With the deep integration of edge computing technologies and AI algorithms, the traditional sensing domain is undergoing an unprecedented transformation toward intelligentization. By synergistically optimizing distributed computing architectures with machine learning models, sensor devices that were once reliant on central processors now possess real-time self-calibration, dynamic self-diagnosis, and intelligent adaptive capabilities. Take high-precision MEMS inertial chips as an example: their built-in edge computing units can perform real-time filtering and error compensation on raw accelerometer and gyroscope data, while AI algorithms dynamically model environmental disturbances such as temperature drift and nonlinear errors. As a result, within a wide operating temperature range of −40°C to 85°C, the navigation accuracy of these devices improves by more than 300% compared with conventional solutions. The pressure-sensor field has also achieved breakthrough progress. Intelligent pressure sensors optimized through neural networks, deployed with lightweight AI models at the edge, can achieve high-frequency data acquisition at 2,000 samples per second and real-time feature extraction. This architecture not only reduces data-transmission bandwidth requirements by 80%, but also continuously refines pressure–temperature cross-sensitivity compensation algorithms via self-learning mechanisms, enabling industrial-grade pressure sensors to maintain long-term stability for over ten years without recalibration. In terms of extreme-environment adaptability, radiation-hardened intelligent pressure sensors have been successfully deployed in deep-sea probes and Mars rovers, with a dynamic response range five times wider than that of conventional devices. These technological advancements are profoundly reshaping the operational paradigms of smart equipment. In the autonomous-driving sector, edge-computing platforms that fuse multi-source heterogeneous sensor data enable millisecond-level path replanning and dynamic obstacle avoidance, increasing the pass rate of L4-level autonomous vehicles in complex urban environments by 40%. In drone-swarm control systems, a distributed attitude-sensing network based on MEMS inertial chips, coupled with AI-driven anti-interference algorithms, ensures that formations of up to one hundred drones maintain formation accuracy within 0.5 meters even in winds of Beaufort scale 7. In the industrial-robotics domain, intelligent six-axis force sensors leverage edge computing to perform real-time solution of force/position hybrid control, and when combined with AI-powered vision-guidance systems, achieve repeat positioning accuracy of ±0.02 mm for precision assembly tasks. From energy systems to smart manufacturing, intelligent-sensing technologies are laying the digital foundation for a new industrial ecosystem. In the renewable-energy sector, smart-grid sensors integrated with AI algorithms can locate microgrid faults and initiate self-healing within seconds, boosting the power-supply reliability of distributed-energy systems to 99.999%. In the green industrial revolution, AI-driven predictive-maintenance models, supported by real-time collection of equipment-operation parameters and environmental data through digital-twin-based intelligent-sensing networks, enable large-scale industrial equipment to