Structural dynamic load test parameter adaptive method and system

Abstract

The application belongs to the technical field of education, and discloses a structural dynamic load test parameter self-adaptive method and system, the method comprising: obtaining a sensor type information list, the list containing sensor parameters of commonly used sensors in teaching experiments; selecting a sensor type used in the experiment from the sensor type information list and loading the sensor parameters; selecting simplified model parameters from a simplified model parameter information set for the used structural model; obtaining vibration data collected by a data collector; combining the loaded sensor parameters, the selected simplified model parameters, and the vibration data, and using a frequency response function peak value method or a characteristic frequency identification method to evaluate the dynamics parameters of the structural model; matching the final dynamics parameters with the loaded sensor parameters, applying the matched parameters to a modal analysis algorithm, calculating structural modal shapes and frequency response curves, and performing graphical real-time display; thereby effectively reducing experiment preparation time and improving experiment efficiency.

Description

Technical Field

[0001] This application relates to the field of educational technology, and more specifically, to an adaptive method and system for structural dynamic load test parameters. Background Technology

[0002] In the undergraduate civil engineering curriculum, structural dynamic load testing is a crucial practical teaching component, particularly important in modal analysis instruction. Currently, the teaching model commonly employs structural dynamic load testing equipment, typically consisting of a horizontal vibration table, a data acquisition system, and a computer. During the experiment, students manually install and fix the structural model (e.g., beams, frames) on the vibration table and manually deploy various sensors to collect structural vibration response data. The collected vibration data is transmitted to the computer via the data acquisition system, and finally, modal parameter identification and analysis are performed using specialized software.

[0003] However, existing teaching models have gradually revealed several limitations in practical applications. First, the experimental preparation phase is excessively time-consuming, requiring students to invest significant time and effort in sensor selection, installation, and complex wiring. These tedious preparations distract students from understanding and mastering the core principles of structural dynamics, directly impacting teaching efficiency. Second, considering the funding constraints commonly faced by undergraduate teaching laboratories, it is difficult to equip them with large quantities of expensive automated sensor identification equipment and highly complex parameter identification algorithms. Therefore, under the principle of economy, parameter identification methods must prioritize computational efficiency and reduce excessive reliance on hardware and algorithm complexity. Furthermore, due to students' relatively limited experimental experience, human error is easily introduced during experiments, inevitably leading to noise or configuration errors in the sensor data, posing a serious challenge to the accuracy and reliability of parameter identification results. Finally, modal analysis teaching demonstrations typically require high real-time performance; the parameter identification process must be completed quickly to avoid long waiting times for students, ensuring a smooth teaching process and a positive learning experience.

[0004] In current undergraduate teaching practice of dynamic load experiments on civil engineering structures, there is an urgent need for a low-cost, high-efficiency, and automated parameter identification method that can effectively address student operational errors and data noise. The shortcomings and deficiencies of existing technologies have severely hampered further improvements in teaching efficiency and experimental demonstration effects. Therefore, how to achieve rapid, robust, and automated sensor type identification and structural model parameter matching in a teaching laboratory environment with relatively limited funding, considering the actual operating conditions of students and the real-time teaching requirements, has become a key technical challenge that urgently needs to be addressed to improve the quality of undergraduate civil engineering experimental teaching.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide an adaptive method and system for structural dynamic load test parameters, which can effectively reduce experimental preparation time and improve experimental efficiency.

[0007] Firstly, this application provides an adaptive method for structural dynamic load test parameters, the method comprising:

[0008] S1. Obtain a list of sensor type information, which includes sensor parameters of commonly used sensors in teaching experiments;

[0009] S2. Select the sensor type to be used in the experiment from the sensor type information list and load the sensor parameters;

[0010] S3. For the structural model used, select simplified model parameters from the simplified model parameter information set;

[0011] S4. Acquire vibration data collected by the data acquisition device;

[0012] S5. Combining the loaded sensor parameters with the selected simplified model parameters and vibration data, the dynamic parameters of the structural model are evaluated using the peak frequency response function method or the characteristic frequency identification method; the dynamic parameters include natural frequency and damping ratio.

[0013] S6. Match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural mode shapes and frequency response curves, and display them graphically in real time.

[0014] Preferably, step S2 includes:

[0015] S201. Display a list of sensor type information, including sensor model, sensitivity, and range, on the user interface;

[0016] S202. In response to the user's selection of sensor model on the user interface, determine the sensor model used in the experiment;

[0017] S203. Based on the determined sensor model, retrieve the corresponding sensitivity and range parameters from the pre-stored sensor parameter database;

[0018] S204. Load the retrieved sensitivity and range parameters and display the loading results on the user interface in real time for user confirmation.

[0019] Preferably, step S3 includes:

[0020] S301. Presents a structural model selection interface, which includes multiple simplified model options corresponding to commonly used structural models in teaching experiments. Each simplified model option corresponds to a different degree of simplification.

[0021] S302. Respond to the user's selection operation on the structural model selection interface and determine the simplified model option selected by the user;

[0022] S303. Based on the determined simplified model option, retrieve the corresponding simplified model parameter from the pre-stored simplified model parameter information set;

[0023] S304. Assess students' cognitive level of structural dynamics according to the pre-set cognitive level assessment rules; the cognitive level assessment rules involve students' past theoretical course grades, experimental operation records, and their understanding of basic concepts of structural dynamics.

[0024] S305. Based on the student's cognitive level assessment results and the degree of simplification corresponding to the simplified model options, determine whether the simplified model options match the student's cognitive level. If they do not match, provide a prompt on the structural model selection interface to guide the student to select a simplified model option that matches their cognitive level. If they match, load the retrieved simplified model parameters.

[0025] Preferably, step S304 includes:

[0026] Collect students' exam scores in structural dynamics-related courses, assign weights according to exam type, calculate a weighted average score, and use it as the theoretical knowledge assessment score.

[0027] Obtain students' historical operations in structural dynamic load tests, and calculate experimental operation evaluation scores based on the standardization, efficiency, and accuracy of the historical operations.

[0028] Obtain the accuracy rate of students' pre-completed tests involving basic concepts of structural dynamics, and calculate the concept comprehension assessment score;

[0029] The total score of students' structural dynamics cognitive level is calculated by combining the scores of theoretical knowledge assessment, experimental operation assessment, and concept understanding assessment, according to the preset weight ratio.

[0030] The total score of the structural dynamics cognitive level is mapped to a preset cognitive level.

[0031] Preferably, after step S4 and before step S5, the following step is also included:

[0032] S7. Evaluate the noise level of the vibration data. If the noise level exceeds the preset threshold, filter the vibration data.

[0033] Preferably, step S7 includes:

[0034] S701. Calculate the power spectral density of the vibration data to determine the average power value within a preset frequency band, which serves as an indicator for noise level assessment.

[0035] S702. Determine whether the noise level assessment index exceeds the preset level threshold. If it does, filter the vibration data.

[0036] Preferably, step S5 includes:

[0037] S501. Based on the vibration data, calculate the frequency domain data using the fast Fourier transform algorithm, and construct the frequency response function of the structural model by combining the loaded sensor parameters;

[0038] S502. Evaluate the matching degree between the simplified model corresponding to the selected simplified model parameters and the constructed frequency response function. If the matching degree is higher than the preset matching degree threshold, select the frequency response function peak method; otherwise, select the feature frequency identification method.

[0039] S503. If the peak frequency response function method is selected, the maximum amplitude point is searched according to the amplitude spectrum of the constructed frequency response function, the frequency corresponding to the maximum amplitude point is identified as the natural frequency of the structural model, and the damping ratio of the structural model is calculated based on the amplitude on both sides of the maximum amplitude point.

[0040] S504. If the characteristic frequency identification method is selected, time-frequency analysis is performed on the vibration data to obtain a time-frequency distribution map. The natural frequency of the structural model is identified based on the energy concentration area in the time-frequency distribution map, and the damping ratio of the structural model is calculated based on the energy decay rate near the natural frequency.

[0041] Preferably, step S502 includes:

[0042] Calculate the theoretical frequency response function of the simplified model corresponding to the simplified model parameters within a preset frequency range, and extract the amplitude spectrum of the theoretical frequency response function;

[0043] The cross-correlation coefficient between the amplitude spectrum of the constructed frequency response function and the amplitude spectrum of the extracted theoretical frequency response function is calculated as the degree of matching between the simplified model and the constructed frequency response function.

[0044] Determine whether the matching degree is higher than a preset matching degree threshold. If it is higher than the preset matching degree threshold, select the frequency response function peak method; otherwise, select the feature frequency recognition method.

[0045] Preferably, step S6 includes:

[0046] S601. Construct a comprehensive parameter set, which matches the natural frequency, damping ratio, and applied sensor sensitivity and range parameters of the final structural model;

[0047] S602. Based on the comprehensive parameter set, select the modal analysis algorithm and configure the input parameters of the modal analysis algorithm; the input parameters include the geometric parameters of the structural model, material properties, boundary conditions, sensor locations, and excitation force information;

[0048] S603. Run the configured modal analysis algorithm to calculate the mode shapes and frequency response curves of the structural model;

[0049] S604. The calculated structural mode shapes and frequency response curves are graphically processed and displayed on the user interface in real time.

[0050] Secondly, this application provides an adaptive system for structural dynamic load test parameters, which includes a vibration table, a data acquisition unit and a computer, both of which are electrically connected to the computer.

[0051] The vibration table is used to drive the vibration of the structural model;

[0052] The data acquisition device is used to collect vibration data through sensors installed on the structural model and upload it to the computer;

[0053] The computer is equipped with modal analysis software, which is configured with:

[0054] The sensor list acquisition module is used to obtain a list of sensor type information, which includes the sensor parameters of commonly used sensors in teaching experiments.

[0055] The sensor type selection module is used to select the type of sensor to be used in the experiment from the sensor type information list and load the sensor parameters.

[0056] The structural model parameter configuration module is used to select simplified model parameters from the simplified model parameter information set for the structural model being used.

[0057] The receiving module is used to receive vibration data collected by the data acquisition device;

[0058] The preliminary parameter evaluation module is used to evaluate the dynamic parameters of the structural model by combining the loaded sensor parameters with the selected simplified model parameters and vibration data, using the peak frequency response function method or the characteristic frequency identification method; the dynamic parameters include natural frequency and damping ratio.

[0059] The modal analysis module is used to match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural mode shapes and frequency response curves, and display them graphically in real time.

[0060] Beneficial effects: The adaptive method and system for structural dynamic load test parameters provided in this application can effectively reduce experimental preparation time and improve experimental efficiency by automatically selecting sensor parameters and simplified model parameters, combining vibration data to evaluate dynamic parameters, and applying the matched parameters to modal analysis. Attached Figure Description

[0061] Figure 1 A flowchart of the adaptive method for structural dynamic load test parameters provided in the embodiments of this application.

[0062] Figure 2 A schematic diagram of the adaptive system for structural dynamic load test parameters provided in this application embodiment.

[0063] Figure 3 A connection block diagram of the adaptive system for structural dynamic load test parameters provided in the embodiments of this application.

[0064] Figure 4 This is a schematic diagram of a simple vibration table.

[0065] Figure 5 for Figure 4 An enlarged view of the S part in the image.

[0066] Labeling Explanation: 1. Vibration Table; 101. Base; 102. Guide Rail; 103. Slide Table; 104. Drive Mechanism; 105. Support; 106. Guide Cylinder; 107. Slide Rod; 108. Connecting Rod; 109. Swing Rod; 110. Servo Motor; 111. Waist Hole; 112. Slide Seat; 113. Drive Device; 2. Data Acquisition Unit; 3. Computer; 301. Sensor List Acquisition Module; 302. Sensor Type Selection Module; 303. Structural Model Parameter Configuration Module; 304. Receiving Module; 305. Preliminary Parameter Evaluation Module; 306. Modal Analysis Module; 90. Structural Model. Detailed Implementation

[0067] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] refer to Figure 1 This application proposes an adaptive method for structural dynamic load test parameters, the method comprising:

[0070] S1. Obtain a list of sensor type information, which includes sensor parameters of commonly used sensors in teaching experiments;

[0071] S2. Select the sensor type to be used in the experiment from the sensor type information list and load the sensor parameters;

[0072] S3. For the structural model used, select simplified model parameters from the simplified model parameter information set;

[0073] S4. Acquire vibration data collected by the data acquisition device;

[0074] S5. Combining the loaded sensor parameters with the selected simplified model parameters and vibration data, the dynamic parameters of the structural model are evaluated using the peak frequency response function method or the characteristic frequency identification method; the dynamic parameters include natural frequency and damping ratio.

[0075] S6. Match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural mode shapes and frequency response curves, and display them graphically in real time.

[0076] In step S1, the sensor type information list is pre-stored in the system, and the list contains parameters such as model, sensitivity, and range of various commonly used sensors in teaching experiments.

[0077] In step S2, the user can view a list of sensor type information through the user interface and select the sensor model to be used in this experiment. The system responds to the user's selection by retrieving and loading the corresponding sensor's sensitivity and range parameters from the sensor parameter database.

[0078] In step S3, the simplified model parameter information set contains simplified model parameters corresponding to commonly used structural models in teaching experiments (such as beam models, frame models, etc.). Users can select the corresponding simplified model option on the structural model selection interface based on the actual structural model being used. The system loads the corresponding simplified model parameters according to the user's selection. These simplified model parameters can include the model's geometric dimensions, material properties, etc. A simplified model refers to a model that simplifies the actual structure to a certain extent to facilitate theoretical analysis and calculation. Simplified models typically ignore some minor factors, such as the complex geometry of the structure, nonlinear material properties, or complex boundary conditions, thus simplifying the actual structure into an idealized model with fewer degrees of freedom and simpler parameters.

[0079] In step S4, the data acquisition device collects vibration data of the structural model under excitation in real time, and the vibration data is transmitted to the computer for further processing.

[0080] In step S5, the peak frequency response method determines the natural frequency and damping ratio of the structural model by identifying the peak points of the amplitude spectrum of the frequency response function. The characteristic frequency identification method determines the natural frequency and damping ratio of the structural model by analyzing the time-frequency distribution of vibration data and identifying the frequency domain where energy is concentrated. The dynamic parameters obtained from the evaluation include the natural frequency and damping ratio of the structural model.

[0081] In step S6, the final dynamic parameters are matched and integrated with the sensor parameters loaded in step S2 to form a comprehensive parameter set. This comprehensive parameter set is then applied to a modal analysis algorithm. Based on input parameters such as the geometric parameters, material properties, and boundary conditions of the structural model, the modal analysis algorithm calculates the structural mode shapes and frequency response curves. The calculation results are displayed to the user in real time through a graphical interface.

[0082] Specifically, the adaptive method for structural dynamic load test parameters works as follows: First, a pre-set list of information containing sensor and model parameters enables rapid configuration of experimental parameters. Users only need to make selections to load sensor and model parameters, thus shortening experimental preparation time. Second, after vibration data is collected, the system automatically evaluates the dynamic parameters of the structural model using the peak frequency response function method or the characteristic frequency identification method. The parameter evaluation process requires no manual intervention, thus reducing human error. Third, the evaluated dynamic parameters are matched with the sensor parameters, and the matched parameters are used for modal analysis calculations. The modal analysis results can be displayed graphically in real time, improving the efficiency of presenting experimental results. Through these steps, the adaptive method for structural dynamic load test parameters automates the determination of structural dynamic load test parameters, improving experimental teaching efficiency and demonstration effects.

[0083] In some specific implementations, for dynamic load tests on beam structure models, a preset sensor type information list includes parameters for various sensor types such as accelerometers, velocity sensors, and displacement sensors. After the user selects an accelerometer, the system automatically loads its sensitivity and range parameters. A simplified model parameter information set includes beam model parameters at different simplification levels, such as Euler beam models and Timoshenko beam models. After the user selects an Euler beam model, the system loads parameters such as the cross-sectional dimensions and material elastic modulus of the Euler beam model. The data acquisition unit collects the acceleration response data of the beam model under vibration. The system combines the loaded sensor parameters and beam model parameters, and uses the peak frequency response function method to evaluate the first-order natural frequency and damping ratio of the beam model. After matching the evaluation results with the accelerometer parameters, they are applied to the modal analysis algorithm to calculate the first-order mode shapes and frequency response curves of the beam model, and the mode shape animation and frequency response curve graphs of the beam model are displayed in real time on the user interface.

[0084] In some implementations, step S2 includes:

[0085] S201. Display a list of sensor type information, including sensor model, sensitivity, and range, on the user interface;

[0086] S202. In response to the user's selection of sensor model on the user interface, determine the sensor model used in the experiment;

[0087] S203. Based on the determined sensor model, retrieve the corresponding sensitivity and range parameters from the pre-stored sensor parameter database;

[0088] S204. Load the retrieved sensitivity and range parameters and display the loading results on the user interface in real time for user confirmation.

[0089] In step S201, the way the sensor type information list is presented on the user interface is not limited. For example, the list can be implemented as a drop-down menu, a scrollable list, or a table, so that users can browse and select. The data that makes up the list is pre-stored in the software's configuration file or database.

[0090] In step S202, the user responds to the sensor model selection operation on the user interface, determining the sensor model to be used in the experiment. This response is achieved through a user interface event listening mechanism, and the selected sensor model is stored as an internal software variable for use in subsequent steps.

[0091] In step S203, the system performs a search operation in a pre-stored sensor parameter database based on the sensor model determined in step S202. The database stores sensor models and their corresponding sensitivity and range parameters. The search operation aims to obtain the sensitivity and range values ​​associated with the selected sensor model.

[0092] In step S204, the sensitivity and range parameters retrieved in step S203 are loaded into the software system for subsequent calculations and analysis. Simultaneously, these parameters are displayed in real-time on the user interface, such as in text boxes or labels, so that the user can verify and confirm the correctness of the loaded parameters.

[0093] Specifically, the proposed method aims to address the problem of manually inputting sensor parameters in structural dynamic load tests. Initially, the user sees a list of sensors through a user-friendly interface, displaying available sensor models and key parameters such as sensitivity and range. Step S201 provides this visual presentation, facilitating sensor selection. After the user selects a sensor model in step S202, the system automatically recognizes the selection. Subsequently, in step S203, the system accesses a pre-stored database and automatically retrieves the sensitivity and range parameters associated with the selected sensor model. This eliminates the need for manual searching and input of sensor parameters. Finally, step S204 ensures that the retrieved parameters are loaded into the system and displayed on the user interface for user confirmation. This confirmation step is crucial for preventing errors caused by incorrect parameter loading. By automating sensor parameter loading and providing a user-friendly interface with confirmation, this method significantly shortens experimental preparation time, reduces user errors, and improves the efficiency and accuracy of structural dynamic load tests, making it particularly suitable for teaching environments. Thus, the problems of excessively long experimental preparation times and student errors are effectively solved, improving the teaching efficiency and reliability of experimental results in structural dynamic load tests.

[0094] In some implementations, step S3 includes:

[0095] S301. Presents a structural model selection interface, which includes multiple simplified model options corresponding to commonly used structural models in teaching experiments. Each simplified model option corresponds to a different degree of simplification.

[0096] S302. Respond to the user's selection operation on the structural model selection interface and determine the simplified model option selected by the user;

[0097] S303. Based on the determined simplified model option, retrieve the corresponding simplified model parameter from the pre-stored simplified model parameter information set;

[0098] S304. Assess students' cognitive level of structural dynamics according to the pre-set cognitive level assessment rules; the cognitive level assessment rules involve students' past theoretical course grades, experimental operation records, and their understanding of basic concepts of structural dynamics.

[0099] S305. Based on the student's cognitive level assessment results and the degree of simplification corresponding to the simplified model options, determine whether the simplified model options match the student's cognitive level. If they do not match, provide a prompt on the structural model selection interface to guide the student to select a simplified model option that matches their cognitive level. If they match, load the retrieved simplified model parameters.

[0100] In step S301, the structural model selection interface is configured as a graphical user interface, which presents multiple simplified model options for selection. These simplified model options correspond to structural models commonly used in teaching experiments, such as simply supported beam models, cantilever beam models, or frame models. Each simplified model option is preset with a different degree of simplification, which can be differentiated based on the number of model degrees of freedom or the complexity of model parameters.

[0101] In step S302, the user makes a selection on the structural model selection interface by clicking or touching, and the system responds to the user's operation to determine the simplified model option selected by the user.

[0102] In step S303, a pre-stored set of simplified model parameter information is stored in a database. This set contains simplified model parameters corresponding to each simplified model option, and these parameters may include the model's geometric dimensions, material properties, or boundary conditions. Once a simplified model option is determined, the system retrieves and loads the corresponding simplified model parameters from the database.

[0103] In step S304, the cognitive level assessment rules are pre-set as a method for quantitatively assessing students' cognitive level in structural dynamics. The assessment rules can include multiple assessment dimensions, such as theoretical course grades, experimental operation records, and conceptual understanding. Theoretical course grades can be based on students' exam scores in structural dynamics-related courses; experimental operation records can be based on students' standardized records of past experimental operations; and conceptual understanding can be based on students' scores on tests of basic structural dynamics concepts.

[0104] In step S305, the student's cognitive level assessment result and the degree of simplification corresponding to the simplified model option are used for matching judgment. Matching judgment can use a preset matching threshold; when the difference between the simplification degree of the simplified model option and the student's cognitive level assessment result exceeds the threshold, it is judged as a mismatch. If there is a mismatch, the system provides a prompt on the structural model selection interface, which can be in text or graphic form, guiding the student to select a simplified model option that matches their cognitive level. If there is a match, the system loads the retrieved simplified model parameters to prepare for the subsequent experimental parameter adaptation process.

[0105] Specifically, in teaching structural dynamic load experiments, to address the issue of students potentially choosing simplified models that are incompatible with their abilities, thus affecting teaching effectiveness, the technical solution in step S3 is adopted. First, through steps S301 and S302, the system provides a structural model selection interface, allowing students to choose a suitable simplified model based on experimental needs and their own judgment. The interface clearly presents model options with different levels of simplification; for example, the simply supported beam model offers options such as "first-order beam model" and "third-order beam model." Then, step S303 automatically loads preset simplified model parameters based on the student's model selection, reducing the tedious manual parameter configuration. More importantly, step S304 introduces a cognitive level assessment mechanism. The system comprehensively considers the student's theoretical foundation, experimental experience, and conceptual mastery to quantitatively assess their cognitive level. The assessment results are used in step S305 for model matching judgment, determining whether the student's chosen model is appropriate for their cognitive level. If a student chooses an overly complex model, the system provides a prompt, such as "The model complexity is high; it is recommended to choose a lower-order model," guiding the student to select a more suitable model. Conversely, if students choose overly simplistic models, the system may provide prompts, encouraging them to tackle more complex models. In this way, the configuration of simplified model parameters is no longer fixed but can be adaptively adjusted according to students' cognitive levels, ensuring the personalization and effectiveness of the teaching content. Thus, students can conduct experiments on models that match their abilities, improving the effectiveness of experimental teaching and solving the problem of model selection mismatch with student capabilities.

[0106] Preferably, step S304 may include:

[0107] Collect students' exam scores in structural dynamics-related courses, assign weights according to exam type, calculate a weighted average score, and use it as the theoretical knowledge assessment score.

[0108] Obtain students' historical operations in structural dynamic load tests, and calculate experimental operation evaluation scores based on the standardization, efficiency, and accuracy of the historical operations.

[0109] Obtain the accuracy rate of students' pre-completed tests involving basic concepts of structural dynamics, and calculate the concept comprehension assessment score;

[0110] The total score of students' structural dynamics cognitive level is calculated by combining the scores of theoretical knowledge assessment, experimental operation assessment, and concept understanding assessment, according to the preset weight ratio.

[0111] The total score of the structural dynamics cognitive level is mapped to a preset cognitive level.

[0112] The theoretical knowledge assessment score is obtained by configuring the system to access the student's learning management system. Exam scores for all courses related to structural dynamics are collected from the learning management system. The exam types are divided into midterm exams and final exams, with the final exam having a higher weight than the midterm exam. Thus, the weighted average score is calculated and used as the theoretical knowledge assessment score.

[0113] The experimental operation evaluation score is obtained by acquiring the student's operation records of all structural dynamic load tests. The operation records include sensor installation steps, data acquisition parameter settings, and model adjustment process. The system's evaluation algorithm analyzes these operation records. Standardization is measured by whether the operation steps conform to the standard procedure, efficiency is evaluated by the time taken to complete the experimental operation, and accuracy is examined by the quality of data acquisition and the rationality of parameter settings. The experimental operation evaluation score is calculated by combining these three aspects.

[0114] The concept comprehension assessment score is obtained by having students complete a pre-set test on basic structural dynamics concepts. The test includes multiple-choice, fill-in-the-blank, and short-answer questions. The system automatically grades the test or provides manual grading assistance. The accuracy rate is then calculated and used as the concept comprehension assessment score.

[0115] In the calculation of the total cognitive level score, the theoretical knowledge assessment score, experimental operation assessment score, and concept understanding assessment score are combined according to a preset weight ratio. For example, the theoretical knowledge weight is 40%, the experimental operation weight is 30%, and the concept understanding weight is 30%. The total score of the student's structural dynamics cognitive level is calculated by weighted summation.

[0116] The mapping of cognitive level involves pre-setting a mapping relationship between the total cognitive score and the cognitive level level. For example, a total score of 85 or above is considered advanced, 70-84 is intermediate, 60-69 is beginner, and below 60 is introductory. Students' total cognitive scores are mapped to the corresponding cognitive level level. This allows for a quantitative assessment of students' structural dynamics cognitive level. The assessment results provide a basis for the subsequent adaptive selection of simplified model parameters, ensuring the relevance and effectiveness of the teaching content.

[0117] Specifically, the cognitive level assessment method is detailed, ensuring accurate and consistent evaluation of students' cognitive levels. By collecting students' exam scores in the structural dynamics course, the system reflects their mastery of theoretical knowledge; by acquiring their experimental operation records, it assesses their practical skills; by conducting basic concept tests, it directly quantifies their understanding of fundamental structural dynamics knowledge; and by weightedly combining these three scores, a comprehensive assessment of students' structural dynamics cognitive level can be achieved. The total cognitive level score is further mapped to a cognitive level grade, making the assessment results easier to understand and apply, and facilitating the system to match appropriate simplified models to students based on their cognitive level. The cognitive level assessment method allows the system to be adjusted according to individual student needs, improving the effectiveness of experimental teaching. The effectiveness of the adaptive parameter selection process is guaranteed, thereby enhancing the teaching value of the experiments.

[0118] In some specific implementations, a student cognitive level assessment process is executed for bridge structure dynamic load tests. First, the learning management system's data interface is invoked, and students' midterm and final exam scores for Structural Mechanics and Bridge Dynamics courses are collected. The final exam score is weighted at 0.6, and the midterm exam score at 0.4; the weighted average score is calculated as the theoretical knowledge assessment score. Second, students' historical bridge model experiment operation records are retrieved. The operational standardization, efficiency, and accuracy are scored by the experiment instructors based on the records and experiment videos, with a maximum score of 100 points, converted into an experiment operation assessment score. Then, a basic bridge dynamics concept test, consisting of 20 multiple-choice questions and 10 fill-in-the-blank questions, is completed online by the students. The system automatically grades the papers, and the accuracy rate is calculated as the concept comprehension assessment score. When calculating the total cognitive level score, the weights for theoretical knowledge, experiment operation, and concept comprehension are set to 0.5, 0.3, and 0.2, respectively, and the weighted total score is calculated. Finally, a total score of 90 or above is rated as advanced, 80-89 as high level, 70-79 as intermediate level, 60-69 as beginner level, and below 60 as introductory level. The cognitive level is displayed on the model selection interface, guiding students to choose a simplified bridge model of appropriate difficulty. For example, advanced students are recommended to choose a complex model considering the elasticity of stay cables and piers, while introductory students are recommended to choose a simply supported beam model. Thus, the accuracy and operability of the cognitive level assessment are verified in the teaching of bridge structure dynamic load experiments.

[0119] In some implementations, after step S4 and before step S5, the following step is also included:

[0120] S7. Evaluate the noise level of the vibration data. If the noise level exceeds the preset threshold, filter the vibration data.

[0121] The noise level assessment of vibration data can be implemented as follows: First, analyze the noise intensity in the vibration data. For example, by calculating the power spectral density of the vibration data, determine the average power value within a preset frequency band, and use this average power value as the noise level assessment index. Then, compare the noise level assessment index with a preset level threshold. The preset level threshold is a pre-set standard value representing the maximum acceptable noise level. If the noise level assessment index exceeds the preset level threshold, it indicates that the vibration data is significantly affected by noise. In this case, to reduce the impact of noise on data quality and to improve the accuracy of subsequent dynamic parameter assessment, filtering is performed on the vibration data. Filtering aims to weaken or eliminate noise components in the vibration data, retaining the true and valid structural vibration signal. The filtering method can be a digital filter, such as a finite impulse response filter or an infinite impulse response filter. The filter type and parameters can be selected and adjusted according to the noise characteristics and signal features. Thus, by adding a noise assessment and filtering step before the dynamic parameter assessment step, it is ensured that the vibration data, which may be contaminated by noise, is preprocessed before parameter assessment, improving the robustness and reliability of the entire adaptive method for structural dynamic load test parameters.

[0122] Specifically, in structural dynamic load tests, after the data acquisition unit collects vibration data from the structural model, a data preprocessing step is added before evaluating the dynamic parameters of the structural model using the peak frequency response function method or the characteristic frequency identification method. This step first assesses the quality of the collected vibration data by calculating the average power spectral density of the vibration data within a specific frequency band to quantify the noise level. A preset threshold is set as the standard for determining whether the data needs filtering. The filtering process uses a low-pass filter, such as a Butterworth low-pass filter, with a cutoff frequency designed slightly higher than the expected highest natural frequency of the structural model. For example, if the expected first-order natural frequency of the structural model is 10Hz, the cutoff frequency of the low-pass filter can be set to 20Hz. Through the application of the low-pass filter, high-frequency noise components are effectively filtered out, while low-frequency structural vibration signals are preserved, improving the signal-to-noise ratio. The filtered vibration data is then used for subsequent dynamic parameter evaluation, yielding more accurate natural frequencies and damping ratios of the structural model.

[0123] Preferably, step S7 may include:

[0124] S701. Calculate the power spectral density of the vibration data to determine the average power value within a preset frequency band, which serves as an indicator for noise level assessment.

[0125] S702. Determine whether the noise level assessment index exceeds the preset level threshold. If it does, filter the vibration data.

[0126] The calculation of power spectral density aims to transform time-domain vibration data into the frequency domain for analysis. The method for calculating power spectral density is existing technology and will not be detailed here. This allows for a comprehensive reflection of the signal's frequency components and energy distribution. Noise typically exhibits specific distribution characteristics in the frequency domain; therefore, through power spectral density analysis, the energy distribution of noise in the frequency domain can be accurately identified and quantified. A preset frequency band is determined, and within this band, the average power value is calculated as a noise level assessment indicator. For example, the preset frequency band can be set according to the specific experimental conditions and sensor type; for instance, avoiding the main frequency range of structural vibration and selecting the high-frequency portion as the noise assessment band.

[0127] The noise level assessment metric is then compared to a preset level threshold. This preset threshold, determined empirically or through experimental calibration, represents the upper limit of acceptable noise. If the noise level assessment metric exceeds the preset level threshold, it indicates that the vibration data is significantly affected by noise, and filtering is performed. Filtering can employ various digital signal processing methods, such as finite impulse response (FIR) or infinite impulse response (IR) filters, to attenuate or eliminate noise components and improve the signal-to-noise ratio.

[0128] Specifically, the aforementioned techniques address the challenge of accurately assessing the noise level of vibration data to more effectively determine whether filtering is necessary. First, the power spectral density of the vibration data is calculated, transforming the noise assessment from the time domain to the frequency domain, fully utilizing the frequency domain characteristics of noise. By calculating the average power value within a preset frequency band, the noise level is quantified into a specific assessment index. This index, compared to simple time-domain threshold judgment, more accurately reflects the actual noise level. Subsequently, the noise level assessment index is compared with a preset level threshold, determining whether filtering is required. Filtering is only triggered when the noise level exceeds an acceptable range, avoiding unnecessary filtering and ensuring timely filtering even when noise levels are high. The purpose of filtering is to reduce the impact of noise on vibration data, thereby improving data quality and laying the foundation for accurate assessment of subsequent dynamic parameters. Thus, through these steps, a more refined and accurate assessment of the noise level of vibration data is achieved, making filtering more targeted and effective, and enhancing the robustness and reliability of the adaptive method for structural dynamic load test parameters.

[0129] In some specific implementations, a structural dynamic load test is underway, and a data acquisition device collects a segment of vibration data. To assess the noise level of this data, it is first input into modal analysis software on a computer. The software uses a Fast Fourier Transform algorithm to calculate the power spectral density of the vibration data. A preset frequency band is set to 200 Hz to 500 Hz; this band is chosen because it is higher than the first natural frequency of the structural model and is more likely to include sensor and environmental noise. Within the preset frequency band, the average power spectral density is calculated as a noise level assessment metric. A preset noise level threshold is set to -60 dB / Hz, determined based on extensive experimental experience. The noise level assessment metric is compared to the preset threshold. If the calculated average power value is higher than -60 dB / Hz, the noise level is considered to exceed the threshold, and the software automatically activates a Butterworth low-pass filter to filter the vibration data, removing high-frequency noise. The filtered data is then used for subsequent dynamic parameter assessments, ensuring the accuracy of the evaluation results. Therefore, through the above implementation method, the noise level in the vibration data can be effectively evaluated and processed, ensuring the reliability of the structural dynamic load test.

[0130] In some implementations, step S5 includes:

[0131] S501. Based on the vibration data, calculate the frequency domain data using the fast Fourier transform algorithm, and construct the frequency response function of the structural model by combining the loaded sensor parameters;

[0132] S502. Evaluate the matching degree between the simplified model corresponding to the selected simplified model parameters and the constructed frequency response function. If the matching degree is higher than the preset matching degree threshold, select the frequency response function peak method; otherwise, select the feature frequency identification method.

[0133] S503. If the peak frequency response function method is selected, the maximum amplitude point is searched according to the amplitude spectrum of the constructed frequency response function, the frequency corresponding to the maximum amplitude point is identified as the natural frequency of the structural model, and the damping ratio of the structural model is calculated based on the amplitude on both sides of the maximum amplitude point.

[0134] S504. If the characteristic frequency identification method is selected, time-frequency analysis is performed on the vibration data to obtain a time-frequency distribution map. The natural frequency of the structural model is identified based on the energy concentration area in the time-frequency distribution map, and the damping ratio of the structural model is calculated based on the energy decay rate near the natural frequency.

[0135] Step S501 can be implemented as follows: First, acquire structural vibration data (time-domain data) from sensors; then, use a fast Fourier transform algorithm to convert the time-domain data to the frequency domain to obtain frequency-domain data; finally, calibrate the frequency-domain data using pre-loaded sensor sensitivity parameters to construct a frequency response function that reflects the dynamic characteristics of the structure. The construction of the frequency response function provides a data foundation for subsequent dynamic parameter evaluation.

[0136] Specifically, in step S502, the matching degree evaluation can employ various methods. For example, it can calculate the cross-correlation coefficient between the theoretical frequency response function of the simplified model and the constructed frequency response function; the higher the cross-correlation coefficient, the higher the matching degree. A preset matching degree threshold can be set according to actual needs, for example, to 0.8. The introduction of the method selection mechanism allows the parameter evaluation method to adaptively select based on the model matching degree. When the matching degree is high, the peak frequency response function method is selected; when the matching degree is low, the feature frequency identification method is selected, thereby ensuring the accuracy and reliability of the parameter evaluation.

[0137] Specifically, in step S503, the peak frequency response function method is a frequency domain-based parameter identification method. Its principle lies in the fact that the natural frequency of a structure corresponds to the peak point of the amplitude spectrum of the frequency response function. By searching for the maximum amplitude point in the amplitude spectrum, the frequency corresponding to that point is identified as the natural frequency of the structural model. The damping ratio can be calculated based on the amplitudes on both sides of the peak point. For example, the half-power bandwidth method can be used to calculate the damping ratio based on the frequency width where the amplitudes on both sides of the peak point drop to half the peak amplitude. The peak frequency response function method has high computational efficiency and is suitable for scenarios with high model matching.

[0138] Specifically, in step S504, the characteristic frequency identification method is a parameter identification method based on time-frequency analysis, such as short-time Fourier transform and wavelet transform. Time-frequency analysis decomposes the vibration signal into a joint time and frequency domain, obtaining a time-frequency distribution map, which clearly shows the frequency components of the signal changing over time. The natural frequencies of the structural model are represented as energy concentration regions on the time-frequency distribution map; by identifying these energy concentration regions, the natural frequencies can be determined. The damping ratio is related to the energy decay rate; by analyzing the energy decay rate near the natural frequencies, the damping ratio can be calculated. The characteristic frequency identification method has low sensitivity to model errors and is suitable for scenarios with low model matching.

[0139] Specifically, in the adaptive method for structural dynamic load test parameters, step S5 is a crucial step in evaluating the dynamic parameters of the structural model. First, in step S501, a frequency response function is constructed using Fast Fourier Transform and sensor parameters, providing accurate frequency domain data support for subsequent parameter identification. Then, step S502 proposes an adaptive method selection mechanism, intelligently selecting either the peak frequency response function method or the characteristic frequency identification method by evaluating the model matching degree. When the model matching degree is high, the peak frequency response function method can effectively identify parameters; when the model matching degree is low, the characteristic frequency identification method ensures robustness of the identification. Thus, step S502 achieves optimized selection of the evaluation method. Next, steps S503 and S504 describe in detail the implementation process of the two evaluation methods: the peak frequency response function method identifies parameters using the peak characteristics of the frequency response function, while the characteristic frequency identification method identifies parameters using time-frequency analysis techniques. Through the refined design of step S5, the technical solution can adaptively select the parameter evaluation method according to the model matching degree, and use a suitable algorithm to identify the natural frequency and damping ratio, thereby solving the problem of blind method selection in the prior art and improving the accuracy of dynamic parameter evaluation.

[0140] As a preferred embodiment, the solution of this application is specifically implemented as follows:

[0141] Suppose a dynamic load test is conducted on a simply supported beam structure model, and vibration data is collected using an accelerometer.

[0142] First, step S501 is executed, which uses the fast Fourier transform algorithm to process the collected vibration data to obtain frequency domain data, and combines the sensitivity parameters of the accelerometer to construct the frequency response function of the simply supported beam structure model.

[0143] Then, step S502 is executed to evaluate the matching degree between the selected simplified simply supported beam model and the constructed frequency response function. The evaluation method is as follows: calculate the cross-correlation coefficient between the theoretical frequency response function of the simplified model and the amplitude spectrum of the constructed frequency response function. A preset matching degree threshold is set to 0.8. If the cross-correlation coefficient is higher than 0.8, the peak value method of the frequency response function is selected; otherwise, the characteristic frequency identification method is selected.

[0144] If the peak frequency response function method is selected, step S503 is executed to search for the maximum amplitude point in the amplitude spectrum of the frequency response function, and the frequency corresponding to this point is identified as the first natural frequency of the simply supported beam structure model. The half-power bandwidth method is used to calculate the damping ratio based on the frequency width where the amplitude on both sides of the peak point drops to half of the peak amplitude (the specific calculation method is existing technology and will not be described in detail here).

[0145] If the characteristic frequency identification method is selected, step S504 is executed to perform a short-time Fourier transform on the vibration data to obtain a time-frequency distribution map. In the time-frequency distribution map, the region of concentrated energy corresponds to the first natural frequency of the simply supported beam structure model. The energy decay rate near the natural frequency is analyzed, and the damping ratio is calculated (the specific calculation method is existing technology and will not be detailed here).

[0146] Through the above technical solution, this application can better select the dynamic parameter evaluation method and improve the accuracy of the evaluation. By evaluating the model fit, the peak frequency response function method or the characteristic frequency identification method is adaptively selected, making the parameter evaluation method adaptable to the model fit, thereby improving the accuracy and reliability of the parameter evaluation.

[0147] Preferably, step S502 may include:

[0148] Calculate the theoretical frequency response function of the simplified model corresponding to the simplified model parameters within a preset frequency range, and extract the amplitude spectrum of the theoretical frequency response function;

[0149] The cross-correlation coefficient between the amplitude spectrum of the constructed frequency response function and the amplitude spectrum of the extracted theoretical frequency response function is calculated as the degree of matching between the simplified model and the constructed frequency response function.

[0150] Determine whether the matching degree is higher than a preset matching degree threshold. If it is higher than the preset matching degree threshold, select the frequency response function peak method; otherwise, select the feature frequency recognition method.

[0151] The theoretical frequency response function can be calculated using various methods. For example, for single-degree-of-freedom or multi-degree-of-freedom systems, the theoretical frequency response function can be derived analytically using the system's dynamic equations and model parameters. Alternatively, for more complex systems, numerical methods such as finite element analysis can be used to simulate the system's response in the frequency domain to obtain the theoretical frequency response function. The preset frequency range can be set according to the actual situation of the structural model and experimental requirements. For example, it can be set to a frequency band that includes the expected natural frequencies of the structural model. The amplitude spectrum can be extracted by directly taking the amplitude of the frequency response function, or by further processing the frequency response function, such as smoothing filtering, to reduce noise interference and improve the quality of the amplitude spectrum.

[0152] The cross-correlation coefficient is an indicator that measures the similarity between two signals, ranging from -1 to 1. A cross-correlation coefficient closer to 1 indicates a higher positive correlation and similarity between the two signals; a cross-correlation coefficient closer to -1 indicates a higher negative correlation; and a cross-correlation coefficient close to 0 indicates a very low correlation. In this application, the cross-correlation coefficient is used as an indicator to evaluate the similarity between the amplitude spectrum of the theoretical frequency response function and the experimentally constructed amplitude spectrum of the frequency response function. A higher cross-correlation coefficient indicates a better fit between the simplified model and the actual structural behavior, and a higher degree of matching. The cross-correlation coefficient can be calculated using the standard cross-correlation formula and efficiently in the frequency domain using the Fast Fourier Transform algorithm.

[0153] The preset matching degree threshold is a pre-defined critical value used to judge the matching degree. It can be set according to actual application needs and empirical data; for example, it can be set to 0.8 or 0.9. When the calculated matching degree is higher than the preset matching degree threshold, it indicates that the simplified model and the constructed frequency response function have a high matching degree. In this case, choosing the peak frequency response function method for parameter identification is reasonable. The peak frequency response function method is a computationally efficient and simple parameter identification method, suitable for situations where the model matching degree is high. When the matching degree is lower than the preset matching degree threshold, it indicates that the simplified model and the constructed frequency response function have a low matching degree. In this case, choosing the characteristic frequency identification method is more robust. The characteristic frequency identification method is a parameter identification method with a certain degree of robustness to model errors and noise, suitable for situations where the model matching degree is low or the experimental data quality is not high. By introducing a matching degree threshold judgment mechanism, the adaptive selection of parameter identification methods can be achieved, balancing computational efficiency and the accuracy of identification results.

[0154] Specifically, in the adaptive method for structural dynamic load test parameters, step S502 is introduced as a key step in evaluating the matching degree between the simplified model and the constructed frequency response function. First, based on the selected simplified model parameters, the theoretical frequency response function is calculated, and its amplitude spectrum is extracted to provide a benchmark for matching degree evaluation. Then, the amplitude spectrum of the experimentally constructed frequency response function is cross-correlation analyzed with the theoretical amplitude spectrum to calculate the cross-correlation coefficient, which is defined as the matching degree. The cross-correlation coefficient effectively quantifies the similarity between the two amplitude spectra, thereby objectively evaluating the matching degree between the simplified model and the actual structure. Finally, by setting a matching degree threshold and making comparisons, the adaptive selection of parameter identification methods is achieved. When the matching degree is higher than the threshold, the computationally efficient peak frequency method of the frequency response function is selected to quickly identify the dynamic parameters of the structural model; when the matching degree is lower than the threshold, the method switches to the more robust characteristic frequency identification method to ensure that relatively accurate parameter identification results can still be obtained even when the model matching degree is low or the data quality is poor. Thus, step S502 enables adaptive selection of parameter identification methods based on matching degree evaluation, making the entire parameter identification process more intelligent and efficient, ensuring both computational efficiency and accuracy of identification results.

[0155] Through the above technical solution, this application can effectively evaluate the matching degree between the simplified model and the constructed frequency response function, and adaptively select an appropriate parameter identification method according to the matching degree. When the simplified model can well represent the actual structure, the peak frequency response function method with high computational efficiency is selected to quickly complete the parameter identification; while when there is a certain deviation between the simplified model and the actual structure, the more robust characteristic frequency identification method is selected to ensure the accuracy of parameter identification, thereby improving the intelligence and adaptability of parameter identification in structural dynamic load test.

[0156] In some implementations, step S6 includes:

[0157] S601. Construct a comprehensive parameter set, which matches the natural frequency, damping ratio, and applied sensor sensitivity and range parameters of the final structural model;

[0158] S602. Based on the comprehensive parameter set, select the modal analysis algorithm and configure the input parameters of the modal analysis algorithm; the input parameters include the geometric parameters of the structural model, material properties, boundary conditions, sensor locations, and excitation force information;

[0159] S603. Run the configured modal analysis algorithm to calculate the mode shapes and frequency response curves of the structural model;

[0160] S604. The calculated structural mode shapes and frequency response curves are graphically processed and displayed on the user interface in real time.

[0161] Step S601, constructing the comprehensive parameter set, refers to building a dataset containing multiple parameters. The core of this comprehensive parameter set lies in achieving a precise match between the dynamic parameters used in the final evaluation of the structural model—namely, the natural frequency and damping ratio—and the pre-loaded sensor parameters—namely, sensitivity and range. This matching is not a simple data listing but emphasizes the inherent correlation and mutual influence between the parameters. In practice, data structures, such as hash tables or associative arrays, can be used to store and index these parameters, ensuring they can be quickly and accurately retrieved and used in subsequent steps.

[0162] In step S602, based on the comprehensive parameter set constructed in step S601, a modal analysis algorithm suitable for the current experimental scenario and parameter characteristics is determined. The selection of the modal analysis algorithm can be based on a preset rule base, which stores the applicable conditions and performance characteristics of different algorithms. In the input parameter configuration stage, various parameters in the comprehensive parameter set, such as the geometric parameters, material properties, boundary conditions, sensor positions, and excitation force information of the structural model, need to be converted into a data format that the selected modal analysis algorithm can recognize and process. The input parameter configuration process can employ parameter mapping and conversion techniques to achieve compatibility between different data types and formats. It is noteworthy that the input parameters particularly emphasize sensor positions (which can be obtained manually) and excitation force information (during operation, the computer controls the vibration table based on the target excitation force information, so the excitation force information can be directly obtained from the computer), with the aim of enabling the modal analysis model to more realistically reflect the experimental state.

[0163] In step S603, the modal analysis algorithm can be a frequency domain analysis method, such as the frequency response function method, or a time domain analysis method, such as the modal parameter identification method. The calculation results mainly include the mode shapes and frequency response curves of the structural model. The mode shapes reflect the vibration patterns of the structure under different modes, while the frequency response curves describe the response characteristics of the structure under different frequency excitations. The calculation process can employ parallel or distributed computing techniques to improve computational efficiency and shorten computation time.

[0164] In step S604, the modal analysis results calculated in step S603 are visualized. Graphical processing can employ computer graphics algorithms, such as 3D rendering, color mapping, and animation techniques, to transform abstract modal shapes and frequency response curves into intuitive graphical images. Real-time display ensures that users can simultaneously observe the calculation process and result changes of the modal analysis. The user interface can utilize graphical user interface (GUI) technology, providing a user-friendly interactive operation method for convenient parameter adjustment and result viewing. The comprehensive parameter set constructed in step S601 provides a data foundation for the subsequent selection of modal analysis algorithms and parameter configuration. Step S602 selects a modal analysis algorithm and configures its input parameters to ensure the accuracy and reliability of the modal analysis. Step S603 runs the configured modal analysis algorithm to calculate the modal shapes and frequency response curves of the structural model, obtaining the modal analysis results. Step S604 graphically processes the calculated structural modal shapes and frequency response curves and displays them in real-time on the user interface, realizing the visualization and real-time monitoring of the modal analysis results. Through the above steps, the experimental parameters and modal analysis were effectively combined and displayed in real time graphically.

[0165] Specifically, in performing modal analysis, this application first constructs a comprehensive parameter set in step S601, integrating the structural model dynamic parameters (natural frequency, damping ratio) obtained in step S5 and the sensor parameters (sensitivity, range) applied in step S2. This integration ensures that the subsequent modal analysis is not conducted in isolation, but fully considers the characteristics of the sensors used in the experiment and the dynamic characteristics of the structure itself. Subsequently, in step S602, a suitable modal analysis algorithm is selected based on this comprehensive parameter set. The algorithm selection can be based on factors such as parameter type and data quality in the parameter set. When configuring the algorithm input parameters, in addition to the conventional parameters of the structural model (geometric parameters, material properties, boundary conditions), sensor location and excitation force information are also specifically added. The addition of this information makes the modal analysis model closer to the real experimental scenario, improving the confidence of the analysis results. After completing the parameter configuration, step S603 runs the modal analysis algorithm to calculate the mode shapes and frequency response curves of the structural model. These results are key data for evaluating the dynamic characteristics of the structure. Finally, step S604 displays these abstract analysis results graphically on the user interface in real time. Graphical processing makes the results more intuitive and easier to understand, while real-time display allows users to monitor the experiment and analysis process immediately. This solution, through the collaborative work of steps S601 to S604, achieves a complete closed loop from experimental parameters to modal analysis results, solving the problem of insufficient utilization of experimental parameters in the modal analysis process in the prior art, ensuring the accuracy and real-time nature of the modal analysis results, and improving the effectiveness of experimental teaching.

[0166] Through the above technical solution, this application achieves an effective integration of structural dynamic load test parameters and modal analysis. By constructing a comprehensive parameter set, it ensures that the modal analysis algorithm can fully utilize the sensor parameters applied during the experiment and the structural dynamic parameters obtained from the evaluation, thereby improving the accuracy and reliability of the modal analysis results. Through the real-time graphical display of the modal analysis results, users can intuitively and dynamically grasp the modal characteristics of the structural model, improving the efficiency and quality of structural dynamic load experimental teaching.

[0167] refer to Figure 2 , Figure 3 This application provides an adaptive system for structural dynamic load test parameters. The system includes a vibration table 1, a data acquisition unit 2, and a computer 3. Both the vibration table 1 and the data acquisition unit 2 are electrically connected to the computer 3.

[0168] The vibration table 1 is used to drive the structural model 90 to vibrate;

[0169] The data acquisition device 2 is used to collect vibration data through the sensor 4 installed on the structural model 90 and upload it to the computer 3;

[0170] The computer 3 is equipped with modal analysis software, which is configured with:

[0171] The sensor list acquisition module 301 is used to acquire a list of sensor type information. The list includes the sensor parameters of commonly used sensors in teaching experiments (refer to step S1 above for details).

[0172] The sensor type selection module 302 is used to select the type of sensor used in the experiment from the sensor type information list and load the sensor parameters (refer to step S2 above for details).

[0173] The structural model parameter configuration module 303 is used to select simplified model parameters from the simplified model parameter information set for the structural model 90 used (for details, refer to step S3 above).

[0174] The receiving module 304 is used to receive vibration data collected by the data acquisition unit 2 (for details, refer to step S4 above).

[0175] The preliminary parameter evaluation module 305 is used to combine the loaded sensor parameters with the selected simplified model parameters and vibration data, and to evaluate the dynamic parameters of the structural model 90 using the peak frequency response function method or the characteristic frequency identification method; the dynamic parameters include natural frequency and damping ratio (refer to step S5 above for the specific process).

[0176] Modal analysis module 306 is used to match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural mode shape and frequency response curve, and display them graphically in real time (refer to step S6 above for details).

[0177] The vibration table 1, data acquisition unit 2, and computer 3 are electrically connected via wires to transmit data and control signals. The vibration table 1 is configured to generate controllable vibrations, such as sine waves, random waves, or shock waves, to excite the structural model 90. The data acquisition unit 2 contains multiple channels, each connected to a sensor 4, for synchronously acquiring vibration signals from multiple sensors 4. The computer 3 serves as the system's control and computing center, and its installed modal analysis software integrates various functional modules. The sensor list acquisition module 301 reads a list of sensor type information from a preset database or configuration file; each item in the list includes parameters such as sensor model, sensitivity, and range. The sensor type selection module 302 provides a user interface where users can select the sensor model used in the experiment. The structural model parameter configuration module 303 provides a structural model selection interface where users can select a simplified model corresponding to the experimental structural model. The receiving module 304 monitors the data port of the data acquisition unit 2 in real time, receiving the acquired vibration data. The preliminary parameter evaluation module 305 executes signal processing and parameter identification algorithms. The peak frequency method identifies the natural frequency and damping ratio by finding the peak value of the frequency response function amplitude spectrum. The characteristic frequency identification method identifies the characteristic frequencies of the structural model through time-frequency analysis methods, such as wavelet transform or Hilbert-Huang transform. The modal analysis module 306 configures modal analysis algorithms, such as the finite element method or modal superposition method, based on the identified dynamic parameters and the parameters of the applied sensors to calculate the mode shapes and frequency response curves. The graphical real-time display function displays the calculation results graphically, such as mode shape animations and frequency response curves, on the user interface in real time.

[0178] Specifically, in the teaching of structural dynamic load experiments, students first install and fix the structural model 90 on the vibration table 1, and place sensors 4 at key locations on the structural model 90. The signal lines of the sensors 4 are connected to the data acquisition unit 2. After starting the modal analysis software installed on the computer 3, the sensor list acquisition module 301 automatically loads the preset sensor type information list, which is presented on the user interface through the sensor type selection module 302. Students select the sensor model according to the actual sensor model they are using, and the system automatically loads the corresponding sensor parameters. Subsequently, on the structural model selection interface provided by the structural model parameter configuration module 303, students select a simplified model that matches the current experimental structural model, and the system loads the corresponding simplified model parameters. After completing the parameter configuration, the data acquisition unit 2 is started to collect vibration data, and the receiving module 304 receives the vibration data uploaded by the data acquisition unit 2. The parameter preliminary evaluation module 305 combines the loaded sensor parameters, simplified model parameters, and received vibration data, and uses the frequency response function peak method or characteristic frequency identification method to automatically evaluate the dynamic parameters of the structural model 90, such as its natural frequency and damping ratio. Finally, the modal analysis module 306 matches the evaluated dynamic parameters with the loaded sensor parameters, applies the matched parameters to the modal analysis algorithm, calculates the structural mode shapes and frequency response curves, and displays the modal analysis results to students in real time via a graphical real-time display function. This achieves adaptive configuration of structural dynamic load test parameters and rapid identification of structural dynamic parameters, improving teaching efficiency and effectiveness.

[0179] In some preferred embodiments, the modal analysis software is further configured with:

[0180] The filtering module is used to evaluate the noise level of the vibration data. If the noise level exceeds the preset threshold, the vibration data is filtered (see step S7 above for details).

[0181] For cost-saving purposes, the excitation table 1 can adopt... Figure 4 , Figure 5 The simplified vibration table shown includes a base 101, at least two guide rails 102 parallel to each other mounted on the base 101, a slide 103 slidably mounted on the guide rails 102, and a drive mechanism 104 for driving the slide 103 to reciprocate. In use, the structural model 90 is mounted on the slide 103 (e.g., by screw connection, snap-fit ​​connection, or other connection methods), and then the drive mechanism 104 drives the slide 103 to move, thereby driving the structural model 90 to vibrate.

[0182] Furthermore, the drive mechanism 104 includes a bracket 105 fixed to the base 101, a guide cylinder 106 fixed to the bracket 105, a slide rod 107 slidably passing through the guide cylinder 106, a connecting rod 108, a swing rod 109, and a servo motor 110. The first end of the slide rod 107 is fixedly connected to the slide table 103. The servo motor 110 is used to drive the swing rod 109 to rotate. The first end of the connecting rod 108 is hinged to the second end of the slide rod 107, and the second end of the connecting rod 108 is rotatably connected to a position offset from the rotation center of the swing rod 109. During operation, by controlling the rotation of the servo motor 110, the swing rod 109 drives the slide rod 107 to reciprocate axially through the connecting rod 108, thereby driving the slide table 103 to reciprocate.

[0183] Among some preferred implementation methods, see Figure 5 The rocker arm 109 is provided with a waist hole 111 extending radially along the center of rotation, and the second end of the connecting rod 108 is connected to the waist hole 111. The length of the connecting rod 108 is adjustable, and / or the position of the rocker arm 109 in the axial direction of the slide rod 107 is adjustable. By adjusting the length of the connecting rod 108 and / or the position of the rocker arm 109, the radial distance between the second end of the connecting rod 108 and the center of rotation of the rocker arm 109 can be adjusted, thereby adjusting the amplitude of the reciprocating movement of the slide table 103, that is, the vibration amplitude can be adjusted.

[0184] To achieve adjustable length of the connecting rod 108, it can be configured as a structure including a sleeve and a sliding rod, wherein the sliding rod is slidably inserted into the sleeve, and its position is locked by a locking screw. The length of the connecting rod 108 can be adjusted by adjusting the position of the sliding rod.

[0185] To achieve adjustable position of the rocker arm 109 along the axis of the slide bar 107, the following can be used: Figure 5 The structure shown includes a bracket 105 with a slide block 112 capable of reciprocating along the axial direction of the slide rod 107 and a drive device 113 (which can be a cylinder, hydraulic cylinder, electric telescopic rod, linear motor, etc.) for driving the slide block 112. The bracket 105 also has a sliding hole 114 extending along the axial direction of the slide rod 107. A servo motor 110 is mounted on the slide block 112, and a rotating shaft is located at the rotation center of the swing rod 109, passing through the sliding hole 114 and connecting to the servo motor 110. The position of the slide block 112 can be adjusted by controlling the drive device 113, thereby adjusting the position of the swing rod 109 along the axial direction of the slide rod 107. This structure allows for online adjustment of the slide block 112 position via a computer 3, eliminating the need to stop the machine for amplitude adjustment during experiments and improving operational flexibility.

[0186] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A structural dynamic load test parameter adaptive method, characterized in that, The method comprises: S1. Obtain a sensor type information list, the list containing sensor parameters of commonly used sensors in teaching experiments; S2. Select a sensor type used in the experiment from the sensor type information list, and load the sensor parameters; S3. Select simplified model parameters from a simplified model parameter information set for the used structure model; S4. Obtain vibration data collected by a data collector; S5. Combine the loaded sensor parameters and selected simplified model parameters with the vibration data, and use the frequency response function peak value method or the characteristic frequency identification method to evaluate the dynamic parameters of the structure model; the dynamic parameters include natural frequency and damping ratio; S6. Match the final dynamic parameters with the loaded sensor parameters, and apply the matched parameters to a modal analysis algorithm to calculate the structure modal shape and frequency response curve, and perform graphical real-time display; Step S3 comprises: S301. Present a structure model selection interface, the interface containing a plurality of simplified model options corresponding to commonly used structure models in teaching experiments, each simplified model option corresponding to a different degree of simplification; S302. Determine the simplified model option selected by the user in response to the selection operation of the user on the structure model selection interface; S303. Retrieve the corresponding simplified model parameters from the pre-stored simplified model parameter information set according to the determined simplified model option; S304. Evaluate the cognitive level of the student on structure dynamics according to a pre-set cognitive level evaluation rule; the cognitive level evaluation rule involves the student's past theoretical course scores, experimental operation records, and understanding degree of basic concepts of structure dynamics; S305. Determine whether the simplified model option matches the cognitive level of the student according to the student's cognitive level evaluation result and the degree of simplification corresponding to the simplified model option, if not, give a prompt information on the structure model selection interface to guide the student to select a simplified model option matching the student's cognitive level, if yes, load the retrieved simplified model parameters; Step S304 comprises: Collect the student's past test scores in structure dynamics related courses, assign weights according to the test types, and calculate the weighted average score as the theoretical knowledge evaluation score; Obtain the student's historical operation in structure dynamic load test, calculate the experimental operation evaluation score according to the normativity, efficiency and accuracy of the historical operation; Obtain the accuracy of the test completed by the student in advance related to the basic concepts of structure dynamics, and calculate the concept understanding evaluation score; Integrate the theoretical knowledge evaluation score, the experimental operation evaluation score and the concept understanding evaluation score, calculate the total score of the student's structure dynamics cognitive level according to the pre-set weight proportion; Map the total score of the structure dynamics cognitive level to a pre-set cognitive level grade; Step S5 comprises: S501. Calculate the frequency domain data using the fast Fourier transform algorithm according to the vibration data, and construct the frequency response function of the structure model in combination with the loaded sensor parameters; S502. Evaluate the matching degree of the selected simplified model and the constructed frequency response function, if the matching degree is higher than a pre-set matching degree threshold, select the frequency response function peak value method, otherwise select the characteristic frequency identification method; S503. If the frequency response function peak value method is selected, search for the amplitude maximum value point in the amplitude spectrum of the constructed frequency response function, identify the frequency corresponding to the amplitude maximum value point as the natural frequency of the structure model, and calculate the damping ratio of the structure model according to the amplitudes on both sides of the amplitude maximum value point; S504. If the characteristic frequency identification method is selected, perform time-frequency analysis on the vibration data to obtain a time-frequency distribution diagram, identify the natural frequency of the structure model according to the energy concentration area in the time-frequency distribution diagram, and calculate the damping ratio of the structure model according to the energy decay rate near the natural frequency; Step S502 comprises: calculating the theoretical frequency response function of the simplified model corresponding to the simplified model parameters in a preset frequency range, and extracting the amplitude spectrum of the theoretical frequency response function; calculating the cross-correlation coefficient between the amplitude spectrum of the constructed frequency response function and the extracted amplitude spectrum of the theoretical frequency response function as the matching degree of the simplified model and the constructed frequency response function; determining whether the matching degree is higher than a preset matching degree threshold, if higher than the preset matching degree threshold, selecting the frequency response function peak value method, otherwise selecting the characteristic frequency identification method.

2. The method of claim 1, wherein, Step S2 comprises: S201. Present a sensor type information list containing sensor model, sensitivity and range on the user interface; S202. In response to the user's selection operation of the sensor model on the user interface, determine the sensor model used in the experiment; S203. According to the determined sensor model, retrieve the corresponding sensitivity and range parameters from the pre-stored sensor parameter database; S204. Load the retrieved sensitivity and range parameters, and display the loading result in real time on the user interface for user confirmation.

3. The method of claim 1, wherein, After step S4 and before step S5, the system further comprises the following steps: S7. Evaluate the noise level of the vibration data, and if the noise level exceeds a preset level threshold, filter the vibration data.

4. The method of claim 3, wherein, Step S7 comprises: S701. Calculate the power spectral density of the vibration data to determine the average power value in the preset frequency band range as a noise level evaluation index; S702. Determine whether the noise level evaluation index exceeds the preset level threshold, if it does, filter the vibration data.

5. The method of claim 2, wherein, Step S6 comprises: S601. Construct a comprehensive parameter set, which matches the natural frequency and damping ratio of the final structure model and the loaded sensor sensitivity and range parameters; S602. According to the comprehensive parameter set, select a modal analysis algorithm and configure the input parameters of the modal analysis algorithm; the input parameters include the geometric parameters, material properties, boundary conditions, sensor positions and excitation force information of the structure model; S603. Run the configured modal analysis algorithm to calculate the modal shape and frequency response curve of the structure model; S604. Perform graphical processing on the calculated structure modal shape and frequency response curve, and display them in real time on the user interface.

6. A structural dynamic load testing parameter adaptive system, characterized by, The system comprises an excitation platform, a data collector and a computer, and the excitation platform and the data collector are electrically connected to the computer; The excitation platform is used to drive the structure model to vibrate; The data collector is used to collect vibration data through sensors arranged on the structure model and upload them to the computer; The computer is installed with modal analysis software, and the modal analysis software is configured with: The sensor list acquisition module is configured to acquire a sensor type information list, which contains sensor parameters of commonly used sensors in teaching experiments. The sensor type selection module is configured to select a sensor type used in the experiment from the sensor type information list and load the sensor parameters. The structural model parameter configuration module is configured to select simplified model parameters from the simplified model parameter information set for the used structural model. The receiving module is configured to receive vibration data collected by the data collector. The parameter preliminary evaluation module is configured to combine the loaded sensor parameters, the selected simplified model parameters, and the vibration data, and evaluate the dynamic parameters of the structural model by using a frequency response function peak value method or a characteristic frequency identification method. The dynamic parameters include natural frequencies and damping ratios. The modal analysis module is configured to match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to a modal analysis algorithm, calculate structural modal shapes and frequency response curves, and perform real-time graphical display. When the structural model parameter configuration module selects simplified model parameters from the simplified model parameter information set for the used structural model, the following steps are performed: S301. A structural model selection interface is presented, which contains multiple simplified model options corresponding to commonly used structural models in teaching experiments. Each simplified model option corresponds to a different degree of simplification. S302. In response to a selection operation of a user on the structural model selection interface, a simplified model option selected by the user is determined. S303. According to the determined simplified model option, corresponding simplified model parameters are retrieved from the pre-stored simplified model parameter information set. S304. According to a pre-set cognitive level evaluation rule, the cognitive level of the student on structural dynamics is evaluated. The cognitive level evaluation rule involves the student's past theoretical course scores, experimental operation records, and understanding degree of basic concepts of structural dynamics. S305. According to the student cognitive level evaluation result and the degree of simplification corresponding to the simplified model option, it is judged whether the simplified model option matches the cognitive level of the student. If not, a prompt information is given on the structural model selection interface to guide the student to select a simplified model option that matches the cognitive level of the student. If it matches, the retrieved simplified model parameters are loaded. Step S304 includes: The student's past examination scores in structural dynamics related courses are collected. According to the type of the examination, a weight is assigned, and a weighted average score is calculated as a theoretical knowledge evaluation score. The student's historical operation in structural dynamic load test is obtained. According to the standardization, efficiency and accuracy of the historical operation, an experimental operation evaluation score is calculated. The accuracy of the test completed by the student in advance, which involves basic concepts of structural dynamics, is obtained. A concept understanding evaluation score is calculated. The theoretical knowledge evaluation score, the experimental operation evaluation score and the concept understanding evaluation score are integrated. According to a pre-set weight proportion, a total score of the student's cognitive level of structural dynamics is calculated. The total score of the cognitive level of structural dynamics is mapped to a pre-set cognitive level grade. The parameter preliminary evaluation module executes when evaluating the dynamic parameters of the structure model by using the frequency response function peak value method or the characteristic frequency identification method in combination with the loaded sensor parameters and the selected simplified model parameters and the vibration data: S501. According to the vibration data, the frequency domain data is calculated by using the fast Fourier transform algorithm, and the frequency response function of the structure model is constructed in combination with the loaded sensor parameters; S502. The matching degree of the selected simplified model and the constructed frequency response function is evaluated, if the matching degree is higher than the preset matching degree threshold, the frequency response function peak value method is selected, otherwise the characteristic frequency identification method is selected; S503. If the frequency response function peak value method is selected, the frequency response function of the structure model is constructed, the amplitude maximum value point is searched according to the amplitude spectrum of the constructed frequency response function, the frequency corresponding to the amplitude maximum value point is identified as the natural frequency of the structure model, and the damping ratio of the structure model is calculated according to the amplitudes on both sides of the amplitude maximum value point; S504. If the characteristic frequency identification method is selected, the time-frequency analysis is performed on the vibration data to obtain a time-frequency distribution diagram, the natural frequency of the structure model is identified according to the energy concentration area in the time-frequency distribution diagram, and the damping ratio of the structure model is calculated according to the energy decay rate near the natural frequency; Step S502 includes: calculating the theoretical frequency response function of the simplified model corresponding to the simplified model parameters in the preset frequency range, and extracting the amplitude spectrum of the theoretical frequency response function; calculating the cross-correlation coefficient between the amplitude spectrum of the constructed frequency response function and the extracted amplitude spectrum of the theoretical frequency response function as the matching degree of the simplified model and the constructed frequency response function; determining whether the matching degree is higher than the preset matching degree threshold, if higher than the preset matching degree threshold, the frequency response function peak value method is selected, otherwise the characteristic frequency identification method is selected.

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