text, images, and tables) to determine user intent and identifies key information (such as battery model and query parameters) through neural network entity recognition (NER).

[0045] Using the output of the BERT model, the intent probability is assigned to each category using the Softmax activation function:

[0046] p intent =Softmax(f(Q))

[0047] Among them, f(Q) represents the question vector after processing by the large model, p is the probability value of each intent category, and the task category is determined by calculating the maximum value.

[0048] (2) Task routing and module selection:

[0049] Task distribution is dynamically adjusted using a reinforcement learning (RL) model. Each task path (e.g., database query, large language model generation, etc.) has a corresponding "reward function," which is dynamically adjusted based on factors such as task complexity, execution time, and return accuracy. The application of reinforcement learning allows task routing to not only dynamically adjust based on user input, but also gradually optimize task allocation based on historical data. Reinforcement learning achieves dynamic routing selection by updating the Q-value function:

[0050]

[0051] Among them, Q task (s,a) is the Q value of selecting action a in task state s, r is the immediate reward, γ is the discount factor, and α is the learning rate.

[0052] 1.3Prompt generation and scheduling module

[0053] The prompt generation and scheduling module is responsible for converting user questions into queries that the system can understand and process. The system dynamically generates queries based on the type of user question using preset prompt templates. Each query type (such as battery life or charge rate) corresponds to a specific template, and the variables in the template are dynamically populated based on the user's question.

[0054] For example, for the question "What is the cycle life of battery X123 at high temperatures?", the system automatically fills in the battery model and ambient temperature information through a prompt template, generating a standardized query request: "Query the cycle life of battery X123 at high temperatures."

[0055] 1.4 Database Interface Module

[0056] The database interface module is responsible for connecting to the new energy battery database and supports multiple types of databases, including relational databases (such as MySQL and PostgreSQL) and NoSQL databases (such as MongoDB). This module receives query requests from the prompt generation module, executes the query operation, and returns the query results.

[0057] For example, when the system generates a query request: "Query the cycle life of battery X123 in a high-temperature environment," the database interface module will query the database through SQL and return the relevant data: "The cycle life of battery X123 in a high-temperature environment is 560 times."

[0058] 1.5 Large model generation module

[0059] The large model generation module generates natural language answers to user questions using a large language model (such as GPT-4). This module receives the results of database queries and combines them with the user's question to generate a complete answer. The large language model ensures that the generated answers are both accurate and natural and fluent during the natural language generation process.

[0060] For example, after receiving the database query result "The cycle life of the X123 battery in a high-temperature environment is 560 times," the large language model module converts it into a user-friendly natural language answer: "The cycle life of the X123 battery in high-temperature conditions is approximately 560 times."

[0061] 1.6 Context Management Module

[0062] Functional Description: The context management module is responsible for storing and managing contextual information in multi-round conversations, ensuring that the system can understand the user's intentions and generate logical responses during long-term interactions.

[0063] Technical implementation:

[0064] Context storage and retrieval:

[0065] The system uses a Redis database to store contextual information and a sliding window technique to preserve the most recent N rounds of conversation. When a user asks a question, the system automatically retraces the context, extracts relevant information, and generates a more appropriate response based on it.

[0066] Context update formula:

[0067] The system automatically adjusts the context based on the window size N and the conversation history:

[0068] C context =SlidingWindow(H dialog ,N)

[0069] Among them, H dialog is the history of the dialogue, N is the window size, C context Represents the current context.

[0070] The working principle of the system of the present invention is as follows:

[0071] (1) User questions: Users input natural language questions through the front-end interactive module, such as "What is the cycle life of the X123 battery at room temperature?"

[0072] (2) Agent intention recognition: The agent management module recognizes the user's intention based on the question and determines that the query targets are "battery model" and "cycle life".

[0073] (3) Generate database query request: Based on the question type, the system generates a request that matches the database query through prompt, for example:

[0074] SELECT cycle number FROM battery database WHERE model = 'X123' AND temperature = 'normal temperature';

[0075] (4) Database query: The database interface module receives the query request, executes the SQL query, and returns the query results.

[0076] (5) Natural language answer generation: The large model generation module combines the query results with the user's question and generates a natural language answer: "The cycle life of the X123 battery at room temperature is 560 times."

[0077] (6) User response: The system returns the generated answer to the user, who can continue to ask questions, and the system will continue to track the conversation with the support of the context management module.

[0078] Example 1:

[0079] In a real-world application scenario, a battery R&D team needs to quickly query battery data at different times. Traditional methods require complex SQL queries and data analysis, but the system of this invention automatically generates query requests and returns results simply by asking questions in natural language.

[0080]

[0081]

[0082] Overall answer: Figure 3 As shown in the screenshot.

[0083] Example 2: Multi-round dialogue with memory function

[0084]

[0085] Overall answer: Figure 3 As shown in the screenshot.

[0086] Compared with existing database query systems and AI question-answering systems, this invention has the following essential differences and technical advantages:

[0087] Key Difference 1: The “Bridging Mechanism” Between Large Language Models and Structured Databases

[0088] Currently, large models are mostly used for plain text semantic tasks and cannot directly access real-time databases. This paper introduces a Prompt template-driven + DSL-to-SQL + data backfill mechanism to enable large models to have real-time data access capabilities. Through DifyAgent unified scheduling, a closed-loop linkage of "semantics-structure-semantics" is achieved.

[0089] Key Difference 2: Agent Routing Mechanism Based on Task Intent

[0090] Different from the existing "fixed question and answer template" system, the present invention uses an intelligent agent to autonomously identify the intention of the question and dynamically select the processing path (database query / preset answer / external API call); supports complex task scheduling and free combination between modules, and improves the scalability and intelligence of the system.

[0091] Key difference three: Optimized design based on proprietary knowledge in the new energy battery field

[0092] Prompt templates, field mappings, and answer corpus are all designed specifically for common parameters of new energy batteries (model, rate, temperature, lifespan, etc.);

[0093] This makes the model's understanding of questions in this vertical field and its professional answers significantly better than general systems.

[0094] The present invention is not limited to the above-mentioned optimal implementation mode. Anyone can derive other forms of products under the inspiration of the present invention. However, no matter what changes are made in the shape or structure, any technical solution that is the same or similar to that of the present application falls within the scope of protection of the present invention.

Claims

1. A battery database access method based on Dify and a large model question-answering mechanism, characterized by: First, the user inputs a natural language question; second, it is converted into a database query; finally, a natural language answer is generated through a large language model.

2. The battery database access method based on Dify and large model question-answering mechanism according to claim 1 is characterized in that: Specifically, it includes the following steps: S1: the user inputs a natural language question through the front-end interaction module; S2: The agent management module identifies the user's intention based on the question; S3: Generates a request that matches the database query through Prompt based on the question type; S4: The database interface module receives the query request, executes the SQL query, and returns the query results; S5: The large model generation module combines the query results with the user's questions to generate natural language answers; S6: Return the generated answer to the user.

3. The battery database access method based on Dify and large model question-answering mechanism according to claim 1, characterized in that: After step S6 , the user can continue to ask questions and continue to track the conversation with the support of the context management module.

4. A battery database access system based on Dify and large model question-answering mechanism, characterized in that: include: The front-end interaction module is responsible for interacting with users, receiving natural language questions from users, and passing the questions to the back-end for processing; Dify agent management module, responsible for task distribution and user intent recognition; The prompt generation and scheduling module is responsible for converting user questions into query requests that the system can understand and process; Database interface module, responsible for connecting with the new energy battery database; The large model generation module generates natural language answers to user questions through a large language model; The context management module is responsible for storing and managing context information in multiple rounds of dialogue.

5. The battery database access system based on Dify and large model question-answering mechanism according to claim 4 is characterized in that: In the Dify agent management module, intent recognition is performed through the BERT model.

6. The battery database access system based on Dify and large model question-answering mechanism according to claim 5 is characterized by: Combine multimodal input to determine user intent and identify key information through entity recognition technology.

7. The battery database access system based on Dify and large model question-answering mechanism according to claim 4 is characterized in that: In the Prompt generation and scheduling module, query requests are dynamically generated according to the type of user questions through a preset Prompt template.

8. The battery database access system based on Dify and large model question-answering mechanism according to claim 4 is characterized in that: The database interface module supports multiple types of databases, including relational databases and NoSQL databases.

9. The battery database access system based on Dify and large model question-answering mechanism according to claim 4, characterized in that: The large model generation module receives the results of the database query and combines them with the user's question to generate a complete answer.

10. The battery database access system based on Dify and large model question-answering mechanism according to claim 4, characterized in that: In the context management module, a Redis database is used to store context information, and a sliding window technology is used to save the most recent N rounds of conversations.

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