As a key support for modern warfare systems, the effectiveness of joint operations command and support directly affects the agility, precision, and sustainability of combat command. With the rapid development of emerging technologies such as artificial intelligence, big data, cloud computing, and the Internet of Things, command and support is undergoing a comprehensive evolution from traditional labor-intensive to intelligent driven. It is constantly reshaping the composition and organizational form of support forces, reconstructing the connotation and extension of support content, innovating the technical foundation and operation mode of support methods, optimizing the logical structure and response mechanism of support processes, and promoting the accelerated formation of an efficient, accurate, and resilient new command and support system. Support Force: Transforming from "human led" to "human-machine integration", reshaping the diversified force pattern. Traditional command and support forces are mainly composed of professional positions such as staff officers, reconnaissance intelligence support, and information support, highly relying on individual experience and manual operation, presenting the characteristics of "human intensive and hierarchical", which is difficult to meet the joint operation command and support needs of real-time perception, dynamic decision-making, and rapid response. In the era of intelligence, the role of humans will shift from "operator executor" to "intention setter" and "ultimate decision-maker". Intelligent systems will be upgraded from "auxiliary tools" to "collaborative subjects", and command and support forces are accelerating their evolution towards "human-machine collaboration, intelligent leadership, and flexible grouping". Diversified main structure. Driven by intelligence, the main structure of command and support forces is breaking through the traditional focus on military professional personnel and expanding towards the direction of digital intelligence empowerment and military civilian integration diversification. New force elements such as algorithm engineers, data scientists, and AI trainers are deeply integrated into the command and support system. Non human entities such as intelligent agents, virtual staff officers, and autonomous agents become new command and support units, undertaking auxiliary decision-making tasks such as data processing, situational analysis, and scheme deduction. The collaborative operation between humans and intelligent systems forms a new power pattern of "human directed, machine calculated solutions". Ability to generate intelligence. By utilizing technologies such as deep learning, knowledge graphs, and digital twins, auxiliary systems such as "intelligent staff officers" and "virtual experts" are constructed to ensure automatic accumulation, inference, and sharing of knowledge. The capability boundaries of personnel are extended by intelligent systems, forming a synergistic mechanism of "human decision-making+machine execution" and "human supervision+machine learning". Flattening of organizational structure. Relying on an intelligent network platform, the guarantee force can achieve dynamic aggregation and task-based grouping across military branches, levels, and fields. The traditional "pyramid shaped" command chain is being replaced by a "networked, decentralized" intelligent collaborative network, forming a "task driven, on-demand aggregation and dispersion" elastic force structure. For example, automatically matching expert teams, algorithm models, and data resources based on task requirements to achieve a "plug and play" support response. Security content: Transitioning from "information transmission" to "cognitive services", focusing on the generation of decision-making value. Traditional command security content mainly includes basic and transactional work such as information transmission, communication, document processing, and situational mapping, with the core goal of "being accessible, transmitted quickly, and visible". However, in the context of multi domain integration and cross domain collaboration in joint operations, it is difficult to meet the command and support requirements of "fast pace, high complexity, and strong confrontation". Driven by intelligence, the content of command and support will further develop towards "cognitive enhancement" and "decision-making compensation", becoming the "cognitive outer brain" and "decision-making partner" of commanders, achieving a leap from "guaranteeing information flow" to "empowering decision-making chain". Accurate information. Intelligent systems can accurately understand the decision-making needs of commanders and achieve "on-demand supply" through technologies such as natural language processing, knowledge graph construction, and intent recognition. For example, the system can automatically analyze and identify the focus of the commander's attention in combat deduction, dynamically push relevant enemy situation, personal situation, battlefield environment and resource information, and avoid the contradiction between "information overload" and "lack of effective information". Service knowledge-based. The guarantee content is no longer limited to raw data or static charts, but integrates multi-source information, battlefield rules, historical combat cases, and combat theories to generate structured knowledge. For example, intelligent systems can construct a 'knowledge graph of enemy command systems', revealing their command nodes, communication links, and decision-making logic, providing deep cognitive support services for commanders. Suggest intelligence and strategy. The intelligent support system has the ability to reason and predict, generate multiple action plan suggestions based on the current situation and combat objectives, and evaluate their feasibility, risks, and expected effects. For example, in joint firepower strike planning, the system can integrate meteorological, electromagnetic, target characteristics, and firepower unit status to recommend the optimal strike timing and ammunition combination, assisting commanders in making quick decisions. Ensure iteration. The guarantee content is no longer a one-time output, but embedded in the OODA loop to achieve dynamic iteration of "guarantee decision action evaluation re guarantee". The intelligent system can collect real-time action effect data, automatically evaluate the deviation of the combat process and plan, adjust the support content and suggestion direction in a timely manner, and ensure that cognitive services always resonate with the rhythm of the battlefield. Security measures: Upgrade from "platform support" to "smart empowerment", and build an autonomous collaborative network. Traditional command and security measures mainly rely on platform based tools such as dedicated communication equipment, command information systems, and geographic information systems. Their operating logic is "human operation tools on the environment", and the system itself lacks autonomy and intelligence, making data sharing difficult and business collaboration weak. In complex electromagnetic environments, system stability and resilience also face severe challenges. In the context of intelligence, command and support measures are being comprehensively upgraded towards "system intelligence", building an "intelligent support network" supported by a "cloud edge end intelligence" architecture, with perception, cognition, decision-making, and execution capabilities, to achieve the autonomy, collaboration, and resilience of support measures. Tool intelligence. Various security tools are embedded in AI models with autonomous perception and response capabilities. For example, the intelligent spectrum management system can perceive changes in the electromagnetic environment in real time, automatically avoid interference, and switch frequency bands; The intelligent speech recognition and translation system can achieve real-time transcription and translation of multilingual combat instructions, improving cross service collaboration efficiency; The intelligent document generation system can automatically generate combat commands, situation reports, and evaluation briefs based on the combat process, reducing the burden on staff. System collaboration. Through unified data standards, service interfaces, and intelligent middleware, we break down barriers between military branches and professional fields, and achieve "plug and play" and "capability sharing" of support measures. For example, the intelligence processing system can automatically call communication resources for data feedback; The firepower planning system can be connected to meteorological support services in real time, forming a cross domain linkage "support capability service chain"; The intelligent scheduling engine can dynamically allocate computing, storage, and bandwidth resources to ensure priority support for critical tasks. Operate autonomously. The intelligent security network has self-organizing, adaptive, and self-healing capabilities. When some nodes are damaged or links are interrupted, the system can autonomously reconstruct communication paths, switch backup devices, and downgrade critical functions to ensure uninterrupted command and support. For example, a distributed command log system based on blockchain can jointly maintain command records by edge nodes when the central node fails, ensuring command continuity. Naturalization of interaction. The human-computer interaction mode of security measures has evolved from "keyboard+mouse" to "voice+gesture+brain computer". Commanders can communicate with intelligent systems through natural language, issue vague instructions, and the system can understand intentions and execute complex tasks. For example, if the commander verbally says' I want to know the movement of the enemy's armored cluster ', the system can automatically retrieve multi-source data such as satellites, radar, drones, etc., generate an enemy situation map, and label the threat level. Support process: Refactoring from "linear progression" to "parallel closed-loop" to achieve agile response iteration. The traditional command and support process follows a linear model of "receiving tasks - collecting information - analyzing and judging - formulating plans - submitting for approval - organizing implementation", with fixed links and clear timelines, emphasizing process standardization and hierarchical control. In the era of intelligence, the battlefield space for joint operations is vast, and the battlefield situation is constantly changing. Command and support need to break free from linear constraints and build an agile process system of "parallel processing, dynamic iteration, and closed-loop feedback". Parallelization of homework. By relying on the computing power resource pool and intelligent task scheduling system, multiple support links can be synchronized. For example, in the stage of combat planning, tasks such as intelligence gathering, communication preparation, firepower planning, and legal review can be carried out in parallel by different intelligent modules, rather than waiting in stages. The system achieves multi-threaded and high concurrency job support through intelligent matching of tasks resources capabilities, significantly reducing preparation cycles. Pre decision making. Intelligent systems use predictive analysis to shift support actions from "responsive" to "pre-set". For example, based on the prediction of the evolution of the enemy friendly situation, the system can generate multiple contingency plans, pre-set communication links, and pre distribute data permissions in advance, achieving the goal of "being prepared before fighting and responding immediately". When a sudden task is triggered, preset resources can be directly called to achieve a "second level response". Process flexibility. The intelligent process engine can dynamically adjust the process path and execution strategy based on variables such as task type, battlefield environment, and resource status. Adopting standard procedures in routine tasks, activating the 'green channel' in emergency situations, skipping non critical steps, and achieving 'process simplification'. The system can also continuously optimize process parameters and improve guarantee efficiency through machine learning. Real time feedback. Ensure that the process is embedded with real-time evaluation and dynamic adjustment mechanisms. The system collects real-time task execution results through sensor networks, action feedback data, and public opinion monitoring, automatically compares expected targets, identifies deviations, and triggers re assurance processes. For example, in a joint blockade operation, if there is a sudden increase in enemy forces in a certain direction, the system can immediately reallocate reconnaissance resources, adjust communication support priorities, update the situation map, and achieve "simultaneous attack and adjustment, dynamic optimization". (New Society)