Structural dynamic load test parameter self-adaption method and system

Abstract

The invention belongs to the technical field of education, and discloses a structure dynamic load test parameter self-adaption method and system, and the method comprises the steps: obtaining a sensor type information list which comprises sensor parameters of sensors commonly used in teaching experiments; selecting a sensor type used by an experiment from the sensor type information list, and loading sensor parameters; selecting simplified model parameters from the simplified model parameter information set for the used structural model; acquiring vibration data acquired by a data acquisition unit; the loaded sensor parameters, the selected simplified model parameters and vibration data are combined, and a frequency response function peak value method or a characteristic frequency identification method is adopted to evaluate kinetic parameters of the structure model; matching the final kinetic parameters with the loaded sensor parameters, applying the matched parameters to a modal analysis algorithm, calculating a structural modal shape and a frequency response curve, and performing graphical real-time display; therefore, the experiment preparation time can be effectively shortened, and the experiment efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of educational technology, and in particular to a method and system for adaptively controlling parameters of a structural dynamic load test. Background Art

[0002] In the undergraduate teaching system of civil engineering, structural dynamic load test is an important practical teaching link, especially in the teaching of modal analysis. At present, the teaching mode generally adopts the structural dynamic load test device, and the typical device consists of a horizontal vibration table, a data acquisition device, and a computer. During the experiment, students need to manually complete the installation and fixation of the structural model (such as beams, frames, etc.) on the vibration table, and manually lay out a variety of sensors to collect structural vibration response data. The collected vibration data is transmitted to the computer through the data acquisition device, and finally the modal parameters are identified and analyzed using professional software.

[0003] However, the existing teaching model has gradually revealed many limitations that are difficult to ignore in practical applications. First, the experimental preparation stage takes too long, and students need to invest a lot of time and energy in the selection, installation, and complex line connection of sensors. These tedious preparations have distracted students from understanding and mastering the core principles of structural dynamics to a certain extent, which directly affects the improvement of teaching efficiency. Secondly, considering the funding constraints generally faced by undergraduate teaching laboratories, it is difficult to equip a large number of expensive automated sensor identification equipment and highly complex parameter identification algorithms. Therefore, under the consideration of the principle of economy, the parameter identification method must pursue computational efficiency as much as possible and reduce the excessive dependence on hardware equipment and algorithm complexity. In addition, due to the relatively insufficient experimental operation experience of students, it is very easy to introduce human errors during the experiment, resulting in the inevitable mixing of noise or configuration errors in the data collected by the sensor, which poses a severe challenge to the accuracy and reliability of the parameter identification results. Finally, the modal analysis teaching demonstration usually has high requirements for real-time performance, and the parameter identification process must be completed quickly to avoid students waiting for a long time during the experiment, so as to ensure the fluency of the teaching process and the good learning experience of students.

[0004] In the current teaching practice of dynamic load test of undergraduate civil engineering structures, there is an urgent need for a low-cost, high-efficiency, and automated parameter identification method that can effectively deal with student operation errors and data noise. The shortcomings and deficiencies of existing technologies have seriously restricted the further improvement of teaching efficiency and experimental demonstration effects. Therefore, how to achieve fast, robust and automated sensor type identification and structural model parameter matching based on the actual situation of student operations and real-time teaching needs in a teaching laboratory environment with relatively limited funds has become a key technical problem that needs to be solved urgently to improve the quality of undergraduate civil engineering experimental teaching.

[0005] In view of the above problems, the existing technology urgently needs to be improved. Summary of the Invention

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

[0007] In a first aspect, this application provides a method for self-adapting structural dynamic load test parameters, and the method includes: S1. Obtain a list of sensor type information, where the list contains the sensor parameters of commonly used sensors in teaching experiments; S2. Select the sensor type used in the experiment from the list of sensor type information, and load the sensor parameters; S3. For the structural model in use, select the simplified model parameters from the set of simplified model parameter information; S4. Obtain the vibration data collected by the data collector; S5. Combine the loaded sensor parameters, the selected simplified model parameters, and the vibration data, and use the peak method of frequency response function or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model; the dynamic parameters include natural frequency and damping ratio; S6. Match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural modal vibration mode and frequency response curve, and perform graphical real-time display.

[0008] Preferably, step S2 includes: S201. Present a list of sensor type information including sensor model, sensitivity, and range on the user interface; S202. Respond to the user's selection operation of the sensor model on the user interface to determine the sensor model used in the experiment; S203. Retrieve the corresponding sensitivity and range parameters from the pre-stored sensor parameter database according to the determined sensor model; S204. Load the retrieved sensitivity and range parameters, and display the loading result on the user interface in real time for the user to confirm.

[0009] Preferably, step S3 includes: S301. Present a structural model selection interface, where the interface contains multiple simplified model options corresponding to commonly used structural models in teaching experiments, and each simplified model option corresponds to a different degree of simplification; S302. Respond to the user's selection operation on the structural model selection interface to determine the selected simplified model option; S303. Retrieve the corresponding simplified model parameters from the pre-stored set of simplified model parameter information according to the determined simplified model option; S304. Evaluate the students' cognitive level of structural dynamics according to the preset cognitive level evaluation rules; the cognitive level evaluation rules involve the students' past theoretical course grades, experimental operation records, and the degree of understanding of the basic concepts of structural dynamics; S305. According to the evaluation result of the students' cognitive level and the degree of simplification corresponding to the simplified model option, determine whether the simplified model option matches the students' cognitive level. If not, give a prompt message on the structural model selection interface to guide the students to select a simplified model option that matches their cognitive level. If it matches, load the retrieved simplified model parameters.

[0010] Preferably, step S304 includes: Collect the students' previous exam scores in the courses related to structural dynamics, assign weights according to the exam types, and calculate the weighted average score as the theoretical knowledge evaluation score; Obtain the students' historical operations in the structural dynamic load test, and calculate the experimental operation evaluation score according to the normativity, efficiency, and accuracy of the historical operations; Obtain the correct rate of the test involving the basic concepts of structural dynamics completed by the students in advance, and calculate the concept understanding evaluation score; Integrate the theoretical knowledge evaluation score, experimental operation evaluation score, and concept understanding evaluation score, and calculate the total score of the students' cognitive level of structural dynamics according to the preset weight ratio; Map the total score of the students' cognitive level of structural dynamics to the preset cognitive level grade.

[0011] Preferably, after step S4 and before step S5, there is also a step: S7. Evaluate the noise level of the vibration data. If the noise level exceeds the preset level threshold, filter the vibration data.

[0012] Preferably, step S7 includes: S701. Calculate the power spectral density of the vibration data to determine the average power value within the preset frequency band range as the noise level evaluation index; S702. Judge whether the noise level evaluation index exceeds the preset level threshold. If it exceeds, filter the vibration data.

[0013] Preferably, step S5 includes: S501. Calculate the frequency domain data according to the vibration data using the fast Fourier transform algorithm, and combine the loaded sensor parameters to construct the frequency response function of the structural model; 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 peak method of the frequency response function; otherwise, select the characteristic frequency identification method. S503. If the peak method of frequency response function is selected, search for the maximum amplitude point in the amplitude spectrum of the constructed frequency response function, identify the frequency corresponding to the maximum amplitude point as the natural frequency of the structural model, and calculate the damping ratio of the structural model according to the amplitudes on both sides of the maximum amplitude 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 structural model according to the energy concentration region in the time-frequency distribution diagram, and calculate the damping ratio of the structural model according to the energy attenuation rate near the natural frequency.

[0014] Preferably, step S502 includes: 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; Calculate 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 as the matching degree between the simplified model and the constructed frequency response function; 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 peak method of frequency response function; otherwise, select the characteristic frequency identification method.

[0015] Preferably, step S6 includes: S601. Construct a comprehensive parameter set, and the comprehensive parameter set matches the natural frequency and damping ratio of the final structural model with the sensitivity and range parameters of the loaded sensors; 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 structural model; S603. Run the configured modal analysis algorithm to calculate the modal vibration mode and frequency response curve of the structural model; S604. Graphically process the calculated structural modal vibration mode and frequency response curve and display them in real time on the user interface.

[0016] In a second aspect, the present application provides a structural dynamic load test parameter adaptive system, which includes a shaking table, a data collector, and a computer. The shaking table and the data collector are both electrically connected to the computer; The shaking table is used to drive the structural model to vibrate; The data collector is used to collect vibration data through sensors arranged on the structural model and upload it to the computer; The computer is installed with modal analysis software, and the modal analysis software is configured with: A sensor list acquisition module, which is used to acquire a list of sensor type information, and the list contains the sensor parameters of commonly used sensors in teaching experiments; A sensor type selection module, which is used to select the sensor type used in the experiment from the sensor type information list and load the sensor parameters; A structural model parameter configuration module, which is used to select the simplified model parameters from the simplified model parameter information set for the used structural model; A receiving module, which is used to receive the vibration data collected by the data collector; A parameter preliminary evaluation module, which is used to combine the loaded sensor parameters, the selected simplified model parameters, and the vibration data, and adopt the peak method of frequency response function or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model; the dynamic parameters include natural frequency and damping ratio; A modal analysis module, which 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 modal vibration mode and frequency response curve, and perform graphical real-time display.

[0017] Beneficial effects: A structural dynamic load test parameter adaptive method and system provided by the present application can effectively reduce the experimental preparation time and improve the experimental efficiency by automatically selecting sensor parameters and simplified model parameters, combining vibration data for dynamic parameter evaluation, and applying the parameters after matching to modal analysis. Description of the Drawings

[0018] Figure 1 It is a flowchart of the structural dynamic load test parameter adaptive method provided by the embodiment of the present application.

[0019] Figure 2 It is a structural schematic diagram of the structural dynamic load test parameter adaptive system provided by the embodiment of the present application.

[0020] Figure 3 It is a connection block diagram of the structural dynamic load test parameter adaptive system provided by the embodiment of the present application.

[0021] Figure 4 It is a structural schematic diagram of a simple excitation table.

[0022] Figure 5 It is Figure 4 an enlarged view of part S in

[0023] Label description: 1. Vibration table; 101. Base; 102. Guide rail; 103. Slide table; 104. Driving mechanism; 105. Bracket; 106. Guide cylinder; 107. Slide bar; 108. Connecting rod; 109. Swing bar; 110. Servo motor; 111. Kidney-shaped hole; 112. Slide seat; 113. Driving device; 2. Data collector; 3. Computer; 301. Sensor list acquisition module; 302. Sensor type selection module; 303. Structural model parameter configuration module; 304. Receiving module; 305. Parameter preliminary evaluation module; 306. Modal analysis module; 90. Structural model. Detailed implementation

[0024] The technical solutions in this application will be clearly and completely described below with reference to the accompanying drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. The components of this application described and illustrated in the accompanying drawings here can 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 this application that is required to be protected, but only represents the selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative efforts fall within the scope of protection of this application.

[0025] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, terms such as "first" and "second" are only used for differential description and cannot be understood as indicating or implying relative importance.

[0026] Referring to Figure 1 , this application proposes a method for self-adapting structural dynamic load test parameters, and the method includes: S1. Obtain a list of sensor type information, and the list includes the sensor parameters of commonly used sensors in teaching experiments; S2. Select the sensor type used in the experiment from the list of sensor type information and load the sensor parameters; S3. For the used structural model, select the simplified model parameters from the simplified model parameter information set; S4. Obtain the vibration data collected by the data collector; S5. Combine the loaded sensor parameters, the selected simplified model parameters, and the vibration data, and use the peak method of frequency response function or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model; the dynamic parameters include natural frequency and damping ratio; S6. Match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural modal vibration modes and frequency response curves, and display them graphically in real time.

[0027] Among them, in step S1, the sensor type information list is pre-stored in the system, and the list contains parameters such as the models, sensitivities, and measurement ranges of various sensors commonly used in teaching experiments.

[0028] Among them, in step S2, the user can view the sensor type information list through the user interface and select the sensor model used in this experiment. The system responds to the user's selection, retrieves and loads the sensitivity and measurement range parameters of the corresponding sensor from the sensor parameter database.

[0029] Among them, 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.). The user can select the corresponding simplified model option on the structural model selection interface according to the actual structural model used. The system loads the corresponding simplified model parameters according to the user's selection. The simplified model parameters can include the geometric dimensions, material properties, etc. of the model. Among them, the simplified model refers to a model that simplifies the actual structure to a certain extent for theoretical analysis and calculation; the simplified model usually ignores some secondary factors, such as the complex geometric shape of the structure, non-linear material properties, or complex boundary conditions, so as to simplify the actual structure into an idealized model with fewer degrees of freedom and simple parameters.

[0030] Among them, in step S4, the data collector collects the vibration data of the structural model under the excitation in real time, and the vibration data is transmitted to the computer for subsequent processing.

[0031] Among them, in step S5, the peak method of frequency response function 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 the vibration data and identifying the frequency domain where the energy is concentrated. The evaluated dynamic parameters include the natural frequency and damping ratio of the structural model.

[0032] Among them, in step S6, the final dynamic parameters can be matched and integrated with the sensor parameters loaded in step S2 to form a comprehensive parameter set. The comprehensive parameter set is then applied to the modal analysis algorithm. The modal analysis algorithm calculates the structural modal vibration modes and frequency response curves based on input parameters such as the geometric parameters, material properties, and boundary conditions of the structural model. The calculation results are displayed to the user in real time through the graphical interface.

[0033] Specifically, the working principle of the adaptive method for structural dynamic load test parameters is as follows: First, a list of information containing sensors and model parameters is preset to achieve rapid configuration of experimental parameters. The user only needs to perform a selection operation to complete the loading of sensor parameters and model parameters, thereby shortening the experimental preparation time. Second, after the vibration data is collected, the system automatically uses the peak method of frequency response function or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model. The parameter evaluation process does not require manual intervention, thus reducing human errors. Third, the evaluated dynamic parameters are matched with the sensor parameters, and the matched parameters are used for modal analysis calculations. The results of modal analysis can be graphically displayed in real time, thereby improving the presentation efficiency of experimental results. Through the above steps, the adaptive method for structural dynamic load test parameters realizes the automatic determination of structural dynamic load test parameters, improving the efficiency of experimental teaching and demonstration effects.

[0034] In some specific embodiments, for the dynamic load test of a beam structure model, the preset list of sensor type information includes parameters of various types of sensors such as acceleration sensors, velocity sensors, and displacement sensors. After the user selects an acceleration sensor, the system automatically loads the sensitivity and range parameters of the acceleration sensor. The simplified model parameter information set includes beam model parameters with different degrees of simplification, 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 collector collects the acceleration response data of the beam model under excitation. The system combines the loaded sensor parameters and beam model parameters and uses the peak method of frequency response function to evaluate the first natural frequency and damping ratio of the beam model. After the evaluation results are matched with the acceleration sensor parameters, they are applied to the modal analysis algorithm to calculate the first modal vibration mode and frequency response curve of the beam model, and the vibration mode animation and frequency response curve graph of the beam model are displayed in real time on the user interface.

[0035] In some embodiments, step S2 includes: S201. Present a list of sensor type information including sensor model, sensitivity, and range on the user interface; S202. Respond to the user's selection operation of the sensor model on the user interface to determine the sensor model used in the experiment; S203. Retrieve the corresponding sensitivity and range parameters from the pre-stored sensor parameter database according to the determined sensor model; S204. Load the retrieved sensitivity and range parameters and display the loading results in real time on the user interface for the user to confirm.

[0036] Among them, in step S201, the presentation manner of the sensor type information list on the user interface is not limited. For example, the list can be implemented in the form of a drop-down menu, a scrollable list, or a table, etc., so that users can browse and select. The data constituting the list is pre-stored in the configuration file or database of the software.

[0037] Among them, in step S202, the user responds to the selection operation of the sensor model on the user interface to determine the sensor model used in the experiment. This response is implemented through the user interface event listening mechanism, and the selected sensor model is stored as an internal variable of the software for subsequent steps to use.

[0038] Among them, in step S203, the system performs a retrieval operation in the pre-stored sensor parameter database according to the sensor model determined in step S202. The database stores the sensor model and its corresponding sensitivity and range parameters. The retrieval operation aims to obtain the sensitivity and range values associated with the selected sensor model.

[0039] Among them, in step S204, the sensitivity and range parameters retrieved in step S203 are loaded into the software system for subsequent calculation and analysis. At the same time, these parameters are displayed in real time on the user interface, such as in a text box or a label, so that users can check and confirm the correctness of the parameter loading.

[0040] Specifically, the proposed method aims to solve the problem of manually inputting sensor parameters in the structural dynamic load test. In the initial state, the user sees a sensor list through a friendly user interface, and the list presents the models of available sensors and key parameters such as sensitivity and range. Step S201 realizes this visual presentation, facilitating the user to select sensors. After the user selects the sensor model in step S202, the system automatically recognizes the user's selection. Subsequently, in step S203, the system accesses the pre-stored database and automatically retrieves the sensitivity and range parameters related to the selected sensor model. This eliminates the steps of manually searching for and inputting sensor parameters. Finally, step S204 ensures that the retrieved parameters are loaded into the system and displayed on the user interface for the user to confirm. This confirmation step is crucial for preventing errors caused by incorrect parameter loading. By automating the sensor parameter loading and providing a user-friendly interface with a confirmation link, the method significantly shortens the experiment preparation time, reduces user operation errors, and improves the efficiency and accuracy of the structural dynamic load test, especially suitable for the teaching environment. Thus, the problems of excessive time consumption in the experiment preparation stage and easy errors in student operations are effectively solved, and the teaching efficiency of the structural dynamic load test and the reliability of the experimental results are improved.

[0041] In some embodiments, step S3 includes: S301. Present a structural model selection interface, which contains multiple simplified model options corresponding to commonly used structural models in teaching experiments, and each simplified model option corresponds to a different degree of simplification; S302. Respond to the user's selection operation on the structural model selection interface, and determine the simplified model option selected by the user; S303. According to the determined simplified model option, retrieve the corresponding simplified model parameters from the pre-stored set of simplified model parameter information; S304. According to the preset cognitive level evaluation rules, evaluate the students' cognitive level of structural dynamics; the cognitive level evaluation rules involve the students' past theoretical course grades, experimental operation records, and understanding of the basic concepts of structural dynamics; S305. According to the evaluation result of the students' cognitive level and the degree of simplification corresponding to the simplified model option, determine whether the simplified model option matches the students' cognitive level. If not, give a prompt message on the structural model selection interface to guide the students to select a simplified model option that matches their cognitive level. If it matches, load the retrieved simplified model parameters.

[0042] Among them, in step S301, the structural model selection interface is configured as a graphical user interface, on which multiple selectable simplified model options are presented. These simplified model options correspond to commonly used structural models 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, and the distinction of the degree of simplification can be based on the number of degrees of freedom of the model or the complexity of the model parameters.

[0043] Among them, in step S302, the user performs a selection operation on the structural model selection interface by clicking, touching, etc., and the system responds to the user's operation to determine the selected simplified model option.

[0044] Among them, in step S303, the pre-stored set of simplified model parameter information is stored in a database. The information set contains the simplified model parameters corresponding to each simplified model option, and the parameters can include the geometric dimensions, material properties, or boundary conditions of the model, etc. When the simplified model option is determined, the system retrieves and loads the corresponding simplified model parameters from the database.

[0045] Among them, in step S304, the cognitive level evaluation rules are preset as a method for quantitatively evaluating the students' cognitive level of structural dynamics. The evaluation rules can include multiple evaluation dimensions, such as theoretical course grades, experimental operation records, and understanding of concepts. The theoretical course grades can be the exam scores of the students in the courses related to structural dynamics, the experimental operation records can be the normative records of the students in historical experimental operations, and the understanding of concepts can be the scores of the students in the tests of the basic concepts of structural dynamics.

[0046] Among them, in step S305, the evaluation result of the student's cognitive level and the degree of simplification corresponding to the simplified model option are used for matching judgment. The matching judgment can adopt a preset matching degree threshold. When the difference between the degree of simplification of the simplified model option and the evaluation result of the student's cognitive level exceeds the threshold, it is determined as a mismatch. If there is a mismatch, the system gives a prompt message on the structural model selection interface. The prompt message can be in the form of text or graphics to guide the student to select a simplified model option that matches their own cognitive level. If it matches, the system loads the retrieved simplified model parameters to prepare for the subsequent experimental parameter adaptation process.

[0047] Specifically, in the teaching of structural dynamic load tests, to solve the problem that students may choose a simplified model that does not match their own abilities and affect the teaching effect, the technical solution of step S3 is adopted. First, through steps S301 and S302, the system provides a structural model selection interface, and students can select a suitable simplified model according to the experimental requirements and their own judgment. On the interface, model options with different degrees of simplification are clearly presented. For example, for a simply supported beam model, options with different degrees of simplification such as "first-order beam model" and "third-order beam model" can be provided. Then, step S303 automatically loads the preset simplified model parameters according to the student's model selection, reducing the cumbersome operation of manually configuring parameters. More importantly, step S304 introduces a cognitive level evaluation mechanism. The system comprehensively considers the student's theoretical basis, experimental experience, and concept mastery degree to quantitatively evaluate the student's cognitive level. The evaluation result is used for model matching judgment in step S305, and the system judges whether the model selected by the student is suitable for their cognitive level. If a student selects a model that is too complex, the system will give a prompt, such as "The model complexity is relatively high. It is recommended to select a lower-order model", to guide the student to select a more suitable model. On the contrary, if a student selects a model that is too simple, the system may also give a prompt to encourage the student to challenge a more complex model. In this way, the configuration of the simplified model parameters is no longer fixed, but can be adaptively adjusted according to the student's cognitive level, ensuring the personalization and effectiveness of the teaching content. Thus, students can conduct experiments on a model that matches their own abilities, improving the experimental teaching effect and solving the problem of the mismatch between model selection and student abilities.

[0048] Preferably, step S304 may include: Collect the previous exam scores of the student in the courses related to structural dynamics, assign weights according to the exam types, and calculate the weighted average score as the theoretical knowledge evaluation score; Obtain the historical operations of the student in the structural dynamic load test, and calculate the experimental operation evaluation score according to the standardization, efficiency, and accuracy of the historical operations; Obtain the correct rate of the test on the basic concepts of structural dynamics pre-completed by students, and calculate the concept understanding evaluation score; Integrate the theoretical knowledge evaluation score, experimental operation evaluation score, and concept understanding evaluation score, and calculate the total score of the students' cognitive level of structural dynamics according to the preset weight ratio; Map the total score of the cognitive level of structural dynamics to the preset cognitive level grades.

[0049] Among them, the way to obtain the theoretical knowledge evaluation score is that the system is configured to be able to access the students' learning management system, and the examination results of previous courses related to structural dynamics are collected from the learning management system. The examination types are divided into mid-term examinations and final examinations, and the weight of the final examination is higher than that of the mid-term examination. Thus, the weighted average score is calculated as the theoretical knowledge evaluation score.

[0050] Among them, the way to obtain the experimental operation evaluation score is that the system obtains the operation records of the students' previous structural dynamic load test operations. The operation records include sensor installation steps, data acquisition parameter settings, and model adjustment processes. The evaluation algorithm of the system analyzes these operation records. The standardization is measured by whether the operation steps conform to the standard process, the efficiency is evaluated by the time used to complete the experimental operation, and the accuracy examines the data acquisition quality and the rationality of parameter settings. Combining these three aspects, the experimental operation evaluation score is calculated.

[0051] Among them, the way to obtain the concept understanding evaluation score is that students complete a pre-set test on the basic concepts of structural dynamics. The test question types include multiple-choice questions, fill-in-the-blank questions, and short-answer questions. The system automatically corrects or corrects with manual assistance, and the correct rate is finally calculated as the concept understanding evaluation score.

[0052] Among them, in the calculation link of the total score of the cognitive level, the theoretical knowledge evaluation score, the experimental operation evaluation score, and the concept understanding evaluation score are integrated according to the preset weight ratio. For example, the theoretical knowledge weight is 40%, the experimental operation weight is 30%, and the concept understanding weight is 30%. Through weighted summation, the total score of the students' cognitive level of structural dynamics is calculated.

[0053] Among them, the mapping process of the cognitive level grades is to preset the mapping relationship between the total score of the cognitive level and the cognitive level grades. For example, above 85 points is advanced, 70 - 84 points is intermediate, 60 - 69 points is primary, and below 60 points is entry-level. The total score of the students' cognitive level is mapped to the corresponding cognitive level grades. Thus, the students' cognitive level of structural dynamics is quantitatively evaluated, and the evaluation results provide a basis for the subsequent adaptive selection of simplified model parameters, ensuring the pertinence and effectiveness of teaching content.

[0054] Specifically, the cognitive level assessment method is specified in detail, enabling the accurate and consistent assessment of students' cognitive levels. By collecting students' exam scores in the Structural Dynamics course, the degree of their mastery of theoretical knowledge can be reflected; by obtaining students' experimental operation records, their practical operation skills can be evaluated; through basic concept tests, students' understanding of the basic knowledge of structural dynamics can be directly quantified; by comprehensively weighting the scores of these three aspects, students' cognitive levels of structural dynamics can be comprehensively evaluated. The total cognitive level score is further mapped to cognitive level grades, making the evaluation results easier to understand and apply, and facilitating the system to match students with appropriate simplified models according to their cognitive level grades. The cognitive level assessment method enables the system to be adjusted according to students' individual needs, improving the effect of experimental teaching. The effectiveness of the adaptive parameter selection process is ensured, thereby enhancing the teaching value of the experiment.

[0055] In some specific embodiments, for the dynamic load test of bridge structures, the student cognitive level assessment process is executed. First, the learning management system data interface is called, and the mid-term and final exam scores of students in the Structural Mechanics and Bridge Dynamics courses are collected. The weight of the final exam score is set to 0.6, and the weight of the mid-term exam score is set to 0.4. The weighted average score is calculated as the theoretical knowledge assessment score. Second, the historical experimental operation records of students' bridge models are retrieved, and the operation standardization, efficiency, and accuracy are scored by the experimental instructor based on the records and experimental videos, with a full score of 100 points, which is converted into the experimental operation assessment score. Then, a basic concept test of bridge dynamics consisting of 20 multiple-choice questions and 10 fill-in-the-blank questions is completed online by students, and the system automatically grades the papers, and the correct rate is calculated as the concept understanding assessment score. When calculating the total cognitive level score, the weights of the scores of theoretical knowledge, experimental operation, and concept understanding 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 is rated as a relatively high level, 70 - 79 is rated as a medium level, 60 - 69 is rated as a primary level, and below 60 is rated as an entry level. The cognitive level grades are displayed on the model selection interface to guide students to select bridge simplified models with appropriate difficulty. For example, advanced students are recommended to select complex models considering the elasticity of stay cables and bridge piers, while entry-level students are recommended to select simply supported beam models. Thus, the accuracy and operability of the cognitive level assessment are verified in the teaching of the dynamic load test of bridge structures.

[0056] In some embodiments, after step S4 and before step S5, the following step is further included: S7. Evaluate the noise level of the vibration data. If the noise level exceeds the preset level threshold, filter the vibration data.

[0057] Among them, for the evaluation of the noise level of vibration data, it 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 range, and use this average power value as the noise level evaluation index. Then, compare the noise level evaluation index with a preset level threshold. The preset level threshold is a standard value set in advance, representing the maximum acceptable noise level. If the noise level evaluation index exceeds the preset level threshold, it indicates that the vibration data is significantly interfered by noise. In this case, in order to reduce the impact of noise on data quality and improve the accuracy of subsequent dynamic parameter evaluation, filtering processing is performed on the vibration data. The filtering processing aims to weaken or eliminate the noise components in the vibration data and retain the real and effective structural vibration signals. The filtering method can choose a digital filter, such as a finite impulse response filter or an infinite impulse response filter. The type and parameters of the filter can be selected and adjusted according to the noise characteristics and signal characteristics. Thus, by adding a noise evaluation and filtering step before the dynamic parameter evaluation step, it is ensured that the vibration data that may be contaminated by noise is preprocessed before parameter evaluation, improving the robustness and reliability of the entire structural dynamic load test parameter adaptive method.

[0058] Specifically, in the structural dynamic load test, after the data collector collects the vibration data of the structural model but before using the peak method of frequency response function or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model, a data preprocessing step is added. This step first evaluates the quality of the collected vibration data, specifically by calculating the average power spectral density of the vibration data within a specific frequency band to quantify the noise level. The preset level threshold is set as the standard for judging whether the data needs to be filtered. The filtering processing specifically uses a low-pass filter, such as a Butterworth low-pass filter, and the cut-off frequency is designed to be slightly higher than the expected highest natural frequency of the structural model. For example, if the first-order natural frequency of the structural model is expected to be 10 Hz, the cut-off frequency of the low-pass filter can be set to 20 Hz. Through the application of the low-pass filter, the high-frequency noise components are effectively filtered out, and the low-frequency structural vibration signals are retained, improving the signal-to-noise ratio. The vibration data after filtering processing is then used for subsequent dynamic parameter evaluation, and more accurate natural frequency and damping ratio of the structural model can be obtained.

[0059] Preferably, step S7 may include: S701. Calculate the power spectral density of the vibration data to determine the average power value within a preset frequency band range as the noise level evaluation index; S702. Judge whether the noise level evaluation index exceeds the preset level threshold. If it exceeds, filter the vibration data.

[0060] Among them, calculating the power spectral density aims to transform the time-domain vibration data into the frequency domain for analysis. The calculation method of the power spectral density is a prior art and will not be elaborated here. Thus, the frequency components and energy distribution of the signal can be comprehensively reflected. Noise usually has 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 range is determined, and within this frequency band range, the average power value is calculated and used as an evaluation index for the noise level. For example, the preset frequency band range can be set according to the specific circumstances of the experiment and the type of sensor. For example, avoiding the main frequency range of structural vibration and selecting the high-frequency part as the noise evaluation frequency band.

[0061] The noise level evaluation index is then compared with a preset level threshold. The preset level threshold is determined based on experience or experimental calibration and represents the acceptable upper limit of noise. If the noise level evaluation index exceeds the preset level threshold, it indicates that the vibration data is significantly affected by noise, and a filtering operation is performed. The filtering process can adopt various digital signal processing methods, such as finite impulse response filters or infinite impulse response filters, to attenuate or eliminate the noise components and improve the signal-to-noise ratio.

[0062] Specifically, regarding the problem of how to accurately evaluate the noise level of vibration data to more effectively decide whether to perform filtering processing, the above technical means provide the working principle. First, the power spectral density of the vibration data is calculated, which enables the noise evaluation to be transformed from the time domain to the frequency domain, making full use of the characteristics of noise in the frequency domain. By calculating the average power value within the preset frequency band range, the noise level is quantified into a specific evaluation index. This index can more accurately reflect the actual level of noise compared to simple time-domain threshold judgment. Subsequently, the noise level evaluation index is compared with the preset level threshold, and this step determines whether filtering processing is required. Only when the noise level exceeds the acceptable range will the filtering operation be triggered, avoiding unnecessary filtering and ensuring timely filtering when the noise is high. The purpose of the filtering process is to reduce the impact of noise on the vibration data, thereby improving the data quality and laying a foundation for the accurate evaluation of subsequent dynamic parameters. Thus, through the above steps, a more refined and accurate evaluation of the noise level of vibration data is achieved, making the filtering process more targeted and effective, and enhancing the robustness and reliability of the entire structural dynamic load test parameter adaptive method.

[0063] In some specific embodiments, a structural dynamic load test is being carried out, and a data collector has collected a segment of vibration data. To evaluate the noise level of this data, first, this vibration data is input into the modal analysis software of a computer. In the software, the fast Fourier transform algorithm is called to calculate the power spectral density of the vibration data. A preset frequency band range is set from 200 Hz to 500 Hz. This frequency band range is selected because it is higher than the first natural frequency of the structural model and is more likely to contain sensor and environmental noise. Within the preset frequency band range, the average value of the power spectral density is calculated as the noise level evaluation index. A preset level threshold is set at -60 dB / Hz, and this threshold is determined based on multiple experimental experiences. The noise level evaluation index is compared with the preset level threshold. If the calculated average power value is higher than -60 dB / Hz, it is determined that the noise level exceeds the threshold, and the software automatically activates a Butterworth low-pass filter to filter the vibration data and remove high-frequency noise. The filtered data is used for subsequent dynamic parameter evaluation, ensuring the accuracy of the evaluation results. Thus, through the above embodiments, the noise level in the vibration data can be effectively evaluated and processed, ensuring the reliability of the structural dynamic load test.

[0064] In some embodiments, step S5 includes: S501. According to the vibration data, use the fast Fourier transform algorithm to calculate the frequency-domain data, and combine the loaded sensor parameters to construct the frequency response function of the structural model; 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, then select the frequency response function peak method; otherwise, select the characteristic frequency identification method; S503. If the frequency response function peak method is selected, then search for the amplitude maximum point according to the amplitude spectrum of the constructed frequency response function, identify the frequency corresponding to the amplitude maximum point as the natural frequency of the structural model, and calculate the damping ratio of the structural model according to the amplitudes on both sides of the amplitude maximum point; S504. If the characteristic frequency identification method is selected, then perform time-frequency analysis on the vibration data to obtain a time-frequency distribution diagram, identify the natural frequency of the structural model according to the energy concentration region in the time-frequency distribution diagram, and calculate the damping ratio of the structural model according to the energy attenuation rate near the natural frequency.

[0065] Among them, for step S501, the following method can be specifically used to implement it: First, collect the structural vibration data (time-domain data) obtained by the sensor; then, use the fast Fourier transform algorithm to convert the time-domain data to the frequency domain to obtain the frequency-domain data; finally, calibrate the frequency-domain data in combination with the pre-loaded sensor sensitivity parameters to construct a frequency response function that can reflect the dynamic characteristics of the structure. The construction of the frequency response function provides a data basis for subsequent dynamic parameter evaluation.

[0066] Among them, in step S502, specifically, multiple methods can be used for the matching degree evaluation. For example, the cross-correlation coefficient between the theoretical frequency response function of the simplified model and the constructed frequency response function is calculated. The higher the cross-correlation coefficient, the higher the matching degree. The preset matching degree threshold can be set according to actual requirements. For example, it can be set to 0.8. The introduction of the method selection mechanism enables the parameter evaluation method to adaptively select according to the model matching degree. When the matching degree is high, the peak value method of the frequency response function is selected; when the matching degree is low, the characteristic frequency identification method is selected, thereby ensuring the accuracy and reliability of the parameter evaluation.

[0067] Among them, in step S503, specifically, the peak value method of the frequency response function is a parameter identification method based on the frequency domain. Its principle lies in that the natural frequency of the 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 and identifying the frequency corresponding to this point 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 is used to calculate the damping ratio according to the frequency width at which the amplitudes on both sides of the peak point drop to half of the peak amplitude. The peak value method of the frequency response function has high calculation efficiency and is applicable to scenarios with a relatively high model matching degree.

[0068] Among them, in step S504, specifically, the characteristic frequency identification method is a parameter identification method based on time-frequency analysis. Time-frequency analysis methods such as short-time Fourier transform, wavelet transform, etc. Time-frequency analysis can decompose the vibration signal into the time and frequency joint domain to obtain a time-frequency distribution diagram. The time-frequency distribution diagram can clearly show the change of the frequency components of the signal over time. The natural frequency of the structural model appears as an energy concentration area on the time-frequency distribution diagram, and the natural frequency can be determined by identifying the energy concentration area. The damping ratio is related to the energy decay rate, and the damping ratio can be calculated by analyzing the energy decay rate near the natural frequency. The characteristic frequency identification method is less sensitive to model errors and is applicable to scenarios with a relatively low model matching degree.

[0069] Specifically, in the adaptive method for structural dynamic load test parameters, step S5 is a key step for evaluating the dynamic parameters of the structural model. First, in step S501, the frequency response function is constructed by fast Fourier transform and combining sensor parameters, providing accurate frequency-domain data support for subsequent parameter identification. Then, step S502 proposes an adaptive method selection mechanism. By evaluating the model matching degree, it intelligently selects the peak method of the frequency response function or the characteristic frequency identification method. When the model matching degree is high, the peak method of the frequency response function can effectively identify parameters; when the model matching degree is low, the characteristic frequency identification method can ensure the robustness of the identification. Thus, step S502 realizes the optimized selection of the evaluation method. Next, steps S503 and S504 respectively describe in detail the implementation processes of the two evaluation methods. The peak method of the frequency response function identifies parameters using the peak characteristics of the frequency response function, and the characteristic frequency identification method identifies parameters using time-frequency analysis technology. 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 appropriate algorithms to identify the natural frequency and damping ratio, thus solving the problem of blindness in method selection in the prior art and improving the accuracy of dynamic parameter evaluation.

[0070] As a preferred embodiment, the solution of the present application is specifically implemented as follows: Suppose a dynamic load test is performed on a simply supported beam structure model, and an acceleration sensor is used to collect vibration data.

[0071] First, step S501 is executed. The vibration data collected is processed using the fast Fourier transform algorithm to obtain frequency-domain data, and combined with the sensitivity parameter of the acceleration sensor, the frequency response function of the simply supported beam structure model is constructed.

[0072] Then, step S502 is executed to evaluate the matching degree between the selected simplified model of the simply supported beam and the constructed frequency response function. The evaluation method is: 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. The preset matching degree threshold is set to 0.8. If the cross-correlation coefficient is higher than 0.8, the peak method of the frequency response function is selected; otherwise, the characteristic frequency identification method is selected.

[0073] If the peak method of the frequency response function is selected, then step S503 is executed. Search for the maximum amplitude point in the amplitude spectrum of the frequency response function, and identify the frequency corresponding to this point as the first natural frequency of the simply supported beam structure model. Using the half-power bandwidth method, calculate the damping ratio according to the frequency width at which the amplitude on both sides of the peak point drops to half of the peak amplitude (the specific calculation method is the prior art and will not be elaborated here).

[0074] If the characteristic frequency identification method is selected, step S504 is executed to perform short-time Fourier transform on the vibration data to obtain a time-frequency distribution diagram. In the time-frequency distribution diagram, the region where the energy is concentrated corresponds to the first natural frequency of the simply supported beam structure model. Analyze the attenuation rate of the energy near the natural frequency and calculate the damping ratio (the specific calculation method is a prior art and will not be elaborated here).

[0075] Through the above technical solution, the present application can more preferably select the dynamic parameter evaluation method and improve the accuracy of the evaluation. Through the model matching degree evaluation, the peak method of the frequency response function or the characteristic frequency identification method is adaptively selected, so that the parameter evaluation method is adapted to the model matching degree, thereby improving the accuracy and reliability of the parameter evaluation.

[0076] Preferably, step S502 may include: 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; Calculate 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 as the matching degree between the simplified model and the constructed frequency response function; Judge whether the matching degree is higher than a preset matching degree threshold. If it is higher than the preset matching degree threshold, select the peak method of the frequency response function, otherwise select the characteristic frequency identification method.

[0077] Among them, the theoretical frequency response function can be calculated by various methods. For example, for a single-degree-of-freedom or multi-degree-of-freedom system, the theoretical frequency response function can be derived by an analytical method using the dynamic equation of the system and combining the model parameters; or, for a more complex system, numerical methods such as finite element analysis can be used to simulate the response of the system 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 the experimental requirements. For example, it can be set as a frequency band range including the expected natural frequency of the structural model. The extraction of the amplitude spectrum can be realized 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.

[0078] Among them, the cross-correlation coefficient is an index to measure the similarity degree of two signals, and its value range is from -1 to 1. The closer the cross-correlation coefficient is to 1, the higher the positive correlation and the higher the similarity degree of the two signals; the closer the cross-correlation coefficient is to -1, the higher the negative correlation of the two signals; the cross-correlation coefficient close to 0 indicates that the correlation between the two signals is very low. In this application, the cross-correlation coefficient is used as an index to evaluate the similarity between the amplitude spectrum of the theoretical frequency response function and the amplitude spectrum of the frequency response function constructed experimentally. The higher the cross-correlation coefficient, the better the simplified model fits the actual structural behavior and the higher the matching degree. The calculation of the cross-correlation coefficient can adopt the standard cross-correlation formula and be efficiently calculated in the frequency domain by using the fast Fourier transform algorithm.

[0079] Among them, the preset matching degree threshold is a critical value preset for judging the high or low matching degree, which can be set according to actual application requirements 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 matching degree between the simplified model and the constructed frequency response function is relatively high. At this time, it is reasonable to select the frequency response function peak method for parameter identification. The frequency response function peak method is a parameter identification method with high calculation efficiency and simple implementation, which is suitable for the case of high model matching degree. When the matching degree is lower than the preset matching degree threshold, it indicates that the matching degree between the simplified model and the constructed frequency response function is relatively low. At this time, it is more robust to select the characteristic frequency identification method. The characteristic frequency identification method is a parameter identification method with certain robustness to model errors and noises, which is suitable for the case of low model matching degree or low quality of experimental data. By introducing the matching degree threshold judgment mechanism, the adaptive selection of the parameter identification method can be realized, taking into account both the calculation efficiency and the accuracy of the identification result.

[0080] Specifically, in the adaptive method for structural dynamic load test parameters, step S502 is introduced as a key link for evaluating the matching degree between the simplified model and the constructed frequency response function. First, according to the selected simplified model parameters, the theoretical frequency response function is calculated, and its amplitude spectrum is extracted to provide a benchmark for the matching degree evaluation. Then, the cross-correlation analysis is performed on the amplitude spectrum of the experimentally constructed frequency response function and the theoretical amplitude spectrum, the cross-correlation coefficient is calculated, and this coefficient is defined as the matching degree. The cross-correlation coefficient can effectively quantify 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 a comparison judgment, the adaptive selection of the parameter identification method is realized. When the matching degree is higher than the threshold, the peak method of the frequency response function with high calculation efficiency is selected to quickly identify the dynamic parameters of the structural model; when the matching degree is lower than the threshold, the more robust characteristic frequency identification method is switched to ensure that relatively accurate parameter identification results can still be obtained when the model matching degree is not high or the data quality is poor. Thus, step S502 realizes the adaptive selection of the parameter identification method based on the matching degree evaluation, making the entire parameter identification process more intelligent and efficient, and taking into account the accuracy of the identification results while ensuring the calculation efficiency.

[0081] Through the above technical solution, the present application can effectively evaluate the matching degree between the simplified model and the constructed frequency response function, and adaptively select a suitable parameter identification method according to the high or low matching degree, so that when the simplified model can better represent the actual structure, the peak method of the frequency response function with high calculation efficiency is selected to quickly complete the parameter identification; 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 the parameter identification, thereby improving the intelligent and adaptive level of the structural dynamic load test parameter identification.

[0082] In some embodiments, step S6 includes: S601. Construct a comprehensive parameter set, and the comprehensive parameter set matches the natural frequency, damping ratio of the final structural model and the sensitivity and range parameters of the loaded sensors; 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 structural model; S603. Run the configured modal analysis algorithm to calculate the modal vibration mode and frequency response curve of the structural model; S604. Graphically process the calculated structural modal vibration mode and frequency response curve and display them on the user interface in real time.

[0083] Among them, constructing the comprehensive parameter set in step S601 means constructing a data set containing parameters in multiple aspects. The core of this comprehensive parameter set lies in achieving an exact match between the dynamic parameters for the final evaluation of the structural model, namely the natural frequency and damping ratio, and the pre-loaded sensor parameters, namely the sensitivity and range parameters. This match is not simply a list of data, but emphasizes the internal correlation and mutual influence between the parameters. When specifically implemented, data structures such as hash tables or associative arrays can be used to store and index these parameters to ensure that they can be quickly and accurately called and used in subsequent steps.

[0084] Among them, 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 library, which stores the applicable conditions and performance characteristics of different algorithms. In the input parameter configuration link, it is necessary to convert the 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, into a data format that the selected modal analysis algorithm can recognize and process. The input parameter configuration process can adopt parameter mapping and conversion technologies to achieve compatibility between different data types and formats. It is worth noting that the input parameters particularly emphasize the sensor position (which can be obtained through manual input) and the excitation force information (during operation, the computer controls the shaker based on the target excitation force information, so the excitation force information can be directly obtained from the computer), aiming to make the modal analysis model more realistically reflect the experimental state.

[0085] Among them, 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 modal vibration modes and frequency response curves of the structural model. The modal vibration modes reflect the vibration forms of the structure in different modes, and the frequency response curves describe the response characteristics of the structure under different frequency excitations. The calculation process can adopt parallel computing or distributed computing technologies to improve the calculation efficiency and shorten the calculation time.

[0086] Among them, in step S604, the modal analysis results calculated in step S603 are visually presented. Graphic processing can adopt computer graphics algorithms, such as 3D rendering, color mapping, and animation techniques, to transform the abstract modal vibration modes and frequency response curves into intuitive graphic images. The real-time display function ensures that users can synchronously observe the calculation process and result changes of the modal analysis. The user interface can adopt graphical user interface (GUI) technology to provide a friendly interaction operation method, facilitating users to adjust parameters and view results. The comprehensive parameter set constructed in step S601 provides a data basis for the selection and parameter configuration of subsequent modal analysis algorithms. In step S602, a modal analysis algorithm is selected and the input parameters of the modal analysis algorithm are configured to ensure the accuracy and reliability of the modal analysis. In step S603, the configured modal analysis algorithm is run to calculate the modal vibration modes and frequency response curves of the structural model, obtaining the modal analysis results. In step S604, the calculated structural modal vibration modes and frequency response curves are graphically processed and real-time displayed on the user interface, realizing the visualization and real-time monitoring of the modal analysis results. Through the above steps, the effective combination of test parameters and modal analysis is achieved, and real-time graphical display is carried out.

[0087] Specifically, when the solution of the present application performs modal analysis, first, a comprehensive parameter set is constructed through step S601, integrating the dynamic parameters (natural frequency, damping ratio) of the structural model evaluated in step S5 and the sensor parameters (sensitivity, range) loaded in step S2. This integration ensures that the subsequent modal analysis is not carried out in isolation, but fully considers the characteristics of the sensors used in the experimental process and the dynamic characteristics of the structure itself. Subsequently, in step S602, based on this comprehensive parameter set, a suitable modal analysis algorithm is selected. The basis for algorithm selection can be factors such as the parameter types and data quality in the parameter set. When configuring the input parameters of the algorithm, in addition to the conventional parameters of the structural model (geometric parameters, material properties, boundary conditions), the sensor position and excitation force information are particularly added. The addition of this information makes the modal analysis model closer to the real experimental scenario and improves the confidence level of the analysis results. After completing the parameter configuration, step S603 runs the modal analysis algorithm to calculate the modal vibration modes and frequency response curves of the structural model. These results are key data for evaluating the dynamic characteristics of the structure. Finally, step S604 visually displays these abstract analysis results in real-time on the user interface through graphical means. Graphic processing makes the results more intuitive and easy to understand, and real-time display facilitates users to instantaneously monitor the experimental and analysis processes. Through the collaborative work of steps S601 to S604, the present solution realizes a complete closed-loop from experimental parameters to modal analysis results, solves the problem of insufficient utilization of experimental parameters in the existing technical solutions proposed in the background art, ensures the accuracy and real-time nature of the modal analysis results, and improves the experimental teaching effect.

[0088] Through the above technical solution, the present application realizes the effective integration of structural dynamic load test parameters and modal analysis. By constructing a comprehensive parameter set, it is ensured that the modal analysis algorithm can make full use of the sensor parameters loaded during the experiment and the evaluated structural dynamics parameters, improving the accuracy and reliability of the modal analysis results. Through the graphical real-time display of the modal analysis results, users can intuitively and dynamically master the modal characteristics of the structural model, improving the efficiency and quality of structural dynamic load experiment teaching.

[0089] Reference Figure 2 、 Figure 3 、, the present application provides a structural dynamic load test parameter adaptive system, which includes an excitation table 1, a data collector 2 and a computer 3. The excitation table 1 and the data collector 2 are both electrically connected to the computer 3; The excitation table 1 is used to drive the structural model 90 to vibrate; The data collector 2 is used to collect vibration data through sensors 4 arranged on the structural model 90 and upload it to the computer 3; The computer 3 is installed with modal analysis software, and the modal analysis software is configured with: A sensor list acquisition module 301, which is used to acquire a sensor type information list, and the list contains the sensor parameters of commonly used sensors in teaching experiments (the specific process refers to step S1 in the previous text); A sensor type selection module 302, which is used to select the sensor type used in the experiment from the sensor type information list and load the sensor parameters (the specific process refers to step S2 in the previous text); A structural model parameter configuration module 303, which is used to select simplified model parameters from the simplified model parameter information set for the used structural model 90 (the specific process refers to step S3 in the previous text); A receiving module 304, which is used to receive the vibration data collected by the data collector 2 (the specific process refers to step S4 in the previous text); A parameter preliminary evaluation module 305, which is used to combine the loaded sensor parameters, the selected simplified model parameters, and the vibration data, and adopt the peak method of frequency response function or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model 90; the dynamic parameters include natural frequency and damping ratio (the specific process refers to step S5 in the previous text); A modal analysis module 306, which 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 modal vibration mode and frequency response curve, and perform graphical real-time display (the specific process refers to step S6 in the previous text).

[0090] Among them, the shaking table 1, the data collector 2, and the computer 3 are electrically connected through wires to achieve the transmission of data and control signals. The shaking 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 collector 2 contains multiple channels, and each channel is connected to a sensor 4 for synchronously collecting the vibration signals of multiple sensors 4. The computer 3 serves as the control and calculation center of the system, and the installed modal analysis software integrates various functional modules. The sensor list acquisition module 301 reads the sensor type information list from a preset database or a configuration file, and each item in the list contains parameters such as the sensor model, sensitivity, and range. The sensor type selection module 302 provides a user interface, and the user can select the model of the sensor used in the experiment on the interface. The structural model parameter configuration module 303 provides a structural model selection interface, and the user can select a simplified model corresponding to the experimental structural model. The receiving module 304 monitors the data port of the data collector 2 in real time and receives the collected vibration data. The parameter preliminary evaluation module 305 executes signal processing and parameter identification algorithms. The frequency response function peak method identifies the natural frequency and damping ratio by finding the peak of the amplitude spectrum of the frequency response function, and the characteristic frequency identification method identifies the characteristic frequency 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 the modal superposition method, according to the identified dynamic parameters and the loaded sensor parameters, and calculates the modal vibration mode and the frequency response curve. The graphical real-time display function displays the calculation results in a graphical manner, such as vibration mode animation and frequency response curve graph, on the user interface in real time.

[0091] Specifically, in the teaching of structural dynamic load tests, first, students install and fix the structural model 90 on the shaker 1, and arrange sensors 4 at key positions of the structural model 90. The signal lines of the sensors 4 are connected to the data collector 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 and presents it on the user interface through the sensor type selection module 302. Students select according to the actually used sensor models, and the system automatically loads the corresponding sensor parameters. Subsequently, students select a simplified model that matches the current experimental structural model on the structural model selection interface provided by the structural model parameter configuration module 303, and the system loads the corresponding simplified model parameters. After completing the parameter configuration, start the data collector 2 to collect vibration data, and the receiving module 304 receives the vibration data uploaded by the data collector 2. The parameter preliminary evaluation module 305 combines the loaded sensor parameters, simplified model parameters, and the received vibration data, and uses the peak value method of frequency response function or the characteristic frequency identification method to automatically evaluate the dynamic parameters such as the natural frequency and damping ratio of the structural model 90. Finally, the modal analysis module 306 matches the evaluated dynamic parameters with the loaded sensor parameters, and applies the matched parameters to the modal analysis algorithm to calculate the structural modal vibration mode and frequency response curve, and displays the modal analysis results to students in real time through the graphical real-time display function. Thus, the adaptive configuration of structural dynamic load test parameters and the rapid identification of structural dynamic parameters are realized, improving the teaching efficiency and teaching effect.

[0092] In some preferred embodiments, the modal analysis software is further configured with: A filtering module for evaluating the noise level of the vibration data. If the noise level exceeds the preset level threshold, filter the vibration data (the specific process is shown in step S7 of the foregoing text).

[0093] For the purpose of cost saving, the shaker 1 can adopt Figure 4 , Figure 5 The shown simple shaker, which includes a base 101, at least two guide rails 102 arranged parallel to each other on the base 101, a slide table 103 slidably arranged on the guide rails 102, and a driving mechanism 104 for driving the slide table 103 to slide reciprocally. During use, install the structural model 90 on the slide table 103 (for example, install it by screw connection, snap connection or other connection methods), and then drive the slide table 103 to move by the driving mechanism 104, so as to vibrate the driving structural model 90.

[0094] Further, the driving mechanism 104 includes a bracket 105 fixed on the base 101, a guide cylinder 106 fixed on the bracket 105, a slide bar 107 slidably inserted in the guide cylinder 106, a connecting rod 108, a swing rod 109, and a servo motor 110. The first end of the slide bar 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 bar 107. The second end of the connecting rod 108 is rotatably connected to a position deviating 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 bar 107 to reciprocate axially through the connecting rod 108, thereby driving the slide table 103 to reciprocate.

[0095] In some preferred embodiments, see Figure 5 , a waist-shaped hole 111 extending radially along the rotation center is provided on the swing rod 109. The second end of the connecting rod 108 is connected to the waist-shaped hole 111; the length of the connecting rod 108 is adjustable, and / or the position of the swing rod 109 in the axial direction of the slide bar 107 is adjustable. By adjusting the length of the connecting rod 108 and / or the position of the swing rod 109, the radial distance between the second end of the connecting rod 108 and the rotation center of the swing rod 109 can be adjusted, so that the amplitude of the reciprocating movement of the slide table 103 can be adjusted, that is, the vibration amplitude can be adjusted.

[0096] To achieve the adjustable length of the connecting rod 108, the connecting rod 108 can be set to include a sleeve rod and a sliding rod. The sliding rod is slidably inserted into the sleeve rod, and the position of the sliding rod is locked by a locking screw. By adjusting the position of the sliding rod, the length of the connecting rod 108 can be adjusted.

[0097] To achieve the adjustable position of the swing rod 109 in the axial direction of the slide bar 107, the structure shown in Figure 5 can be adopted. Among them, a slide seat 112 capable of reciprocating axially along the slide bar 107 and a driving device 113 (which can be a cylinder, a hydraulic cylinder, an electric telescopic rod, a linear motor, etc.) for driving the slide seat 112 to move are provided on the bracket 105. A slide hole 114 extending axially along the slide bar 107 is also provided on the bracket 105. The servo motor 110 is arranged on the slide seat 112. A rotating shaft is provided at the rotation center of the swing rod 109, and the rotating shaft passes through the slide hole 114 and is connected to the servo motor 110. By controlling the driving device 113, the position of the slide seat 112 can be adjusted, and further the position adjustment of the swing rod 109 in the axial direction of the slide bar 107 can be realized; this structure can realize the on-line adjustment of the position of the slide seat 112 through the computer 3, so that the amplitude adjustment does not need to stop the machine during the experiment, improving the use flexibility.

[0098] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for adaptive parameters of structural dynamic load test, characterized in that: The method includes: S1. Obtain a list of sensor type information, which includes sensor parameters of sensors commonly used in teaching experiments; S2. Select the sensor type used in the experiment from the sensor type information list and load the sensor parameters; S3. For the structural model used, select simplified model parameters from the simplified model parameter information set; S4. Obtain vibration data collected by the data collector; S5. Combine the loaded sensor parameters with the selected simplified model parameters and the vibration data, and use the frequency response function peak method or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model; the dynamic parameters include the natural frequency and the damping ratio; S6. Match the final dynamic parameters with the loaded sensor parameters, apply the matched parameters to the modal analysis algorithm, calculate the structural modal vibration shape and frequency response curve, and display them graphically in real time.

2. A method for adaptive structural dynamic load test parameters according to claim 1, characterized in that: Step S2 includes: S201. Presenting a list of sensor type information including sensor model, sensitivity and range on the user interface; S202. In response to the user's selection of a sensor model on the user interface, determine the sensor model used in the experiment; S203. According to the determined sensor model, the corresponding sensitivity and range parameters are retrieved from the pre-stored sensor parameter database; S204. Load the retrieved sensitivity and range parameters, and display the loading results in real time on the user interface for user confirmation.

3. A method for adaptive structural dynamic load test parameters according to claim 1, characterized in that: Step S3 includes: S301. Presenting a structural model selection interface, the interface includes a plurality of simplified model options corresponding to commonly used structural models in teaching experiments, each simplified model option corresponding to a different degree of simplification; S302. In response to the user's selection operation on the structural model selection interface, determine the simplified model option selected by the user; S303. According to the determined simplified model option, the corresponding simplified model parameters are retrieved from the pre-stored simplified model parameter information set; S304. Evaluate the students' cognitive level of structural dynamics according to the preset cognitive level evaluation rules; the cognitive level evaluation rules involve the students' past theoretical course scores, experimental operation records, and the degree of understanding of the basic concepts of structural dynamics; S305. According to the student cognitive level assessment results and the corresponding simplification degree of the simplified model option, determine whether the simplified model option matches the student cognitive level. If not, a prompt message is given on the structural model selection interface to guide the student to select a simplified model option that matches his or her own cognitive level. If it matches, the retrieved simplified model parameters are loaded.

4. A method for adaptive structural dynamic load test parameters according to claim 3, characterized in that: Step S304 includes: Collect students' previous test scores in structural dynamics-related courses, assign weights according to the test types, and calculate the weighted average score as the theoretical knowledge evaluation score; Obtain students' historical operations in the structural dynamic load test, and calculate the experimental operation evaluation score based on the standardization, efficiency and accuracy of the historical operations; Obtain the correct percentage of the test involving basic concepts of structural dynamics completed by students in advance and calculate the conceptual understanding assessment score; The total score of the student's structural dynamics cognition level is calculated based on the theoretical knowledge assessment score, experimental operation assessment score and concept understanding assessment score according to the preset weight ratio; Map the total score of the structural dynamics cognition level to the preset cognition level level.

5. A method for adaptive structural dynamic load test parameters according to claim 1, characterized in that: After step S4 and before step S5, the method further includes 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.

6. A method for adaptive structural dynamic load test parameters according to claim 5, characterized in that: Step S7 includes: S701. Calculate the power spectral density of the vibration data to determine the average power value within a preset frequency band as a noise level evaluation indicator; S702. Determine whether the noise level evaluation index exceeds a preset level threshold. If so, filter the vibration data.

7. A method for adaptive structural dynamic load test parameters according to claim 1, characterized in that: Step S5 includes: S501. According to the vibration data, the frequency domain data is calculated using the fast Fourier transform algorithm, and the frequency response function of the structural model is constructed in combination with the loaded sensor parameters; 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 a preset matching degree threshold, select the frequency response function peak method; otherwise, select the characteristic frequency identification method. S503. If the frequency response function peak 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 according to the amplitudes on both sides of the maximum amplitude point; S504. If the characteristic frequency identification method is selected, a time-frequency analysis is performed on the vibration data to obtain a time-frequency distribution diagram, the natural frequency of the structural model is identified according to the energy concentration area in the time-frequency distribution diagram, and the damping ratio of the structural model is calculated according to the energy attenuation rate near the natural frequency.

8. A method for adaptive structural dynamic load test parameters according to claim 7, characterized in that: Step S502 includes: Calculating a theoretical frequency response function of the simplified model corresponding to the simplified model parameters within a preset frequency range, and extracting an amplitude spectrum of the theoretical frequency response function; Calculating the mutual correlation coefficient between the amplitude spectrum of the constructed frequency response function and the amplitude spectrum of the extracted theoretical frequency response function as the matching degree between the simplified model and the constructed frequency response function; It is determined whether the matching degree is higher than a preset matching degree threshold. If it is higher than the preset matching degree threshold, the frequency response function peak method is selected, otherwise the characteristic frequency recognition method is selected.

9. A method for adaptive structural dynamic load test parameters according to claim 2, characterized in that: Step S6 includes: S601. Construct a comprehensive parameter set, wherein the comprehensive parameter set matches the natural frequency, damping ratio, and sensitivity and range parameters of the final structural model; S602. According to the comprehensive parameter set, a modal analysis algorithm is selected and input parameters of the modal analysis algorithm are configured; the input parameters include geometric parameters of the structural model, material properties, boundary conditions, sensor positions and exciting force information; S603. Run the configured modal analysis algorithm to calculate the modal vibration shape and frequency response curve of the structural model; S604. Graphically process the calculated structural modal vibration shape and frequency response curve, and display them in real time on the user interface.

10. A structural dynamic load test parameter adaptive system, characterized in that: The system includes an excitation table, a data acquisition device and a computer, wherein the excitation table and the data acquisition device are both electrically connected to the computer; The vibration table is used to drive the structural model to vibrate; The data collector is used to collect vibration data through sensors arranged on the structural model and upload the data 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 used to obtain a list of sensor type information, which includes sensor parameters of commonly used sensors in teaching experiments; A sensor type selection module is used to select the sensor type used in the experiment from the sensor type information list and load the sensor parameters; A structural model parameter configuration module, used for selecting simplified model parameters from a simplified model parameter information set for the used structural model; A receiving module, used for receiving vibration data collected by a data collector; The parameter preliminary evaluation module is used to combine the loaded sensor parameters with the selected simplified model parameters and the vibration data, and use the frequency response function peak method or the characteristic frequency identification method to evaluate the dynamic parameters of the structural model; the dynamic parameters include the natural frequency and the damping ratio; 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 modal vibration shape and frequency response curve, and display them graphically in real time.

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