CN-122019932-A - Vibration wire sensor acquisition frequency precision self-adaptive optimization method based on Kalman filtering algorithm

CN122019932ACN 122019932 ACN122019932 ACN 122019932ACN-122019932-A

Abstract

The invention discloses a vibration wire sensor acquisition frequency precision self-adaptive optimization method based on a Kalman filtering algorithm, which belongs to the technical field of civil engineering structure health monitoring and comprises the steps of S1, establishing a Kalman filtering model taking frequency as an observation and taking a structure state as a target, S2, setting a filtering initial value and carrying out self-adaptive optimization setting on the model, and S3, embedding the optimized algorithm into a sensor microcontroller. The invention directly outputs the optimal state estimation value of the tunnel structure at each monitoring moment by establishing a proper Kalman filtering model, dynamically adjusts noise in real time to reduce error accumulation, introduces an fading factor to match with Kalman optimal gain, effectively reduces adverse effects on the current state due to model errors or priori statistical characteristic errors in a recursion process, enhances the adaptability of the system to the current state change, and improves the reliability of the monitoring system by embedded real-time processing and dynamic parameter adjustment.

Inventors

LIU XUEZENG
SHA SHA
SUN ZHOU
JIANG XI
LI MING
LI YINPING

Assignees

同济大学
上海同岩土木工程科技股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260123

Claims (10)

1. The adaptive optimization method for the acquisition frequency precision of the vibrating wire sensor based on the Kalman filtering algorithm is characterized by comprising the following steps of: s1, establishing a Kalman filtering model aiming at a monitored structure, and establishing a proper state equation and an observation equation by using the Kalman filtering model; S2, carrying out self-adaptive optimization setting on the Kalman filtering model, wherein the self-adaptive optimization setting comprises the steps of setting a filtering initial value, setting noise self-adaptive adjustment and model self-adaptive adjustment; S3, embedding the algorithm program established and optimized in the steps S1 and S2 into a microcontroller of the vibrating wire sensor, so that the sensor can acquire frequency signals and output optimized structural state information in real time.
2. The adaptive optimization method for the acquisition frequency accuracy of the vibrating wire sensor based on the Kalman filtering algorithm of claim 1, wherein the specific steps in the step S1 comprise the following steps of; S11, defining a frequency signal acquired by a vibrating wire sensor as an observation variable, and defining a parameter reflecting a physical quantity to be monitored of a civil structure as a state variable; S12, establishing a dynamic model describing the evolution rule of the state variable along with time, and constructing a state equation; s13, establishing a measurement model describing the mapping relation between the observation variable and the state variable, and constructing an observation equation.
3. The method for adaptively optimizing the acquisition frequency accuracy of a vibrating wire sensor based on a Kalman filtering algorithm according to claim 1, wherein the initial filtering values in the step S2 comprise an initial state vector, an initial state error covariance matrix, an observation noise covariance matrix and a process noise covariance matrix.
4. The adaptive optimization method for the acquisition frequency accuracy of the vibrating wire sensor based on the Kalman filtering algorithm of claim 1, wherein the specific step of setting the adaptive noise adjustment in the step S2 comprises the following steps: S221, calculating a state estimation residual error at the current moment; s222, constructing a noise covariance sample at the current moment based on the residual error; s223, using the self-adaptive filtering algorithm, fusing the historical noise covariance with the current sample, and updating the noise covariance matrix estimation value at the next moment.
5. The adaptive optimization method for acquisition frequency accuracy of a vibrating wire sensor based on a Kalman filtering algorithm according to claim 4, wherein the adaptive filtering algorithm adopts an exponential weighted moving average algorithm or a sliding window estimation algorithm, and iteratively updates the observed noise covariance matrix and the process noise covariance matrix based on state estimation residuals at the current moment.
6. The adaptive optimization method for the acquisition frequency accuracy of the vibrating wire sensor based on the Kalman filtering algorithm of claim 1, wherein the adaptive adjustment of the model in the step S2 comprises the following steps: the Kalman optimal gain calculation is used for ensuring that the square sum of state errors is minimum and obtaining the optimal unbiased estimation closest to the real state; the fading factors are introduced to strengthen the suppression of the state error covariance matrix growth.
7. The adaptive optimization method for acquisition frequency accuracy of vibrating wire sensor based on Kalman filtering algorithm of claim 6, wherein the Kalman optimal gain calculation step specifically comprises the following steps: S2321, constructing an optimization target of Kalman gain by taking the trace of the minimized state error covariance matrix as a criterion; s2322, calculating to obtain a Kalman optimal gain value by solving the partial derivative of the trace of the state error covariance matrix to the Kalman gain and enabling the partial derivative to be zero; s2323, updating the state estimation value and the state error covariance matrix by using the Kalman optimal gain value.
8. The adaptive optimization method for the acquisition frequency accuracy of the vibrating wire sensor based on the Kalman filtering algorithm of claim 6, wherein the method for introducing the fading factor to correct the model is characterized by comprising the following steps: s2331, dynamically calculating the fading factor according to the size of the observation state estimation residual error; s2332, inhibiting the growth of a state error covariance matrix by using the fading factors; and S2333, updating the state estimation value based on the amplified state prediction error covariance matrix.
9. The adaptive optimization method for acquisition frequency accuracy of vibrating wire sensor based on Kalman filtering algorithm of claim 1, wherein the step S2 further comprises setting and calibrating initial parameters of Kalman filtering model, specifically: filtering by adopting a preset initial state error covariance matrix in a first preset time period after monitoring and starting; after entering a second preset time period, updating the initial state error covariance matrix according to the state error covariance matrix statistical value actually calculated in the time period, and fixing the updated value in subsequent monitoring.
10. The adaptive optimization method for the acquisition frequency precision of the vibrating wire sensor based on the Kalman filtering algorithm is characterized in that a microcontroller of the vibrating wire sensor adopts an MCU chip or an SOC chip.

Description

Vibration wire sensor acquisition frequency precision self-adaptive optimization method based on Kalman filtering algorithm Technical Field The invention relates to the technical field of health monitoring of civil engineering structures, in particular to a vibration wire sensor acquisition frequency precision self-adaptive optimization method based on a Kalman filtering algorithm. Background In the process of monitoring the health of the civil structure, a vibrating wire sensor is often used for carrying out health monitoring on the civil structure, the stress deformation state of the civil structure is indirectly expressed through the monitoring frequency of the vibrating wire sensor, and if the acquired frequency value has errors, the accurate stress deformation state of the structure cannot be obtained. Meanwhile, the acquired signals of the vibrating wire sensor are often influenced by a plurality of interference factors such as environmental noise, equipment noise and the like. Noise of the vibrating wire sensor is usually calibrated in a laboratory, when conditions such as sensor aging, construction operation, environmental mutation and the like occur, the noise also changes, if fixed factory noise is continuously adopted, a filter cannot filter out real noise, and finally data errors are larger and larger, and reliable civil structure state monitoring data cannot be provided. The technical means for improving the acquisition precision of the vibrating wire sensor in the prior art mainly comprises two types, namely a first type of filtering noise from a source by designing a hardware anti-interference circuit, a second type of filtering noise by designing a hardware anti-interference circuit, wherein the scheme has the problems of complex circuit, high cost, increased fault risk and the like, the parameters of the scheme are usually fixed, the capability of self-adaptive adjustment according to signal change is lacking, and a second type of filtering noise is performed on acquired frequency data by performing software post-processing, wherein a Kalman filtering algorithm is a common state estimation and denoising tool. However, the scheme generally takes Kalman filtering as a post-processing algorithm of an upper computer or a server, and has the following limitations that a data window is required to be established for signals, frequency spectrum leakage is easy to occur, meanwhile, the quality requirement on original vibrating wire signals is high, fixed factory noise is adopted, the noise cannot be dynamically adjusted in real time, an actual state value cannot be output in one step, and problems of algorithm delay, error accumulation and the like are caused. Disclosure of Invention The invention overcomes the defects of the prior art and provides a vibration wire sensor acquisition frequency precision self-adaptive optimization method based on a Kalman filtering algorithm. In order to achieve the purpose, the technical scheme adopted by the invention is that the adaptive optimization method for the acquisition frequency precision of the vibrating wire sensor based on the Kalman filtering algorithm comprises the following steps: s1, establishing a Kalman filtering model aiming at a monitored structure, and establishing a proper state equation and an observation equation by using the Kalman filtering model; S2, carrying out self-adaptive optimization setting on the Kalman filtering model, wherein the self-adaptive optimization setting comprises the steps of setting a filtering initial value, setting noise self-adaptive adjustment and model self-adaptive adjustment; S3, embedding the algorithm program established and optimized in the steps S1 and S2 into a microcontroller of the vibrating wire sensor, so that the sensor can acquire frequency signals and output optimized structural state information in real time. In a preferred embodiment of the present invention, the specific steps in the step S1 include; S11, defining a frequency signal acquired by a vibrating wire sensor as an observation variable, and defining a parameter reflecting a physical quantity to be monitored of a civil structure as a state variable; S12, establishing a dynamic model describing the evolution rule of the state variable along with time, and constructing a state equation; s13, establishing a measurement model describing the mapping relation between the observation variable and the state variable, and constructing an observation equation. In a preferred embodiment of the present invention, the initial filtering values in step S2 include an initial state vector, an initial state error covariance matrix, an observed noise covariance matrix, and a process noise covariance matrix. In a preferred embodiment of the present invention, the specific step of setting the noise adaptive adjustment in the step S2 includes: S221, calculating a state estimation residual error at the current moment; s222, constructing a noise covariance sam