Thinking on large model empowerment of safety management innovation

[Abstract] This study focuses on constructing a vertical domain-specific large model for military safety management applications. By leveraging extensive safety risk management data during the training process, the model achieves more accurate understanding and generation of military safety management-related professional content. It provides support for enhancing safety risk monitoring and early warning effectiveness, while offering reference for intelligent transformation in safety management.

【Keywords】Safety Management｜Large Models｜Challenges and Countermeasures

党的二十大报告明确提出"推进国家安全体系和能力现代化"，党的二十届三中全会作出"推进国家安全科技赋能"部署。以（ - ）为代表的人工智能大语言模型（简称大模型）通过模拟人类的思维和行为，具备了强大的学习、推理和决策能力，为众多领域带来前所未有的变革。

In recent years, with the restructuring of institutional frameworks and the expansion of functional missions, the frequency and duration of large-scale, long-distance, and cross-regional military operations have significantly increased compared to the past, presenting numerous new challenges for military safety management. Large models offer advantages in environmental perception, command decision-making, and intelligent response, showcasing broad application prospects. To proactively adapt to the changes of the times, technological advancements, and institutional reforms, it is essential to apply large models across various scenarios of military safety management to enhance its quality and efficiency.

I. The Value and Advantages of Large Models in Empowering Safety Management

Large models, by deeply inheriting, skillfully combining, and effectively enhancing the advanced technologies in the current field of artificial intelligence, possess powerful generative capabilities and generalization performance. Their application in the realm of military security management will demonstrate significant advantages.

提升安全风险评估质效，缓解专家资源稀缺问题。目前，安全风险评估主要依赖个人经验和专业知识来识别潜在风险点，对其导致事故发生的可能性与严重性进行分析并提出相应安全对策措施。然而，传统安全风险评估中存在任务现场的复杂性和动态性易使风险"辨不全"，单纯依靠人工进行判断易使风险"识不快"，分析工作的专业性易使风险"评不准"等难题。大模型依托超级算力，可以在军事任务执行前比较全面地对某个任务场景下影响安全的各类因素进行数据收敛与建模分析，并辅助生成安全风险评估报告。

完善风险监测预警体系，实现实时预测智能归因。现有的风险监测预警主要基于人工巡查监测，"不及时、不顺畅"，安全监测体系缺乏全局安全风险视图。针对营区内可能存在的安全隐患，可以在各类活动的安全检查场景中应用人工智能大模型对安全风险进行预测和归因。大模型赋能完善风险监测预警体系，针对各类传感器返回的数据，通过基于图像识别技术的视觉智能实现风险识别功能，通过基于智能算法技术的报警锁定和分级推送实现协同管理功能，通过基于大数据技术的快速定位问题来源实现可追溯性功能等，能够使安全风险监测向主动预警转变。

增强整体应急处置能力，深化知识共享管理协同。建立由"三库"构成的离线知识库，对安全管理知识进行体系化管理。其中，应急资源库囊括军地安全管理专家团队信息、应急物资、应急力量情况，应急预案库由上下承接、联动一体的应急预案体系构成，教育与训练资源库包含先进安全管理理论、军地事故问题资料、安全法律法规等。一是实现知识共享和交互功能，使知识的更新和维护更高效；二是促进高效联动和协同配合，在紧急情况下得到精准快速的支援；三是进行个性化教育与训练，有针对性地提升官兵安全知识和技能。

驱动安全管理模式转型，适应打赢未来战争需要。以美国为首的西方国家军事智能化进程正逐步加快，战争形态正悄然向以"智能泛在、万物互联、人机共生、跨域协同、控网夺脑"为主要特征的智能化战争转变。服务作战、聚焦打赢的军队管理对高质量安全风险管理的需求也越来越强。大模型应用于军队安全管理领域，具备通常以隐性知识形式存在的安全管理专家经验，能有效减少人工劳动，驱动安全管理模式向智能化转型，更加适应未来智能化战争的需求。

2. Constraints on Large Models Empowering Security Management

大模型是"大数据""大算例""强算法"结合的产物，随着技术的不断演进与研究应用的不断深化，大模型将推动军队安全管理迈向更高效智能的新阶段。然而，其构建与应用过程中仍存在数据质量、领域生态、算力资源和风险挑战等一系列制约因素。

数据质量参差不齐，模型训练性能不佳。大模型中数据的"质"与"量"是决定其准确性和泛化能力的重要因素。一是数据数量问题。由于缺乏共建共享机制，不同单位间数据壁垒严重，限制了数据的流通，导致数据规模不足、数据多样性缺乏，将无法有效支撑模型训练。二是数据质量问题。缺乏科学的顶层设计，管理各场景、环节数据结构不统一，导致数据质量参差不齐，缺乏高质量数据为模型微调提供支撑。数据的质量、规模和多样性等难以得到保证，将影响模型的准确性、稳定性与鲁棒性。

There are significant ecological differences across domains, which constrain model capabilities. As a product of the co-development of various intelligent technologies, existing foundational large models lack in-depth thinking ability for vertical industries and cannot meet users' professional needs, resulting in insufficient practicality. First, while general-purpose large models possess broad universal knowledge, they struggle with understanding military semantics and adapting to specific scenarios, lacking the depth of knowledge required to address complex demands in certain specialized fields. Second, in private, offline environments or on edge platforms, large model products suffer from severe functional deficiencies and lack the capability to embed customized large models. Third, large models are essentially black boxes based on deep neural networks, leading to issues such as insufficient reliability of outputs and unstable performance.

Computing resources are in short supply, and model inference costs are high. Large models are artificial intelligence models with billions to hundreds of billions or even more trainable parameters, requiring substantial computing power for both training and inference. Firstly, training and running large models demand enormous computational and storage resources, necessitating high-performance computing equipment and distributed computing systems. Secondly, with the continuous development and application of large models, traditional computing architectures and infrastructure struggle to meet the explosively growing demand for computing power, leading to increasingly tight and unevenly distributed resources. Thirdly, in practical applications, the performance of computing power is constrained by factors such as storage and network environments, and current advancements in storage and bandwidth technologies have yet to fully meet the demands of massive data processing.

Risks and challenges have garnered significant attention, yet model security remains difficult to ensure. First, there are inherent risks within the models. Current large models and their related products have yet to resolve issues such as the opacity of AI algorithms, susceptibility to data sample influences, and biases or hallucinations, leading to insufficient accuracy and controllability in output results. Second, there is the risk of operational failure. If general-purpose large models are subjected to security vulnerabilities such as data poisoning attacks or model theft attacks, they could pose potential threats to vertical-domain large models trained based on these general-purpose models. Third, there are data security risks. Large models for security management are trained using massive amounts of data from military security management, particularly data related to major military missions, which often involve classified military secrets and sensitive information. Any leakage of such data could have severe consequences.

3. Strategies for Advancing Safety Management Empowered by Large Models

Due to constraints such as the sensitivity of data and the high demands on computing power, establishing a dedicated large-scale model for military security management and achieving the desired application vision remains a long and challenging journey. However, the advent of large-scale models brings immeasurable potential for military security management. To better promote the application and innovation of large-scale models in the field of military security management, ensure their safe and reliable operation, and deliver greater benefits and advantages for decision-making and combat operations, it is essential to conduct in-depth research on the data governance framework for large-scale models, accelerate the development of application capabilities in vertical domains, continuously optimize model algorithms and computing resource deployment strategies, and enhance the security of the large-scale model application ecosystem across multiple dimensions.

Conduct in-depth research on the governance framework system for large model data. By effectively planning, monitoring, and controlling each stage of data collection, processing, storage, usage, protection, and disposal, the value and performance of large models throughout their lifecycle can be enhanced. First, establish an observable risk data lifecycle chain, implementing processes that ensure data traceability, labeling, rollback capability, and encryption. Second, on the premise of ensuring data security, enable the sharing of military data resources within defined scopes and conditions to fully unlock the value and efficiency of the data. Third, classify and tier data according to specific application scenarios to optimize the efficiency and effectiveness of data training. This will improve the understanding of military security management terminology and issues, thereby significantly enhancing the reliability of large model decision-making.

Accelerate the development of large model applications in vertical domains. To meet the demands and data requirements of military safety management, develop large models tailored for this vertical domain. Deeply embed safety concepts, the knowledge system of the safety management industry, safety standards, and the real-world needs of core safety management scenarios into the underlying architecture of the algorithmic models. This approach can effectively avoid data ambiguity issues and more accurately understand and generate professional content relevant to the field. 1. **Theoretical Research**: Explore theoretical foundations that can explain large model performance from perspectives such as feature extraction, modal alignment, scaling laws, and emergent phenomena. 2. **Technical Research**: Based on management practices and needs, rationally select key technical methods for model customization. For example, enhance the model's domain-specific capabilities by integrating an external professional military safety management knowledge base during deployment.

Continuously optimize model algorithms and computing power deployment strategies. First, develop more efficient model compression and acceleration techniques, such as knowledge distillation, model pruning, and vectorization, to enhance model inference efficiency. Second, explore more efficient training algorithms and specialized hardware designs while reducing computing power demands by focusing on application scenarios. Third, implement private deployment of computing power resource pools. Deploy sandbox environments for high-computing-power model secondary training resource pools, medium-computing-power model fine-tuning and optimization training resource pools, and low-computing-power model inference resource pools to ensure more robust and secure management of the military’s core data. High-speed network systems enable efficient connectivity between computing resources, while computing power pools facilitate resource allocation, scheduling, and monitoring to improve the efficiency and quality of resource utilization.

Enhance the security level of the large model application ecosystem across multiple dimensions. Strengthen proactive prevention and regulatory guidance from various aspects to minimize risks and ensure the security of the large model application ecosystem for military safety management. First, conduct data security governance. Build multi-level, full-cycle security capabilities from the database side to safeguard the collection, transmission, processing, storage, exchange, use, and disposal of training data. Second, implement algorithm security governance. Ensure generated content meets management requirements through supervised fine-tuning and reinforcement learning from human feedback. Third, establish secure usage mechanisms. Prevent unauthorized access and data leaks through user authentication mechanisms. Fourth, continuously reinforce oversight mechanisms. Conduct regular security audits of military safety management large models to ensure stable operation and continuous evolution of security capabilities.

references

Wang Chengming, Wang Gaokai, Li Yongnan. Construction of a Security Risk Situational Awareness Framework Based on Large Model Intelligent Agents. , (): -.

Wang Jingya, Shen Hua, Shen Yan.