As the core component of information perception, sensor is the key bridge linking the physical world and the digital world, and it is the important cornerstone of new generation information technology. Firstly, the irreplaceable strategic value of sensing technology is discussed in basic science innovation, industrial upgrading and national defense engineering, etc., under the empowerment of artificial intelligence (AI) . Then, the technological breakthroughs and development trends of high-end sensors are analyzed in device upgrading, intelligent algorithms and system integration driven by AI convergence, and China's structural challenges in the global competition of high-end sensing technology are deeply deconstruct. Then, it systematically explains the application scenarios and core roles of high-end sensors in various fields in China and puts forward specific development paths to realize autonomous control of intelligent sensor. Finally, the prospective study is constructed from the dual perspectives of technology evolution trends and industrial ecology construction. This study provides a theoretical and practical guidance framework for China to break through the bottleneck of sensing technology and build a safe and controllable intelligent sensor industry system.

Micro-hemispherical resonator gyroscopes are widely used in aerospace, precision navigation and other fields due to their high stability and accuracy. However, the orthogonal error caused by structural defects seriously limits the application of microhemispherical resonator gyroscopes in high-precision scenarios. Firstly, the mechanism of orthogonal error is analyzed by a dynamic model considering the damping-mass-stiffness coupling effect. Then, based on the analysis of the orthogonal error mechanism, a micro-hemispherical resonator gyroscope orthogonal error compensation system based on the orthogonal coupling stiffness correction method is designed. Electrostatic negative stiffness is generated through a dedicated orthogonal control electrode configuration to directly offset the orthogonal coupling stiffness. Finally, a closed-loop compensation system is implemented and test verification is carried out. The test results show that after compensation by orthogonal coupling stiffness correction method, the angle random wander of the micro-hemispherical resonator gyroscope is reduced from 9.767°/h to 1.327°/h, the zero bias instability is reduced from 2.884°/h to 0.047°/h, and the zero bias stability is reduced from 29.954°/h to 0.402°/h after 10 s smoothing. The performance of the gyroscope has been significantly improved after compensation, which verifies the effectiveness of the orthogonal coupling stiffness correction method. The research strongly promotes the development of inertial navigation systems.

Eddy current sensors are widely used in railroad detection, but temperature variations can lead to sensitivity and zero drift, thus affecting sensors accuracy. For this reason, a temperature compensation model based on black widow optimization (BWO) -support vector machine (SVM) is proposed. The model overcomes the shortcomings of the traditional model that is easy to fall into local optimum by globally optimizing the parameters of the SVM kernel function and the penalty factor. The data of eddy current displacement sensors at different temperatures are obtained through two-dimensional calibration tests, and the temperature compensation models of BWO-SVM, firefly optimization algorithm-least squares support vector machine (FOA-LSSVM) and improved genetic algorithm-back propagation (GA-BP) are established and compared respectively. The experimental results show that the BWO-SVM temperature compensation model reduces the sensitivity temperature coefficient of the sensor from 1.58×10-2/℃ to 3.28×10-4/℃ and the zero temperature coefficient from 1.54×10-2/℃ to 2.96×10-4/℃. Compared with the FOA-LSSVM and GA-BP temperature compensation models, the BWO-SVM temperature compensation model shows more robustness and adaptability in complex environments. The research not only provides an effective and intelligent temperature compensation model for the health monitoring and maintenance of railroad infrastructure, but also provides a feasible idea for the other areas of nonlinear temperature compensation scenario.

Aiming at the problems of large error and complicated calculation in the existing blade remaining useful life (RUL) prediction methods, an RUL prediction method considering blade fatigue failure mode is proposed. Firstly, the blade damage model is established, and the crack extension process is observed under basic operating conditions. Then, for the nonlinearity and uncertainty of crack expansion, unscented Kalman filter algorithm is used for prediction. The tracking accuracy of the crack expansion trend is improved by fusing the fatigue failure mode with the crack observation data. Finally, simulation tests are conducted using a National Renewable Energy Laboratory (NREL) 5 MW wind turbine. Different average wind speeds and initial crack lengths are set by test. The simulation results show that the crack extension prediction result agrees with the observation data by 98.75%, and the RUL prediction error is greatly reduced. Comparative analysis results show that the proposed method can effectively suppress the interference of nonlinear factors and reduce the calculation time, which can be effectively applied to crack prediction. The research can provide theoretical support and engineering application reference for wind turbine blade health management.

The traditional fast Fourier transform (FFT) method is prone to spectral leakage and fence effect under frequency variation and non-synchronized sampling conditions, which leads to a decrease in the accuracy of active power metering. For this reason, a new power metering method is researched, and a power metering method based on multiple second-order generalized integrator (MSOGI) is proposed. The MSOGI is used to extract the fundamental and each harmonic component and their quadrature components of single-phase voltage and current signals respectively, and realize the accurate measurement of active power based on separating the fundamental and harmonics. Meanwhile, a frequency adaptive module is introduced to improve the metering accuracy under frequency fluctuation. Simulation results show that the MSOGI-based power metering method can maintain high metering accuracy under different grid frequencies and harmonic conditions. The method innovatively combines MSOGI with instantaneous active power calculation, which provides a reliable solution for high-precision power metering in complex grid environments and can provide reference for subsequent related research.

The combined vacuum nickel-based brazing and heat treatment process is proposed for the deformation of Coriolis mass flowmeter measuring tubes and residual internal stresses caused by the traditional silver-based manual brazing process. The process optimizes the welding process of the flowmeter, reduces the generation of internal stresses, and improves the symmetry of the vibration system through the steps of precision positioning and calibration, surface plasma treatment, gradient brazing material laying, and vacuum brazing, etc.To further eliminate internal stresses, the annealing and insulation process is used, which significantly improved the mechanical properties and measurement stability of the flowmeter. The test results show that the improved process enables the flowmeter to have an average error within 0.14% and a repeatability of up to 0.07%, which meets the requirements of class 0.15 accuracy. This research not only significantly improves the measurement performance of Coriolis mass flowmeter, but also provides an effective stress control method for the manufacture of vibration type precision sensors, which has important engineering application value and promotion prospects.

With the widespread application of piston pressure gauges, traditional pressure measurement devices are unable to meet the demands of modern industrial and technological development for high precision, high stability, and low cost, etc. To address these challenges, a new type of pressure standardizer, the absolute pressure gas piston pressure gauge, has been developed. The design parameters of the pressure gauge are as follows： measurement range of 6 kPa to 7.5 MPa, minimum resolution of 2 kPa, and accuracy grade as high as 0.005. The pressure gauge features efficient loading of weights and pollution-free. Test results indicate that the pressure gauge exhibits stable performance and high accuracy, enabling precise measurement and calibration of gauge pressure, absolute pressure, and vacuum pressure. The design has enhanced China's high-precision pressure measurement technology capabilities, and provided a crucial technical support for the development of related industries. The design holds significant importance for promoting national defense construction and industrial technological progress.

Shield machine hob is the core component for excavating rock and soil in tunnel construction. The failure inspection of shield machine tool is an important bottleneck that restricts the safety and efficiency of tunnel construction. Through the study of hob failure mechanisms and reasonable selection of detection physical quantity, a multi-parameter integrated sensor device based on hob speed, hob temperature and hob wear are designed. Wireless acquisition and communication technology is adopted to realize the transmission of measurement signals and real-time on-line detection of hob condition. The sensor device has been verified by industrialized application, and it can meet the requirements of on-line detection of hob under the harsh working environment of shield tunneling in terms of technical design and hob condition evaluation. This research has important reference value for the intelligent diagnosis and evaluation of tool failure.

When solving the attitude of a driverless micro-electro-mechanical systems (MEMS) gyroscope, it is difficult to accurately assess the statistical characteristics of the system noise and the measurement noise due to the influence of the noise in the operating environment, which leads to a poor filtering effect. To this end, an adaptive optimization algorithm of Kalman filter parameters that can be applied in complex system environments is designed. Based on the demand for real-time and curve smoothness of the filtering algorithm for gyroscope attitude solving, the filtering real-time and curve curvature are used as evaluation functions, and an approximation iteration method is used to obtain the optimal system noise and measurement noise. The superiority of the algorithm is proved by mathematical simulation and semi-physical simulation test. The algorithm realizes the independent acquisition of the key parameters of the Kalman filter and improves the real-time filtering and the smoothness of the filter curve.

Aiming at the fluctuation problem of phase modulation depth of sinusoidal phase modulation interferometer, a phase modulation depth measurement method based on fast Fourier transform (FFT) and a proportional integral differential (PID) control scheme are proposed. A computational model for the measurement of phase modulation depth is established. The model performs an FFT of the original interference signal to calculate the amplitude attenuation ratio of the low-frequency component of the signal after the carrier frequency shift and solves the phase modulation depth according to the functional relationship. The incremental PID control combined with direct digital synthesis (DDS) is used to adjust the carrier signal strength in real time to realize the precise control of the phase modulation depth. Numerical simulation results show that the phase modulation depth measurement method can accurately realize the measurement function. Several sets of measurement experiments are carried out on solid surface micro vibrations excited by acoustic radiation using a sinusoidal phase modulation interferometer. The experimental results show that the interferometric signal demodulation results are better, and the signal-to-noise distortion ratio is better than 99.85% under the closed-loop control of the phase modulation depth. The results validate the effectiveness of the proposed method. The proposed scheme is of great application value for improving the measurement performance of laser sinusoidal phase modulation interferometers.

With the complexity and aging of rotating equipment, the frequency of equipment failure gradually increases. To ensure the normal operation of rotating equipment in thermal power plants, the condition monitoring and fault early warning model of rotating equipment is researched. It is innovatively proposed to use wavelet packet transform and multivariate state estimation technology to extract the fault characteristics of rotating equipment and build a fault early warning model based on similarity model, K-means clustering algorithm and principal component analysis method. The experimental results show that the proposed model can significantly improve the accuracy of the feature parameters;the early warning threshold is 0.77. The model issues an alarm when the similarity of the data is lower than the early warning threshold in about 58 min. This is consistent with the actual fault generation time. Therefore, the proposed model has good fault early warning effect and has certain feasibility and practical application value. This research helps to promote the development of fault monitoring and early warning technology, and can provide technical support for the management of rotating equipment in thermal power plants.

A passenger flow optimization model based on improved ant colony optimization (ACO) algorithm is proposed for the passenger flow pressure faced by urban rail transit systems during peak hours. The model takes platform passing capacity, train carrying capacity and platform carrying capacity as constraints, and aims to reduce the total passenger waiting time and improve the operational efficiency. By constructing the passenger flow control model, the relationship between passenger flow and capacity constraints is analyzed, and a new passenger flow control model is proposed. The improved ACO algorithm improves the convergence speed and the quality of the solution of the algorithm through a dynamically weighted pheromone update strategy. The case study results show that compared with the existing algorithms, the proposed algorithm performs better in terms of searching ability and convergence and is able to effectively reduce the number of waiting passengers, lower the train carrying rate and increase the boarding rate. This study is of significance for optimizing passenger flow management in urban rail transit systems and provides new perspectives and methods for improving passenger travel experience.

Traditional totem pole bridge-less power factor correction converter (PFCC) operating in continuous current mode (CCM) are limited in ability to achieve high power density due to high switching losses. To this, a new auxiliary circuit has been added to the PFCC to enable soft switching, thereby reducing switching losses and improving power density. Addressing the limitations of traditional dual-loop proportional integral (PI) controllers in balancing the speed and stability of totem pole bridge-less PFCC, an adaptive fuzzy PI controller for totem pole bridge-less PFCC is proposed. By designing a fuzzy controller, the parameters of the voltage-loop PI controller are adaptively adjusted. Simulation tests validated the correctness and feasibility of the proposed auxiliary circuit. The proposed adaptive fuzzy PI controller outperforms the traditional dual-loop PI controller in terms of performance metrics such as overshoot suppression and regulation time, etc. The controller has faster response speed, more stable voltage control performance, and stronger interference resistance. This study provides guidance for achieving high power density and excellent performance in such converters.

Aiming at the problems of low efficiency and irregular management process due to the uncertainty and stochasticity of renewable energy sources when managing the energy of a hybrid distribution grid with distributed photovoltaic, an optimization method based on two-stage optimization is proposed for energy storage of a hybrid distribution grid with distributed photovoltaic. In the first stage, the day-ahead optimization is implemented based on the predicted output power of wind turbines and photovoltaic, predicted electricity and hydrogen demand, time-price variation information, and physical and economic constraints of system operation, aiming to optimize the resource allocation and minimize the total operating cost of the system. In the second stage, the intraday adjustment model based on backward horizon optimization is introduced to respond to and adjust the energy storage strategy, which effectively compensates for the power fluctuations caused by prediction errors. The experimental results show that the proposed method reduces the operating cost by 13.0% and the deviation by 16.2% compared with the grid-connected energy storage method. The proposed method can improve the efficiency of energy storage in hybrid distribution grids with distributed photovoltaic and has certain practical application value.

To improve the intelligent inspection effect of main equipment of converter station, the three-dimensional augmented reality (AR) inspection method of main equipment of converter station is designed. The AR intelligent inspection terminal device is utilized to collect the image information of main equipment of converter station. The three-dimensional inspection scene of main equipment of converter station is constructed through augmented rendering. The inspection scene and image are matched by parameter estimation within the server to complete the positioning of main equipment in the virtual scene. Using series connection of traditional attention module and channel attention module to form a hybrid attention mechanism, the attention matrix composed of weighted feature signals is constructed to obtain the detection results of main equipment operation state, so as to realize the three-dimensional inspection of main equipment of converter station. The test results show that the positioning accuracy of the method for the equipment is higher than 90% and the accuracy of the detection results for different states of the equipment operation is above 0.97, which can effectively collect the data of main equipment of converter station and realize the construction of the three-dimensional inspection scene of main equipment of converter station. The method realizes the three-dimensional inspection operation of main equipment of converter station and has strong applicability.