MATLAB-based structure operation parameter identification method and system

Abstract

The invention discloses a structure operation parameter identification method and system based on MATLAB, and belongs to the technical field of modal parameter identification technology and MATLAB program development, and the method comprises the following steps: constructing a structure model; a multi-channel response signal is imported, and signal preprocessing is carried out; selecting a modal parameter identification algorithm, and identifying core parameters including inherent frequency, damping ratio and vibration mode data; after the recognition algorithm is completed, collaborative output of parameters, graphs and data files is carried out, and static vibration mode visualization and vibration mode animation display are realized; the steps are respectively and correspondingly realized by a structure construction module, a signal processing module, a parameter identification module and a structure visualization module which are formed by the system. According to the invention, based on MATLAB environment development, commercial software authorization and hardware cost are greatly saved, and through algorithm white box, the technical threshold is reduced, and teaching popularization and industry innovation are assisted.

Description

Technical Field

[0001] This invention belongs to the field of modal parameter identification technology and MATLAB program development technology, specifically involving a method and system for identifying structural operation parameters based on MATLAB. Background Technology

[0002] With the rapid development of science and technology, vibration and dynamics problems of large machinery and engineering structures during operation are becoming increasingly prominent. For example, long-span bridges and high-rise buildings may experience severe vibrations under environmental excitations such as ground tremors and wind loads, leading to structural damage or even failure; while the vibration behavior of equipment such as machine tools, vehicles, and aerospace vehicles directly affects their working performance and product quality. Therefore, in-depth research on the dynamic characteristics of structures under actual working conditions is of great engineering significance for ensuring safety, optimizing design, and realizing condition monitoring.

[0003] Currently, the theories, technologies, and methods for modal parameter identification are advancing rapidly. Well-known modal analysis software both domestically and internationally includes Simcenter Testlab from Germany, Bruel & Kjaer modal analysis software from Denmark, ME'scopeVES software from the United States, EDM-Modal modal analysis software from Crystal Semiconductor in the United States, DHDAS analysis software developed by Donghua Testing in China, and N-Modal modal analysis software from the Modern Analysis Center of Nanjing University. These software programs, through iteration and optimization, demonstrate powerful functions, clear interfaces, and strong capabilities in modal analysis data processing. However, their inherent limitations restrict the in-depth development and widespread adoption of mainstream domestic and international software in specific high-end application scenarios. First, their core algorithms are typically encapsulated as "black boxes," making it difficult for users to understand their internal processing logic and judgment details. This hinders both the teaching demonstrations and principle verification of modal analysis and creates barriers to the research, comparison, and innovation of advanced algorithms. Second, these mainstream software programs, both domestically and internationally, sacrifice flexibility in pursuit of versatility. Their workflows and parameter identification are often rigid, making it difficult for users to customize algorithm improvements and process optimizations for special structures such as ultra-large bridges, micro-nano devices, and extreme conditions, thus failing to meet the needs of cutting-edge scientific research and specialized tasks. Third, these software programs are usually independent and closed systems, presenting significant obstacles to data interaction and process integration with user-developed finite element analysis, model updating, or structural health monitoring systems, making it difficult to build an integrated analysis loop. Finally, the complex user interface and numerous functions increase the learning cost for users, and the software's large size makes it difficult to deploy in a lightweight manner and embed it into real-time online monitoring systems.

[0004] Therefore, there is an urgent need to develop a new tool for identifying operational modal parameters that is transparent, flexible, customizable, low-cost, and easy to integrate. Summary of the Invention

[0005] To address the problems existing in the prior art, this invention proposes a method and system for identifying structural operating parameters based on MATLAB. It is developed based on the integrated software platform independently developed by MATLAB App Designer, realizing a "white-box" operation of the entire process from structural modeling and signal preprocessing to parameter identification and visualization.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0007] The MATLAB-based method for identifying structure execution parameters includes the following steps:

[0008] Step 1: Construct the structural model: Import the coordinates of structural points and element information to quickly define the structural model; and add structural point orientation association information and constraint information;

[0009] Step 2, Signal Import and Preprocessing: Import the multi-channel structural response signal acquired under environmental excitation, check the correspondence between the measurement point directions contained in the input signal and the structural point directions of the structural model, and perform signal preprocessing;

[0010] Step 3: Modal parameter identification: Select a modal parameter identification algorithm to identify the core dynamic parameters of the structure, including natural frequency, damping ratio, and mode shape data;

[0011] Step 4: Result Output and Visualization: After the recognition algorithm is completed, the parameters, graphs and data files obtained from the recognition are output in a coordinated manner, and the static mode shape visualization and mode shape animation are displayed.

[0012] Furthermore, signal preprocessing includes signal display and truncation, calculation and plotting of the autocorrelation function and cross-correlation function of each signal, and calculation and plotting of the power spectral density function of the full channel signal; wherein, the signal truncation length is determined by the user, and the signal length of each channel is the same.

[0013] Furthermore, the identification algorithm includes at least one of the following: covariance-driven subspace method (SSI-COV), data-driven space method (SSI-DATA), auxiliary variable method (IV), and enhanced frequency domain decomposition method (EFDD).

[0014] Furthermore, the collaborative output of parameters, graphs, and data files in step four is specifically as follows:

[0015] The identified parameters, including natural frequency and damping ratio, are directly displayed in a table on the user interface.

[0016] The complete mode shape data, including the identification algorithm, timestamps, and displacement in each direction at each measurement point, is written into a predefined TXT file in a structured format.

[0017] Within the specified graphical interface coordinate area, a steady-state diagram is plotted with frequency as the horizontal axis and model order as the vertical axis, and different markers are used to distinguish each stability level.

[0018] Furthermore, the static array visualization allows users to select the modal order via a drop-down menu, which then displays the corresponding modal shape diagram and structural point coordinates.

[0019] The mode shape animation generates a continuous motion sequence based on a sine interpolation algorithm and supports playback, pause, step control, and amplitude scaling.

[0020] Furthermore, the specific process for visualizing static mode shapes is as follows:

[0021] The user selects the natural mode order via a drop-down menu. For the selected mode order, the callback function of the "Display Static Mode Shape" button is triggered. The callback function reads the contents of a text file, uses regular expressions to match each item of the mode shape information, including the mode order, natural frequency, damping ratio, and displacement of the structural point, and stores the mode order, natural frequency, damping ratio, and displacement of the structural point in different variables, thereby reading the mode shape displacement vector of the selected mode order.

[0022] Obtain the initial coordinates of the structural points in the structural model, and match the measurement point information in the text file with the structural points in the structural model to obtain the coordinates of the deformed structural points;

[0023] Using plotting functions, scatter plots and connecting lines of structural points are drawn respectively, and finally static mode shape diagrams of the corresponding modal orders are generated.

[0024] Furthermore, the specific implementation process of mode shape animation is as follows:

[0025] Trajectory interpolation algorithm: The custom timed callback function performs mathematical interpolation on the motion trajectory between the static limit positions determined by the mode displacement based on the symmetry of the mode motion, and uses a sine function to control the structure motion speed to generate a complete motion sequence with high frame rate and controllable rhythm.

[0026] Animation Loop and State Control: An animation loop is constructed using MATLAB's timer object and graphics refresh function. Based on a preset sampling frequency, the spatial coordinate data of the graphics object is continuously updated to create smooth animation. The start and stop of the animation loop are accurately controlled by monitoring the playback status flag.

[0027] Precise step control: Establish a mapping relationship between each frame of the animation sequence and the parameter values, and realize the jump of animation frames by adjusting the parameters; during the parameter adjustment process, the animation is not played continuously, but the static vibration mode diagram corresponding to the corresponding frame is redrawn to realize the erase method of drawing animation.

[0028] This invention also protects a MATLAB-based structural operation parameter identification system, developed using MATLAB AppDesigner, which supports data integration with finite element analysis and structural health monitoring systems. The system includes: a structural construction module for importing and displaying structural models; a signal processing module for importing multi-channel structural response signals, checking the correspondence between the measurement point directions contained in the input signals and the structural point directions of the structural model based on a check-signal intelligent check mechanism, and then calculating the correlation function and power spectral density function; a parameter identification module integrating at least four modal identification algorithms for modal parameter identification; and a structural visualization module supporting static mode display and mode animation.

[0029] Furthermore, the specific implementation process of the inspection-signal intelligent inspection mechanism is as follows: all directions of all structural points in the structural model are listed as variables in the format of a hybrid cell array matrix, and the directions of associated, constrained, and input structural points are processed in the same way; then, the custom compare_cell_arrays function is used to remove the directions of constrained and associated structural points in turn; finally, the remaining structural point directions are compared with the measurement point directions contained in the input signal to obtain the filtered hybrid cell array matrix. After inspection, if the filtered hybrid cell array matrix is ​​not empty, a message box pops up to explicitly list the undefined structural point directions; if it is empty, it means that all structural point directions have been defined.

[0030] Furthermore, the structure visualization module also includes:

[0031] A vibrational animation engine that achieves high frame rate animation based on trajectory interpolation algorithms and timer objects;

[0032] Interactive control components, including a slider and a play / pause button, support frame-by-frame viewing and amplitude adjustment. When the slider is dragged, the slider control maps the parameter value corresponding to the slider position to the animation frame sequence through a parameter change callback function. The play / pause button controls the start and stop of the animation loop.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] This invention, through its integrated design of the entire process from "model-signal-recognition-result," completely eliminates the need for users to frequently switch between multiple independent commercial software programs and import / export data, thereby improving the efficiency of the recognition process.

[0035] The unique "model-signal" intelligent inspection mechanism of this invention can automatically diagnose the matching integrity between the measurement point layout and the structural model, eliminating recognition failure or result distortion caused by sensor channel omission or incorrect constraint setting from the source, thus ensuring the reliability and accuracy of the recognition results.

[0036] Channel association technology reduces hardware dependence by filling virtual channels where sensors cannot be placed with information from associated channels. It can also actively suppress local non-interest modes through software algorithms, making the recognition results more focused on the overall structural mode shape, and effectively improving the recognition accuracy and clarity of core modal parameters.

[0037] The "step-by-step" mode animation function provided by this invention allows users to observe the shape of the structure at any precise position on the motion path as if analyzing video frames. This provides a way to identify local complex deformations and verify modal confidence, upgrading the result display from "dynamic demonstration" to "static detailed analysis", which helps to discover the subtle structural behavior under higher-order or complex modes.

[0038] This invention is developed based on the MATLAB environment, which greatly saves on commercial software licensing and hardware costs. By white-boxing the algorithm, it lowers the technical threshold and helps popularize teaching and promote industry innovation. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method of the present invention;

[0040] Figure 2 The following is a model diagram of a two-story tower structure provided for a specific embodiment of the present invention, wherein (1) is a physical diagram of the test piece and (2) is a diagram of the distribution of measuring points;

[0041] Figure 3 This is a flowchart for experimental verification.

[0042] Figure 4 A schematic diagram of the interface for constructing a two-story tower model using the structural construction module of this invention;

[0043] Figure 5 This is a schematic diagram of the interface of the signal processing module for the two-layer tower model of the present invention.

[0044] Figure 6 This is a schematic diagram of the interface of the parameter identification module for a two-story tower model according to the present invention.

[0045] Figure 7 This is a schematic diagram of the interface of the result display module for the two-layer tower model of the present invention;

[0046] Figure 8 The diagram shows a comparison between the mode shape identification results of the system of the present invention and the identification results of commercial software. (1) corresponds to the first mode order, (2) corresponds to the second mode order, (3) corresponds to the third mode order, (4) corresponds to the fourth mode order, and (5) corresponds to the fifth mode order.

[0047] Figure 9 This is the mode confidence diagram of the identification algorithm of this invention. Detailed Implementation

[0048] To make the technical solution of the present invention clearer, the technical solution of the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments.

[0049] like Figure 1 As shown, the MATLAB-based method for identifying structural runtime parameters provided by this invention includes the following steps:

[0050] Step 1: Construct the structural model: Import the coordinates and connection information of structural points to quickly define the structural model; and add the orientation association information and constraint information of structural points;

[0051] Step 2, Signal Import and Preprocessing: Import the multi-channel structural response signals acquired under environmental excitation, check the correspondence between the measurement point directions contained in the input signals and the structural point directions of the structural model, and perform signal preprocessing, including signal display and truncation, calculation and plotting of the autocorrelation function and cross-correlation function of each signal, and calculation and plotting of the power spectral density function of the full-channel signal; the signal truncation length is determined by the user, and the signal length of each channel is the same;

[0052] Step 3: Modal parameter identification: Select a modal parameter identification algorithm, including Covariance-Driven Subspace (SSI-COV), Data-Driven Space (SSI-DATA), Auxiliary Variable Method (IV), and Enhanced Frequency Domain Decomposition (EFDD), to identify the core dynamic parameters of the structure, including natural frequencies, damping ratios, and mode shapes.

[0053] Step 4: Result Output and Visualization: After the recognition algorithm is completed, parameters, graphs and data files are output collaboratively, and static mode visualization and mode animation are displayed.

[0054] Specifically, the collaborative output of parameters, graphs, and data files in step four is as follows:

[0055] The identified parameters, including natural frequency and damping ratio, are directly displayed in a table on the user interface.

[0056] The built-in private function phi2txt writes the complete mode shape data, including the identification algorithm, timestamps, and displacement of each measurement point in each direction, into a predefined TXT file in a structured format.

[0057] The plotstab function can be used to plot a steady-state graph with frequency on the x-axis and model order on the y-axis in a specified graphical user interface coordinate area, and different labels can be used to distinguish each stability level.

[0058] Specifically, the process of visualizing static mode shapes is as follows:

[0059] A dropdown control is used to allow users to select the natural mode order from a dropdown menu. For the selected mode order, the "Show Static Mode Shape" button callback function (show_modeButtonPushed) is triggered. The callback function reads the contents of a text file, uses regular expressions to match each item of the mode shape information, including the mode order, natural frequency, damping ratio, and displacement of the structural point, and stores the mode order, natural frequency, damping ratio, and displacement of the structural point in different variables, thereby reading the mode shape displacement vector of the selected mode order.

[0060] Obtain the initial coordinates of the structural points in the structural model, and match the measurement point information in the text file with the structural points in the structural model to obtain the positions of the deformed structural points;

[0061] The scatter3 and plot3 functions are used to plot the scatter plots and connecting lines of the structural points in the coordinate area, and finally the static mode shape diagrams of the corresponding modal orders are generated.

[0062] Specifically, the implementation process of dynamic mode shape animation is as follows:

[0063] Trajectory interpolation algorithm: A custom timerCallback function performs mathematical interpolation on the motion trajectory between the static limit positions determined by the mode displacement based on the symmetry of the mode motion, and uses a sine function to control the structure's motion speed to generate a complete motion sequence with high frame rate and controllable rhythm;

[0064] Animation Loop and State Control: An animation loop is constructed using MATLAB's timer object and the graphics refresh function (drawnow). Based on a preset sampling frequency, the spatial coordinate data of the graphics object is continuously updated to create a smooth animation. The start and stop of the animation loop are accurately controlled by monitoring the playback status flag.

[0065] Precise step control: Establish a mapping relationship between each frame of the animation sequence and the parameter values, and realize the jump of animation frames by adjusting the parameters; during the parameter adjustment process, the animation is not played continuously, but the static vibration mode diagram corresponding to the corresponding frame is redrawn to realize the erase method of drawing animation.

[0066] The structural operation parameter identification system based on MATLAB provided by this invention is developed using MATLAB APP Designer and supports data integration with finite element analysis and structural health monitoring systems. It includes: a structural construction module for importing and displaying structural models; a signal processing module for importing multi-channel structural response signals, checking the correspondence between the measurement point directions contained in the input signals and the structural point directions of the structural model based on a check-signal intelligent check mechanism, and calculating the correlation function and power spectral density function; a parameter identification module integrating four modal identification algorithms; and a structural visualization module supporting static mode shape display and mode shape animation.

[0067] Specifically, the implementation process of the intelligent signal inspection mechanism is as follows: All directions of all structural points in the structural model are listed as variables in the format of a hybrid cell array matrix, while the directions of associated, constrained, and input structural points are processed in the same way. Then, the custom function `compare_cell_arrays` is used to sequentially remove the constrained and associated structural point directions. Finally, the remaining structural point directions are compared with the measurement point directions contained in the input signal to obtain the filtered hybrid cell array matrix. Upon inspection, if the filtered hybrid cell array matrix is ​​not empty, a message box pops up, explicitly listing the undefined structural point directions; if it is empty, it indicates that all structural point directions have been defined.

[0068] Specifically, the structure visualization module also includes:

[0069] A vibrational animation engine that achieves high frame rate animation based on trajectory interpolation algorithms and timer objects;

[0070] Interactive control components, including a slider and a play / pause button, support frame-by-frame viewing and amplitude adjustment. When the slider is dragged, the slider control (pace long) maps the parameter value corresponding to the slider position to the animation frame sequence through the parameter change callback function (ValueChangedFcn). By listening to the state switching event of the play / pause button, the start and stop of the animation loop are controlled.

[0071] Example

[0072] In one specific embodiment, the following is selected: Figure 2The two-story tower model shown is constructed from three plates and eight L-shaped steel sections welded together, using carbon steel. The plates are 600mm long and 300mm wide, with a thickness of 30mm, and the L-shaped steel sections are 400mm long. A concentrated white noise force excitation is applied to the middle section of the second-story tower members using a vibrator. The center positions of the thickness at the four corners of the upper two plates are selected as measurement points to obtain the time-domain response at these points. The sampling frequency is 1000Hz, and the total sampling time is 5s. A 16-channel signal acquisition device is used to collect the acceleration in the x and y directions at eight measurement points. The acquisition results are then imported into the commercial software N-modal and the system provided in this invention for modal parameter identification. The experimental verification logic is as follows: Figure 3 As shown in Table 1, the time-domain response data obtained in the experiment is in the format of the first column, which represents the sampling time with a sampling interval of 0.001s. The first row represents the channel number, the corresponding structural point number and direction, and records the acceleration in the x and y directions of each measuring point.

[0073] Table 1

[0074] Time (s) 1：1x 2:1y 3:2x ··· 16：8y 0.001 0.27198 -0.32841 0.113351 ··· 0.390214 0.002 3.94534 -3.86799 3.80893 ··· -0.332606 ··· ··· ··· ··· ··· ··· 5.000 9.13087 -6.43595 8.82119 ··· 1.00775

[0075] The parameter identification method based on MATLAB provided by this invention includes the following steps:

[0076] Step 1: Construct a two-story tower model: Import the structural point coordinates and connection information of the two-story tower model to quickly build its geometric model, such as... Figure 4 As shown in Table 2, constraints are applied to each structural point of the constructed structural model, where numbers represent structural point numbers and letters represent constraint directions.

[0077] Step 2: Signal Import and Preprocessing: Import the acquired time-domain signal. The system automatically matches each channel with the structural point and displays it in the signal list, such as... Figure 5 As shown; calculate and plot the relevant functions and the power spectral density functions of all channels;

[0078] Step 3: Modal Parameter Identification: Select a modal parameter identification algorithm, including Covariance-Driven Subspace (SSI-COV), Data-Driven Subspace (SSI-DATA), Auxiliary Variable Method (IV), and Enhanced Frequency Domain Decomposition (EFDD). After selecting the identification method, input the identification order as 5, perform multiple identifications, record the numerical results of each identification, take the average of multiple identifications as the final identification result, and observe the peak value of the steady-state graph (e.g., Figure 6 As shown in the figure), the correspondence between the natural frequencies and the identified natural frequencies is shown in Table 3. It can be seen from the table that the errors between the natural frequencies identified by the four identification algorithms and the N-modal reference values ​​are all controlled within 0.64%. Considering the measurement uncertainty, this invention has good accuracy and reliability in frequency identification.

[0079] Step 4: Result Output and Visualization: After the recognition algorithm is completed, parameters, graphs and data files are output collaboratively, and static mode visualization and mode animation are displayed.

[0080] Table 2

[0081] Constraint direction 1Z 2Z 3Z 4Z 5Z 6Z 7Z 8Z 9ALL 10ALL 11ALL 12ALL

[0082] Table 3

[0083] order N-modal EFDD IV SSI-DATA SSI-COV 1 17.371 17.532 17.356 17.332 17.428 2 26.525 26.413 26.391 26.536 26.612 3 39.351 39.122 39.012 39.174 39.284 4 45.415 45.550 45.481 45.631 45.741 5 71.823 72.120 71.024 72.280 71.472 error - 0.528％ 0.640％ 0.366％ 0.410％

[0084] Figure 7 A schematic diagram of the interface of the result display module of the present invention is shown; Figure 8 The figure shows a comparison between the mode shape identification results of the system provided by this invention and the identification results of commercial software. As can be seen from the figure, the first five modes can be accurately matched, which proves the correctness of the system provided by this invention in terms of mode shape identification and display.

[0085] The mode shape data obtained from any two identification algorithms of this invention are organized and plotted to obtain the mode shape confidence score (MAC). The results are as follows: Figure 9 As shown, the MAC values ​​of each order are close to 1, indicating that the mode shape identification of the system provided by the present invention has high accuracy.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made by those skilled in the art within the scope of the technology disclosed in this invention, based on the technical solution and concept of the present invention, should be included within the protection scope of this invention. Therefore, the protection scope of this invention should be determined by the scope of the claims.

Claims

1. A method for identifying structural runtime parameters based on MATLAB, characterized in that, Includes the following steps: Step 1: Construct the structural model: Import the coordinates of structural points and element information to quickly define the structural model; and add structural point orientation association information and constraint information; Step 2, Signal Import and Preprocessing: Import the multi-channel structural response signal acquired under environmental excitation, check the correspondence between the measurement point directions contained in the input signal and the structural point directions of the structural model, and perform signal preprocessing; Step 3: Modal parameter identification: Select a modal parameter identification algorithm to identify the core dynamic parameters of the structure, including natural frequency, damping ratio, and mode shape data; Step 4: Result Output and Visualization: After the recognition algorithm is completed, the parameters, graphs and data files obtained from the recognition are output in a coordinated manner, and the static mode shape visualization and mode shape animation are displayed.

2. The method for identifying structural operation parameters based on MATLAB according to claim 1, characterized in that, Signal preprocessing includes signal display and truncation, calculation and plotting of the autocorrelation and cross-correlation functions of each signal, and calculation and plotting of the power spectral density function of the full-channel signal; among which, the signal truncation length is determined by the user, and the signal length of each channel is the same.

3. The method for identifying structural operation parameters based on MATLAB according to claim 1, characterized in that, The recognition algorithm includes at least one of the following: covariance-driven subspace method, data-driven space method, auxiliary variable method, and enhanced frequency domain decomposition method.

4. The MATLAB-based structural operation parameter identification method according to claim 1, characterized in that, The collaborative output of parameters, graphs, and data files in step four is as follows: The identified parameters, including natural frequency and damping ratio, are directly displayed in a table on the user interface. The complete mode shape data, including the identification algorithm, timestamps, and displacement in each direction at each measurement point, is written into a predefined TXT file in a structured format. Within the specified coordinate area of ​​the graphical interface, a steady-state diagram is plotted with frequency as the horizontal axis and model order as the vertical axis, and different markers are used to distinguish each stability level.

5. The MATLAB-based structural operation parameter identification method according to claim 1, characterized in that, The static array visualization allows users to select the modal order via a drop-down menu, which then displays the corresponding modal order vibration mode diagram and structural point coordinates. The vibration mode animation generates a continuous motion sequence based on a sine interpolation algorithm and supports playback, pause, step control, and amplitude scaling.

6. The MATLAB-based structural operation parameter identification method according to claim 5, characterized in that, The specific process of visualizing static mode shapes is as follows: The user selects the natural mode order via a drop-down menu. For the selected mode order, the callback function of the "Display Static Mode Shape" button is triggered. The callback function reads the contents of a text file, uses regular expressions to match each item of the mode shape information, including the mode order, natural frequency, damping ratio, and displacement of the structural point, and stores the mode order, natural frequency, damping ratio, and displacement of the structural point in different variables, thereby reading the mode shape displacement vector of the selected mode order. Obtain the initial coordinates of the structural points in the structural model, and match the measurement point information in the text file with the structural points in the structural model to obtain the coordinates of the deformed structural points; Using plotting functions, scatter plots and connecting lines of structural points are drawn respectively, and finally static mode shape diagrams of the corresponding modal orders are generated.

7. The MATLAB-based structural operation parameter identification method according to claim 6, characterized in that, The specific implementation process of mode shape animation is as follows: Trajectory interpolation algorithm: The custom timed callback function performs mathematical interpolation on the motion trajectory between the static limit positions determined by the mode displacement based on the symmetry of the mode motion, and uses a sine function to control the structure motion speed to generate a complete motion sequence with high frame rate and controllable rhythm. Animation Loop and State Control: An animation loop is constructed using MATLAB's timer object and graphics refresh function. Based on a preset sampling frequency, the spatial coordinate data of the graphics object is continuously updated to create smooth animation. The start and stop of the animation loop are accurately controlled by monitoring the playback status flag. Precise step control: Establish a mapping relationship between each frame of the animation sequence and the parameter values, and realize the jump of animation frames by adjusting the parameters; during the parameter adjustment process, the animation is not played continuously, but the static vibration mode diagram corresponding to the corresponding frame is redrawn to realize the erase method of drawing animation.

8. A MATLAB-based structural operation parameter identification system, characterized in that, The method for identifying structural operating parameters based on MATLAB, as described in any one of claims 1 to 7, is developed using the MATLAB App Designer and supports data integration with finite element analysis and structural health monitoring systems. It includes: a structural construction module for importing and displaying structural models; a signal processing module for importing multi-channel structural response signals, checking the correspondence between the measurement point directions contained in the input signals and the structural point directions of the structural model based on a check-signal intelligent check mechanism, and then calculating the correlation function and power spectral density function; a parameter identification module integrating at least four modal identification algorithms for modal parameter identification; and a structural visualization module supporting static mode display and mode animation display.

9. The MATLAB-based structural operation parameter identification system according to claim 7, characterized in that, The specific implementation process of the intelligent signal inspection mechanism is as follows: all directions of all structural points in the structural model are listed as variables in the format of a hybrid cell array matrix, and the same processing is performed on the directions of constrained, associated, and input structural points; then, the custom compare_cell_arrays function is used to remove the directions of constrained and associated structural points in turn; finally, the remaining structural point directions are compared with the measurement point directions contained in the input signal to obtain the filtered hybrid cell array matrix. After inspection, if the filtered hybrid cell array matrix is ​​not empty, a message box pops up to explicitly list the undefined structural point directions; if it is empty, it means that all structural point directions have been defined.

10. The MATLAB-based structural operation parameter identification system according to claim 7, characterized in that, The structure visualization module also includes: A vibrational animation engine that achieves high frame rate animation based on trajectory interpolation algorithms and timer objects; Interactive control components, including a slider and a play / pause button, support frame-by-frame viewing and amplitude adjustment. When the slider is dragged, the slider control maps the parameter value corresponding to the slider position to the animation frame sequence through a parameter change callback function. The play / pause button controls the start and stop of the animation loop.

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