The "Wenlu" Brain System for Multimodal Cognition and Embodied Decision-Making: A Novel Secure Architecture for Deep Integration of General Large Models and Domain Knowledge

Geng Liang^{1,2,3}

^{1}Shijiazhuang Key Laboratory of Agricultural Robot Intelligent Perception, Shijiazhuang 050035
Email: liang_geng@bupt.edu.cn (L.Geng)

As artificial intelligence rapidly penetrates diverse industries and scenarios, a critical challenge emerges: how to effectively integrate the linguistic understanding strengths of general large models with proprietary industry knowledge bases in complex real-world applications. This paper proposes an embodied brain system named "Wenlu" based on multimodal cognitive decision-making, aiming to achieve secure fusion of private knowledge with public models, unified processing of multimodal data such as images and speech, and closed-loop decision-making from cognition to automatic hardware code generation. Through a brain-inspired memory tagging and replay mechanism, the system organically combines user private data, industry-specific knowledge, and general language models to provide precise and efficient multimodal services for enterprise decision support, medical analysis, autonomous driving, robot control, and more. Compared to existing technical solutions, "Wenlu" offers significant advantages in multimodal processing, privacy security, end-to-end hardware control code generation, self-learning, and sustainable updates, laying the foundation for building a new generation of intelligent hubs.

Keywords: multimodal cognition; embodied brain; private data; general large model; automatic code generation; brain-inspired memory replay

In the evolving wave of artificial intelligence technology, large-scale language models (LLM) driven by deep learning have become important cornerstones for building intelligent systems. However, current mainstream language models mostly focus on pure text input-output patterns, with relatively weak capabilities for processing multimodal data. Simultaneously, how to efficiently integrate industry-specific knowledge with general models while protecting user private information has long been a challenge troubling both academia and industry. To fill this gap and promote the deployment of next-generation intelligent applications, this paper proposes an embodied brain system named "Wenlu," aiming to achieve a complete closed loop from multimodal cognition to automatic hardware code generation, while fully demonstrating characteristics of security and efficient compatibility in private data and industry knowledge application scenarios.

Over the past few years, challenges facing intelligent decision-making and multimodal information processing have mainly come from several aspects: First, different types of data (such as text, images, speech, sensor readings, etc.) exhibit enormous differences in dimensionality, feature distribution, and temporal characteristics, making it difficult to efficiently integrate them using a single network structure or general model. Second, deep knowledge for specific industries is often embedded in private or proprietary datasets, requiring more cautious security compliance measures. However, general large models are typically trained on massive public data sources, lacking built-in confidentiality and access control mechanisms, making direct application in sensitive domains difficult. Third, many AI systems often remain at the "perception-cognition" stage, struggling to automatically transform intelligent decisions into actual execution processes of machine control or code generation, hindering efficient deployment in embodied scenarios such as robots, wearable devices, and autonomous driving.

To effectively address these issues, the "Wenlu" system proposes a multi-level architecture design that tightly coordinates user private data decision modules, industry multimodal decision and service modules, hardware control and automatic code generation modules, and general model fusion units. Private knowledge units provide secure isolation and controlled access for user sensitive information through encrypted sandbox and tagging management approaches. Multimodal decision modules enable the system to process not only text but also comprehensively judge and reason about multi-source data such as images, speech, and sensors. The hardware control module further breaks barriers between language understanding and physical execution, automatically generating control instructions based on high-level cognitive or task descriptions, thereby enabling real-time feedback and actions on robots or other devices. The underlying general model fusion unit is responsible for overall language understanding and generation, providing powerful semantic support for all upper-level modules while deeply coupling with private knowledge bases and industry knowledge bases.

Furthermore, drawing inspiration from how biological brains tag and replay memories, the "Wenlu" system incorporates a brain-inspired memory reinforcement mechanism: when executing complex decision-making tasks, the system automatically tags key information or important reasoning paths and replays these memories during subsequent idle or offline periods for reinforcement learning, continuously optimizing multimodal understanding and reasoning performance. This brain-inspired memory mechanism not only enhances the system's deep cognition and reuse capabilities for specific scenarios but also lays the foundation for self-learning and continuous iteration.

At the application level, the "Wenlu" embodied brain system aims to cover multi-scenario and multi-industry needs: such as automatic inspection and control in industrial manufacturing, diagnostic assistance for medical imaging, environmental perception for autonomous driving, human-robot interaction for service robots, and intelligent monitoring for wearable devices. By combining industry experience with general semantic understanding, "Wenlu" can provide professional decision support and automated execution solutions for users while ensuring data security and privacy compliance. Compared to traditional systems relying on manual configuration or plugin-based invocation, it can significantly reduce communication costs and error rates while achieving self-learning and adaptation through continuous iteration.

In summary, the goal of the "Wenlu" system is to become a general platform for multimodal cognition and embodied decision-making that inherits the advantages of large language models in text understanding and generation while strengthening processing capabilities for images, sound, sensor data, and other information. It can utilize private information in secure environments while deeply coupling proprietary knowledge for various industry scenarios, and simultaneously transform intelligent decision results into automated hardware execution code, achieving a true "perception-cognition-decision-action" integrated closed loop. This paper systematically elaborates on the technical ideas of the "Wenlu" embodied brain in multimodal fusion, private information management, automatic code generation, and brain-inspired memory reinforcement, and discusses in detail its potential value across different industries and application scenarios. Through such system design, we hope to provide a new approach for transitioning AI from academic research to industrial application and to lay a feasible technical foundation for building next-generation general intelligent hubs.

2 Theoretical Background and Existing Technical Limitations

Artificial intelligence has developed rapidly over the past decade, gradually transitioning from early statistical learning models to deep learning, large-scale language models, and multimodal fusion technologies. Particularly in natural language processing (NLP), general language models based on Transformer architecture (Large Language Model, LLM) have emerged, triggering widespread discussion about the potential for "general artificial intelligence." However, the current mainstream technical system still faces numerous challenges in multimodal information integration, private data security, automatic code generation, and deep coupling of industry knowledge. To deeply analyze these problems, the following sections elaborate on existing technical backgrounds and limitations in combination with several typical solutions.

2.1 Challenges in Multimodal Fusion and Cognitive Decision-Making

Early AI applications mainly focused on single modalities, such as image recognition, speech recognition, and text generation. However, information in the real world is typically multimodal: images, audio, text, and sensor data often appear simultaneously and are interrelated. Against this background, multimodal fusion technology emerged, attempting to improve perception and decision-making accuracy by establishing unified feature spaces or semantic representations for different modal data.

Although existing multimodal technologies have achieved preliminary success in tasks such as image-text matching and visual question answering, they still show deficiencies in deep-level cognition and complex decision-making, mainly attributable to the following points:

1. Difficulty in Feature Alignment and Semantic Projection. Data from different modalities exhibit large differences in dimensionality and semantic distribution, requiring unified network structures or feature projection mechanisms. Once the alignment method between modalities is inappropriate, the model's reasoning effectiveness will be limited.

2. Lack of Cross-Modal Long-Term Memory and Reasoning Frameworks. Most multimodal fusion research focuses on short-term or one-time reasoning, unable to support long-term, multi-step cognitive processes. For example, industrial inspection or medical diagnosis often requires continuous temporal information and iterative judgment, posing higher requirements for multimodal systems.

3. Insufficient Model Interpretability and Causal Reasoning. Multimodal models often rely on large-scale training data for pattern matching and have not yet formed "common sense" or "causal" understanding of the world. When deployed in industries, the lack of interpretable mechanisms also affects user trust and decision-making safety.

2.2 Limitations of General Large Models and Bottlenecks in Industry Knowledge Fusion

Since the emergence of Transformer and its improved models, general language models trained on massive public corpora (such as the GPT family, BERT family, etc.) have achieved breakthroughs in language understanding and generation. However, directly transplanting such large models to specific industries or private data scenarios encounters several difficulties:

1. Lack of Industry-Specific Knowledge. The training data used by general large models has broad coverage but limited depth in each domain. When facing rigorous professional scenarios (such as medical diagnosis or financial analysis), the lack of deep domain knowledge significantly reduces the accuracy and reliability of model outputs.

2. Privacy and Compliance Challenges. General large models are often open-ended, with training data and inference paths lacking strict security isolation. Once user private information is mixed with public corpora, it can easily lead to data leakage or misuse. As industry regulations on data compliance and user privacy become increasingly strict, how to safely link private data with large models has become an urgent problem to solve.

3. Lack of Continuous Learning and Memory Mechanisms. Although current large models have massive parameters, their "memory" is mainly reflected in trained weight parameters and is not flexible enough. When new knowledge needs to be absorbed during operation, they often face high costs of "secondary fine-tuning" or even "large-scale retraining," making rapid iteration and knowledge accumulation impossible.

4. The "Breakpoint" from Abstract Understanding to Concrete Execution. Even if large models can accurately generate text descriptions, it is difficult to directly transform them into automatically executable hardware instructions or robot control scripts. This prevents "general models" from fully realizing their end-to-end intelligent potential in embodied scenarios.

2.3 Traditional Approaches to Robotics and Hardware Control and Their Drawbacks

In the field of robotics or hardware control, mainstream development models typically require human engineers to manually program action strategies and control logic first, translating natural language requirements into low-level instructions or API calls. This process has multiple limitations:

1. High Cost of Manual Programming and Interface Adaptation. Each robot platform or wearable device has its specific API or SDK, and transplantation and adaptation often require engineers to spend considerable time writing code and debugging, resulting in lengthy development cycles and high error rates.

2. Insufficient Adaptation to Dynamic External Environments. When the physical environment changes or device hardware is updated, programs need to be substantially modified again, making it difficult to form a dynamically adaptive closed loop.

3. Difficulty in Integrating with High-Level Semantic Reasoning. Robot control mostly remains at the level of sensor data processing and behavior planning, lacking deep understanding of high-level information such as natural language and industry knowledge, and cannot be uniformly managed with multimodal decision-making systems.

2.4 Compromise Solutions Based on Plugins/External APIs and Their Problems

To bridge the gap between general large models and domain applications, some technical practices attempt to add plugins or call external APIs on top of large models. For example, robot APIs or wearable device interfaces are encapsulated as plugins to provide extended capabilities to language models through corresponding calling examples. Although such methods provide certain hardware interaction capabilities for large models, they still have the following shortcomings in practical applications:

1. Lack of Context Management. Plugin-based solutions essentially rely on temporary API calls, lacking unified memory and reasoning management mechanisms, resulting in limited context connection capabilities for large models in multi-turn conversations or cross-modal scenarios.

2. Complex Cross-Domain Call Chains. If a task requires multiple plugins to collaborate simultaneously (for example, reading sensor data, controlling a robotic arm, and outputting diagnostic reports), the plugin call chain becomes cumbersome with high coupling and high maintenance costs.

3. Unstable Decision Results. Stability issues and call timing problems of plugin interfaces often lead to unexpected system failures or errors. Once anomalies occur, large models themselves lack global perception of hardware or external APIs and cannot self-correct.

2.5 Knowledge Graph-Based Question Answering and Decision-Making Systems

In specific industries such as medical, financial, and manufacturing, there are also question answering or reasoning systems centered on knowledge graphs. They encapsulate structured expert knowledge into retrievable graph nodes and edge relationships for auxiliary reasoning and decision-making. However, such solutions also face several constraints:

1. High Construction and Update Costs. Knowledge graphs require specialized teams for data annotation and maintenance updates, and multimodal features (images, audio, sensors, etc.) are difficult to express efficiently in graphs.

2. Lack of Strong Support for Language Generation. Compared with general large models, traditional knowledge graphs mainly handle retrieval-based question answering or logical reasoning, lacking natural language understanding and generation capabilities, and insufficient ability to present complex domain data in readable formats.

3. Inability to Achieve Automated Hardware Output. Knowledge graph-based systems focus more on "information retrieval" and "logical reasoning," with a large distance from automatically generating robot or device control instructions, making it difficult to form an embodied closed loop.

2.6 Summary and Limitation Analysis

Combining the above typical solutions, we can summarize that existing technologies have significant limitations in the following aspects:

1. Insufficient Multimodal Fusion. Whether relying on general large models or knowledge graphs, most existing systems lack the ability to simultaneously process multi-source information such as images, audio, text, and sensors for deep decision-making, or can only perform limited functional splicing through plugin-based approaches.

2. Lack of Secure and Controllable Private Data Management. Existing large models mostly ignore privacy protection needs, while traditional industry systems often lack support for open language understanding; efficient and secure integration between the two is difficult.

3. The "Disconnect" from Cognition to Execution. General large models can perform text understanding and generation, but still require manual "translation" for physical hardware control, leading to a separation between actual decision-making and execution, hindering self-learning and rapid iteration.

4. Lack of Continuous Self-Learning and Memory Reinforcement Mechanisms. Whether large models or knowledge graphs, updates and fine-tuning often require high costs. Most solutions lack brain-inspired memory tagging and replay links, unable to accumulate new experiences and knowledge in continuous interaction and application.

Thus, to break through the comprehensive barriers of multimodal cognition, private information protection, and embodied decision-making, a new architecture is needed that combines general language understanding, efficient domain knowledge integration, private data security protection, and automated hardware code generation capabilities. Driven by this background and demand, the "Wenlu" embodied brain system emerges, committed to building an intelligent hub oriented toward multimodal cognitive decision-making, focusing on private information security, and capable of directly outputting executable instructions, forming a complete closed loop from "perception-cognition-decision-action."

3 Wenlu System Architecture

The "Wenlu" embodied brain system aims to bridge the gaps between multimodal cognition, private information protection, and hardware control execution in AI applications, thereby providing more complete, flexible, and secure intelligent services for real-world scenarios. To achieve this goal, "Wenlu" adopts a multi-level, modular architecture: it includes both front-end perception and interaction, as well as a core back-end where large models and private knowledge bases are highly integrated; it also reserves modules for hardware control generation and industry knowledge docking to meet end-to-end application requirements.

The first layer is the user private knowledge understanding and question-answering decision unit, which primarily addresses how to securely use user-sensitive information and industry-specific data within large models. This module employs technical means such as secure sandboxing, encrypted storage, and tagging management to ensure that private data's read/write permissions are strictly controlled after entering the system. Once user queries touch sensitive content, the system automatically invokes specific access strategies to parse and desensitize the required private data. This unit not only effectively isolates public training data from user private data but also provides highly customized support for subsequent decision-making.

The second layer is the industry multimodal decision-making and service support unit, whose core function is to fuse industry-specific multimodal inputs (images, speech, text, sensors, etc.) and perform deep semantic-level docking with general large models. By converting image recognition, audio analysis, and text understanding into unified feature vectors or embedding representations, this module can comprehensively determine the information correlation in multi-source signals and perform deep decision-making. In high-demand fields such as industrial inspection, medical imaging analysis, or autonomous driving monitoring, this multimodal decision unit can significantly improve result accuracy and reliability while outputting richer explainable analysis reports.

The third layer is the robot manipulation and hardware device code generation unit, which serves as the bridge "from cognition to action" in the entire "Wenlu" system. When the multimodal decision unit and private knowledge decision unit collaboratively arrive at a determined execution plan, the robot manipulation unit can automatically generate low-level code scripts from high-level semantic instructions, adapting to various hardware platforms such as ROS2, wearable device APIs, and intelligent robotic arm interfaces. Through this end-to-end automation process, the system forms an integrated closed loop from natural language description, image/sensor fusion analysis to specific device execution. Unlike traditional manual programming approaches, "Wenlu" can achieve rapid migration by simply updating the adaptation layer when hardware or environmental changes occur, significantly reducing engineering costs.

The underlying foundation supporting the entire system is the deep language model base and the "general-specialty combined" knowledge fusion unit. This unit includes both the general language understanding and generation capabilities of general large models (such as DeepSeek) and integrates industry expert knowledge bases to improve question-answering and reasoning accuracy in vertical domains. Simultaneously, this unit embeds a brain-inspired memory tagging and replay mechanism: during each interaction or reasoning process, the system automatically annotates key decisions or important scenarios and replays and reinforces this information during idle or offline periods, enabling the model to establish firmer "long-term memory" for high-value knowledge and frequent tasks. This brain-inspired memory consolidation approach can significantly improve system reliability and customization capabilities after multiple iterative interactions, also providing theoretical and technical guarantees for integrating large models with private information and industry knowledge.

It is worth emphasizing that the various units are not isolated components but achieve efficient collaboration through a unified bus and communication protocol: the private knowledge decision unit can share core large model capabilities with the multimodal decision unit and call encrypted data based on permission policies; multimodal analysis results can be directly passed to the hardware control generation unit, ultimately presented as executable scripts or instructions; the underlying general model and industry knowledge base continuously iterate精华 content from multiple interactions into the model kernel through the memory tagging mechanism, providing increasingly accurate support for subsequent question-answering and decision-making.

Through this layered, modular architecture, "Wenlu" builds a closed system loop between multimodal perception, semantic decision-making, private data processing, and robot control, providing powerful and flexible solutions for intelligent applications across industries. Under this framework, the system not only possesses the ability to deeply reason about multi-source data in complex environments but can also output automated hardware execution scripts while fully protecting user-sensitive information. Ultimately, "Wenlu" is expected to become a new generation of general intelligent hub with both cross-industry scalability and personalized professional knowledge accumulation, providing reliable technical support for transitioning AI from laboratories to actual production and living scenarios.

4 Core Modules and Implementation Mechanisms

In the "Wenlu" embodied brain architecture, all modules are designed around multimodal fusion, privacy protection, and end-to-end decision-making closed loops. To more clearly demonstrate its technical ideas and internal logic, this section provides an academic and systematic exposition of the structure, mechanisms, and internal coordination relationships of four core modules according to system functions and implementation processes.

4.1 User Private Knowledge Decision Unit

4.1.1 Module Functions

This module focuses on solving the problem of how to securely fuse user-sensitive information with general large models, ensuring both strict access and processing controls for confidential data within the system and meeting the needs for efficient inference and customized question-answering. Its core functions include:

1. Sandbox-style encrypted storage and tagging management of private data. User or leadership questions external network (public knowledge sources or interfaces) external large model/external API/external resources This is sample label internal security zone (local deployment) secure sandbox (filtering and desensitization control) local large model (DeepSeek, etc.) + brain-inspired computing private data (encrypted storage) fusion decision unit (local + external knowledge) secure output (return to user) return external results optional call to external network Composition Four (general model fusion unit) Composition Three (hardware control generation unit) large model base + brain-inspired computing architecture automatic code generation (robot, wearable device) robot or hardware device private data (encrypted storage) Composition One (user private knowledge decision unit) private decision unit (secure question-answering, permission policy) multimodal input (industrial, medical, other) Composition Two (industry multimodal decision unit) multimodal decision unit (text, image, audio, sensor)

2. Security sandbox and encryption strategies. Adopt symmetric or asymmetric encryption algorithms for private data storage, supplemented by role-based access control (RBAC). Construct a "private knowledge base index" table, assigning encryption keys and security tags to each piece of confidential information.

3. Permission verification and desensitized output. When user queries touch data indicated by security tags, the permission verification module is first triggered to determine whether access is allowed or if desensitization procedures need to be executed. After authorization, the private knowledge unit decrypts the original data and performs semantic vectorization; after merging with general model inference results, secondary review and sensitive information filtering are conducted.

4. Collaboration between private knowledge and general large models. Using encrypted indexes, private corpora share implicit feature spaces with general models in an embedded manner, enabling large models to dynamically retrieve relevant confidential information during inference. Before final question-answering or decision results are published, they undergo security policy detection to ensure sensitive information is not unauthorized.

4.2 Industry Multimodal Decision and Service Unit

4.2.1 Module Functions

The "Wenlu" multimodal decision unit aims to organically fuse different types of industry data (images, speech, text, sensor signals, etc.) and output more refined and explainable decision results under the support of industry-specific knowledge bases and general language models. It undertakes the following functions:

1. Feature fusion and semantic projection. For image, audio, sensor data, etc., specialized deep neural networks are adopted to extract feature vectors; these are then projected and fused with text input in a unified semantic space. Contextual information between different modalities is interconnected through structures such as multi-head attention or cross-modal transformer to generate multimodal comprehensive representations.

2. Interaction between industry knowledge base and general model. Based on this fused representation, the system calls the general model and domain-specific knowledge base for reasoning. If private data is involved, tag matching with the private knowledge unit is also required. Ultimately, explainable industry diagnoses, fault detection reports, predictive analysis results, or service plans are formed.

3. Explainability and service interface. To support high-reliability scenarios such as industrial and medical applications, the system has built-in explainable components that can provide core evidence (such as annotated defect areas, semantic keypoints, etc.) while outputting decision conclusions. Final results can be returned through APIs or front-end interfaces for user reference or further operation.

4.3 Robot Manipulation and Hardware Device Code Generation Unit

4.3.1 Module Functions

This unit is the key link for "Wenlu" to achieve embodied decision-making, providing direct execution solutions for physical platforms such as robots and wearable devices through automatic translation from high-level semantic understanding to specific hardware control instructions. Specific functions include:

1. Mapping from high-level semantics to instructions. Based on the language model's sequence-to-sequence generation capability, "natural language requirements" or "multimodal reasoning results" are converted into hardware control languages, such as ROS2 node scripts, embedded C++, Python execution scripts, etc. Compilation or interpretation can be performed at the output stage to adapt to different operating environment specifications.

2. Adaptation layer and interface management. A unified adaptation layer is designed for mainstream robot frameworks and hardware platforms; this layer translates intermediate instructions generated by the general model into function calls or configuration files that comply with specific APIs, facilitating rapid switching of hardware devices. When hardware or environment configuration changes, engineers only need to modify the adaptation layer without large-scale adjustments to high-level logic.

3. Real-time feedback and closed-loop iteration. Status or sensor data generated by hardware devices during task execution can be fed back into the multimodal decision unit for subsequent decision updates, achieving adaptive and self-learning capabilities. Through continuous iteration, the system can better adapt to complex or dynamic external environments.

4.4 General Model Fusion Unit

4.4.1 Module Functions

This unit is the foundational support for the entire "Wenlu" system, integrating open-source or commercial general large models (such as DeepSeek) with industry knowledge bases through a "general-specialty combined" fusion approach. It is responsible for both cross-domain language understanding and generation and provides a unified underlying semantic foundation for multimodal and private data reasoning.

1. Pre-trained model loading and industry knowledge base docking. The system deploys pre-trained general large models in server or cloud environments and incorporates industry expert knowledge bases into the semantic representation space through fine-tuning or incremental training. When facing different domain requirements, corresponding knowledge sub-bases can be dynamically loaded to achieve more targeted industry reasoning capabilities.

2. Brain-inspired memory tagging and replay. Drawing inspiration from how biological brains tag and replay memories, the system marks important scenarios or reasoning paths after completing complex decisions or services. During idle or offline periods, the annotated information is replayed and reinforced, achieving secondary consolidation of potential high-value knowledge and gradually improving prediction accuracy and execution efficiency for similar tasks.

3. Multi-module collaboration and adaptive update. Private knowledge modules, multimodal decision modules, and hardware control modules can all access the general language model or update internal knowledge indexes through bidirectional communication with the model base. When the system receives new industry-specific data or private information, these knowledge are gradually embedded into the model structure through encrypted indexes and incremental fusion strategies, improving its deep reasoning and customized service capabilities.

4.5 Inter-Module Collaboration and Overall Process

In actual operation, "Wenlu" forms a closed loop through the above four modules via a unified interface bus and communication protocol. The process is illustrated as follows:

1. External multimodal data and user request reception: The multimodal decision unit completes feature extraction or semantic matching, and if private data is involved, the private knowledge unit is called.

2. Core language model reasoning and memory tagging: The general model fusion unit retrieves the base model and industry knowledge base, performs deep analysis of multimodal information, and performs memory tagging.

3. Private information security processing: If the reasoning process or results involve sensitive data, the private knowledge unit is triggered to perform sandbox decryption, permission judgment, and necessary desensitized output.

4. Decision result generation and hardware control: After determining high-level decisions, the robot manipulation unit converts them into scripts or API instructions and issues them to target hardware.

5. Execution feedback and self-learning: Feedback from devices or the external environment is processed again by the multimodal unit and core model, triggering memory replay and reinforcement learning to continuously optimize the overall performance of the "Wenlu" system.

Through this modular, extensible system design, "Wenlu" can flexibly handle the entire process from pure software-level question-answering decisions to hardware execution, achieving true multimodal cognition and embodied artificial intelligence. The modules complement each other, ensuring private data security while providing deep decision support and automatic execution capabilities for a wide range of industry scenarios.

5 System Innovations and Technical Advantages

Based on the deep coupling of multimodal fusion, large model semantic understanding, and security privacy protection, the "Wenlu" embodied brain system has formed a relatively complete, closed-loop AI innovation architecture. The following elaborates on the system's main innovations and technical advantages from three levels: technology, application, and ecosystem.

5.1 Technical Innovations

1. Multimodal Brain-Inspired Memory Tagging and Replay Mechanism. "Wenlu" does not solely rely on the powerful reasoning and generation capabilities of general large models in text semantics but incorporates a "memory reinforcement" principle similar to biological brains: it tags core decision paths for multimodal data (text, images, audio, sensor information) and completes replay and consolidation during system idle or offline periods. Through this mechanism, the system accumulates industry-specific experience through continuous interaction, gradually improving understanding and reasoning accuracy for complex scenarios.

2. Deep Integration and Security Management of Private Data. Addressing common privacy and compliance needs in industry applications, "Wenlu" specially designed private knowledge decision units and encrypted sandbox mechanisms: user confidential information and public general corpora are isolated and managed through tagging, eliminating the risk of mixing private data with public training data. During inference, if sensitive information is called, permission verification and desensitization processing are performed first, strictly ensuring controllable information leakage risks.

3. Embodied Closed Loop from Cognition to Automatic Code Generation. Traditional AI often remains at the "cognition-decision" level, requiring manual writing of hardware control logic for actual execution. In contrast: the "Wenlu" system has a built-in robot manipulation and hardware device code generation unit that automatically maps high-level decisions to specific hardware instructions or executable scripts; without repeated manual translation, it significantly reduces labor costs and development cycles and enables rapid adaptation to environmental changes.

4. Deep Coupling of General Large Models and Industry Knowledge. To bridge the gap between industry-specific data and open large models, "Wenlu" implements a "general-specialty combined" strategy in the general model fusion unit: through small-scale fine-tuning or incremental training, professional content from industry knowledge bases is organically integrated into the general language model's representation space. When facing vertical domain scenarios, the model can leverage both general and professional knowledge advantages to complete more refined question-answering and reasoning.

5.2 Application Advantages

1. Integrated Multimodal Processing. The system can simultaneously handle text, images, audio, and various sensor data, with multimodal inputs fused in a unified semantic space. In scenarios requiring multi-source information collaboration such as industrial inspection, medical diagnosis, and autonomous driving, decision results achieve higher precision and stronger interpretability.

2. Secure and Controllable Private Data Management. "Wenlu" constructs rigorous security barriers for user-sensitive information through independent private knowledge decision modules and tagging encryption strategies. While balancing data utilization value and compliance risks, it also provides a feasible path for enterprise-level private deployment of large models.

3. End-to-End Embodied Code Generation. The system's unique hardware control generation unit can directly transform natural language descriptions or multimodal analysis results into executable instructions, significantly shortening the path from requirement to action. This capability extends not only to robotics but also to various wearable devices, intelligent manufacturing robotic arms, and other hardware forms, achieving a true "perception-cognition-decision-execution" closed loop.

4. Self-Learning and Sustainable Evolution. With the help of brain-inspired memory replay mechanisms, "Wenlu" can reinforce high-value information after multiple decision-making and task execution processes and relatively low-cost integrate new industry data or private information. Without frequent full retraining of large models, the system can continue to grow through iteration.

5. Balance Between General and Specialized Capabilities. Through the "general-specialty combined" model fusion approach, "Wenlu" not only retains the broad coverage of general knowledge by large models but also demonstrates professional standards in vertical domains. This dual capability is particularly valuable for enterprises, medical care, autonomous driving, and other environments with high precision requirements for intelligent decision-making and diverse application scenarios.

5.3 Ecosystem Advantages

1. Cross-Industry, Multi-Domain Deployment. Relying on multimodal fusion and private data management architecture, "Wenlu" can provide adapted solutions for different fields such as medical, financial, manufacturing, and transportation. Professional knowledge and experience accumulated in various industries will also feed back into the system, gradually forming a virtuous cycle of data and model co-evolution.

2. Compatibility with Existing Large Model Systems. "Wenlu" is compatible with mainstream open-source general models (such as DeepSeek, GPT family, etc.), allowing migration and deployment based on public platforms or customized development. While meeting more application needs, it also absorbs cutting-edge research results from the industry.

3. Continuous Learning Capability. The brain-inspired memory replay mechanism adopted by the system provides diverse experimental and improvement space for subsequent research, such as integrating causal reasoning models or reinforcement learning frameworks to further improve adaptability to complex scenarios and long-term dynamic environments.

In summary, the "Wenlu" embodied brain system breaks the inherent barriers between multimodal fusion, privacy protection, and hardware decision-making at the technical level, and demonstrates high scalability and portability potential at the application level. Through forward-looking architecture design and brain-inspired memory management, the system provides innovative ideas and technical paths for deep industry deployment and continuous evolution of artificial intelligence.

6 Typical Applications and Extensions

Under the multiple advantages of multimodal cognition, private data protection, and hardware code generation, the "Wenlu" embodied brain system brings considerable application value and development potential to numerous industries and scenarios. The following elaborates on its typical application methods and extensible directions in practical scenarios from different perspectives and levels.

6.1 Enterprise Management and Decision Support

6.1.1 Enterprise Management and Business Strategy. Using the "Wenlu" system, massive text reports, market data, and industry expert documents can be semantically analyzed, combined with real-time financial indicators and environmental perception data to assist enterprise executives in making scientific decisions. The private knowledge unit ensures strict protection of internal confidential documents and financial data during use, effectively reducing data leakage risks. Multimodal decision-making functions allow unified input of text, tables, images, and other data for comprehensive judgment, achieving more comprehensive and precise business strategies.

6.1.2 Complex Prediction and Early Warning Analysis. For scenarios such as financial markets and supply chain management that require cross-domain, multi-stage data analysis, "Wenlu" can deeply integrate text news, historical transaction data, and sensor monitoring indicators to generate risk warning or trend prediction reports. The brain-inspired memory replay mechanism can mark and reinforce recent abnormal fluctuations or high-risk events, continuously improving the model's response capabilities.

6.2 Robotics and Automation

6.2.1 Service Robots and Smart Home. Combining multimodal decision-making with automatic hardware code generation provides a complete solution for service robots from voice understanding to action execution. For example, household robots directly execute tasks such as cleaning, handling, or remote monitoring after receiving voice commands. The private knowledge management module can protect family users' privacy data while allowing robots to access restricted information when necessary (such as home security deployment plans) and execute corresponding actions.

6.2.2 Industrial Manufacturing and Unmanned Production Lines. In intelligent manufacturing, "Wenlu" can judge production line health status in real-time based on sensor data, monitoring images, and production logs, and automatically generate operation scripts for industrial robots to complete flexible manufacturing tasks. When abnormalities occur, the multimodal decision unit combines private knowledge (such as manufacturer proprietary technology) for fault location; the hardware generation unit issues temporary control commands to avoid risks, thereby building a highly reliable, low-latency production system.

6.3 Medical and Autonomous Driving

6.3.1 Medical Imaging Diagnosis. In medical scenarios, the system can integrate patient text medical records, imaging examination results (X-ray, CT, MRI, etc.), and physiological signals to output more accurate diagnostic suggestions or treatment plan recommendations. The private data unit ensures that sensitive patient case information and hospital internal materials are not leaked; while the general model fusion unit can deeply couple public medical knowledge with proprietary medical institution databases to form a personalized diagnosis and treatment assistant.

6.3.2 Autonomous Driving and Intelligent Transportation. For autonomous vehicles, the system can comprehensively analyze visual images, radar/lidar sensor data, voice navigation instructions, and traffic flow information to generate optimal driving strategies in real-time. If confidential maps or security strategies are involved, the private knowledge unit can manage them through encryption and call encrypted data in key decisions such as route planning and traffic prediction; automatically generated execution scripts are directly applied to vehicle controllers, achieving highly autonomous vehicle operation.

6.4 Human-Computer Interaction

6.4.1 Intelligent Customer Service and Expert Systems. Enterprises or public institutions can deploy "Wenlu" in customer service fields to provide users with consultation and services integrating voice, text, images, and other multimodal inputs, significantly improving interaction experience. Through the "general-specialty combined" knowledge base fusion model, customer service systems can not only handle general question-answering but also call deep industry knowledge to provide authoritative answers for professional scenarios (such as legal consultation, insurance claims, etc.). If user privacy data is involved (such as ID cards, policy information, etc.), the private decision unit can strictly control it and output customized answers after security review.

6.4.2 Virtual Assistants and Wearable Devices. The system can interface with AR/VR devices, smartwatches, smart glasses, etc., analyze real-time camera footage or sensor data, generate personalized suggestions through language models, and feedback to users in natural language. In healthcare scenarios, virtual assistants can obtain sensor data such as heart rate, blood pressure, and exercise steps, combine it with the general model's health knowledge base to output exercise and dietary advice, while strictly complying with user personal privacy protection requirements.

6.5 Future Extensions

6.5.1 Integration of Open-Source Models and Industry Data. "Wenlu" can be deeply integrated with various open-source large models, efficiently coupling industry data, private materials, and public corpora through directional fine-tuning or incremental learning of general models. Relying on the basic language understanding and generation capabilities provided by general models, supplemented by industry-specific knowledge bases and private data management, a more targeted and scalable next-generation intelligent hub is achieved.

6.5.2 Self-Learning Ecosystem. The system accumulates large amounts of multimodal task data through multiple interactions and continuous use, forming a self-learning cycle combined with brain-inspired memory replay mechanisms. In the long term, "Wenlu" will continuously enhance its mastery of key tasks and algorithmic strategies across industries, promoting the evolution from a general large model platform to a broader general artificial intelligence ecosystem.

6.5.3 Future Extension Directions. Future extensions may include: deeper causal reasoning and explainable machine learning by integrating causal reasoning frameworks and reinforcement learning ideas to further improve system interpretability and robustness in complex decision-making and uncertain scenarios; cross-modal generation and reverse engineering, exploring multimodal generation beyond code generation (such as text-to-image, text-to-3D action simulation) to assist design, research, and art fields; and multi-level privacy and compliance strategies, adding more fine-grained access control and audit functions within the private knowledge decision unit for stricter or more detailed data compliance scenarios (such as medical privacy laws, financial regulations).

In summary, the "Wenlu" embodied brain system demonstrates significant advantages in multimodal fusion, efficient decision-making, and private data protection across multiple industry applications. In fields such as medical, financial, industrial manufacturing, and new human-computer interaction, the system can provide end-to-end intelligent services. With the continuous strengthening of its self-learning mechanism, it brings users AI experiences with both breadth and depth. As large models continue to break through in multimodal understanding, "Wenlu" will still have vast space for innovation and expansion, laying a solid foundation for the development of next-generation general intelligent platforms.

7 Implementation Methods and Workflow

To maximize the effectiveness of the "Wenlu" embodied brain system in practical applications, systematic process design and implementation from initial deployment to daily operation are required. This section elaborates on its typical workflow and key implementation details, focusing on the linkage and information flow among the system's main modules.

7.1 System Initialization and Deployment

1. General model deployment. In cloud or local server environments, pre-deploy open-source or commercial large-scale language models (such as DeepSeek) and configure corresponding computing resources (GPU/TPU). According to target industry requirements, perform necessary fine-tuning or incremental training on the model to achieve higher question-answering and reasoning accuracy in domain scenarios.

2. Industry knowledge base and private data management module preparation. Import or create industry knowledge bases (such as medical, manufacturing, financial) to prepare data support for multimodal decision-making and reasoning. Configure the private knowledge decision unit, encrypt and index materials that may contain sensitive information (text, tables, images, confidential documents, etc.). Establish access control strategies, including role permissions and desensitization mechanisms, to ensure private data is securely stored independently from public training corpora.

3. Multimodal input channel setup. Connect interfaces for image capture, voice collection, and sensor data reading to achieve real-time connection or timed data fetching with external devices/databases. For scenarios requiring physical interaction (such as robots, wearable devices), initialize communication protocols or adaptation layers to ensure smooth command sending and receiving after deployment.

7.2 Private Knowledge Management Workflow

1. Private data upload and identification. Users or system administrators upload private files (such as internal documents, medical records, confidential technical solutions, etc.) through the backend, and the system automatically performs encryption processing and generates unique identifiers. Using text analysis or predefined metadata, the system performs preliminary semantic parsing of file content, dividing it into tags for sensitivity level, subject matter, and access permissions.

2. Permission management and index establishment. Based on role-based access control (RBAC) strategies, assign access levels to private data (such as administrator-only or specific business department-only). Establish private index tables inside the system, pairing document content embedding vectors or keywords with general models for subsequent question-answering or reasoning phases.

3. Encrypted storage and sandbox isolation. Adopt secure sandbox mechanisms to store private data separately from public corpora, combined with encryption algorithms (symmetric or asymmetric) to protect files. External applications must pass authorization verification processes before they can read or write sensitive content when initiating requests involving private information.

7.3 Multimodal Data Processing Flow

1. Feature extraction module. For image data, adopt convolutional neural networks or other visual models for target detection, classification, or feature embedding; for audio data, utilize acoustic feature extraction or speech recognition models; sensor data is processed through filtering, normalization, and other methods. Convert raw information from different modalities into unified or alignable vector representations and store them in intermediate buffers.

2. Semantic fusion and context encoding. Relying on the "Wenlu" system's multimodal decision unit, combine image, speech, text, and other feature vectors into the same context encoding model; use attention mechanisms to achieve semantic association learning between modalities. When necessary, dynamically call industry knowledge bases to further identify professional information points in features (such as lesion locations in medical images, fault points in industrial inspection).

3. Privacy detection and filtering. When multimodal data contains content that may map to private indexes (such as internal identifiers or confidential parameters), privacy identification checks are performed first; if triggered, coordinate with the private knowledge unit to verify permissions before deciding subsequent steps. For parts without sensitive tags, they are directly handed over to the general model for deep semantic parsing and reasoning.

7.4 Decision and Reasoning Process

1. User request/task input. Users can initiate queries or decision requests to the system through natural language, image-text mixing, voice, etc.; they can also be triggered by external events (such as industrial equipment failure alerts or urgent medical diagnosis needs). If tasks involve references to private data or require high-sensitivity decision-making, the system immediately calls the private knowledge unit to verify access permissions.

2. General model fusion unit reasoning. User requests, multimodal inputs, and industry-specific knowledge (and private information, if permissions allow) are input together into the general language model for reasoning. The brain-inspired memory tagging mechanism is introduced to preliminarily mark important knowledge points or reasoning paths involved in this reasoning process.

3. Output decision or question-answering results. The general model fusion unit outputs preliminary decision conclusions or reply content, and the multimodal decision unit annotates and explains visual objects (such as diagnostic graphs). If private data or sensitive information is involved, desensitization checks are performed before result publication to ensure that output text or images do not exceed user permission scopes.

7.5 Hardware Control Execution

1. Hardware control request trigger. Once system decision results contain robot actions, wearable device scheduling, or other hardware control needs, the hardware control generation unit is triggered. For example, user input such as "Please control the robotic arm to grasp the red object and place it in area X," or multimodal detection results showing "Need to close specific valves to prevent fault escalation."

2. Script/instruction automatic generation. Based on ROS2 or target hardware API calling rules, the hardware control generation unit converts natural language or multimodal decision results into control scripts (such as Python, C++, etc.). For different models or brands of robot systems, the underlying adaptation layer translates unified intermediate instructions into specific driver commands.

3. Execution and status feedback. The system issues generated scripts or commands to target hardware in real-time; if devices have feedback functions (sensors, status logs, etc.), they are automatically incorporated into the multimodal analysis module for secondary judgment. If obstacles or environmental mutations are encountered during execution, the system can quickly output new control strategies through re-reasoning, achieving dynamic adaptive closed loops.

7.6 Memory Replay and Reinforcement Learning

1. Key decision point tagging. After completing a complete interaction, reasoning, or hardware operation, the system adds memory annotations to key information and reasoning links in the process, recording their weight distribution in multimodal fusion and knowledge base calls. If private data was called during the task, its "call path" is also securely recorded for subsequent audit and policy optimization.

2. Offline replay and reinforcement. During system idle periods or offline batch processing, past important decisions are replayed according to memory annotations; reinforcement learning or parameter optimization methods are used to improve execution efficiency and accuracy for similar tasks. This process does not require retraining the entire large model, only fine-tuning policy parameters or fusion units within the system to achieve continuous improvement.

3. Update indexes and strategies. After replay, the system writes reinforcement results into knowledge index tables or private data management strategies, allowing subsequent tasks to directly benefit. This forms a positive cycle of "multiple interactions—memory tagging—offline reinforcement—performance improvement," driving system self-evolution.

7.7 System Maintenance and Expansion

1. New industry knowledge base integration. When expanding to new industries (such as agriculture, construction, energy, etc.), relevant knowledge bases can be imported into the system through incremental learning, and corresponding model plugins can be added in the multimodal feature extraction phase. The general language model only needs lightweight parameter updates, reducing repeated training overhead.

2. Private policy and security compliance updates. For constantly changing laws, regulations, or internal compliance requirements, permission rules, tagging management schemes, and desensitization algorithms in the private knowledge unit can be flexibly configured. Encryption algorithms and security protocols are regularly checked to maintain the system's latest adaptation for privacy and compliance.

3. Hardware interface and adaptation layer maintenance. As hardware devices upgrade or new platforms are integrated, only the adaptation layer scripts and API mappings in the hardware control generation unit need to be updated, without affecting high-level decision logic. During long-term operation, data on different hardware failures or abnormal scenarios can be collected to further optimize the stability of code generation.

7.8 Complete Workflow Example

1. Scenario description. Industrial scenario: A production line experiences a failure alarm, and the system receives data streams from sensors (temperature, pressure), images from monitoring cameras, and text descriptions input by operators.

2. Multimodal fusion and private determination. The multimodal decision unit performs feature extraction to identify fault locations; if manufacturer-exclusive confidential component information is involved, the private knowledge unit performs secure access determination.

3. General model reasoning and output. Combining industry knowledge bases and general models to determine the most likely cause of failure, recommending replacement of certain parts or parameter adjustments.

4. Automatic generation of maintenance robot instructions. The hardware control unit translates the fault handling solution into robot movement, grasping, detection, and other action scripts; after robot execution, data is fed back for secondary verification.

5. Offline replay and reinforcement learning. During system idle periods, the decision path of this incident is marked and replayed, recording features causing the fault, repair methods, and execution efficiency to form gains for next decision-making.

Through the above complete process, the "Wenlu" embodied brain system can continuously absorb multimodal information, industry experience, and user private data in daily operation, achieving self-learning and functional iteration with minimal additional training costs. Meanwhile, the end-to-end hardware execution capability gives the system a closed-loop characteristic from cognition to action, significantly shortening the cycle from intelligent decision-making to actual deployment while providing higher reliability and security in critical scenarios.

Geng Liang received his master's degree from Hebei University of Technology (Tianjin) in 2012. He is currently a Ph.D. candidate at the School of Artificial Intelligence, Beijing University of Posts and Telecommunications (Beijing), and concurrently serves as an assistant researcher at the School of Mechanical and Electrical Engineering, Shijiazhuang University, and an assistant researcher at the Shijiazhuang Key Laboratory of Agricultural Robot Intelligent Perception.