An offline-deployed AI agent solution

Abstract

The application discloses an AI intelligent agent system based on offline deployment and a method thereof, and relates to the technical field of artificial intelligence and data processing.The application comprises a local knowledge base module, an Agent workflow arrangement module and a personal intelligent agent space module.Through constructing a localized vector database and a retrieval enhancement generation mechanism, local closed-loop processing of sensitive data is realized;by using a visual arrangement canvas to define the task logic and tool call chain of the Agent, the automatic execution of complex business processes is supported.The application solves the problems of high data privacy leakage risk, large response delay and weak customization capability of the existing cloud big model scheme, and significantly improves the data security and processing efficiency of enterprise-level applications.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and information security technology, specifically to an AI agent solution based on offline deployment. This solution is based on the privately deployed Qwen-R1 large model and, through a "five-unified" architecture (unified computing power, unified AI, unified scheduling, unified application, and unified architecture), enables full lifecycle management of privately owned intelligent agents for individuals and enterprises. It is suitable for office automation, knowledge management, and decision support scenarios with extremely high requirements for data security and privacy protection. Background Technology

[0002] With the popularization of generative AI technology, large-scale models are increasingly being applied in fields such as office work and government affairs. However, most existing general-purpose large-scale model services adopt the public cloud SaaS model, which has the following technical bottlenecks: 1. Data security risks: Sensitive data such as office documents, meeting minutes, and certificates need to be uploaded to third-party servers, which can easily lead to the leakage of enterprise or personal privacy and fail to meet the requirements of information technology innovation standards and data compliance; 2. Strong network dependence: Existing services must maintain a real-time network connection. Once the network is interrupted or in a confidential intranet environment, the service becomes unavailable immediately; 3. Low scenario adaptability: General-purpose models lack in-depth optimization for specific industry (such as government affairs and finance) office processes (such as official document writing, meeting minutes, and PPT creation), and are difficult to integrate deeply with local OA systems and file systems. Therefore, there is an urgent need for an offline AI agent solution that can be fully deployed locally, has multimodal generation capabilities, and supports visual workflow orchestration. Summary of the Invention

[0003] The purpose of this invention is to provide an AI agent solution based on offline deployment, which solves the data security and network dependence problems in the existing technology by building a private platform that is "technology-driven, scenario-oriented, and data-empowered". 1. Technical solution: This invention provides an AI agent solution based on offline deployment. The core architecture adopts a "five-unified" design: (1) Unified computing power: adapts to localized hardware environment and provides stable and efficient inference computing power support; (2) Unified AI: based on the privately deployed Qwen-R1 large model as the underlying base; (3) Unified scheduling: responsible for parsing user intent and coordinating computing power and AI resources; (4) Unified application: provides specific office applications such as AI writing, meeting minutes, PPT generation, and visual creation; (5) Unified architecture: provides basic support for system configuration, permission management and security auditing. 2. Core functional modules: (1) Personal intelligent agent space: Provides an independent management interface for each account, including sections such as "My Intelligent Agent", "My Knowledge Base", and "My Office Applications", and supports the construction of a personal AI privacy library; (2) Local knowledge base and RAG (retrieval enhancement generation): Supports users to upload local files (documents, pictures, etc.) and connect to OA system data, and establishes an index through vector search technology to realize fast knowledge retrieval and Q&A, and supports hierarchical permission sharing (all staff / departments / sections); (3) Agent workflow orchestration: Provides a visual drag-and-drop canvas, allowing users to combine AI inference, data retrieval, condition judgment and other nodes into complex automated processes to achieve a closed loop of business logic across applications; (4) Multimodal AI office suite, including: AI writing and meeting minutes: Supports intelligent generation of official documents, summaries and notices, as well as audio transcription and minutes extraction; Intelligent creation (PPT / poster / video / audio): Supports text-to-image, text-to-video, text-to-speech and one-click generation of presentations; Intelligent vision and translation: Supports document and invoice recognition (business license, ID card, etc.) and translation of multiple document formats; (5) Security and permission management: Adopts the standard RBAC (role-based access control) model to achieve fine-grained function menus, buttons, interfaces and data permission control. 3. Workflow: (1) Initialization: Deploy the Qwen-R1 large model and vector database on the local server, and configure system parameters and permissions; (2) Knowledge Injection: Users upload local files or connect to external data sources, and the system automatically performs vectorization processing and stores them in the local knowledge base; (3) Task Execution: Users input instructions in their personal intelligent agent space (such as "Write a research report on XX"), the unified scheduling layer parses the instructions, triggers the AI ​​writing subsystem, the system retrieves relevant information from the local knowledge base, generates a draft in combination with the Qwen-R1 model, and the results are returned to the interface for display, while recording Token consumption and operation logs. 4. Statistical Analysis: The background automatically generates statistical reports such as business access volume and knowledge base call count to assist in operational decision-making. Beneficial effects

[0004] Ultimate Security: End-to-end private deployment ensures data remains within the internal network, completely eliminating the risk of sensitive information leakage; Rich Scenarios: Covers all dimensions of office scenarios, including writing, speaking, drawing, viewing, listening, searching, and browsing, significantly improving work efficiency; Flexible Expansion: Through visual workflow orchestration, even non-technical personnel can quickly build customized AI applications; Easy Management: Based on the RBAC permission system and multi-dimensional statistical reports, it achieves refined resource management and data-driven operation. Detailed Implementation

[0005] In practice, the solution software is first installed and deployed in the user's local environment (such as a physical server or private cloud). The administrator configures model parameters and assigns RBAC role permissions through the system configuration module. After logging in, the user enters their personal agent space and uploads local documents to build a knowledge base. When the user needs to handle office tasks, they select the corresponding agent application (such as agent PPT), input a topic, and the system calls the local Qwen-R1 model to generate and format the content, ultimately outputting a PPTX file for the user to download. The entire process does not require a public network connection, and all data is stored on the local hard drive. Attached Figure Description

[0006] Figure 1 A system architecture diagram provided for embodiments of the present invention; Figure 2 This is a diagram of the personal intelligent agent space function interface provided in an embodiment of the present invention.

Claims

1. An AI agent solution based on offline deployment, characterized in that, The solution is built on a privately deployed large-scale model, Qwen-R1, and includes a unified computing power layer, a unified AI engine layer, a unified scheduling layer, a unified application layer, and a unified architecture layer. The unified application layer includes a personal intelligent agent space, which is configured with a visual interactive interface for receiving natural language commands input by the user. The unified AI engine layer includes a local knowledge base management module and an agent workflow orchestration module. The local knowledge base management module stores and retrieves user-uploaded local files and real-time data. The agent workflow orchestration module defines business processes, including AI inference nodes, approval nodes, and data call nodes, through a visual drag-and-drop interface. The unified scheduling layer parses the intent of the natural language commands, calls the computing resources of the unified computing power layer, drives the unified AI engine layer to execute the corresponding intelligent agent application, and completes closed-loop processing from task planning to result generation in an offline environment, ultimately outputting structured results through the unified application layer.

2. The AI ​​intelligent agent solution based on offline deployment according to claim 1, characterized in that, The unified application layer specifically includes: an AI writing subsystem, which leverages the capabilities of the Qwen-R1 large model to provide document generation and text editing, supporting full-scenario output of work summaries and meeting notifications; an intelligent agent meeting minutes subsystem, which performs speech-to-text transcription of uploaded audio files or real-time recordings in an offline environment, and intelligently extracts and generates structured meeting minutes; an intelligent agent PPT subsystem, which generates outlines with one click based on user-input topics, and generates exportable presentations by combining multiple scenario templates; an intelligent agent vision and multimedia subsystem, which provides text-to-image, text-to-video, text-to-speech, and document / invoice information recognition services; and an intelligent agent translation subsystem, which provides real-time multilingual translation services for text, documents, audio, and images.

3. The AI ​​intelligent agent solution based on offline deployment according to claim 1, characterized in that, The local knowledge base management module specifically performs the following operations: receiving user-uploaded documents, database query results, or real-time data, and indexing and constructing the data using vector search technology; when receiving a user question, locating the most relevant content in the local knowledge base through vector retrieval, and generating an answer by combining it with the Qwen-R1 large model; the local knowledge base management module is also configured with an access control unit, supporting hierarchical access control modes such as sharing among all staff, sharing within departments, and sharing across departments.

4. The AI ​​intelligent agent solution based on offline deployment according to claim 1, characterized in that, The Agent workflow orchestration module includes: a node library containing AI inference nodes, condition judgment nodes, API call nodes, and data processing nodes; a canvas unit for users to connect the nodes into a directed acyclic graph by dragging and dropping; and an execution engine for executing the tasks of each node sequentially in the local environment according to the logical order of the directed acyclic graph, thereby realizing automated business flow across applications.

5. The AI ​​intelligent agent solution based on offline deployment according to claim 1, characterized in that, The unified architecture layer is configured with an access control system based on the RBAC model. The access control system includes: a role definition unit for defining the roles of system administrators, department administrators, and ordinary users; a permission allocation unit for binding function menu permissions, interface button permissions, interface call permissions, and data operation permissions to the corresponding roles; and an access control unit for verifying the role identity of users when they log in and controlling the user's access to system resources according to the bound permissions.

6. The AI ​​intelligent agent solution based on offline deployment according to claim 1, characterized in that, The solution also includes a statistical reporting module, which is used to collect data on business access volume, knowledge base call count, interface call volume, and token consumption, and generate multi-dimensional visual statistical charts.

7. An AI agent control method based on offline deployment, applied to the solution as described in any one of claims 1-6, characterized in that, The method Includes the following steps: S1: In an offline environment, receive user commands and data input through the visual interface of the personal intelligent agent space; S2: Use the unified scheduling layer to parse user instructions and determine the target intelligent agent application and the required workflow; S3: Call upon unified computing power layer resources to drive the unified AI engine layer to perform knowledge retrieval, AI generation, or workflow node tasks; S4: Render and display the execution results in the unified application layer, and feed the interactive data back to the local knowledge base for indexing and updating.

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