Modernization Doctrine Cyber & Info

Study Military Affairs, Study War, Study Combat Operations | Intelligentized Means Reshape the Logic of Striking Command Nodes

研究军事、研究战争、研究打仗丨智能化手段重塑指挥节点击破逻辑

PLA Daily (解放军报) 28 July 2026

Summary

A PLA academic article in the 'Study Military Affairs, Study War, Study Combat Operations' series argues that intelligentized technology has restructured the logic of striking command nodes across four dimensions: target identification (shifting from physical to signal and behavioral cross-validation), strike-chain compression (replacing serial manual decision nodes with parallel algorithm-assisted planning), damage logic (moving from physical destruction to 'functional deconstruction' (功能解构) and 'systemic disintegration' (体系瓦解)), and offensive-defensive contest tempo (from pre-war contingency planning to real-time algorithmic adversarial evolution). The article documents how PLA doctrinal writing is systematizing the concept of 'inducing incapacitation' (制造失能) as a distinct strike objective separate from physical kill, which extends a visible pattern in PLA operational theory of reframing lethality in network and system terms rather than platform or munition terms. The framing points to an institutional effort to translate intelligentized warfare concepts into concrete targeting and damage-assessment criteria, though which units or programs are operationalizing these frameworks remains unspecified.

Translation

Whether in traditional warfare or in today's informatized and intelligentized warfare, command nodes have always been the nerve center of the operational system, and their state of survival directly determines whether overall operational effectiveness can be effectively released. Therefore, striking enemy command nodes with greater efficiency and speed has always been the key to battlefield victory. As intelligentized means penetrate the entire chain of operations, the detection, location, strike, and assessment of command nodes exhibit characteristics markedly different from any previous form of warfare, driving a profound transformation in the logic of striking command nodes. Against this backdrop, clarifying how intelligentized means reshape the logic of striking command nodes is of significant importance for winning future wars.

From the "central command tent" (中军大帐) of the cold-weapons era to the rear command posts of mechanized warfare, and then to the distributed command centers of informatized and intelligentized warfare, the outward form of command nodes has always been in dynamic flux. What remains constant, however, are the physical characteristics, signal characteristics, and behavioral characteristics present during their operation—these three together form the basis upon which command nodes can be detected, identified, and discovered. Traditional sensing systems focus their identification on the physical characteristics of command nodes, concentrating identification methods on physical approaches such as scout reconnaissance and high-altitude surveillance, with identification precision constrained by information transmission speed, personnel analytical capacity, and battlefield cognitive load. In modern warfare, the substantial development of informatized and intelligentized technology has caused the boundary of physical characteristics between command nodes and non-command nodes to become increasingly blurred. If sensing systems rely solely on identifying the physical characteristics of command nodes, they are highly susceptible to deception by enemy camouflage works, tactical feints, and false electromagnetic emissions, making it difficult to precisely lock onto the enemy's core command units. The application of intelligentized sensing means has changed the emphasis of feature extraction, placing greater importance on the extraction, identification, and judgment of signal characteristics and behavioral characteristics.

Specifically, the sophistication of intelligentized sensing means is manifested primarily in cross-validation and continuous identification. Intelligent learning models can establish high-dimensional feature mappings across massive multi-domain datasets, integrating the diverse and disorganized signals dispersed across the electromagnetic spectrum, infrared radiation, terrain masking, and communication timing sequences into a holistic "portrait." Abstract data unique to command nodes—such as decision-cycle rhythms, information-flow pathways, and power-radiation patterns—can all be rendered by intelligent learning models into concrete, observable characteristics for cross-validation. At the same time, networked and distributed sensing nodes can use intelligent algorithms to form a sensing matrix; even if enemy command nodes are able to maintain silence across certain time periods and certain domains, as long as they engage in communication behavior or signal anomalies, they will be incorporated into a continuously tracked identification sequence. Under these circumstances, the identification pattern no longer relies on the "single answer" presented by a single explicit characteristic, but instead seeks the "optimal answer" through the probabilistic superposition of multi-dimensional characteristics, thereby exposing enemy concealment methods to the greatest possible extent. The key to better leveraging intelligentized sensing means lies in building distributed sensing networks and categorized feature libraries, parameterizing, vectorizing, and correlating the physical forms, behavioral patterns, electromagnetic emissions, and other characteristic parameters of command nodes across broad operational spaces according to algorithmic models—striving to "observe the finest details" (明察秋毫) and "perceive the significant from the subtle" (见微知著) in an informatized environment, continuously improving the capacity to "get to the root of the matter" (寻根究底), so that enemy command nodes have nowhere to hide.

In intelligentized warfare, where tempo has accelerated to an unprecedented degree, the importance of "time" is increasingly prominent: the longer the strike chain, the more time consumed, and the broader the window available to enemy command nodes to complete relocation, go silent, and deploy deception measures. In traditional strike chains, moving from target confirmation to fire delivery requires passing through multiple manual decision nodes; the linear process of order circulation and situational assessment continuously accumulates the time consumed by decision-making, which is clearly ill-suited to the future battlefield characterized by highly complex situations and highly intense confrontation. Algorithm-assisted mechanisms within intelligentized technology can formulate optimal strike plans within a shorter period of time through target assignment and fire planning, evolving various decisions from serial approval into parallel circulation. Once enemy command nodes are precisely identified, the scheduling of strike resources, the planning of strike routes, and the generation of penetration strategies can proceed simultaneously, compressing to a great extent the time required to complete the strike chain.

The deeper-level impact of intelligentized technology on command decision-making is reflected in the closed-loop adjustment of strike operations. Once a traditional strike chain is initiated, the cost of mid-course adjustment is extremely high. In future warfare, however, command nodes—as high-value time-sensitive targets—will inevitably be subject to frequent and intensive adjustment by the enemy with respect to their location, echelon, and domain, which means strike chains must be prepared to adjust at any time; intelligentized technology prepares the conditions for reducing costs and increasing efficiency in this process. On one hand, intelligentized technology can continuously "collect" frontline situational information during the strike process, assess changes in target status in real time, and correct strike parameters, transforming strike operations from an unadjustable "arrow loosed from the bow" into a "guided munition" whose direction can be adjusted at the appropriate moment, significantly reducing the difficulty and cost of plan changes. On the other hand, intelligentized swarm strike means can achieve autonomous mission assignment and path coordination within the swarm, subjecting the defending side to saturating, simultaneous multi-directional penetration pressure that makes comprehensive defensive adjustment difficult to achieve, thereby ensuring that overall strike effectiveness does not diminish. On this basis, compressing the strike chain through intelligentized decision-making requires not only achieving system-operational "speed," but also emphasizing the "agility" (活) of dynamic lethality, and attending to the "comprehensiveness" (全) of strike angles—thereby shaping a three-dimensional advantage of "precise strikes, rapid change, and sustained pressure" to achieve effective strikes against enemy command nodes.

The purpose of striking enemy command nodes is not simply to destroy their physical entities, but to pursue the failure of the decision-making function of their operational system. Traditional damage models rely primarily on the physical destruction wrought by munition yield, achieving hard destruction through blast overpressure, fragmentation, and thermal effects. The effective range of this damage mode is relatively coarse—one might say it "grabs everything at once" (眉毛胡子一把抓)—and often requires a relatively large damage radius to achieve the strike objective. In informatized and intelligentized warfare, the development of distributed command nodes has caused their spatial positions to exhibit high dispersion and strong variability. Under these circumstances, the input-output ratio of traditional damage models is relatively low; even expanding the damage radius to a very broad space makes it difficult to "catch all" enemy command nodes in one net. The development of intelligentized technology has enriched damage means, changed damage logic, and deepened damage layers, and is expanding strike effects from the single layer of "physical destruction" to the multi-dimensional layer of "functional deconstruction" (功能解构). For example, algorithm-empowered precision strikes can implement pinpoint damage against specific functional modules of command nodes, achieving multi-layered disintegration of the enemy's overall operational system by triggering chain reactions—this means that the strike effect of smaller-yield weapons may rival that of heavy weapons.

At a deeper level, the deepening of damage layers by intelligentized lethality is essentially a cognitive leap regarding the question of "what damage is." The traditional view holds that striking a command node is equivalent to eliminating its physical existence—a view rooted in the material-confrontation logic of mechanized warfare, which simplifies war to the direct collision of firepower against firepower and treats command nodes as isolated, independently removable hardware units. But in intelligentized warfare, command nodes are no longer purely physical entities; they are "information-decision" hubs embedded in the entire operational system, with their value concentrated in information-flow functions, decision-generation functions, and order-distribution functions. This means that intelligentized strike means should shift from "eliminating entities" to "inducing incapacitation" (制造失能), from "hardware destruction" to "severing functions," and from "single-point isolation" to "systemic disintegration" (体系瓦解). In other words, intelligentized lethality pursues not "how much is destroyed" in the physical sense, but "how widely failure spreads" in the systemic sense; not the accumulation of yield at the point of strike, but the hierarchical diffusion through the operational network. Only in this way can strike effects be driven, from the surface to the core and from point to plane, to continuously deepen from the single layer of physical damage toward the multi-dimensional layers of functional deconstruction and systemic collapse.

War is an offensive-defensive contest between two opposing sides; as the means of "offense" continuously develop, so too do the techniques of "defense" continuously improve. Within the traditional contest framework, the capability iteration of both attacking and defending sides relies primarily on the linear improvement of equipment performance and the accumulation of experience in tactics and methods; the cycle for changes in the mode of contest is relatively long, the predictability of both sides' strategies is relatively high, and the overall character is one of gradual evolution. The proliferation of intelligentized means is accelerating the iteration rate and strategic complexity of the offensive-defensive contest: the attacker's intelligent algorithms can rapidly generate diverse combinations of penetration strategies, "probing" the vulnerabilities of the defensive system through multi-domain coordination, dynamic path planning, and mixed decoy groupings; the defender's intelligent systems can simultaneously learn attack patterns and dynamically adjust defensive deployment and resource scheduling. Strategy generation by both sides no longer relies entirely on the commander's experiential deduction, but is instead completed by algorithms in simulated environments through extensive strategy traversal and selection of superior over inferior options, with the dimensions and depth of the contest space expanding simultaneously. The tempo of offensive-defensive confrontation over command nodes thereby shifts from "pre-war contingency plans plus improvised adjustment" to "real-time contest plus dynamic evolution."

To seize the initiative and gain the upper hand in this new mode of contest, on one hand it is necessary to build a high-fidelity digital twin adversarial environment. Deploy highly realistic digital twin simulation platforms, simulate complex and variable adversarial scenarios, and conduct iterative development of offensive-defensive strategies and verification of tactical effectiveness. Use advanced algorithmic models to mine and optimize massive adversarial datasets, screening out optimal tactical combinations and resource allocation plans, providing the physical system's deployment with a thoroughly validated, rapidly responsive contingency library, and effectively transferring trial-and-error costs to the virtual domain. On the other hand, it is necessary to establish a mechanism for adversarial evolution and continuous iteration of algorithmic capabilities. Build a closed-loop algorithmic capability evolution system: through normalized adversarial machine learning, enable core algorithms to reveal weaknesses and identify vulnerabilities through continuous "offensive-defensive confrontation" deductions; build a rigorous algorithmic effectiveness evaluation metric system to monitor in real time the performance of algorithms in combat simulation environments; manage data feedback and model updating well, rapidly feeding back to the algorithmic model training process the vulnerabilities discovered and strategies optimized during adversarial deductions, driving continuous optimization and iteration of models, ensuring that algorithmic capabilities always lead those of potential adversaries, and achieving dynamic evolution and generational leaps in offensive and defensive capabilities.

Original Chinese

无论是传统战争，还是当下的信息化智能化战争，指挥节点始终是作战体系的神经中枢，其存续状态直接决定了整体作战效能能否有效释放。因此，以更高效率、更快速度击破敌指挥节点始终是制胜战场的关键。随着智能化手段向作战全链条渗透，指挥节点的发现、定位、打击与评估各环节呈现出区别于以往任何战争形态的显著特点，带动指挥节点击破逻辑发生深刻变革。在此背景下，厘清智能化手段如何重塑指挥节点击破逻辑，对打赢未来战争具有重要意义。从冷兵器时代的“中军大帐”到机械化战争时代的后方指挥所，再到信息化智能化战争中的分布式指挥中心，指挥节点的外在形态始终处于动态变化之中，但其中不变的是运行过程中的物理特征、信号特征与行为特征，三者共同构成了指挥节点可被探测、被识别、被发现的基础。传统感知系统的识别内容聚焦于指挥节点的物理特征，识别手段集中在斥候探查、高空侦察等物理化方法，识别精度受限于信息传递速度、人员分析能力和战场认知负荷。在现代战争中，信息化、智能化技术的长足发展使得指挥节点与非指挥节点之间的物理特征边界趋于模糊，感知系统如果仅仅依赖于识别指挥节点的物理特征，就极易被敌方的伪装工事、战术佯动和虚假电磁辐射所欺骗，难以精准锁定敌方核心指挥单元。而智能化感知手段的应用，改变了特征提取的侧重点，更加重视对信号特征和行为特征的提取、识别与判断。具体而言，智能化感知手段的高明之处主要体现在交叉验证和持续识别上。智能学习模型能够在海量的多域数据中建立高维特征映射，将分散在电磁频谱、红外辐射、地形遮蔽、通联时序中的多元、杂乱信号整合为整体性“画像”，指挥节点所特有的决策周期节律、信息流转路径、功率辐射模式等抽象数据，均可被智能学习模型具象化为可观测的具体特征进行交叉验证。与此同时，网络化、分布式的感知节点能够借助智能算法构成感知矩阵，敌方指挥节点即便能够在部分时间、部分领域保持静默，只要其发生通联行为和信号异动，就会被纳入持续跟踪的识别序列。在这种情况下，识别模式不再依赖单一显性特征所呈现的“唯一答案”，而是通过多维度特征的概率叠加寻求“最优答案”，从而最大程度上识破敌方的隐匿手段。更好地发挥智能化感知手段的作用，关键在于搭建分布式感知网络和分类别特征库，根据算法模型将广域作战空间中指挥节点的物理形态、行为模式、电磁辐射等特征参数化、向量化、关联化，力求在信息化环境中“明察秋毫”“见微知著”，不断提高“寻根究底”的能力，使得敌方指挥节点无处遁形。在节奏空前加快的智能化战争中，“时间”的重要性愈加凸显：打击链路越长，花费时间越多，敌方指挥节点完成转移、静默、诱骗部署的窗口期就越“广阔”。传统的打击链路中，从目标确认到火力投放需要经过多个人工决策节点，线性的指令流转与态势研判过程会不断累积决策所用的时间，这显然难以适应局势高度复杂、对抗高度激烈的未来战场。智能化技术中的算法辅助机制，能够通过目标分配与火力规划，在较短时间内制定最优打击方案，各类决策从串行审批演化为并行流转。敌方指挥节点一旦被精准识别，打击资源的调度、打击路径的规划、突防策略的生成可同步展开，极大程度上压缩了打击链路的贯通时间。智能化技术对指挥决策的更深层次影响，体现在打击行动的闭环调整上。传统打击链路一旦启动，中途调整的成本极高。而在未来战争中，指挥节点作为高价值时敏目标，敌方对其所处的位置、层级、领域的调整必然是频繁的、密集的，这使得打击链路不得不做好随时调整的准备，智能化技术为这一过程的降本增效准备了条件。一方面，智能化技术可以在打击过程中持续“回收”前线态势信息，实时评估目标状态变化并修正打击参数，打击行动从不可调整的“离弦之箭”变为可适时调整方向的“制导武器”，显著降低方案变化的难度和成本。另一方面，智能化集群打击手段可以在集群内部实现自主任务分配与路径协同，使得防御方面临饱和式、多方向同时突防压力，难以实现防御调整的全覆盖，从而保证整体打击效能不衰减。基于此，通过智能化决策压缩打击链路，不仅要做到系统运转之“快”，更要注重动态杀伤之“活”，还要关注打击角度之“全”，以此塑造“精打、快变、持续压”的立体优势，实现对敌方指挥节点的有效打击。击破敌方指挥节点的目的，并非单纯摧毁其物理实体，而是追求其作战体系决策功能的失效。传统毁伤模式主要依靠弹药当量的物理破坏，通过爆炸冲击波、破片与热效应实现硬摧毁。这种毁伤方式的作用范围相对粗放，可谓“眉毛胡子一把抓”，往往需要较大的毁伤半径才能实现打击目的。在信息化智能化战争中，分布式指挥节点的发展使得其空间位置呈现高分散性、强变化性。在这种情况下，传统毁伤模式的投入产出比较低，即便是将毁伤半径扩展至非常广阔的空间，也难以将敌方指挥节点“一网打尽”。智能化技术的发展，丰富了毁伤手段、改变了毁伤逻辑、深化了毁伤层次，正在将击破效果从“物理摧毁”的单一层次拓展到“功能解构”的多维层次。比如，算法赋能的精确打击，能够针对指挥节点的特定功能模块实施定点毁伤，通过引发连锁反应对敌方整体作战体系实现多层次瓦解，这使得较小当量武器的打击效果可能与重型武器相媲美。从更深层看，智能化毁伤深化瓦解层次，本质上是对“毁伤是什么”这一问题的认知跃迁。传统观念认为，指挥节点的击破等同于消灭其物理存在，这一观念根植于机械化战争的物质对抗逻辑，将战争简化为火力与火力的直接碰撞，把指挥节点视为一个孤立的、可被单独剥离的硬件单元。但在智能化战争中，指挥节点不再是单纯的物理实体，而是嵌入整个作战体系的“信息—决策”枢纽，其价值集中在信息流转功能，决策生成功能与指令分发功能。这就意味着，智能化打击手段应从“消灭实体”转向“制造失能”，从“硬件破坏”转向“斩断功能”，从“单点剥离”转向“体系瓦解”。换言之，智能化毁伤追求的不是物理意义上的“摧毁多少”，而是体系意义上的“失效多广”；不是打击点上的当量叠加，而是作战网中的层级扩散。只有这样，才能由表及里、从点到面，推动打击效果从物理毁伤的单一层次向功能解构、体系崩塌的多维层次持续深化。战争是对抗双方的攻防博弈，“攻”的手段不断发展，“防”的技巧也在不断提高。在传统博弈框架下，攻防双方的能力迭代主要依赖装备性能的线性提升与战术战法的经验积累，博弈方式发生改变的周期较长，双方策略的可预见性相对较高，整体呈现出渐进式的演进特征。智能化手段的普及，正在加速攻防博弈的迭代速率与策略复杂度：攻击方的智能算法可快速生成多样化突防策略组合，通过多域协同、路径动态规划、诱饵混合编组等方式“试探”防御体系的漏洞；防御方的智能系统则可以同步学习攻击模式，动态调整防御部署与资源调度。双方的策略生成不再完全依赖指挥员的经验推演，而是由算法在仿真环境中完成大量策略遍历与优劣筛选，博弈空间的维度和深度同步扩展。指挥节点的攻防对抗节奏，由此从“战前预案+临机调整”转向“实时博弈+动态演化”。要在这样的新型博弈模式下抢得先机、赢得主动，一方面要构建高拟真数字孪生对抗环境。部署高度逼真的数字孪生仿真平台，模拟复杂多变的对抗场景，进行攻防策略迭代与战法效能验证。通过先进的算法模型对海量对抗数据进行挖掘与优选，筛选出最优战术组合和资源配置方案，为实体系统的部署提供经过充分验证的、可快速响应的预案库，将试错成本有效转移至虚拟域。另一方面要建立算法能力的对抗进化与持续迭代机制。构建闭环的算法能力进化体系：通过常态化的对抗性机器学习，使核心算法在持续的“攻防对抗”推演中揭示弱点、识别漏洞；构建严谨的算法效能评估指标体系，实时监测算法在实战模拟环境中的表现；做好数据回流与模型更新，将对抗推演中发现的漏洞、优化的策略，快速反馈至算法模型训练流程，驱动模型持续优化迭代，确保算法能力始终领先于潜在对手，实现攻防能力的动态进化与代际跃升。