A monitoring method of a multi-component physical sensor and a sensor

Abstract

The application relates to a monitoring method and a sensor of a multi-component physical sensor, the sensor comprising an MCU, an inertial measurement unit (IMU), a three-axis magnetometer, a temperature sensor and at least one extension unit which are integrally sealed in the inside of a shell; the MCU is configured with two groups of independent communication pin groups, namely a first communication pin group and a second communication pin group; the IMU, the magnetometer and the temperature sensor are mounted on the same communication bus, and the communication bus is electrically connected with the first communication pin group; each extension unit is cascaded with each other and is electrically connected with the second communication pin group through a communication line. The technical scheme relies on synchronous acquisition and data integration of multiple physical quantities, realizes stereoscopic perception of a structural health state, early risk identification and predictive maintenance, and thus effectively meets the safe operation and maintenance requirements of various infrastructures.

Description

Technical Field

[0001] This invention relates to the field of sensor technology, and more specifically to a monitoring method and sensor for a multi-component physical sensor. Background Technology

[0002] Artificial intelligence health monitoring and detection, as a new generation of detection and monitoring methods that are being vigorously promoted globally, aims to work in conjunction with sensors and IoT monitoring to achieve remote online monitoring through a control center.

[0003] Although IoT monitoring technology has matured in the monitoring of building equipment, fire protection and other subsystems, there is still a lack of efficient and reliable technical means for monitoring the safety status of special building structures and complex infrastructure. Taking the vibration incident of the Shenzhen SEG Plaza as an example, such buildings are prone to perceptible structural vibration and even overall tilting under the coupled effects of complex external loads such as wind fields, subway operation, geological changes, earthquakes and equipment vibrations, which pose significant safety hazards and risks of damage. Therefore, developing specialized monitoring for key status parameters such as vibration and tilt of super high-rise buildings has become an urgent need for the safe operation and maintenance of urban infrastructure.

[0004] Traditional sensors are typically designed as single-function units. Each sensor can only provide measurement information of a specific physical quantity for one spatial point of the object being measured. Taking a triaxial sensor as an example, it is essentially still a single physical quantity measurement unit. Its principle is to decompose and measure the components of a specific physical quantity, such as acceleration, in three-dimensional space. It can only provide three-dimensional data of a single point and cannot achieve simultaneous sensing of multiple physical quantities, nor can it perform three-dimensional measurement through the linkage of multiple sensors. Therefore, such sensors are difficult to deal with complex scenarios such as building tilt and vibration monitoring, bridge structure assessment, dam and slope stability monitoring, and vibration, deformation and early fault diagnosis of various industrial equipment. Summary of the Invention

[0005] To address the above issues and overcome the problems of traditional sensors in existing monitoring technologies having a single monitoring dimension and isolated data, which cannot achieve simultaneous perception of multiple physical quantities or complete three-dimensional measurement, making it difficult to monitor the vibration and tilt status of special building structures and complex infrastructures under multiple external impacts in real time, and lacking early warning functions, this invention provides a monitoring method and sensor for multi-component physical sensors that relies on simultaneous acquisition and integration of multiple physical quantities to achieve three-dimensional perception of structural health status, early risk identification, and predictive maintenance, thereby effectively meeting the safety operation and maintenance needs of various infrastructures.

[0006] To achieve the above objectives, the technical solution of the present invention is: A multi-component physical sensor monitoring method includes the following steps: Data acquisition: At least the raw time-domain signals of triaxial angular velocity, triaxial acceleration and triaxial magnetic induction intensity are acquired, and the triaxial attitude angle is calculated based on the triaxial angular velocity. The triaxial magnetometer is also calibrated in a non-magnetic environment to eliminate drift and the monitoring reference zero point is set. Calculation of relative displacement and angular displacement of magnetometer: Based on the change in magnetic induction intensity, attitude angle and geometric dimensions of the measured object, the relative displacement and angular displacement of magnetometer are calculated accordingly; Axis system transformation and synchronization processing: The Earth axis quantity is converted into the body axis quantity using the angle transformation matrix, the gravitational acceleration projection component is removed to obtain the body axis acceleration, and the phase and transmission rate of acceleration and attitude angle are unified through the transfer function; Acceleration-displacement calculation: The body axis acceleration is sequentially smoothed by mean and processed by multi-stage hysteresis dead zone. Combined with the set-reset latch logic with continuous sampling judgment and feedforward feedback control, the integral is completed to obtain the three-axis velocity and acceleration integral displacement. Cross-verification: Compare and verify the integral displacement of acceleration, relative displacement of magnetometer and angular displacement, identify abnormal signals and complete data reconfirmation; Frequency domain analysis: Perform Fourier transform on the preprocessed time-domain signal to obtain frequency, amplitude, and phase characteristics, and distinguish between the structure's natural frequency and instantaneous frequency; Deflection calculation: The vibration wavelength is calculated based on the propagation speed of the medium, and multiple multi-component physical sensors are deployed at different points of the object under test. The linear deflection and angular deflection are solved by the wavelength and amplitude differences of the multi-component physical sensors at different locations. Finally, the static deflection and dynamic deflection are calculated by combining the time-domain displacement parameters. Status determination and early warning: Combining time-domain velocity, displacement, and frequency-domain frequency, wavelength, and disturbance parameters, a hierarchical determination method is adopted, which combines the threshold of a single multi-component physical sensor with the linkage threshold of multiple multi-component physical sensors, to realize deformation monitoring, fault diagnosis, and early warning of the measured object.

[0007] To make the sensor data robust, in the axis conversion and synchronization processing steps, the angle conversion matrix is ​​first constructed by the attitude angle to complete the conversion. The gravity under the Earth's axis is decomposed into the projected components on each axis. The body axis acceleration is obtained by subtracting the corresponding gravity projection component from the three-axis acceleration and removing the influence of gravity.

[0008] In the axis conversion and synchronization processing steps, angle transfer functions and acceleration transfer functions are configured respectively. The angle transfer function can be the following transfer functions or combinations thereof: low-pass filter, lead / lag transfer function, multiple unit delay equations, or equations composed of various transfer functions, etc. In processing these steps, the output frequency of the angle transfer function is first fixed to maintain the attitude angle signal at a preset response speed and ensure the signal peak accuracy. Then, the parameters of the acceleration transfer function are adjusted to keep the body axis acceleration and attitude angle synchronized in phase and bandwidth, thereby suppressing jumps in the body axis acceleration data.

[0009] In the acceleration-displacement calculation step, the mean equation and dead zone equation are configured first. The mean equation is used to smooth the body axis acceleration, reducing acceleration fluctuations and integral drift. The dead zone equation uses the minimum change in the body axis acceleration caused by zero drift, random walk, and external vibration disturbances as the threshold. The white noise or colored noise interval corresponding to the acceleration is set as the dead zone. When the acceleration is within the white noise dead zone, its integral result is set to zero. This can eliminate the drift of acceleration caused by white noise due to the integration time sequence.

[0010] Mean smoothing divides the data statistical intervals according to the output frequencies of multi-component physical sensors; the dead zone equations include the basic linear dead zone equation, the piecewise linear hysteresis dead zone equation, and the nonlinear parabolic hysteresis dead zone equation; the basic linear dead zone equation takes the original acceleration as input, calibrates the parameters based on the maximum vibration amplitude of the measured object under natural conditions, and uses a fixed slope to calculate the linear white noise threshold interval; both the piecewise linear hysteresis dead zone equation and the nonlinear parabolic hysteresis dead zone equation take the mean acceleration as input to calculate the nonlinear white noise threshold interval; the piecewise linear hysteresis dead zone equation sets a jump threshold and a regression threshold, or linear and nonlinear white noise threshold intervals, and the regression threshold is less than the jump threshold, and independent slopes are configured for the jump and regression processes respectively; the nonlinear parabolic hysteresis dead zone equation uses nonlinear equations to complete the calculation.

[0011] In the acceleration-displacement calculation step, a vibration logic algorithm and latching logic software switch are configured, and dead-zone operation is used to complete the logic judgment. When the absolute value of acceleration is greater than or equal to the jump threshold, the setting is executed and integration is started, and the set state is maintained. When the absolute value of acceleration is less than the regression threshold and the condition is met by multiple consecutive samplings, the reset is executed and the integration result is cleared to zero. Feedforward and feedback control are introduced into the integration process, and the start, hold and reset states of integration are switched by latching logic, and finally the three-axis velocity and body axis displacement are obtained.

[0012] In the frequency domain analysis step, the Fourier algorithm is used to realize time-frequency conversion; the highest measurable frequency for spectrum analysis is half of the output frequency of the multi-component physical sensor data, and binary sequence values ​​are selected for the spectrum test points; different durations of sampling data can be extracted to draw spectrum diagrams. The larger the amount of data extracted, the higher the accuracy of the calculated frequency and amplitude; the frequency, amplitude, and displacement meet the preset accuracy indicators, and the frequency accuracy, amplitude, and displacement all correspond to the working frequency range of the multi-component physical sensor, and a dedicated accuracy algorithm is configured to complete error suppression and error calculation; the time domain data is converted into frequency domain data containing natural frequency, instantaneous frequency, amplitude, and phase through the Fourier algorithm, and structural fault diagnosis and status early warning are realized by combining the changes in frequency domain data.

[0013] In the frequency domain analysis step, wavelength calculation is performed based on the vibration frequency obtained from the frequency domain analysis. The wavelength is solved according to the rule that the wavelength is equal to the propagation speed of the medium divided by the vibration frequency. During the monitoring process, the vibration frequency is divided into the natural frequency of the measured object and the instantaneous frequency generated by the dynamic force. The natural frequency of the measured object is the inherent stable vibration frequency of the structure itself. The instantaneous frequency changes with the magnitude and direction of the external force. After the external force disappears, the instantaneous frequency returns to the natural frequency. The calculated wavelength is combined with the velocity and displacement data obtained from the time domain solution to complete the structural deflection solution.

[0014] In the deflection calculation step: multi-component physical sensors are deployed at multiple points within the monitoring area of ​​the object under test, and dedicated computational logic circuits and signal links are configured to complete the deflection-related calculations; for any coordinate axis direction of the object under test, the displacement measured in the static state of the object under test is taken as the static displacement, the additional deformation caused by the impact is taken as the absolute dynamic deflection, and the total deformation measured under the impact is taken as the relative dynamic deflection; the impact coefficient is defined as the ratio of the relative dynamic deflection to the static displacement, which is equal to 1 and the sum of the ratios of the absolute dynamic deflection and the static displacement; the linear deflection and angular deflection are solved by combining the wavelength and amplitude differences of multiple corresponding multi-component physical sensors, and the static and dynamic deflection calculations are completed by combining the aforementioned parameters, and the degree of structural damage to the object under test is determined based on the calculation results.

[0015] The multi-component physical sensor meets the following accuracy and performance specifications: angular accuracy of ±0.01°, and angular response bandwidth of 1 / 3 to 1 / 4 of the angular output frequency. For example, when the angular output frequency is 100Hz, the angular response bandwidth is approximately 33 to 25Hz. Similarly, the acceleration accuracy is ±0.01m / s². 2 The acceleration response bandwidth is 1 / 3 to 1 / 4 of the acceleration output frequency, when the acceleration accuracy is... 0.01m / s 2The displacement accuracy within the aforementioned bandwidth, resulting in drift, can be controlled within 5mm through the integration of acceleration, because the frequency accuracy of the multi-component physical sensor has been adjusted to... Within 0.5%, so taking a test frequency of 400Hz as an example, the frequency accuracy is within ±2.0Hz; when the test frequency is less than 200Hz, the accuracy is within ±1.0Hz. The amplitude algorithm accuracy of Fourier spectrum analysis is ±0.5%. Because the spectrum has high accuracy, the accuracy of the wavelength obtained from the spectrum analysis will be greatly improved, and the value of the true amplitude calculated by the velocity and displacement algorithms will be controlled within... Within 0.5%.

[0016] The accuracy and algorithm of the above multi-component physical sensors must be combined with the installation and arrangement of the sensors. Specifically, multiple sets of multi-component physical sensors are arranged along the three axes of length, width and height of the object being measured. The length and width directions are arranged at front, middle and rear points, and the height direction is arranged at top, middle and bottom points to achieve three-dimensional length, width and height monitoring.

[0017] In the state determination and early warning steps: the determination calculation of disturbance and deformation is completed based on the preset logic circuit; when the measured object is not deformed, the wavelength and amplitude of each multi-component physical sensor remain consistent; when the structure undergoes local deformation, the wavelength of the multi-component physical sensor corresponding to the deformation location becomes longer and the amplitude increases; by comparing the differences in wavelength and amplitude measured by each multi-component physical sensor, the disturbance change and bending degree of the measured object are determined; based on the maximum amplitude and peak frequency of the triaxial acceleration, triaxial velocity, triaxial displacement, and triaxial angle data output by the multi-component physical sensors, a hierarchical determination method combining the threshold of a single multi-component physical sensor and the linkage threshold of multiple multi-component physical sensors is adopted. The threshold of a single multi-component physical sensor includes a fixed threshold, an elastic threshold, and a nonlinear threshold. The fixed threshold is the product of the static maximum vibration amplitude and the preset amplitude multiple coefficient; the elastic threshold is the sum of the static maximum vibration amplitude and the dynamic adjustment parameter; and the nonlinear threshold is the product of the static maximum vibration amplitude and the preset amplitude multiple coefficient plus the dynamic adjustment parameter.

[0018] In the state determination and early warning steps: threshold determination of multiple multi-component physical sensors is realized by relying on the linkage operation logic circuit; the linkage threshold of multiple multi-component physical sensors is determined based on each other's data, and each group of multi-component physical sensors performs a linkage alarm process in coordination while performing individual multi-component physical sensor threshold alarm monitoring; this process includes: firstly, collecting time-domain and frequency-domain data of the accelerometers of each group of multi-component physical sensors, and calculating velocity and displacement from the time-domain data; then converting the frequency and amplitude data into wavelength, displacement, and static disturbance; then combining displacement and static disturbance to further obtain dynamic disturbance; finally, setting the linkage threshold based on the dynamic disturbance data of multiple groups of multi-component physical sensors to realize the coordinated alarm determination among multiple multi-component physical sensors.

[0019] A multi-component physical sensor, implemented using the aforementioned monitoring method, includes an MCU, an inertial measurement unit (IMU), a triaxial magnetometer, a temperature sensor, and at least one expansion unit, all sealed and integrated within the same housing. The MCU is configured with two independent communication pin groups, namely a first communication pin group and a second communication pin group. The IMU, magnetometer, and temperature sensor are mounted on the same communication bus, which is electrically connected to the first communication pin group. Each expansion unit is cascaded and electrically connected to the second communication pin group via a communication line.

[0020] The multi-component physical sensor can also be used with the following expansion units, such as any one or more combinations of Beidou positioning modules, global positioning modules, aerodynamic sensors, relative humidity sensors, acoustic sensors, stress / strain sensors, LVDT / RVDT distance sensors, and distance sensors in wire displacement gauges, to achieve comprehensive three-dimensional monitoring regardless of any external interference factors, such as temperature, humidity, wind vibration, vehicle vibration, earthquakes, rainstorms, etc.

[0021] Compared with the prior art, the advantages of the present invention are as follows: This invention quantifies the vibration amplitude and real-time deformation of the tested object using time-domain velocity and displacement data, providing direct evidence for deformation monitoring. Simultaneously, it obtains natural frequency, instantaneous frequency, wavelength, and deflection parameters through frequency-domain analysis, reflecting the inherent mechanical properties of the tested object and its dynamic changes under stress. Specifically, natural frequency shift, the inability of instantaneous frequency to return to a reference value, and abnormal changes in deflection and wavelength can serve as characteristic indicators of structural performance degradation or localized damage. By combining fixed thresholds, elastic thresholds, and nonlinear thresholds of a single multi-component physical sensor, as well as a hierarchical judgment logic involving thresholds linked to multiple multi-component physical sensors, and comparing the wavelength and amplitude differences of multi-component physical sensors at different locations, it can identify deformation, vibration, and structural response deviations exceeding safe limits. This enables comprehensive verification of the local and overall state of the tested object, ultimately achieving deformation monitoring, fault diagnosis, and early warning. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the composition of the multi-component physical sensor of the present invention; Figure 2 This is a schematic diagram illustrating the calculation of acceleration, velocity, and displacement using the multi-component physical sensor of the present invention; Figure 3 This is a schematic diagram of the conversion between the Earth axial quantity and the body axis of the multi-component physical sensor acceleration of the present invention; Figure 4 This is a schematic diagram of the velocity equation for the multi-component physical sensor acceleration of the present invention; Figure 5 This is a schematic diagram of the basic linear dead zone equation for acceleration of the multi-component physical sensor of the present invention; Figure 6 This is a schematic diagram of the piecewise linear hysteresis dead zone equation for the multi-component physical sensor of the present invention; Figure 7 This is a schematic diagram of the acceleration nonlinear parabolic hysteresis dead zone equation of the multi-component physical sensor of the present invention; Figure 8 This is a schematic diagram of the acceleration logic software switch of the multi-component physical sensor of the present invention; Figure 9 This is a flowchart of the data spectrum mode conversion function of the multi-component physical sensor of the present invention; Figure 10 This is a rendering of the multi-component physical sensor deployment of the present invention (taking a bridge as an example, and along the length of the bridge). Figure 11 This is a planar schematic diagram of the multi-component physical sensor layout of the present invention (taking a bridge as an example, and along the length of the bridge). Figure 12 This is a planar schematic diagram of the multi-component physical sensor layout of the present invention (taking a bridge as an example, and along the width direction of the bridge). Figure 13 This is a three-dimensional schematic diagram of the multi-component physical sensor deployment of the present invention (taking a bridge as an example, and along the height direction of the bridge). Figure 14 This is a schematic diagram illustrating the vibration relationship between the multi-component physical sensor of the present invention and the wavelength; Figure 15 This is a schematic diagram of the multi-component physical sensor data collection and threshold alarm process of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application and how they solve the aforementioned technical problems will be clearly and completely described below with reference to specific embodiments and the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0024] In the description of this application, it should be understood that the terms "upper", "lower", "front", "back", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0025] In this application, the terms "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any implementation or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other implementations or design options. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0026] As mentioned in the background section, AI-powered health monitoring and detection systems aim to achieve remote online monitoring through sensors and IoT. Although IoT monitoring technology has matured in monitoring subsystems such as building equipment and fire protection, it lacks efficient methods for safety monitoring of special building structures and complex infrastructure. Taking the vibration incident of the Shenzhen SEG Plaza as an example, such buildings have significant safety hazards under the coupled effects of complex external loads such as wind fields and subway operation. Traditional triaxial sensors can only output three-dimensional data for a single physical quantity. For example, a triaxial accelerometer can only collect acceleration signals in three orthogonal axes and does not have the ability to simultaneously sense multiple physical quantities. It also cannot achieve three-dimensional monitoring by deploying multiple sensors. This limitation makes it difficult to adapt to complex engineering scenarios that require multi-dimensional and multi-parameter analysis, such as building tilt monitoring, bridge structure assessment, dam stability monitoring, and early fault diagnosis of industrial equipment.

[0027] Based on this, the present invention further provides a multi-component physical sensor monitoring method, which includes the following steps: Data Acquisition: The multi-component physical sensor of this application integrates expansion units such as IMU, triaxial magnetometer and temperature sensor. It can natively output thirteen-axis raw data. Through software algorithm, the raw data can be expanded to obtain 28-axis or more derived data. The sensor uses the three orthogonal axes of the earth as a reference. The IMU collects the triaxial angular velocity and triaxial acceleration in the x, y and z directions, and the triaxial magnetic induction intensity in the x, y and z directions is collected by the triaxial magnetometer to form the raw time domain signal.

[0028] A three-axis gyroscope is used to collect angular velocity data about three orthogonal axes, defined as roll angular velocity, pitch angular velocity, and yaw angular velocity, denoted as ( G x , G y , G z ) or written as ( P , Q , R The triaxial accelerometer is used to collect acceleration data in three orthogonal directions, defined as acceleration x, acceleration y, and acceleration z, denoted as ( A x , A y , A z A triaxial magnetometer collects spatial triaxial magnetic induction intensity data, defined as magnetic induction intensity x, magnetic induction intensity y, and magnetic induction intensity z, denoted as ( M x , M y , M z Before collecting data, the triaxial magnetometer needs to be calibrated in a non-magnetic environment and the monitoring reference zero point needs to be set. After calibration, its measurement results will not drift with time or geographical location. It can adapt to the geomagnetic field specifications of different locations around the world. At a fixed monitoring point, the magnetometer output will remain constant, thus ensuring the consistency of data output.

[0029] Magnetometer relative displacement and attitude angular displacement calculation: The three-axis angular velocity can be converted into angular displacement using an angle algorithm. Taking high-rise building monitoring as an example, a multi-component physical sensor is installed on the top floor of the building, and the vertical height of the sensor from the ground is set as... The building has a square floor plan, and the side length of the floor plan is defined as follows: The overall dimensions of the building are: The sensor is installed with its x-axis facing the building's main entrance, and the building's left-right offset, front-back offset, and torsional deformation are respectively mapped to the roll angle. Pitch angle Heading angle By combining the attitude angles and geometric dimensions, the displacements corresponding to the left-right, front-back, and torsional directions of the building can be calculated separately. The calculation formulas are as follows: ; In the formula, This represents the displacement along the x-axis corresponding to the roll angle. This represents the displacement along the y-axis corresponding to the pitch angle. This represents the displacement along the z-axis corresponding to the heading angle.

[0030] Furthermore, those skilled in the art will understand that the three-axis angular velocity can be converted into three-axis angles using known attitude calculation algorithms (such as quaternion methods, Euler angle methods, or direct integration methods). These algorithms are existing technologies, and their specific implementations will not be elaborated here. In this embodiment, a conventional angular velocity integration combined with a magnetometer-assisted Kalman filter method can be used to obtain a stable three-axis angle output.

[0031] After the magnetometer is calibrated, the magnetometer collects triaxial magnetic flux density data ( M x , M y , M z Under a stable magnetic field, rotating about each axis will result in the following three relationships: Using the x-axis plane as the rotation reference, M x The value will remain at a constant value: M z / M y The corresponding magnetic roll angle output can be calculated as atan2 ( M z , M y ).

[0032] Using the y-axis plane as the rotation reference, M y The value will remain at a constant value: M x / M z The corresponding magnetic pitch angle output can be calculated as atan2 ( M x , M z ).

[0033] Using the z-axis plane as the rotation reference, M z The value will remain at a constant value: M y / M xThis can be used to calculate the corresponding magnetic heading angle, output as atan2 ( M y , M x ).

[0034] When the triaxial magnetic induction intensity ( M x , M y , M z When the changes occur synchronously, it indicates that the measured object has undergone three-dimensional displacement, and the magnitude of the displacement is related to the change in the triaxial magnetic field. M x , M y , M z It is directly proportional to the value. If the change is caused by external electromagnetic interference, the triaxial magnetic induction intensity will eventually return to the calibration zero point or near zero point.

[0035] Therefore, the change in triaxial magnetic induction intensity can be used to reconfirm the spatial displacement of the measured object. At the same time, the difference between the acceleration integral displacement and the attitude angle geometric displacement can be calculated to comprehensively determine the authenticity of the displacement.

[0036] The above-mentioned raw time-domain data are all output based on the Earth's axial dimension. In order to realize the measurement reference based on the sensor itself, the Earth's axial dimension data needs to be converted into the sensor's own axial dimension data. This conversion process involves trigonometric function calculations, which can easily produce phase differences and signal delays, thus causing the conversion error to accumulate continuously and the detection accuracy to decrease. At the same time, the three-axis gyroscope and three-axis accelerometer have non-white noise problems such as random walk and zero bias instability, which can also lead to divergence in calculation accuracy. Therefore, it is necessary to specifically solve the above-mentioned accuracy interference and error accumulation problems.

[0037] like Figure 3 As shown, axis transformation and synchronization processing: An angle transformation matrix is ​​constructed using attitude angles, and the Earth's axial quantities are converted into body axial quantities using the angle transformation matrix. The transformation matrices for the three orthogonal axes x, y, and z are defined as follows: ; ; ; Therefore, the transformation matrix [T] between the Earth's axial mass and volume axis is: ; Decompose the gravity along the Earth's axis into its projected components along each axis: ; The gravity vector after conversion to body axis is: ; By removing the gravitational acceleration projection component (i.e., subtracting the corresponding gravitational projection component from the three-axis acceleration), we obtain the body axis acceleration: ; The above acceleration equation is the body axis acceleration. When performing acceleration integration to solve for velocity and displacement, body axis acceleration data must be used, and the phase difference between acceleration and attitude angle must be eliminated; otherwise, the calculation accuracy will be reduced.

[0038] Figure 3 As shown, by adjusting the triaxial acceleration A x A y A z By performing integration, it can be converted into velocity and displacement, but synchronous acceleration A is required. x A y A z With attitude angle , , To minimize phase error and ensure consistent response rates for both types of signals, thus reducing body axis acceleration data fluctuations caused by phase interference, the transmission rate of acceleration data must be reduced based on the gyroscope's original transmission rate, and the acceleration response must be matched during attitude angle calculation. Specifically, in the process of calculating gravitational acceleration components based on attitude angles and correcting the original acceleration, two sets of transfer functions are added for signal synchronization processing. These two sets of transfer functions are angle transfer functions (…). f ( ), f ( ), and acceleration transfer function ( f (A x ), f (A y ), f (A z )).

[0039] Figure 3 As shown, (A) x A y A z ) represents the raw acceleration value collected by the sensor, and represents the acceleration value of the Earth's axial force. , The values ​​represent the roll and pitch angles output by the sensor. In body axis measurements, when the attitude angle changes, the acceleration due to gravity on the body axis will change. How to instantaneously calculate the difference between the body axis measurement caused by gravitational acceleration and its own acceleration response using a transfer function, ensuring synchronization between the two, is crucial. These factors affect the accuracy of the displacement algorithm; therefore, they are used to process the roll angle. Pitch angle Angle transfer function f ( ), f ( The equation is composed of a phase transformation equation and multiple unit delay functions, which can be a low-pass filter, a lead / lag transfer function, multiple unit delay equations, or an equation composed of the above transfer functions.

[0040] Figure 3 As shown, the transfer function corresponding to the triaxial accelerometer f (A x ), f (A y ), f (A z This is used to achieve synchronous optimization of raw acceleration and attitude angle data, minimizing synthetic data fluctuations and effectively reducing drift during velocity and displacement integration. This transfer function is similar to the angle transfer function. f ( ), f ( When used in conjunction with other functions, the function is transferred at a fixed angle during operation. f ( ), f ( The output frequency of the ) ensures the response speed and peak accuracy of the attitude angle signal. Then, the parameters of the acceleration transfer function are adjusted to keep the body axis acceleration and attitude angle synchronized in phase and bandwidth, thereby suppressing the jump of the body axis acceleration data.

[0041] like Figure 4As shown, acceleration-displacement calculation: After synchronizing acceleration and attitude angle data, external interference can still cause slight fluctuations in the acceleration signal, affecting the accuracy of velocity and displacement calculations using acceleration integration. Here, mean smoothing of the body axis acceleration integral term can suppress signal fluctuations and reduce long-term integral drift, but it cannot completely eliminate the problem. Therefore, a multi-stage hysteresis dead zone processing is further introduced, setting the white noise or colored noise range of acceleration as the dead zone. When the acceleration is within the white noise dead zone, its integration result is set to zero, thereby eliminating the drift caused by white noise due to the integration timing and stabilizing the calculation of body axis velocity. This effectively avoids computational instability caused by acceleration random walk and external noise, while suppressing various interferences such as zero-bias instability, temperature drift, long-term integration errors, and external vibrations.

[0042] Mean smoothing relies on the following mean equation accomplish: ; Mean value equation of acceleration Mean smoothing needs to be dynamically determined based on the sensor's data output frequency. Taking an accelerometer with a sampling frequency of 2000Hz as an example, a mean calculation period of 0.1 seconds can be set, meaning 200 acceleration data samples are collected every 0.1 seconds. Smoothing is then performed using the mean equation. Since this mean is calculated based on 200 sets of data, the processed signal curve has high smoothness. Extending the mean calculation period to 0.2 seconds further improves the smoothing effect, but reduces the output bandwidth and response speed of the velocity data. Therefore, a trade-off must be struck between measurement accuracy and signal bandwidth. Similarly, if the sensor output frequency is 200Hz, only 20 sets of data can be collected in 0.1 seconds, and the mean smoothing accuracy will be significantly lower compared to 2000Hz sampling. Therefore, for scenarios requiring high measurement accuracy and response speed for velocity and displacement, a high sampling rate accelerometer is preferred to ensure sufficient accuracy of the mean-processed velocity and displacement data.

[0043] Multi-stage lag dead zone handling relies on the dead zone equation The implementation, whose parameter settings are directly related to the external vibration disturbance characteristics of the measured object, is an adaptive dead-zone algorithm based on vibration characteristics. It needs to be calibrated in conjunction with measured vibration data. The dead-zone equation is... This includes basic linear dead zone equations, piecewise linear lag dead zone equations, and nonlinear parabolic lag dead zone equations.

[0044] like Figure 5 As shown, the basic linear dead zone equation takes the original acceleration as input, calibrates the interval parameters based on the maximum vibration amplitude of the measured object under natural conditions, and obtains the linear white noise threshold interval using a fixed slope calculation. This linear dead zone equation... The design rules are as follows: Set the maximum vibration amplitude of the object under natural conditions as the dead zone threshold. When the acceleration value is at to When the acceleration is within this range, it is considered white noise interference, and the integral output is set to zero; when the acceleration exceeds this range, it participates in the calculation according to a fixed slope m, as shown in the following formula: ; In the above formula, the slope m is an adjustable parameter. The value of the slope m is dynamically adjusted according to the response rate of the acceleration signal. When the acceleration signal exceeds the dead zone threshold, the integration process will be based on the acceleration value after dead zone processing to ensure the stability and accuracy of the velocity calculation and effectively avoid the integration divergence problem caused by noise in the dead zone.

[0045] Both the piecewise linear lag dead zone equation and the nonlinear parabolic lag dead zone equation use the mean acceleration as input to solve for the nonlinear white noise threshold interval. The piecewise linear lag dead zone equation distinguishes between two types of white noise threshold intervals: the escape threshold and the regression threshold. The regression threshold is smaller than the escape threshold. Furthermore, it configures independent calculation slopes for the escape and regression processes, such as... Figure 6 As shown, the graph uses acceleration ( Acc ,unit: m / s 2 The horizontal axis represents the output value after processing by the dead-zone algorithm, and the vertical axis represents the output value after processing by the dead-zone algorithm. The specific formula is as follows: ; In the formula, Similarly, the maximum vibration amplitude of the tested object under natural conditions is defined as the jump constant. Unlike the basic linear dead zone equation, the piecewise linear hysteresis dead zone equation additionally sets a regression constant. The algorithm's signal escape path differs from the regression path, and the regression constant is smaller than the escape constant, ensuring that the regression constant is within a small dead zone. The aforementioned double-threshold hysteresis function effectively improves the accuracy of acceleration integral solutions while enabling rapid switching between positive and negative velocity states.

[0046] also, The jump slope is positive or negative. The slopes represent positive and negative regression rates, and neither needs to be fixed values. They can be set to various variations according to actual working conditions. The escape constant and regression constant are respectively... and And satisfy > This ensures that the regression threshold is within a smaller dead zone, guaranteeing a faster response during the positive-to-negative switching process of the acceleration signal.

[0047] The nonlinear parabolic hysteresis dead zone equation is calculated using nonlinear equations. It defines two computational paths—signal escape and regression—based on multiple sets of quadratic parabolic equations, such as... Figure 7 As shown, the graph uses the mean acceleration ( Acc ,unit: m / s 2 () as the horizontal axis, with () Acc Algorithm output with mean acceleration as parameter The specific formula is as follows: ; In the formula, and These represent the jump threshold and regression threshold of the test object under unloaded, natural conditions, with the corresponding intervals being the jump dead zone and regression dead zone, respectively. Compared to and , Multiple sets of nonlinear equations are used to define the signal escape path and the return path respectively, and the range of the return dead zone is smaller than that of the escape dead zone. The whole structure constitutes a nonlinear parabolic hysteresis dead zone equation. This design can further improve the speed and accuracy of integral calculation, while accelerating the switching response speed between positive and negative speeds.

[0048] In the equation for the hysteresis dead zone of a nonlinear parabola and Let it be its positive and negative escape parameters. and All are positive values, when ( When =0), it will be ( and The minimum value is obtained at ().

[0049] These nonlinear curves can be flexibly configured and do not need to be fixed to a single form. Among them, the escape constant... and regression constant ,satisfy > This ensures that the threshold of the regression process is within a smaller dead zone, thereby achieving a rapid response when the acceleration signal switches between positive and negative directions. By adjusting the fineness of the nonlinear hysteresis curve, the accuracy of velocity and displacement calculations can be significantly improved.

[0050] The three equations above cover the typical forms of lag control: for example, when = When the hysteresis loop is 0, it degenerates into a standard hysteresis curve. The multi-form hysteresis curve design makes the velocity and displacement algorithms more flexible, allowing for adjustments to software complexity and computational load as needed, adapting to different accuracy requirements, and ensuring compatibility with MCU or CPU platforms of varying performance and cost, effectively enhancing the product's market competitiveness.

[0051] like Figure 4 As shown, in the acceleration-displacement calculation steps: A set-reset latch logic with continuous sampling judgment is used, specifically a latch logic switch composed of vibration logic algorithm and latch switch algorithm, which, together with dead-zone operation, completes the logic judgment; when the absolute value of acceleration is greater than or equal to the jump threshold, a set position is executed and integration is started, and the set state is maintained continuously; when the absolute value of acceleration is less than the regression threshold, and this state is maintained for at least five consecutive samples, a reset is executed and the integration result is cleared to zero; the integration process combines feedforward and feedback control, relying on the latch logic to switch the start, hold, and reset states of integration.

[0052] The right side of the diagram contains two functional modules: a vibration logic algorithm and a latching switch algorithm. These two modules are used to assist in dead zone judgment and decision-making. Their core function is to detect when the data output exceeds the absolute value. When the set logic is triggered, the integral equation can begin execution; when the data output exceeds the absolute value... When at least five consecutive samples are taken, the reset logic is triggered, and the integral output automatically returns to zero.

[0053] If the data output parameter is After processing with the mean and dead zone equations, the output is the mean acceleration of each axis. Taking the x-axis as an example, the logic equations for the vibration set / reset logic and the latch switch algorithm are as follows: ; The above logic can be extended to the y-axis and z-axis to achieve set and reset control across all three axes.

[0054] like Figure 8 As shown, the above equation can be converted into a logic diagram. In this diagram, firstly... Take its absolute value, and then compare it with the jump constant of the dead zone. and regression constant Compare the data to determine if the output data has been converted into an integral function. , or .

[0055] like Figure 8 As shown, body axis acceleration After processing through the mean equation and dead zone equation in sequence, the output is the triaxial mean acceleration. To prevent interference data such as random walks and zero-bias instability from entering the integration algorithm and causing the integral output to diverge, a bit reset auxiliary logic is added.

[0056] like Figure 8 As shown, It is an absolute value function. The unit is the signal delay unit, and 3shot is the condition for determining that the signal is true for five consecutive samples. Figure 7 In and All are local parameters. The constants defined in the above equations are determined by the vibration amplitude of the measured object under unloaded natural conditions. Since the characteristics of the x, y, and z axes under natural vibration differ, the values ​​of the corresponding constants are dynamically adjusted according to the actual monitoring conditions. Therefore, the logical structure design of the x, y, and z axes is similar, but the constant values ​​corresponding to each axis are different.

[0057] like Figure 8 As shown, the integral stage incorporates both feedforward and feedback control algorithms to achieve real-time switching of control actions. The switching logic is as follows: Figure 8 The control circuit shown is determined by the state control of the logic switch. By adding hold and reset functions to the integral stage and cooperating with the logic switching switch, operations such as integral initialization, automatic zeroing, state holding, and displacement accumulation can be completed automatically without the need for additional logic circuit support, realizing integral calculation and state switching with the simplest logic structure.

[0058] like Figure 4 and Figure 8 As shown, once the triaxial velocity and displacement are calculated, they will no longer be affected by zero-bias instability, temperature drift, and white noise, thus overcoming the data divergence problem. The obtained data can be further used for calculations and state estimation in infrastructure monitoring.

[0059] Cross-verification: The three types of displacement data calculated are integral acceleration displacement, relative magnetometer displacement, and angular displacement calculated based on attitude angle and the geometry of the measured object. Using multi-component physical sensors, three different displacement or position-related outputs will be generated: integral acceleration displacement, relative magnetometer displacement, and angular displacement. The system can compare and verify these three types of displacement data, identify abnormal signals, and complete data reconfirmation. By using multi-source data cross-verification, the false alarm rate of sensors can be effectively reduced, highly reliable alarm judgment can be achieved, and the reliability, robustness, controllability, and measurement accuracy of the entire monitoring solution can be improved.

[0060] The algorithms employed in the aforementioned hardware signal acquisition and processing stages can suppress zero bias, instability, and noise interference, yielding stable velocity and displacement data. These operations are all time-domain spatial operations. This invention's multi-component physical sensor not only achieves high-precision time-domain operations but also possesses high-precision frequency-domain operational capabilities. To ensure the stability and consistency of data output, the multi-component physical sensor requires multiple calibration processes, among which orthogonality calibration, temperature compensation calibration, and magnetic sensor calibration of the Earth's magnetic field are core mandatory calibration items. After calibration, frequency-domain spatial operations can be performed based on the time-domain data.

[0061] Frequency domain analysis: Perform Fourier transform on the preprocessed time-domain signal. For example, when the output frequency is 400Hz, according to the Nyquist sampling theorem, the highest measurable frequency is half of that, i.e., 200Hz.

[0062] The spectrum test points can be configured as follows: The default setting is the spectrum output frequency (e.g., 200Hz in the example above), and configured using binary sequence values, such as 2. 6 =64、2 7 =128、2 8 =256、2 9 =512、2 10 =1024、2 11 =2048 etc.

[0063] Simultaneously, sampling data of different durations can be extracted to plot spectrum graphs: for example, 400 data sets per second, 800 data sets per two seconds, 1200 data sets per three seconds, 1600 data sets per four seconds, or 2000 data sets per five seconds. The larger the amount of data extracted and the denser the test points, the higher the accuracy of the calculated frequency and amplitude.

[0064] After Fourier transform, the time-domain signal can be converted into a frequency-amplitude spectrum analysis graph. The spectrum graph provides a clear view of the inherent frequencies and corresponding amplitudes of the infrastructure structure: when the structure is undamaged, uncorroded, or fractured, the inherent frequencies and amplitudes will stabilize at their initial reference values; when the structure is subjected to external forces and exhibits a dynamic response, the relevant parameters will also be reflected synchronously in the spectrum graph.

[0065] Natural frequencies are determined by the structural materials and geometry, and are inherent, fixed vibration parameters of infrastructure. Instantaneous frequencies are dynamic parameters generated by external forces on the structure, changing with the magnitude and direction of the force, and exhibiting various forms of variation. After the external force disappears, the instantaneous frequency gradually decays and returns to the natural frequency. Therefore, the numerical shift, amplitude fluctuation, and phase change of the natural frequency can all serve as indicators of the health status of the object under test. Obtaining accurate parameters in the frequency domain, such as frequency, amplitude, and phase, is a crucial step in monitoring the health of the object under test.

[0066] The relationship between frequency and wavelength satisfies the following formula: ; in, Vibration frequency (unit: Hz). λ is the wave propagation speed in the medium (unit: m / s), and λ is the corresponding vibration wavelength (unit: m).

[0067] The propagation speed of waves varies with the type of medium. Typical reference values ​​for materials are as follows: approximately 5000 m / s for steel structures; approximately 3200 m / s for cement / concrete; approximately 2000-6000 m / s for stone media; and approximately 343 m / s for air (20°C) media.

[0068] In health monitoring of the tested object, the corresponding wave velocity can be determined based on the material of the tested object. Combined with measured frequency Substituting the above formula into the vibration wavelength, the corresponding vibration wavelength is obtained. Then, combined with the deployment data and algorithms of multiple corresponding multi-component physical sensors, the disturbance parameters of the infrastructure are further calculated.

[0069] The multi-component physical sensor meets the following accuracy and performance specifications: angular accuracy of ±0.01°, and angular response bandwidth of 1 / 3 to 1 / 4 of the angular output frequency. For example, when the angular output frequency is 100Hz, the angular response bandwidth is approximately 33 to 25Hz. Similarly, the acceleration accuracy is ±0.01m / s². 2 The acceleration response bandwidth is 1 / 3 to 1 / 4 of the acceleration output frequency, when the acceleration accuracy is... 0.01m / s 2 The displacement accuracy within the aforementioned bandwidth, resulting in drift, can be controlled within 5mm through the integration of acceleration, because the frequency accuracy of the multi-component physical sensor has been adjusted to... Within 0.5%, so taking a test frequency of 400Hz as an example, the frequency accuracy is within ±2.0Hz; when the test frequency is less than 200Hz, the accuracy is within ±1.0Hz. The amplitude algorithm accuracy of Fourier spectrum analysis is ±0.5%. Because the spectrum has high accuracy, the accuracy of the wavelength obtained from the spectrum analysis will be greatly improved, and the value of the true amplitude calculated by the velocity and displacement algorithms will be controlled within... Within 0.5%.

[0070] like Figures 9 to 13As shown, the accuracy and algorithm of the above multi-component physical sensors must be combined with the installation and arrangement of the sensors to achieve multi-dimensional measurement of the deflection of special building structures and complex infrastructure. Specifically, multiple sets of multi-component physical sensors are arranged along the length, width, and height axes of the object being measured. The length and width directions are arranged at front, middle, and rear points, and the height direction is arranged at top, middle, and bottom points, achieving three-dimensional length, width, and height monitoring. In other words, the present invention requires at least three multi-component physical sensors. Taking a bridge as an example, three sets of multi-component physical sensors are linearly arranged along the traffic flow direction (defined as the x-axis) to detect changes in deflection along the traffic flow direction. In addition, multi-component physical sensors can also be arranged along the side of the bridge (defined as the y-axis). If vertical deflection needs to be measured, they can be arranged at top, middle, and bottom positions along the height direction (defined as the z-axis).

[0071] In the deflection calculation step: the wavelength analysis diagram of the multi-component physical sensor is constructed by the data of the front, middle and rear multi-component physical sensors. Each multi-component physical sensor outputs linear wavelength and angular wavelength data respectively. By calculating the linear wavelength difference and angular wavelength difference between the three multi-component physical sensors, the deflection change of the structure, including linear deflection and angular deflection, can be calculated.

[0072] In this scheme, the deflection is divided into linear deflection and angular deflection according to the deformation mode; and into static deflection and dynamic deflection according to the load condition. Among them, static deflection is equivalent to the static displacement of the measured object, which is the basic displacement generated when the measured object is in a static steady state without external force or impact. It is also the benchmark reference value for subsequent calculation of dynamic deflection and impact coefficient.

[0073] Based on the above triaxial displacement calculation results, the z-axis is used as an example for specific explanation: the displacement measured by the multi-component physical sensor under the static steady state of the measured object is defined as... The static displacement is called static disturbance; the additional deformation caused by external impact is defined as absolute dynamic disturbance. The total deformation of the measured object under impact is defined as the relative dynamic disturbance. The relative dynamic perturbation With static perturbation (static displacement) The ratio is defined as the impact coefficient. The calculation formula is as follows: ; The relative dynamic perturbation is the sum of the static perturbation (static displacement) and the absolute dynamic perturbation, satisfying the following relationship: ; By simultaneously deriving the two equations, we can obtain: ; Taking bridge monitoring as an example, multi-component physical sensors can simultaneously collect the static disturbance (static displacement) and the relative dynamic disturbance under impact. When the bridge body is undamaged and unbroken, its disturbance response is mainly concentrated in the long wavelength range below 25Hz, and the amplitude change corresponding to the disturbance in this frequency band is basically at the millimeter level.

[0074] Acceleration signals measured by multi-component physical sensors have an inherent zero-bias error, expressed as: ; In the formula, ε represents the zero bias error of acceleration measurement.

[0075] When integrating acceleration to solve for velocity, the error accumulates linearly with time: ; In the formula, δ represents the initial velocity error.

[0076] When further integrating the velocity to solve for the displacement, the error will accumulate twice: ; In the formula, η is the initial displacement error.

[0077] Clearly, the above three types of errors will directly reduce the calculation accuracy of static displacement (static perturbation) and dynamic perturbation, and are the interference terms that the algorithm needs to focus on suppressing.

[0078] like Figure 14 As shown, the implementation method of wavelength-based deflection calculation is as follows. In this embodiment, the bridge is still used as the test object, and three sets of multi-component physical sensors are deployed on the bridge deck in the test area: front, middle, and rear. When the bridge is not deformed, the wavelengths and amplitudes of all multi-component physical sensors are basically consistent. When the middle section of the bridge undergoes flexural deformation, the wavelengths and amplitudes of the multi-component physical sensors at the front and rear positions remain basically unchanged, while the wavelengths and amplitudes of the multi-component physical sensors at the middle position increase. By combining the differences in wavelengths and amplitudes of multiple corresponding multi-component physical sensors with the reference static deflection (static displacement) data, the overall deflection change and bending degree of the test object can be accurately calculated.

[0079] like Figure 15 As shown, the multi-component physical sensor data is based on spectrum analysis results, with acceleration output data as the foundation for extracting frequency and amplitude information. After the above calculations, the following parameters can be obtained: Turbulence calculation, based on acceleration data, involves spectral analysis to extract frequency and amplitude characteristics. The resulting output parameters primarily include triaxial acceleration (unit: m / s²). 2 The system provides outputs of three-axis velocity (unit: mm / s), three-axis displacement (unit: mm), and three-axis angle (unit: °), with each parameter corresponding to the maximum amplitude and peak frequency.

[0080] Status determination and early warning: In static monitoring scenarios, the above maximum amplitude data will be used as the benchmark for setting alarm thresholds. To ensure alarm accuracy and reduce false alarm rate, an alarm mechanism combining independent thresholds for a single multi-component physical sensor and linkage thresholds for multiple multi-component physical sensors is adopted.

[0081] Alarm settings for a single multi-component physical sensor: For monitored components such as acceleration, angle, velocity, displacement, static disturbance, and dynamic disturbance, based on the maximum amplitude of static vibration. It is designed with three judgment methods: fixed threshold, flexible threshold and nonlinear threshold, which can be flexibly selected according to the object being measured.

[0082] The fixed threshold is set as follows: ; In the formula, Acceleration threshold limiting; This is the preset amplitude multiple coefficient.

[0083] The elastic threshold is set as follows: ; In the formula, The parameter is dynamically adjusted to add a compensation value on top of the existing maximum amplitude. This parameter can be dynamically adjusted according to working conditions such as time, traffic flow, temperature, and climate. Taking bridge monitoring as an example, the time period is divided into peak period (7:00-9:00, 17:30-19:30), off-peak period (23:00-5:00), and normal period (the remaining time after removing the peak and off-peak periods) based on the traffic volume.

[0084] The non-linear threshold setting method is as follows: ; The nonlinear threshold setting is a combination of a fixed threshold setting and a flexible threshold setting, wherein... This is the amplitude multiplier, a multiplier parameter corresponding to a fixed threshold setting; The method dynamically adjusts the parameters and provides compensation parameters corresponding to the elastic threshold setting. It combines the advantages of both fixed threshold and elastic threshold, making it suitable for more complex monitoring conditions.

[0085] Although the above three threshold setting methods are explained using acceleration threshold as an example, they can be directly applied to threshold alarm settings for other monitoring parameters such as angle, velocity, displacement, and disturbance.

[0086] like Figures 11 to 13 as well as Figure 15As shown, multiple multi-component physical sensor threshold alarm settings, that is, while each group of multi-component physical sensors performs individual multi-component physical sensor threshold monitoring, simultaneously runs a linkage alarm process. The main steps are as follows: acquire the frequency and amplitude of the accelerometers of three groups of multi-component physical sensors. First, collect the time domain data and frequency domain data of the accelerometers, and calculate parameters such as velocity and displacement through time domain data integration. Convert the frequency / amplitude data into wavelength, displacement, and static disturbance (i.e., static displacement). Among them, velocity, displacement, and wavelength can be calculated by the above algorithm. After obtaining displacement and static disturbance (i.e., static displacement), dynamic disturbance can also be obtained by the above algorithm. Wavelength is obtained by frequency conversion. Finally, displacement, static disturbance, dynamic disturbance, and wavelength parameters can be obtained. When applied to bridge monitoring, at least 3 multi-component physical sensors are generally arranged according to the bridge length and installation method. The dynamic disturbance data of each multi-component physical sensor can be obtained through the above steps.

[0087] This invention quantifies the vibration amplitude and real-time deformation of the tested object using time-domain velocity and displacement data, providing direct evidence for deformation monitoring. Simultaneously, it obtains natural frequency, instantaneous frequency, wavelength, and deflection parameters through frequency-domain analysis, reflecting the inherent mechanical properties of the tested object and its dynamic changes under stress. Specifically, natural frequency shift, the inability of instantaneous frequency to return to a reference value, and abnormal changes in deflection and wavelength can serve as characteristic indicators of structural performance degradation or localized damage. By combining fixed thresholds, elastic thresholds, and nonlinear thresholds of a single multi-component physical sensor, as well as a hierarchical judgment logic involving thresholds linked to multiple multi-component physical sensors, and comparing the wavelength and amplitude differences of multi-component physical sensors at different locations, it can identify deformation, vibration, and structural response deviations exceeding safe limits. This enables comprehensive verification of the local and overall state of the tested object, ultimately achieving deformation monitoring, fault diagnosis, and early warning.

[0088] like Figure 1 As shown, this application also provides a multi-component physical sensor. Applying the aforementioned monitoring method, the multi-component physical sensor includes a microcontroller unit (MCU), an inertial measurement unit (IMU), a triaxial magnetometer, a temperature sensor, and at least one expansion unit, all hermetically integrated within the same housing. All components are packaged within the same housing to achieve miniaturization, high integration, and extended service life in harsh environments.

[0089] The MCU, as the core control unit, is configured with two independent communication pin groups, namely the first communication pin group and the second communication pin group. The first communication pin group is used to connect the basic sensors inside except for the expansion unit, and the second communication pin group is used to connect the expansion unit. The two pin groups are completely independent in electrical and logical terms and can communicate at different rates or protocols at the same time to avoid data conflicts.

[0090] The IMU, triaxial magnetometer, and temperature sensor are mounted on the same communication bus, which is electrically connected to the first communication pin group. The IMU integrates a gyroscope and accelerometer to measure the triaxial acceleration and angular velocity of the object being measured. The triaxial magnetometer measures the direction of the Earth's magnetic field. The temperature sensor monitors the ambient temperature and provides temperature compensation data for the IMU and magnetometer. Meanwhile, the MCU periodically reads data from these sensors through the first communication pin group for aggregation and processing.

[0091] Each expansion unit is cascaded and electrically connected to the second communication pin group via a communication line. Each expansion unit has input and output ports, and data is transmitted sequentially and finally aggregated to the second communication pin group of the MCU. This allows for flexible addition or removal of the number of sensors, and only requires a small number of MCU pins. The communication line can use a single bus, a custom protocol, etc., depending on the type of expansion unit.

[0092] This embodiment further defines the specific type of the expansion unit. The expansion unit can be any one or more combinations of a BeiDou positioning module, a global satellite positioning module, an aerodynamic sensor, a relative humidity sensor, an acoustic sensor, a stress / strain sensor, an LVDT / RVDT, or a distance sensor from a wire displacement gauge. This enables comprehensive, three-dimensional monitoring regardless of external interference factors such as temperature, humidity, wind vibration, vehicle vibration, earthquakes, and heavy rain. Specifically, one or more expansion units can be selected according to the monitoring scenario requirements; multiple expansion units can be deployed in a cascaded manner.

[0093] For example, when location monitoring is required, a global satellite positioning module is cascaded, and the positioning data is transmitted to the MCU via the second communication pin group; when monitoring environmental conditions is required, a relative humidity sensor is selected; when detecting structural mechanical deformation is required, a stress-strain sensor is selected. When configuring a single expansion unit, the unit communicates directly with the second communication pin group via a communication line; when configuring multiple expansion units, all units communicate with the MCU in a cascaded topology. The MCU reads the data collected by each expansion unit according to a preset priority or polling strategy.

[0094] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0095] The embodiments and descriptions in the above specification are merely illustrative of the principles and best practices of this application. Various changes and modifications may be made to this application without departing from the spirit and scope thereof, and all such changes and modifications fall within the scope of this application as claimed.

Claims

1. A multi-component physical sensor monitoring method, characterized in that, Includes the following steps: Data acquisition: At least the raw time-domain signals of triaxial angular velocity, triaxial acceleration and triaxial magnetic induction intensity are acquired, and the triaxial attitude angle is calculated based on the triaxial angular velocity. The triaxial magnetometer is also calibrated in a non-magnetic environment to eliminate drift and the monitoring reference zero point is set. Calculation of relative displacement and angular displacement of magnetometer: Based on the change in magnetic induction intensity, attitude angle and geometric dimensions of the measured object, the relative displacement and angular displacement of magnetometer are calculated accordingly; Axis system transformation and synchronization processing: The Earth axis quantity is converted into the body axis quantity using the angle transformation matrix, the gravitational acceleration projection component is removed to obtain the body axis acceleration, and the phase and transmission rate of acceleration and attitude angle are unified through the transfer function; Acceleration-displacement calculation: The body axis acceleration is sequentially smoothed by mean and processed by multi-stage hysteresis dead zone. Combined with the set-reset latch logic with continuous sampling judgment and feedforward feedback control, the integral is completed to obtain the three-axis velocity and acceleration integral displacement. Cross-verification: Compare and verify the integral displacement of acceleration, relative displacement of magnetometer and angular displacement, identify abnormal signals and complete data reconfirmation; Frequency domain analysis: Perform Fourier transform on the preprocessed time-domain signal to obtain frequency, amplitude, and phase characteristics, and distinguish between the structure's natural frequency and instantaneous frequency; Deflection calculation: The vibration wavelength is calculated based on the propagation speed of the medium, and multiple multi-component physical sensors are deployed at different points of the object under test. The linear deflection and angular deflection are solved by the wavelength and amplitude differences of the multi-component physical sensors at different locations. Finally, the static deflection and dynamic deflection are calculated by combining the time-domain displacement parameters. Status determination and early warning: Combining time-domain velocity, displacement, and frequency-domain frequency, wavelength, and disturbance parameters, a hierarchical determination method is adopted, which combines the threshold of a single multi-component physical sensor with the linkage threshold of multiple multi-component physical sensors, to realize deformation monitoring, fault diagnosis, and early warning of the measured object.

2. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the axis conversion and synchronization processing steps, the angle conversion matrix is ​​constructed by the attitude angle to complete the conversion. The gravity under the Earth's axis is decomposed into the projected components on each axis. The body axis acceleration is obtained by subtracting the corresponding gravity projection component from the three-axis acceleration and removing the influence of gravity.

3. The multi-component physical sensor monitoring method according to claim 2, characterized in that, In the axis conversion and synchronization processing steps, angle transfer functions and acceleration transfer functions are configured respectively. The angle transfer function is a low-pass filter, a lead / lag transfer function, multiple unit delay equations, or an equation composed of various transfer functions. First, the output frequency of the angle transfer function is fixed to maintain the attitude angle signal at a preset response speed and ensure the signal peak accuracy. Then, the parameters of the acceleration transfer function are adjusted to keep the body axis acceleration and attitude angle synchronized in phase and bandwidth, thereby suppressing the jump of body axis acceleration data.

4. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the acceleration-displacement calculation step, mean equations and dead zone equations are configured respectively. The mean equation is used to smooth the body axis acceleration, reducing acceleration fluctuations and minimizing integral drift. The dead zone equation uses the minimum change in body axis acceleration caused by zero drift, random walk, and external vibration disturbances as the threshold. The corresponding white noise or colored noise interval of the acceleration is set as the dead zone. When the acceleration is within the white noise dead zone, the integral result is set to zero, eliminating the drift of acceleration caused by white noise due to the integration time sequence.

5. The multi-component physical sensor monitoring method according to claim 4, characterized in that, Mean smoothing divides the data statistical interval according to the output frequency of the multi-component physical sensor; the dead zone equation includes the basic linear dead zone equation, the piecewise linear hysteresis dead zone equation, and the nonlinear parabolic hysteresis dead zone equation; the basic linear dead zone equation takes the original acceleration as input, calibrates the parameters based on the maximum vibration amplitude of the measured object under natural conditions, and uses a fixed slope to calculate the linear white noise threshold interval. Both the piecewise linear hysteresis dead zone equation and the nonlinear parabolic hysteresis dead zone equation use the mean acceleration as input to derive the nonlinear white noise threshold range. The piecewise linear lag dead zone equation sets a jump threshold and a regression threshold, with the regression threshold being less than the jump threshold, and assigns independent slopes to the jump and regression processes respectively; the nonlinear parabolic lag dead zone equation uses a nonlinear equation to complete the calculation.

6. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the acceleration-displacement calculation step, the vibration logic algorithm and latch logic software switch are configured, and the logic judgment is completed in conjunction with the dead zone operation; when the absolute value of acceleration is greater than or equal to the jump threshold, the setting is executed and integration is started, and the set state is maintained; when the absolute value of acceleration is less than the regression threshold and the condition is met by multiple consecutive samplings, the reset is executed and the integration result is cleared to zero. The integration process incorporates feedforward and feedback control, relying on latching logic to switch the start, hold, and reset states of the integration process, ultimately obtaining the three-axis velocity and body axis displacement.

7. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the frequency domain analysis step, the Fourier algorithm is used to realize time-frequency conversion; the highest measurable frequency for spectrum analysis is half of the output frequency of the multi-component physical sensor data, and binary sequence values ​​are selected for the spectrum test points; different durations of sampling data can be extracted to draw spectrum diagrams, and the larger the amount of data extracted, the higher the accuracy of the calculated frequency and amplitude; the frequency, amplitude, and displacement meet the preset accuracy indicators, and the frequency accuracy, amplitude, and displacement all correspond to the working frequency range of the multi-component physical sensor, and a dedicated accuracy algorithm is configured to complete error suppression and error calculation; the time domain data is converted into frequency domain data containing natural frequency, instantaneous frequency, amplitude, and phase through the Fourier algorithm, and structural fault diagnosis and status early warning are realized by combining the changes in frequency domain data.

8. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the frequency domain analysis step, wavelength calculation is performed based on the vibration frequency obtained from the frequency domain analysis. The wavelength is solved according to the rule that the wavelength is equal to the propagation speed of the medium divided by the vibration frequency. During the monitoring process, the vibration frequency is divided into the natural frequency of the measured object and the instantaneous frequency generated by the dynamic force. The natural frequency of the measured object is the inherent stable vibration frequency of the structure itself. The instantaneous frequency changes with the magnitude and direction of the external force. After the external force disappears, the instantaneous frequency returns to the natural frequency. The calculated wavelength is combined with the velocity and displacement data obtained from the time domain solution to complete the structural deflection solution.

9. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the deflection calculation step: multi-component physical sensors are deployed at multiple points within the monitoring area of ​​the object under test, and dedicated computing logic circuits and signal links are configured to complete the deflection-related calculations; for any coordinate axis direction of the object under test, the displacement measured in the static state of the object under test is taken as the static displacement, the additional deformation caused by the impact is taken as the absolute dynamic deflection, and the total deformation measured under the impact is taken as the relative dynamic deflection; the impact coefficient is defined as the ratio of the relative dynamic deflection to the static displacement, which is equal to 1 and the sum of the ratios of the absolute dynamic deflection and the static displacement; the linear deflection and angular deflection are solved by combining the wavelength and amplitude differences of multiple corresponding multi-component physical sensors, and the static and dynamic deflection calculations are completed by combining the aforementioned parameters, and the degree of structural damage of the object under test is determined based on the calculation results.

10. A multi-component physical sensor monitoring method according to claim 1, characterized in that, The multi-component physical sensor meets the following accuracy and performance specifications: angular accuracy of ±0.01°, angular response bandwidth of 1 / 3 to 1 / 4 of the angular output frequency, and acceleration accuracy of ±0.01 m / s². 2 The acceleration response bandwidth is 1 / 3 to 1 / 4 of the acceleration output frequency, and the displacement accuracy is ±5mm. After the multi-component physical sensor is debugged, the frequency measurement accuracy is ±0.5%, and the amplitude algorithm accuracy of Fourier spectrum analysis is ±0.5%.

11. The multi-component physical sensor monitoring method according to claim 1, characterized in that, Multiple sets of multi-component physical sensors are arranged along the length, width, and height of the object being measured. The length and width are arranged at front, middle, and rear points, and the height is arranged at top, middle, and bottom points to achieve three-dimensional monitoring.

12. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the state determination and early warning steps: the determination calculation of deflection and deformation is completed based on the preset logic circuit; when the object under test is not deformed, the wavelength and amplitude of each multi-component physical sensor remain consistent; when the structure is locally deformed, the wavelength of the multi-component physical sensor corresponding to the deformation location becomes longer and the amplitude increases; by comparing the differences in wavelength and amplitude measured by each multi-component physical sensor, the deflection change and bending degree of the object under test are determined. Based on the maximum amplitude and peak frequency of triaxial acceleration, triaxial velocity, triaxial displacement, and triaxial angle data output by multi-component physical sensors, a hierarchical judgment method is adopted, which combines the threshold of a single multi-component physical sensor with the linkage threshold of multiple multi-component physical sensors. The threshold of a single multi-component physical sensor includes a fixed threshold, an elastic threshold, and a nonlinear threshold. The fixed threshold is the product of the static maximum vibration amplitude and a preset amplitude multiplication factor. The elastic threshold is the sum of the static maximum vibration amplitude and a dynamic adjustment parameter. The nonlinear threshold is the product of the static maximum vibration amplitude and a preset amplitude multiplication factor, plus the dynamic adjustment parameter.

13. The multi-component physical sensor monitoring method according to claim 1, characterized in that, In the status determination and early warning steps: threshold determination of multiple multi-component physical sensors is achieved by relying on the linkage operation logic circuit; the linkage threshold of multiple multi-component physical sensors is determined based on each other's data, and each group of multi-component physical sensors performs a linkage alarm process in coordination while performing individual multi-component physical sensor threshold alarm monitoring; this process includes: firstly, collecting time-domain and frequency-domain data of the accelerometers of each group of multi-component physical sensors, and calculating velocity and displacement from the time-domain data; then converting the frequency and amplitude data into wavelength, displacement, and static disturbance; then combining displacement and static disturbance to further obtain dynamic disturbance; finally, setting the linkage threshold based on the dynamic disturbance data of multiple groups of multi-component physical sensors to achieve coordinated alarm determination among multiple multi-component physical sensors.

14. A multi-component physical sensor, implemented using the multi-component physical sensor monitoring method as described in any one of claims 1 to 13, characterized in that, It includes an MCU, an inertial measurement unit (IMU), a triaxial magnetometer, a temperature sensor, and at least one expansion unit, all sealed and integrated within the same housing. The MCU is configured with two independent communication pin groups, namely a first communication pin group and a second communication pin group. The IMU, magnetometer, and temperature sensor are mounted on the same communication bus, which is electrically connected to the first communication pin group. The expansion units are cascaded together and electrically connected to the second communication pin group via communication lines.

15. A multi-component physical sensor according to claim 14, characterized in that, The expansion unit is any one or more combinations of Beidou positioning module, global positioning module, aerodynamic sensor, relative humidity sensor, acoustic sensor, stress / strain sensor, LVDT / RVDT, and distance sensor in wire displacement gauge.

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