# 基于人脑与ai脑共享技能库工具脚本的可控本地安全助理平台研究与实现
**摘要：**
即使零token也能手机多平台联动电脑运行脚本自由安全地运行各种技能工具脚本，通过"自行预设"的关键词来触发运行脚本的方法达到绝对安全、高效、和自由完全掌控平台的碾压核心优势！打造十分适合机关单位、内网环境、私有部署场景的安全助理，选择不走云端ai通道！当然也可以自由选择本地部署的人工智能ai，即使没有ai照样能跑！比沙箱模式更加安全高效和极其节省成本的终极理想设定！
论文网址 [](https://doi.org/10.5281/zenodo.19500478)
git仓库 https://github.com/huajl530/Human-Machine-Shared-Skill-Library
**关键词：** 本地化助手；人机协作；技能共享；隐私计算；自动化办公
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## 1. 引言
在机关单位及关键信息基础设施领域，数据安全是智能化应用的前提。当前的云端AI助理模式存在“黑盒化”与“不可控性”，且高度依赖外部网络环境。为了解决这一痛点，本文设计了一套强调“主权可控”与“人机同构”的助理平台，将AI定位为“协作层”与“路由层”，而将核心执行能力沉淀于本地脚本。
## 2. 系统架构设计
本系统采用分层架构设计，旨在实现执行与决策的解耦：
### 2.1 命令层（Command Layer）
基于正则表达式与关键词匹配的指令集。该层允许用户通过预设关键词（如“备份”、“排版”）直接触发本地脚本，实现“零推理延迟”的精确控制。
### 2.2 路由层（Routing Layer）
系统根据输入任务的复杂度自动分流：
- **短路路由：** 匹配到精确关键词时，直接调用本地脚本。
- **智能路由：** 面对自然语言请求时，由本地轻量化模型或受控云端模型进行意图识别，并映射至共享技能库。
### 2.3 执行层（Execution Layer - 技能仓库）
由一系列独立的 Python/JavaScript 脚本组成。这些脚本对人脑（手动输入）和AI脑（自动调用）透明，确保了逻辑的一致性与可维护性。
### 2.4 安全沙箱（Security Sandbox）
针对云端AI接入，系统设立了严格的物理沙箱目录（如 `\sandbox_workspace\`）。所有AI生成的操作均受限于该目录，并通过审计日志（Guardian Log）进行实时记录与阻断。
## 3. 关键创新点
### 3.1 人机同构的技能库
不同于传统的 AI Agent 插件，本平台的技能脚本具备双重触发特性。这种设计不仅提升了系统在无网环境下的鲁棒性，还允许人类专家随时接管、审计和优化AI的执行路径。
### 3.2 极低 Token 消耗模型
通过“关键词短路”机制，约 70% 的高频重复任务无需通过 LLM 推理，极大地降低了运行成本，并消除了云端服务的延迟感。
### 3.3 声明式安全策略
系统引入了基于白名单的脚本执行策略，确保只有经过备案的本地工具才能被调用。这种“主权在人”理念，解决了 AI 生成代码在内网环境下运行的安全隐患。
## 4. 典型应用场景
1. **复杂公文自动化：** 结合 `beauty_word.py` 等脚本，实现 Word/PDF 文档的本地化一键排版与合规性检查。
2. **多维情报研判：** 通过 `smart_commander.py` 调度爬虫与本地分析模型，生成内网专用的行业研判报告。
3. **异构系统联动：** 在不改变原有内网架构的前提下，通过本地助理实现跨软件的数据流转。
## 5. 结论
本文提出的本地安全助理平台，通过“人机共享技能”的创新模式，成功探索了 AI 在高安全需求场景下的落地路径。未来研究将集中在本地小模型的端侧微调，以进一步提升复杂指令识别的精确度，同时保持系统的闭环安全性。
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# Research and Implementation of a Controllable Local Security Assistant Platform Based on Human-AI Shared Skill Libraries and Tool Scripts
**Abstract:**
This system enables safe and free execution of various skill scripts via cross-platform linkage between mobile devices and PCs, even with zero token consumption. By employing "user-preset keywords" to trigger local scripts, it achieves absolute security, high efficiency, and the ultimate advantage of complete platform control. Designed as a specialized security assistant for government agencies, intranet environments, and private deployment scenarios, it offers the choice to bypass cloud AI channels entirely. Locally deployed AI can be optionally integrated, yet the system remains fully functional without it. This represents a definitive ideal configuration—safer, more efficient, and significantly more cost-effective than traditional sandboxing modes.
**Keywords:** Local Assistant; Human-AI Collaboration; Skill Sharing; Privacy Computing; Office Automation
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## 1. Introduction
In government agencies and critical information infrastructure sectors, data security is the prerequisite for intelligent applications. Current cloud-based AI assistant models suffer from "black-box" opacity and "uncontrollability," relying heavily on external network environments. To address these pain points, this paper designs an assistant platform emphasizing "sovereignty and controllability" and "human-AI isomorphism." It positions AI as a "collaboration and routing layer," while embedding core execution capabilities within local scripts.
## 2. System Architecture Design
The system adopts a layered architecture to decouple execution from decision-making:
### 2.1 Command Layer
An instruction set based on regular expressions and keyword matching. This layer allows users to trigger local scripts directly via preset keywords (e.g., "backup," "typesetting"), achieving precise control with "zero inference latency."
### 2.2 Routing Layer
The system automatically shunts tasks based on complexity:
- **Short-circuit Routing:** Directly invokes local scripts when a precise keyword is matched.
- **Intelligent Routing:** For natural language requests, an on-device lightweight model or a controlled cloud model performs intent recognition and maps the request to the shared skill library.
### 2.3 Execution Layer (Skill Warehouse)
Consists of independent Python/JavaScript scripts. These scripts are transparent to both the human brain (manual input) and the AI brain (automated calls), ensuring consistency and maintainability of logic.
### 2.4 Security Sandbox
For cloud AI access, the system establishes strict physical sandbox directories (e.g., `\sandbox_workspace\`). All AI-generated operations are restricted to this directory and are monitored/blocked in real-time via audit logs (Guardian Log).
## 3. Key Innovations
### 3.1 Human-AI Isomorphic Skill Library
Unlike traditional AI Agent plugins, the skill scripts in this platform feature dual-triggering capabilities. This design not only enhances system robustness in offline environments but also allows human experts to take over, audit, and optimize AI execution paths at any time.
### 3.2 Ultra-low Token Consumption Model
Through the "keyword short-circuit" mechanism, approximately 70% of high-frequency repetitive tasks bypass LLM inference. This significantly reduces operating costs and eliminates the latency associated with cloud services.
### 3.3 Declarative Security Policy
The system introduces a whitelist-based script execution policy, ensuring that only verified local tools can be invoked. This "sovereignty-in-human" philosophy resolves the security risks of running AI-generated code within intranet environments.
## 4. Typical Scenarios
1. **Complex Document Automation:** Utilizing scripts like `beauty_word.py` for local one-click formatting and compliance checks of Word/PDF documents.
2. **Multidimensional Intelligence Synthesis:** Scheduling crawlers and local analysis models via `smart_commander.py` to generate industry intelligence reports for internal use.
3. **Heterogeneous System Linkage:** Enabling data flow across different software platforms through the local assistant without altering the existing intranet architecture.
## 5. Conclusion
The local security assistant platform proposed in this paper successfully explores the deployment path for AI in high-security scenarios through the innovative "human-AI shared skill" model. Future research will focus on the edge-side fine-tuning of local small models to further enhance the precision of complex instruction recognition while maintaining closed-loop system security.
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**Author's Note:** This research aims to provide a low-cost, high-security, and autonomously controlled intelligent upgrade solution for government and enterprise sectors.
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