CN117171985B - A real-time monitoring method, device, equipment and storage medium for nonlinear structure - Google Patents

Abstract

The present application discloses a real-time monitoring method, device, equipment and storage medium for nonlinear structures. The method combines unknown external excitation, structural parameters and structural state, and constructs an augmented state vector that combines three vectors including unknown external excitation, structural parameter vector and structural state vector. According to the partial acceleration response and augmented state vector actually collected, the unscented Kalman filter is used to realize the real-time prediction of unknown structural state, structural parameters and external excitation. The method can be used for engineering structures subjected to loads such as earthquakes, so as to understand the health status of the structure in real time and provide effective guarantee for structural evaluation.

Description

A real-time monitoring method, device, equipment and storage medium for nonlinear structure

Technical Field

The present invention relates to the field of civil engineering technology, and in particular to a real-time monitoring method, device, equipment and storage medium for a nonlinear structure.

Background technique

There are three main types of unknown quantities in the application of structural health monitoring of engineering structures, namely external excitation (such as excitation caused by earthquakes, typhoons, etc.), unknown structural states and unknown structural parameters. Structural states include velocity and displacement, and structural parameters include stiffness, damping, nonlinear parameters, etc. Effective identification of unknown external excitations of engineering structures is a prerequisite for safety evaluation and vibration control of engineering structures. Reliable estimation of structural states can be used to identify fatigue damage of structures to be monitored. The structural performance of engineering structures can be reflected by parameters such as stiffness and damping of the structure. Health monitoring results can directly reflect the health status of engineering structures. However, in general, the structural parameters of engineering structures are unknown, and it is difficult to directly measure all structural states and external excitations of a structure.

When an engineering structure is subjected to loads such as earthquakes and typhoons, and the structure undergoes large displacements and deformations, strong nonlinear characteristics will appear, and nonlinearity will cause more complex dynamic phenomena. At present, most of the research on the identification of nonlinear structures is to identify the structure state and structure parameters when the external excitation is known. The state vector contains the structure state vector and the unknown structure parameter vector. However, in actual engineering, it is usually difficult to directly measure the external excitation, so it is difficult to estimate the structure state vector and structure parameter vector of the engineering structure.

Summary of the invention

The present application aims to at least solve the technical problems existing in the prior art. To this end, the present application proposes a real-time monitoring method, device, equipment and storage medium for nonlinear structures, which can understand the health status of the structure in real time and provide effective guarantee for structural evaluation.

According to the real-time monitoring method of a nonlinear structure in the first aspect of the present application, the real-time monitoring method of a nonlinear structure comprises the following steps:

Collect partial acceleration response of nonlinear structure under external excitation;

Simulating the structural parameters of the nonlinear structure and the external excitation as a random walk model, and constructing an augmented state vector consisting of a structural parameter vector, the external excitation, and a state vector of the nonlinear structure according to the random walk model;

According to the partial acceleration response and the augmented state vector, an unscented Kalman filter is used to predict in real time the structural state, structural parameters and the external excitation of the nonlinear structure.

The present application provides a real-time monitoring method for nonlinear structures, which has at least the following beneficial effects:

At present, the identification research of nonlinear structures is to identify the structural state and structural parameters when the external excitation is known. The state vector contains the structural state vector and the uncertain structural parameter vector. However, it is usually difficult to directly measure the external excitation in actual engineering. In this method, the unknown external excitation, structural parameters and structural state are combined. By constructing an augmented state vector containing the unknown external excitation, structural parameter vector and structural state vector, the unscented Kalman filter is used to estimate the unknown structural state, structural parameters and external excitation based on the partial acceleration response and augmented state vector actually collected, which can improve the efficiency and accuracy of the estimation. This method can be used for engineering structures subjected to loads such as earthquakes, so as to understand the health status of the structure in real time and provide effective protection for structural evaluation.

According to some embodiments of the present application, based on the state estimate and error covariance at the current moment, an unscented transformation is used to generate a first sigma point set of the augmented state vector and calculate the corresponding weights;

Converting the first sigma point set into a state prediction vector, combining the state prediction vector according to the weights to obtain a one-step-ahead state estimate and calculating a corresponding error covariance;

Generate a second sigma point set of the state prediction vector by using an unscented transformation according to the state estimate of the previous step and its corresponding error covariance;

converting the second sigma point set into a measurement prediction vector corresponding to a sigma point, and combining the measurement prediction vectors corresponding to the sigma point according to the weight to obtain a measurement prediction;

Calculating a measurement prediction covariance and a state-measurement covariance based on the state estimate of the one step forward, the state prediction vector, the measurement prediction vector corresponding to the sigma point, and the measurement prediction;

Calculating a Kalman gain based on the measurement prediction covariance and the state-measurement covariance;

The state estimate at the next moment is calculated based on the state estimate of the previous step, the Kalman gain, the partial acceleration response and the measurement prediction, so as to obtain the structural state, structural parameters and external excitation at the next moment from the state estimate at the next moment; wherein the current moment and the next moment are separated by a sampling time period.

According to some embodiments of the present application, the calculating the measurement prediction covariance and the state-measurement covariance according to the one-step-ahead state estimate, the state prediction vector, the measurement prediction vector and the measurement prediction includes:

Among them, P _{yy, (i+1|i)} is the measurement prediction covariance, is the measurement prediction vector corresponding to the sigma point, is the measurement prediction, n is the dimension of the augmented state vector, is the weight of the covariance, for The transposed matrix of , the subscript (i+1|i) is the transition from time i to time (i+1), P _{uy, (i+1|i)} is the state-measurement covariance, is the state prediction vector, is the state estimate one step ahead, and R is the covariance matrix of the measurement noise.

According to some embodiments of the present application, the calculating the Kalman gain according to the measurement prediction covariance and the state-measurement covariance includes:

in, is the Kalman gain, is the inverse matrix of P _{yy, (i+1|i)} .

According to some embodiments of the present application, the calculating the state estimate at the next moment according to the state estimate of the previous step, the Kalman gain, the partial acceleration response and the measured prediction value includes:

Among them, _yi+1 is the partial acceleration response at time (i+1), is the state estimate at time (i+1).

According to some embodiments of the present application, after calculating the state estimate at the next moment, the error covariance at the next moment is also calculated in the following manner:

in, is the error covariance at the next moment, is the error covariance corresponding to the state estimate one step ahead, for The inverse matrix of .

According to some embodiments of the present application, simulating the structural parameters of the nonlinear structure and the external excitation as a random walk model includes:

According to the external excitation and the first Gaussian white noise, simulating the external excitation as a random walk model;

And, determining the structural parameters of the nonlinear structure, and simulating the structural parameters as a random walk model according to the structural parameters and a second Gaussian white noise.

According to a real-time monitoring device for a nonlinear structure of an embodiment of the second aspect of the present application, the real-time monitoring device for a nonlinear structure comprises:

A data acquisition unit, used to acquire partial acceleration response of the nonlinear structure under external excitation;

A state vector construction unit, used for simulating the structural parameters of the nonlinear structure and the external excitation as a random walk model, and constructing an augmented state vector consisting of a structural parameter vector, the external excitation and a state vector of the nonlinear structure according to the random walk model;

The recursive solution unit is used to use unscented Kalman filtering to real-time estimate the structural state, structural parameters and the external excitation of the nonlinear structure according to the partial acceleration response and the augmented state vector.

An electronic device according to an embodiment of the third aspect of the present application includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the above-mentioned real-time monitoring method of nonlinear structure.

According to a computer-readable storage medium of an embodiment of the fourth aspect of the present application, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned real-time monitoring method for nonlinear structures.

Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing the present application.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and/or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

FIG1 is a schematic flow chart of a real-time monitoring method for a nonlinear structure provided in an embodiment of the present application;

2 is a schematic diagram of using unscented Kalman filtering to estimate the structural state, structural parameters and external excitation of a nonlinear structure according to an embodiment of the present application;

FIG3 is a schematic diagram of a five-layer Duffing-type layer shear frame structure according to an embodiment of the present invention;

FIG4 is a comparison diagram of predicted and actual displacement identification according to an embodiment of the present invention;

FIG5 is a comparison diagram of predicted and actual speed recognition according to an embodiment of the present invention;

FIG6 is a diagram of stiffness parameter estimation results according to an embodiment of the present invention;

FIG7 is a diagram of nonlinear parameter estimation results according to an embodiment of the present invention;

FIG8 is a diagram showing the damping proportional coefficient estimation result according to an embodiment of the present invention;

FIG9 is a comparison diagram of external stimulus recognition results according to an embodiment of the present invention;

FIG. 10 is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application.

Detailed ways

The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are described. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations:

(1) Unscented Kalman Filter (UKF) is a nonparametric filtering algorithm used to process nonlinear systems. It can infer the value of hidden states from observation and measurement data. The basic idea of UKF is to use a series of weighted state estimates to predict future states based on the state of state variables and the observation of measurement variables, and to correct the predicted values through observations and measurements. The unscented transformation refers to obtaining some sampling points in a deterministic sampling method so that their statistical properties are equal to the mean and covariance matrix of this Gaussian distribution. After these points are transformed nonlinearly, the mean and covariance matrix of the new function are obtained in a deterministic weighted manner.

(2) Three unknown quantities and one known quantity of the nonlinear structure involved in the embodiments of the present disclosure:

External excitation refers to unknown excitation acting on nonlinear structures. External excitation is usually generated by typhoons, earthquakes, etc.

Structural state, including velocity and displacement of nonlinear structures under external excitation;

Structural parameters, usually including mass, stiffness, damping and nonlinear parameters of nonlinear structures;

Partial acceleration response refers to the measurement obtained by an acceleration sensor installed on a partial area of the nonlinear structure.

First embodiment

1 , an embodiment of the present application provides a real-time monitoring method for a nonlinear structure, and the method includes the following steps S110 to S130:

Step S110: collecting in real time partial acceleration responses of the nonlinear structure under external excitation.

Step S120: Simulate the structural parameters and external excitation of the nonlinear structure as a random walk model, and construct an augmented state vector consisting of the structural parameter vector, the external excitation and the state vector of the nonlinear structure according to the random walk model.

Step S130: According to the partial acceleration response and the augmented state vector, an unscented Kalman filter is used to estimate in real time the structural state, structural parameters and external excitation of the nonlinear structure.

At present, the identification research of nonlinear structures is all based on the structural state-structural parameter identification when the external excitation is known. The state vector contains the structural state vector and the uncertain structural parameter vector. However, it is usually difficult to directly measure the external excitation in actual engineering.

In this embodiment, based on the prior art, the unknown external excitation, structural parameters and structural state are combined, and an augmented state vector including the unknown external excitation, structural parameter vector and structural state vector is constructed. According to the partial acceleration response and augmented state vector actually acquired, the unscented Kalman filter is used to realize the estimation of the unknown structural state, structural parameters and external excitation, which can improve the efficiency and accuracy of the estimation. This method can be used for engineering structures subjected to loads such as earthquakes, so as to understand the health status of the structure in real time and provide effective guarantee for structural evaluation.

Second embodiment

This embodiment provides a specific implementation of the method shown in the first embodiment, including steps S210 to S240:

Step S210: obtaining a partial acceleration response of the structure to be monitored under an unknown external excitation.

The sensor is placed in the structure to be monitored (nonlinear structure), and when the structure to be monitored is subjected to external excitation, the partial acceleration response of the structure read by the sensor is obtained. In this method, only part of the acceleration response is observed, and the actual observation value is used in the subsequent formula (33).

Step S220: establishing a random walk model of external excitation and structural parameters.

When the structure to be monitored is subjected to external excitation, the motion equation of its continuous-time system can be expressed as follows (taking the Duffing-type layer shear structure as an example):

Among them, M is the mass matrix of the structure to be monitored, C is the damping matrix of the structure to be monitored, K is the stiffness matrix of the structure to be monitored, and W is the nonlinear parameter of the structure to be monitored; x(t) represents the acceleration, velocity, and displacement vector of the structure to be monitored, and f(t) is the unknown external excitation. It should be noted that the motion equations of different structures are slightly different.

Define the state vector Then the equation of motion of formula (1) can be rewritten into the form of the following state space equation:

Among them, θ(t) represents the structural parameter vector of the structure to be monitored, including mass, stiffness, damping and nonlinear parameters, ω(t) represents the process noise, ω can be simulated as zero mean, and the covariance matrix is the Gaussian distribution of Q; g(.) is the nonlinear function of the state vector χ(t), expressed as follows:

The measurement equation (or observation equation) can be expressed as:

z(t)=h(χ(t), θ(t), f(t))+v(t) (4)

Where h(.) is a nonlinear function of the state vector χ(t), v(t) is the measurement noise (or observation noise), and v can be modeled as a Gaussian distribution with zero mean and covariance matrix R. The measurement equation is used to calculate the predicted acceleration response.

State Space Equations The and measurement equations can be discretized using the sampling time period Δt. The discretized state space equation (i.e., discrete formula (2)) is expressed as follows at the (i+1)Δt moment:

χ _i+1 =F(χ _i , θ _i , _fi )+ω _j (5)

Wherein, F(.) is a nonlinear function of the discretized state vector χ _i+1 . i = 0, 1, ..., N; N = t/Δt, t is the total duration of the structural acceleration response observation period.

The discrete measurement equation (i.e., discrete formula (4)) is:

The random walk model is used to represent the unknown external excitation and changes in structural parameters of the structure to be monitored. The calculation formula of the discrete time system is as follows:

_fi+1 = _fi +ωf _,i (8)

θ _i+1 =θ _i +ω _θ，i (9)

Among them, _fi represents the unknown external excitation at time i; _θi represents the unknown structural parameter vector at time i, usually including stiffness parameters, damping parameters and nonlinear parameters; ωf _,i and ωθ _,i are Gaussian white noise with zero mean, and their covariance matrices are Pf _,i and Pθ _,i respectively.

Step S230: introducing an augmented state vector including a state vector, a structural parameter vector and an external excitation.

u _i =[χ _i ^T , θ _i ^T , f _i ^T ] ^T (10)

Among them, χ _i represents the state vector at time i, and the state vector includes displacement and velocity.

For the external excitation input-structural state-structural parameter identification problem of the nonlinear structure of this embodiment, the expression of the augmented state space equation is as follows:

Where, the new process noise η _j = [ω _j ^T ω _{θ, j} ^T ω _{f, j} ^T ] ^T , is the new nonlinear equation for the new state vector and process noise, which is expressed as follows:

where G(·) is a nonlinear function of the augmented state vector u _j+1 .

The observation equation based on the augmented state vector can be written as:

y _j+1 =H(u _j+1 )+v _j+1 (13)

In formula (13), H(·) is the new nonlinear function of the measurement equation.

Traditionally, only the structural state and structural parameters are used together to construct the state vector, while the present application adds external excitation to achieve the combination of the three parameter vectors of structural state, structural parameters and external excitation.

Step S240: using the partial acceleration response of step S210 and the augmented state vector of step S230, the structural state, structural parameters and external excitation of the structure to be monitored are estimated based on the unscented Kalman filter algorithm.

2 , the above step S240 specifically includes the following steps S241 to S248:

First, the unscented Kalman filter algorithm is a recursive algorithm. The initial state value of the unscented Kalman filter algorithm is set as:

Where u ₀ represents the augmented state vector of the structure to be monitored expanded at the initial zero time, represents the predicted value of the augmented state vector; P ₀ represents the error covariance matrix of the augmented state vector at the initial zero time, Represents the predicted values of the error covariance matrix.

In this embodiment, the current moment is taken as moment i, and the next moment is taken as moment (i+1). The calculation process from moment i to moment (i+1) is described in detail. The interval between moment i and moment (i+1) is a sampling time Δt. The state estimation and error covariance of the current moment i are known. Since the following steps describe the calculation process from moment i to moment i+1, the state estimation and error covariance of moment i can be inferred according to a similar process, which will not be described in detail here.

Step S241: According to the current state estimation and error covariance, an unscented transformation is used to generate the first sigma point set of the augmented state vector and calculate the corresponding weights.

In step S241, the augmented state vector is untraceably transformed to generate its corresponding sigma point (in this embodiment, (2n+1) points are set). The above formulas (16) to (18) are the process of obtaining the sigma point. are the state estimate and error covariance at time i respectively.

is the weight corresponding to the mean, is the weight corresponding to the covariance, n is the dimension of the augmented state vector, λ is the scale parameter, λ＝α ² (n+κ)-n, where α＝1, β＝2, κ＝0.

Step S242: convert the first sigma point set into a state prediction vector, and combine the state prediction vectors according to the weights to obtain a state estimate one step forward and calculate the corresponding error covariance.

Using the state space equation of formula (11) above The sigma point Converted into state prediction vector Then use the weights to combine the state prediction vector Get the state estimate of the first step forward at time i And calculate the error covariance of the state estimate one step ahead

Step S243: Generate a second sigma point set of the state prediction vector using unscented transformation according to the state estimate of the previous step and its corresponding error covariance.

Step S243 is measurement update, i.e. using the state estimation of the previous step and error covariance Generate a set of sigma points again through unscented transformation

Step S244: convert the second sigma point set into measurement prediction vectors corresponding to the sigma points, and merge the measurement prediction vectors corresponding to the sigma points according to the weights to obtain the measurement prediction.

The sigma point is converted into a measurement prediction vector using the nonlinear measurement equation H(.) shown in the above formula (13) Merge Vectors Get the measurement prediction at time (i+1)

Step S245, calculate the measurement prediction covariance and state-measurement covariance of the measurement noise based on the state estimation one step forward, the state prediction vector, the measurement prediction vector corresponding to the sigma point, and the measurement prediction.

Consider the measurement prediction covariance P _{yy, (i+1|i)} and the state-measurement covariance P _{uy, (i+1|i)} of the measurement noise:

Step S246: Calculate the Kalman gain according to the measurement-prediction covariance and the state-measurement covariance.

In order to update the state measurement, the Kalman gain is calculated first

Step S247, calculate the state estimate at the next moment and the corresponding error covariance based on the state estimate of the previous step, Kalman gain, partial acceleration response and measurement prediction, so as to obtain the structural state, structural parameters and external excitation at the next moment from the state estimate.

According to the Kalman gain Update the state vector at time (i+1)

Here, _yi+1 is the partial acceleration response at time i+1 collected in step S210.

The state vector at this time It includes the structural state and structural parameters at time (i+1) and the external excitation acting on the structure to be monitored at time (i+1).

The error covariance at time i+1 obtained in step S247 It is used in the next round of recursive process. It should be noted that the state estimation at time i can also be inferred from the above formula and error covariance This is not discussed in detail here.

In the next cycle, that is, from time (i+1) to time (i+2), the calculation of the next structural state, structural parameters and external excitation is also performed according to the above steps S241 to S247 until all calculations at N times are completed.

At present, the identification research of nonlinear structures is to identify the structural state and structural parameters when the external excitation is known. The state vector includes the structural state vector and the uncertain structural parameter vector. However, it is usually difficult to directly measure the external excitation in actual engineering. In this embodiment, based on the prior art, the unknown external excitation, structural parameters and structural state are combined, and an augmented state vector including the unknown external excitation, structural parameter vector and structural state vector is constructed. According to the partial acceleration response and augmented state vector actually collected, the unscented Kalman filter is used to realize the estimation of the unknown structural state, structural parameters and external excitation, which can improve the efficiency and accuracy of the estimation. This method can be used for engineering structures subjected to loads such as earthquakes, so as to understand the health status of the structure in real time and provide effective guarantee for structural evaluation.

Third embodiment

To facilitate understanding by those skilled in the art, a set of embodiments is provided below:

Take a five-story Duffing-type layer shear frame structure as an example. The schematic diagram of the structure is shown in Figure 3. The structural parameters are: the mass of each layer m _L = 400kg, the stiffness of each layer k _L = 320kN/m, L = 1, 2, ..., 5, so the stiffness-to-mass ratio of each layer is 0.8, and the nonlinear parameter W = 30kN/m; the Rayleigh damping model is adopted, and the damping coefficient is a = 0.5996, b = 0.0032, so the first two-order damping ratio ξ is taken as 5%. The first five natural frequencies of the structure are 1.2813Hz, 3.7400Hz, 5.8958Hz, 7.5739Hz, and 8.6358Hz respectively. The structure is subjected to horizontal earthquake excitation, and the ground acceleration is simulated as zero-mean Gaussian white noise with a spectral intensity of ^0.49m2 / ^s3 ; the entire monitoring period t is 300s, and the acceleration response of the 1st, 3rd and 5th layers of this nonlinear structure is observed, and the sampling frequency of the monitoring data is 1000Hz. Moreover, at the 100th second, the stiffness of the 1st and 4th layers of the structure degrades by 10% on the original basis. Considering the influence of observation noise, Gaussian white noise with a signal root mean square value of 5% is added to the observation information; the unknown structural parameters and unknown external excitations of the structure are expanded into the state vector to obtain the augmented state vector. Considering the large difference in order of magnitude between the structural response and the parameters, in order to avoid the influence of calculation rounding errors, the parameter items in the augmented state vector are parameterized; at this time, the augmented state vector of the unscented Kalman filter algorithm (UKF algorithm) is

Among them, θ _k,L represents the stiffness parameter vector after parameterization, θ _a and θ _b represent the damping proportional coefficients after parameterization, and θ _w represents the nonlinear parameter after parameterization. Specifically:

Step S310: Obtain the acceleration response of the part of the structure under unknown excitation. Arrange the sensor in the structure, and when the structure is subjected to external excitation, use the accelerometer to obtain the acceleration response of the 1st, 3rd and 5th layers of the structure.

Step S320: Establish a model.

Step S321: Establish a mathematical model of unknown external excitation and unknown structural parameters.

_fi+1 = _fi +ωf _,i (35)

θ _i+1 = θ _i +ω _θ,i (36)

Step S322: determine the augmented state vector:

Step S323: Establish the time-discrete augmented state equation of the system containing process noise:

Step S324: According to the state vector, the following observation equation can be obtained:

_yi+1 =H(ui ₊₁ )+ _vi+1 (39)

Step S330: Real-time joint estimation of structural excitation, state and parameters (taking the period from time i to time (i+1) as an example).

Step S331, setting the initial state value of the unscented Kalman filter algorithm:

Step S332: state prediction:

Through untraceable transformation, 2n+1 sampling points (sigma point set) are generated and the corresponding weights are calculated:

Merge Vectors Get the one-step-forward state estimate and the estimated covariance

Step S333: Measurement update:

Generate (2n+1) sampling points (sigma point set) again through unscented transformation:

Step S334: measurement prediction:

Step S335: Consider the measurement prediction covariance P _yy and the state measurement covariance P _uy of the measurement noise:

Step S336: Status update;

Kalman gain:

State vector update:

Error covariance:

Step S337, repeat steps S310 to S330 until time i=N.

It should be noted that the explanations of the above formulas (35) to (60) refer to those in the second embodiment and will not be repeated here.

Fourth embodiment

4 to 9 , for better explanation, the present embodiment conducted the following experiments:

Figures 4 and 5 compare the actual response and estimated response of the engineering structure, and select a time interval of 5s for enlarged comparison. It can be seen that the state estimation value can match the actual value well. Figure 6 shows the results of the stiffness parameter estimation. It can be seen that the initial moment can quickly converge to the true value. After the stiffness of the first and fourth layers degrades at the 100th second, it can also quickly converge to the true value. Figures 7 and 8 are the results of the identification of nonlinear parameters and damping proportional coefficients. The identification error is greater than the stiffness parameter because the stiffness contributes more to the structural response and is easier to identify. It can be seen from the figure that the identification result is contained in the 95% confidence interval and is within the acceptable error range. Figure 9 compares the actual excitation and the estimated excitation, and selects a comparison chart with a time interval of 1s for enlarged comparison. The comparison results show that the estimated excitation is very close to the actual excitation.

Fifth embodiment

Some embodiments of the present application provide a real-time monitoring device for a nonlinear structure, and the real-time monitoring device for a nonlinear structure includes: a data acquisition unit 1100, a state vector construction unit 1200, and a recursive solution unit 1300, which specifically include:

The data acquisition unit 1100 is used to acquire in real time a partial acceleration response of the nonlinear structure under external excitation.

The state vector construction unit 1200 is used to simulate the structural parameters and external excitation of the nonlinear structure into a random walk model, and construct an augmented state vector consisting of the structural parameter vector, the external excitation and the state vector of the nonlinear structure according to the random walk model.

The recursive solution unit 1300 is used to estimate the structural state, structural parameters and external excitation of the nonlinear structure in real time by using unscented Kalman filtering according to the partial acceleration response and the augmented state vector.

The device of this embodiment and the above-mentioned method embodiment are based on the same inventive concept, so the relevant contents of the above-mentioned method embodiment are also applicable to the contents of this device, and will not be repeated here.

Sixth embodiment

Referring to FIG. 10 , an embodiment of the present application further provides an electronic device, the electronic device comprising:

at least one memory;

at least one processor;

at least one program;

The programs are stored in the memory, and the processor executes at least one program to implement the real-time monitoring method of the nonlinear structure described above in the present disclosure.

The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a vehicle-mounted computer, etc.

The electronic device according to the embodiment of the present application is described in detail below.

The processor 1600 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present disclosure;

The memory 1700 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1700 can store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1700, and the processor 1600 calls and executes the real-time monitoring method of the nonlinear structure of the embodiment of the present disclosure.

Input/output interface 1800, used to implement information input and output;

The communication interface 1900 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

Bus 2000 , which transmits information between various components of the device (e.g., processor 1600 , memory 1700 , input/output interface 1800 , and communication interface 1900 );

The processor 1600 , the memory 1700 , the input/output interface 1800 , and the communication interface 1900 are connected to each other in communication within the device via the bus 2000 .

The embodiment of the present disclosure further provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned real-time monitoring method for nonlinear structures.

The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The embodiments described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present disclosure, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules/units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And/or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and/or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character "/" generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store programs. The above is a detailed description of the embodiments of the present application in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments. Within the scope of knowledge possessed by ordinary technicians in the relevant technical field, various changes can be made without departing from the purpose of the present application.

Claims (8)

1. A real-time monitoring method for a nonlinear structure, characterized in that the real-time monitoring method for a nonlinear structure comprises the following steps: Real-time acquisition of partial acceleration response of nonlinear structure under external excitation; The structural parameters of the nonlinear structure and the external excitation are simulated as a random walk model, and an augmented state vector consisting of a structural parameter vector, the external excitation and a state vector of the nonlinear structure is constructed according to the random walk model; wherein: According to the external excitation and the first Gaussian white noise, simulating the external excitation as a random walk model; and determining structural parameters of the nonlinear structure, and simulating the structural parameters as a random walk model according to the structural parameters and a second Gaussian white noise; According to the partial acceleration response and the augmented state vector, an unscented Kalman filter is used to estimate the structural state, structural parameters and the external excitation of the nonlinear structure in real time, wherein: According to the current state estimate and error covariance, an unscented transformation is used to generate the first sigma point set of the augmented state vector and calculate the corresponding weights; Converting the first sigma point set into a state prediction vector, combining the state prediction vector according to the weights to obtain a one-step-ahead state estimate and calculating a corresponding error covariance; Generate a second sigma point set of the state prediction vector by using an unscented transformation according to the state estimate of the previous step and its corresponding error covariance; converting the second sigma point set into a measurement prediction vector corresponding to a sigma point, and combining the measurement prediction vectors corresponding to the sigma point according to the weight to obtain a measurement prediction; Calculating a measurement prediction covariance and a state-measurement covariance based on the state estimate of the one step forward, the state prediction vector, the measurement prediction vector corresponding to the sigma point, and the measurement prediction; Calculating a Kalman gain based on the measurement prediction covariance and the state-measurement covariance; The state estimate at the next moment is calculated based on the state estimate of the previous step, the Kalman gain, the partial acceleration response and the measurement prediction, so as to obtain the structural state, structural parameters and external excitation at the next moment from the state estimate at the next moment; wherein the current moment and the next moment are separated by a sampling time period. 2. The real-time monitoring method of a nonlinear structure according to claim 1, characterized in that the step of calculating the measurement prediction covariance and the state-measurement covariance based on the one-step-ahead state estimate, the state prediction vector, the measurement prediction vector and the measurement prediction comprises: Among them, P _yy,(i+1|i) is the measurement prediction covariance, is the measurement prediction vector corresponding to the sigma point, is the measurement prediction, n is the dimension of the augmented state vector, is the weight of the covariance, for The transposed matrix of , the subscript (i+1|i) is the transition from time i to time (i+1), P _uy,(i+1|i) is the state-measurement covariance, is the state prediction vector, is the state estimate one step ahead, and R is the covariance matrix of the measurement noise. 3. The real-time monitoring method of a nonlinear structure according to claim 2, characterized in that the calculating of the Kalman gain according to the measurement prediction covariance and the state-measurement covariance comprises: in, is the Kalman gain, is the inverse matrix of P _yy,(i+1|i) . 4. The real-time monitoring method for nonlinear structures according to claim 3, characterized in that the step of calculating the state estimate at the next moment based on the state estimate one step forward, the Kalman gain, the partial acceleration response and the measured predicted value comprises: Among them, _yi+1 is the partial acceleration response at time (i+1), is the state estimate at time (i+1). 5. The real-time monitoring method of nonlinear structure according to claim 4, characterized in that after calculating the state estimation at the next moment, the error covariance at the next moment is also calculated in the following manner: in, is the error covariance at the next moment, is the error covariance corresponding to the state estimate one step ahead, for The inverse matrix of . 6. A real-time monitoring device for a nonlinear structure, characterized in that the real-time monitoring device for a nonlinear structure comprises: A data acquisition unit, used for real-time acquisition of partial acceleration response of the nonlinear structure under external excitation; A state vector construction unit is used to simulate the structural parameters of the nonlinear structure and the external excitation as a random walk model, and construct an augmented state vector consisting of the structural parameter vector, the external excitation and the state vector of the nonlinear structure according to the random walk model; wherein: According to the external excitation and the first Gaussian white noise, simulating the external excitation as a random walk model; and determining structural parameters of the nonlinear structure, and simulating the structural parameters as a random walk model according to the structural parameters and a second Gaussian white noise; A recursive solution unit is used to use an unscented Kalman filter to estimate in real time the structural state, structural parameters and the external excitation of the nonlinear structure according to the partial acceleration response and the augmented state vector, wherein: According to the current state estimate and error covariance, an unscented transformation is used to generate the first sigma point set of the augmented state vector and calculate the corresponding weights; Converting the first sigma point set into a state prediction vector, combining the state prediction vector according to the weights to obtain a one-step-ahead state estimate and calculating a corresponding error covariance; Generate a second sigma point set of the state prediction vector by using an unscented transformation according to the state estimate of the previous step and its corresponding error covariance; converting the second sigma point set into a measurement prediction vector corresponding to a sigma point, and combining the measurement prediction vectors corresponding to the sigma point according to the weight to obtain a measurement prediction; Calculating a measurement prediction covariance and a state-measurement covariance based on the state estimate of the one step forward, the state prediction vector, the measurement prediction vector corresponding to the sigma point, and the measurement prediction; Calculating a Kalman gain based on the measurement prediction covariance and the state-measurement covariance; The state estimate at the next moment is calculated based on the state estimate of the previous step, the Kalman gain, the partial acceleration response and the measurement prediction, so as to obtain the structural state, structural parameters and external excitation at the next moment from the state estimate at the next moment; wherein the current moment and the next moment are separated by a sampling time period. 7. An electronic device, characterized in that it includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the real-time monitoring method of nonlinear structures described in any one of claims 1 to 5. 8. A computer-readable storage medium, characterized in that: the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the real-time monitoring method for nonlinear structures according to any one of claims 1 to 5.