1. Introduction
1.1 Research Background and Significance
Against the backdrop of the rapid development of Industry 4.0 and intelligent manufacturing, control network platforms have become a key support for modern industrial systems. Industry 4.0 aims to achieve the transformation and upgrading of industrial production through the deep integration of digital, networked, and intelligent technologies, constructing highly flexible, personalized, and intelligent production models. Intelligent manufacturing emphasizes the use of advanced information technology and automation technology to enhance the intelligence level of production processes, achieving efficient, precise, and sustainable development of production.
The control network platform, as a key hub connecting various devices, systems, and personnel in industrial production, is responsible for important tasks such as data transmission, information interaction, and collaborative control. In the architecture of Industry 4.0 and intelligent manufacturing, it is the foundation for achieving automation, intelligence, and flexibility in production processes. Through the control network platform, various production devices can interconnect in real-time, achieving real-time data sharing and collaborative work, thereby improving production efficiency, optimizing production processes, reducing production costs, and enhancing the market competitiveness of enterprises. For example, in smart factories, the control network platform can organically connect robots, automation devices, sensors, etc., on the production line, achieving real-time monitoring and precise control of the production process, timely adjusting production parameters to cope with various changes, ensuring the stability and consistency of product quality.
Multifunctional sensors, communication, and effectors, as important components of the control network platform, play a crucial role in the performance and functional realization of the control network platform. Multifunctional sensors can perceive various physical quantities, chemical quantities, and state information in the production process, such as temperature, pressure, speed, position, etc., and convert this information into electrical or digital signals, providing real-time and accurate data support for the control network platform. For example, on automobile manufacturing production lines, various sensors installed on robotic arms, material conveying devices, and processing tools can monitor the operating status of equipment, processing accuracy of parts, and location information of materials in real-time, providing a basis for subsequent production decisions.
Communication technology is responsible for transmitting data and instructions between multifunctional sensors, control centers, and effectors, ensuring fast and accurate information delivery. With the continuous development of communication technology, from traditional wired communication to wireless communication, and now to new generation communication technologies like 5G and 6G, the speed, reliability, and coverage of communication have been greatly enhanced. High-speed, stable communication networks can achieve real-time transmission of large amounts of data, allowing control centers to timely obtain data collected by sensors and quickly send control instructions to effectors, achieving real-time control of the production process. For example, in smart grids, communication technology has enabled real-time monitoring and scheduling of distributed energy, ensuring the stable operation of the power system.
Effectors execute corresponding actions based on instructions from the control center, achieving control and adjustment of the production process. They can be various actuators, such as motors, valves, robots, etc., completing specific tasks by changing their own states or actions. On automated production lines, effectors precisely control the motion trajectory of robots and adjust the parameters of processing equipment according to control instructions, achieving precise processing and assembly of products.
The collaborative work of multifunctional sensors, communication, and effectors is the core of achieving intelligent and efficient operation of the control network platform. Their close cooperation can achieve comprehensive perception, real-time analysis, and precise control of the production process, thereby enhancing the intelligence level of industrial production and promoting the in-depth development of Industry 4.0 and intelligent manufacturing. For example, in intelligent warehousing and logistics systems, sensors monitor the location and status of goods in real-time, communication technology transmits this information to the control system, and the control system issues instructions to effectors based on analysis results, achieving automatic storage, retrieval, and handling of goods, improving the efficiency and accuracy of warehousing logistics.
1.2 Research Objectives and Content
This study aims to deeply analyze the collaborative working mechanism of multifunctional sensors, communication, and effectors in control network platforms, revealing their internal connections and collaborative laws, providing theoretical support and technical guidance for enhancing the performance and intelligence level of control network platforms. The specific research content includes:
1.Characteristics and Data Processing of Multifunctional Sensors: In-depth study of the working principles, performance characteristics, and adaptability of multifunctional sensors in complex industrial environments. Analyze the advantages and disadvantages of different types of sensors, discussing how to select suitable sensors based on actual application needs. At the same time, focus on the methods of data collection, preprocessing, and feature extraction of sensors, improving the accuracy and reliability of data, providing high-quality data support for subsequent data analysis and decision-making. For example, in chemical production processes, it is necessary to accurately monitor various parameters such as temperature, pressure, and flow. By studying the adaptability of different types of sensors in complex chemical environments, selecting suitable sensors and optimizing their data collection and processing methods can reflect the state of the production process more accurately, providing reliable basis for production control.
2.Application and Optimization of Communication Technology in Control Networks: A comprehensive study of the application of communication technology in control network platforms, including wired and wireless communication technologies. Analyze the characteristics and applicable scenarios of different communication protocols, discussing how to improve the reliability, real-time performance, and security of communication. Study the topology design, network optimization, and fault diagnosis methods of communication networks to reduce communication delays, improve data transmission efficiency, and ensure the stable operation of communication networks. For example, in smart factories, a large number of devices require real-time communication. By optimizing the communication network topology and selecting suitable communication protocols, communication conflicts can be reduced, and the timeliness of data transmission can be improved, ensuring efficient collaboration in the production process.
3.Control Strategies and Optimization of Effectors: Study the working principles, control methods, and execution accuracy of effectors, analyzing their performance under different control tasks. Discuss how to formulate reasonable control strategies for effectors based on control objectives and sensor feedback information to achieve precise control of the production process. At the same time, study the optimization design and fault diagnosis methods of effectors to improve their working efficiency and reliability, reducing energy consumption and maintenance costs. For example, in robot control, optimizing the control strategies of effectors based on the task requirements and environmental information can enable robots to perform operational tasks more accurately, improving production efficiency and quality.
4.Collaborative Mechanism of Multifunctional Sensors, Communication, and Effectors: In-depth study of the collaborative working mechanism among multifunctional sensors, communication, and effectors, analyzing their interactions and influences in information exchange, task allocation, and collaborative control processes. Establish mathematical models and simulation platforms for collaborative work, optimizing collaborative strategies through simulation and experimental verification, enhancing the overall performance and intelligence level of the system. Research how to achieve seamless docking and collaborative optimization among the three to meet the complex and changing industrial production needs. For example, in intelligent warehousing logistics systems, by establishing a collaborative working model of sensors, communication, and effectors, optimizing their information exchange and task allocation can achieve efficient storage, retrieval, and handling of goods, enhancing the intelligence level and operational efficiency of warehousing logistics.
1.3 Research Methods and Innovations
1.Research Methods:
◦Literature Research Method: Widely collect relevant literature on control network platforms, multifunctional sensors, communication, and effectors from both domestic and international sources, including academic journal papers, thesis papers, research reports, patent literature, etc. Systematically sort and analyze these literatures to understand the current research status, development trends, and existing problems in this field, providing a theoretical basis and research ideas for the study. Through literature research, grasp the working principles, performance characteristics, and application cases of different types of multifunctional sensors in the industrial field; understand the application status and development trends of communication technology in control networks, as well as the characteristics and applicable scenarios of various communication protocols; analyze the control strategies and optimization methods of effectors and their application effects in actual production.
◦Case Analysis Method: Select typical industrial production cases, such as automobile manufacturing, chemical production, and intelligent warehousing logistics, to analyze the collaborative working situation of multifunctional sensors, communication, and effectors in control network platforms. Through detailed research on cases, summarize successful experiences and existing problems, providing practical basis for proposing targeted improvement measures and optimization strategies. For example, in the automobile manufacturing case, analyze how sensors monitor the operating status of equipment and the processing accuracy of parts in real-time, how communication technology achieves fast data transmission and accurate instruction delivery, and how effectors complete the assembly of automobile parts based on control instructions, identifying key factors and optimization directions in the collaborative working process.
◦Experimental Research Method: Build an experimental platform to simulate the actual industrial production environment and conduct experimental research on the collaborative work of multifunctional sensors, communication, and effectors. Through experiments, validate the results of theoretical analysis, testing the performance indicators of the system under different parameters and conditions, such as the accuracy and real-time of data transmission, the control accuracy and response speed of effectors, etc. Based on experimental results, optimize the design and parameter configuration of the system to improve the overall performance of the system. For example, on the experimental platform, test the data collection accuracy and stability of different types of sensors in complex environments, study the transmission performance of communication networks under different loads, and the working efficiency and energy consumption of effectors under different control strategies.
1.Innovations:
◦Cross-domain Collaborative Innovation: This research breaks traditional disciplinary boundaries, deeply integrating knowledge and technologies from control engineering, sensor technology, communication technology, computer science, and other fields, studying the collaborative working mechanism of multifunctional sensors, communication, and effectors in control network platforms. This cross-domain research method helps to integrate the advantageous resources of various fields, achieving technological innovation and breakthroughs, providing new ideas and methods for solving complex industrial production problems. Through cross-domain collaboration, it is possible to better understand the interactions and influences between different technologies, thereby optimizing the overall performance of the system and enhancing the intelligence level of industrial production.
◦Technological Fusion Application Innovation: In the research process, explore the integration of emerging technologies such as artificial intelligence, big data, and cloud computing with traditional multifunctional sensor, communication, and effector technologies, achieving technological fusion application innovation. Utilize artificial intelligence algorithms to perform real-time analysis and prediction of data collected by sensors, providing more accurate basis for production decisions; leverage big data technology to store, manage, and mine massive production data, discovering potential production rules and optimization opportunities; through cloud computing technology, achieve distributed storage and computation of data, enhancing the processing capacity and response speed of the system. The fusion application of these technologies will bring new opportunities and challenges for the development of control network platforms, and is expected to promote industrial production towards a more intelligent and efficient direction.
◦Collaborative Mechanism Optimization Innovation: In-depth study of the collaborative mechanisms among multifunctional sensors, communication, and effectors, proposing new collaborative strategies and optimization methods. By establishing more precise mathematical models and simulation platforms, comprehensively simulate and analyze the collaborative working process, identifying key factors affecting collaborative effectiveness, and optimizing accordingly. For example, optimize the data collection and transmission strategies of sensors to improve the accuracy and timeliness of data; improve the topology and routing algorithms of communication networks to reduce communication delays and packet loss rates; design smarter control strategies for effectors to achieve precise control and optimization of the production process. These collaborative mechanism optimization innovations will help improve the overall performance and intelligence level of the control network platform, meeting the high demands of Industry 4.0 and intelligent manufacturing for production systems.
2. Overview of Control Network Platforms
2.1 Concept and Architecture of Control Network Platforms
A control network platform is a comprehensive platform that deeply integrates control technology, network technology, and information technology. It achieves centralized control and management of various devices and systems through networks, widely applied in industrial automation, smart buildings, transportation, and many other fields. Its core lies in utilizing network communication technology to connect sensors, controllers, effectors, and other devices distributed in different geographical locations, achieving real-time data transmission and interaction, thereby enabling precise control and optimized management of the entire system.
From the perspective of functional characteristics, control network platforms have high reliability, ensuring stable operation in complex industrial environments, reducing the probability of failures, and ensuring production continuity; they possess real-time capabilities, meeting the strict requirements of industrial production for data transmission and control response times, processing and feedback information in a timely manner; they also have openness, supporting various communication protocols and device interfaces, facilitating system integration and expansion, enabling seamless docking with devices and systems from different manufacturers.
Control network platforms typically adopt a layered architecture, which helps achieve modular and hierarchical management of functions, improving the maintainability and scalability of the system. Common layered architectures include the perception layer, network layer, control layer, and application layer.
The perception layer, as the foundation of the control network platform, is mainly composed of various multifunctional sensors, responsible for collecting various information from the physical world, such as temperature, pressure, flow, position, etc. These sensors convert physical quantities into electrical or digital signals, providing raw data for the entire system. For example, in smart factories, temperature sensors installed on production lines can monitor the operating temperature of equipment in real-time, and pressure sensors can detect pressure changes in pipelines. This data is crucial for monitoring and controlling the production process.
The network layer is the key channel for data transmission, responsible for transmitting data collected by the perception layer to the control layer and transmitting instructions from the control layer to the effectors. It includes wired communication networks and wireless communication networks, such as Ethernet, Wi-Fi, Bluetooth, ZigBee, etc., as well as various communication protocols such as TCP/IP, UDP, Modbus, etc. Different communication technologies and protocols are suitable for different application scenarios. Ethernet, with its high-speed and stable characteristics, is commonly used for device connections in industrial automation; Wi-Fi is convenient for the access of mobile devices, suitable for wireless device communication in smart buildings.
The control layer is the core of the control network platform, mainly composed of controllers and servers. The controller is responsible for receiving data transmitted from the network layer, analyzing and processing it according to preset control strategies and algorithms, generating control instructions, and sending them to the effectors. The server is used for data storage and management, providing data analysis and decision support functions. In industrial control systems, programmable logic controllers (PLCs) are often used as controllers, which can achieve logical control and sequential control of production equipment based on input signals and preset programs.
The application layer is the interface between the control network platform and users, providing various application services such as monitoring interfaces, data analysis reports, remote control, etc. Users can understand the system’s operating status in real-time through the application layer, perform parameter settings, and control operations. For example, in intelligent building management systems, users can view environmental parameters such as temperature and humidity in real-time through mobile apps or computer clients and remotely control the operating status of equipment such as air conditioning and lighting.
The layers communicate with each other through standard interfaces and protocols, achieving collaborative work. The data collected by the perception layer is transmitted to the control layer through the network layer, the control layer analyzes the data and makes decisions, generates control instructions, and transmits them to the effectors through the network layer, which execute the corresponding actions based on the instructions, achieving control of the system. The application layer provides users with an entry point for monitoring and managing the entire system, allowing users to configure and adjust the system to meet different application needs.
2.2 Development History and Current Status of Control Network Platforms
The development history of control network platforms is a history of continuous evolution and innovation, closely accompanied by the development of industrial automation and information technology. Early control network platforms were mainly based on simple point-to-point communication methods to achieve basic control of production equipment. At this stage, the functions of control systems were relatively simple, mainly relying on the connection of hardware devices and simple logic control, with slow data transmission speeds and limited communication distances. As the scale of industrial production continued to expand and the demand for production efficiency increased, traditional point-to-point communication methods gradually became unable to meet the needs of complex production systems.
To address this challenge, fieldbus technology emerged, opening a new chapter in the development of control network platforms. Fieldbus technology connects multiple devices to the same bus, achieving digital communication between devices, greatly improving the efficiency and reliability of data transmission. Through fieldbus, devices such as sensors, controllers, and effectors can exchange information in real-time, achieving centralized control and management of the production process. For example, in automobile manufacturing factories, fieldbus can connect various devices on the production line, achieving precise control of the production process, improving production efficiency and product quality. Different types of fieldbuses have been widely used in the industrial field, such as PROFIBUS, CAN, FF, etc., each with unique characteristics and advantages, suitable for different industrial scenarios.
With the rapid development of information technology, industrial Ethernet has gradually become the mainstream technology for control network platforms. Industrial Ethernet inherits the advantages of traditional Ethernet, such as high speed, openness, and good compatibility, while optimizing and improving it according to the needs of industrial applications, providing better real-time performance, reliability, and security. It can achieve high-speed data transmission, meeting the industrial production’s requirements for real-time processing of large amounts of data. At the same time, industrial Ethernet supports various communication protocols, facilitating integration with enterprise information systems and enabling sharing and collaborative management of production data. In smart factories, industrial Ethernet connects devices from various production links into an organic whole, achieving comprehensive monitoring and intelligent management of the production process.
In recent years, with the continuous emergence of new technologies such as the Internet of Things, big data, and artificial intelligence, control network platforms are accelerating their development towards intelligence, integration, and cloud computing. The application of IoT technology enables more devices to connect to control network platforms, achieving interconnectivity and real-time data collection of devices. Big data technology provides powerful data processing and analysis capabilities for control network platforms, enabling the discovery of potential production rules and optimization opportunities through mining and analyzing massive production data, providing a more scientific basis for production decisions. The introduction of artificial intelligence technology enables control network platforms to have autonomous learning and intelligent decision-making capabilities, allowing them to automatically adjust control strategies based on real-time data in the production process, optimizing production control. For example, in chemical production, using artificial intelligence algorithms to analyze production data can timely identify abnormal situations in the production process and automatically adjust production parameters to ensure the safety and stability of production.
Under the current status, control network platforms exhibit diversification in network architecture. In addition to traditional star, bus, and ring topologies, with the development of wireless network technology, new network architectures such as wireless sensor networks and Mesh networks are gradually being applied to control network platforms. These new network architectures have advantages such as flexible deployment, low cost, and strong scalability, meeting the needs of different industrial scenarios. In some industrial sites where wiring is difficult, wireless sensor networks can conveniently achieve connections and data collection of devices.
Communication protocols, as a key component of control network platforms, are also continuously evolving and improving. Currently, in addition to the widely used TCP/IP protocol in industrial Ethernet, many communication protocols specifically targeting the industrial control field have emerged, such as OPC UA and MQTT. OPC UA has characteristics such as cross-platform, high security, and strong scalability, enabling seamless communication and data sharing between devices from different manufacturers. MQTT, on the other hand, is a lightweight communication protocol suitable for resource-constrained devices and low-bandwidth, high-latency network environments, widely used in IoT device communication.
Security technology has also become increasingly important in control network platforms. With the deep integration of control network platforms and enterprise information systems, as well as the rapid development of the industrial Internet, the security threats faced by control network platforms are increasing. To ensure the secure operation of control network platforms, various security technologies and measures continue to emerge, such as firewalls, intrusion detection systems, data encryption, and identity authentication. Firewalls can filter network traffic, blocking unauthorized access and attacks; intrusion detection systems can monitor abnormal behaviors in the network in real-time, timely identifying potential security threats; data encryption technology can encrypt data during transmission and storage, preventing data from being stolen or tampered with; identity authentication ensures that only authorized users can access the control network platform.
Currently, significant progress has been made in the areas of network architecture, communication protocols, and security technologies in control network platforms. However, with the in-depth development of Industry 4.0 and intelligent manufacturing, control network platforms still face many challenges and require continuous technological innovation and optimization to meet the growing industrial production needs.
2.3 Challenges and Development Trends Faced by Control Network Platforms
In the wave of digital transformation, control network platforms, as the core support for the intelligence of industrial production, are facing many severe challenges while also giving rise to unprecedented development opportunities, presenting clear development trends.
Network security is the primary challenge faced by control network platforms. As control network platforms deeply integrate with enterprise information systems and the Internet, their attack surface is continuously expanding, and security threats are becoming increasingly complex and diverse. Hackers may invade control network platforms through malware, tampering with critical data, disrupting the production process, and even leading to the paralysis of the entire production system, causing significant economic losses to enterprises. For example, the “Stuxnet” virus incident in 2010 specifically targeted industrial control systems, infecting Siemens’ industrial control software and damaging centrifuges in Iran’s nuclear facilities, resulting in severe consequences. This incident fully exposed the vulnerability of control network platforms in the face of cyber attacks. Furthermore, internal personnel’s operational errors and improper permissions management may also trigger security risks. To address these challenges, enterprises need to strengthen the construction of network security protection systems, adopting multiple security measures such as firewalls, intrusion detection systems, encryption technologies, and identity authentication, establishing a multi-layered security protection barrier. At the same time, it is also necessary to enhance employees’ security awareness training, standardize operational processes, and improve the ability to respond to security incidents.
Data integration and management are also significant challenges faced by control network platforms. In industrial production, control network platforms need to integrate large amounts of data from different devices and manufacturers, which may have various formats and standards. How to achieve effective integration and unified management of this data is a challenge. Data collected by different types of sensors may have different sampling frequencies, data precisions, and encoding methods, and the diversity of communication protocols also poses compatibility issues during data transmission and interaction. Furthermore, as the volume of data continues to grow, how to efficiently store, process, and analyze this data to extract valuable information to support production decisions is also a key issue that control network platforms need to solve. Enterprises need to establish unified data standards and interface specifications, adopting data cleaning, transformation, and integration technologies to consolidate and process multi-source heterogeneous data. Meanwhile, utilizing big data technology, they should build data storage and analysis platforms to achieve efficient management and in-depth mining of massive data.
The increasing demands for real-time performance and reliability present higher challenges for control network platforms. In industrial automation production, many control tasks have high real-time requirements for data transmission and processing, such as precise control of robots and high-speed operation of production lines. Any slight delay may lead to deviations in the production process, affecting product quality and production efficiency. Moreover, the reliability of control network platforms is directly related to the continuity and stability of production. Once a failure occurs, it may trigger serious production accidents. Traditional network technologies and communication protocols have certain limitations in meeting the real-time and reliability requirements in complex industrial environments, and issues such as network delays and packet loss are difficult to completely avoid. To meet these requirements, it is necessary to develop new types of network communication technologies and protocols, optimize network topology, and adopt redundancy, fault diagnosis, and self-healing technologies to enhance the real-time performance and reliability of control network platforms.
While facing numerous challenges, control network platforms also exhibit clear development trends. Protocol standardization is one of the important directions for future development. As the application range of control network platforms continues to expand, the demand for interconnectivity between different devices and systems is becoming increasingly urgent. Unified communication protocols and interface standards can break down compatibility barriers between devices, enabling seamless integration and collaborative work of devices from different manufacturers. Currently, some international organizations and industry associations are actively promoting the standardization of control network protocols, such as the OPC UA (Open Platform Communications Unified Architecture) protocol, which provides unified data access and interaction standards for the industrial automation field, promoting interoperability between different devices and systems. In the future, with the continuous advancement of protocol standardization, control network platforms will become more open and flexible, facilitating system integration and expansion.
Enhancing security is an inevitable trend in the development of control network platforms. In the face of increasingly severe network security situations, enterprises will increase their investments in network security, adopting more advanced security technologies and management measures. In addition to traditional firewalls and encryption technologies, artificial intelligence and machine learning technologies will also be widely applied in the field of network security. By learning and analyzing large amounts of network data, artificial intelligence algorithms can monitor network traffic in real-time, identify abnormal behaviors, timely discover potential security threats, and automatically take corresponding defensive measures. Blockchain technology, due to its decentralized and tamper-proof characteristics, provides new ideas for the security protection of control network platforms. Utilizing blockchain technology can achieve secure storage and transmission of data, ensuring the integrity and authenticity of data, improving the security and credibility of control network platforms.
Enhancing intelligence and automation levels will be the core trend in the development of control network platforms. With the continuous development of emerging technologies such as artificial intelligence, big data, and cloud computing, control network platforms will possess stronger intelligent decision-making and automatic control capabilities. By performing real-time analysis and mining of massive data generated during production processes, control network platforms can predict equipment failures, optimize production processes, and improve production efficiency and product quality. For example, using machine learning algorithms to analyze equipment operating data can predict potential failures of equipment in advance, enabling timely maintenance and avoiding losses caused by equipment downtime. At the same time, continuous advancements in automatic control technology will enable control network platforms to achieve more precise and efficient production control, reducing human intervention and lowering production costs.
While facing numerous challenges, control network platforms are also ushering in new development opportunities. By actively addressing challenges, seizing development trends, and continuously advancing technological innovation and applications, control network platforms will play an increasingly important role in the process of Industry 4.0 and intelligent manufacturing, providing strong support for achieving intelligent, efficient, and sustainable industrial production.
3. Principles and Applications of Multifunctional Sensors
3.1 Working Principles of Multifunctional Sensors
3.2 Typical Application Cases of Multifunctional Sensors
4.1 Forms of Application of Communication Technology in Control Networks
5.1 Basic Working Principles of Effectors
6.1 Collaboration Between Multifunctional Sensors and Communication
7.1 Optimization Measures at the Technical Level
8.1 Summary of Research Results
This article contains 34,100 words of absolute technical content. Join the knowledge community to access it;
The following excellent articles have been uploaded to this knowledge community:
In-depth Analysis and Outlook on the Construction of Intelligent Combat Forces by the US Army – 18,250 Words of Absolute Technical Content
Artificial Intelligence Empowering Underwater Warfare: Applications, Challenges, and Future Outlook – 31,900 Words of Absolute Technical Content
Insight into the US Army’s Swarm Simulation Test Technology: Innovation, Applications, and Strategic Insights – 26,700 Words of Absolute Technical Content
Multidimensional Analysis of the US Army’s Intelligent Combat Force Construction Strategy and Practice – 23,100 Words of Absolute Technical Content
Autonomous Behavior System Clusters: Technological Evolution, Application Expansion, and Challenge Response – 17,000 Words of Absolute Technical Content
Multidimensional Analysis of Starlink’s Strategic Significance in the Russia-Ukraine War – 19,400 Words of Absolute Technical Content
Analysis of Link-16, Link-11, and Link-11B Data Link Technologies and Application Prospects – 41,100 Words of Absolute Technical Content
Link 22 Data Link Technology: Principles, Applications, and Future Trends – 25,300 Words of Absolute Technical Content
21st Century Troops’ Universal Tactical Data Link: Technological Evolution, Application Efficiency, and Future Outlook – 30,100 Words of Absolute Technical Content
Analysis of Advanced Bomber Combat Capabilities of the US and Russia and Future Trends – 30,400 Words of Absolute Technical Content
Development of New Quality Space Equipment by the US: Insight into Trends and Challenges – 24,700 Words of Absolute Technical Content
Link 16 System: Definition, Component Analysis, and Combat Deployment Strategy Research – 26,800 Words of Technical Content
Communication and Information Systems: Current Status, Innovation, and Future Trends – 24,900 Words of Technical Content
Link 11 System Technology: Principles, Applications, and In-depth Analysis of Development – 20,400 Words of Technical Content
Analysis of the US Star Network’s High-Dynamic Communication Environment: Technology, Challenges, and Prospects – 28,530 Words of Absolute Technical Content
Satellite Warfare and Space-based Military Surveillance: Transforming Military Power and Future Challenges – 22,200 Words of Technical Content
Analysis of the US Star Network: Multilevel Network Protection System Analysis and Satellite Node Security Assessment – 27,000 Words of Technical Content
Future Combat Networks Empowering Joint All-Domain Command and Control: Transformations, Challenges, and Perspectives – 27,300 Words of Absolute Technical Content
In-depth Analysis and Optimization Strategies for Network Security Situation Awareness and Intrusion Detection Algorithms under the Star Network System – 33,700 Words of Absolute Technical Content
Analysis of Network Security Architecture under the US Star Network System and Exploration of Dynamic Threat Response Mechanisms – 38,700 Words of Absolute Technical Content
Communication Countermeasures Technology: Current Status, Challenges, and Future Outlook – 21,300 Words of Technical Content
More than 1,000 technical documents related to network warfare, information warfare, artificial intelligence, and weapon systems have been uploaded to this knowledge community.
