Detailed explanation of the characteristics of artificial intelligence-based monocrystalline silicon pressure transmitters

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What will an artificial intelligence-powered silicon crystal pressure transmitter look like? Overall, artificial intelligence (AI) is developing at a rapid pace, and the initiatives of Made in China 2025 and Industry 4.0 aimed at achieving intelligent manufacturing are still in the process of being put into practice. It is still too early to discuss the precise characteristics of AI-powered pressure transmitters. However, it is certain that intelligent pressure transmitters used in the context of Made in China 2025 and Industry 4.0 should possess a higher level of intelligence compared to the older generation of metal-capacitive analog industrial pressure transmitters. Such transmitters should not only have functions such as menu operation, range adjustment, remote control, and temperature compensation, but also possess advanced capabilities such as self-status awareness, real-time monitoring, autonomous decision-making, and accurate execution. The key difference lies in whether they have the ability to learn and improve over time; only then can they be considered true AI-powered silicon pressure transmitters. In the future, high-grade, highly intelligent pressure transmitters will be referred to as Artificial Intelligence Pressure Transmitters. These are new types of pressure transmitters made using single-crystal silicon technology, designed specifically for initiatives such as China Manufacturing 2025 or Industry 4.0’s concept of intelligent manufacturing. To avoid confusion with the currently used term “Smart Pressure Transmitters,” they should be called Artificial Intelligence Pressure Transmitters. Such transmitters possess the following characteristics: Firstly, an Artificial Intelligence Pressure Transmitter is a device that, in addition to its basic functions, also has additional capabilities such as digital communication, configuration, optimization, diagnosis, and maintenance. It generally has the abilities to sense, analyze, reason, make decisions, and control processes. It represents a new generation of pressure transmitters that result from the integrated and deep integration of advanced manufacturing technologies, information technologies, and intelligent technologies ； Some artificial intelligence pressure transmitters possess advanced intelligent capabilities; they have network communication functions, the ability to self-perceive their own status, the surrounding environment, and the processes involved, as well as the capacity for analysis, reasoning, decision-making, and execution. They also have adaptive and optimization capabilities, can provide various types of data to support data analysis and mining, and can be integrated with CPS systems ； Finally, artificial intelligence-based pressure transmitters are also part of the entire product life cycle (PLM), which includes market demand analysis, product research and development, product production and management, product quality control, supply chain management, marketing management, customer management, after-sales service, and more – all of these aspects are driven by initiatives such as Made in China 2025 and Industry 4.0 smart manufacturing. The theoretical foundation of artificial intelligence pressure transmitters stems from theoretical research in artificial intelligence. Focusing on industrial safety applications, it aims at identifying fundamental theoretical areas that have the potential to drive advancements in artificial intelligence technology, thereby strengthening research in areas such as big data intelligence, cross-media perception computing, human-machine hybrid intelligence, swarm intelligence, as well as autonomous coordination and decision-making. Details: 1. Big data intelligence theory. Research on new artificial intelligence methods that combine data-driven approaches with knowledge-guided strategies, cognitive computing theories and methods centered on natural language understanding and images/graphs, theories and methods that integrate deep reasoning with creative artificial intelligence, fundamental theories and frameworks for intelligent decision-making under incomplete information, as well as data-driven mathematical models and theories for general artificial intelligence.   2. Cross-media perceptual computing theory. Research on perception acquisition beyond human visual capabilities, active visual perception and computation for the real world, auditory perception and computation in natural acoustic scenarios, speech perception and computation in natural interaction environments, human-like perception and computation for asynchronous sequences, autonomous learning for media intelligence perception*, and an omnidimensional intelligent perception and reasoning engine for cities.   3. Hybrid enhanced intelligence theory. Research on hybrid enhanced intelligence of \"human in the loop,\" behavior enhancement and brain-computer collaboration for human-machine intelligent symbiosis, machine intuitive reasoning and causal models, associative memory models and knowledge evolution methods, hybrid enhanced intelligence approaches for complex data and tasks, cloud robot collaborative computing methods, situation understanding in real-world environments, and human-machine team collaboration. 4. Swarm intelligence theory. Study the theoretical structures and organizational methods of swarm intelligence, the incentive mechanisms and emergence principles of swarm intelligence, the theoretical and methodological aspects of swarm intelligence, as well as the general computational paradigms and models for swarm intelligence.   5. Autonomous collaborative control and optimization decision theory. Research on theories such as collaborative perception and interaction for autonomous unmanned systems, collaborative control and optimal decision-making for autonomous unmanned systems, and knowledge-driven collaboration and interoperability among humans, machines, and objects.   6. Advanced Machine Learning* Theory. It studies theoretical foundations of statistics*, theories of uncertainty reasoning and decision-making, distributed learning* and interaction, privacy protection*, small-sample learning*, deep reinforcement learning*, unsupervised learning*, semi-supervised learning*, active learning*, and other related theories as well as efficient models.   7. Theoretical framework of brain-inspired intelligent computing. Research on theories and methods such as brain-inspired perception, brain-inspired computing*, the integration of brain-inspired memory mechanisms with computing, brain-inspired complex systems, and brain-inspired control.   8. Theory of quantum intelligent computing. Explore the quantum patterns and underlying mechanisms of brain cognition, and study efficient quantum intelligence models and algorithms, high-performance and high-bit quantum artificial intelligence processors, as well as real-time quantum artificial intelligence systems capable of interacting with the external environment. The application of artificial intelligence pressure transmitters in the field of safety will further contribute to the development of functional safety pressure transmitters for safety instrumented systems. Moving from users relying on their own experience gained from practical use of pressure transmitters to utilizing big data to improve the performance of existing monocrystalline silicon pressure transmitters, it is possible to prevent issues such as corrosion of the measuring diaphragm, static pressure overload, pressure surges (water hammer effect), and hydrogen-induced brittleness that can occur due to operating conditions. It also helps to avoid faults caused by factors other than the pressure transmitter itself, such as blockages in process connections or breaks in electronic signal wires, thereby enabling safety instrumented systems to provide real-time, continuous protection for production safety.