Table of Contents
What is industrial computing?
Industrial computing refers to the application of computing technology to improve the efficiency, automation and data-driven decision-making capabilities of each link in the industrial field. It covers many aspects from production and manufacturing to resource management, logistics and supply chain, and optimizes and intelligentizes industrial processes by using advanced computing technology and tools.
The development of Industrial Computing
Mechanization (late 18th century to 19th century)
The industrial revolution promoted the application of mechanical equipment in production and greatly improved production efficiency.
Computing mainly relies on mechanical devices such as gears and levers.
Electrification (late 19th century to early 20th century)
The widespread use of electricity has driven the automation of industrial production.
Equipment based on motors and electrical control has replaced some mechanical systems.
Automation (mid-20th century)
With the development of electronic technology, industrial production began to introduce electronic control systems.
The invention of programmable logic controller (PLC) has achieved partial automation of the production process.
Digitalization (late 20th century)
The development of computer technology has enabled industrial production to enter the digital stage.
The application of CAD/CAM software has improved the efficiency of design and manufacturing.
Intelligence (21st century to present)
The Internet of Things (IoT), cloud computing and artificial intelligence (AI) have driven the development of Industry 4.0.
Industrial systems have become more intelligent and interconnected, enabling data-driven decision-making.
Core concepts of industrial computing
Automation and control
Use computer control systems to automate production processes to improve efficiency and consistency.
Including programmable logic controllers (PLCs), distributed control systems (DCSs) and industrial robots.
Data acquisition and analysis
Collect production and operation data through sensors and network devices.
Use big data analysis and machine learning techniques to identify trends, predict problems, and optimize processes.
Interconnection
Use Internet of Things (IoT) technology to connect devices and systems to achieve real-time sharing and interaction of information.
Support remote monitoring and management to improve collaboration efficiency.
Intelligent decision-making
Apply artificial intelligence (AI) technology for predictive maintenance, quality control and supply chain optimization.
Help companies make smarter business decisions through data-driven insights.
Industrial computers and industrial computing
Industrial computers are computer systems designed specifically for industrial environments to control and monitor industrial processes. They are usually highly reliable, durable and adaptable to harsh environments. It is an important component of industrial computing, providing basic hardware support for the realization of intelligence and automation, while industrial computing improves the overall efficiency and intelligence level of industrial systems by combining a variety of advanced technologies.
Difference between industrial computers and industrial computing
1. Scope
Industrial computers are hardware devices that focus on performing specific tasks in industrial environments.
Industrial computing is a broader concept that covers the overall process of applying computing technology to improve efficiency and intelligence in the industrial field.
2. Role
Industrial computers act as actuators and controllers in industrial computing, processing field data and controlling equipment.
Industrial computing includes a variety of technical means and methods, including industrial computers, for overall optimization of industrial processes.
Industrial Computing vs Embedded Systems vs Embedded Computing
Industrial computing, embedded systems and embedded computing complement each other in modern industrial and technological applications. Industrial computing uses the power of embedded systems and embedded computing to promote the development of intelligent manufacturing and automation, while embedded systems and embedded computing provide dedicated and reliable computing solutions for various devices and applications
Features | Industrial computing | Embedded system | Embedded computing |
Definition | Apply computing technology in the industrial field to improve efficiency and automation. | A computing system that integrates hardware and software for specific functions, usually as part of a larger system. | Computing capabilities embedded in devices to perform specific tasks. |
Application scope | Covering the entire industrial operation and management, including automation, optimization, monitoring, etc. | Focus on the implementation of functions in a specific device or application. | Computing for specific functions or tasks, emphasizing efficient resource utilization. |
Hardware composition | Industrial-grade computers, servers, PLCs, sensors, network equipment, etc. | Microcontroller (MCU), microprocessor (MPU), sensor, interface module, etc. | Usually MCU or MPU integrated in the device. |
Software features | Including SCADA systems, MES systems, data analysis software, real-time operating systems (RTOS), etc. | Firmware, real-time operating system, driver, application software. | Application-specific firmware or software, often running in resource-constrained environments. |
Real-time performance | Support real-time data processing and analysis to meet the dynamic needs of industrial processes. | Real-time responsiveness is required for many tasks, especially in safety-critical applications. | Achieve real-time computing in response to inputs and events. |
Environmental adaptability | Able to operate in harsh industrial environments with high reliability and durability. | Designed to adapt to specific environmental conditions, which may be harsh or restricted environments. | Often face resource limitations and environmental constraints in embedded systems. |
Scalability | Requires flexible expansion and integration of multiple technologies and equipment to support complex industrial applications. | Generally relatively fixed, customized design is required for different application requirements. | Limited by the scalability of hardware design and functional requirements. |
Typical applications | Smart manufacturing, process control, energy management, logistics optimization, etc. | Home appliances, automotive electronics, medical equipment, communication equipment, etc. | Various devices that require integrated computing functions, such as sensors, wearable devices, industrial controllers, etc. |
Data processing capabilities | Processing large-scale data and complex analysis, and achieving efficient processing through cloud computing and edge computing. | Usually handles smaller-scale data and specific application tasks. | Efficient data processing and computing in resource-constrained environments. |
Technology integration | Integrate multiple advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and artificial intelligence. | Mainly integrates necessary hardware and software to achieve specific functions. | Focus on the integration of computing power to support device intelligence. |
Key technologies of industrial computing
Internet of Things (IoT): Industrial Internet of Things (IIoT) collects and analyzes data in real time through sensors and equipment interconnection to improve factory automation and equipment maintenance. IoT technology makes real-time data collection and monitoring possible, improving production transparency and responsiveness.
Cloud computing: It provides powerful computing resources and storage capabilities, enabling enterprises to process large amounts of data at lower costs and higher efficiency. Through cloud services, enterprises can monitor and manage production processes in real time and optimize resource allocation.
Big data analysis: By analyzing production data, enterprises can identify production bottlenecks, optimize supply chain management, and improve product quality.
Artificial intelligence and machine learning: Used for predictive maintenance, quality control, and production process optimization. AI can analyze historical data and predict future trends to help enterprises make better decisions.
Edge computing: Edge computing reduces latency and improves real-time data processing capabilities by processing data on-site where data is generated, which is suitable for industrial applications that require fast response.
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Industrial computing is changing the way industries operate through the integration and application of new technologies, driving the development of Industry 4.0. The future industrial environment will be more intelligent, interconnected and efficient, providing new opportunities and challenges for various industries.
